Survival analysis under imperfect record linkage using historic census data.

BACKGROUND: Advancements in linking publicly available census records with vital and administrative records have enabled novel investigations in epidemiology and social history. However, in the absence of unique identifiers, the linkage of the records may be uncertain or only be successful for a subset of the census cohort, resulting in missing data. For survival analysis, differential ascertainment of event times can impact inference on risk associations and median survival. METHODS: We modify some existing approaches that are commonly used to handle missing survival times to accommodate this imperfect linkage situation including complete case analysis, censoring, weighting, and several multiple imputation methods. We then conduct simulation studies to compare the performance of the proposed approaches in estimating the associations of a risk factor or exposure in terms of hazard ratio (HR) and median survival times in the presence of missing survival times. The effects of different missing data mechanisms and exposure-survival associations on their performance are also explored. The approaches are applied to a historic cohort of residents in Ambler, PA, established using the 1930 US census, from which only 2,440 out of 4,514 individuals (54%) had death records retrievable from publicly available data sources and death certificates. Using this cohort, we examine the effects of occupational and paraoccupational asbestos exposure on survival and disparities in mortality by race and gender. RESULTS: We show that imputation based on conditional survival results in less bias and greater efficiency relative to a complete case analysis when estimating log-hazard ratios and median survival times. When the approaches are applied to the Ambler cohort, we find a significant association between occupational exposure and mortality, particularly among black individuals and males, but not between paraoccupational exposure and mortality. DISCUSSION: This investigation illustrates the strengths and weaknesses of different imputation methods for missing survival times due to imperfect linkage of the administrative or registry data. The performance of the methods may depend on the missingness process as well as the parameter being estimated and models of interest, and such factors should be considered when choosing the methods to address the missing event times.

A tale of many neighborhoods: Latent profile analysis to derive a national neighborhood typology for the US.

INTRODUCTION: Neighborhoods are complex and multi-faceted. Analytic strategies used to model neighborhoods should reflect this complexity, with the potential to better understand how neighborhood characteristics together impact health. We used latent profile analysis (LPA) to derive a residential neighborhood typology applicable for census tracts across the US. METHODS: From tract-level 2015-2019 American Community Survey (ACS) five-year estimates, we selected five indicators that represent four neighborhood domains: demographic composition, commuting, socioeconomic composition, and built environment. We compared model fit statistics for up to eight profiles to identify the optimal number of latent profiles of the selected neighborhood indicators for the entire US. We then examined differences in national tract-level 2019 prevalence estimates of physical and mental health derived from CDC's PLACES dataset between derived profiles using one-way analysis of variance (ANOVA). RESULTS: The 6-profile LPA model was the optimal categorization of neighborhood profiles based on model fit statistics and interpretability. Neighborhood types were distinguished most by demographic composition, followed by commuting and built environment domains. Neighborhood profiles were associated with meaningful differences in the prevalence of health outcomes. Specifically, tracts characterized as "Less educated non-immigrant racial and ethnic minority active transiters" (n = 3,132, 4%) had the highest poor health prevalence (Mean poor physical health: 18.6 %, SD: 4.30; Mean poor mental health: 19.6 %, SD: 3.85), whereas tracts characterized as "More educated metro/micropolitans" (n = 15, 250, 21%) had the lowest prevalence of poor mental and physical health (Mean poor physical health: 10.6 %, SD: 2.41; Mean poor mental health: 12.4 %, SD: 2.67; p < 0.001). CONCLUSION: LPA can be used to derive meaningful and standardized profiles of tracts sensitive to the spatial patterning of social and built conditions, with observed differences in mental and physical health by neighborhood type in the US.

Bayesian areal disaggregation regression to predict wildlife distribution and relative density with low-resolution data.

For species of conservation concern and human-wildlife conflict, it is imperative that spatial population data be available to design adaptive-management strategies and be prepared to meet challenges such as land use and climate change, disease outbreaks, and invasive species spread. This can be difficult, perhaps impossible, if spatially explicit wildlife data are not available. Low-resolution areal counts, however, are common in wildlife monitoring, that is, the number of animals reported for a region, usually corresponding to administrative subdivisions, for example, region, province, county, departments, or cantons. Bayesian areal disaggregation regression is a solution to exploit areal counts and provide conservation biologists with high-resolution species distribution predictive models. This method originated in epidemiology but lacks experimentation in ecology. It provides a plethora of applications to change the way we collect and analyze data for wildlife populations. Based on high-resolution environmental rasters, the disaggregation method disaggregates the number of individuals observed in a region and distributes them at the pixel level (e.g., 5 × 5 km or finer resolution), thereby converting low-resolution data into a high-resolution distribution and indices of relative density. In our demonstrative study, we disaggregated areal count data from hunting bag returns to disentangle the changing distribution and population dynamics of three deer species (red, sika, and fallow) in Ireland from 2000 to 2018. We show an application of the Bayesian areal disaggregation regression method and document marked increases in relative population density and extensive range expansion for each of the three deer species across Ireland. We challenged our disaggregated model predictions by correlating them with independent deer surveys carried out in field sites and alternative deer distribution models built using presence-only and presence-absence data. Finding a high correlation with both independent data sets, we highlighted the ability of Bayesian areal disaggregation regression to accurately capture fine-scale spatial patterns of animal distribution. This study uncovers new scenarios for wildlife managers and conservation biologists to reliably use regional count data disregarded so far in species distribution modeling. Thus, it represents a step forward in our ability to monitor wildlife population and meet challenges in our changing world.

Collaborations Between IPUMS and Genealogical Organizations, 1999-2022.

From 1999 to 2019, IPUMS collaborated with genealogical organizations to develop massive individual-level census datasets spanning the 1790 through 1940 period, and we are currently working on the 1950 census. This research note describes how our genealogical collaborations came about. We focus on our collaborations with the Church of Jesus Christ of Latter-Day Saints Family and Church History Department (later known as FamilySearch) and the private genealogical companies HeritageQuest and Ancestry.com.

Quantifying the number of US men with erectile dysfunction who are potential candidates for penile prosthesis implantation.

Introduction: Penile prosthesis implantation (PPI) is a treatment option recommended in clinical guidelines for erectile dysfunction (ED). However, a limited number of urologists perform PPI procedures in the United States. Aim: To quantify the number of insured men with ED in the United States and project the number of potential candidates for PPI in 2022. Methods: An Excel-based disease impact model was constructed using a top-down estimation approach. The starting US male population consisted of adult men from 2022 US Census data after exclusion of age-specific mortality rates from the National Vital Statistics Reports. Men with health insurance were included in the model based on insurance status data from the US Census database. ED prevalence and ED treatment rates were obtained from administrative claims data analyses-the Merative MarketScan Commercial Database (18-64 years) and the 5% Medicare Standard Analytical Files (≥65 years)-and literature-based estimates of patient-reported ED prevalence. Outcomes: The number of men with ED in the United States and the number of potential candidates for PPI were estimated. Results: By utilizing ED prevalence based on administrative claims, an estimated 8.3% of insured men (10,302,540 estimated men [8,882,548 aged 18-64 years and 1,419,992 aged ≥65 years]) had a diagnosis of ED and sought ED care, out of 124,318,519 eligible US men aged ≥18 years in 2022. An estimated 17.1% of men with an ED diagnosis claim could benefit from PPI in 2022 (1,759,248 men aged ≥18 years). Patient self-reported ED prevalence across all ages ranged from 5.1% to 70.2%. Scenario analyses applying the patient self-reported ED prevalence range revealed the number of men in the United States who could benefit from PPI could have been higher than 1.7 million if their ED symptoms were diagnosed by health care providers. Clinical Implications: Most men with ED in the United States are undertreated, and many could benefit from PPI. Strengths and Limitations: This analysis is a US population-level estimation. However, given this study utilized a variety of assumptions, the results may vary if different model assumptions are applied. Conclusions: This disease impact model estimated that approximately 10.3 million men were diagnosed with ED by their health care providers and sought ED care in the United States in 2022. Of those, 1.7 million men could be PPI candidates and benefit from the treatment option.

Impact of COVID-19 Restrictions on the Urban Thermal Environment of Edmonton, Canada.

The effects of the COVID-19 pandemic on urban environments are addressed in many recent studies. However, limited research has been conducted to examine the impact of the pandemic on anthropogenic emissions over urban land use types, and their relation to socioeconomic characteristics. Anthropogenic heat, as the main contributor to the urban temperature, is changed by the sudden halt imposed by COVID-19 lockdowns. This study thus focuses on previously under-explored urban thermal environments by quantifying the impact of COVID-19 on urban thermal environments across different land-use types and related socioeconomic drivers in Edmonton, Canada. Using Landsat images, we quantified and mapped the spatial pattern of land surface temperature (LST) for business, industrial, and residential land use areas during both the pandemic lockdown and pre-pandemic periods in the study area. Results show that temperature declined in business and industrial areas and increased in residential areas during the pandemic lockdown. Canadian census and housing price data were then used to identify the potential drivers behind the LST anomaly of residential land use. The most important variables that affected LST during the lockdown were found to be median housing price, visible minority population, postsecondary degree, and median income. This study adds to the expanding body of literature about the impact of the COVID-19 pandemic by providing unique insights into the effect of lockdown on a city's thermal environments across different land use types and highlights critical issues of socioeconomic inequalities, which is useful for future heat mitigating and health equity-informed responses.

Relationship between Urban Environmental Components and Dengue Prevalence in Dhaka City-An Approach of Spatial Analysis of Satellite Remote Sensing, Hydro-Climatic, and Census Dengue Data.

Dengue fever is a tropical viral disease mostly spread by the Aedes aegypti mosquito across the globe. Each year, millions of people have dengue fever, and many die as a result. Since 2002, the severity of dengue in Bangladesh has increased, and in 2019, it reached its worst level ever. This research used satellite imagery to determine the spatial relationship between urban environmental components (UEC) and dengue incidence in Dhaka in 2019. Land surface temperature (LST), urban heat-island (UHI), land-use-land-cover (LULC), population census, and dengue patient data were evaluated. On the other hand, the temporal association between dengue and 2019 UEC data for Dhaka city, such as precipitation, relative humidity, and temperature, were explored. The calculation indicates that the LST in the research region varies between 21.59 and 33.33 degrees Celsius. Multiple UHIs are present within the city, with LST values ranging from 27 to 32 degrees Celsius. In 2019, these UHIs had a higher incidence of dengue. NDVI values between 0.18 and 1 indicate the presence of vegetation and plants, and the NDWI identifies waterbodies with values between 0 and 1. About 2.51%, 2.66%, 12.81%, and 82% of the city is comprised of water, bare ground, vegetation, and settlement, respectively. The kernel density estimate of dengue data reveals that the majority of dengue cases were concentrated in the city's north edge, south, north-west, and center. The dengue risk map was created by combining all of these spatial outputs (LST, UHI, LULC, population density, and dengue data) and revealed that UHIs of Dhaka are places with high ground temperature and lesser vegetation, waterbodies, and dense urban characteristics, with the highest incidence of dengue. The average yearly temperature in 2019 was 25.26 degrees Celsius. May was the warmest month, with an average monthly temperature of 28.83 degrees Celsius. The monsoon and post-monsoon seasons (middle of March to middle of September) of 2019 sustained higher ambient temperatures (>26 °C), greater relative humidity (>80%), and at least 150 mm of precipitation. The study reveals that dengue transmits faster under climatological circumstances characterized by higher temperatures, relative humidity, and precipitation.

Data analysis supports monitoring and surveillance of goat health and welfare in the Netherlands.

Monitoring and surveillance systems have an increasingly important role in contemporary society ensuring high levels of animal health and welfare, securing export positions, and protecting public health by ensuring animal health and product safety. In the Netherlands, a voluntary monitoring and surveillance system is in place since 2003 to provide a broad overview of livestock trends in addition to disease-specific surveillance systems, including insight into the occurrence and prevalence of new and emerging non-notifiable diseases and disorders. Being a major surveillance component of this monitoring and surveillance system for small ruminant health in the Netherlands, an annual data analysis on routine census data is performed to retrospectively monitor trends and developments regarding goat health and welfare. This paper aims to describe the process of the data analysis on goat farms in the Netherlands in 2020 and subsequent results are discussed. The data analysis provides key monitoring indicators such as animal and farm density, mortality, animal movements, and numbers and origin of imported small ruminants. Trends were analysed over a five-year, period and associations between herd characteristics and herd health are evaluated. Results showed that in 2020 the Dutch goat population consisted of 670,842 goats, distributed over 14,730 unique herds and increased by 2.3 % compared to 2019. Between 2016 and 2020, although probably underestimated, recorded mortality rates showed a decline on both small-scale and professional farms, with a strongest decrease on farms with herd sizes over more than 200 animals. Seventy-five percent of all professional farms registered animal introductions, in addition to 63 % of small-scale farms, including 2439 imported goats. Performing risks analyses requires demographic knowledge of the goat industry. During and after several disease outbreaks, such as bluetongue and Schmallenberg virus disease, the data analysis proved to function as a valuable tool, however, appeared just as important for recording outbreak-free data. Since its start in 2006, the concept of the data-analysis has continuously been improved, and will in the future be further developed, especially if more complete data sets become available. Subsequently, data analysis will increasingly support monitoring and surveillance of goat health and welfare.

Decomposing trends in educational homogamy and heterogamy - The case of Ireland.

Employing Irish Census microdata, we analyze trends in educational homogamy and heterogamy between 1991 and 2016 and examine how they can be explained by concurrent trends in three theoretically relevant socio-demographic components - (a) educational attainment, (b) the educational gradient in marriage, and (c) educational assortative mating (i.e., non-random matching). Our study proposes a novel counterfactual decomposition method to estimate the contribution of each component to changing sorting outcomes in marriages. Findings indicate rising educational homogamy, an increase in non-traditional unions in which women partner 'down' in education, and a decline in traditional unions. Decomposition results suggest that these trends are predominantly attributable to changes in women's and men's educational attainment. Furthermore, changes in the educational gradient in marrying contributed to rising homogamy and the decline in traditional unions, a fact largely overlooked in previous research. Although assortative mating has also undergone changes, they barely contribute to trends in sorting outcomes.

Changes in visits to green spaces due to the COVID-19 pandemic: Focusing on the proportion of repeat visitors and the distances between green spaces and visitors' places of residences.

Through a quantitative approach, this study aimed to clarify the changes in the number of visitors and visits to green spaces according to green space type before and after the COVID-19 pandemic. We explored the changes in the proportion of repeat visitors and the distance between green spaces and visitors' places of residence. We used KDDI Location Analyzer, which performs novel analysis using mobile phone GPS and census data. The study area included 10 target sites (urban parks and nature trails in the backcountry) located in the Sapporo metropolitan area in Japan. The survey period included snow-free seasons from 2019 to 2021, and 2019 was considered the period "before the pandemic." The results revealed that the number of visits during the pandemic increased compared with those before the pandemic, except for those of urban parks near the city center. In 2020, the proportion of repeat visitors increased for all 10 target sites. In addition, since the outbreak of the pandemic, distances between all urban parks and visitors' residences decreased. The same trend was observed for nature trails in the backcountry close to the city center. These findings indicate a generally decreasing trend in the number of visits to green spaces as many people have been refraining from visiting the site since the outbreak of the pandemic. Contrastingly, the number of visits by repeat visitors who reside close to the target sites has increased in some cases, which compensated for the general decreases.

Interpretable machine learning analysis to identify risk factors for diabetes using the anonymous living census data of Japan.

Purpose: Diabetes mellitus causes various problems in our life. With the big data boom in our society, some risk factors for Diabetes must still exist. To identify new risk factors for diabetes in the big data society and explore further efficient use of big data, the non-objective-oriented census data about the Japanese Citizen's Survey of Living Conditions were analyzed using interpretable machine learning methods. Methods: Seven interpretable machine learning methods were used to analysis Japan citizens' census data. Firstly, logistic analysis was used to analyze the risk factors of diabetes from 19 selected initial elements. Then, the linear analysis, linear discriminate analysis, Hayashi's quantification analysis method 2, random forest, XGBoost, and SHAP methods were used to re-check and find the different factor contributions. Finally, the relationship among the factors was analyzed to understand the relationship among factors. Results: Four new risk factors: the number of family members, insurance type, public pension type, and health awareness level, were found as risk factors for diabetes mellitus for the first time, while another 11 risk factors were reconfirmed in this analysis. Especially the insurance type factor and health awareness level factor make more contributions to diabetes than factors: hypertension, hyperlipidemia, and stress in some interpretable models. We also found that work years were identified as a risk factor for diabetes because it has a high coefficient with the risk factor of age. Conclusions: New risk factors for diabetes mellitus were identified based on Japan's non-objective-oriented anonymous census data using interpretable machine learning models. The newly identified risk factors inspire new possible policies for preventing diabetes. Moreover, our analysis certifies that big data can help us find helpful knowledge in today's prosperous society. Our study also paves the way for identifying more risk factors and promoting the efficiency of using big data.

Evaluation of population estimation methods for wastewater-based epidemiology in a metropolitan city.

This study evaluated the effect of population estimation on the calculation of drug biomarker consumption using wastewater-based epidemiology. Population estimates using mobile phone data, census data, and wastewater quality parameters, such as biological oxygen demand (BOD), total nitrogen (TN), and total phosphorus (TP), were evaluated in six different wastewater treatment plant catchment areas of Busan Metropolitan City, South Korea. The population based on mobile phone data was affected by the patterns of non-resident population movements in each area. The population-normalized daily loads (PNDLs) of methamphetamine were compared according to the different population results. The PNDLs using the population based on mobile phone data (PNDLMobile) was 5.87-27.0 mg/d/1000 people. The PNDLMobile values were notably different from the PNDLs using wastewater quality parameters (PNDLWastewater) (PNDLWastewater/PNDLMobile: 51-148 %, mean 93 %, relative standard deviation (RSD) 36 %), indicating the unsuitability of population estimation using BOD, TN, and TP. In areas with a large concentration of workplaces, the PNDLs using census data (PNDLCensus) differed from the PNDLMobile values (PNDLCensus/PNDLMobile: 57-124 %, mean 94 %, RSD 27 %), whereas other areas showed similar values for PNDLCensus and PNDLMobile (PNDLCensus/PNDLMobile: 95-108 %, mean 102 %, RSD 4.2 %). In particular, the total population estimates of the six survey areas using census data were approximately the same as those based on mobile phone data (RSD: 0.8 %), indicating a decrease in the influence of the non-residential active population in the entire metropolitan city.

Forecasting of cohort fertility by educational level in countries with limited data availability: The case of Brazil.

The Brazilian period total fertility rate (PTFR) dropped to 1.8 in 2010 (1.5 among those with high education). Due to shifts in fertility timing, the PTFR may provide a misleading picture of fertility levels. The consequences of these changes for the cohort total fertility rate (CTFR)-a measure free from tempo distortions-and for educational differences in completed fertility remain unknown. Due to data limitations, CTFR forecasts in low- and middle-income countries are rare. We use Brazilian censuses to reconstruct fertility rates indirectly and forecast the CTFR for all women and by educational level. Four forecasting methods indicate that the CTFR is unlikely to fall to the level of the PTFR. Educational differences in the CTFR are likely to be stark, at 0.7-0.9, larger than in many high-income countries with comparable CTFRs. We show how the CTFR can be forecasted in settings with limited data and call for more research on educational differences in completed fertility in low- and middle-income countries.

Using national census data to facilitate healthcare research.

National censuses are conducted at varying intervals across both the developed and developing world and collect detailed data on a wide range of societal, economic and health questions. This immense volume of data has many potential uses in the field of healthcare research and can be utilised either in isolation or in conjunction with other information sources such as hospital records. At a governmental level census data can be used for healthcare service planning by providing accurate population density information but also, through the use of more detailed data collection, by helping to identify high-risk populations that may require increased resource allocation. It can also be a key tool in addressing and improving healthcare inequality and deprivation by both identifying those populations with poorer healthcare outcomes and through helping researchers to better understand the causes of this inequality. Similarly, it has utility when studying the complex causes of disease and assessing the success of strategies designed to tackle these aetiologies. However, the maximum benefit from these various uses can only be realised if the data collection and analysis processes utilised are robust and this requires that census bureaus regularly review and modify their methods in a transparent and thorough way.

Spatial heterogeneity in drinking water sources in the Greater Accra Metropolitan Area (GAMA), Ghana.

Universal access to safe drinking water is essential to population health and well-being, as recognized in the Sustainable Development Goals (SDG). To develop targeted policies which improve urban access to improved water and ensure equity, there is the need to understand the spatial heterogeneity in drinking water sources and the factors underlying these patterns. Using the Shannon Entropy Index and the Index of Concentration at the Extremes at the enumeration area level, we analyzed census data to examine the spatial heterogeneity in drinking water sources and neighborhood income in the Greater Accra Metropolitan Area (GAMA), the largest urban agglomeration in Ghana. GAMA has been a laboratory for studying urban growth, economic security, and other concomitant socio-environmental and demographic issues in the recent past. The current study adds to this literature by telling a different story about the spatial heterogeneity of GAMA's water landscape at the enumeration area level. The findings of the study reveal considerable geographical heterogeneity and inequality in drinking water sources not evidenced in previous studies. We conclude that heterogeneity is neither good nor bad in GAMA judging by the dominance of both piped water sources and sachet water (machine-sealed 500-ml plastic bag of drinking water). The lessons from this study can be used to inform the planning of appropriate localized solutions targeted at providing piped water sources in neighborhoods lacking these services and to monitor progress in achieving universal access to improved drinking water as recognized in the SDG 6 and improving population health and well-being.

A New Strategy for Linking U.S. Historical Censuses: A Case Study for the IPUMS Multigenerational Longitudinal Panel.

This paper presents a probabilistic method of record linkage, developed using the U.S. full count censuses of 1900 and 1910 but applicable to many sources of digitized historical records. The method links records using a two-step approach, first establishing high confidence matches among men by exploiting a comprehensive set of individual and contextual characteristics. The method then proceeds to link both men and women by leveraging links between households established in the first step. While only the first stage links can be directly comparable to other popular methods in research on the U.S., our method yields both considerably higher linkage rates and greater accuracy while only performing negligibly worse than other algorithms in resembling the target population.

Longitudinal SARS-CoV-2 RNA wastewater monitoring across a range of scales correlates with total and regional COVID-19 burden in a well-defined urban population.

Wastewater-based epidemiology (WBE) is an emerging surveillance tool that has been used to monitor the ongoing COVID-19 pandemic by tracking SARS-CoV-2 RNA shed into wastewater. WBE was performed to monitor the occurrence and spread of SARS-CoV-2 from three wastewater treatment plants (WWTP) and six neighborhoods in the city of Calgary, Canada (population 1.44 million). A total of 222 WWTP and 192 neighborhood samples were collected from June 2020 to May 2021, encompassing the end of the first-wave (June 2020), the second-wave (November end to December 2020) and the third-wave of the COVID-19 pandemic (mid-April to May 2021). Flow-weighted 24-hour composite samples were processed to extract RNA that was then analyzed for two SARS-CoV-2-specific regions of the nucleocapsid gene, N1 and N2, using reverse transcription-quantitative polymerase chain reaction (RT-qPCR). Using this approach SARS-CoV-2 RNA was detected in 98.06% (406/414) of wastewater samples. SARS-CoV-2 RNA abundance was compared to clinically diagnosed COVID-19 cases organized by the three-digit postal code of affected individuals' primary residences, enabling correlation analysis at neighborhood, WWTP and city-wide scales. Strong correlations were observed between N1 & N2 gene signals in wastewater and new daily cases for WWTPs and neighborhoods. Similarly, when flow rates at Calgary's three WWTPs were used to normalize observed concentrations of SARS-CoV-2 RNA and combine them into a city-wide signal, this was strongly correlated with regionally diagnosed COVID-19 cases and clinical test percent positivity rate. Linked census data demonstrated disproportionate SARS-CoV-2 in wastewater from areas of the city with lower socioeconomic status and more racialized communities. WBE across a range of urban scales was demonstrated to be an effective mechanism of COVID-19 surveillance.

Prevalence estimation of intellectual disability using national administrative and household survey data: The importance of survey question specificity.

BACKGROUND: Variability in prevalence estimation of intellectual disability has been attributed to heterogeneity in study settings, methodologies, and intellectual disability case definitions. Among studies based on national household survey data specifically, variability in prevalence estimation has partly been attributed to the level of specificity of the survey questions employed to determine the presence of intellectual disability. SPECIFIC AIMS & METHOD: Using standardised difference scoring, and 'intellectual disability' survey data from the 2007 Northern Ireland Survey on Activity Limitation and Disability (NISALD) (N=23,689) and the 2011 Northern Ireland Census (N=1,770,217) the following study had two aims. First, we aimed to demonstrate the effects of survey question specificity on intellectual disability prevalence estimation. Second, we aimed to produce reliable estimates of the geographic variation of intellectual disability within private households in Northern Ireland while also assessing the socio-demographic, health-related and disability characteristics of this population. FINDINGS: Prevalence estimates generated using the more crudely classified intellectual disability Census data indicated a prevalence of 2% for the overall population, 3.8% for children aged between 0 and 15 years, and 1.5% for citizens aged 16 years or older. Intellectual disability prevalence estimates generated using the more explicitly defined 2007 NISALD data indicated a population prevalence of 0.5% for the overall population, 1.3% for children aged between 0 and 15 years, and 0.3% for citizens aged 16 years or older. The NISALD estimates were consistent with most recent international meta-analysis prevalence estimates. According to the NISALD data, the majority of those with an intellectual disability were male, lived outside Belfast, and experienced severe intellectual disability, with multiple comorbid health conditions. DISCUSSION: The current findings highlight the importance of survey question specificity in the estimation of intellectual disability prevalence and provide reliable prevalence estimates of intellectual disability in Northern Ireland. The findings also demonstrate the utility of administrative data for detecting and understanding intellectual disability, and inform recommendations on how to maximise use of future intellectual disability Census data.

Describing socio-economic variation in life expectancy according to an individual's education, occupation and wage in England and Wales: An analysis of the ONS Longitudinal Study.

People who live in more deprived areas have poorer health outcomes, and this inequality is a major driver of health and social policy. Many interventions targeting these disparities implicitly assume that poorer health is predominantly associated with area-level factors, and that these inequalities are the same for men and women. However, health differentials due to individual socio-economic status (SES) of men and women are less well documented. We used census data linked to the ONS Longitudinal Study to derive individual-level SES in terms of occupation, education and estimated wage, and examined differences in adult mortality and life expectancy. We modelled age-, sex- and SES-specific mortality using Poisson regression, and summarised mortality differences using life expectancy at age 20. We compared the results to those calculated using area-level deprivation metrics. Wide inequalities in life expectancy between SES groups were observed, although differences across SES groups were smaller for women than for men. The widest inequalities were found across men's education (7.2-year (95% CI: 3.0-10.1) difference in life expectancy between groups) and wage (7.0-year (95% CI: 3.5-9.8) difference), and women's education (5.4-year (95% CI: 2.2-8.1) difference). Men with no qualifications had the lowest life expectancy of all groups. In terms of the number of years' difference in life expectancy, the inequalities measured here with individual-level data were of a similar magnitude to inequalities identified previously using area-level deprivation metrics. These data show that health inequalities are as strongly related to individual SES as to area-level deprivation, highlighting the complementary usefulness of these different metrics. Indeed, poor outcomes are likely to be a product of both community and individual influences. Current policy which bases health spending decisions on evidence of inequalities between geographical areas may overlook individual-level SES inequalities for those living in affluent areas, as well as missing important sex differences.

Influence of Family and Community Socioeconomic Status on the Risk of Adolescent Drug Use.

Background: Adolescent drug use increases the risk of mental, physical and social problems later in life and so it is important to understand its complex etiology that likely includes socioeconomic status (SES). We undertook the present analysis using data from a population-based retrospective cohort study to examine the influence of family and community SES in relation to adolescent drug use. We hypothesized that lower levels of community and parental SES would increase the risk of use and that there would be stronger associations for the more proximate family-level factors. Methods: We used self-administered questionnaires (N=1,402) to obtain information on use of marijuana, inhalants, heroin, cocaine/crack, psychedelics/hallucinogens, Ritalin without a prescription, and club drugs during adolescence. Family SES was gathered from birth certificate data on maternal educational level and paternal occupation. Community SES characteristics at birth, age 10 and age 18 were obtained from the US Census Bureau. Results: An increased risk of adolescent drug use was associated with lower maternal education, non-white collar occupations among fathers, and lower community median income, and poverty and unemployment levels at age 18. The strongest associations were seen for the use of multiple drugs (Risk Ratio (RR): 1.7, 95% CI: 1.4-2.2), inhalants (RR: 2.5, 95% CI: 1.5-2.2), crack/cocaine (RR: 2.8, 95% CI: 1.7-4.5), psychedelics/hallucinogens (RR: 1.8, 95% CI: 1.4-2.4), and club/designer drugs (RR: 1.8, 95% CI: 1.2-2.7) among adolescents whose mothers had only a high school education. Conclusions: These results suggest that use of certain drugs during adolescence is associated with both family and community SES measures. However, maternal education appears to have the greatest influence on use, suggesting that a multi-level approach that engages mothers is needed to prevent adolescent drug use.