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In the United States, male-to-male sexual transmission accounts for the greatest number of new human immunodeficiency virus (HIV) diagnoses and a substantial number of sexually transmitted infections (STI) annually. However, the prevalence and annual incidence of HIV and other STIs among men who have sex with men (MSM) cannot be estimated in local contexts because demographic data on sexual behavior, particularly same-sex behavior, are not routinely collected by large-scale surveys that allow analysis at state, county, or finer levels, such as the US decennial census or the American Community Survey (ACS). Therefore, techniques for indirectly estimating population sizes of MSM are necessary to supply denominators for rates at various geographic levels.
Our objectives were to indirectly estimate MSM population sizes at the county level to incorporate recent data estimates and to aggregate county-level estimates to states and core-based statistical areas (CBSAs).
We used data from the ACS to calculate a weight for each county in the United States based on its relative proportion of households that were headed by a male who lived with a male partner, compared with the overall proportion among counties at the same level of urbanicity (ie, large central metropolitan county, large fringe metropolitan county, medium/small metropolitan county, or nonmetropolitan county). We then used this weight to adjust the urbanicity-stratified percentage of adult men who had sex with a man in the past year, according to estimates derived from the National Health and Nutrition Examination Survey (NHANES), for each county. We multiplied the weighted percentages by the number of adult men in each county to estimate its number of MSM, summing county-level estimates to create state- and CBSA-level estimates. Finally, we scaled our estimated MSM population sizes to a meta-analytic estimate of the percentage of US MSM in the past 5 years (3.9%).
We found that the percentage of MSM among adult men ranged from 1.5% (Wyoming) to 6.0% (Rhode Island) among states. Over one-quarter of MSM in the United States resided in 1 of 13 counties. Among counties with over 300,000 residents, the five highest county-level percentages of MSM were San Francisco County, California at 18.5% (66,586/359,566); New York County, New York at 13.8% (87,556/635,847); Denver County, Colorado at 10.5% (25,465/243,002); Multnomah County, Oregon at 9.9% (28,949/292,450); and Suffolk County, Massachusetts at 9.1% (26,338/289,634). Although California (n=792,750) and Los Angeles County (n=251,521) had the largest MSM populations of states and counties, respectively, the New York City-Newark-Jersey City CBSA had the most MSM of all CBSAs (n=397,399).
We used a new method to generate small-area estimates of MSM populations, incorporating prior work, recent data, and urbanicity-specific parameters. We also used an imputation approach to estimate MSM in rural areas, where same-sex sexual behavior may be underreported. Our approach yielded estimates of MSM population sizes within states, counties, and metropolitan areas in the United States, which provide denominators for calculation of HIV and STI prevalence and incidence at those geographic levels.
In the United States, male-to-male sexual transmission accounted for 58-65% of human immunodeficiency virus (HIV) diagnoses from 2009 to 2013 [
Having male sex partners is not necessarily the same as self-identification as gay, bisexual, or queer
Data regarding cohabitating same-sex partners are collected by the US Census Bureau, but behavioral data on same-sex behavior among men are not. Therefore, researchers studying MSM populations often use estimates from national probability surveys such as the General Social Survey (GSS) [
Several methods have been proposed to estimate state and local population sizes of MSM. Some researchers begin with HIV prevalence assumptions and work backward to determine the population size of MSM in a given area. For example, Lieb and colleagues [
One recent approach used both data from the American Community Survey (ACS) and NHSLS estimates [
Here, we create a new method to estimate the population sizes of MSM in US states, counties, and core-based statistical areas (CBSAs). Our approach uses elements of Lieb et al.’s [
We used data from the ACS 5-year summary file, 2009 to 2013, to obtain the total number of households, total number of SSM households (male householder and male partner), and total number of men aged 18 years and older for each county in the United States (
ACS data are publicly available as 1-, 3-, or 5-year summary files or as a Public Use Microdata Sample (PUMS), which contains a de-identified and unaggregated sample of ACS data. The 1- and 3-year summary files are limited to areas with populations of 65,000 or 20,000 or more, respectively. However, the 5-year ACS summary files contain data at all available geographic areas. We did not include data from US territories.
To more accurately describe where MSM reside, we supplemented data from the ACS using the urbanicity categories produced by the National Center for Health Statistics (NCHS) [
We developed a method to estimate small-area MSM populations by combining two models reported by Lieb et al. [
Equations 1–3
By calculating the MSM Index within strata of urbanicity, we expected to reduce inter-urbanicity differences in same-sex cohabitation and reporting among MSM due to stigma. However, 35.4% (1112/3143) counties had no reported SSM households, and consequently had MSM Index values and estimated MSM population sizes of zero, which likely reflected these biases in detection of MSM. To impute MSM in these areas for our final estimates, while preserving the relative population sizes based on SSM households, we added households to both the numerator and denominator of the above equations. For each county, we increased the number of SSM households and the number of total households by adding the urbanicity-specific percentage of SSM households (
Equation 4.
As an example of our imputation method, we will use two hypothetical nonmetropolitan counties. The total percentage of SSM households among all households in nonmetropolitan counties in our data was approximately 0.1%. For a county with 1000 households, of which zero were SSM households, we added one SSM household, or 0.1% of 1000. This meant that, for the part of our model that calculated urbanicity-specific indices, the new totals for that county were 1001 households, of which one was a SSM household. For another nonmetropolitan county with 20,000 households, of which 15 were SSM households, we added 20 SSM households, for a new total of 20,020 households and 35 SSM households. By adding a proportionate number of SSM households to all counties, we effectively maintained the relative representation of SSM households within urbanicity strata while estimating at least some MSM in counties with no SSM households. Because the index was used as a way of weighting the percentage of MSM among adult men in each county and not as a direct method of estimation, adding SSM households did not add MSM to our final population estimates.
For our analysis, we chose to estimate the number of men who had sex with men within the past 5 years, rather than the past 12 months or over the lifetime, as others have reported [
All analyses were conducted using R Studio, version 0.98.953 [
Using Purcell et al.’s [
Of the 3143 counties or county-equivalent areas in the United States, we estimated that over one-half of the total US MSM population resided in only 51 (
Estimated MSM populations in 50 states and the District of Columbia, ranked by size of MSM population, using housing and population estimates from the American Community Survey, 2009-2013.
Adult males | MSM | US MSM | |||
Rank | State | N | n (%) | % of total | Cumulative % of MSM |
1 | California | 13,997,953 | 792,750 (5.7%) | 17.6% | 17.6% |
2 | Texas | 9,189,027 | 371,781 (4.0%) | 8.3% | 25.9% |
3 | New York | 7,247,605 | 371,087 (5.1%) | 8.2% | 34.1% |
4 | Florida | 7,283,572 | 340,163 (4.7%) | 7.6% | 41.7% |
5 | Illinois | 4,728,089 | 199,486 (4.2%) | 4.4% | 46.1% |
6 | Pennsylvania | 4,798,340 | 162,745 (3.4%) | 3.6% | 49.7% |
7 | Ohio | 4,263,691 | 144,367 (3.4%) | 3.2% | 52.9% |
8 | New Jersey | 3,257,962 | 132,520 (4.1%) | 2.9% | 55.8% |
9 | Georgia | 3,522,525 | 131,374 (3.7%) | 2.9% | 58.8% |
10 | Michigan | 3,671,762 | 113,860 (3.1%) | 2.5% | 61.3% |
11 | Virginia | 3,030,663 | 112,785 (3.7%) | 2.5% | 63.8% |
12 | Washington | 2,590,196 | 111,960 (4.3%) | 2.5% | 66.3% |
13 | Massachusetts | 2,477,594 | 111,625 (4.5%) | 2.5% | 68.8% |
14 | Arizona | 2,393,283 | 110,344 (4.6%) | 2.5% | 71.2% |
15 | North Carolina | 3,536,017 | 103,010 (2.9%) | 2.3% | 73.5% |
16 | Maryland | 2,136,890 | 84,465 (4.0%) | 1.9% | 75.4% |
17 | Minnesota | 2,000,472 | 83,027 (4.2%) | 1.8% | 77.2% |
18 | Tennessee | 2,357,860 | 73,639 (3.1%) | 1.6% | 78.9% |
19 | Colorado | 1,939,236 | 73,357 (3.8%) | 1.6% | 80.5% |
20 | Missouri | 2,219,565 | 70,783 (3.2%) | 1.6% | 82.1% |
21 | Indiana | 2,389,263 | 70,103 (2.9%) | 1.6% | 83.6% |
22 | Oregon | 1,472,740 | 61,607 (4.2%) | 1.4% | 85.0% |
23 | Wisconsin | 2,154,753 | 59,078 (2.7%) | 1.3% | 86.3% |
24 | Nevada | 1,038,437 | 51,726 (5.0%) | 1.1% | 87.4% |
25 | Kentucky | 1,621,844 | 47,034 (2.9%) | 1.0% | 88.5% |
26 | Connecticut | 1,334,105 | 43,313 (3.2%) | 1.0% | 89.4% |
27 | Louisiana | 1,665,801 | 41,492 (2.5%) | 0.9% | 90.4% |
28 | Alabama | 1,754,583 | 40,600 (2.3%) | 0.9% | 91.3% |
29 | Oklahoma | 1,394,881 | 37,739 (2.7%) | 0.8% | 92.1% |
30 | District of Columbia | 239,916 | 36,775 (15.3%) | 0.8% | 92.9% |
31 | South Carolina | 1,726,807 | 36,316 (2.1%) | 0.8% | 93.7% |
32 | Utah | 962,285 | 33,294 (3.5%) | 0.7% | 94.5% |
33 | Rhode Island | 395,905 | 23,815 (6.0%) | 0.5% | 95.0% |
34 | Kansas | 1,054,271 | 22,900 (2.2%) | 0.5% | 95.5% |
35 | Iowa | 1,145,708 | 20,753 (1.8%) | 0.5% | 96.0% |
36 | Arkansas | 1,076,736 | 19,264 (1.8%) | 0.4% | 96.4% |
37 | Mississippi | 1,063,557 | 18,992 (1.8%) | 0.4% | 96.8% |
38 | New Mexico | 762,051 | 17,969 (2.4%) | 0.4% | 97.2% |
39 | Hawaii | 534,961 | 15,411 (2.9%) | 0.3% | 97.6% |
40 | Maine | 511,631 | 15,071 (2.9%) | 0.3% | 97.9% |
41 | New Hampshire | 507,277 | 14,122 (2.8%) | 0.3% | 98.2% |
42 | Nebraska | 678,518 | 13,199 (1.9%) | 0.3% | 98.5% |
43 | West Virginia | 716,528 | 13,063 (1.8%) | 0.3% | 98.8% |
44 | Delaware | 335,554 | 13,049 (3.9%) | 0.3% | 99.1% |
45 | Idaho | 574,213 | 9,907 (1.7%) | 0.2% | 99.3% |
46 | Vermont | 243,332 | 7,069 (2.9%) | 0.2% | 99.5% |
47 | Montana | 386,653 | 6,374 (1.6%) | 0.1% | 99.6% |
48 | South Dakota | 309,108 | 5,171 (1.7%) | 0.1% | 99.7% |
49 | Alaska | 278,464 | 5,074 (1.8%) | 0.1% | 99.8% |
50 | North Dakota | 270,992 | 4,447 (1.6%) | 0.1% | 99.9% |
51 | Wyoming | 220,518 | 3,225 (1.5%) | 0.1% | 100.0% |
Total | 115,463,694 | 4,503,080 (3.9%) |
The 51 US counties with the largest estimated MSM populations, representing approximately one-half of the estimated US MSM population and ranked according to size of MSM population, using housing and population estimates from the American Community Survey, 2009-2013.
Adult males | MSM | US MSM | ||||
Rank | County | State | N | n (%) | % of total | Cumulative % of MSM |
1 | Los Angeles County | CA | 3,666,190 | 251,521 (6.9%) | 5.6% | 5.6% |
2 | Cook County | IL | 1,905,622 | 125,923 (6.6%) | 2.8% | 8.4% |
3 | Maricopa County | AZ | 1,408,797 | 87,894 (6.2%) | 2.0% | 10.3% |
4 | New York County | NY | 635,847 | 87,556 (13.8%) | 1.9% | 12.3% |
5 | Harris County | TX | 1,490,581 | 83,401 (5.6%) | 1.9% | 14.1% |
6 | San Diego County | CA | 1,204,728 | 80,968 (6.7%) | 1.8% | 15.9% |
7 | Riverside County | CA | 794,695 | 70,803 (8.9%) | 1.6% | 17.5% |
8 | San Francisco County | CA | 359,566 | 66,586 (18.5%) | 1.5% | 19.0% |
9 | Dallas County | TX | 855,958 | 64,385 (7.5%) | 1.4% | 20.4% |
10 | Orange County | CA | 1,134,443 | 62,190 (5.5%) | 1.4% | 21.8% |
11 | King County | WA | 769,969 | 61,752 (8.0%) | 1.4% | 23.2% |
12 | Kings County | NY | 895,148 | 59,767 (6.7%) | 1.3% | 24.5% |
13 | Miami-Dade County | FL | 956,927 | 59,733 (6.2%) | 1.3% | 25.8% |
14 | Broward County | FL | 664,314 | 58,629 (8.8%) | 1.3% | 27.1% |
15 | Clark County | NV | 744,929 | 46,529 (6.2%) | 1.0% | 28.2% |
16 | Queens County | NY | 855,853 | 45,656 (5.3%) | 1.0% | 29.2% |
17 | Alameda County | CA | 578,149 | 40,924 (7.1%) | 0.9% | 30.1% |
18 | Hennepin County | MN | 441,369 | 37,611 (8.5%) | 0.8% | 30.9% |
19 | Santa Clara County | CA | 689,137 | 37,041 (5.4%) | 0.8% | 31.7% |
20 | District of Columbia | DC | 239,916 | 36,775 (15.3%) | 0.8% | 32.5% |
21 | Sacramento County | CA | 517,617 | 34,556 (6.7%) | 0.8% | 33.3% |
22 | Tarrant County | TX | 645,094 | 34,529 (5.4%) | 0.8% | 34.1% |
23 | Philadelphia County | PA | 550,353 | 33,549 (6.1%) | 0.7% | 34.8% |
24 | Bexar County | TX | 621,564 | 32,401 (5.2%) | 0.7% | 35.5% |
25 | Franklin County | OH | 431,661 | 31,220 (7.2%) | 0.7% | 36.2% |
26 | Travis County | TX | 407,740 | 30,741 (7.5%) | 0.7% | 36.9% |
27 | Orange County | FL | 438,963 | 30,732 (7.0%) | 0.7% | 37.6% |
28 | Fulton County | GA | 348,541 | 30,169 (8.7%) | 0.7% | 38.3% |
29 | Wayne County | MI | 638,235 | 30,161 (4.7%) | 0.7% | 38.9% |
30 | Multnomah County | OR | 292,450 | 28,949 (9.9%) | 0.6% | 39.6% |
31 | Hillsborough County | FL | 461,567 | 28,246 (6.1%) | 0.6% | 40.2% |
32 | Middlesex County | MA | 577,698 | 28,122 (4.9% | 0.6% | 40.8% |
33 | Allegheny County | PA | 466,388 | 26,666 (5.7%) | 0.6% | 41.4% |
34 | Suffolk County | MA | 289,634 | 26,338 (9.1%) | 0.6% | 42.0% |
35 | Cuyahoga County | OH | 460,353 | 25,837 (5.6%) | 0.6% | 42.6% |
36 | Denver County | CO | 243,002 | 25,465 (10.5%) | 0.6% | 43.2% |
37 | Pinellas County | FL | 358,997 | 25,204 (7.0%) | 0.6% | 43.7% |
38 | Suffolk County | NY | 556,340 | 24,597 (4.4%) | 0.5% | 44.3% |
39 | San Bernardino County | CA | 722,111 | 24,060 (3.3%) | 0.5% | 44.8% |
40 | Salt Lake County | UT | 372,182 | 23,244 (6.2%) | 0.5% | 45.3% |
41 | Palm Beach County | FL | 510,352 | 22,727 (4.5%) | 0.5% | 45.8% |
42 | Bronx County | NY | 469,573 | 22,370 (4.8%) | 0.5% | 46.3% |
43 | Mecklenburg County | NC | 336,345 | 20,920 (6.2%) | 0.5% | 46.8% |
44 | DeKalb County | GA | 249,589 | 20,302 (8.1%) | 0.5% | 47.2% |
45 | Marion County | IN | 323,768 | 19,553 (6.0%) | 0.4% | 47.7% |
46 | Wake County | NC | 331,066 | 19,021 (5.7%) | 0.4% | 48.1% |
47 | Contra Costa County | CA | 387,213 | 18,974 (4.9%) | 0.4% | 48.5% |
48 | Erie County | NY | 344,098 | 18,706 (5.4%) | 0.4% | 48.9% |
49 | Hudson County | NJ | 251,902 | 18,523 (7.4%) | 0.4% | 49.3% |
50 | Milwaukee County | WI | 339,381 | 18,428 (5.4%) | 0.4% | 49.7% |
51 | Shelby County | TN | 321,669 | 17,466 (5.4%) | 0.4% | 50.1% |
Estimated percentage of adult men who had sex with a man in the past 5 years, using housing and population estimates from the 2009-2013 American Community Survey.
By aggregating our county-level findings to CBSAs, we found that 97.4% (4,384,172/4,503,080) of the MSM in our model resided in the 917 CBSAs in the United States. One-half (2,251,068/4,503,080) lived in one of 16 CBSAs (
The 16 CBSAs with the largest estimated MSM populations, representing one-half of the US MSM population and ranked according to size of MSM population, using housing and population estimates from the American Community Survey, 2009-2013.
Adult males | MSM | US MSM | |||
Rank | State | N | n (%) | % of total | Cumulative % of MSM |
1 | New York-Newark-Jersey City | 7,239,158 | 397,399 (5.5%) | 8.8% | 8.8% |
2 | Los Angeles-Long Beach-Anaheim | 4,800,633 | 313,711 (6.5%) | 7.0% | 15.8% |
3 | Chicago-Naperville-Elgin | 3,443,489 | 175,118 (5.1%) | 3.9% | 19.7% |
4 | San Francisco-Oakland-Hayward | 1,700,219 | 145,972 (8.6%) | 3.2% | 22.9% |
5 | Miami-Fort Lauderdale-West Palm Beach | 2,131,593 | 141,088 (6.6%) | 3.1% | 26.1% |
6 | Dallas-Fort Worth-Arlington | 2,320,338 | 133,944 (5.8%) | 3.0% | 29.0% |
7 | Washington-Arlington-Alexandria | 2,113,258 | 122,895 (5.8%) | 2.7% | 31.8% |
8 | Houston-The Woodlands-Sugar Land | 2,159,519 | 103,722 (4.8%) | 2.3% | 34.1% |
9 | Atlanta-Sandy Springs-Roswell | 1,899,899 | 102,642 (5.4%) | 2.3% | 36.3% |
10 | Philadelphia-Camden-Wilmington | 2,189,761 | 100,293 (4.6%) | 2.2% | 38.6% |
11 | Riverside-San Bernardino-Ontario | 1,516,806 | 94,863 (6.3%) | 2.1% | 40.7% |
12 | Phoenix-Mesa-Scottsdale | 1,557,094 | 92,825 (6.0%) | 2.1% | 42.7% |
13 | Boston-Cambridge-Newton | 1,729,903 | 92,527 (5.3%) | 2.1% | 44.8% |
14 | Seattle-Tacoma-Bellevue | 1,342,052 | 82,002 (6.1%) | 1.8% | 46.6% |
15 | San Diego-Carlsbad | 1,204,728 | 80,968 (6.7%) | 1.8% | 48.4% |
16 | Minneapolis-St. Paul-Bloomington | 1,247,688 | 71,099 (5.7%) | 1.6% | 50.0% |
We used recent estimates of the population size of US MSM [
Our findings are consistent with other studies, although ours is the first to use this method at this fine of a geographic level for the entire country. For example, Gallup’s March, 2015 [
Despite similarities with other studies, our results were different from other recent publications, notably the ones from which we derived part of our method. We estimated fewer MSM at the state level than Lieb and colleagues [
In addition to the different definitions of urbanicity, the percentages cited by Lieb et al. [
Our findings substituted the Laumann et al. [
We made several assumptions and adjustments to prior methods that may limit the interpretation and use of our results. First, we decided that computing the MSM Index according to stratum would more accurately compare geographic areas, given possible within-urbanicity tendencies for MSM either not to cohabitate or to underreport SSM households. However, it may be that it is more accurate to compare all geographic areas, rather than to generate urbanicity-specific MSM Index values. Second, we used urbanicity-specific MSM percentages from Oster and colleagues [
In addition to our method, our findings may be limited by our use of ACS data. The ACS is a sample of the population that is weighted, unlike the decennial census, which contains more data. As a result, inferences based on the ACS may be less accurate than data from the decennial census. ACS might also miss some of the same-sex households that are not in urban areas, particularly if they are less likely to respond to a survey other than the decennial census. It could also be due to more cohabitation, including marriage, among same-sex couples due to differences in legislation permitting marriage. However, because our data span several years, we cannot determine the extent to which policies and laws regarding marriage influence geographic differences.
Small-area estimates of MSM populations that incorporate the most recent data and estimates available may provide a useful tool to public health practitioners and policy makers for determining the burden of HIV and STIs among MSM in local contexts and planning prevention and treatment responses. Our method produced similar results to a recent effort to estimate MSM population sizes in San Francisco County but different from other studies that used a similar method, largely due to differences in the assumptions underlying the models. The method we presented can be updated annually as new ACS data are released, which would provide counties and larger geographic areas with up-to-date population sizes and, potentially, incidence and prevalence rates. These local statistics would allow for better resource allocation, intervention development, and service delivery. For data from the current analysis and for future updates, visit the study website [
The findings and conclusions in this manuscript are solely the responsibility of the authors and do not necessarily represent the official views of the Centers for Disease Control and Prevention or the Department of Health and Human Services.
Variable names and locations of key data from the American Community Survey, 2009-2013.
American Community Survey
core-based statistical area
General Social Survey
human immunodeficiency virus
metropolitan statistical area
men who have sex with men
National Health and Nutrition Examination Survey
National Health and Social Life Survey
National Survey of Family Growth
standard deviation
same-sex male
sexually transmitted infection
The authors disclose receipt of the following financial support for the research and/or authorship of this article: Emory Center for AIDS Research (P30 AI050409) and the National Center for HIV, Viral Hepatisis, STDs, and TB Prevention Epidemiologic and Economic Modeling Agreement (NEEMA) (U38 Ps004646-01).
The authors would like to thank the Applied Public Health Advisory Group, a collaborative set of state and local public health professionals convened for this project, for their guidance and expertise. We would like to specifically thank Dr. Pascale Wortley, Rebecca Filipowicz, Dr. Jennifer Kates, Dr. Preeti Pathela, Dr. Jane Kelly, Dr. Jim Curran and Dr. David Dowdy for reviewing this paper and providing feedback.
None declared.