tag:blogger.com,1999:blog-25523827374693374162024-03-05T01:48:11.643-08:00Bill and TunaI'm Bill. These are my observations on queer health, and other things I care about for one reason or another. Tuna was my adorable dog, a companion of 16 years.Bill Jesdalehttp://www.blogger.com/profile/14477168137262967470noreply@blogger.comBlogger189125tag:blogger.com,1999:blog-2552382737469337416.post-29815594558735262502024-01-20T07:42:00.000-08:002024-01-20T16:35:57.306-08:00Support for Same Sex Marriage in the Americas<div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiXaujzXiMPiGqiRxfUIyPhWADqKFflBS5KhAtansQQMtlKKtMSUEJ2nqg5cQAxj5OVM9sJWK_nJQTpcp2fTMOG-ia7Rv1rdU42Lv-fZJsiqYMZ_BSf-4LUD1jMi-4AwOA6B77Pvmq_KH362N678PcROzhZFsZ3ljJso0ge7sRsOVOpEVWb4mji1V6tkA9a/s663/d6%20map.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="663" data-original-width="609" height="667" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiXaujzXiMPiGqiRxfUIyPhWADqKFflBS5KhAtansQQMtlKKtMSUEJ2nqg5cQAxj5OVM9sJWK_nJQTpcp2fTMOG-ia7Rv1rdU42Lv-fZJsiqYMZ_BSf-4LUD1jMi-4AwOA6B77Pvmq_KH362N678PcROzhZFsZ3ljJso0ge7sRsOVOpEVWb4mji1V6tkA9a/w613-h667/d6%20map.png" width="613" /></a></div><br />Let's look at the latest data from the <a href="https://www.vanderbilt.edu/lapop/" target="_blank">Americas Barometer</a>, a frequent survey of most countries in the Americas. I've used it before to examine state-level <a href="https://billandtuna.blogspot.com/2022/01/how-homophobic-is-my-state.html">homophobia</a> and <a href="https://billandtuna.blogspot.com/2022/07/how-transphobic-is-my-state-part-i.html">transphobia</a> in prior posts. Since my last losts, there is new data from 2023, and some new questions. So, just jumping right in, here is the mean of an 11-point scale of how strongly respondents approve (10) or disapprove (0) of same sex marriage (averaged from 2010, 2012, 2014, 2016, 2018 & 2023), and grouped by region. Higher values indicate higher average support for same sex marriage.<div class="separator" style="clear: both; text-align: center;"><div class="separator" style="clear: both; text-align: center;"><br /></div><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh8Ddh8DYdGEme2wLkxFH7ElwkC5Az4PqOQp7CBCVJ52b27X45ivpovOeUrE0bat2MPCZPhG8SmKWi_ug_Pl6B2N-jzfBR27KyJrEfGOJ4SdZB1hdarn0-nZjkYfsEfzj2MatdHR7ygcKOgYGr6R2I0RIL8FRE4CjpMAE-5zsRhIdDixjxMknXr71nVQ21f/s1198/D6%20by%20country.png" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="660" data-original-width="1198" height="350" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh8Ddh8DYdGEme2wLkxFH7ElwkC5Az4PqOQp7CBCVJ52b27X45ivpovOeUrE0bat2MPCZPhG8SmKWi_ug_Pl6B2N-jzfBR27KyJrEfGOJ4SdZB1hdarn0-nZjkYfsEfzj2MatdHR7ygcKOgYGr6R2I0RIL8FRE4CjpMAE-5zsRhIdDixjxMknXr71nVQ21f/w638-h350/D6%20by%20country.png" width="638" /></a></div><br /><div style="text-align: left;"><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhMTAgPBOMPjcXjX9-H04d1GjJGjk2DUc7I9-LRUZexT7-kKKgpjWbUrBGR538c_X25PJA3hNv1RplzL1uOLCa4Ksjk2MJPEX5dAWEK5UuqIm79myg3X-z3mNlvGDeAvq-9EUOQ_qr28nuDI5IjjgA0nWIBt0EuyD_UXtlYYfuYs2WvZk6IhhyCOxvKaceU/s801/D6%20by%20country%20(table).png" style="clear: right; float: right; margin-bottom: 1em; margin-left: 1em;"><img border="0" data-original-height="801" data-original-width="381" height="705" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhMTAgPBOMPjcXjX9-H04d1GjJGjk2DUc7I9-LRUZexT7-kKKgpjWbUrBGR538c_X25PJA3hNv1RplzL1uOLCa4Ksjk2MJPEX5dAWEK5UuqIm79myg3X-z3mNlvGDeAvq-9EUOQ_qr28nuDI5IjjgA0nWIBt0EuyD_UXtlYYfuYs2WvZk6IhhyCOxvKaceU/w335-h705/D6%20by%20country%20(table).png" width="335" /></a></div>And the same information, in tabular format, indicates that the highest support of same sex marriage in the Americas can be found in Canada (7.36), Uruguay (7.18), Argentina (6.68), the United States (5.90), Chile (5.82), Mexico (5.46), and Brazil (5.42), with considerably lower support throughout the Caribbean, the northernmost countries of South America, and Central America.</div></div><br /><br />Bill Jesdalehttp://www.blogger.com/profile/14477168137262967470noreply@blogger.com0tag:blogger.com,1999:blog-2552382737469337416.post-42546047140052990542023-03-28T12:06:00.002-07:002023-03-28T12:07:03.834-07:00Interview Completion: Are Sexual and Gender Minority People More or Less Likely to Engage in Research?This post is part of a series about engagement of sexual and gender minority populations in survey research. For an overview, <a href="http://billandtuna.blogspot.com/2023/02/queer-and-trans-representation-in.html" target="_blank">start here</a>.<div><br /></div><div>One of the most direct measures of research participation I'm looking at is interview completion, getting to the end of the survey. Certainly there are many reasons for cutting an interview short; some of these are more closely related to a lack of interest in engaging in the research effort than others, but on the whole, the more a respondent is engaged, the more likley they are to get to the end of the survey.</div><div><br /></div><div>Alas, out of the 25 surveys I have tabulated so far, only 3 report whether the interview was completed (the National Health Interview Survey (NHIS), the Health Information National Trends Survey (HINTS), and the American National Election Survey (ANES). I also created mwasures of interview completion for 2 more surveys (the Behavioral Risk Factor Surveillance System (BRFSS) and the Household Pulse Survey (HPS)) by looking at patterns of missing values―if all values sequentially after a given point in the interview are missing, then I code that as an interview termination. Developing those measures is time-consuming, and I doubt I'll do it for any others.</div><div><br /></div><div>So, here's what I found in these 5 surveys, from most to fewest nummber of respondents:</div><div><br /></div><div><span style="font-size: large;">Household Pulse Survey</span> (waves 34-55, 2021-2023, Internet survey)</div><div><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/a/AVvXsEh4uJZHMVTLno4E8Poen8VQAcPmNpGYS6xCsZ2pGM_gpk1UK5SXDhiQzVZ1GifjWmQ51YHC3jGxzCwWtWyFQASbgOz3pHsNaUq3azXw81bqDNKBMBCghDsjlsAOxleFpiEv_hECizVxd9lW9xVqiYhHvqz2bNAoRJkZ6Xn3yXLrGXe5ZkAHgtJO52Ilww" style="margin-left: 1em; margin-right: 1em;"><img alt="" data-original-height="1034" data-original-width="1424" height="470" src="https://blogger.googleusercontent.com/img/a/AVvXsEh4uJZHMVTLno4E8Poen8VQAcPmNpGYS6xCsZ2pGM_gpk1UK5SXDhiQzVZ1GifjWmQ51YHC3jGxzCwWtWyFQASbgOz3pHsNaUq3azXw81bqDNKBMBCghDsjlsAOxleFpiEv_hECizVxd9lW9xVqiYhHvqz2bNAoRJkZ6Xn3yXLrGXe5ZkAHgtJO52Ilww=w648-h470" width="648" /></a></div>Overall, LGBT interview completion was a bit lower than among cisgender heterosexuals, lower among transgender respondents, perhaps higher for gay cismen, and lower for cismen of "another" sexual orientation. These are raw (weighted) percents, so to get an estimate of relative likelihood of interview completion after adjusting for respondent age, state or residence, and time trend, I estimated a logistic model to get adjusted odds ratios fro interview completion:</div><div><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/a/AVvXsEhSjFyNyuwvLnNIfxb4EyEfK9YM6ynqZ_KcKBUVNYabWVPqTjn6qGZsJjlDNKcIcrUjxfOifFRyfAkIofRYFXkjwJLwhPqsr1YlI9jJ925QlAs18emDVpWXS22Zw_n2OermbeiOC1-L403dhnWNkzqC8qf5OeZfoLWoY2hi4rJIL_npHTq9xnwmlo-Iug" style="margin-left: 1em; margin-right: 1em;"><img alt="" data-original-height="1034" data-original-width="1423" height="468" src="https://blogger.googleusercontent.com/img/a/AVvXsEhSjFyNyuwvLnNIfxb4EyEfK9YM6ynqZ_KcKBUVNYabWVPqTjn6qGZsJjlDNKcIcrUjxfOifFRyfAkIofRYFXkjwJLwhPqsr1YlI9jJ925QlAs18emDVpWXS22Zw_n2OermbeiOC1-L403dhnWNkzqC8qf5OeZfoLWoY2hi4rJIL_npHTq9xnwmlo-Iug=w643-h468" width="643" /></a></div>After adjustment, interview completion was actually higher for LGBT people averaged together (on this chart, 1.0 means equally likely as the comparison group), for cisgender sexual minority women, cisgender gay and bisexual men, and about the same as cisgender heterosexuals for transgender respondents.</div><div><br /></div><div><br /></div><div><span style="font-size: large;">Behavioral Risk Factor Surveillance Survey</span> (2014-2021, Telephone survey, restricted to states where SOGI items were asked in the demographics section)<br /><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/a/AVvXsEjvlKxlavIm6RJu3SN_4hBD58yOZMcOuMCpi-z6wmQjg5eqqNwMDpoLdILeiikC2HxXQfrnhKERnBhrDYcEbCEILVMQREwk6er88LOK4PyU5jiRTfWWqOUXqEMq9IjqvQcBJVrsjfaqsM9CdDnWkybsf6Uz7ExTomDPiyEpq15kcrIvMokfkb4y9VkJrQ" style="margin-left: 1em; margin-right: 1em;"><img alt="" data-original-height="1034" data-original-width="1424" height="469" src="https://blogger.googleusercontent.com/img/a/AVvXsEjvlKxlavIm6RJu3SN_4hBD58yOZMcOuMCpi-z6wmQjg5eqqNwMDpoLdILeiikC2HxXQfrnhKERnBhrDYcEbCEILVMQREwk6er88LOK4PyU5jiRTfWWqOUXqEMq9IjqvQcBJVrsjfaqsM9CdDnWkybsf6Uz7ExTomDPiyEpq15kcrIvMokfkb4y9VkJrQ=w647-h469" width="647" /></a></div>In the crude rates, LGBT respondents were just about as likely to complete the interview as cisgender heterosexuals. Transgender people somewhat less likely, and as in HPS, gay and bisexual cismen more likely to complete the interview, while cismen of another seuxal orientation were less likely to complete the interview.</div><div>Again, I did a logistic model to adjust for respondent age and state of residence, and time trend.</div><div><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/a/AVvXsEgwWa-v28XEvV4yurFdMgIxueBqLXOAga3p95QXtKntXm1q9a6uplEWxlMbqu7PDjQLuBkjYy-Qvmd646ta6swj-5LFjb1aWzD_rIGrJAc1tdvss5nCW8cw8s78NnnsfbAsZw-qsj7PWTH1JgrOIl7vHCW6TIYszNTtbMEpSZ5W-R3lbpX4AU7RgERgfg" style="margin-left: 1em; margin-right: 1em;"><img alt="" data-original-height="1034" data-original-width="1423" height="471" src="https://blogger.googleusercontent.com/img/a/AVvXsEgwWa-v28XEvV4yurFdMgIxueBqLXOAga3p95QXtKntXm1q9a6uplEWxlMbqu7PDjQLuBkjYy-Qvmd646ta6swj-5LFjb1aWzD_rIGrJAc1tdvss5nCW8cw8s78NnnsfbAsZw-qsj7PWTH1JgrOIl7vHCW6TIYszNTtbMEpSZ5W-R3lbpX4AU7RgERgfg=w648-h471" width="648" /></a></div>After adjustment, LGBT people were slightly more likely to complete interviews, transgender people less likely to, gay and bi cismen more likely, and cismen of another seuxal orientation were less likely to complete interviews.</div><div><br /></div><div><br /></div><div><span style="font-size: large;">National Health Interview Survey</span> (2014-2021, Face-to-face interviews)</div><div><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/a/AVvXsEjb9PcH2eO2x_-zDlNzn9gx-D6DDn7ds9mmSK3N9a3nB_z1eceJJnLsowsYuNS4QAvQa125h92Q-Dyu496TrCGKuffaZ_IaA-staDOgERDfo12zSgxYx8kdOV8HgDeXy01p_nXsS-WR6pAzVOSViuQEHzg0Gl1o4jcbegEWzKEqn8FPjskwpE558XIRNQ" style="margin-left: 1em; margin-right: 1em;"><img alt="" data-original-height="1034" data-original-width="1424" height="471" src="https://blogger.googleusercontent.com/img/a/AVvXsEjb9PcH2eO2x_-zDlNzn9gx-D6DDn7ds9mmSK3N9a3nB_z1eceJJnLsowsYuNS4QAvQa125h92Q-Dyu496TrCGKuffaZ_IaA-staDOgERDfo12zSgxYx8kdOV8HgDeXy01p_nXsS-WR6pAzVOSViuQEHzg0Gl1o4jcbegEWzKEqn8FPjskwpE558XIRNQ=w649-h471" width="649" /></a></div>Although I was able to combine several years of data here, the sample size of NHIS is considerably smaller than HPS or BRFSS, so the comparison between sexual minority adults and heterosexuals is robust, getting into some of the subgroups gets harder to interpret. NHIS did not collect gender identity, but they did identify people who said they weren't sure about their seuxal orientation.</div><div>Overall, sexual minority respondents were about as likely to complete interviews, and it looks like the questioning groups may have been less likely to complete the interview.</div><div>Again, a logistic model, adjusted for respondent age, region of residence, and time trend:</div><div><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/a/AVvXsEglTnDU_yqcTURVHoyDgyM4GLkf5zFiO03ivz-S--KTvz4TGC8slyaGtYUizj1OcajkLMFiPnB9Vi20LJpyquhxFN5RFn5gbAO_uoqjaWYD5emu5sb2iYYSAPh1fWCnGGs4fC8aK9ZyzTBg_hN8Zo-woq_C1mVWeGs3_HRXybMh_ZM9yfIzKpNTSUAXzw" style="margin-left: 1em; margin-right: 1em;"><img alt="" data-original-height="1034" data-original-width="1423" height="465" src="https://blogger.googleusercontent.com/img/a/AVvXsEglTnDU_yqcTURVHoyDgyM4GLkf5zFiO03ivz-S--KTvz4TGC8slyaGtYUizj1OcajkLMFiPnB9Vi20LJpyquhxFN5RFn5gbAO_uoqjaWYD5emu5sb2iYYSAPh1fWCnGGs4fC8aK9ZyzTBg_hN8Zo-woq_C1mVWeGs3_HRXybMh_ZM9yfIzKpNTSUAXzw=w639-h465" width="639" /></a></div>Overall, the relative likelihood of interview completion for sexual minority respondents was slightly higher, in the same range as the two larger surveys above. Subgroups are too small to interpret here.</div><div><br /></div><div><br /></div><div><span style="font-size: large;">Health Information for National Trends Survey</span> (2017-2020, Internet & Mail)<br /><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/a/AVvXsEiFFT71UTGMgoL6wgw6luCJH-vlmIDhByRC8v-LFq_tAzqVi-trTUgHI1Ju2HqoNre0xK6bAUgYZBl6SQRxe42YbJEAYOGuEeah77jhwRqFrEN5l-53CWqoqgCQMiMe3VHrJhvkv9uNv7Eh67ZE9_n_8LY2nxjlDZPksDBaEpgoyk79zNVTzoc6jZhxUA" style="margin-left: 1em; margin-right: 1em;"><img alt="" data-original-height="1034" data-original-width="1424" height="464" src="https://blogger.googleusercontent.com/img/a/AVvXsEiFFT71UTGMgoL6wgw6luCJH-vlmIDhByRC8v-LFq_tAzqVi-trTUgHI1Ju2HqoNre0xK6bAUgYZBl6SQRxe42YbJEAYOGuEeah77jhwRqFrEN5l-53CWqoqgCQMiMe3VHrJhvkv9uNv7Eh67ZE9_n_8LY2nxjlDZPksDBaEpgoyk79zNVTzoc6jZhxUA=w641-h464" width="641" /></a></div>HINTS is a very rich survey, lots of in-depth information about experience with cancer and beliefs about cancer prevention. However, with about 15,000 respondents after pooling 4 annual surveys, the sample is just too small to say anything confidently about sexual minority respondents relative to heterosexuals. Also, the reported interview completion rate is very high, which is great, but it also means there's not a lot of variation to look at from a statistical perspective.</div><div>I'm hesitant even to show model results because of this, but for the sake of completeness, here they are:</div><div><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/a/AVvXsEjPNfG-WjObxk3BXGj3nVcMTVNeOJu0O6uqaGr1_XGBDlNE-eVsa4Kt4PJmbFTGv0kzbzMErDE33gIai7udm7pD160OM5ijjXETVEwj4oJ9BgjA5nzPy0iH4Ney3HrX0cFyUnoKoVBoD6AQHfx8hrwq8yV2PxXdspftKOlNysI_1pmdGWbEjMdTn-iCKQ" style="margin-left: 1em; margin-right: 1em;"><img alt="" data-original-height="1034" data-original-width="1423" height="466" src="https://blogger.googleusercontent.com/img/a/AVvXsEjPNfG-WjObxk3BXGj3nVcMTVNeOJu0O6uqaGr1_XGBDlNE-eVsa4Kt4PJmbFTGv0kzbzMErDE33gIai7udm7pD160OM5ijjXETVEwj4oJ9BgjA5nzPy0iH4Ney3HrX0cFyUnoKoVBoD6AQHfx8hrwq8yV2PxXdspftKOlNysI_1pmdGWbEjMdTn-iCKQ=w639-h466" width="639" /></a></div><br /><br /><br /><span style="font-size: large;">American National Election Survey</span> (2016, 2020, mostly Internet, some face-to-face, televideo, and telephone interviews)<br /><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/a/AVvXsEjl5r3X50RsVt1eeOMOjD9HrFNK9eKB_s_Y7teG0tF_FX0kEYV7Gakxwu5Evf3qYYSd1PaTz6QnRwxBJHsOEzHaogZqh0dhDUcFBACec-hTvXoUq3zJd-HxGcgrEunXzhyrXVtzYt4m0-Z4sjgwEpgVw9mB7zMZpM2Ry3mi94cHMsoVLwn7h8b8yqjC5Q" style="margin-left: 1em; margin-right: 1em;"><img alt="" data-original-height="1034" data-original-width="1424" height="462" src="https://blogger.googleusercontent.com/img/a/AVvXsEjl5r3X50RsVt1eeOMOjD9HrFNK9eKB_s_Y7teG0tF_FX0kEYV7Gakxwu5Evf3qYYSd1PaTz6QnRwxBJHsOEzHaogZqh0dhDUcFBACec-hTvXoUq3zJd-HxGcgrEunXzhyrXVtzYt4m0-Z4sjgwEpgVw9mB7zMZpM2Ry3mi94cHMsoVLwn7h8b8yqjC5Q=w637-h462" width="637" /></a></div>Really nothing to say about this survey, given that it is shy of 10,000 respondents, and again with such a high interview completion rate that there isn't much statistical variation to play with.<br /><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/a/AVvXsEjER5p3XtqQfXQHke0ydFY8e26vRlAnBTNZZn4evDzZ7e-WKbiBEzDVrAXlIW5EFMuTXod3fjUu6lhBSFXlNbqMDvqbOxBOMCxht8MzCaYaxwSOLXLOxz6AkqFUlvT-lhwEyMdYvwG43whp_WUeGMwEoFP50t6wjxDuGawB163JoyB32mGfWLxEDbQnDw" style="margin-left: 1em; margin-right: 1em;"><img alt="" data-original-height="1034" data-original-width="1423" height="468" src="https://blogger.googleusercontent.com/img/a/AVvXsEjER5p3XtqQfXQHke0ydFY8e26vRlAnBTNZZn4evDzZ7e-WKbiBEzDVrAXlIW5EFMuTXod3fjUu6lhBSFXlNbqMDvqbOxBOMCxht8MzCaYaxwSOLXLOxz6AkqFUlvT-lhwEyMdYvwG43whp_WUeGMwEoFP50t6wjxDuGawB163JoyB32mGfWLxEDbQnDw=w642-h468" width="642" /></a></div><div><br /></div><div><br /></div><span style="font-size: large;">All 5 Surveys Together</span></div><div>The main value of looking at these 5 surveys, with different methodologies, covering different subject matter, and over (somewhat) different time frames is being able to look at them all together. Here are the results of the five logistic models for LGB(T) populations compared to cisgender heterosexuals, all on the same scale:</div><div><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/a/AVvXsEiO5B93POXO8mMt_7zaSPIqNj_0mpWPpDENjPDutBdQz7jF51jCHwqG1LBSuaC76AoQbIViZylzhHcLnYsfIK6Z8KVPvin1f-u-lgqH_vRCn9htPhb9-Y9cPdDQjR6rM-pIy8UTJm7UF0WibvHzv6Tjizv_jVm8C42Uko3_KHA-_eiaQnzY1gmALhSKBA" style="margin-left: 1em; margin-right: 1em;"><img alt="" data-original-height="1034" data-original-width="1423" height="467" src="https://blogger.googleusercontent.com/img/a/AVvXsEiO5B93POXO8mMt_7zaSPIqNj_0mpWPpDENjPDutBdQz7jF51jCHwqG1LBSuaC76AoQbIViZylzhHcLnYsfIK6Z8KVPvin1f-u-lgqH_vRCn9htPhb9-Y9cPdDQjR6rM-pIy8UTJm7UF0WibvHzv6Tjizv_jVm8C42Uko3_KHA-_eiaQnzY1gmALhSKBA=w641-h467" width="641" /></a></div>With only 5 surveys, it doesn't make sense to do a formal meta-analysis, especially given that they surveys are really quite different from one another. Nonetheless, it is reassuring to see that the three largest studies have relative completion rates that are compatible with one another (the 2 smaller studies are also compatible with these, but also compatible with such a wide range of alternate possibilities that they are simply not informative).</div><div>It may come as a surprise to some readers that LGBT people are, at least in terms of interview completion, more likely to engage in research, and thus perhaps slightly over-represented in research datasets.<br /><br /><br /><br /><br /><br /><br /></div>Bill Jesdalehttp://www.blogger.com/profile/14477168137262967470noreply@blogger.com0tag:blogger.com,1999:blog-2552382737469337416.post-30081270382170987872023-02-22T17:52:00.002-08:002023-02-22T17:52:46.957-08:00Proportion LGBT in 20 Probability Samples<span style="font-size: large;">Six surveys asked about sexual orientation and gender identity:</span><div><br /><div>01) Behavioral Risk Factor Surveillance System (2014-2021; 18+; Telephone; n=1,602,144)<div><span> LGBT: 6.05%</span></div><div><span><span><span><span><span><span><span><span><span><span><br /></span></span></span></span></span></span></span></span></span></span></div><div>02) Household Pulse Surveys (2021-2023; 18+; Internet; n=1,206,436)</div><div><span> LGBT: 9.74%</span></div><div><span><br /></span></div><div><span>03) National Crime Victimization Surveys (2017-2021; 18+; Face-to-face & telephone; n=760,408)</span></div><div><span><span> LGBT: 2.39%</span><br /></span></div><div><br /></div><div>04a) Associated Press VoteCast (2018; 18+; Internet & telephone; n=39,864)</div><div> LGBT: 7.73%<br /></div><div><br /></div><div>04b) Associated Press VoteCast (2020; 18+; Internet & telephone; n=34,868)</div><div> LGBT: 9.27%</div><div><br /></div><div>05) California Health Interview Survey (2021; 18+; Internet & telephone; n=24,441)</div><div><span> LGBT: 10.95%</span><br /></div><div><span><br /></span></div><div><span>06) Collaborative Multi-racial Post-election Surveys (2012, 2016-2017; 18+; Internet; n=12,660)</span></div><div><span><span> LGBT: 8.58%</span></span></div></div></div><div><span><span><span><span><br /></span></span></span></span></div><div><span><span><span><span><span style="font-size: large;">Fifteen more surveys asked about sexual orientation, but not gender identity:</span></span></span></span></span></div><div><span><span><span><span><br /></span></span></span></span></div><div><span><span><span><span>07) National Health Interview Survey (2014-2021; 18+; Face-to-face & telephone; n=240,719)</span></span></span></span></div><div><span><span><span><span><span> LGB: 3.93%</span><br /></span></span></span></span></div><div><span><span><span><span><span><br /></span></span></span></span></span></div><div><span><span><span><span><span>08) National Drug Use and Health Surveys (2015-2020; 18+; Face-to-face; n=236,145)</span></span></span></span></span></div><div><span><span><span><span><span><span> LGB: 5.21%</span><br /></span></span></span></span></span></div><div><span><span><span><span><span><span><br /></span></span></span></span></span></span></div><div><span><span><span><span><span><span>09) New York City Community Health Surveys (2001-2020; 18+; Telephone; n=155,714)</span></span></span></span></span></span></div><div><span><span><span><span><span><span><span> LGB: 4.64%</span><br /></span></span></span></span></span></span></div><div><span><span><span><span><span><span><br /></span></span></span></span></span></span></div><div><span><span><span><span><span><span>10) Health Reform Monitoring Surveys (2013-2020; 18-64; Internet; n=147,203)</span></span></span></span></span></span></div><div><span><span><span><span><span><span><span> LGB: 7.50%</span><br /></span></span></span></span></span></span></div><div><span><span><span><span><span><br /></span></span></span></span></span></div><div><span><span><span><span><span>11) National Adult Tobacco Surveys (2012-2014; 18+; Telephone; n=120,017)</span></span></span></span></span></div><div><span><span><span><span><span><span> LGB: 4.22%</span><br /></span></span></span></span></span></div><div><span><span><span><span><span><span><br /></span></span></span></span></span></span></div><div><span><span><span><span><span><span>12) Canadian Community Health Surveys (2017-2018; 15+; Face-to-face & telephone; n=103,217)</span></span></span></span></span></span></div><div><span><span><span><span><span><span><span> LGB: 3.35%</span><br /></span></span></span></span></span></span></div><div><span><span><span><span><span><span><br /></span></span></span></span></span></span></div><div><span><span><span><span><span><span>13) National Survey of Family Growth (2011-2019; 15-49; Face-to-face; n=41,174)</span></span></span></span></span></span></div><div><span><span><span><span><span><span><span> LGB: 6.94%</span><br /></span></span></span></span></span></span></div><div><span><span><span><span><span><span><span><br /></span></span></span></span></span></span></span></div><div><span><span><span><span><span><span><span>14a) Population Assessment of Tobacco and Health, Wave 1 (2011; 18+; Face-to-face; n=31,515)</span></span></span></span></span></span></span></div><div><span><span><span><span><span><span><span><span> LGB: 4.92%</span><br /></span></span></span></span></span></span></span></div><div><span><span><span><span><span><span><span><span><br /></span></span></span></span></span></span></span></span></div><div><span><span><span><span><span><span><span><span>14b) Population Assessment of Tobacco and Health, Wave 4 (2016; 18+; Face-to-face; n=33,415)</span></span></span></span></span></span></span></span></div><div><span><span><span><span><span><span><span><span><span> LGB: 8.68%</span><br /></span></span></span></span></span></span></span></span></div><div><span><span><span><span><span><span><br /></span></span></span></span></span></span></div><div><span><span><span><span><span><span>15) Well-Being and Basic Needs Surveys (2017-2020; 18-64; Internet; n=27,449)</span></span></span></span></span></span></div><div><span><span><span><span><span><span><span> LGB: 7.50%</span><br /></span></span></span></span></span></span></div><div><span><span><span><span><span><span><span><br /></span></span></span></span></span></span></span></div><div><span><span><span><span><span><span><span>16) National Health and Nutrition Examination Surveys (2001-2016; 18-64; Face-to-face; n=25,529)</span></span></span></span></span></span></span></div><div><span><span><span><span><span><span><span><span> LGB: 5.38%</span><br /></span></span></span></span></span></span></span></div><div><span><span><span><span><span><span><span><span><br /></span></span></span></span></span></span></span></span></div><div><span><span><span><span><span><span><span>17) General Social Surveys (2008, 2010, 2012, 2014, 2016, 2018, 2021; 18+; Face-to-face, Internet & telephone; n=12,815)</span></span></span></span></span></span></span></div><div><span><span><span><span><span><span><span><span> LGB: 4.72%</span><br /></span></span></span></span></span></span></span></div><div><span><span><span><span><span><span><span><span><br /></span></span></span></span></span></span></span></span></div><div><span><span><span><span><span><span><span><span>18) American National Election surveys (2016, 2020; 18+; Internet, face-to-face, video call & telephone; n=9,254)</span></span></span></span></span></span></span></span></div><div><span><span><span><span><span><span><span><span><span> LGB: 6.57%</span><br /></span></span></span></span></span></span></span></span></div><div><span><span><span><span><span><span><span><span><br /></span></span></span></span></span></span></span></span></div><div><span><span><span><span><span><span><span><span>19a) Supplementary Empirical Teaching Units in Political Science (2016; 18+; Telephone, Internet & video call; n=3,464)</span></span></span></span></span></span></span></span></div><div><span><span><span><span><span><span><span><span><span> LGB: 6.26%</span><br /></span></span></span></span></span></span></span></span></div><div><span><span><span><span><span><span><span><span><span><br /></span></span></span></span></span></span></span></span></span></div><div><span><span><span><span><span><span><span><span><span>19b) Supplementary Empirical Teaching Units in Political Science (2020; 18+; Face-to-face & Internet; n=7,089)</span></span></span></span></span></span></span></span></span></div><div><span><span><span><span><span><span><span><span><span><span> LGB: 6.99%</span><br /></span></span></span></span></span></span></span></span></span></div><div><span><span><span><span><br /></span></span></span></span></div><div><span><span><span><span>20) National Social Life, Health, and Aging Project (2015-2016; 50+; Face-to-face; n=3,392)</span></span></span></span></div><div><span><span><span><span><span> LGB: 2.41%</span><br /></span></span></span></span></div>Bill Jesdalehttp://www.blogger.com/profile/14477168137262967470noreply@blogger.com0tag:blogger.com,1999:blog-2552382737469337416.post-40263599676101138722023-02-02T11:18:00.002-08:002023-02-02T11:18:34.870-08:00Queer and Trans Representation in Research<p><span>WHAT IS REPRESENTATION?</span></p><p><span> </span>A common refrain in research on LGBT populations is that we are underrepresented in research. In many ways, that is undoubtedly true. Many data collection systems do not include items on sexual orientation, and even fewer include gender identity. And many of those that do are small enough that there is not enough of a queer/trans population to provide reliable estimates. Arguably funding for research on (not often enough <i>with</i>) sexual and gender minority populations is decades overdue and falls short of the mark. And publications about sexual and gender minority comprise a tiny fraction of the published scientific literature.</p><p><span> And yet. The number of research datasets with reliable information on sexual orientation and gender identity has expanded rapidly, first in large survey datasets, more recently in infectious disease tracking systems, and coming soon in medical records datasets and large administrative databases. Funding has increased dramatically in recent years, and there are now established journals dedicated to sexual and gender minority population research.</span></p><p><span><span> In a more limited sense of 'representation', we really don't know the degree to which sexual and gender minority populations are represented in these datasets - in other words, how much more or less likely are these populations to be included in research? With age, race/ethnicity, and geography, we can use Census records to compare the distribution of people included in a study with respect to their expected distribution in the population, and "sex" in a broad sense, although this breaks down when considering gender identity. When we know these distributions, we can re-weight the analytic dataset to reflect the population at large.</span></span><br /></p><p><span><span><span> But, with sexual and gender minority populations, there is no Census standard - in fact, these surveys themselves are the closest thing we have to a standard. But there is considerable variability from one survey to another in terms of the proportion of people identifying as sexual and gender minorites, as well as variation in the questions asked - and responses options offered.</span><br /></span></span></p><p><span><span><span><br /></span></span></span></p><p><span><span><span>DATA SOURCES</span></span></span></p><p><span><span><span><span> In a series of posts I plan to explore here, I'll be looking at representation in this narrow sense (likelihood of responding to an invitation to engage in survey research) across a wide range of large probability surveys in the US, namely:</span></span></span></span></p><p><span><span><span><span> <a href="https://www.cdc.gov/brfss/data_documentation/index.htm" target="_blank">Behavioral Risk Factor Surveillance System (2014-2021)</a><br /></span></span></span></span></p><p><span><span><span> <a href="https://www.census.gov/data/experimental-data-products/household-pulse-survey.html" target="_blank">Household Pulse Survey (2021-2023)</a><br /></span></span></span></p><p><span><span> <a href="https://www.cdc.gov/nchs/nhis/data-questionnaires-documentation.htm" target="_blank">National Health Interview Survey (2013-2021)</a><br /></span></span></p><p><span> <a href="https://wwwn.cdc.gov/nchs/nhanes/" target="_blank">National Health and Nutrition Examination Survey (1999-2019)</a><br /></span></p><p> <a href="https://nsduhweb.rti.org/respweb/homepage.cfm" target="_blank">National Survey of Drug Use and Health (2015-2020)</a><br /></p><p> <a href="https://www.cdc.gov/nchs/nsfg/index.htm" target="_blank">National Survey of Family Growth (2011-2019)</a><br /></p><p> <a href="https://www.cdc.gov/tobacco/data_statistics/surveys/nats/index.htm" target="_blank">National Adult Tobacco Survey (2012-2014)</a><br /></p><p> <a href="https://pathstudyinfo.nih.gov/" target="_blank">Population Assessment of Tobacco and Health (2011, 2016)</a><br /></p><p> <a href="https://healthpolicy.ucla.edu/chis/Pages/default.aspx" target="_blank">California Heath Interview Survey (2021)</a><br /></p><p> <a href="https://www.nyc.gov/site/doh/data/data-sets/community-health-survey.page" target="_blank">New York City Community Health Survey (2003-2020)</a><br /></p><p> <a href="https://bjs.ojp.gov/data-collection/ncvs" target="_blank">National Crime Victimization Survey (2017-2021)</a><br /></p><p> <a href="https://www.urban.org/policy-centers/health-policy-center/projects/health-reform-monitoring-survey" target="_blank">Health Reform Monitoring Surveys (2013-2020)</a><br /></p><p> <a href="https://www.urban.org/policy-centers/health-policy-center/projects/well-being-and-basic-needs-survey" target="_blank">Well-Being and Basic Needs Survey (2017-2020)</a><br /></p><p> <a href="https://gss.norc.org/" target="_blank">General Social Survey (2008, 2010, 2012, 2014, 2016, 2018, 2021)</a><br /></p><p> <a href="https://electionstudies.org/" target="_blank">American National Election Surveys (2016, 2020)</a><br /></p><p> <a href="https://cmpsurvey.org/">Collaborative Multiracial Post-election Surveys (2012, 2016, 2017)</a><br /></p><p> <a href="https://www.ap.org/content/politics/elections/ap-votecast/about" target="_blank">Associated Press VoteCast (2018)</a><br /></p><p><span> These 17 large surveys with public use data reflect a broad range of sampling strategies (random telephone dial, internet recruitment from Census lists, panels recruited by established survey firms, quite a few in-person interviews based on physical addresses, and one using televideo interviews), on a variety of topics (puplic opinion polling, health surveys, crime), using a variety of question wording and response options. They are heavily weighted towards recent years, but there are some going back decades. Is there a dataset you think I've overlooked? Let me know!</span><br /></p><p><span><br /></span></p><p><span>MEASURING REPRESENTATION</span></p><p><span> If there are no Census data (or other standard) for the distribution of sexual and gender identity data, how do I propose to look at relative representation of these groups in these research datasets? Indirectly. I plan to use measures that are fairly inuitive correlates of research participation, as determined in prior research.</span></p><p><span><span> </span>One of the most intuitive is how many attempts it took to get a successful interview. Presumably, people who answer the call to participate immediately are "easy" interviews, and those who take 20-50 attempts to connect with are less eager to participate. So, we can look at the distribution of how many contact attmepts were made to connect for an interview as a proxy for eagerness to participate. Alas, this measure is only reported publically in 2 of the above studies.</span></p><p><span><span> </span>Another fairly intuitive proxy is how likely a respondent is to complete the interview once started. Presumably people who hang on to the end of an interview are more invested in the research endeavor than those who break off after a short period. This measure is available for 3 of the above surveys - many of these surveys only report out complete interviews (or impute values for missing data) so that there are no "short" interviews to compare to. For others, the sexual orientation and/or gender identity items are asked late enough in the interview that there is not information on these items among those who cut the interview short.</span><br /></p><p><span><span> A measure that seems to make sense (but may be less useful than it appears) is the weight assigned to a respondent. If the weighting system the survey is using works well enough, respondents who are harder to reach will have a higher weight, and those easy to reach will have a lower weight. The factors that go into these weights typically include sex, race/ethnicity, age, geography, how many phones the respondent uses and how many people could answer the phone, and interactions betweeen these factors. So, to the degree that how much more or less likely sexual and gender minority people are to respond because of these factors, the relative weighting could be informative. But, to the extent that sexual and gender minority respondents elect to engage with researchers is related to being LGTBQ over and above those delineated factors that go into the weighting, the relative weights will fail to reflect participation.</span><br /></span></p><p><span><span><span> A less intuitive measure is called the "fraction of missing information", or how many items the respondent used a "don't know" or "not sure" response, or declined to answer. Presumably, a person who declines to answer a larger proportion of questions is less invested in the research than a person who answers everything. Of course, there are many reasons to leave questions blank or say "don't know" that have nothing to do with eagerness to participate. And I have to be careful to distinguish between questions left blank because the respondent heard it and didn't answer vs. the question was skipped on purpose, or skipped because the interview already ended. Another difficulty with this measure is that an awful lot of people answer every question, even when they truly don't know or aren't sure, so the median number of blank items is 0, a rough distribution to work with from a statistical perspective. On the plus side, these measures are available for all the studies above, except one that imputes missing and don't know values to "known" values before releasing the public use datatset.</span></span></span></p><p><span><span><span><span> I've gone ahead and split these missing information measures into two categories: one based on demographic items (race/ethnicity, marital status, educational attainment, employment status, income, household composition, citizenship, language), and another based on all other items on the survey, which I've called 'substantive items' for lack of a better generic term for "everything else", whether that be a history of cancer to presidential candidate preference.</span><br /></span></span></span></p><p><span><span><span><br /></span></span></span></p><p><span><span><span>STATEMENT OF EXPECTED HYPOTHESES</span></span></span></p><p><span><span><span><span> What do I expect to see in all this? It's hard to say for sure, which is what makes it especially interesting to me. I do expect to see heterogeneity. I expect to see greater participation from sexual and gender minority populations from Internet-based recruiting than telephone, for instance. I expect to see greater participation from gay men than lesbian women, and greater still than bisexual men and women; from cisgender people than transgender. Overall, I think that LGBT people will probably be somewhat more likley to participate in research, but if I had to guess, I'd say the difference is probably pretty small, compared to differences in participation related to age, race/ethnicity and sex. I suspect that the variation between sexual and gender minority groups will be greater than the difference between LGBT people as a whole and cisgender heterosexual adults.</span><br /></span></span></span></p><p><span><span><span><span><span> I would say I don't have a strong expectation about participation between transfeminine and transmasculine people. I don't have as solid a foundation of experience to draw from. I'm also not sure about what to expect about younger or older LGBT people relative to younger or older cisgender heterosexuals, or about LGBT people belonging to minoritized racial/ethnic groups relative to cisgender heterosexual non-Hispanic Whites.</span><br /></span></span></span></span></p><p><br /></p><p>WHAT YOU SHOUOLD EXPECT</p><p><span> Over the next weeks to months, I plan to post a variety of analyses here related to this topic. Expect to see analyses based on one survey at a time. Expect to see an analysis of the same quetion or proxy outcome across multiple surveys. Expect to see analyses of missing data due to particular items across multiple surveys and populations. Expect to see analyses looking at trends over time, differences across survey methodologies, differences with respect to survey topics (drug use, general health, crime victimization, politics). In other words, this topic is too big (at least in my mind) for synthesis into a single paper for publication. I want to explore it with you and figure out along the way what the paper(s) within the topic are to pursue for publication in a more formal setting.</span><br /></p>Bill Jesdalehttp://www.blogger.com/profile/14477168137262967470noreply@blogger.com0tag:blogger.com,1999:blog-2552382737469337416.post-42164892456367377952022-07-31T13:30:00.005-07:002022-07-31T13:54:16.224-07:00How Transphobic is my State (Part I)<p> I think it's safe to say that the social climate in every state can be characterized as transphobic. It also depends on who you ask, and what you ask them.</p><p>But if some states are more transphobic than others, is there a way to measure those differences in degree?</p><p>To date, most researchers have used measures based on legislation and policy to describe the climate of each state. The <a href="https://www.lgbtmap.org/equality-maps" target="_blank">Movement Advancement Project</a> has the most comprehensive listing of policies affecting transgender, genderqueer, non-binary & agender people in the United States.</p><p>Public opinion is also a promising way to measure the transphobic climate of the states, and a number of polling firms have reported public opinion on items related to transphobia. A few recent examples include: <a href="https://www.npr.org/2022/06/29/1107484965/transgender-athletes-trans-rights-gender-transition-poll" target="_blank">NPR</a> reporting that a majority of Americans oppose allowing girls and women who are trans to participate in womens' sports; <a href="https://www.pewresearch.org/social-trends/2022/06/28/americans-complex-views-on-gender-identity-and-transgender-issues/" target="_blank">Pew</a> reports that fewer than half of Americans favor requiring health insurance to cover gender affirming therapies; and <a href="https://news.gallup.com/poll/350174/mixed-views-among-americans-transgender-issues.aspx" target="_blank">Gallup</a> reports that support for transgender people being able to serve openly in the military has declined since 2019. For a comprehensive assessment of Americans' views, check out <a href="https://williamsinstitute.law.ucla.edu/publications/public-opinion-trans-rights-us/" target="_blank">this poll conducted by IPSOS on behalf of the WIlliams Insititue</a>. Unfortunartely, none of these polls report state-level results in a way that researchers like me can use them to develop measures of state-level climate, as I was able to do for measures of homophobia derived from the <a href="http://billandtuna.blogspot.com/2022/01/how-homophobic-is-my-state.html" target="_blank">AmericasBarometer</a> and the <a href="http://billandtuna.blogspot.com/2022/02/how-homophobic-is-my-state-part-ii.html" target="_blank">American National Election Survey</a>.</p><p>For today's blog, I have to thank Maggi Price (<a href="https://twitter.com/MaggiPrice" target="_blank">@MaggiPrice</a>) and colleagues for bringing to my attention (through a terrific preprint, don't know if I can link to it) that <a href="https://www.projectimplicit.net/" target="_blank">Project Implicit</a> publishes a <a href="https://osf.io/y9hiq/" target="_blank">dataset ideal for assessing state-level transphobic attitudes</a>. It's a bonanza and I've been working on for a couple weeks & am finally ready to share findings with you-all!</p><p>OK, here are the first set of findings, methodology below:</p><p></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/a/AVvXsEjnuDvURKC8Ow4JkwQVt-k_J2Hd82VF-S_N-0eprCnbOB8kbOpWJy9jZAXqtB6eD-zbTKx55bonKC1ciUvQ-86uTc4G1Jrj__PyvqPpQl9Ji0Tv2QNyJuQIz_hGLAI3ePT-ppL8i-RrJ8JDz8bjpJSzM-0g44cE4p8yw6e4y0CC_9lgv9XjeTjejq0Lww" style="margin-left: 1em; margin-right: 1em;"><img alt="" data-original-height="1034" data-original-width="1873" height="358" src="https://blogger.googleusercontent.com/img/a/AVvXsEjnuDvURKC8Ow4JkwQVt-k_J2Hd82VF-S_N-0eprCnbOB8kbOpWJy9jZAXqtB6eD-zbTKx55bonKC1ciUvQ-86uTc4G1Jrj__PyvqPpQl9Ji0Tv2QNyJuQIz_hGLAI3ePT-ppL8i-RrJ8JDz8bjpJSzM-0g44cE4p8yw6e4y0CC_9lgv9XjeTjejq0Lww=w649-h358" width="649" /></a></div><br />The diamonds indicate the average score for each state, after weighting to the state population (more details below). The states are ranked from lowest mean transphobia as assessed on a 9-item scale (Vermont) to highest (Guam, or Mississippi if you're just interested in states) <i>on this particular measure</i>. The vertical whiskers are 95% confidence intervals indicating the degree of uncertainty in the state-specific measures. A long line incidates a low level of certainty in the state's score, and a short line indicates a hgiher degree of certainty - but it is worth noting that none of these confidence intervals are short enough to have a high degree of confidence the the exact ranking of each state. Also, these confidence intervals are based on sampling variation only, and are thus far smaller than they should be if I had accurately accounted for additional sources of uncertainty - a decent rule of thumb is that the confidence intervals from a sample like this should be about doubled in size. The absolute values on the y-axis (15-50) have no simple direct interpretation, it is the sum of 9 items-see details below to how they are calculated.<p></p><p><i><span style="font-size: large;">Methodology</span></i></p><p>A couple generic methodologic notes that apply to all measures I've developed from this Project Implicit dataset. The survey is designed to measure an individual's <i>implicit bias</i> against or for a given group (usually a minoritized or marginalized group), based on how quickly they associate "good" and "bad" words with that group compared to another group (usually the socially dominant group). The survey also asks a number of questions about <i>explicit bias</i>. For my purposes, I am not terribly interested in how individuals respond, but the overall tenor of a state (or in the future, metro area or smaller geography, as sample size allows), and I am interested in both the implicit and explicit measures.</p><p>This is a "convenience" sample, meaning that the questionnaire was filled out by whoever elected to do it, not from a systematic sample of Americans. Thus it is important to pay attention to <i>who</i> elected to fill it out, especially since I am trying to estimate these measures for the state as a whole, not for the people who elected to take the survey. Some people took it as part of a school project, or because they were encouraged to (perhaps even required to) by their employer. Some people took it because they heard about it in the news, on social media, or from a friend. Presumably, those required to take it may be more representative of the state climate (thanks to Jarvis Chen for this insight), because they would be closer to a representative population, but I've decided (for now anyway) to include respondents regardless of their reason for taking the test. In any event, a large number of people have done so (about 200,000 over 2020-2021 for the Transgender Implicit Associations Test), and I have constructed weights based on people's state of residence, sex (admittedly an imperfect variable in this case!), age group, and race/ethnicity, to attempt to make the sample more closely resemble the general population of each state according to the Census's July 2020 population estimates (or as close as I could get to that for the teritories of American Samoa, Guam, Puerto Rico and the US Virgin Islands). (In the future, watch for variations on this weighting theme, such as weighting for educational attainment, gender identity, policital ideology, county-level geography, etc. Jarvis gave me many great ideas about trying out different weighting schemes).</p><p>I'd also like to explore, in future variations, restricting to respondents aged 18-64, and/or those who are cisgender, to reflect the attitudes in the socially dominant population, but for now, I have included all respondents aged 10 to 85+, and of all gender identities.</p><p>There is some evidence of occasional "goofball" respondents (like people who say that they are all races, and that they are all genders (cismale, cisfemale, transmasculine and transfeminine), etc.), but I have not yet done anything about excluding or assigning low weights to the goofballs. It can be a bit of a tightrope walk defining who is a goofball and who is not - there is a risk of assigning someone with multiply marginalized positions as a goofball by mistake.</p><p>Eager to hear any suggestions people may have for further refinements!</p><p>Now, on to the specific measures:</p><p><br /></p><p><i>Nine-Item Transphobia Scale</i></p><p>This is a measure of explicit bias against transgender people, measured using <a href="https://link.springer.com/article/10.1007/s11199-008-9458-7" target="_blank">a 9-item scale</a>, with responses ranging from strongly disagree (1) to strongly agree (7) on a 7-point scale. Thus, the scale can range from 7 to 63. Here are the items:</p><blockquote style="border: none; margin: 0px 0px 0px 40px; padding: 0px; text-align: left;"><p></p><ol style="text-align: left;"><li>I don't like it when someone is flirting with me, and I can't tell if they are a man or a woman.</li><li>I think there is something wrong with a person who says that they are neither a man nor a woman.</li><li>I would be upset, if someone I'd known a long time revealed to me that they used to be another gender.</li><li>I avoid people on the street whose gender is unclear to me.</li><li>When I meet someone, it is important for me to be able to identify them as a man or a woman.</li><li>I believe that the male/female dichotomy is natural.</li><li>I am uncomfortable around people who don't conform to traditional gender roles, e.g., aggressive women or emotional men.</li><li>I believe that a person can never change their gender.</li><li>A person's genitalia define what gender they are, e.g., a penis defines a person as being a man, a vagina defines a person as a woman.</li></ol><p></p></blockquote><p>I required answers to all 9 items, a future version may include multiple imputation to be able to include respondents who missed a couple items - often people skip items for good reasons (like the wording doesn't make sense, or they just don't know how they feel), and it is a shame to exclude people. I also didn't do anything fancy like factor analysis or trying to account for the relative importance of each item-I just added them together.</p><p>This scale was asked of about half of the people taking the test. I have done nothing special to account for that fact - notably the weights are the same as those for the whole dataset, not in any way adjusted to reflect the subsample that was given this scale.</p><p>I think it's worth noting that these items don't talk about transgender people explicity. Arguably, these items are more about a form of sexism I call "stereosexism" - the notion that there are but two genders and that these are permanent attributes. However, whatever that concept is, it is arguably very closely linked to transphobia.</p><p><br /></p><p><i>Single-Item Preference for Transgender People</i></p><p></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/a/AVvXsEiRXXh_d2jirK3-G3bx2Qaqrets7jsKNJkTwdEO6kGMRCJcBcQ4PXEZaZt8-m_vYHRwSnWQQZYJVnLICtNgdvA-SY9o2V0-ymOhZ8JoI-PDn7xYbf05JaIo5RuiJmu8NkoDMdCZ-GmssVyvrQ2L_4Xqr26fAuaAaJJHRJYZ8aE6gVKHllTvwtTamgn1-g" style="margin-left: 1em; margin-right: 1em;"><img alt="" data-original-height="1034" data-original-width="1873" height="354" src="https://blogger.googleusercontent.com/img/a/AVvXsEiRXXh_d2jirK3-G3bx2Qaqrets7jsKNJkTwdEO6kGMRCJcBcQ4PXEZaZt8-m_vYHRwSnWQQZYJVnLICtNgdvA-SY9o2V0-ymOhZ8JoI-PDn7xYbf05JaIo5RuiJmu8NkoDMdCZ-GmssVyvrQ2L_4Xqr26fAuaAaJJHRJYZ8aE6gVKHllTvwtTamgn1-g=w639-h354" width="639" /></a></div><br />This next measure is based on a single item, with 7 possible responses:<div><div><ol style="text-align: left;"><li>I strongly prefer transgender people to cisgender people. </li><li>I moderately prefer transgender people to cisgender people.</li><li>I slightly prefer transgender people to cisgender people.</li><li>I like cisgender people and transgendper people equally.</li><li>I slightly prefer cisgender people and transgendper people.</li><li>I moderately prefer cisgender people and transgendper people.</li><li>I strongly prefer cisgender people and transgendper people.</li></ol></div><div><i><span style="font-size: x-small;">Note, I copied these responses from the data documentation, including the misspelling of "transgendper" instead of "transgender" for responses 4-7, and presumably a typo in items 5-7 "cisgender and" instead of "cisgender to". I assume these are errors in the documentation, not in the original survey, but buyer beware...</span></i></div><div><i><br /></i></div><div>For the chart above, I've re-ranked the states and territories from the least likely to prefer cisgender people (Vermont) to the most likely (Arkansas), with the diamonds indicating the average response for the tates's residents, and the vertical whiskers indicating the 95% confidence interval based on sampling variability only (and again, these should be interpreted as though the confidence intervals are about twice as wide as they are calculated, given that this is not a random sample, and due to the influence of additional sources of variation not accounted for in the standard calculations).</div><div><br /></div><div>This item was asked of everyone who took the test (but not answered by some). I have done nothing special to the weights to account for missing-ness on this item, just included people who actually answered.</div><div><br /></div>Those of you with eagle eyes for this sort of thing may have noticed that, even though this measure has nearly double the sample as the transphobia scale above, the uncertainty around these state-level estimates are generally larger than for the 9-item scale (on a relative scale), meaning that the relative ranking of states and territories on this measure is even less stable than the previous measure. Could be a bunch of reasons for that - my leading hypothesis would be that the 9-item scale has lower variability between individuals (which is just a way of saying it is more stably measured, not that it is a <i>closer</i> measure of transphobia). I do like that this measure directly asks people about transgender and cisgender people, that seems much more of a direct linkage to me than the items in the scale above. On the other hand, many people (particularly the cisgender majority) do not have a strong internalized notion of what "cisgender" means, and may not be able to answer this item accurately, even though IAT defines the terms.</div><div><br /></div><div style="text-align: left;"><i>Implicit Bias</i><br /><a href="https://blogger.googleusercontent.com/img/a/AVvXsEiEA32SNGiEB7BKDa_bMkQfCvcHiCzzV0_b9eAJebST4nQGSgNGIdzMX-jT-9Uc1SKVEfqZLnh8b5_aXr5NSYXpupU0fjsIt1Jndzl0lMZmnYa38TULT16oP2-WZGSFj21nErEJkOVo3tofqEF6XrjstjkyaUButKQzd-kfdtU_Wa4cdeLZRTnChazVqA" style="margin-left: 1em; margin-right: 1em; text-align: center;"><img alt="" data-original-height="1034" data-original-width="1873" height="355" src="https://blogger.googleusercontent.com/img/a/AVvXsEiEA32SNGiEB7BKDa_bMkQfCvcHiCzzV0_b9eAJebST4nQGSgNGIdzMX-jT-9Uc1SKVEfqZLnh8b5_aXr5NSYXpupU0fjsIt1Jndzl0lMZmnYa38TULT16oP2-WZGSFj21nErEJkOVo3tofqEF6XrjstjkyaUButKQzd-kfdtU_Wa4cdeLZRTnChazVqA=w642-h355" width="642" /></a><br />You may well wonder why I didn't lead with the implicit bias measure, especially since this is the whole purpose for the data collection by Project Implicit in the first place!<br />The main reason I put this third is that it is the measure I have the least familiarity with, and thus the least confidence I am aware of potential measurement issues inherent to it. To be completely honest with you, I have not delved into the methodology deeply enough to even be able to describe how this measure is calculated, let alone what the scale (y-axis) means.<br />As before, I have ranked the states and territories from the least implicit bias against transgender people (Guam, or Utah if you're only counting states) to the most implicit bias (North Dakota), with the diamonds representing my method's best guess as to the average level of anti-trans implicit bias in the state, and the vertical whiskers indicating 95% confidence intervals - with the wider ranges meaning less confidence and the narrower ranges higher confidence.<br />Although this measure was asked of all respondents, it had more missing-ness than the single-item preference for transgender people measure above - and I have not looked into why these are missing - could be that people got tired out before the test was finished, could be that there is a data-cleaning step to remove measures if people were too slow or too quick to answer, or some other factors I haven't thought of.<br /></div><div style="text-align: left;">Another concern I have is that many respondents are pretty unfamiliar with the term "cisgender", and the implicit association test, which counts on a knee-jerk rapid response comparing "transgender" and "cisgender" may not adequately tap into this automatic level of thinking if people have to keep reminding themselves what the terms mean, as opposed to "Black" and "White" which are concepts most Americans have deeply emplanted understandings of.</div><div style="text-align: left;">It also appears that the variability around each state's measure is higher still, relative to the variability between states, suggesting that the relative ranking between states is less stable than the other two measures. I'm worried about reading too much into that at this stage, but initally, it suggests to me that either implict bias is tough to measure precisely, or that the level of implicit bias is relatively uniform (and presumably high) across the country, whereas for the explicit measures, they may be tapping into how socially acceptable it is to express bias, which may well vary more from state-to-state.</div><div style="text-align: left;"><br /></div><div style="text-align: left;"><i>Contact with Transgender People</i></div><div style="text-align: left;"><div class="separator" style="clear: both; font-style: italic; text-align: center;"><a href="https://blogger.googleusercontent.com/img/a/AVvXsEij8WKWxLItfq5RqN3CaEoFNVWs-W3XxJoqMYySivinQ7VaKC7qs_-wzWNPwnYB-XikSbjVr4Wih5AMjzWMlfsXgOsl3PxD0_H_W7HcfmNhzbHW85W3pCdbJ_JnryLcc8i7vKgTtdP9IVTFMErE9RzHYCFMblCHTzOuqHGkJvyPY5mq8JLHwYlvgSvl4A" style="margin-left: 1em; margin-right: 1em;"><img alt="" data-original-height="1034" data-original-width="1873" height="358" src="https://blogger.googleusercontent.com/img/a/AVvXsEij8WKWxLItfq5RqN3CaEoFNVWs-W3XxJoqMYySivinQ7VaKC7qs_-wzWNPwnYB-XikSbjVr4Wih5AMjzWMlfsXgOsl3PxD0_H_W7HcfmNhzbHW85W3pCdbJ_JnryLcc8i7vKgTtdP9IVTFMErE9RzHYCFMblCHTzOuqHGkJvyPY5mq8JLHwYlvgSvl4A=w647-h358" width="647" /></a></div><br />And one more measure - this one is about contact with transgender people. It is based on 4 items, each with a yes/no response. I have simply added these up, resulting in a scale from 4 (no contact) to 8 (contact in each category):</div><div style="text-align: left;"><ol style="text-align: left;"><li>Do you have a family member who is transgender?</li><li>Do you have a friend who is transgender?</li><li>Do you have friendly interactions with transgender people on a regular basis?</li><li>Have you even met a transgender person?</li></ol></div><div style="text-align: left;">These items were asked of nearly half the people who took the test (with no overlap bewteen this half and the half who were asked the 9-item transphobia scale). I have only included people who answered all 4 items, and not done anything to account for those who are missing one or more, and as before, used the overall weighting, nothing special for this particular measure.</div><div style="text-align: left;"><br /></div><div style="text-align: left;">As with the other measures, I have ranked the states, this time from the least contact (The US Virgin Islands, or West Virginia) to the states with the highest levels of contact (Guam, or Vermont). Note that the ranking on this measure is conceptually in the opposite direction from the prior measures, but generally the states end up in nearly the same ranking (once you take the reversed ranking into account).</div><div style="text-align: left;"><br /></div><div style="text-align: left;">I think it's interesting that the variabilty between states is pretty low on this measure, with most states bunched in a pretty narrow range indicating an affirmative response to 1-2 of the items above.</div><div style="text-align: left;"><br /></div><div style="text-align: left;">This type of measure is often interpreted as a precursor to people's attitudes towards a group, under the "Contact Hypothesis" elaborated by Allport in the 1950's, fairly early on in America's fascination with sociology and efforts to understand how eugenics in World War II and racializing attitudes in post-war America came into the mainstream of public thought and political action.</div><div style="text-align: left;">However, I think it has a much more complicated relationship than simply reflecting prior contact as a driver of current attitudes. In this case, that people who are aware of contact with transgender people are more likely to have had some awareness and openness to that possibility before they meet (or become aware of) transgender people in their lives. Also, I would argue that the state climate is likely to be a strong determinant of how open people are to be about being transgender (and thus come to the conscious attention of others), and potentially even of recognizing that experiences around gender are compatible with a transgender identity. Thus, I think these contact measures are better considered as reflections of the state climate than causes of it.</div><div style="text-align: left;"><br /></div><div style="text-align: left;"><i>Closing Queries</i></div><div style="text-align: left;">Hey, thanks for bearing with me through all this. I am super interested to hear your thoughts...</div><div style="text-align: left;"><ul style="text-align: left;"><li>about the conceptual idea of measuring state-level transphobia - does this even make sense to you? Does this kind of quantification seem viable to you?</li><li>what the different measures are telling you about the contours os between-state variation in transphobia - are these 4 measures all measuring the same underlying construct or are the differences between contact, implicit bias, and the 2 explicit measures of transphobia speaking to you about something more than there being a single "thing" to measure?</li><li>how could we use these measures to examine the causal role of state-level climate on mood disorders? Gender euphoria? The impact of inhibiting gender expression on stunting the development of cisgender, as well as transgender, people?</li></ul>If you want to chat, but don't want to leave a public comment, go ahead and get to me through twitter <a href="https://twitter.com/billandtuna">@billandtuna</a>. I look forward to hearing from you.</div><div><p></p></div>Bill Jesdalehttp://www.blogger.com/profile/14477168137262967470noreply@blogger.com0tag:blogger.com,1999:blog-2552382737469337416.post-85665374575512691942022-02-01T08:02:00.002-08:002022-02-01T08:07:41.937-08:00How Homophobic is my State (Part II)<p> This is the second post in a series on using public opinion polling to assess structural heteronormativity. The first (based on the LatinoBarometer surveys) can be found <a href="http://billandtuna.blogspot.com/2022/01/how-homophobic-is-my-state.html" target="_blank">here</a>.</p><p>For today's installment, I collated responses to a "feeling thermometer" from the <a href="https://electionstudies.org/" target="_blank">American National Election Survey</a>, a survey of Americans of voting age conducted to assess a wide variety of attitudes and voting pattern data. For this measure, I averaged responses from surveys conducted in advance of major elections in 2008, 2012, and 2016. Please note that these averages DO NOT use the appropriate weighting supplied with the data, and are not adjusted for anything other than which election wave the data were collected in. Thus, the means may be off what they should be (either higher or lower, impossible to predict), and the confidence intervals are probably narrower than they should be.</p><p>The question wording for the "feeling thermometer" is reproduced below, after which a number of groups are asked about - the order of the groups presented is randomized from one respondent to another.</p><p><i>"We’d also like to get your feelings about some groups in American society. When I read the name of a group, we’d like you to rate it with what we call a feeling thermometer. Ratings between 50 degrees-100 degrees mean that you feel favorably and warm toward the group; ratings between 0 and 50 degrees mean that you don’t feel favorably towards the group and that you don’t care too much for that group. If you don’t feel particularly warm or cold toward a group you would rate them at 50 degrees. If we come to a group you don’t know much about, just tell me and we’ll move on to the next one."</i></p><p><i>"Gay Men and Lesbians"</i></p><p>As before, the states are ranked from least heteronormative rating to most heteronormative rating.</p><p></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/a/AVvXsEiijHR7i_JlrW6sIA3hMnyGkhlIi8rn7V3P5lx9OpWtpOXUM40ezCrnOmIQfF9cUn-Tsz3tDcSBQVVW-injk5XBTOaNugtLRUa9OV5Mfa18qd3oR9KV1S2FXNLDscesB7CUCwFNld80zGUc_Uf52iYT5Lq1YZ-W0GeepOJw23zwShPLXQM03UE19daiDg" style="margin-left: 1em; margin-right: 1em;"><img alt="" data-original-height="1081" data-original-width="361" height="1131" src="https://blogger.googleusercontent.com/img/a/AVvXsEiijHR7i_JlrW6sIA3hMnyGkhlIi8rn7V3P5lx9OpWtpOXUM40ezCrnOmIQfF9cUn-Tsz3tDcSBQVVW-injk5XBTOaNugtLRUa9OV5Mfa18qd3oR9KV1S2FXNLDscesB7CUCwFNld80zGUc_Uf52iYT5Lq1YZ-W0GeepOJw23zwShPLXQM03UE19daiDg=w377-h1131" width="377" /></a></div><br /><br /><p></p>Bill Jesdalehttp://www.blogger.com/profile/14477168137262967470noreply@blogger.com0tag:blogger.com,1999:blog-2552382737469337416.post-41847330071271871082022-01-21T10:48:00.001-08:002022-01-21T10:48:39.140-08:00How Homophobic is my State?I've been working on developing measures of heteronormativity for decades. Typically, I end up using legislation / policy because these measures are 1) tightly related to the concept of structural heteronormativity (homophobia at the societal, not individual, level), 2) easy to explain to others, 3) perhaps more importantly, legislation points to a direct method for redressing heteronormativity.<div>Another approach is to look at public opinion. In some ways, this is a more direct measure of public sentiment about QTBLG people, but it is often difficult to get access to data that is based on a large enough group of people, reported in sufficient detail to make state-level estimates, and uses consistent methodology across enough surveys to make decent state-level estimates.</div><div>Here, I present data from the <a href="https://www.vanderbilt.edu/lapop/" target="_blank">AmericasBarometer</a> that allows state-level estimates of some decent questions for the United States (and many other countries in North, Central, and South America and the Caribbean).</div><div>In order to generate these estimates, I combined data collected in multiple waves (2006, 2008, 2010, 2012, 2014 & 2016). There is definitely a time trend towards greater acceptance over this time period, but just to simplify things, I'm ignoring that for now. I have also suppressed estimates for any state based on fewer than 20 respondents (across all 6 waves combined), which means I can't rank Alaska, North Dakota, or Wyoming.</div><div>Below are (weighted, but otherwise unadjusted) estimates for three questions asked in the AmericasBarometer survey. As you can see, there is very wide variability across the states, and a decent correlation between the results of the different questions.</div><div><br /></div><div>D5: And now on a different topic, thinking about homosexuals, how strongly do you
approve or disapprove of such people being permitted to run for public office?</div><div>¿Con qué firmeza aprueba o desaprueba que los homosexuales puedan postularse para
cargos pĂşblicos?</div><div>Scaled 1-10, with 1=strongly disapprove, 10=strongly approve (asked in all waves)</div><div>States ranked by mean score, from least homophobic to most homophobic:</div><div><div class="separator" style="clear: both; text-align: center;"><br /></div><div class="separator" style="clear: both; text-align: center;"><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjiIsj94JpwLAD2SP91YbPP8yp78sayErN-d3BSqPn5-MIvmG1GfQnMILsY8-jXIA0IKx69L1q_WodAfMBu-V0RDguLNgIeYyMrf6iV8hmjikfevOQlWOBghE1PTIM6uBwoKq6OA6Ul9wBO/" style="margin-left: 1em; margin-right: 1em;"><img alt="" data-original-height="1020" data-original-width="368" height="1048" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjiIsj94JpwLAD2SP91YbPP8yp78sayErN-d3BSqPn5-MIvmG1GfQnMILsY8-jXIA0IKx69L1q_WodAfMBu-V0RDguLNgIeYyMrf6iV8hmjikfevOQlWOBghE1PTIM6uBwoKq6OA6Ul9wBO/w380-h1048/image.png" width="380" /></a></div><br /></div><br /></div><div>D6: How strongly do you approve or disapprove of same-sex couples having the right to
marry?</div><div>¿Con qué firmeza aprueba o desaprueba que las parejas del mismo sexo puedan
tener el derecho a casarse?</div><div>Scaled 1-10, with 1=strongly disapprove, 10=strongly approve (asked in 2010, 2012, 2014 & 2016)</div><div>(Data suppressed for District of Columbia, Hawai'i, Idaho, Montana, Rhode Island & Vermont)</div><div> <div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgyowoET4uQESp_nD5q5wIA4GrrBzkvkmpYcod0xpNENPpF5p8gEVR7zIIdlNEqFtdeCz5eLRI3ssLc4YEs-kmXOp8xc_m6HU7_GMRcW8koPIR9M01jmz8xd_ESdf_E31HvNXKBv-pb-u5j/" style="margin-left: 1em; margin-right: 1em;"><img alt="" data-original-height="900" data-original-width="326" height="1023" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgyowoET4uQESp_nD5q5wIA4GrrBzkvkmpYcod0xpNENPpF5p8gEVR7zIIdlNEqFtdeCz5eLRI3ssLc4YEs-kmXOp8xc_m6HU7_GMRcW8koPIR9M01jmz8xd_ESdf_E31HvNXKBv-pb-u5j/w371-h1023/image.png" width="371" /></a></div><br /></div><div>DIS35a: Now you are going to read a list of several groups of people. Can you check off any groups that you
would not want to have as neighbors? Gays. Would you mind
having them as neighbors?</div><div>Vamos a mostrarle una lista de varios grupos de personas. ÂżPodrĂa decirme si hay
algunos de ellos que no le gustarĂa tener como vecinos? Homosexuales. ÂżNo los quisiera tener
de vecinos?</div><div>Yes=1, No=0 (asked only in 2012)</div><div><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhTdrG4ZZ4rmRCbh1EduDO6yZYHXc97iqTfOCT_fsJ_7IASYOQJ9bljtjPQEvkDz-06hxL-oXtL_fwGYtege4Qc-EPsR8AbfyqKRBbIVYbJQfzKyrv2RgRhdFPowzPsxafVYEt1qhND7v45/" style="margin-left: 1em; margin-right: 1em;"><img alt="" data-original-height="360" data-original-width="333" height="476" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhTdrG4ZZ4rmRCbh1EduDO6yZYHXc97iqTfOCT_fsJ_7IASYOQJ9bljtjPQEvkDz-06hxL-oXtL_fwGYtege4Qc-EPsR8AbfyqKRBbIVYbJQfzKyrv2RgRhdFPowzPsxafVYEt1qhND7v45/w441-h476/image.png" width="441" /></a></div><br /><br /></div>Bill Jesdalehttp://www.blogger.com/profile/14477168137262967470noreply@blogger.com0tag:blogger.com,1999:blog-2552382737469337416.post-16365149801113120172021-09-23T10:48:00.004-07:002021-09-23T11:34:26.517-07:00Census Household Pulse Survey - Tips for Analyzing Sexual Orientation and Gender Identity<p> Hooray! The US Census has finally provided estimates of the sexual and gender minority populations in the United States!</p><p>I am in the process of learning how these numbers work, and am eager to pass along what I've learned to other researchers.</p><p>As part of the "Household Pulse Survey", a weekly survey of the entire US population designed to gather vital information on the COVID-19 pandemic and related topics, the Census included items on sexual orientation and gender identity starting on week 34. As of this writing, there are 3 weeks of data to work with - a bit over 200,000 respondents, already approaching the sample size of a full year of BRFSS data! (and even more important, not preselected by which state you live in, or whether you are answering an out-of-state cell phone (see <a href="https://www.ajpmonline.org/article/S0749-3797(21)00215-4/fulltext" target="_blank">my recent article in AJPM</a> for more detail on that)).</p><p><br /></p><p><span style="font-size: medium;"><b><i>Comparability to BRFSS</i></b></span></p><p>Many of the questions are identical to those fielded in BRFSS, or can be easily transformed to a comparable format. The sexual orientation item is nearly identical, simply requiring a recode of 5="I don't know" to 7, and -99 to 9.</p><p>The sex at birth and gender identity questions are not exactly comparable, however. There are some complications that require a bit of finesse before using the gender identity variables.</p><p>The raw data files can be downloaded from: <a href="https://www.census.gov/programs-surveys/household-pulse-survey/datasets.html#phase3.2">www.census.gov/programs-surveys/household-pulse-survey/datasets.html#phase3.2</a> .</p><p><br /></p><h3 style="text-align: left;"><span style="font-size: medium;"><i>Tip #1: Restrict to <span style="color: #2b00fe; font-family: courier;">AGENID_BIRTH=2</span>.</i></span></h3><p>For both sexual orientation and gender identity, any analysis should be restricted to cases where <span style="color: #2b00fe; font-family: courier;">AGENID_BIRTH=2</span>. <span style="color: #2b00fe; font-family: courier;">AGENID_BIRTH</span> is a variable indicating whether sex at birth was imputed (1) or not (2). Census used a "hot deck" imputation technique to impute missing values for several key variables, including sex at birth (<span style="color: #2b00fe; font-family: courier;">EGNEID_BIRTH</span>) and current gender identity (<span style="color: #2b00fe; font-family: courier;">GENID_DESCRIBE</span>). When sex at birth or current gender identity are imputed, Census replaces these missing values with values from other respondents, in a (not quite) random fashion. As a result, about half of the respondents randomly assigned male at birth are assigned a current gender identity of female (and <i>vice versa</i>), which would indicate that they are transgender. Because sex at birth is imputed for about 3% of the total population, about 1.5% of people are unintentionally imputed to be transgender when they are in fact cisgender - a common enough occurrence that it overwhelms the population of people who are actually transgender.</p><p>The great majority of researchers who don't want to go to all the trouble of performing a full multiple imputation where these variables strongly inform one another (as opposed to being treated as nearly independent as this particular hot deck imputation technique appears to assume), should just take the simple route of restricting the analysis to <span style="color: #2b00fe; font-family: courier;">AGENID_BIRTH=2</span>.</p><p>By implication, anyone looking at sexual orientation should probably also make this restriction, especially when also looking at sex (which one should <i>always</i> do when looking at sexual orientation), otherwise you'll get gay men in your lesbian group, and so on. Not as large an error as for gender identity, but why use analytic groups you know are premixed in such a way as to minimize distinctions between the groups?</p><p><br /></p><h3 style="text-align: left;"><span style="font-size: medium;"><i>Tip #2: Use an expansive definition of transgender.</i></span></h3><p>Don't be fooled by the simplicity of the "current gender identity" variable (<span style="color: #2b00fe; font-family: courier;">GENID_DESCRIBE</span>), which looks like it differentiates between people who are transgender and cisgender male or female (and another group "none of these" - I'm holding this group out separately because I haven't yet examined this group in detail).</p><p>But <span style="color: #2b00fe; font-family: courier;">GENID_DESCRIBE</span> is about respondent's current gender identity, and many transgender people prefer to identify as "male" or "female" rather than as "transgender". Therefore, to identify transgender people in the Household Pulse Survey, one should also look for people whose sex at birth was male and whose current gender identity is female (and <i>vice versa</i>).</p><p>Here is SAS code to accomplish that recode. It puts the results into a format closer to BRFSS (but where there is no "gender non-conforming" option (BRFSS=3), and "none of these" is held out as a separate category (HPS=4, recoded to 5 for convenience).</p><blockquote style="border: none; margin: 0px 0px 0px 40px; padding: 0px; text-align: left;"><div style="text-align: left;"><span style="font-family: courier; font-size: x-small;">if <span style="color: #2b00fe;">AGENID_BIRTH</span>=2 then do;<br /><span style="color: #6aa84f;">* Male to Female Transgender<span style="white-space: pre;"> </span>; </span><br />if <span style="color: #2b00fe;">EGENID_BIRTH</span>=1 and <span style="color: #2b00fe;">GENID_DESCRIBE</span> in(2,3) then <span style="color: #2b00fe;">TRNSGNDR</span>=1; <br /><span style="color: #6aa84f;">* Female to Male Transgender<span style="white-space: pre;"> </span>;</span><br />else if <span style="color: #2b00fe;">EGENID_BIRTH</span>=2 and <span style="color: #2b00fe;">GENID_DESCRIBE</span> in(1,3) then <span style="color: #2b00fe;">TRNSGNDR</span>=2; <br /><span style="color: #6aa84f;">* Cisgender<span style="white-space: pre;"> </span>;</span><br />else if (<span style="color: #2b00fe;">EGENID_BIRTH</span>=1 and <span style="color: #2b00fe;">GENID_DESCRIBE</span> in(1,-99))<br /><span style="white-space: pre;"> </span>or (<span style="color: #2b00fe;">EGENID_BIRTH</span>=2 and <span style="color: #2b00fe;">GENID_DESCRIBE</span> in(2,-99)) then <span style="color: #2b00fe;">TRNSGNDR</span>=4;<br /><span style="color: #6aa84f;">* None of these<span style="white-space: pre;"> </span>; </span><br />else if <span style="color: #2b00fe;">GENID_DESCRIBE</span>=4 then <span style="color: #2b00fe;">TRNSGNDR</span>=5;<br />end;</span></div></blockquote><p style="text-align: left;">In many surveys, this sort of recoding is not recommended, because any slip-up in coding sex at birth or current gender identity is much too likely to result in falsely identifying cisgender respondents as transgender. However, in the Household Pulse Survey (and some other surveys), there is a follow-up question to confirm when people identify as one sex at birth and a different current gender, so this data-cleaning as it happens is probably sufficient protection against miscoding.</p><p style="text-align: left;"><br /></p><h3 style="text-align: left;"><i>Tip #3: Combine waves, but adjust the weights</i></h3><p style="text-align: left;">While each weekly wave of the Household Pulse Survey is a large survey, breaking the numbers down into subpopulations (e.g. by age, state, health status, etc.) can result in some pretty unstable estimates. Combining multiple waves is a great way to combat this instability - but be warned, the weights should to be adjusted to account for the fact that each week's weights are intended to represent the whole US population. The quick and dirty way to do this is simply to divide the weights by the number of waves you are combining. For instance, I started with 3 waves, so my adjusted weights are simply generated as <span style="color: #2b00fe; font-family: courier;">PWEIGHT/3</span>. Eventually, I'll probably do something a bit more sophisticated with adjusting the weights when combining waves, particularly if the sample size starts changing dramatically from one wave to another, or the balance between state-level sampling fractions is fiddled with. I may also want to multiply some sort of "recency bias" into the weights if the outcome is one where up-to-the-minute estimation is more conceptually important (i.e. making more recent observations weigh more than distantly past ones). But all that is in the future. For now, a simple division by the number of waves concatenated is sufficient.</p><p style="text-align: left;">I have also included the "wave" identifier as a <span style="color: #2b00fe; font-family: courier;">stratum</span> in <span style="color: #2b00fe; font-family: courier;">proc surveyfreq</span>. No strong theoretical basis for doing so, but it seemed like a good idea. Very much open to suggestions from others about how to best utilize the <span style="color: #2b00fe; font-family: courier;">stratum</span> and <span style="color: #2b00fe; font-family: courier;">psu</span> specifications.</p><p style="text-align: left;"><i>more to come...</i></p>Bill Jesdalehttp://www.blogger.com/profile/14477168137262967470noreply@blogger.com0tag:blogger.com,1999:blog-2552382737469337416.post-23055175391727750052021-01-09T10:03:00.003-08:002021-01-09T10:33:51.847-08:00Vaccines. Risk Groups. Scarcity. Efficiency.<p><span style="font-family: trebuchet; font-size: medium;">Vaccinating the high risk people first is (usually) not the most efficient way to get the greatest number of high risk people vaccinated first.</span></p><p><span style="font-family: trebuchet;">Sounds like the opposite of a tautology, whatever that is called. Let me break it down.</span></p><p><span style="font-family: trebuchet;">I'm starting from the premise that we all want to see the highest risk people (especially the oldest among us, and particularly the most vulnerable African American, Latinx & Native elders) protected from the ravages of SARS-CoV-2 infections through vaccination, as fast as possible. So, the question I'm addressing is, what is the fastest, most efficient means to meet that goal?</span></p><p><span style="font-family: trebuchet;">It may seem obvious that morality dictates that we must get the vaccine to the highest risk people first, to minimize the toll of this terrible pandemic. I agree. But there are some hidden obstacles that thinking about people and populations in term of "risk" that get in the way.</span></p><p><span style="font-family: trebuchet;"><span style="font-size: medium;"><b>First</b></span> among these is that there are inefficiencies in identifying "high risk" people, and scheduling them to come to a specific place at a specific time to get vaccinated. In the first few weeks, to maybe a month or two, this inefficiency will not be particularly apparent, so long as the supply of vaccines is a strongly limiting factor. But once the supply of vaccine outstrips our ability to administer them in a heavily calculated risk-first approach (which may already be upon us), it may serve us better to reframe our efforts around getting vaccines out efficiently, rather than focusing as heavily on who "deserves" or "needs" it most. I have to stress that I am still thinking in a social justice frame here - my goal is to get the vaccines to the most vulnerable among us as fast as possible. I understand it may not feel like that yet...</span></p><p><span style="font-family: trebuchet;"><span style="font-size: medium;"><b>Another hidden obstacle</b></span> that a risk orientation places before us is that few people like to think of themselves as being "at risk". Ironically enough, putting a lot of emphasis and importance on deciding who is at risk, and who is at highest risk sets up ways of thinking in most people's mind that are contrary to what we've been taught in public health school.</span></p><p><span style="font-family: trebuchet;"><span>People who are told that they are "at risk" often go through a thought process like: "</span><span><i>I'm a good, honest, careful, responsible person. Maybe I've made a mistake here or there, but I'm doing OK, and I'll be OK. Even though they are telling me I'm 'at risk', I know and do things to considerably lower my risk, and therefore there are other people out there who need the vaccine more than I do</i>.</span><span>" It may be hard for public health types to hear this, but even the people </span><i>you</i><span> think of as being totally irresponsible and very high risk often have thoughts like that, displacing the stigma of risk even further to the margins of society than they see themselves.</span></span></p><p><span style="font-family: trebuchet;"><span style="font-size: medium;"><b>A third hidden obstacle</b></span> is that this is not the first time these populations have been identified as being "at risk". "At risk" usually means something on the range of unpleasant to bad news. </span><span style="font-family: trebuchet;">Otherwise, we would call it "privileged" or some such verbal indicator of elevated, but infrequent, status; or maybe "normal", the blandest of accolades. When you've experienced being treated as "at risk", particularly multiple times, you come to learn that "at risk" is literally stigmatizing - elided with stereotypes of being childish, immature or dim-witted (should know better, or poor thing didn't get the right education); untrustworthy (what else are they hiding from me); even dirty. It may come as no surprise then that the very act of identifying particular populations as "at risk" feeds directly into hard-earned insights about prior treatment by healthcare and other parentalist social structures. You may interpret that as being about a "lack of trust in medicine"; I agree, just putting it in a slightly different frame to encourage my public health colleagues to see things from a different perspective. In other words, b</span><span style="font-family: trebuchet;">eing part of a "target" population for purposes of healthcare outreach sounds different when you are a target population, in a very literal sense, on the streets of America.</span></p><p><span style="font-family: trebuchet;"><b><span style="font-size: medium;">Getting to a solution</span></b> is even more important than laying out a polemic set of (admittedly) hypothetical concerns. I mean, we have a real-life full-blown health crisis to deal with here, and vaccines are a critical juncture in turning the tide. In my opinion, we need to shift as quickly as practicable (i.e. when the pace of vaccine supply outstrips the pace of getting it into people's arms - a point we have already reached in many states) to mass vaccination strategies. We can still encourage older folks to get to the head of the line, but we need to start getting the vaccines out in a more haphazard fashion than scheduling people and hoping they will show up on time, in the right place. Anecdotally, we are already experiencing the tragedy of tossing out otherwise perfectly good vaccine because only 60-80% of the people scheduled for vaccinations showed up in the right place at the right time, or close enough that they could get processed. We need to shift to older models, like what we did for polio and smallpox. Line 'em up and go go go.</span></p><p><span style="font-family: trebuchet;"><b><span style="font-size: medium;">Let history guide us.</span></b> I will cite four examples where risk-oriented vaccination campaigns were less effective at reaching high risk (and particularly the highest risk) individuals with vaccines than mass vaccination approaches.</span></p><p><span style="font-family: trebuchet;"><b><span style="font-size: medium;">Hepatitis B vaccine</span></b> is a true life-saver. And the most vulnerable population is infants, who can get it from their mother during (and after?) childbirth. Once hep B vaccines became available, CDC bent itself into knots trying to identify mothers at highest risk and getting the vaccine to them. Problem was, the highest risk populations, including Alaska Native mothers, had persistently low vaccination rates, lower than populations with a lower prevalence of hepatitis B infection, and much lower than moderate risk groups like doctors and other healthcare workers, in whom vaccination was nearly universal. How did they fix this? By abandoning the "high risk" approach, and making HBV vaccination routinely recommended for all mothers. This recommendation made it logistically easier to get the vaccine out into clinics (rather than requiring a separate visit, on a particular day, when the vaccine would be available and the correct cold chain could be guaranteed). Vaccination rates grew dramatically higher in the highest risk mothers once the vaccine was simply recommended for all children. I wrote more detail about this story 13 years ago in a post called "<a href="https://billandtuna.blogspot.com/2007/12/new-camera.html" target="_blank">New camera & national vaccine strategy</a>".</span></p><p><span style="font-family: trebuchet;"><b><span style="font-size: medium;">Human Papillomavirus (HPV)</span></b> vaccine is now widely adopted, finally. But at first, it was targeted towards girls in their tweens and early teens. The rationale was that the vaccine would prevent against cancer due to infection with the (largely sexually transmitted) HPV virus. And since girls were at risk for the most commonly-caused HPV-related cancer, cervical cancer, it was completely logical to vaccinate girls. Turns out, this made a lot of parents think about their girls having sex. And vaccination uptake was not particularly fast, particularly in adherents to some Protestant communities. So, there was great umbrage taken in public health circles about the ignorance of this way of thinking, with a large investment in trying to think of clever ways to preempt or combat the narratives of these backwards backwoods clowns (or that's how many of us perceived this form of vaccine hesitancy). In a post from 11 years ago "<a href="https://billandtuna.blogspot.com/2009/10/hpv-vaccine-for-boys-no-and-yes.html" target="_blank">HPV Vaccine for Boys?</a>", I detail how expanding the vaccine recommendation to boys actually got more girls vaccinated.</span></p><p><span style="font-family: trebuchet;"><b><span style="font-size: medium;">Influenza, the pandemic that never left</span></b>. These days, we've heard all kinds of parallels to the great influenza epidemic of 1917/18. While influenza killed unfathomable millions in those years, the death toll in the century since has been absolutely crippling. You probably get a flu shot every year, and still, influenza is a major killer. In part because the vaccines aren't as effective as we'd like them to be, and in great part because a lot of people still see the flu shot as optional, and really only necessary for high risk people (and remember, "high risk" almost always means "someone else", even among the riskiest populations - see above). For years, we put our efforts into identifying the most vulnerable and getting them vaccinated. That actually worked reasonably well in nursing homes, but failed to get high enough rates of vaccination in non-institutional elderly persons to keep the "great scythe of mortality" from reaping a horrific annual harvest. In recent years, the goal has been to get as many adults vaccinated as possible, through as many means as possible, rather than spending a lot of time trying to identify the highest risk adults and making special arrangements to get them vaccinated. While there's still a long way to go, we have been blessed with lower incidence of circulating influenza virus (because more people are vaccinated), and more importantly, higher vaccination rates in the most vulnerable elderly.</span></p><p><span style="font-family: trebuchet;"><b>Long time, no smallpox.</b> Or polio (in the Americas). The reason I bring up these vaccination campaigns is that they occurred in an earlier era of public health. An era where we weren't tempted to risk stratify the population and reach for the lowest-hanging fruit. We just went all out and vaccinated (nearly) everyone - enough people to reach a "herd immunity" so robust it resulted in eradication. Wait, why didn't we identify the highest risk people and vaccinate where it would be most "efficient"? If history documents a line of progress, we immediately think we weren't sophisticated enough to, or our understanding of the world was impaired. We just got lucky that mass vaccination turns out to have been so successful, but we know better now. Except that we eliminated smallpox and polio using a mass, undifferentiated public health approach. And now, in our more enlightened age, we use more sophisticated, targeted, efficient approaches that fail for years, often decades, to make a substantial dent in the public health problems of our day.</span></p><p><span style="font-family: trebuchet;"><br /></span></p><p><span style="font-family: trebuchet;">Thanks for bearing with me on this journey. Hope you got something out of it!</span></p>Bill Jesdalehttp://www.blogger.com/profile/14477168137262967470noreply@blogger.com2tag:blogger.com,1999:blog-2552382737469337416.post-26915763489439922472018-02-03T16:50:00.003-08:002018-02-03T17:23:22.108-08:00Adventures in Cognitive ImpairmentToday was a rough day. In a rough week.<br />
I've struggled with fatigue, physically and cognitively, since taking two doses of chemo this summer - one in July, one in August.<br />
Theoretically, I shouldn't have any remaining difficulty.<br />
But I do. And boy, do I.<br />
<br />
<b>The Symptoms</b><br />
1) I make spelling mistakes I never made before. I make the same spelling errors I made before (words with ie or ei always threw me). I amke teh same transposition errors I always made before. And from time to time, my fingers hit the wrpng keys. But now I mis-spell words in ways I never did before - sometimes I put the wrong vowel in - or I leave a letter out completely. And I can't see what's wrong with it, at least not right away.<br />
<br />
2) My brain won't let the words out. I've had flashes of aphasia throughout my life. Aphasia (for me anyway) is when you know there's a word, but it won't come to you. You probably know the first letter, or syllable, or your brain picks up another word that sounds similar at first, but the right word, as a whole, refuses to make an appearance. You draw a blank. I'm experiencing something like that now, but quite different. Now, I know the word, but my brain won't let me say it. Like today, I wanted to get a postcard from the museum store. But they moved the store, so I had to ask someone where it was now. I walked up to the ticket taker at the front of the museum and I wanted to ask her where the museum store was, to buy a postcard. I could feel the word "store" rolling around in my brain. But somehow, the word "postcard" kept me from being able to figure out how to actually say "store". I knew I was struggling with saying the words, so when I stood at the counter, I held up my hand in a gesture to indicate I was trying to say something, and ... nothing. So I waited. And waited. And waited for the word "postcard" to stand down so I could finally ask "Where would I find the ... store ... to buy a postcard?"<br />
One interesting thing about the words is that it doesn't seem to affect <i>writing</i>, only <i>speaking</i>. Moments before starting to write this blog piece, I had to fumble around in informal sign language with the guy making my sandwich, because I couldn't speak, at least, not when I needed to. And yet, I think it's arguable that my writing remains lucid.<br />
<br />
3) Then there is the thing that bothers me the most - I have trouble moving confidently in the world. I don't have any fear of falling, or a lost sense of balance. It's more like I have to break my motions down and consciously step through the stages. So, again at the museum today, I started walking up a short flight of six stairs. By the time I got to the second stair, I could feel my brain clamping down, so I grabbed the handrail. Hard. And then I had to think through bringing my other foot up from the first stair to meet the pioneering foot on the second stair. Now up to the next stair with one foot. Bring the other foot up to that stair. Stop. Get my bearings. Move my hand up the rail. Repeat. I had to stop and wait at the fourth stair for a few seconds to figure out where I was and how to get where I was going. And once I got to the top of the sixth stair, I moved myself out to the end of the railing, then turned my head first, then my body, towards the next objective, before releasing the rail and starting to walk in that direction.<br />
<br />
<b>The Sensations</b><br />
Of these three, it's the second one - not being able to bring my words into the world - that shocks me. The spelling stuff is a curiosity. These days all my machines try to correct me anyway, and if I mis-spell something in my programming, it doesn't run. But long ago I learned to write code a few lines at a time and test them at every stage, so this doesn't cause any new problems. And as disturbing as the movement impairments are, I can feel it coming on, and I just slow my life down to accommodate.<br />
<br />
But when my brain won't let the words out - it often comes as a surprise - and is accompanied by a strong pique of frustration. These days, I often start crying without warning from that <span style="background-color: white; color: #222222; font-family: "roboto" , "arial" , sans-serif; font-size: 16px;">»</span>bzzzzt<span style="background-color: white; color: #222222; font-family: "roboto" , "arial" , sans-serif; font-size: 16px;">«</span> of frustration. This week, I broke out in tears on Tuesday, Wednesday, Friday, and Saturday - sometimes several times a day. I get frustrated with having my brain seize up. Frustrated with <i>not understanding</i> why my brain works differently now. Frustrated with <i>not knowing what to do</i> to make it better. Frustrated with <i>not knowing my future -</i> is this who I'm going to be for the rest of my life? If so, I can live with it, but it's rough not knowing.<br />
<br />
<b>The Science</b><br />
I'm still pretty new at all this, but I've been eagerly reading about what I may be experiencing and why.<br />
I've had two oncologists tell me that the chemo I had (two doses of carboplatin) couldn't possibly explain my symptoms, and from what I've read so for, I'm inclined to agree. Which is strange. I know that a bunch of new symptoms first appeared when I started the chemo, it was pretty bad while I was taking it, and it got better afterwards. What's even weirder is that, in the past few weeks, they've gotten <i>more</i> prominent, not less. It could be "deconditioning" from the chemo laying me out flat in bed for a month, so that's the practical angle I'm pursuing at the moment.<br />
<br />
But here are a few interesting things from the scientific literature: first, a Swedish study did an amazing job to characterize the cognitive impairment that testicular cancer survivors experience. Rather than starting by conducting the standard neuropsych tests, they asked open-ended questions of a few dozen guys who had had testicular cancer <i>and </i> chemo about their experiences. Then, they drew up a list of a few hundred descriptions from what these men told them, and then asked over a thousand testicular cancer survivors (nearly all the survivors in Sweden) to figure out which were the most frequently endorsed, and which were particularly associated with chemo.<br />
They found that items related to language processing were the most specific to chemo, and that for many of these items, it was an occasional thing, not the sort of constant level of impairment you might expect to identify using the standard neuropsych battery. That rang true to my own experience - most of the time I have no trouble at all. And I can't produce these symptoms "on demand".<br />
But what was interesting to me is that there was really no difference between guys who had taken no chemo at all from those who had taken one to four cycles, it was really only the guys who had taken 5 or more cycles that had higher levels of impairment. And they did the right kind of analysis to show a step up from a basically flat nothing-burger to a jump to a qualitatively different experience among the guys who had taken quite a bit of chemo.<br />
<br />
Second, a Danish study did another interesting thing. They used the standard neuropsych testing battery (which is not specific to the kind of symptoms that the Swedish study identified). Like many other studies, found essentially no difference between testicular cancer survivors who had taken chemo from those who had just had surgery. But, when they compared their results to what the standard population norms are for men of the same age, and with the same educational background (both of which affect the performance on these tests), they found that testicular cancer survivors were much more likely to be cognitively impaired on just about every test than they "should" be, regardless of chemo or not.<br />
That tells me that there is something about having the cancer itself that causes cognitive changes. I don't have any idea what that is, let alone how long it lasts, or even whether it continually gets worse with age.<br />
Maybe it is something about the cancer itself. But, why the cancer itself would continue to cause problems years after it was cured is a mystery to me.<br />
Maybe it is something about having surgery. But, the surgery for testicular cancer is no more intense or invasive than a hernia repair, and there must be millions of relatively young men who have had a comparably intense surgery - you'd think if there was an epidemic of surgery-induced cognitive impairment, somebody would have noticed it decades ago.<br />
Maybe it is something about <i>the stress</i> of learning one has cancer at a relatively young age, rather than a direct physiologic effect of the cancer itself or the surgery. Maybe. But there are so many sources of stress that are more traumatic. Like being shot. Or imprisoned. I wonder what people have found out about stressful life-altering events and cognitive impairment - and particularly if it manifests in the peculiar ways I identified for myself, or the Swedes identified in their cohort.<br />
<br />
So far, I don't feel like I have anything close to an understanding of what this is all about.<br />
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<b>The Search ... Continues</b><br />
I want to re-read these articles, and as many others as I can find, with a few ideas in mind.<br />
First, what do we know about <i>characterizing the phenomenon</i> (like the Swedish study), and can we do a better job of it.<br />
Second, I want to learn more about the <i>trajectory</i> of cognitive impairment in testicular cancer survivors. The studies I've looked at so far are cross-sectional, I've got one in the bin that follows men over time, including starting with assessments after surgery but before chemo - that should be an interesting read. But I also want to know how does this play out over years - decades - after the initial surgery (with or without chemo or radiation).<br />
Third, I want to see whether the specific cognitive impairments experienced by testicular cancer survivors are similar to those reported by people who have survived other forms of cancer - particularly other forms that are infrequently treated with anything more than surgery. That is, is there something unique about the cognitive impairments associated with testicular cancer?Bill Jesdalehttp://www.blogger.com/profile/14477168137262967470noreply@blogger.com2tag:blogger.com,1999:blog-2552382737469337416.post-50519691096169752442017-11-10T17:43:00.001-08:002017-11-10T18:07:57.093-08:00What John Snow's Pump Handle Portends for Precision MedicineJohn Snow (the physician) traced the 1854 London cholera outbreak to the Broad Street water pump, had the pump handle removed, and saved the day. That's the founding narrative of public health we were taught.<br />
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Turns out, the story is more complicated. The pump handle was removed long after the threat of cholera passed from that spot, John Snow had a difficult time convincing people of his theory that cholera was spread by invisible particles borne in water, and London was rocked by Cholera many times in the following decades. Yet, we should remember John Snow for being innovative, and identifying a microbial cause for cholera, an accurate description of how the disease is transmitted.<br />
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Cholera rocked London until a bunch of people (including <a href="https://en.wikipedia.org/wiki/William_Farr" target="_blank">William Farr</a>), pursuing the <i>wrong</i> idea about how cholera spread, acted to protect the population at large by digging out the city streets and putting in an effective sewer system. The sewers of London were designed to cultivate a more productive working class by removing the filth that the city's "better classes" were convinced kept much of their workforce sick, caring for others, or dying before their working years had come to an end. At best, they were part right, in identifying filth. But they were very wrong in their thinking that it was harmful ethers emanating from the filth that was the problem.<br />
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Today, the US government has committed a huge sum to the <a href="https://allofus.nih.gov/" target="_blank">Precision Medicine Initiative</a>, with the goal of recruiting a million Americans into a cohort, and measuring our genes, with the promise of providing individualized medical information to improve the delivery of medical care. The promise is that Precision Medicine will deliver us from the hapless poking and prodding of medical practitioners groping in the dark - to a gleaming future where, after reading our genes, a precisely-guided medication will be identified to provide us with maximal benefit. Turns out, the promise of a gleaming future where we will be known completely and our dis-eases will be readily dispatched is as old as medicine itself (see this great <a href="https://www.theatlantic.com/health/archive/2016/12/the-peril-of-overhyping-precision-medicine/510326/" target="_blank"><i>Atlantic</i> post by Nathaniel Comfort</a>).<br />
But perhaps <i>this time</i>, medicine, or particularly Precision Medicine, will deliver on the promise. I would argue, that even if it does deliver miraculous rescues of many people from the clutches of truly horrifying diseases, it will likely have little impact on our health as a population, perhaps even diverting our attention from those interventions most likely to have the greatest beneficial impacts.<br />
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John Snow correctly identified the causal mechanism behind the transmission of cholera from one person to another. What he did not do (and I would argue could not do) was use that information to effectively prevent the spread of cholera from one person to another. Imagine, if you will, that he had been able to convince the authorities in London of the water-borne nature of cholera, and that it's spread could be interrupted by the timely removal of pump handles. One can imagine setting up an infrastructure to identify cases of cholera (that already existed, though could have been improved), leading to the deployment of a team of investigators to identify the probable source, and in turn remove pump handles in affected areas. Would this have stopped cholera? Yes, within days, by which time it would likely have spread to dozens of other people. And in London, a hub of global trade, the frequent re-introduction of cholera was virtually guaranteed. Not to mention the hardship on the population of having to travel farther and farther to get their water from an ever-diminishing set of functioning pumps, and whether some of those people were now carrying cholera with them to other parts of the city. John Snow and his pump handle removers would be playing whack-a-mole. They would, over the years, improve their methods and whack the cholera-laden pump handles faster and faster. But it wouldn't do anything to stop cholera from cropping up in the first place.<br />
Installing the sewers, on the other hand, (largely) prevented cholera from being transmitted, even when it was re-introduced to the city.<br />
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My analogy then, is to whether the knowledge to be gained from the Precision Medicine Initiative (and the knowledge gained will be vast) will have much impact. The first fruits from precision medicine have been in the area of cancer treatment. Some tremendously impressive gains have been made. I imagine it's likely that many more are coming.<br />
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<a href="https://vignette2.wikia.nocookie.net/minority-report/images/f/f0/Wally_and_the_Precogs.jpeg/revision/latest?cb=20170804220557" imageanchor="1" style="clear: right; float: right; margin-bottom: 1em; margin-left: 1em;"><img alt="Image result for minority report" border="0" height="133" src="https://vignette2.wikia.nocookie.net/minority-report/images/f/f0/Wally_and_the_Precogs.jpeg/revision/latest?cb=20170804220557" width="320" /></a>But the promoters of precision medicine also hold out more fantastical ideas. For instance: that we will be better able to assess the risk of future disease. That sounds awesome. If I know I'm at increased risk of heart disease, I can take action - I can eat less red meat, more veggies, exercise more, and so on. But a couple of other things also may happen that should be of great concern. The simplest concern is that people "not" at increased risk of heart disease may think they are wasting their time by doing things to prevent heart disease. Why walk laps at the mall and eat like a bird when I could just eat what I like and spare my knees the agony? But <i>everyone</i> is at risk of heart disease, so what's the great advantage in hair-splitting whether we are 30% likely or 70% likely to die from it?<br />
What about finding out I have an elevated risk of an unpreventable health outcome? Is that knowledge worth knowing? In some cases it may be, but frankly, I'd rather not know if I have a higher risk of Parkinson's or Alzheimer's. It's just one more worry in a life full of worries. I'm glad I didn't waste time worrying about testicular cancer before I got it.<br />
And here's a really scary thought - many of the things we do as individuals to prevent disease are ineffective - either we've got the wrong idea about how the disease works, or we have over-estimated the preventive effect of taking action, or we are taking the right action, but 10 years too late. What then are we doing to people by telling them there is a train coming down the tracks at them, then suggesting they totally change their lives, when those changes may or may not pull them off the tracks - or even worse, put them on more dangerous footing.<br />
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<span style="font-size: large;">Individualizing risk <i>individualizes</i> risk</span><br />
Here I think is the biggest problem, and it's where I come back to John Snow and the individualized approach to prevention. The promise of individualizing our risks - perhaps producing a scorecard with red, orange, yellow and green labels on various potential diseases facing our future selves - this individualization has an insidious impact. It implies that whatever got us to <i>here</i>, we ourselves are responsible for the next step. It localizes prevention to the individual, and as a result, implies we are each responsible in our own way for our health. And re-enforces the notion that others who have befallen ill health may be in some way responsible for it. Especially if we can <i>see</i> that they are lazy, fat, dumb, poorly kept, or even just poor. Sometimes, we just project those qualities upon them. And it takes away from focusing on steps we can take to promote everyone's health. Like sewers. Walkable neighborhoods. Shifting the subsidies for food production that would deliver all of us better options. Fewer handguns. More compassion. More connection.<br />
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I can hear some of you shouting that these goals need not be in conflict. I agree, they <i>need</i> not. but I'm arguing that so much of our nation's research budget, and hype about the future, are devoted to diving deeper into our genes, that leaves the rest of us a bit parched.Bill Jesdalehttp://www.blogger.com/profile/14477168137262967470noreply@blogger.com0tag:blogger.com,1999:blog-2552382737469337416.post-31369245330915249442017-10-13T17:31:00.000-07:002017-10-13T18:20:57.996-07:00Me and my Orchid Tumor<span style="font-size: large;">The Origin</span><br />
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<a href="https://i.pinimg.com/236x/89/34/a7/8934a793db51cbd46faeae04492fe812--purple-orchids-orchid-flowers.jpg" imageanchor="1" style="clear: right; float: right; margin-bottom: 1em; margin-left: 1em;"><img border="0" height="200" src="https://i.pinimg.com/236x/89/34/a7/8934a793db51cbd46faeae04492fe812--purple-orchids-orchid-flowers.jpg" width="152" /></a>Back in May, I felt something odd. I was pretty sure it was a tumor, but I also have a touch of hypochondria.</div>
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I had felt something odd in the same place a year ago and had it checked out. After a few minutes in the exam room though, the urgent care doc had me convinced not to worry about it. And I didn't.</div>
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Until May 18th, when I felt something really and truly odd.</div>
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Went back to the same urgent doc. In an even shorter exam, we decided that I needed to get imaging. And fast.</div>
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<span style="font-size: large;">The Work-Up</span></div>
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Within three days, I was being examined by an ultrasound tech. I am grateful she did not remark on what she was seeing. I was fairly certain I had a tumor at that point. I had read enough about it by now to know that a key symptom was the complete absence of pain under circumstances where one should feel it. But, a haze of plausible denial was crucial for getting through the night. And the next day. During these days, I prepared myself, by saying "cancer" out loud a few times.</div>
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And I told my boss that bad news might very well be coming. And my parents.</div>
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<span style="font-size: large;">The Diagnosis</span></div>
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<a href="https://scontent.fewr1-4.fna.fbcdn.net/v/t1.0-9/18921638_10101437206573311_7020921907373160412_n.jpg?oh=327a24d097e671176fe210374d4b5945&oe=5A6DB2C2" imageanchor="1" style="clear: right; float: right; margin-bottom: 1em; margin-left: 1em;"><img alt="Image may contain: 1 person" border="0" height="199" src="https://scontent.fewr1-4.fna.fbcdn.net/v/t1.0-9/18921638_10101437206573311_7020921907373160412_n.jpg?oh=327a24d097e671176fe210374d4b5945&oe=5A6DB2C2" width="200" /></a></div>
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I waited for the diagnosis. And waited. I checked my health portal a few times a day. No news. A few days later the urgent care doc called. During a work party. That I was hosting.<br />
He delivered the news quickly. Gracefully. And hung up.</div>
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There were to be follow-up appointments to confirm the diagnosis, but this is the moment I knew. I had no doubts, no plausible alternatives to cushion me from the inevitable.</div>
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I was wounded, as this photo I took moments after hearing the news shows.</div>
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At this point, I didn't feel like the tumor was part of me. And I think that's why I didn't think of myself as "having cancer". I had a tumor, a reminder of my embryonic origins, and I wanted it gone.</div>
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<span style="font-size: large;">The Confirmation</span></div>
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<a href="https://scontent.fewr1-4.fna.fbcdn.net/v/t1.0-9/18952657_10101439476659041_8911535618476771094_n.jpg?oh=b19b28ead84359f2d31f80630f3da5f7&oe=5A79ED0A" imageanchor="1" style="clear: right; float: right; margin-bottom: 1em; margin-left: 1em;"><img alt="No automatic alt text available." border="0" height="200" src="https://scontent.fewr1-4.fna.fbcdn.net/v/t1.0-9/18952657_10101439476659041_8911535618476771094_n.jpg?oh=b19b28ead84359f2d31f80630f3da5f7&oe=5A79ED0A" width="193" /></a>I visited the urologist. She wanted it out too. And on June 14th, she delivered a mass that pathology confirmed was a 4.5 centimeter orchid tumor. It's a boy!</div>
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I was relieved. It felt great to have it out. And it felt great to have a singular focus in front of me. I had one job, and only one thing on my mind: recovering from being attacked by a very nice woman with a knife.</div>
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My parents drove to Worcester to pick me up after surgery, and I developed a nice routine of sleeping, watching TV, and allowing myself to be doted on in the most beautiful place on Earth.</div>
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Looking back, I certainly didn't think of myself as a "cancer patient", or a "cancer survivor". I pretty much just focused on the task in front of me - getting back on my feet.</div>
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I wasn't really thinking about whether I had cancer, or <i>had</i> had cancer. Or if I should feel affinity with anyone else who had had cancer.</div>
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That, and every step of the process since May 18th, I was being fed bits and pieces of information. It was all I could do to keep up with what I had in front of me. And I needed some spare bandwidth to make jokes about it all along the way as well.</div>
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<span style="font-size: large;">The Search for Information</span></div>
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Towards the end of my stay in Vermont, I began to read up a bit more on the tumor, mainly to satisfy my parents' curiosity. Early on, I had read up on what clinical trials might be available. And when I saw words like "bleomycin", my stomach sank, and I knew I had to just put it away, and work only with what was right in front of me. To make decisions one at a time, without thinking too hard about them, and definitely without looking back.</div>
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Now that I was going back in to the literature, I knew I needed to start out slow. With a review article or two. I found the claim that "the 20-year survival is indistinguishble from 100%" very re-assuring, and I repeated it often, to everyone who would hear me. But I was also reading about "surveillance", "recurrence", and "retroperitoneal lymph nodes". And for the first two weeks back at work, I had no patience for it. Instead, I pulled dozens of articles about seminoma and its management, organized them into folders, and read them. And I remembered everything I read. It was astonishing to me how clearly I could recall exactly what each of the authors had said. I knew where each cohort was located, how many people were in each one, and how the results differed slightly. And how the standards of care for surveillance strategies varied from place to place.</div>
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So, by the time I met the oncologist, I knew what I wanted to do, and I had the citations in my head to back my decision up. I was well versed in the arguments for and against each strategy.</div>
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I wanted no chemo, and no CT scans. Modestly spaced followup scans for the next 5 years.</div>
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<span style="font-size: large;">The Oncologist</span></div>
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She was not down with my decision. She wanted to follow the US guidelines and do a series of over a dozen CT scans that would produce something like a 2-5% chance of inducing a brand-new tumor. I wanted to do something like the approach endorsed in the UK, Spain, and Norway, countries with much larger cohorts with much better follow-up than in the US (or anywhere else). They do many fewer scans, and over a shorter period. And they do it that way because they have found better survival and fewer side effects than the US approach. I even printed out a schedule to discuss with her. She declined to look at it, and kept repeating the guidelines. I was astonished. I was ready to discuss, to argue if necessary, to listen (if essential). But I was not prepared to find out I had a small role to play in my own care decision. I was furious.</div>
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She did agree to run my preference past the 'tumor board'. And I thought, who are they to decide? Can I trust my oncologist to fairly represent my concerns when she resisted hearing them? Can I appeal their decision? But decided in the end I had to stick to what's in front of me.</div>
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<span style="font-size: large;">Midlife - Crisis Preempted</span></div>
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Oh, did I mention that I first met my oncologist on my birthday? My 50th birthday?</div>
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Which brings me to how I've been travelling through this experience.</div>
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I was not shocked by the diagnosis. I've heard almost everyone else diagnosed with cancer say that they couldn't hear anything else. I was a sponge. I couldn't learn enough. I wanted all the reports, all the lab values, all the articles.</div>
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I've often noticed that I've experienced many parts of this process like a road trip. I don't really know what's coming. I've heard some stuff from other people about what's on the road ahead, but there's nothing like seeing it for yourself. I find the anticipation of these new experiences thrilling, even when they are unpleasant in many aspects. And I find myself focused on the new-ness, the unusual-ness, and the quirky unexpected moments of these experiences more than on the pain or the uncertainty. Like when I was getting the ultrasound, I started laughing in the middle of it. I was annoyed by the sound the computer made every time the tech saved a picture. Bzzzt. Bzzzt. Bzzzt. Bzzzt. And I thought, why did they make it sound like a long series of errors? Denied. Denied. Denied. They could have programmed any noise. How about: Ding. Ding. Ding. A small thing would make such a big difference in the experience. And so that made me laugh.</div>
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<span style="font-size: large;">Revising the Decision</span></div>
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Anyway, back to the decision about what to do now that I had no tumor, no sign of any tumor, a small likelihood of ever seeing this cancer again, and a life expectancy "indistiguishable" from someone who had never had a tumor. But somehow, I was still a cancer patient. With a big decision about how to "keep an eye" on things that might crop up in the future. I kept reading, and getting more and more details about recurrence rates, risk factors, short and long term side effects of the various options in front of me.</div>
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<a href="https://scontent.fewr1-4.fna.fbcdn.net/v/t1.0-9/20139873_10101490146825621_8010958787635578426_n.jpg?oh=1fd8544d6cd0a5f68a39a69cbb716f8e&oe=5A81B4B9" imageanchor="1" style="clear: right; float: right; margin-bottom: 1em; margin-left: 1em;"><img alt="Image may contain: 1 person, smiling, eyeglasses and indoor" border="0" height="150" src="https://scontent.fewr1-4.fna.fbcdn.net/v/t1.0-9/20139873_10101490146825621_8010958787635578426_n.jpg?oh=1fd8544d6cd0a5f68a39a69cbb716f8e&oe=5A81B4B9" width="200" /></a>And then the tumor board ruled. My preference was not an option. I had to choose between getting a long series of CT scans with an unacceptably high level of producing a new (and much less treatable) cancer, or I would have to take chemo. The chemo regimen was carboplatin 7xAUC, taken in two doses, three weeks apart. That chemo regime is one of the "light" ones, it has many possible short term side effects, a low risk of long term debility, and a not inconsequential risk of damaging my heart.</div>
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It took me less than a second to decide. I went with chemo. She didn't even have a chance to finish the sentence.</div>
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<span style="font-size: large;">Chemo sucks</span></div>
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<a href="https://scontent.fewr1-4.fna.fbcdn.net/v/t1.0-9/20525665_10101506391650861_4369675331683878_n.jpg?oh=4d12134554099c0c7010f82beb2a095d&oe=5A798D7A" imageanchor="1" style="clear: right; float: right; margin-bottom: 1em; margin-left: 1em;"><img alt="Image may contain: 1 person, cloud, sky, closeup and outdoor" border="0" height="98" src="https://scontent.fewr1-4.fna.fbcdn.net/v/t1.0-9/20525665_10101506391650861_4369675331683878_n.jpg?oh=4d12134554099c0c7010f82beb2a095d&oe=5A798D7A" width="200" /></a>My first dose of chemo was cool enough. I was back on the road, I had a reason to take myself out of the everyday, and to experience what was happening to my body. I cut my hair short as a ritual to prepare for the chemo. At first, there were no symptoms at all. Over the next few days, I became exquisitely sensitive to sunlight - I got a deep tan in a matter of minutes. I posted updates on social media because I needed to stay in touch. I needed to know for sure people were thinking of me. And I put on a brave face. Thumbs up. But looking back at these photos, I can see I wasn't as upbeat and perky as I felt. I was still able to put in a little bit at work, but that came to a screeching halt with the second dose.</div>
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<a href="https://scontent.fewr1-4.fna.fbcdn.net/v/t1.0-9/20882157_10101515953543751_2447184722841612893_n.jpg?oh=0fd6e7796a523aff8fb4f9f20b99e74b&oe=5A87C4E9" imageanchor="1" style="clear: right; float: right; margin-bottom: 1em; margin-left: 1em;"><img alt="Image may contain: 1 person, outdoor" border="0" height="81" src="https://scontent.fewr1-4.fna.fbcdn.net/v/t1.0-9/20882157_10101515953543751_2447184722841612893_n.jpg?oh=0fd6e7796a523aff8fb4f9f20b99e74b&oe=5A87C4E9" width="200" /></a>It is painful to look back at this picture. I swear I was genuinely happy when I took it. I had just showered, and although showering and walking back upstairs afterwards had taken every bit of energy out of me, I felt clean, I had fresh sheets, I could feel the love of so many people flowing through me. And yet, look at that face. I thought I was beaming.</div>
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I spent most of the month of August in bed. Most of it sleeping. Some TV, some reading, but mostly just laying in bed resting. My folks were terrific. If this scared them, they didn't show it. We had breakfast every morning. And if I was feeling frisky, we would play a few hands of cards. We had dinner every night. And I shouted questions at Alex Trebek.</div>
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But honestly, the fatigue was terrible. For weeks, I couldn't do more than walk downstairs, eat, and I'd be gasping for breath by the time I got back upstairs to bed.</div>
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When I got back to work, I could only last about 5 minutes in conversation before my mind would clamp down. My eyes would flow with tears because I couldn't get words to show their faces. I'd have to explain that I needed to be alone.<br />
I couldn't do any programming. I made spelling errors I've never made before. Lots and lots of spelling errors. But it's all part of the road trip. I found myself fascinated by what I could and couldn't do, and how long it took for my brain or my lungs to wave the white flag. The spelling errors were telling me about how my memories are organized, my lungs were a governor. Exhaustion is sweet in its way. It is a clear and unmistakable break. It gives one full permission to stop. Stop and experience the familiar world as alien and new.</div>
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I've been back teaching for five weeks now. The first two weeks, it was all I could do to show up and mumble through the slides. Someone in my cancer support group suggested methylphenidate, and the requisite professionals agreed. It makes a huge difference, now I'm back to joking with the students, poking the quiet ones, cruising from one side of the room to the other, fielding questions, re-thinking my approach to teaching the basics: person-time. Randomness. Causation.</div>
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I've brought the students right along on this journey with me. They've seen me slouching in the chair, exhausted after an hour but still pushing through. But now they see something very much like my old self.</div>
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Very much like.</div>
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I feel like a counterfactual me. Almost a mirror image, but more of a quantum-entangled actor on the other side, uncannily diverging from what I would have been.</div>
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The road stretches out before us.</div>
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Let's roll.</div>
Bill Jesdalehttp://www.blogger.com/profile/14477168137262967470noreply@blogger.com2tag:blogger.com,1999:blog-2552382737469337416.post-49160732133653574172016-06-19T15:20:00.002-07:002016-06-19T15:20:10.006-07:00Guns Don't Solve Problems. Guns Cause Problems. A week has passed since the Orlando Massacre. I've spend most of the time since grieving, seeking the comfort of others, and sharing what comfort I can. In the back of my mind has been a battle over how to "respond". Personally, publicly, politically. I'm still at a loss, but here's one partial, tentative response, among the many swirling in my head.<br />
So many things got "attached" in one way or another to the profound loss Orlando, the Nation, and beyond experienced: gay blood, the presidential race, violence prevention, overpolicing, I could go on.<br />
Perhaps the thing that most caught my eye was the idea that somehow now that "the gays" were activated, gun control might stand a chance of moving forward in ways it has been slow to in the wake of so many national tragedies. On one hand, I'm perplexed by the idea, but also inspired by the challenge - we've made such huge progress on the "gay agenda", what can we offer?<br />
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<span style="font-size: large;">Levers of Power and Change</span><br />
What can "the gays" offer to gun control efforts? I guess I'd start with a basic breakdown of the levers of power and change in this country thatwe've appealed to at one juncture or another in recent years: the executive, legislative, and judiciary branches of government, the press, entertainment, science, and interpersonal relationships.<br />
It appears that the judiciary is, for the next 3-5 years anyway, unlikely to be of great assistance. The legislature (Federally) is hamstrung, and likely to remain so at least until the 2018 election, and probably beyond. Although some State and local governments have made heroic efforts in recent years, the porous nature of State boundaries put severe limits on what can be accomplished. The Federal executive has done about as much as it can in the face of strident opposition from the other to branches. The press is a strong advocate, and also a strong impediment. More on that later. Science is unlikely to be of great assistance - the basic work demonstrating that possession of a firearm greatly increases one's own risk of death is already well established. In my experience, science itself rarely moves political change. Narratives do what statistics appear to be incapable of. Coming out has been an incredibly powerful method for invoking change, starting at the most atomistic level of power: family and friendship. But frankly, I'm struggling to think of what the corollary in terms of violence reduction through gun control is.<br />
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<span style="font-size: large;">Changing the Narrative</span><br />
I suspect the way to create the greatest momentum now, which can later be leveraged for policy change, is to work on the narrative of how we tell stories about guns. <b>Guns don't solve problems. Guns cause problems.</b><br />
But in the movies and video games, guns are frequently portrayed as solving problems, from a distance, without consequence. Now I love me some violent video games and movies (Reservoir Dogs springs to mind), but to the degree that we don't tell an accurate story about how using guns screws everyone up, we're doing a disservice. We should demand that television and film makers don't turn to guns as a plot device to get rid of a problem to allow the protagonist to progress, but rather every use of a gun should endeavor to show how it makes everyone's life, including the shooter's, much more complicated and less comfortable. That would be a more accurate story.<br />
Narrative moves opinion more than statistics do. With that in mind, a committed, passionate press should tell more stories about the consequences of firearms use, more about the victims, and also more about what happens to those who carry and fire them. Get into the fears that drive people to carry, and to shoot, get into the legal consequenes, sure; and also the long term psychological consequences of what injuring or killing another human causes. Get the stories of people who used to carry and decided not to any longer - give current firearms owners a path out.Bill Jesdalehttp://www.blogger.com/profile/14477168137262967470noreply@blogger.com0tag:blogger.com,1999:blog-2552382737469337416.post-67661955620155186642016-01-24T15:39:00.000-08:002016-01-24T15:48:48.638-08:00New Data. New Opportunities.So, I'm working on a new analysis, and my plan is to describe what I'm doing, why I'm doing it, and what I'm learning along the way, as it happens.<br />
<div>
<br /></div>
<div>
Well, I guess it's not entirely "new", depending on how you count it, I've been working on this for years already.</div>
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<br /></div>
<div>
It's an extension of prior work I've talked about a lot on this blog, the extraordinary finding that gay men are more likely to be in "excellent" health than heterosexual men.</div>
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<br /></div>
<div>
I've looked at this issue in many datasets, and published a paper on it in one particular dataset, the Behavioral Risk Factor Surveillance Survey, where I looked at the self-reported general health of men in same-sex and mixed-sex couples, based on their marital status and household composition.</div>
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<br /></div>
<div>
In that study, I found that about half the general health advantage of men in same-sex couples was explained by the facts that men in same-sex couples were younger on average, better educated, and wealthier. When I looked at some of the specific health characteristics, men in same-sex couples were less likely to be overweight or obese, but more likely to smoke. Taking all the health factors I could into account didn't explain why man in same-sex couples were healthier than men in mixed-sex couples.</div>
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<br /></div>
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But what do I want to do now, in this new analysis? Well, it may yet shift and wiggle from this idea, but I'd like to do a couple of things. First, there's a lot more data to work with now. Instead of having to look at marital status and household composition, we can now look at sexual orientation directly, because a number of states have started to ask people not just who you live with and your marital status, but also whether you think of yourself as heterosexual, gay, lesbian, bisexual, or something else.</div>
<div>
Second, because we now have both sexual orientation <i>and </i>marital status, and same-sex couples can get married in a lot more places than when I did the earlier analyses, I can look at single gay, lesbian and bisexual people, bisexual people in same-sex and mised-sex relationships, and a variety of household composition structures.</div>
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Third, I'd like to try to fold three different surveys into a single analysis, the BRFSS (which is the largest of the surveys, but the sexual orientation questions are asked in only select states), the National Health Interview Surveys (which are smaller, but comprehensively asked all across the country), and the National Health and Nutrition Examination Surveys (which are smaller still, but have a great deal of depth to them).</div>
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<br /></div>
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There are other ideas I'd like to look at too, like the degree to which heterosexuals would be misidentified as being sexual minorities because they live in same-sex households (like my heterosexual roommate). And what geographic and demographic factors predict that misclassification.</div>
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Also on the issue of misclassification, the BRFSS asks interviewers to guess the sex of the person they're on the phone with, and these data give us an estimate of how often they guess wrong.</div>
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Another set of issues I could look at with these data are things like basic demographics - Who do gay men and lesbians live with? How many are in relationships? Believe it or not, these basic issues have barely been touched in the academic literature, because data like this has not been available before.</div>
Bill Jesdalehttp://www.blogger.com/profile/14477168137262967470noreply@blogger.com0tag:blogger.com,1999:blog-2552382737469337416.post-50987440803926764532015-12-26T13:45:00.000-08:002015-12-26T13:50:02.266-08:00Coming out as a blood donorI've been a surreptitious blood donor for 14 years.<br />
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhXe5H5XBUwD3oqFvQEUvSjJ9PquJFwGNvh4dBr4vDIJuw7FVydCdfHqBf3lmOtponVGVomxa4EE9NS8yZAKozFBbZNAYRxvRdTQXOYUeMpsBW2tyLHzLLmoNTVJnif8rNX35Qn1_hu8bou/s1600/blood+donation.jpg" imageanchor="1" style="clear: right; float: right; margin-bottom: 1em; margin-left: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhXe5H5XBUwD3oqFvQEUvSjJ9PquJFwGNvh4dBr4vDIJuw7FVydCdfHqBf3lmOtponVGVomxa4EE9NS8yZAKozFBbZNAYRxvRdTQXOYUeMpsBW2tyLHzLLmoNTVJnif8rNX35Qn1_hu8bou/s320/blood+donation.jpg" /></a><br />
It all started after 9/11. I was stricken with grief. The enormity of the tragedy taking place in New York City weighed heavily and I wanted to do something, anything, to help with the recovery.<br />
A few days later, there was a blood drive at school, and I thought long and hard about donating. I desperately wanted to do something useful, but I also knew it would mean lying to do it.<br />
Eventually, I decided that giving blood would be more important than the lie I'd have to tell to give it. It never felt right, lying, and I'm relieved that I won't have to do it ever again.<br />
<br />
Giving blood is an important part of my life. I'm humbled by the idea that parting with a pint or two here and there can help others get through the worst day of their lives.<br />
And frankly, selfishly, it feels good to feel 'healthy', that my body has something of value for others. That's not a message that gay men get often enough.<br />
<br />
At this point, some of you may be shouting at the screen, wondering how I could be so irresponsible as to endanger the blood supply. I've seen what HIV can do. I've lost friends to it. I've seen what the meds can do. Believe me, I'm under no illusion that HIV is something minor. Sure it's treatable, perhaps even managable at this point, but I could never live with myself if I thought there was even a fraction of a chance I could give HIV to someone through a blood donation.<br />
At the same time, the rule that the blood donation system used is ridiculous - permanent deferment for any man who has had sex with a man since 1978.<br />
<br />
So, I had to make up my own rules. I decided to go with six months since last having sex, and then an HIV test just to make sure, before allowing myself to donate.<br />
I guess I have sex so infrequently that waiting six months isn't a big deal. There were even a few opportunities I passed up because my (secret) identity as a blood donor wasn't worth putting on hold.<br />
<br />
Speaking of secret identities, it was quite jarring to go back into the closet to give blood. And to stay in the closet about being a blood donor everywhere else. I wanted to ask for the pink gauze to wrap up my arm after the donation, but had to bite my tongue. I had to make sure I wore the t-shirts that they give you inside out, and I couldn't accept as a gift in exchange for donation anything that would visibly associate me with being a donor.<br />
<br />
I frequently struggled with who, how and when to "come out" as a donor in the rest of my life. Half of me wanted to come out fully and fight against the injustice of the gay donor ban. Half of my wanted to fly under the radar and help as many people anonymously as I could with my donations. I can't say I ever felt like I made the "right" choice there, often flying a bit close to the sun trying to do both at once.<br />
<br />
For those members of my family, my friends, my coworkers, I'm sorry I didn't feel comfortable coming out to you as a blood donor - I hope you won't feel betrayed that I kept this to myself. And of course to the nurses at the dontation centers I've given at, I apologize for lying right to your face.<br />
But to the mucky-mucks at the FDA - screw you. You've made my life uncomfortable and duplicitous and prevented many valuable donations from being received by others.<br />
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Gentle readers, I'm curious to hear your thoughts.Bill Jesdalehttp://www.blogger.com/profile/14477168137262967470noreply@blogger.com2tag:blogger.com,1999:blog-2552382737469337416.post-71373187275843070872014-12-15T14:14:00.000-08:002014-12-15T14:18:12.371-08:00Observations on 3-fold interactionsSorry about how "mathy" this post is. I'm percolating about what to write about <a href="http://billandtuna.blogspot.com/2013/07/allowing-gay-blood-would-increase-safety.html" target="_blank">gay blood donors</a>, but I need to think on that for another few days.<br />
<br />
The last lecture for my epi class was about effect measure modification (interactions). Most people do it completely wrong, they use an interaction term in a statistical model (Y=a +b<span style="font-size: xx-small;">1</span>X<span style="font-size: xx-small;">1</span> + b<span style="font-size: xx-small;">2</span>X<span style="font-size: xx-small;">2</span> + b<span style="font-size: xx-small;">3</span>X<span style="font-size: xx-small;">1</span>X<span style="font-size: xx-small;">2</span>), and then interpret b<span style="font-size: xx-small;">3</span> as though it's telling you something interesting. It isn't (except in extremely unusual circumstances).<br />
What you really want to know is the degree to which being exposed to X<span style="font-size: xx-small;">1</span> and X<span style="font-size: xx-small;">2</span> produces more disease than you'd expect if all you know was the effect of X<span style="font-size: xx-small;">1</span> in the absence of X<span style="font-size: xx-small;">2</span> and the effect of X<span style="font-size: xx-small;">2</span> in the absence of X<span style="font-size: xx-small;">1</span>.<br />
Or, in mathy terms, let R<span style="font-size: xx-small;">ij</span> be the rate of disease when X<span style="font-size: xx-small;">1</span>=i and X<span style="font-size: xx-small;">2</span>=j<br />
We want to know whether (R<span style="font-size: xx-small;">11</span>-R<span style="font-size: xx-small;">00</span>), The difference that both make when working together, is greater or less than (R<span style="font-size: xx-small;">01</span>-R<span style="font-size: xx-small;">00</span>) + (R<span style="font-size: xx-small;">10</span>-R<span style="font-size: xx-small;">00</span>), the difference each makes in the absence of the other.<br />
<br />
I'm going to skip right to the three-factor effect measure modification - here the idea is whether:<br />
(R<span style="font-size: xx-small;">111</span>-R<span style="font-size: xx-small;">000</span>), the effect of all three together,<br />
is comparable to the effect of each of the three in isolation:<br />
(R<span style="font-size: xx-small;">100</span>-R<span style="font-size: xx-small;">000</span>) + (R<span style="font-size: xx-small;">010</span>-R<span style="font-size: xx-small;">000</span>) + (R<span style="font-size: xx-small;">001</span>-R<span style="font-size: xx-small;">000</span>).<br />
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First implication: In order to make that assessment, your study needs people with none of the exposures, all of the exposures, and at most one of the exposures. It does not need anyone with two of the exposures, so including any such subjects would be inefficient. That's bizarre.<br />
<br />
Second implication: The fact that those people with two exposures are irrelevant actually points to the fact that there could be four quantities of interest: First, the one comparing each of the three in isolation to the effect of all three together, and then three iterations of comparing one in isolation with the other two in combination, i.e.<br />
(R<span style="font-size: xx-small;">110</span>-R<span style="font-size: xx-small;">000</span>) + (R<span style="font-size: xx-small;">001</span>-R<span style="font-size: xx-small;">000</span>)<br />
<i>or</i> (R<span style="font-size: xx-small;">101</span>-R<span style="font-size: xx-small;">000</span>) + (R<span style="font-size: xx-small;">010</span>-R<span style="font-size: xx-small;">000</span>)<br />
<i>or</i> (R<span style="font-size: xx-small;">011</span>-R<span style="font-size: xx-small;">000</span>) + (R<span style="font-size: xx-small;">100</span>-R<span style="font-size: xx-small;">000</span>)<br />
So, there are actually four interaction terms to compare to the joint effect: (R<span style="font-size: xx-small;">111</span>-R<span style="font-size: xx-small;">000</span>).<br />
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Third implication: I love how the math and the concepts circle around and inform one another. In this case, the fact that there is one comparison to make when there are two exposures, but four to make when there are three, suggests to me that our brains are not well suited to thinking about the issue of three factor interactions, and the whole idea should ideally not be attempted at all.<br />
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<br />
But hmmm, what happens when we go to four....<br />
(R<span style="font-size: xx-small;">1111</span>-R<span style="font-size: xx-small;">0000</span>) would be the joint effect of all four.<br />
The single factors adding up would be:<br />
(R<span style="font-size: xx-small;">1000</span>-R<span style="font-size: xx-small;">0000</span>) + (R<span style="font-size: xx-small;">0100</span>-R<span style="font-size: xx-small;">0000</span>) + (R<span style="font-size: xx-small;">0010</span>-R<span style="font-size: xx-small;">0000</span>) + (R<span style="font-size: xx-small;">0001</span>-R<span style="font-size: xx-small;">0000</span>)<br />
Three together + one more would be:<br />
(R<span style="font-size: xx-small;">1110</span>-R<span style="font-size: xx-small;">0000</span>) + (R<span style="font-size: xx-small;">0001</span>-R<span style="font-size: xx-small;">0000</span>)<br />
<div>
(R<span style="font-size: xx-small;">1101</span>-R<span style="font-size: xx-small;">0000</span>) + (R<span style="font-size: xx-small;">0010</span>-R<span style="font-size: xx-small;">0000</span>)</div>
(R<span style="font-size: xx-small;">1011</span>-R<span style="font-size: xx-small;">0000</span>) + (R<span style="font-size: xx-small;">0100</span>-R<span style="font-size: xx-small;">0000</span>)<br />
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(R<span style="font-size: xx-small;">0111</span>-R<span style="font-size: xx-small;">0000</span>) + (R<span style="font-size: xx-small;">1000</span>-R<span style="font-size: xx-small;">0000</span>)</div>
<div>
Two together plus each of the other two alone would be:</div>
(R<span style="font-size: xx-small;">1100</span>-R<span style="font-size: xx-small;">0000</span>) + (R<span style="font-size: xx-small;">0010</span>-R<span style="font-size: xx-small;">0000</span>) + (R<span style="font-size: xx-small;">0001</span>-R<span style="font-size: xx-small;">0000</span>)<br />
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(R<span style="font-size: xx-small;">1010</span>-R<span style="font-size: xx-small;">0000</span>) + (R<span style="font-size: xx-small;">0100</span>-R<span style="font-size: xx-small;">0000</span>) + (R<span style="font-size: xx-small;">0001</span>-R<span style="font-size: xx-small;">0000</span>)</div>
(R<span style="font-size: xx-small;">1001</span>-R<span style="font-size: xx-small;">0000</span>) + (R<span style="font-size: xx-small;">0100</span>-R<span style="font-size: xx-small;">0000</span>) + (R<span style="font-size: xx-small;">0010</span>-R<span style="font-size: xx-small;">0000</span>)<br />
<div>
(R<span style="font-size: xx-small;">0110</span>-R<span style="font-size: xx-small;">0000</span>) + (R<span style="font-size: xx-small;">1000</span>-R<span style="font-size: xx-small;">0000</span>) + (R<span style="font-size: xx-small;">0001</span>-R<span style="font-size: xx-small;">0000</span>)</div>
(R<span style="font-size: xx-small;">0101</span>-R<span style="font-size: xx-small;">0000</span>) + (R<span style="font-size: xx-small;">1000</span>-R<span style="font-size: xx-small;">0000</span>) + (R<span style="font-size: xx-small;">0010</span>-R<span style="font-size: xx-small;">0000</span>)<br />
<div>
(R<span style="font-size: xx-small;">0011</span>-R<span style="font-size: xx-small;">0000</span>) + (R<span style="font-size: xx-small;">1000</span>-R<span style="font-size: xx-small;">0000</span>) + (R<span style="font-size: xx-small;">0100</span>-R<span style="font-size: xx-small;">0000</span>)</div>
<div>
And then two together plus the other two together would be:</div>
(R<span style="font-size: xx-small;">1100</span>-R<span style="font-size: xx-small;">0000</span>) + (R<span style="font-size: xx-small;">0011</span>-R<span style="font-size: xx-small;">0000</span>)<br />
<div>
(R<span style="font-size: xx-small;">1010</span>-R<span style="font-size: xx-small;">0000</span>) + (R<span style="font-size: xx-small;">0101</span>-R<span style="font-size: xx-small;">0000</span>)</div>
(R<span style="font-size: xx-small;">1001</span>-R<span style="font-size: xx-small;">0000</span>) + (R<span style="font-size: xx-small;">0110</span>-R<span style="font-size: xx-small;">0000</span>)<br />
<div>
<br /></div>
<div>
Ai! 14 terms to keep in mind simultaneously.</div>
Bill Jesdalehttp://www.blogger.com/profile/14477168137262967470noreply@blogger.com0tag:blogger.com,1999:blog-2552382737469337416.post-29110091859605118742014-02-02T13:39:00.001-08:002014-02-02T14:23:38.037-08:00Interpreting interaction termsI've had the great privilege of developing a class on social epidemiology this semester, and it's been a lot of fun so far. A ton of work, but fun.<br />
There's a problem that keep cropping up though. A bunch of the articles I've picked out for my class to read have botched the interpretation of interaction terms. Even well-established leaders in the field of social epi routinely botch interpreting their interaction terms.<br />
<br />
It may sound like arcane statistical mumbo jumbo, but interpreting interaction terms is really important in the following context. Let's say I want to see whether X causes more or less disease (Y) in group A or group B. That's a classic setting for an interaction term.<br />
<br />
You could make B the reference group and model it as:<br />
Y = a + b<span style="font-size: xx-small;">1</span>X + b<span style="font-size: xx-small;">2</span>A + b<span style="font-size: xx-small;">3</span>A*X<br />
Or you could make A the reference group and model it as:<br />
Y = a+ b<span style="font-size: xx-small;">1</span>X + b<span style="font-size: xx-small;">2</span>B + b<span style="font-size: xx-small;">3</span>B*X<br />
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You <i>should</i> get the same interpretation either way.</div>
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You do when you interpret things correctly.</div>
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A lot of people interpret b<span style="font-size: xx-small;">1</span> as being the effect of X in the referent group (it is), and b<span style="font-size: xx-small;">3</span> as being the effect of X in the comparison group. Sometimes it is, but usually that's not the case.</div>
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<br /></div>
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Here's some real data. Let's say we were looking for the effect of state tax revenues per capita on mortality among Blacks vs. Whites in the 10 most populous states. <i>(full disclosure, I started out with income inequality, but the data didn't look good. I figured state tax revenues per capita are probably a good indicator of redistributive potential)</i></div>
<div>
<br /></div>
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Age-adjusted mortality in the populous states with the highest tax revenues (CA, MI, NY, NC, PA) was 776.9 per 100,000 Whites per year, and 985.2 per 100,000 Blacks per year.</div>
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Age-adjusted mortality in the populous states with the lowest tax revenues (FL, GA, IL, OH, TX) was 806.4 per 1000,000 Whites per year, and 1,026.0 per 100,000 Blacks per year.</div>
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Let's make White the reference group, as is standard practice. Then, let's do the standard logistic model.</div>
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Mortality = -4.85761 + 0.037268*low tax base + 0.237533*Black + 0.0033101*low tax base*Black</div>
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According to the flawed interpretation, the effect of having a low tax base among Whites is exp(0.037268) = 1.038, or a 3.8% increase in mortality in low tax base states, and the effect among Blacks is exp(0.0033101) = 1.003, or a 0.3% increase in mortality in low tax base states, so sloppy interpretation would lead you to think that living in a low tax base state has more impact on Whites than it does on Blacks.</div>
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<br /></div>
<div>
But what happens when you switch the reference group to Blacks?</div>
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<br /></div>
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Mortality = -4.62008 + 0.040578*low tax base -0.23753*White - 0.0033101*low tax base*White</div>
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<br /></div>
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Using the flawed approach, we would get that the effect of having a low tax base among Blacks is exp(0.040578) = 1.041, or a 4.1% increase in mortality in low tax base states, and the effect among Whites is exp(-0.0033101) = 0.997, or a 0.3% decrease in mortality in low tax base states, so the sloppy interpretation suggests that living in a low tax base state increases mortality more for Blacks than Whites, and might even be beneficial for Whites (laying aside for the moment the very important issue of the role of stochastic error in the measures).</div>
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg_-XO6wx411WnV98uxHw1l_w0hEqQLr1tJkRHugPZVFyew2XwGD_t1AV1llaqbDgrRAQ6-9mkqOaH2gmSQUbyRRa604c0hfrBlSIPHFihmsk7xoJOyknSnqt_blPVnRLHwftZhJHRZosKC/s1600/mortality+by+tax+revenues.png" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><br /></a>What's the real answer? Well it's right there when we look at the two models next to each other. Whites in a low tax base state have a 3.8% increase in mortality, but Blacks have a 4.1% increase in mortality. Not much difference, but the effect <i>appears</i> to be slightly stronger among Blacks than Whites. There is a way to get both the 3.8% and the 4.1% from only one model, but that's a bit more complicated than I want to get into in a blog post...<br />
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg_-XO6wx411WnV98uxHw1l_w0hEqQLr1tJkRHugPZVFyew2XwGD_t1AV1llaqbDgrRAQ6-9mkqOaH2gmSQUbyRRa604c0hfrBlSIPHFihmsk7xoJOyknSnqt_blPVnRLHwftZhJHRZosKC/s1600/mortality+by+tax+revenues.png" imageanchor="1" style="clear: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg_-XO6wx411WnV98uxHw1l_w0hEqQLr1tJkRHugPZVFyew2XwGD_t1AV1llaqbDgrRAQ6-9mkqOaH2gmSQUbyRRa604c0hfrBlSIPHFihmsk7xoJOyknSnqt_blPVnRLHwftZhJHRZosKC/s1600/mortality+by+tax+revenues.png" height="465" width="640" /></a></div>
Bill Jesdalehttp://www.blogger.com/profile/14477168137262967470noreply@blogger.com0tag:blogger.com,1999:blog-2552382737469337416.post-87908074266685741062014-01-11T14:48:00.001-08:002014-01-11T14:49:15.045-08:00Do homophobes really die sooner?Two weeks ago, I posted in my <a href="http://billandtuna.blogspot.com/2014/01/research-worth-reading-homophobia.html" target="_blank">Research Worth Reading</a> series about an article that found that heterosexuals harboring ill-will towards gays lived shorter lives. It seemed like a methodologically sound article, but one thing nagged at the back of my brain. The un-adjusted results were huge, and after controlling for a few sensible factors, the adjusted results were still impressive, but much smaller.<br />
That always makes me worry about uncontrolled (or poorly controlled) confounding, and I figured I'd look into it. There were a bunch of analytic choices I would have made differently, but none of them seemed like they'd be a big deal.<br />
I got excited by their analysis and writeup, and wanted to play with the same data myself, try out a few different things, maybe look at different sub-groups, that sort of thing. I also thought it was a great approach, looking at the degree to which people harboring hatred may lead shorter lives.<br />
So, I downloaded the same GSS files the authors used and fiddled around with it myself.<br />
<br />
The results I got were not quite as impressive as theirs, and suggest that nearly all the main effects can be explained easily by routine confounding factors. Rather than starting out with a 187% increased death rate that is reduced to 25% after adjustment, my analyses showed a 70% increased death rate that was reduced to 8% after adjusting for similar factors.<br />
<br />
There are a few important differences between their approach and mine, but it would be a lot more re-assuring to see similar results despite slightly different approaches, and I'm tempted to put this finding on hold until some replication in another dataset comes forward.Bill Jesdalehttp://www.blogger.com/profile/14477168137262967470noreply@blogger.com0tag:blogger.com,1999:blog-2552382737469337416.post-11546967657986328652014-01-02T16:48:00.000-08:002014-01-03T16:44:57.115-08:00Research Worth Reading - Homophobia Shortens Lives<span style="font-size: x-small;"><a href="http://www.mailman.columbia.edu/our-faculty/profile?uni=mlh2101" target="_blank">Mark L. Hatzenbuehler</a>, <a href="http://soc.unl.edu/anna-bellatorre" target="_blank">Anna Bellatorre</a>, <a href="http://www.mailman.columbia.edu/our-faculty/profile?uni=pm124" target="_blank">Peter Muennig</a>. (2014). <b><a href="http://ajph.aphapublications.org/doi/abs/10.2105/AJPH.2013.301678" target="_blank">Anti-gay prejudice and all-cause mortality among heterosexuals in the United States</a></b>. <i>American Journal of Public Health. </i>Published online ahead of print, Dec 12. 2013.</span><br />
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I'm so glad someone has finally done this study!</div>
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We all know that homophobia is bad for your health. It could be as direct as gay-bashing, or societal disapproval leading to depression, and less directly by causing high blood pressure and that sort of thing.</div>
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But what about the haters? What are the ill effects on people who are themselves homophobic?</div>
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In this paper, the authors used 20,226 people who answered the General Social Survey to figure out how much anti-gay prejudice people feel, specifically heterosexuals, then followed them for 5 to 20 years after the survey to see whether straight people who harbor anti-gay prejudices die sooner than those who don't.</div>
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They found that heterosexuals with a high degree of anti-gay prejudice were much more likely to die, dying at a rate nearly 3 times as fast as heterosexuals with a lower degree of anti-gay prejudice. That may seem implausibly high, and it is. People who harbor anti-gay prejudice tend to have less formal education, and tend to be older, and both of those factors strongly predict mortality.</div>
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But even after adjusting for age and educational attainment (and a few other things), they found that heterosexuals with a high degree of anti-gay prejudice died about 25% faster than heterosexuals with lower anti-gay beliefs. That's more reasonable, but still higher than I'd expect. I suspect that at least some of that difference is due to the fact that the General Social Survey is so long and tedious for respondents that there's a fairly high rate of non-sensical responses in there.</div>
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Promising work, but when you see over 80% of the apparent effect (an excess hazard ratio of 187% dropping to 25%) after being "explained" by control factors, what's left has to be treated very skeptically.</div>
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I'll be eager to see how this line of inquiry pans out in other datasets, although this is clearly the best dataset to start with, and it may be challenging to find another than could produce comparable results for quite some time.</div>
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Well worth reading: the language is pretty accessible even if you're not steeped in the public health world. The methods are a bit challenging, but you can skip the most confusing parts because they don't really make much difference anyway.</div>
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhCYtPhxd4qqKVoVTylDTaIbyZ2F5PAy0pTuLNrMSwmMC7vCmkiD2vMcAoCMQZ7-nxRxKLs8iO3inT-dB3-yE6sHfs_kcSIgIcYeHDe78MMexUcWqt6fULFXfRoM6L41GccIs9mTfUmZZNu/s1600/antigay+over+time.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="464" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhCYtPhxd4qqKVoVTylDTaIbyZ2F5PAy0pTuLNrMSwmMC7vCmkiD2vMcAoCMQZ7-nxRxKLs8iO3inT-dB3-yE6sHfs_kcSIgIcYeHDe78MMexUcWqt6fULFXfRoM6L41GccIs9mTfUmZZNu/s640/antigay+over+time.png" width="640" /></a></div>
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<i>Methodologic critique</i></div>
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This study is actually really well done, much better than most public health research these days. Despite the inherent flukiness of the GSS, the authors used methods that should be pretty robust despite the relatively high rates of non-sense that you find in the GSS.</div>
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Having given high praise overall, I'll move on to the relatively minor things I'd quibble with... First, the measure of whether a person has a high degree of anti-gay prejudice is based on some questions that are horribly out of date, and were horribly out of date when they were asked, from 1988 to 2002. The items are taken from a series of questions designed to assess general social attitudes about communists, atheists, homosexuals and other "undesireables", so the questions can sound a bit strange to us today, especially the first three, which are probably more about civil liberties than prejudice:</div>
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<li>"If some people in your community suggested that a book in favor of homosexuality should be taken out of your public library, would you favor removing this book, or not?"</li>
<li>"Should a man who admits that he is a homosexual be allowed to teach in a college or university, or not?" </li>
<li>"Suppose a man who admits that he is a homosexual wanted to make a speech in your community. Should he be allowed to speak, or not?"</li>
<li>"Do you think that sexual relations between two adults of the same sex is always wrong, almost always wrong, wrong only sometimes, or not wrong at all?"</li>
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If I were doing the study, I'd probably ignore the first three as anachronistic and focus just on the fourth one. But what they did is collapsed the fourth one into a yes/no of "not wrong at all" vs. any of the other responses, and then (as best as I can tell), said that a "yes" to any of the four indicated a high degree of prejudice. It's possible that someone had to say "yes" to any two or more to make it into the high prejudice category. At any rate, it would have been re-assuring to show some kind of dose-response curve from lower endorsement to higher endorsement, and also a check to see if the pattern held when just looking at the fourth item, which is most clearly related to prejudice.</div>
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Of course, it would also be nice to have some sort of response codes indicating a positive inclination towards us, rather than assuming that our words and deeds have only the potential to offend.</div>
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In terms of potential confounders adjusted for, they used pretty much the same list I would have, but I would have modeled some of them a bit differently. Rather than treating age and education as continuous, I'd want to look at them in categories first to make sure that a linear trend makes a logical fit. And I wouldn't use household income itself, but adjust it first to the size and composition of the household relative to poverty. $20,000 for a single person in 1988 would be a lot more comfortable than $20,000 for a household of four in 2002, and log-transforming the household income doesn't help with those issues at all.</div>
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Most importantly, I'd want to explore the year of the survey in a bit more detail. The surveys were conducted from 1988 to 2002, and the follow-up for death ended in 2008, so someone from the early part of the survey could be followed for up to 20 years, while someone interviewed in 2002 might be followed up for as few as five years. They used Cox proportional hazards, which <i>should</i> account for these differences in the length of follow-up, but the fact that anti-gay prejudicial attitudes have shifted rapidly over the same time period makes me less confident that the model did what it was supposed to do. You can probably think of someone who would answer those questions differently in 2002 than they would have in 1988. But the model assumes that they would have answered the same way at both points in time, or at the very least that someone giving a certain answer in 1988 had the same level of prejudice as someone giving the same answer in 2002, despite the fact that it became much less acceptable to express anti-gay attitudes over this time period. It might screw up the model a bit to add year of interview in as a potential confounder, but I'd give it a try anyway, because it's quite possible that what we're seeing is just an artifact of the fact that as the population has developed fewer anti-gay attitudes, they've also been followed for a shorter period of time, and are thus less likely to be seen dying, despite the beauty of the Cox proportional hazards approach in dealing with censored data.</div>
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Bill Jesdalehttp://www.blogger.com/profile/14477168137262967470noreply@blogger.com0tag:blogger.com,1999:blog-2552382737469337416.post-53795823820279240022013-12-29T07:27:00.001-08:002013-12-29T09:18:54.217-08:00Paid to Take Another's PunishmentI am, by any rational measure, a product of extraordinary privilege. I have a prestigious job. I own my own house. I can walk into pretty much anywhere and be taken seriously.<br />
And yet, even though I can see that those things are true, it often doesn't "feel" like that.<br />
It's not because as a gay man, I feel like a second-class citizen. I don't.<br />
It goes back to high school. Really before that, but high school makes a better story.<br />
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<tr><td style="text-align: right;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgbyXCDI5-QBdSJLpQhhxuabJC69ezxqfqFK0s7L7nRmTiYIeQtNRjwChgD6Ugru7Qq8WDRsyeTDuA2US3DteFFnGWoykdC1VYX6oVRy2JAoQMmI6AmYORJd8p8jr1sYxP3ym4EoyGeGB0e/s1600/St._John's_Chapel,_Groton_School,_Groton_MA.jpg" imageanchor="1" style="clear: right; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" height="240" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgbyXCDI5-QBdSJLpQhhxuabJC69ezxqfqFK0s7L7nRmTiYIeQtNRjwChgD6Ugru7Qq8WDRsyeTDuA2US3DteFFnGWoykdC1VYX6oVRy2JAoQMmI6AmYORJd8p8jr1sYxP3ym4EoyGeGB0e/s320/St._John's_Chapel,_Groton_School,_Groton_MA.jpg" width="320" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">St. John's Chapel, Groton School.</td></tr>
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I went to a very prestigious boarding school. The same high school as FDR, and half of JFK's cabinet. When I graduated, I was disappointed because I only got into one Ivy League school, one that I (and most of my compatriots) thought of as a "safety" school.</div>
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But I wasn't like most students there, I was the son of a teacher, a "fac brat". My parents paid pennies on the dollar for tuition, and everyone knew my place, including me.</div>
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One odd tradition they had there was that if you got caught doing something you weren't supposed to do (like skipping services in the lovely chapel shown here), you got assigned to various work duties, the lowest infractions were "punished" by cleaning up the dining hall, wiping the tables down and straightening up the chairs. One of the most severe punishments was to wash dishes, a messy, hot, wet job that lasted for hours.</div>
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In 10th grade, I figured out that you could make a bit of money by doing other people's punishments for them. I used to charge $10 to do a night's worth of dishwashing, then when I figured out you could charge even more than that exorbitant rate, I started raising my rates to $20 and even more if it was a night I didn't want to do it, or if I thought the purchaser was a jerk.</div>
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I could (usually) get away with it because these jobs were also ones that everyone had to do on rotation, so the fact that I was washing dishes even though I didn't often break the rules didn't necessarily raise eyebrows. But occasionally, one of the faculty would notice and ask "Hey Bill, didn't I see you washing dishes earlier this week?" and I'd have to lay low, not taking on any more customers until suspicions would no longer be raised.</div>
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I loved washing dishes, I loved getting messy and wet, pounding the slop down into a trench where it would become feed for the local pig farm; piling the dishes as efficiently as possible into a washing rack; jamming it into the machine, and then yanking the clean dishes off and stacking them in the appropriate piles, throwing the plates airborne as much as possible to minimize skin contact with their scorching hot surfaces. I did it in college too, as a work-study job.</div>
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At the time, it didn't feel the least bit demeaning, I was making money, and having fun while doing it. I even felt a degree of pity for the jerks who paid me to work off their punishments.</div>
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When I wonder whether they ever felt bad about it, I doubt it. Maybe a little. But they learned a valuable lesson too, one that you see every time a bank settles rather than accepts blame for screwing people over. Just pay up and move on. Maybe it's even the same guys.</div>
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Bill Jesdalehttp://www.blogger.com/profile/14477168137262967470noreply@blogger.com0tag:blogger.com,1999:blog-2552382737469337416.post-2132153931583090872013-12-22T16:10:00.000-08:002013-12-22T16:22:25.222-08:00Firearm-related Deaths, United States, 1968-2010<div class="separator" style="clear: both; text-align: left;">
A few months back, I wrote about <a href="http://billandtuna.blogspot.com/2013/05/trends-in-motor-vehicle-accidents.html" target="_blank">trends in motor vehicle accidents</a>, and then about <a href="http://billandtuna.blogspot.com/2013/07/hate-crime-statistics.html" target="_blank">trends in hate crime statistics</a>. Now with all the talk about firearm-related deaths I figured I'd look into those a bit.</div>
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So, the first obvious thing from the chart below is that there was a large increase in the firearm death rate from 1994 or so down to 1999, and it's been pretty level since then. There were also ups & downs before that, too.</div>
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The next thing I see is that changes in the total firearm-related death rate are closely linked to homicides, although the big drop in the late 1990's was due to a drop both in homicides and intentionally self-inflicted injuries, but that trends in homicides and intentionally self-inflicted injuries often follow each other, but not always (especially 2006-2010).</div>
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If 1994 sounds familiar, that could be because that's the year the Brady Handgun Violence Prevention Act took effect, requiring background checks for the sale of handguns. I don't know what happened in 1999-2000 to stop that encouraging trend line.</div>
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhEec2aPGhMHXJAztYT-MXWXqEuvy36jNdbd99gC08BsrXeX-ORsjpAQ4OxvGXGOXDLAmT8K3FhXNERU8FTAbPmU6KYlfTdJxGy283I9UfZy2MzgvfZZ7X2AlOPmOpEyc21APMXcjjEacwV/s1600/firearm+deaths+by+intent.png" imageanchor="1" style="clear: right; float: right; margin-bottom: 1em; margin-left: 1em; text-align: left;"></a></div>
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhEec2aPGhMHXJAztYT-MXWXqEuvy36jNdbd99gC08BsrXeX-ORsjpAQ4OxvGXGOXDLAmT8K3FhXNERU8FTAbPmU6KYlfTdJxGy283I9UfZy2MzgvfZZ7X2AlOPmOpEyc21APMXcjjEacwV/s1600/firearm+deaths+by+intent.png" imageanchor="1" style="display: inline !important; margin-left: 1em; margin-right: 1em;"><img border="0" height="290" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhEec2aPGhMHXJAztYT-MXWXqEuvy36jNdbd99gC08BsrXeX-ORsjpAQ4OxvGXGOXDLAmT8K3FhXNERU8FTAbPmU6KYlfTdJxGy283I9UfZy2MzgvfZZ7X2AlOPmOpEyc21APMXcjjEacwV/s400/firearm+deaths+by+intent.png" width="400" /></a></div>
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This next chart is a lot busier than it should be, but a couple things stand out clearly when you break the time trends down by age.</div>
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First, there are really different trends over time by age. There's an obvious surge in 20-24 year olds dying from 1985 to 1999, but an even more dramatic surge among 15-19 year olds, who start out (and end up) with some of the lowest firearm-related deaths, but really cranked up during the late 80's -early 90's.</div>
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All age groups saw a decline during that critical 1994-1999 period.</div>
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But when you look a little closer, something else becomes clear: the firearm-related death rates for 35-64 year olds pretty much decline throughout the whole time frame, while 75-84 year olds build up through the 80's, then decline through the 90's, and the 85+ year old group inclined through the 80's, but didn't really come down as much since then.</div>
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You may notice a sudden jump in firearm-related deaths among children in 1979, that's actually a fluke due to a change in the coding system (ICD-8 to ICD-9), but the subsequent rise, and dramatic fall in children's firearm-related mortality from that point on is real.</div>
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiQ1B8E3dn1kAL9ciwQ5SDAHUTo4YQsXdiqCKYzhp6o400mFY_59Z-tsvevY68Rf08re4Qe9U-YiMT1FuDO1KMrs0yYpx0sAnbPghgV4OUTUK1fCE9PWOtkXcC88V52Te6RUtV10_i0pSih/s1600/firearm+deaths+by+age.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="290" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiQ1B8E3dn1kAL9ciwQ5SDAHUTo4YQsXdiqCKYzhp6o400mFY_59Z-tsvevY68Rf08re4Qe9U-YiMT1FuDO1KMrs0yYpx0sAnbPghgV4OUTUK1fCE9PWOtkXcC88V52Te6RUtV10_i0pSih/s400/firearm+deaths+by+age.png" width="400" /></a></div>
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One of the frustrating things about working with US mortality data is that it's always 3-4 years out of date. I don't know why that's the case, because before there were computers, the delays in getting the death data out were measured in months. But that's a topic for another day...</div>
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Bill Jesdalehttp://www.blogger.com/profile/14477168137262967470noreply@blogger.com2tag:blogger.com,1999:blog-2552382737469337416.post-52112983283091188292013-11-11T10:45:00.000-08:002013-11-11T11:07:29.988-08:00Where are the Food Deserts?A food desert is an area where healthy food options are out of reach. You know if you're in one, but it's surprisingly difficult for the Ivory Tower crowd (like me) to figure it out. For one thing, there are at least three components in that definition. What's "healthy"? What's "out of reach"? And even what's an "area"?<br />
When I first started thinking about this, I figured, well, you just measure the distance from where a person lives to the nearest supermarket.<br />
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<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjlwxIs3ir_nl4SuMuVut_u0Oh5VaBFUQ9VpLE_mzHGd9diOZhSUjGIW8KsZzklOtbuHH-p9Uv4tLsJw0AV8Pgxmnh8JNQhG-qZDTPV7FWF5k8r_C8hgjXfNce822FlCpn3b4D2iQC5-Mdn/s1600/nearest-grocery-all-red.png" imageanchor="1" style="clear: right; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" height="200" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjlwxIs3ir_nl4SuMuVut_u0Oh5VaBFUQ9VpLE_mzHGd9diOZhSUjGIW8KsZzklOtbuHH-p9Uv4tLsJw0AV8Pgxmnh8JNQhG-qZDTPV7FWF5k8r_C8hgjXfNce822FlCpn3b4D2iQC5-Mdn/s320/nearest-grocery-all-red.png" width="320" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">image from Data Underload, flowingdata.com</td></tr>
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Turns out, that's a lousy definition. Makes for pretty pictures, though, like the one on the right, from Nathan Yau <a href="http://flowingdata.com/2013/08/27/in-search-of-food-deserts/" target="_blank">FlowingData.com</a> (love your site by the way, Nathan).<br />
The main problem with this approach it doesn't take account of of social space. Using this approach, the biggest food deserts are in actual deserts. Which would be fine if you were plopped down in a random part of the country each morning and had to figure out how to eat from scratch every morning.<br />
But we tend to live, work, play, and "get by" in neighborhoods, neighborhoods that are highly structured in physical space in a way that reflects social relations.<br />
<br />
When we looked for food deserts in Alameda county, we found that the "deserts" lay beyond the toney hills, in the outlying commuter suburbs, and the places we expected to see low food availability appeared to be chock-a-block full of supermarkets. Geographic space is part of the food desert picture, but somehow we need to get the idea of social distance in there as well to get at the idea of "out of reach".<br />
And then, there's also the idea of "healthy" food. A supermarket may be short-hand for the availability of affordable healthy food options, but there are plenty of supermarkets whose produce aisle looks like the set for a horror movie, and there are also corner stores with gourmet appeal.<br />
At the APHA meeting, I saw a bunch of posters where people had put a lot of work into figuring out food deserts in their communities, including a very ambitious project to describe food availability in great detail in New Orleans.<br />
But I want to come up with a definition of a "food desert" that I can apply across the country, and without having to visit every supermarket, corner store and farmer's market. Lately, I've been thinking about coming up with some sort of relative distance measure, like the distance to the nearest supermarket, divided by the distance to the nearest outlet that sells tobacco or alcohol. So far, I've downloaded all the supermarket locations across the country, but the number of places that sell tobacco is just too huge. Hmmm.<br />
<br />
Then, there are other important aspects to the social space that defines a food desert. I've got a job, and I drive about 40 miles to get there, so in the course of my day, I come across many food shopping alternatives. But there are many places along my route that someone without a car would have a great deal of difficulty getting to decent food. I can walk into any food vendor and get great service, even while wearing a hoodie. But not everyone wearing a hoodie gets the opportunity to pay cold hard cash for food, let alone get decent helpful service in the aisles.<br />
I'm also re-thinking food deserts as being located in a clearly delineated physical space, and instead as a condition of what an individual or family experiences. I might not be in a food desert, but my neighbor might be.Bill Jesdalehttp://www.blogger.com/profile/14477168137262967470noreply@blogger.com2tag:blogger.com,1999:blog-2552382737469337416.post-23334326883425148262013-11-11T08:48:00.000-08:002013-11-11T08:49:21.085-08:00A Glimpse of ActUp/RI<table cellpadding="0" cellspacing="0" class="tr-caption-container" style="float: right; margin-left: 1em; text-align: right;"><tbody>
<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjZjvqUrd-XcKZao2Aba1Ul16gJ32cpJlEDxo4733jFYOqgyGHq-g3ZkkBa_x20TDCH5CcmKKqKtEgWFaSx9CLJIZG3t7MJKNinAyc-hqW8rt0UY5xF4k3sy4TsHYjpRcCue9LOD3Ei__4q/s1600/Stravaganza.bmp" imageanchor="1" style="clear: right; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" height="214" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjZjvqUrd-XcKZao2Aba1Ul16gJ32cpJlEDxo4733jFYOqgyGHq-g3ZkkBa_x20TDCH5CcmKKqKtEgWFaSx9CLJIZG3t7MJKNinAyc-hqW8rt0UY5xF4k3sy4TsHYjpRcCue9LOD3Ei__4q/s320/Stravaganza.bmp" width="320" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">ActUp/RI at 'Stranvaganza. Photo by Tom Paulhus.<br />
Image from archives at the John Hay library, Brown U.</td></tr>
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I just came across this photo from the heyday of ActUp/RI, it's from a big bash at AS220 called 'Stravaganza. I'm struck by how comfortable I look in a 'teaching' role in this performance piece.<br />
This past weekend I took part in a panel discussion about ActUp/RI after a screening of 'How to Survive a Plague'. It was very confusing to sit through the movie, try to make sense of it, try to make sense of my own feelings, all while trying to respond thoughtfully to an audience.<br />
One thing that became very clear to me is that there was a huge void in my life after AIDS activism. For years, I had had an over-riding purpose, and then, after the protease inhibitors came on-line, and we had a somewhat less hostile President in the White House, it all fell apart. On this Veteran's Day, I'm struck by similarities to the stories I hear about veterans returning to civilian life after combat. You're completely consumed with the daily task of staying alive, and keeping your buddies alive, and then what? Raking leaves out of the driveway? It's impossible to replace that sense of urgency, and often really dangerous to try to.<br />
Being HIV-, I had the privilege to be able to walk away from AIDS. And the new drugs made it seem like most of my HIV+ friends, well I could <i>pretend</i> that they were out of harm's way. Even when my friend <a href="http://youtu.be/jBxfDbDmhzE" target="_blank">Stephen</a> called to let me know he was within a week of dying, I didn't want to burst the bubble, I said I was sorry to hear it, but the next time I saw him was at his funeral.Bill Jesdalehttp://www.blogger.com/profile/14477168137262967470noreply@blogger.com0tag:blogger.com,1999:blog-2552382737469337416.post-28285901038146136442013-10-23T14:27:00.000-07:002013-10-23T14:27:01.047-07:00Interpreting Racial Disparities in MortalityA few months back, I wrote about <a href="http://billandtuna.blogspot.com/2011/05/health-disparities-getting-worse-or.html" target="_blank">one problem plaguing interpretation of racial disparities in mortality</a> rates. It isn't clear when comparing over time whether we should divide or subtract, and you almost always get different answers if you do one vs. the other.<br />
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Here's another problem facing how we interpret racial disparities.<br />
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It gets back to the difference between a fraction and a ratio - a ratio is dividing any two numbers, but for a fraction, the numerator has to be a subset of the denominator. So, miles per gallon is a ratio, but not a fraction. Dividing the number of people who vote in that town by the number of people who live there <i>is</i> a fraction, because everyone who votes there also lives there. Dividing the number of people who shop at a particular store by the number of people who live in the same town the store is located in is<i> not</i> a fraction, because some people who shop there don't live there. That "shopping ratio" looks a lot like a fraction, because you're dividing a target number of people by a broader baseline population, but it's technically a ratio because the numerator isn't a subset of the denominator.<br />
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In a similar fashion, race-specific mortality rates look a look like fractions, but they are actually ratios. That's because we get mortality rates by dividing the number of people counted as dying by the number of people the census counted as living at the beginning of the year. It's true that the dead are a subset of the once living, but here the issue is that the way we classify race differs between the death records and the census records.<br />
On the census form, the head of household usually fills out the form, and describes the race/ethnicity of everyone in the household, someone that s/he knows intimately. On the other hand, death records are usually filled out either the physician certifying the death, or a mortician at a funeral home, so it is based on how someone outside the family perceives the deceased. In other words, denominator race (census) is <i>self-defined</i>, while numerator race (death records) is <i>other-ascribed</i>.<br />
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So, there are potentially problems where there is a large discrepancy between <i>self-defined</i> and <i>other-ascribed</i> race, for example, someone with Mexican ancestry who appears to strangers as White. That's how we end up with a ratio instead of a fraction: we're dividing the number of people who died with a given other-ascribed race by the number who were living with the same racial/ethnic classification, but on the basis of self-definition.<br />
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Camara Jones incorporated a question into the BRFSS that gives us a handle on the difference between self-defined and other-ascribed race: "How do other people usually classify you in this country?". Among self-identified Whites, 98% were perceived by others to be White, and 96% of self-defined Blacks were usually seen as Black by others. So far, not bad.<br />
But only 77% of Asians said others usually saw them as Asian, 63% of Hispanics said others usually saw them as Hispanic, while 27% saw them as White, and a mere 36% of American Indians said that others usually saw them as such, even more (48%) saw them as White.<br />
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So, on that basis, we'd expect that a large proportion of Asians, Hispanics, and especially American Indians would be counted as White on the death records, which would result in lower apparent mortality rates in these groups than one might expect, and somewhat higher mortality rates for Whites than is truly the case.<br />
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And when you look at overall mortality rates, that seems to make sense: Of the major racial/ethnic groups, only Blacks appear to have higher mortality than Whites. American Indians appear to have about the same mortality as Whites, while Hispanics appear to have about 75% of the mortality rate that Whites do, and Asians appear to have about half the mortality that Whites do. But is it credible that American Indian populations have no higher mortality than Whites, or that Asians are twice as likely as Whites to live to old age?<br />
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I've been thinking about how to adjust for this problem, perhaps using the estimates from the survey data above to shift people in the denominator from the census-derived self-defined groups so that they match the other-perceived rates that the death records are based on. That's unsatisfying for two reasons: first because the classification of race on death records is other-ascribed, but it's still by someone who (ideally) has gotten to know the deceased and/or their family at least somewhat, so it's not like a random stranger passing on the street, which is closer to how the survey question is framed. Second, it makes the assumption that the death record classification is "correct" and the census data needs to be adjusted to fit the correct numerator. So, a kludgy tool, but perhaps useful.<br />
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Another way to look at this issue is to think about <i>where</i> in the country the mismatch between self-defined race and other-ascribed race is likely to be greatest, then look at the mortality rate ratios across those areas. So, I figured that there wouldn't be much difference between Blacks and Whites anywhere in the country. Over 95% of both groups say they are usually seen by others as being Black or White, because there's such an extensive history of anti-Black racism in this country, and because there are very few places where Whites never see Blacks and vice-versa. But, the other three groups (Hispanics, Asians, and American Indians) are distributed very unevenly across the country, and there are many Whites who wouldn't run out of fingers before counting all the people they know from one of these groups.<br />
So, I would expect that in places where Whites are least likely to be familiar with people of one of these groups, the level of mis-classification would be highest. And that's in fact what I saw when I crunched the numbers.<br />
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In this graph (just women for the moment), I've arranged the states from those with very few American Indians, Asians or Hispanics (like West Virginia (1.7%) and Maine (2.7%)) to those with the highest proportion of these groups (California (48%), New Mexico (54%), and Hawai'i (73%)). The colored dots indicate the apparent ratio between each groups mortality and that of Whites, the Orange dots are for American Indians relative to Whites, the purple dots for Hispanics relative to Whites, the green dots for Asians relative to Whites, and the red dots for Blacks relative to Whites.</div>
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Looking at the red dots (Blacks vs. Whites), there's not a lot of difference from one side of the graph to the other, a little increase, but not much. Not surprising because there's not a lot of misclassification of these groups.</div>
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There's also not much difference for the Asians, despite the finding from the survey research that almost a quarter of Asians report usually being seen as another race by others. It is interesting to note that the apparent racial disparity is certainly the smallest in Hawai'i, a state where API populations make up the majority of the population.</div>
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But for Hispanics and American Indians, there are really big differences depending on which side of the graph they are on. Hispanics appear to have about 70% lower mortality in West Virginia, but nearly identical mortality to Whites in New Mexico. And although the national average for mortality between American Indians and Whites appears to be about equal, in states at the lower end of the graph the mortality appears to be much lower, and on the upper part of the graph, more mixed, some higher, some lower.<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgBsEXnL5iDH4G_E7GLWmdSSeI2EoqlWU7hfpdiBAo2scdhQw7kMN-Po8BLv1S_hJ634vLJvaq1CbIspBREPsH_kDG2hb38gUjUy94bRwT9LEZw7j8CbN2mrF6GkC9ZlPGYqShY2wSGpjfs/s1600/AIAN+disparity.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="464" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgBsEXnL5iDH4G_E7GLWmdSSeI2EoqlWU7hfpdiBAo2scdhQw7kMN-Po8BLv1S_hJ634vLJvaq1CbIspBREPsH_kDG2hb38gUjUy94bRwT9LEZw7j8CbN2mrF6GkC9ZlPGYqShY2wSGpjfs/s640/AIAN+disparity.jpg" width="640" /></a></div>
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Looking more specifically at Native American populations, here I've ranked the states from those with the fewest American Indians to those with the most, and this graph is spectacularly clear. Most of the states with fewer than 1% American Indians appear to have lower American Indian mortality than Whites, while all but two states with American Indian populations above 1% show higher mortality. That picture is very consistent with the idea that "others" filling out death records in states with a low proportion of American Indians are more likely to classify them as White, while the "true" racial disparity in mortality between American Indians and Whites is likely to be quite a bit higher. (the darker circle is the national average).</div>
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And arranging the states according to the proportion Hispanic also shows a very strong gradient, suggesting that the true racial disparity in mortality is a lot closer to 1 than the national average of about 25% lower.</div>
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Next steps: I figure that accidental and suicidal deaths, and deaths among younger populations, are probably more prone to misclassification on the death records, because the people filling out the death certificates would have less connection to the decedent and their family, so I'd like to break these rates down that way, too.</div>
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Also, there are a bunch of datasets where they follow people up until they die, so in those cases, the numerator really does come from the denominator, so I could look in those to see what the "true" rates should be, even though these represent only a small sample of the whole population.</div>
Bill Jesdalehttp://www.blogger.com/profile/14477168137262967470noreply@blogger.com0tag:blogger.com,1999:blog-2552382737469337416.post-43920954023502754702013-07-29T17:16:00.000-07:002013-07-29T17:27:01.947-07:00Hate Crime Statistics<a href="http://books.google.com/books/about/Measures_of_Normative_Heterosexuality_Su.html?id=szXN5vdCo0EC" target="_blank">My dissertation</a> was about the impact of heteronormativity (a.k.a. societal homophobia) on suicide rates. Well, really it was about how to measure local variation in heteronormativity, and suicide happened to be an convenient health outcome: it's a "hard end-point" meaning that it is captured with little error, it's assessed pretty much the same way everywhere across the country and over time, and it's probably related to heteronormative societal attitudes.<br />
One of the first ideas I had about how to measure local variation in heteronormativity was to look at hate crimes statistics. The logic is that hate crimes are a direct and extreme expression of heteronormativity. The FBI issues a report every year documenting the number of crimes reported as being bias-motivated, and also where they happen and against whom the violence is targeted.<br />
But a strange thing happened when I looked at the data - there were a fair number of bias-motivated crimes reported from San Francisco and New York City, and virtually none from the places I <i>expected</i> to be havens of homophobia. The most likely explanation is that the number of hate crimes <i>reported</i> is a lousy measure of the number of hate crimes committed, and is a better measure of the degree to which a person reporting a hate crime to the police is taken seriously. So, in a way, hate crimes <i>reporting</i> may be a decent measure of heteronormativity, but in the opposite direction of what you'd expect at first: the more hate crimes reported, the friendlier the social environment is for TBLG people.<br />
But, it gets more complicated. There are two ways not to have much conflict between dominant and subordinate groups. One way is for everyone to get along. Another way is for the subordinate group to "mind its manners" and steer clear of offending the sensibilities of the dominant group. So even if the incidence of hate crimes were a good measure of homophobia, it would be complicated because you'd expect the number of crimes to be low in areas where gay people have learned that the best thing to do is stay deeply closeted, or to get out of Dodge. And even though areas that are "gay meccas" allow us to express ourselves more freely, this can incite hardened haters in our midst to violence, like <a href="http://en.wikipedia.org/wiki/Dan_White" target="_blank">Dan White</a>. "Gay meccas" can also attract hardened haters with violent intentions, and thus one often sees violent hate crimes centered around gay bars and cruising areas.<br />
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Anyway, it had been over ten years since I looked at the hate crimes data, and a lot happened since then. So I was curious to see what has changed.<br />
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Not as much as I expected. There are more and more local and state police forces reporting hate crimes to the FBI, but the number of reported hate crimes hasn't changed much, except for a spike in 2001 related to the violent backlash against Arabs and Muslims. If anything, there's a downward trend when you take the growing population into account (which I have not done in these graphs).<br />
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I have to admit, I'm intrigued by data like this. I don't know what story they are telling. I anticipated that with the rapid change in societal attitudes about homosexuality, we'd see a steady growth in the number of <i>reported </i>anti-gay hate crimes. But, as you can see in the graph below, the number of reported anti-gay hate crimes rose pretty steadily until 2001, and has pretty much leveled off since then.<br />
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So maybe that's a good sign - of increasing tolerance, acceptance, and even celebration breaking out in some corners of the country. But it could mean a lot of things, and when you dig down into <i>where</i> these anti-BLG crimes are being reported from, it's still predominantly from the gay meccas - large coastal cities and also university towns all across the country. I suspect that there are <i>lots</i> of anti-gay crimes not being reported at all, especially in rural areas and the South.</div>
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Maybe the peak in 2001 highlights a shift in the attention of bigots, towards a new bogeyman. There's certainly plenty of evidence that anti-Arab (much of the darker orange slice in the graph below), and anti-Muslim (the bright green slice in the next graph down) spiked hard in 2001, and there has been a sustained increase in anti-Islamic crimes since then compared to the 1990's. But I think the idea of bigots turning away from the gays and towards the Muslims is at best a partial story. Also of interest to note in the graph below is that the number of anti-Black crimes reported by the FBI was definitely lower in the first two years of the Obama administration. Evidence of a post-racial America? I strongly doubt it - although the post-racial narrative might explain it if one considers that some of the more "post-racist" (emphasis on racist) police may be harder to convince that a bias-motivated crime has occurred, and thus less likely to report it as such. It would certainly be interesting to look at those trends in the wake of the 2010 retrenchment election.</div>
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So, another interesting thing to note in the graph above, is the absolutely tiny number of hate crimes motivated by anti-atheist sentiments. As a hard-core <a href="http://www.urbandictionary.com/define.php?term=apatheist" target="_blank">aptheist</a> myself, I find it hard to believe that there are so few anti-atheist hate crimes reported. Maybe it's an issue of confusion - how do you classify a religiously-motivated attack when the recipient professes no religion? But I suspect another <i>possible</i> explanation, that theist (after taking the double negative out of "anti-atheist") biases are so entrenched that it is hard for police to see theist motivated crimes as bias-motivated, and therefore not report them as such.<br />
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Another interesting twist to the tospy-turvy world of hate crimes reporting is the biases for which no reporting category is even available. There were no crimes reported as being motivated by ablism before 1997. It's not that a glorious heyday of equanimity passed in 1996, but rather that there was simply no category available even to describe these bias motivations in the FBI's system. Even today (or at least up to 2010), the number of crimes reported as being directed by ablist biases numbers in the dozens per year, across the entire country. So here's another example indicating that the nature of the bias itself prevents it from being recognized and recorded.<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgdJrQYkRAvwKroOhqYyjMwyIbmlPBZtDW8bhqg3H6AOq8oLPRuY6D5I3XSwY7v0ZIBh4_y5OPwR68srt6vEayEtlQ7Lltj6I-TcxxvhuzPib4swtSzQCUIo7EqE46XABINmtZfTEmL5Qba/s1600/Slide5.PNG" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="300" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgdJrQYkRAvwKroOhqYyjMwyIbmlPBZtDW8bhqg3H6AOq8oLPRuY6D5I3XSwY7v0ZIBh4_y5OPwR68srt6vEayEtlQ7Lltj6I-TcxxvhuzPib4swtSzQCUIo7EqE46XABINmtZfTEmL5Qba/s400/Slide5.PNG" width="400" /></a></div>
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So, that seems like a pretty exhaustive list: crimes motivated by bias on the basis of race, ethnicity, religious preference, sexual orientation, and ability. Or does it? Notice that there's simply no category to record crimes motivated by bias against transgender people yet, or intersex, or even bias against women. I wouldn't be surprised if the number of reported hate crimes would double if rapes motivated by misogyny were reported as such.<br />
Also, in a nation where most sources of intolerance are weakening, <a href="http://www.thedailybeast.com/newsweek/2009/08/25/america-s-war-on-the-overweight.html" target="_blank">intolerance against fat people</a> is on the rise. <span style="font-size: xx-small;"><a href="http://jezebel.com/5392810/will-anti+fat-hate-crimes-make-people-take-sizeism-seriously" target="_blank">Plug for a great article on anti-fat bias and media portrayals of disembodied depersonalized fatness.</a></span></div>
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhk6X96Xt9YisERR26xJGJFKMaMmcdO20Mp6WnTCagyYjlIeRMXVmvf8SBY05ZMCbmRBSaTFvobxxUblSj3s2IdKwCqueOkp1lnnR0R98fVTXiUumxJJDf4VeEPCg6Q0DBB1gnEm3i072EN/s1600/24254_621507372091_2300424_n.jpg" imageanchor="1" style="clear: right; float: right; margin-bottom: 1em; margin-left: 1em;"><img border="0" height="255" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhk6X96Xt9YisERR26xJGJFKMaMmcdO20Mp6WnTCagyYjlIeRMXVmvf8SBY05ZMCbmRBSaTFvobxxUblSj3s2IdKwCqueOkp1lnnR0R98fVTXiUumxJJDf4VeEPCg6Q0DBB1gnEm3i072EN/s320/24254_621507372091_2300424_n.jpg" width="320" /></a>I have to admit, I'm pretty ambivalent about organizing around hate crimes as a means to end prejudice. It's not for lack of trying. As my time with ActUp/RI wound down, I turned to advocacy around hate crimes - even made myself into a bit of a spokesmodel in the wake of being beaten about the head on Thayer Street in Providence (that's me standing and gesturing to another victim in that attack). I got involved in training a few police departments in Rhode Island, but I found that re-hashing my story as a hate crime "victim" was a source of re-victimization, and left me feeling dis-empowered and alienated, especially after some of the more intense police training sessions.</div>
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgZapCgfs1hx1Oe5wWI5EwufHIwn3beOjuvstQO0BP6WoFRRUjBNPbzR451eL2R5gF6l6JYfpmpnzvO96w5-YwxAGqrdxT71gM3gkUXV6wlmxTQvAqm8d3yvUZyZY0qzvBbUE9jXMagKTzf/s1600/StopDrLaura.jpg" imageanchor="1" style="clear: right; float: right; margin-bottom: 1em; margin-left: 1em;"><img border="0" height="320" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgZapCgfs1hx1Oe5wWI5EwufHIwn3beOjuvstQO0BP6WoFRRUjBNPbzR451eL2R5gF6l6JYfpmpnzvO96w5-YwxAGqrdxT71gM3gkUXV6wlmxTQvAqm8d3yvUZyZY0qzvBbUE9jXMagKTzf/s320/StopDrLaura.jpg" width="178" /></a>Bill Jesdalehttp://www.blogger.com/profile/14477168137262967470noreply@blogger.com1