Population Percentage and Population Size of Men Who Have Sex With Men in the United States, 2017-2021: Meta-Analysis of 5 Population-Based Surveys.

BACKGROUND: Male-to-male sexual transmission continues to account for the greatest proportion of new HIV diagnoses in the United States. However, calculating population-specific surveillance metrics for HIV and other sexually transmitted infections requires regularly updated estimates of the number and proportion of men who have sex with men (MSM) in the United States, which are not collected by census surveys. OBJECTIVE: The purpose of this analysis was to estimate the number and percentage of MSM in the United States from population-based surveys. METHODS: We used data from 5 population-based surveys to calculate weighted estimates of the proportion of MSM in the United States and pooled these estimates using meta-analytic procedures. We estimated the proportion of MSM using sexual behavior-based questions (encompassing anal or oral sex) for 3 recall periods-past 12 months, past 5 years, and lifetime. In addition, we estimated the proportion of MSM using self-reported identity and attraction survey responses. The total number of MSM and non-MSM in the United States were calculated from estimates of the percentage of MSM who reported sex with another man in the past 12 months. RESULTS: The percentage of MSM varied by recall period: 3.3% (95% CI 1.7%-4.9%) indicated sex with another male in the past 12 months, 4.7% (95% CI 0.0%-33.8%) in the past 5 years, and 6.2% (95% CI 2.9%-9.5%) in their lifetime. There were comparable percentages of men who identified as gay or bisexual (3.4%, 95% CI 2.2%-4.6%) or who indicated that they are attracted to other men (4.9%, 95% CI 3.1%-6.7%) based on pooled estimates. Our estimate of the total number of MSM in the United States is 4,230,000 (95% CI 2,179,000-6,281,000) based on the history of recent sexual behavior (sex with another man in the past 12 months). CONCLUSIONS: We calculated the pooled percentage and number of MSM in the United States from a meta-analysis of population-based surveys collected from 2017 to 2021. These estimates update and expand upon those derived from the Centers for Disease Control and Prevention in 2012 by including estimates of the percentage of MSM based on sexual identity and sexual attraction. The percentage and number of MSM in the United States is an important indicator for calculating population-specific disease rates and eligibility for preventive interventions such as pre-exposure prophylaxis.

Alterations in cell arrangements of group B streptococcus due to virulence factor expression can bias estimates of bacterial populations based on colony count measures.

Group B streptococcus (GBS) is a chain-forming commensal bacterium and opportunistic pathogen that resides in the gastrointestinal and genitourinary tract of healthy adults. GBS can cause various infections and related complications in pregnant and nonpregnant women, adults, and newborns. Investigations of the mechanisms by which GBS causes disease pathogenesis often utilize colony count assays to estimate bacterial population size in experimental models. In other streptococci, such as group A streptococcus and pneumococcus, variation in the chain length of the bacteria that can occur naturally or due to mutation can affect facets of pathogenesis, such as adherence to or colonization of a host. No studies have reported a relationship between GBS chain length and pathogenicity. Here, we used GBS strain 874391 and several derivative strains displaying longer chain-forming phenotypes (874391pgapC, 874391ΔcovR, 874391Δstp1) to assess the impact of chain length on bacterial population estimates based on the colony-forming unit (c.f.u.) assay. Disruption of GBS chains via bead beating or sonication in conjunction with fluorescence microscopy was used to compare chaining phenotypes pre- and post-disruption to detect long- and short-chain forms, respectively. We used a murine model of GBS colonization of the female reproductive tract to assess whether chaining may affect bacterial colonization dynamics in the host during chronic infection in vivo. Overall, we found that GBS exhibiting long-chain form can significantly affect population size estimates based on the colony count assay. Additionally, we found that the length of chaining of GBS can affect virulence in the reproductive tract colonization model. Collectively, these findings have implications for studies of GBS that utilize colony count assays to measure GBS populations and establish that chain length can affect infection dynamics and disease pathogenesis for this important opportunistic pathogen.

Social cartography and satellite-derived building coverage for post-census population estimates in difficult-to-access regions of Colombia.

Effective government services rely on accurate population numbers to allocate resources. In Colombia and globally, census enumeration is challenging in remote regions and where armed conflict is occurring. During census preparations, the Colombian National Administrative Department of Statistics conducted social cartography workshops, where community representatives estimated numbers of dwellings and people throughout their regions. We repurposed this information, combining it with remotely sensed buildings data and other geospatial data. To estimate building counts and population sizes, we developed hierarchical Bayesian models, trained using nearby full-coverage census enumerations and assessed using 10-fold cross-validation. We compared models to assess the relative contributions of community knowledge, remotely sensed buildings, and their combination to model fit. The Community model was unbiased but imprecise; the Satellite model was more precise but biased; and the Combination model was best for overall accuracy. Results reaffirmed the power of remotely sensed buildings data for population estimation and highlighted the value of incorporating local knowledge.

Estimating prevalence of early symptomatic Alzheimer's disease in the United States.

INTRODUCTION: Understanding the prevalence of treatment-eligible Alzheimer's disease (AD) is crucial for policy planning. METHODS: We used a comprehensive literature review and population cascade approach to estimate the number of amyloid-positive, clinically diagnosed patients with mild cognitive impairment (MCI) or mild dementia due to AD in the United States. RESULTS: An estimated 666,646 individuals were identified as having MCI due to AD (range: 351,926-1,227,776) and 620,850 individuals as having mild dementia due to AD (range: 445,082-820,339). In a US population of 76 million individuals aged 60 or older in 2021, the estimates of MCI and mild dementia due to AD increased with age. CONCLUSIONS: As earlier diagnosis of AD and new disease-modifying treatments become available, accurate population estimates are required to reduce uncertainty in the number of clinically diagnosed patients eligible for amyloid-targeting therapies.

Stepwise Proportional Weighting Algorithm for Single-Race Population Estimation Using Hawai'i Census Data.

Many health and health disparities studies require population prevalence information of various race groups, but the estimation of single-race population sizes using the US Census data has been challenging. For each Census race group, Census only provides the counts of those reported being single race ("race alone") and those reported of that specific race regardless of whether the individuals were multiracial or not ("race alone or in (any) combination"). The issue of how to classify Census multiracial individuals is especially important for the state of Hawai'i due to its large multiracial population. The current study developed the Stepwise Proportional Weighting Algorithm (SPWA) for single-race population estimation using US Census data for major race groups in the Census and their nested detailed races. Additionally, given that "partial Native Hawaiian" has often been treated as "Native Hawaiian" in health disparities studies in Hawai'i, the algorithm can also adjust for the unique partial Native Hawaiian race categorization. This paper describes the estimation process with the SPWA and demonstrates its ability to estimate single-races for the 5 most common race groups in Hawai'i. This new methodology addresses an important concern regarding how to classify multiracial individuals to strengthen health and health disparities research in Hawai'i.

Cystic fibrosis prevalence in the United States and participation in the Cystic Fibrosis Foundation Patient Registry in 2020.

BACKGROUND: The Cystic Fibrosis Foundation Patient Registry (CFFPR) collects data on individuals with cystic fibrosis (CF) in the United States (US). In 2012, the US CF population was estimated at 33,292 to 34,327 individuals, with 81-84% CFFPR participation. METHODS: In this study, we update these estimates via simulation to account for uncertainty in CF incidence by race or Hispanic ethnicity, initiation of CF newborn screening (NBS) programs by state, and updated cumulative survival for CF births 1968-2020. We defined registry participation as the proportion of individuals alive as of 2020 with any prior CFFPR participation as well as the proportion with contributing data in 2019 or 2020; we summarize CFFPR participation for those born prior to 1968. RESULTS: We estimated the 2020 prevalent CF population between 1968-2020 to be 38,804 (95% Uncertainty Interval (UI): 38,532 to 39,065) individuals, with 77% of the prevalent CF population contributing recent data. CFFPR participation differs by age (54% of those born in 1968) and exceeds >90% of the population born in 2009 or later. CONCLUSIONS: We demonstrate that the CFFPR remains a valid data source generalizable to the CF population. High participation among younger individuals may reflect the success of newborn screening programs and early referral to CF care. If engagement can be sustained, the percentage of individuals participating in the CFFPR will grow over time and there is an opportunity to identify factors associated with loss to follow up among older individuals to optimize the quality of the CFFPR data.

Relationship Between Age, Comorbidity, and the Prevalence of Erectile Dysfunction.

BACKGROUND: Erectile dysfunction (ED) increases with age. Remarkably, the relationship between age and the risk of ED has only been described in crude categories, such as risk for men aged 50-59 yr, without taking comorbidities into account. OBJECTIVE: To understand how the risk of patient-reported ED varies according to age and comorbidity status. DESIGN, SETTING, AND PARTICIPANTS: This cross-sectional study included a cohort of 17 250 patients with prostate cancer who completed the International Index of Erectile Function erectile function domain (IIEF-EF) questionnaire before any prostate treatment. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: We created a logistic regression model to predict the probability of ED using age and comorbidities such as cardiovascular disease, diabetes, and hypertension as predictors. We used age as a nonlinear term to allow a curvilinear relationship between age and ED. RESULTS AND LIMITATIONS: The prevalence of patient-reported ED among men without any comorbidities increased from 10% to 79% from the age of 40 and 80 yr. The risk of ED increased sharply with comorbidity: the probability of ED for 50- and 75-yr-old individuals was 20% and 68% for healthy men, but 41% and 85% for those with hypertension, obesity, and diabetes. Men with several comorbidities have the same risk of ED as that of healthy men 15-25 yr older. Limitations include a healthier-than-average patient group and lack of information about some comorbidities and the severity of comorbidities. CONCLUSIONS: Our results allow us to better understand how the risk of ED changes with age and comorbidities. Further research should evaluate the impact of other risk factors not considered in the present study and should take risk factor severity into account. PATIENT SUMMARY: Our study shows how the probability of erectile dysfunction (ED) changes with increasing age, analyzed alone and when taking into account the presence of other risk factors for this condition (eg, diabetes, high blood pressure, and cardiovascular disease). Our results help in better understanding the probability of ED for men with and without comorbidities.

A spatial and temporal assessment of nonresponse in the national forest inventory of the U.S.

In nearly all national forest inventories (NFI), some sample plots are unable to be measured such that nonresponse may be an issue of concern. Thus, it is of particular interest to understand the phenomenon in terms of current status and temporal change in nonresponse rates and the associated spatial distribution on the landscape. In the NFI of the USA, denial of access permission on privately owned forest land and hazardous conditions has led to an overall nonresponse rate of 9.8% with some areas exceeding 20% of plots being inaccessible. Further, it was found that nearly 50% of the areas studied were exhibiting increasing rates of nonresponse over time. Comparisons between response and nonresponse plots via remote sensing characteristics suggested there may be systematic differences in some parts of the country, which may cause bias in the sample and resulting estimates. The findings indicate that improved communication strategies with private landowners are needed to reduce nonresponse rates. Due to the unlikelihood of eliminating nonresponse entirely, methods to mitigate potential nonresponse bias should be considered for incorporation into the estimation of population parameters.

Methodology of Natsal-COVID Wave 2: A large, quasi-representative, longitudinal survey measuring the impact of COVID-19 on sexual and reproductive health in Britain.

Background: The National Surveys of Sexual Attitudes and Lifestyles COVID study (Natsal-COVID) was designed to understand the impact of COVID-19 on Britain's sexual and reproductive health (SRH). Natsal-COVID Wave 1 survey and qualitative follow-up interviews were conducted in 2020. The Wave 2 survey was designed to capture one-year prevalence estimates for key SRH outcomes and measure changes over the first year of the pandemic. We describe the Wave 2 survey methodology and assess the sample representativeness. Methods: Natsal-COVID Wave 2 was conducted March-April 2021; approximately one year after the start of Britain's first national lockdown. Data were collected using an online web-panel survey administered by Ipsos. The sample comprised a longitudinal sample of Wave 1 participants who had agreed to re-contact plus a sample of participants residing in Britain, aged 18-59, including a boost sample comprising people aged 18-29. Questions covered reproductive health, relationships, sexual behaviour and SRH service use. Quotas and weighting were used to achieve a quasi-representative sample of the British population. Comparisons were made with recent national probability surveys, Natsal-3 (2010-12) and Natsal-COVID Wave 1 to understand bias. Results: A total of 6,658 individuals completed the survey. In terms of gender, age, ethnicity, and rurality, the weighted Natsal-COVID Wave 2 sample was like the general population. Participants were less likely to be married or to report being in good health than the general population. The longitudinal sample (n=2,098) were broadly like participants who only took part in Wave 1 but were older. Among the sexually active, longitudinal participants were less likely to report multiple sexual partners or a new sexual partner in the past year compared to those who only took part in Wave 1. Conclusions: Natsal-COVID collected longitudinal, quasi-representative population data to enable evaluation of the population-level impact of COVID-19 on SRH and to inform policy.

Fertility trends during successive novel infectious disease outbreaks: Zika and COVID-19 in Brazil / Tendências de fecundidade durante sucessivas epidemias de doenças infecciosas novas: Zika e COVID-19 no Brasil / Tendencias de fecundidad durante los sucesivos brotes de nuevas enfermedades infecciosas: Zika y COVID-19 en Brasil

This study aims to estimate fertility trends in Brazil in the 2010s and early 2020s during a period of back-to-back novel infectious disease outbreaks - Zika virus and COVID-19. We use Brazilian Ministry of Health and Association of Civil Registrar data from 2011-2021 to measure general fertility rates at the national and state levels. We also used seasonal ARIMA model to forecast fertility rates by month and state in 2021 and compared these forecasts with observed fertility rates. We find that fertility rates were steady over 2011-2015 with no statistically significant variation, followed by a sharp decline during the Zika outbreak in 2016 followed by a return to pre-Zika levels after the end of the epidemic. Furthermore, to evaluate the effect of the COVID-19 pandemic, we make comparisons with observed and forecast rates from 2020-2021, showing that declines were generally larger for observed than for forecast rates, yet statistically insignificant. We argue that the resurgence of the COVID-19 pandemic in 2021 might lead to further declines, as women might have not had enough time to adjust rebound from either the effects of the Zika epidemic. We also discuss the importance of timely availability of live births data during a public health crisis with immediate consequences for fertility rates.

O objetivo desta contribuição de dados é estimar as tendências de fecundidade no Brasil nos anos 2010 e início dos anos 2020 durante epidemias consecutivas de doenças infecciosas novas, ou seja, Zika vírus e COVID-19. Utilizamos dados do Ministério da Saúde e do Registro Civil Nacional de 2011-2021 para calcular as taxas mensais de fecundidade nos níveis nacional e estadual. Também utilizamos o modelo ARIMA sazonal para prever taxas de fecundidade por mês e por estado em 2021, e comparamos essas previsões com as taxas de fecundidade observadas. Encontramos que as taxas de fecundidade eram estáveis entre 2011 e 2015, sem variação significativa, seguido por um declínio abrupto durante o surto de Zika em 2016, e seguido por sua vez por um retorno aos níveis pré-Zika depois do fim da epidemia. Além disso, para avaliar o efeito da pandemia de COVID-19, comparamos as taxas observadas e previstas de 2020-2021, mostrando que as quedas geralmente eram maiores nas taxas observadas do que nas previstas, porém sem significância estatística. Argumentamos que o recrudescimento da pandemia de COVID-19 em 2021 poderá levar a mais quedas nas taxas, na medida em que as mulheres não tenham tido tempo suficiente para reagir e se ajustarem aos efeitos da epidemia de Zika. Também discutimos a importância da disponibilidade oportuna de dados sobre nascidos vivos durante uma crise de saúde pública com consequências imediatas para as taxas de fecundidade.

El objetivo de esta aportación de datos es estimar las tendencias de fecundidad en Brasil en la década de 2010 y principios de 2020, durante el período de brotes consecutivos de nuevas enfermedades infecciosas -ZIKV y COVID-19. Se usaron datos procedentes del Ministerio de Salud y del Registro Civil Nacional (ARPEN) desde 2011-2021 para calcular mensualmente las tasas de fecundidad en nivel nacional y en el estado. Se utilizó el ARIMA estacional para pronosticar las tasas de fecundidad por mes y estado en 2021, y se compararon estas predicciones con las tasas de fecundidad observadas. Encontramos que las tasas de fecundidad se mantuvieron estables entre 2011 y 2015, sin variaciones significativas, seguido de un fuerte descenso durante el brote de Zika en 2016, para posteriormente volver a los niveles anteriores al Zika tras el fin de la epidemia. Asimismo, con el fin de evaluar el efecto de la pandemia de COVID-19, hicimos comparaciones con lo observado y la previsión de tasas desde 2020-2021, que muestran que los descensos fueron en general mayores para los índices observados que para los previstos, aunque insignificantes desde el punto de vista estadístico. Sostenemos que el resurgimiento de la pandemia de COVID-19 en 2021 podría provocar nuevos descensos, ya que las mujeres podrían no haber tenido suficiente tiempo para adaptarse a los efectos de la epidemia de Zika. También se discute la importancia de disponer a tiempo de los datos de los nacidos vivos durante una crisis de salud pública con consecuencias inmediatas para las tasas de fecundidad.

Burden of syphilis in Brazil and federated units, 1990-2016: estimates from the Global Burden of Disease Study 2019

ABSTRACT Background: Syphilis is a chronic infectious disease that has created challenging situations for humanity for centuries. Transmission can occur sexually or vertically, with great repercussions on populations, particularly among women and children. The present study presents information on the main burden imposed by syphilis generated by the Global Burden of Disease (GBD) Study 2019 for Brazil and its 27 federated units. Methods: We described the metrics of incidence, deaths, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life years (DALYs), standardized by age and per 100,000 inhabitants, from 1990 to 2019, and we compared the disease burden between the years 1990 and 2019. Results: In Brazil, the disease burden increased between 2005 and 2019 for all metrics. Although a higher incidence of syphilis was found among women in 2019, DALYs [YLLs (males: 15.9%; females: 21.8%), YLDs (males: 25.0%; females: 50.0%), and DALYs (males: 16.2%; females: 22.4%)] were higher among men. In 2019, the highest DALY rate per 100,000 inhabitants was observed in individuals aged above 50 years. The State of Maranhão presented the highest values of DALYs {1990: 165.2 [95% uncertainty interval (UI) 96.2-264.4]; 2005: 43.8 [95% UI 30.3-62.4]; 2019: 29.1 [95% UI 19.8-41.1]} per 100,000 inhabitants in the three years analyzed. Conclusions: The burden of syphilis has increased in recent years. Men presented higher DALYs, although the incidence of the disease was higher in women. Syphilis affects a large number of people across all age groups, causing different degrees of disability and premature death (DALYs).

Using crowdsourcing to estimate populations in communities: Providing a key measurement for coverage assessment of community programmes.

INTRODUCTION: Crowdsourcing pools together dispersed information that is considered public knowledge in an area, to form realistic estimates about the area, or to identify new ideas. The technique can be extremely helpful to develop estimates of public health indicators such as catchment area populations or healthcare providers; however, such uses must be scientifically validated. METHODS: We divided the community into 1040 discrete segments of similar lengths of streets (called spots) and then randomly selected 605 of these spots for crowdsourcing. Local respondents were asked to estimate the maximum and the minimum population residing in those spots. Five informants were interviewed per spot. Median values for the maximum and minimum were averaged to arrive at an estimate for the spot's population. Estimates for all spots were added together to arrive at the population of the community. One hundred spots from the 597 crowdsourced spots were revisited to conduct a household census as a "gold standard". RESULTS: Spots where both crowdsourcing and census estimates were computed had a crowdsource population estimate of 19,255 versus a census estimate of 18,119 - a variation of 5.9% (p: <0.001). However, within spot variation was a mean of 25%. CONCLUSIONS: Crowdsourcing communities for public knowledge information can yield more accurate information about public health indicators such as populations. In turn these estimates can help to better understand public health programme coverage. Other applications to consider may be missed children for immunization or schooling, deaths or births in communities or to identify total formal or informal healthcare providers in a community.

Uncertainty About the Size of the Unauthorized Foreign-Born Population in the United States.

One of the most common methods for estimating the U.S. unauthorized foreign-born population is the residual method. Over the last decade, residual estimates have typically fallen within a narrow range of 10.5 to 12 million. Yet it remains unclear how sensitive residual estimates are to their underlying assumptions. We examine the extent to which estimates may plausibly vary owing to uncertainties in their underlying assumptions about coverage error, emigration, and mortality. Findings show that most of the range in residual estimates derives from uncertainty about emigration rates among legal permanent residents, naturalized citizens, and humanitarian entrants (LNH); estimates are less sensitive to assumptions about mortality among the LNH foreign-born and coverage error for the unauthorized and LNH populations in U.S. Census Bureau surveys. Nevertheless, uncertainty in all three assumptions contributes to a range of estimates, whereby there is a 50% chance that the unauthorized foreign-born population falls between 9.1 and 12.2 million and a 95% chance that it falls between 7.0 and 15.7 million.

Trends in epidemiology: the role of denominator fluctuation in population based estimates.

BACKGROUND: Population estimates are of paramount importance for calculating occurrence and association measures although they can be affected by problems of accuracy and completeness. This study has performed a simulation of the impact of Italian population size variability on incidence rates. METHODS: Data have been obtained by the Italian National Institute of Statistics. For each year expected cases were calculated at increasing fixed rates (up to 1,000/100,000) and were considered constant in the "following year", calculating statistical differences (P < 0.05). RESULTS: In Italy and in other regions, statistically significant higher RRs were found in 2012 vs. 2011 whereas statistically significant lower RRs were found in 2013 vs. 2012 and in 2014 vs. 2013. CONTRIBUTION: The simulation confirms that significant differences due to population fluctuation could be found between consecutive years when investigating diseases with medium-high rates. Researchers should be encouraged to implement actions for reducing the risk of biased population denominators.

Mapathons versus automated feature extraction: a comparative analysis for strengthening immunization microplanning.

BACKGROUND: Social instability and logistical factors like the displacement of vulnerable populations, the difficulty of accessing these populations, and the lack of geographic information for hard-to-reach areas continue to serve as barriers to global essential immunizations (EI). Microplanning, a population-based, healthcare intervention planning method has begun to leverage geographic information system (GIS) technology and geospatial methods to improve the remote identification and mapping of vulnerable populations to ensure inclusion in outreach and immunization services, when feasible. We compare two methods of accomplishing a remote inventory of building locations to assess their accuracy and similarity to currently employed microplan line-lists in the study area. METHODS: The outputs of a crowd-sourced digitization effort, or mapathon, were compared to those of a machine-learning algorithm for digitization, referred to as automatic feature extraction (AFE). The following accuracy assessments were employed to determine the performance of each feature generation method: (1) an agreement analysis of the two methods assessed the occurrence of matches across the two outputs, where agreements were labeled as "befriended" and disagreements as "lonely"; (2) true and false positive percentages of each method were calculated in comparison to satellite imagery; (3) counts of features generated from both the mapathon and AFE were statistically compared to the number of features listed in the microplan line-list for the study area; and (4) population estimates for both feature generation method were determined for every structure identified assuming a total of three households per compound, with each household averaging two adults and 5 children. RESULTS: The mapathon and AFE outputs detected 92,713 and 53,150 features, respectively. A higher proportion (30%) of AFE features were befriended compared with befriended mapathon points (28%). The AFE had a higher true positive rate (90.5%) of identifying structures than the mapathon (84.5%). The difference in the average number of features identified per area between the microplan and mapathon points was larger (t = 3.56) than the microplan and AFE (t = - 2.09) (alpha = 0.05). CONCLUSIONS: Our findings indicate AFE outputs had higher agreement (i.e., befriended), slightly higher likelihood of correctly identifying a structure, and were more similar to the local microplan line-lists than the mapathon outputs. These findings suggest AFE may be more accurate for identifying structures in high-resolution satellite imagery than mapathons. However, they both had their advantages and the ideal method would utilize both methods in tandem.

Steroid hormones in Pacific walrus bones collected over three millennia indicate physiological responses to changes in estimated population size and the environment.

The Pacific walrus (Odobenus rosmarus divergens) is an iconic Arctic marine mammal and an important resource to many Alaska Natives. A decrease in sea ice habitat and unknown population numbers has led to concern of the long-term future health of the walrus population. There is currently no clear understanding of how walrus physiology might be affected by a changing Arctic ecosystem. In this study, steroid hormone concentrations (progesterone, testosterone, cortisol and estradiol) were analysed in walrus bones collected during archaeological [3585-200 calendar years before present (BP)], historical [1880-2006 common era (CE)] and modern (2014-2016 CE) time periods, representing ~ 3651 years, to track changes in reproductive activity and cortisol concentrations (biomarker of stress) over time. Our results show that modern walrus samples have similar cortisol concentrations (median = 43.97 ± standard deviation 904.38 ng/g lipid) to archaeological walruses (38.94 ± 296.17 ng/g lipid, P = 0.75). Cortisol concentrations were weakly correlated with a 15-year average September Chukchi Sea ice cover (P = 0.002, 0.02, r 2 = 0.09, 0.04, for females and males, respectively), indicating a possible physiological resiliency to sea ice recession in the Arctic. All steroid hormones had significant negative correlations with mean walrus population estimates from 1960 to 2016 (P < 0.001). Progesterone in females and testosterone in males exhibited significant correlations with average September Chukchi Sea ice cover for years 1880-2016 (P < 0.001 for both, r2 = 0.34, 0.22, respectively). Modern walruses had significantly lower (P = < 0.001) reproductive hormone concentrations compared with historic walruses during times of rapid population increase, indicative of a population possibly at carrying capacity. This is the first study to apply bone as a tool to monitor long-term changes in hormones that may be associated with changes in walrus population size and sea ice cover.

Methodology of Natsal-COVID Wave 1: a large, quasi-representative survey with qualitative follow-up measuring the impact of COVID-19 on sexual and reproductive health in Britain.

Background: Britain's National Surveys of Sexual Attitudes and Lifestyles (Natsal) have been undertaken decennially since 1990 and provide a key data source underpinning sexual and reproductive health (SRH) policy. The COVID-19 pandemic disrupted many aspects of sexual lifestyles, triggering an urgent need for population-level data on sexual behaviour, relationships, and service use at a time when gold-standard in-person, household-based surveys with probability sampling were not feasible. We designed the Natsal-COVID study to understand the impact of COVID-19 on the nation's SRH and assessed the sample representativeness. Methods: Natsal-COVID Wave 1 data collection was conducted four months (29/7-10/8/2020) after the announcement of Britain's first national lockdown (23/03/2020). This was an online web-panel survey administered by survey research company, Ipsos MORI. Eligible participants were resident in Britain, aged 18-59 years, and the sample included a boost of those aged 18-29. Questions covered participants' sexual behaviour, relationships, and SRH service use. Quotas and weighting were used to achieve a quasi-representative sample of the British general population. Participants meeting criteria of interest and agreeing to recontact were selected for qualitative follow-up interviews. Comparisons were made with contemporaneous national probability surveys and Natsal-3 (2010-12) to understand bias. Results: 6,654 participants completed the survey and 45 completed follow-up interviews. The weighted Natsal-COVID sample was similar to the general population in terms of gender, age, ethnicity, rurality, and, among sexually-active participants, numbers of sexual partners in the past year. However, the sample was more educated, contained more sexually-inexperienced people, and included more people in poorer health. Conclusions: Natsal-COVID Wave 1 rapidly collected quasi-representative population data to enable evaluation of the early population-level impact of COVID-19 and lockdown measures on SRH in Britain and inform policy. Although sampling was less representative than the decennial Natsals, Natsal-COVID will complement national surveillance data and Natsal-4 (planned for 2022).

What is the size of Australia's sexual minority population?

OBJECTIVES: The aim is to present updated estimates of the size of Australia's sexual minority adult population (gay, lesbian, bisexual, and other sexual minority identities). No estimate of this population is currently available from the Australian Bureau of Statistics, and very little is available from other sources. We obtained data on sexual minority identities from three data collections of two national surveys of recent years. Combining averaged prevalence rates from these surveys with official Estimated Resident Population data, we produce estimates of Australia's sexual minority population for recent years. RESULTS: According to percentages averaged across the three survey datasets, 3.6% of males and 3.4% of females described themselves with a minority sexual identity. When applied to Estimated Resident Populations, this gives a sexual minority population at ages 18 + in Australia of 599,500 in 2011 and 651,800 in 2016. Population estimates were also produced by sex and broad age group, revealing larger numbers and higher sexual minority percentages in the younger age groups, and smaller numbers and percentages in the oldest age group. Separate population estimates were also prepared for lesbian, gay, bisexual, and other sexual minority identities.

Over-coverage in population registers leads to bias in demographic estimates.

Estimating the number of individuals living in a country is an essential task for demographers. This study assesses the potential bias in estimating the size of different migrant populations due to over-coverage in population registers. Over-coverage-individuals registered but not living in a country-is an increasingly pressing phenomenon; however, there is no common understanding of how to deal with over-coverage in demographic research. This study examines different approaches to and improvements in over-coverage estimation using Swedish total population register data. We assess over-coverage levels across migrant groups, test how estimates of age-specific death and fertility rates are affected when adjusting for over-coverage, and examine whether over-coverage can explain part of the healthy migrant paradox. Our results confirm the existence of over-coverage and we find substantial changes in mortality and fertility rates, when adjusted, for people of migrating age. Accounting for over-coverage is particularly important for correctly estimating migrant fertility.

Inferring an unobservable population size from observable samples.

Success in the physical and social worlds often requires knowledge of population size. However, many populations cannot be observed in their entirety, making direct assessment of their size difficult, if not impossible. Nevertheless, an unobservable population size can be inferred from observable samples. We measured people's ability to make such inferences and their confidence in these inferences. Contrary to past work suggesting insensitivity to sample size and failures in statistical reasoning, inferences of populations size were accurate-but only when observable samples indicated a large underlying population. When observable samples indicated a small underlying population, inferences were systematically biased. This error, which cannot be attributed to a heuristics account, was compounded by a metacognitive failure: Confidence was highest when accuracy was at its worst. This dissociation between accuracy and confidence was confirmed by a manipulation that shifted the magnitude and variability of people's inferences without impacting their confidence. Together, these results (a) highlight the mental acuity and limits of a fundamental human judgment and (b) demonstrate an inverse relationship between cognition and metacognition.