Adjusting for sampling variability in sparse data: geostatistical approaches to disease mapping

Abstract Background Disease maps of crude rates from routinely collected health data indexed at a small geographical resolution pose specific statistical problems due to the sparse nature of the data. Spatial smoothers allow areas to borrow strength from neighboring regions to produce a more stable estimate of the areal value. Geostatistical smoothers are able to quantify the uncertainty in smoothed rate estimates without a high computational burden. In this paper, we introduce a uniform model extension of Bayesian Maximum Entropy (UMBME) and compare its performance to that of Poisson kriging in measures of smoothing strength and estimation accuracy as applied to simulated data and the real data example of HIV infection in North Carolina. The aim is to produce more reliable maps of disease rates in small areas to improve identification of spatial trends at the local level. Results In all data environments, Poisson kriging exhibited greater smoothing strength than UMBME. With the simulated data where the true latent rate of infection was known, Poisson kriging resulted in greater estimation accuracy with data that displayed low spatial autocorrelation, while UMBME provided more accurate estimators with data that displayed higher spatial autocorrelation. With the HIV data, UMBME performed slightly better than Poisson kriging in cross-validatory predictive checks, with both models performing better than the observed data model with no smoothing. Conclusions Smoothing methods have different advantages depending upon both internal model assumptions that affect smoothing strength and external data environments, such as spatial correlation of the observed data. Further model comparisons in different data environments are required to provide public health practitioners with guidelines needed in choosing the most appropriate smoothing method for their particular health dataset

Adjusting for sampling variability in sparse data: geostatistical approaches to disease mapping

Abstract Background Disease maps of crude rates from routinely collected health data indexed at a small geographical resolution pose specific statistical problems due to the sparse nature of the data. Spatial smoothers allow areas to borrow strength from neighboring regions to produce a more stable estimate of the areal value. Geostatistical smoothers are able to quantify the uncertainty in smoothed rate estimates without a high computational burden. In this paper, we introduce a uniform model extension of Bayesian Maximum Entropy (UMBME) and compare its performance to that of Poisson kriging in measures of smoothing strength and estimation accuracy as applied to simulated data and the real data example of HIV infection in North Carolina. The aim is to produce more reliable maps of disease rates in small areas to improve identification of spatial trends at the local level. Results In all data environments, Poisson kriging exhibited greater smoothing strength than UMBME. With the simulated data where the true latent rate of infection was known, Poisson kriging resulted in greater estimation accuracy with data that displayed low spatial autocorrelation, while UMBME provided more accurate estimators with data that displayed higher spatial autocorrelation. With the HIV data, UMBME performed slightly better than Poisson kriging in cross-validatory predictive checks, with both models performing better than the observed data model with no smoothing. Conclusions Smoothing methods have different advantages depending upon both internal model assumptions that affect smoothing strength and external data environments, such as spatial correlation of the observed data. Further model comparisons in different data environments are required to provide public health practitioners with guidelines needed in choosing the most appropriate smoothing method for their particular health dataset

Race, Obesity, and the Puzzle of Gender Specificity

Obesity is significantly more prevalent among non-Hispanic African-American (henceforth black) women than among non]Hispanic white American (henceforth gwhiteh) women. These differences have persisted without much alteration since the early 1970s, despite substantial increases in the rates of obesity among both groups. Over the same time period, however, we observe little to no significant differences in the prevalence of obesity between black men and white men. Using data from the National Health and Nutrition Examination Surveys (NHANES) and the Behavioral Risk Factor Surveillance System (BRFSS) pertaining to the past two decades, we evaluate an extensive list of potential explanations for these patterns, including race and gender differences in economic incentives, in body size ideals, and in biological factors. We find that the gaps in mean BMI and in obesity prevalence between black women and white women do not narrow substantially after controlling for educational attainment, household income, occupation, location, and marital status-nor do such controls eliminate the gender-specificity of racial differences in obesity. Following these results, we narrow down the list of explanations to two in particular, both of which are based on the idea that black women (but not also black men) face weaker incentives than white women to avoid becoming obese; one explanation involves health-related incentives, the other, sociocultural incentives. While the data show qualified support for both explanations, we find that the sociocultural incentives hypothesis has the potential to reconcile a greater number of stylized facts

Barriers to accessing and using contraception in highland Guatemala: the development of a family planning self-efficacy scale

Emma Richardson,1 Kenneth R Allison,1,2 Dionne Gesink,1 Albert Berry3 1Dalla Lana School of Public Health, University of Toronto, 2Public Health Ontario, 3Department of Economics, Munk Centre for International Studies, University of Toronto, Toronto, ON, Canada Abstract: Understanding the persistent inequalities in the prevalence rates of family planning and unmet need for family planning between indigenous and nonindigenous women in Guatemala requires localized explorations of the specific barriers faced by indigenous women. Based on social cognitive theory, elicitation interviews were carried out with a purposive sample of 16 young women, aged 20–24 years, married or in union, from the rural districts of Patzún, Chimaltenango, Guatemala. Content analysis was carried out using the constant-comparison method to identify the major themes. Based on this qualitative study, the following barriers are incorporated into the development of a self-efficacy scale: lack of knowledge about and availability of methods, fear of side effects and infertility, husbands being against family planning (and related fears of marital problems and abandonment), pressure from in-laws and the community, and the belief that using contraception is a sin. This is the first evidence-informed self-efficacy scale developed with young adult, indigenous women that addresses the issue of family planning in Latin America. Keywords: indigenous, marginalized populations, elicitation interviews, social cognitive theor

Increasing state public health professionals' proficiency in using PubMed

Objective: The paper provides an overview of a strategy to increase utilization of online bibliographic databases by public health workers