Journal of the Royal Statistical Society Series A-Statistics in Society最新文献

Multivariate ordinal data characterised by between-subject heterogeneity or different response styles are prevalent in surveys and other observational studies. It is especially common in surveys designed to assess individual perceptions or knowledge to include a 'don't know' option on some or all survey questions. The latter makes the scales partially ordinal, which precludes the use of well-established models for ordinal data, whereas models for nominal data are inefficient and difficult to interpret. Ignoring the 'don't know' options may introduce bias as the subset of individuals who choose to provide ratings may not be representative of the population of interest. The suggested solution in this manuscript involves jointly modeling the selection of 'don't know' options and the ordinal ratings on multiple variables. The proposed multivariate mixed models are flexible, allow for heterogeneity in responses, response styles, and assessment of the effects of covariates on the ordinal ratings and on choosing the 'don't know' options. Likelihood-based inference and model comparisons are performed. Two case studies: one on financial risk perceptions and one on knowledge about the addictiveness of tobacco products are used for motivation and illustration. A simulation demonstrates that the proposed approach yields unbiased and efficient estimates. The results are straightforward to interpret, effectively capturing the complexity inherent in the data. This makes the proposed models particularly well-suited for analyzing partially ordinal ratings in social and behavioral surveys, providing a robust and reliable framework for such contexts.

We propose a Bayesian hierarchical model to estimate a socio-economic status (SES) index based on mixed dichotomous and continuous variables. In particular, we extend Quinn's ([2004]. Bayesian factor analysis for mixed ordinal and continuous responses. Political Analysis, 12(4), 338-353. https://doi.org/10.1093/pan/mph022) and Schliep and Hoeting's ([2013]. Multilevel latent Gaussian process model for mixed discrete and continuous multivariate response data. Journal of Agricultural, Biological, and Environmental Statistics, 18(4), 492-513. https://doi.org/10.1007/s13253-013-0136-z) factor analysis models for mixed dichotomous and continuous variables by allowing a spatial hierarchical structure of key parameters of the model. Unlike most SES assessment models proposed in the literature, the hierarchical nature of this model enables the use of census observations at the household level without needing to aggregate any information a priori. Therefore, it better accommodates the variability of the SES between census tracts and the number of households per area. The proposed model is used in the estimation of a socio-economic index using 10% of the 2010 Ghana census in the Greater Accra Metropolitan area. Out of the 20 observed variables, the number of people per room, access to water piping and flushable toilets differentiated high and low SES areas the best.

With the precipitous decline in response rates, researchers and pollsters have been left with highly nonrepresentative samples, relying on constructed weights to make these samples representative of the desired target population. Though practitioners employ valuable expert knowledge to choose what variables $X$ must be adjusted for, they rarely defend particular functional forms relating these variables to the response process or the outcome. Unfortunately, commonly used calibration weights-which make the weighted mean of $X$ in the sample equal that of the population-only ensure correct adjustment when the portion of the outcome and the response process left unexplained by linear functions of $X$ are independent. To alleviate this functional form dependency, we describe kernel balancing for population weighting (kpop). This approach replaces the design matrix $\text{X}$ with a kernel matrix, $\text{K}$ encoding high-order information about $\text{X}$ . Weights are then found to make the weighted average row of $\text{K}$ among sampled units approximately equal to that of the target population. This produces good calibration on a wide range of smooth functions of $X$ , without relying on the user to decide which $X$ or what functions of them to include. We describe the method and illustrate it by application to polling data from the 2016 US presidential election.

Different methods for describing health disparities in the distributions of continuous measured health-related variables among groups provide more insight into the nature and impact of the disparities than comparing measures of central tendency. Transformations of the Lorenz curve and analogues of the Gini index used in the analysis of income inequality are adapted to provide graphical and analytical measures of health disparities. Akin to the classical Peters-Belson regression method for partitioning a disparity into a component explained by group differences in a set of covariates and an unexplained component, a new modified Lorenz curve is proposed. The estimation of these curves/measures is adapted for data obtained from surveys with complex sample weighted designs. The statistical properties of sample weighted estimators of the proposed measures and their bootstrap variances are explored through simulation studies. Applications are demonstrated using BMI and blood lead levels among race/ethnic groups of adult females and children, respectively, from the 2013-2018 and 1988-1994 US National Health and Nutrition Examination Surveys. Another application examines disparities in distance to nearest acute care hospital among census blocks in the US state of New York grouped by their level of urbanicity using US census data and the American Hospital Association survey.

Cardiometabolic risk factors (CRFs) during pregnancy are early indicators of maternal diseases, such as stroke and type 2 diabetes. The total number of CRFs typically takes the form of binomial counts that exhibit overdispersion and zero inflation due to correlations among the underlying CRFs. Motivated by an examination of spatiotemporal trends in five CRFs among pregnant women in the U.S. state of South Carolina during the COVID-19 pandemic, we developed a zero-inflated beta-binomial model within a spatiotemporal framework. This model combines a point mass at zero to account for zero inflation and a beta-binomial distribution to model the remaining CRF counts. Given the notable racial disparities in CRFs during pregnancy that vary across the state over time, we incorporate a spatially varying coefficient model to explore the complex relationships between CRFs and geographic and temporal disparities among non-Hispanic White and non-Hispanic Black women. For posterior inference, we developed an efficient hybrid Markov Chain Monte Carlo algorithm that relies on easily sampled Gibbs and Metropolis-Hastings steps. Our analysis of CRFs in South Carolina reveals that certain counties, such as Chesterfield and Clarendon, exhibit gaps in racial health disparities, making them prime candidates for community-level interventions aimed at reducing these disparities.

Healthcare quality metrics refer to a variety of measures used to characterize what should have been done or not done for a patient or the health consequences of what was or was not done. When estimating healthcare quality, many metrics are measured and combined to provide an overall estimate either at the patient level or at higher levels, such as the provider organization or insurer. Racial and ethnic disparities are defined as the mean difference in quality between minorities and Whites not justified by underlying health conditions or patient preferences. Several statistical features of healthcare quality data have been ignored: quality is a theoretical construct not directly observable; quality metrics are measured on different scales or, if measured on the same scale, have different baseline rates; the construct may be multidimensional; and metrics are correlated within-individuals. Balancing health differences across race and ethnicity groups is challenging due to confounding. We provide an approach addressing these features, utilizing exploratory multidimensional item response theory (IRT) models and latent class IRT models to estimate quality, and optimization-based matching to adjust for confounding among the race and ethnicity groups. Quality metrics measured on 93,000 adults with schizophrenia residing in five US states illustrate approaches.

Exposure to fine particulate matter (PM_2.5) poses significant health risks and accurately determining the shape of the relationship between PM_2.5 and health outcomes has crucial policy implications. Although various statistical methods exist to estimate this exposure-response curve (ERC), few studies have compared their performance under plausible data-generating scenarios. This study compares seven commonly used ERC estimators across 72 exposure-response and confounding scenarios via simulation. Additionally, we apply these methods to estimate the ERC between long-term PM_2.5 exposure and all-cause mortality using data from over 68 million Medicare beneficiaries in the United States. Our simulation indicates that regression methods not placed within a causal inference framework are unsuitable when anticipating heterogeneous exposure effects. Under the setting of a large sample size and unknown ERC functional form, we recommend utilizing causal inference methods that allow for nonlinear ERCs. In our data application, we observe a nonlinear relationship between annual average PM_2.5 and all-cause mortality in the Medicare population, with a sharp increase in relative mortality at low PM_2.5 concentrations. Our findings suggest that stricter limits on PM_2.5 could avert numerous premature deaths. To facilitate the utilization of our results, we provide publicly available, reproducible code on Github for every step of the analysis.

Recent years have seen increasing efforts to forecast infectious disease burdens, with a primary goal being to help public health workers make informed policy decisions. However, there has been only limited discussion of how predominant forecast evaluation metrics might indicate the success of policies based in part on those forecasts. We explore one possible tether between forecasts and policy: the allocation of limited medical resources so as to minimize unmet need. We use probabilistic forecasts of disease burden in each of several regions to determine optimal resource allocations, and then we score forecasts according to how much unmet need their associated allocations would have allowed. We illustrate with forecasts of COVID-19 hospitalizations in the U.S., and we find that the forecast skill ranking given by this allocation scoring rule can vary substantially from the ranking given by the weighted interval score. We see this as evidence that the allocation scoring rule detects forecast value that is missed by traditional accuracy measures and that the general strategy of designing scoring rules that are directly linked to policy performance is a promising direction for epidemic forecast evaluation.