About this dataset

Context

The dataset tabulates the population of Gay by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Gay. The dataset can be utilized to understand the population distribution of Gay by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Gay. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Gay.

Key observations

Largest age group (population): Male # 50-54 years (21) | Female # 35-39 years (15). Source: U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Gay population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Gay is shown in the following column.
• Population (Female): The female population in the Gay is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Gay for each age group.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Gay Population by Gender. You can refer the same here

BackgroundIn the Republic of Ireland, the COVID-19 crisis led to sexual health service closures while clinical staff were redeployed to the pandemic response. Gay, bisexual and other men who have sex with men (gbMSM) face pre-existing sexual health inequalities which may have been exacerbated. The aim of this study is to understand sexual health service accessibility for gbMSM in Ireland during the COVID-19 crisis.MethodsEMERGE recruited 980 gbMSM in Ireland (June-July 2021) to an anonymous online survey investigating well-being and service access through geo-location sexual networking apps (Grindr/Growlr), social media (Facebook/Instagram/Twitter) and collaborators. We fit multiple regression models reporting odds ratios (ORs) to understand how demographic and behavioural characteristics (age, sexual orientation, HIV testing history/status, region of residence, region of birth and education) were associated with ability to access services.ResultsOf the respondents, 410 gbMSM accessed sexual health services with some or no difficulty and 176 attempted but were unable to access services during the COVID-19 crisis. A further 382 gbMSM did not attempt to access services and were excluded from this sample and analysis.Baseline: mean age 35.4 years, 88% gay, 83% previously tested for HIV, 69% Dublin-based, 71% born in Ireland and 74% with high level of education.In multiple regression, gbMSM aged 56+ years (aOR = 0.38, 95%CI:0.16, 0.88), not previously tested for HIV (aOR = 0.46, 95%CI:0.23, 0.93) and with medium and low education (aOR = 0.55 95%CI:0.35, 0.85) had lowest odds of successfully accessing services.GbMSM with HIV were most likely to be able to access services successfully (aOR = 2.68 95%CI:1.83, 6.08).Most disrupted services were: STI testing, HIV testing and PrEP.ConclusionsService access difficulties were found to largely map onto pre-existing sexual health inequalities for gbMSM. Future service development efforts should prioritise (re)engaging older gbMSM, those who have not previously tested for HIV and those without high levels of education.

The ratio of the lengths of the 2nd and 4th fingers (2D:4D) is a putative marker for prenatal gonadal hormone signaling and has been linked to human sexual orientation. Although 2D:4D is consistently found to be lower in males than females, the association with sexual orientation is variable across studies, with one meta-analysis finding lower (more masculine) digit ratios in lesbians than heterosexual females, but no overall association in males. However, this previous meta-analysis considered neither unpublished datasets nor bisexual individuals separately from homosexual and heterosexual individuals. Moreover, 17 datasets examining relationships between 2D:4D and sexual orientation have been published since that time, and we located an additional 11 unpublished datasets. We therefore conducted an updated and expanded meta-analysis comprising 51 studies, including 44 male and 34 female datasets, totaling 227,648 participants. This meta-analysis also explored whether 2D:4D differed between heterosexual and bisexual and/or non-exclusive individuals in both sexes. Results indicate lower (more male-typical) digit ratios in homosexual women (right hand g = 0.26, left hand g = 0.16; both adjusted following trim-and-fill), and higher (more female-typical) ratios in homosexual men (right hand g = −0.17, left hand g = −0.20; both adjusted) compared to heterosexual same-sex counterparts. Moderator analyses do not support publication bias for females. For males, positive findings were more likely to be published, but robustness tests, including trim-and-fill and leave-one-out, support the findings’ robustness. No significant differences were observed in 2D:4D between male or female bisexual and heterosexual individuals. These findings are consistent with evidence that prenatal androgens increase attraction to females and/or that prenatal estrogens increase attraction to males.

Indonesia has monitored behaviours that carry a high risk for HIV infection in groups most likely to be affected since 1996. The behavioural sentinel surveillance was originally carried out by the University of Indonesia, with the support of Family Health International under a grant provided by USAID. In 2002, the system was taken over by a team led by the Indonesian Ministry of Health. Surveys were implemented by the Indonesian Bureau of Statistics with the help of NGOs and with technical support from Family Health International, and funded by USAID under a memorandum of understanding which explicitly allows data to be made available for the purposes of planning and evaluating HIV-related interventions. Two rounds of surveillance were carried out under this MOU. In 2002/2003, the team covered only female sex workers and groups of highly mobile men (10 cities). Surveys among male and transgender sex workers, gay men and drug injectors were carried out by University of Indonesia/FHI. In 2004, the government team covered all groups, with data from 15 cities. Variables cover demographic details, sexual and drug taking behaviour, knowledge of HIV, risk perception, contact with HIV prevention and STI care services and use of services. Users particularly interested in data from other groups, including male and transgender sex workers, gay men and drug injectors may contact the owner of this Dataverse. However these data sets have not been so well documented, and some of the documentation is only available in Indonesian

description: Indonesia has monitored behaviours that carry a high risk for HIV infection in groups most likely to be affected since 1996. The behavioural sentinel surveillance was originally carried out by the University of Indonesia, with the support of Family Health International under a grant provided by USAID. In 2002, the system was taken over by a team led by the Indonesian Ministry of Health. Surveys were implemented by the Indonesian Bureau of Statistics with the help of NGOs and with technical support from Family Health International, and funded by USAID under a memorandum of understanding which explicitly allows data to be made available for the purposes of planning and evaluating HIV-related interventions. Two rounds of surveillance were carried out under this MOU. In 2002/2003, the team covered only female sex workers and groups of highly mobile men (10 cities). Surveys among male and transgender sex workers, gay men and drug injectors were carried out by University of Indonesia/FHI. In 2004, the government team covered all groups, with data from 15 cities. Variables cover demographic details, sexual and drug taking behaviour, knowledge of HIV, risk perception, contact with HIV prevention and STI care services and use of services. Users particularly interested in data from other groups, including male and transgender sex workers, gay men and drug injectors may contact the owner of this Dataverse. However these data sets have not been so well documented, and some of the documentation is only available in Indonesian. This dataset can be accessed on the Harvard Dataverse by going to https://dataverse.harvard.edu/dataverse/pisani.; abstract: Indonesia has monitored behaviours that carry a high risk for HIV infection in groups most likely to be affected since 1996. The behavioural sentinel surveillance was originally carried out by the University of Indonesia, with the support of Family Health International under a grant provided by USAID. In 2002, the system was taken over by a team led by the Indonesian Ministry of Health. Surveys were implemented by the Indonesian Bureau of Statistics with the help of NGOs and with technical support from Family Health International, and funded by USAID under a memorandum of understanding which explicitly allows data to be made available for the purposes of planning and evaluating HIV-related interventions. Two rounds of surveillance were carried out under this MOU. In 2002/2003, the team covered only female sex workers and groups of highly mobile men (10 cities). Surveys among male and transgender sex workers, gay men and drug injectors were carried out by University of Indonesia/FHI. In 2004, the government team covered all groups, with data from 15 cities. Variables cover demographic details, sexual and drug taking behaviour, knowledge of HIV, risk perception, contact with HIV prevention and STI care services and use of services. Users particularly interested in data from other groups, including male and transgender sex workers, gay men and drug injectors may contact the owner of this Dataverse. However these data sets have not been so well documented, and some of the documentation is only available in Indonesian. This dataset can be accessed on the Harvard Dataverse by going to https://dataverse.harvard.edu/dataverse/pisani.