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The politics and science of health and disease remain contested terrain among scientists, health practitioners, policy makers, industry, communities, and the public. Stakeholders in disputes about illnesses or conditions disagree over their fundamental causes as well as how they should be treated and prevented. This thought-provoking book crosses disciplinary boundaries by engaging with both public health policy and social science, asserting that science, activism, and policy are not separate issues and showing how the contribution of environmental factors in disease is often overlooked.

Housing tenure sits at the heart of much academic and policy literature across many post-industrial countries, and, while debate is often centred on promoting tenure choice, surprisingly little is known of the underlying ways that the tenure chosen can affect health. While population characteristics tend to vary between tenure types, this largely reflects the forces of broader social and economic selection into those tenures. This paper examines what identifiable effect tenure has upon the mental health of individuals, over and above the characteristics of selection. The analysis is based upon 40 828 responses of 10 245 individuals in the Australian working-age population who participated in the Household, Income and Labour Dynamics in Australia study between 2001 and 2007. It is found that, while mental health varies significantly between tenure types, once tenure population differences are accounted for there is little evidence of an intrinsic relationship between tenure and mental health.

Kinship analyses are important pillars of ecological and conservation genetic studies with potentially far-reaching implications. There is a need for power analyses that address a range of possible relationships. Nevertheless, such analyses are rarely applied, and studies that use genetic-data-based-kinship inference often ignore the influence of intrinsic population characteristics. We investigated 11 questions regarding the correct classification rate of dyads to relatedness categories (relatedness category assignments; RCA) using an individual-based model with realistic life history parameters. We investigated the effects of the number of genetic markers; marker type (microsatellite, single nucleotide polymorphism SNP, or both); minor allele frequency; typing error; mating system; and the number of overlapping generations under different demographic conditions. We found that (i) an increasing number of genetic markers increased the correct classification rate of the RCA so that up to >80% first cousins can be correctly assigned; (ii) the minimum number of genetic markers required for assignments with 80 and 95% correct classifications differed between relatedness categories, mating systems, and the number of overlapping generations; (iii) the correct classification rate was improved by adding additional relatedness categories and age and mitochondrial DNA data; and (iv) a combination of microsatellite and single-nucleotide polymorphism data increased the correct classification rate if <800 SNP loci were available. This study shows how intrinsic population characteristics, such as mating system and the number of overlapping generations, life history traits, and genetic marker characteristics, can influence the correct classification rate of an RCA study. Therefore, species-specific power analyses are essential for empirical studies.

Area level models, such as the Fay–Herriot model, aim to improve direct survey estimates for small areas by borrowing strength from related covariates and from direct estimates across all areas. In their multivariate form, where related population characteristics are jointly modelled, area level models allow for inference about functions of two or more characteristics and may exploit dependence between the response variables to improve small area predictions. When model covariates are observed with random error, such as those drawn from another survey, it is important to account for this error in the modelling. We present a Bayesian analysis of a multivariate Fay–Herriot model with functional measurement error, allowing for both joint modelling of related characteristics and accounting for random observation error in some of the covariates. We apply it to modelling 2010 and 2011 poverty rates of school-aged children for US counties, for predicting 2011 poverty rates and the 2010–2011 changes. For this application, the measurement error model results in great improvements in prediction when compared with the direct estimates, and ignoring the measurement error results in uncertainty estimates that are misleading. We propose a computational approach to implementing this model via an independence chain Markov chain Monte Carlo algorithm and prove the propriety of the posterior distribution under a class of non-informative priors.

The number of studies examining racial and socioeconomic disparities in the geographic distribution of environmental hazards and locally unwanted land uses has grown considerably over the past decade. Most studies have found statistically significant racial and socioeconomic disparities associated with hazardous sites. However, there is considerable variation in the magnitude of racial and socioeconomic disparities found; indeed, some studies have found none. Uncertainties also exist about the underlying causes of the disparities. Many of these uncertainties can be attributed to the failure of the most widely used method for assessing environmental disparities to adequately account for proximity between the hazard under investigation and nearby residential populations. In this article, we identify the reasons for and consequences of this failure and demonstrate ways of overcoming these shortcomings by using alternate, distance-based methods. Through the application of such methods, we show how assessments about the magnitude and causes of racial and socioeconomic disparities in the distribution of hazardous sites are changed. In addition to research on environmental inequality, we discuss how distance-based methods can be usefully applied to other areas of demographic research that explore the effects of neighborhood context on a range of social outcomes.

This study explores whether a relationship exists between sale prices and the presence of registered sex offenders in Jefferson County, Kentucky after accounting for observed and unobserved neighbourhood characteristics in accompaniment with property characteristics. The sale prices of single-family properties sold in 2015 were estimated as a function of the characteristics of the property, the housing and population characteristics of the neighbourhood, block group fixed effects and two separate measures of sex offender presence: a) the distance of the nearest registered sex offender to sold single-family properties; and b) the density of registered sex offenders within a half mile distance to sold single-family properties. Registered sex offender distance and density are associated with sale price when controlling for property characteristics and observed neighbourhood characteristics of the property, but these relationships cease to exist when unobserved neighbourhood characteristics are accounted for in the model. 本研究探讨了肯塔基州杰斐逊县的房屋销售价格与注册性犯罪者之间是否存在关系，并考虑了伴随着物业特征的观察到的和未观察到的街区特征。我们估计2015年出售的单户住宅物业的销售价格与以下因素相关：物业的特征，街区居民的住房和人口特征，组团固定效应、以及两个单独的性犯罪者存在的衡量标准：a）售出的单户住宅与距离最近的注册性犯罪者之间的距离；b）售出单户住宅半英里半径内注册性侵犯者的密度。在剔除物业特征和观察到的物业所处街区的特征后，登记的性犯罪者距离和密度与销售价格相关，但是当在模型中考虑未观察到的邻域特征时，这种关系不复存在。

Early research hypothesized impacts of COVID-19 on agricultural workers, food supply, and rural health systems based on population characteristics from data collected preceding the pandemic. Trends confirmed a vulnerable workforce and limits to field sanitation, housing quality, and healthcare. Less is known about eventual, realized impacts. This article uses the Current Population Survey’s COVID-19 monthly core variables from May 2020 through September 2022 to document actual impacts. Summary statistics and statistical models for the probability of being unable to work reveal that 6 to 8% of agricultural workers were unable to work early in the pandemic and that impacts were disproportionately negative for Hispanics and those with children. An implication is that targeted policies based on vulnerabilities may minimize disparate impacts of a public health shock. Understanding the full impacts of COVID-19 on essential labor remains important for economics, public policy, and food systems in addition to public health.

A SARS-CoV2 super-spreading event occurred during carnival in a small town in Germany. Due to the rapidly imposed lockdown and its relatively closed community, this town was seen as an ideal model to investigate the infection fatality rate (IFR). Here, a 7-day seroepidemiological observational study was performed to collect information and biomaterials from a random, household-based study population. The number of infections was determined by IgG analyses and PCR testing. We found that of the 919 individuals with evaluable infection status, 15.5% (95% CI:[12.3%; 19.0%]) were infected. This is a fivefold higher rate than the reported cases for this community (3.1%). 22.2% of all infected individuals were asymptomatic. The estimated IFR was 0.36% (95% CI:[0.29%; 0.45%]) for the community and 0.35% [0.28%; 0.45%] when age-standardized to the population of the community. Participation in carnival increased both infection rate (21.3% versus 9.5%, p  < 0.001) and number of symptoms (estimated relative mean increase 1.6, p  = 0.007). While the infection rate here is not representative for Germany, the IFR is useful to estimate the consequences of the pandemic in places with similar healthcare systems and population characteristics. Whether the super-spreading event not only increases the infection rate but also affects the IFR requires further investigation. Here the authors present a SARS-CoV2 seroepidemiological observational study from a random, household-based study population in a small town in Germany, showing the effect of a super-spreading event on infection rate, severity, and potentially infection fatality rate.

Accurate and timely estimates of population characteristics are a critical input to social and economic research and policy. In industrialized economies, novel sources of data are enabling new approaches to demographic profiling, but in developing countries, fewer sources of big data exist. We show that an individual's past history of mobile phone use can be used to infer his or her socioeconomic status. Furthermore, we demonstrate that the predicted attributes of millions of individuals can, in turn, accurately reconstruct the distribution of wealth of an entire nation or to infer the asset distribution of microregions composed of just a few households. In resource-constrained environments where censuses and household surveys are rare, this approach creates an option for gathering localized and timely information at a fraction of the cost of traditional methods.