Historical Differences in School Term Length and Measured Blood Pressure: Contributions to Persistent Racial Disparities among US-Born Adults

Introduction Legally mandated segregation policies dictated significant differences in the educational experiences of black and white Americans through the first half of the 20th century, with markedly lower quality in schools attended by black children. We determined whether school term length, a common marker of school quality, was associated with blood pressure and hypertension among a cohort of older Americans who attended school during the de jure segregation era. Methods National Health and Nutrition Examination Survey I and II data were linked to state level historical information on school term length. We used race and gender-stratified linear regression models adjusted for age, state and year of birth to estimate effects of term length on systolic and diastolic blood pressure (SBP and DBP) and hypertension for US-born adults. We also tested whether correcting years of schooling for term length differences attenuated estimated racial disparities. Results Among black women, 10% longer school term was associated with lower SBP, DBP and hypertension prevalence (2.1 mmHg, 1.0 mmHg, and 5.0 percentage points respectively). Associations for whites and for black men were not statistically significant. Adjustment for education incorporating corrections for differences in school term length slightly attenuated estimated racial disparities. Conclusions Longer school term length predicted better BP outcomes among black women, but not black men or whites

Benefits, pitfalls, and future design of population-based registers in neurodegenerative disease

Population-based disease registers identify and characterize all cases of disease, including those that might otherwise be neglected. Prospective population-based registers in neurodegeneration are necessary to provide comprehensive data on the whole phenotypic spectrum and can guide planning of health services. With the exception of the rare disease amyotrophic lateral sclerosis, few complete population-based registers exist for neurodegenerative conditions. Incomplete ascertainment, limitations and uncertainty in diagnostic categorization, and failure to recognize sources of bias reduce the accuracy and usefulness of many registers. Common biases include population stratification, the use of prevalent rather than incident cases in earlier years, changes in disease understanding and diagnostic criteria, and changing demographics over time. Future registers are at risk of funding shortfalls and changes to privacy legislation. Notwithstanding, as heterogeneities of clinical phenotype and disease pathogenesis are increasingly recognized in the neurodegenerations, well-designed longitudinal population-based disease registers will be an essential requirement to complete clinical understanding of neurodegenerative diseases.Health Research Board (Ireland

The role of literacy in the association between educational attainment and depressive symptoms

There is a consistent association between education and depressive symptoms, but research on the mechanisms to explain this association remains limited. No study has formally evaluated the extent to which the association between education and depressive symptoms is mediated through a foundational skill such as literacy. Inverse odds ratio weighting (IORW) was used to estimate total, natural direct, and natural indirect effects in examining literacy as a mediator of the association between education and depressive symptoms. Health and Retirement Study participants born in the U.S. between 1900 and 1947 were interviewed biennially for up to 12 years (N = 16,718). Literacy was assessed with a brief vocabulary measure. Depressive symptoms were measured using the 8-item Centers for Epidemiologic Studies-Depression (CES-D) scale. Decomposition estimates were derived using regression analyses of repeated measures of depressive symptoms. Standard errors were obtained using a nonparametric bootstrap with the individual as the independent unit to account for dependence of observations within an individual. In a large cohort of older Americans, a one standard deviation difference in educational attainment (~ 3 years) was associated with a 0.35-point decrement in CES-D score (95% CI: -0.38, -0.32). This decrement represents a 0.22 standard deviation difference in depressive symptoms. Using IORW, the estimated effect of education on depressive symptoms mediated through literacy was -0.10 (95% CI: -0.18, -0.01), which represents 28% of the total effect. Education confers many benefits; as demonstrated by this study for depressive symptoms, one important benefit is literacy

Causal graphs for the analysis of genetic cohort data.

The increasing availability of genetic cohort data has led to many genome-wide association studies (GWAS) successfully identifying genetic associations with an ever-expanding list of phenotypic traits. Association, however, does not imply causation, and therefore methods have been developed to study the issue of causality. Under additional assumptions, Mendelian randomization (MR) studies have proved popular in identifying causal effects between two phenotypes, often using GWAS summary statistics. Given the widespread use of these methods, it is more important than ever to understand, and communicate, the causal assumptions upon which they are based, so that methods are transparent, and findings are clinically relevant. Causal graphs can be used to represent causal assumptions graphically and provide insights into the limitations associated with different analysis methods. Here we review GWAS and MR from a causal perspective, to build up intuition for causal diagrams in genetic problems. We also examine issues of confounding by ancestry and comment on approaches for dealing with such confounding, as well as discussing approaches for dealing with selection biases arising from study design

Simpson's Paradox, Lord's Paradox, and Suppression Effects are the same phenomenon – the reversal paradox

This article discusses three statistical paradoxes that pervade epidemiological research: Simpson's paradox, Lord's paradox, and suppression. These paradoxes have important implications for the interpretation of evidence from observational studies. This article uses hypothetical scenarios to illustrate how the three paradoxes are different manifestations of one phenomenon – the reversal paradox – depending on whether the outcome and explanatory variables are categorical, continuous or a combination of both; this renders the issues and remedies for any one to be similar for all three. Although the three statistical paradoxes occur in different types of variables, they share the same characteristic: the association between two variables can be reversed, diminished, or enhanced when another variable is statistically controlled for. Understanding the concepts and theory behind these paradoxes provides insights into some controversial or contradictory research findings. These paradoxes show that prior knowledge and underlying causal theory play an important role in the statistical modelling of epidemiological data, where incorrect use of statistical models might produce consistent, replicable, yet erroneous results

Effects of childhood socioeconomic position on subjective health and health behaviours in adulthood: how much is mediated by adult socioeconomic position?

<p>Abstract</p> <p>Background</p> <p>Adult socioeconomic position (SEP) is one of the most frequently hypothesised indirect pathways between childhood SEP and adult health. However, few studies that explore the indirect associations between childhood SEP and adult health systematically investigate the mediating role of multiple individual measures of adult SEP for different health outcomes. We examine the potential mediating role of individual measures of adult SEP in the associations of childhood SEP with self-rated health, self-reported mental health, current smoking status and binge drinking in adulthood.</p> <p>Methods</p> <p>Data came from 10,010 adults aged 25-64 years at Wave 3 of the Survey of Family, Income and Employment in New Zealand. The associations between childhood SEP (assessed using retrospective information on parental occupation) and self-rated health, self-reported psychological distress, current smoking status and binge drinking were determined using logistic regression. Models were adjusted individually for the mediating effects of education, household income, labour market activity and area deprivation.</p> <p>Results</p> <p>Respondents from a lower childhood SEP had a greater odds of being a current smoker (OR 1.70 95% CI 1.42-2.03), reporting poorer health (OR 1.82 95% CI 1.39-2.38) or higher psychological distress (OR 1.60 95% CI 1.20-2.14) compared to those from a higher childhood SEP. Two-thirds to three quarters of the association of childhood SEP with current smoking (78%), and psychological distress (66%) and over half the association with poor self-rated health (55%) was explained by educational attainment. Other adult socioeconomic measures had much smaller mediating effects.</p> <p>Conclusions</p> <p>This study suggests that the association between childhood SEP and self-rated health, psychological distress and current smoking in adulthood is largely explained through an indirect socioeconomic pathway involving education. However, household income, area deprivation and labour market activity are still likely to be important as they are intermediaries in turn, in the socioeconomic pathway between education and health.</p

Reducing bias through directed acyclic graphs

<p>Abstract</p> <p>Background</p> <p>The objective of most biomedical research is to determine an unbiased estimate of effect for an exposure on an outcome, i.e. to make causal inferences about the exposure. Recent developments in epidemiology have shown that traditional methods of identifying confounding and adjusting for confounding may be inadequate.</p> <p>Discussion</p> <p>The traditional methods of adjusting for "potential confounders" may introduce conditional associations and bias rather than minimize it. Although previous published articles have discussed the role of the causal directed acyclic graph approach (DAGs) with respect to confounding, many clinical problems require complicated DAGs and therefore investigators may continue to use traditional practices because they do not have the tools necessary to properly use the DAG approach. The purpose of this manuscript is to demonstrate a simple 6-step approach to the use of DAGs, and also to explain why the method works from a conceptual point of view.</p> <p>Summary</p> <p>Using the simple 6-step DAG approach to confounding and selection bias discussed is likely to reduce the degree of bias for the effect estimate in the chosen statistical model.</p

Measures and models for causal inference in cross-sectional studies: arguments for the appropriateness of the prevalence odds ratio and related logistic regression

<p>Abstract</p> <p>Background</p> <p>Several papers have discussed which effect measures are appropriate to capture the contrast between exposure groups in cross-sectional studies, and which related multivariate models are suitable. Although some have favored the Prevalence Ratio over the Prevalence Odds Ratio -- thus suggesting the use of log-binomial or robust Poisson instead of the logistic regression models -- this debate is still far from settled and requires close scrutiny.</p> <p>Discussion</p> <p>In order to evaluate how accurately true causal parameters such as Incidence Density Ratio (IDR) or the Cumulative Incidence Ratio (CIR) are effectively estimated, this paper presents a series of scenarios in which a researcher happens to find a preset ratio of prevalences in a given cross-sectional study. Results show that, provided essential and non-waivable conditions for causal inference are met, the CIR is most often inestimable whether through the Prevalence Ratio or the Prevalence Odds Ratio, and that the latter is the measure that consistently yields an appropriate measure of the Incidence Density Ratio.</p> <p>Summary</p> <p>Multivariate regression models should be avoided when assumptions for causal inference from cross-sectional data do not hold. Nevertheless, if these assumptions are met, it is the logistic regression model that is best suited for this task as it provides a suitable estimate of the Incidence Density Ratio.</p

Variations in cardiovascular disease under-diagnosis in England: national cross-sectional spatial analysis

BACKGROUND: There is under-diagnosis of cardiovascular disease (CVD) in the English population, despite financial incentives to encourage general practices to register new cases. We compared the modelled (expected) and diagnosed (observed) prevalence of three cardiovascular conditions- coronary heart disease (CHD), hypertension and stroke- at local level, their geographical variation, and population and healthcare predictors which might influence diagnosis. METHODS: Cross-sectional observational study in all English local authorities (351) and general practices (8,372) comparing model-based expected prevalence with diagnosed prevalence on practice disease registers. Spatial analyses were used to identify geographic clusters and variation in regression relationships. RESULTS: A total of 9,682,176 patients were on practice CHD, stroke and transient ischaemic attack, and hypertension registers. There was wide spatial variation in observed: expected prevalence ratios for all three diseases, with less than five per cent of expected cases diagnosed in some areas. London and the surrounding area showed statistically significant discrepancies in observed: expected prevalence ratios, with observed prevalence much lower than the epidemiological models predicted. The addition of general practitioner supply as a variable yielded stronger regression results for all three conditions. CONCLUSIONS: Despite almost universal access to free primary healthcare, there may be significant and highly variable under-diagnosis of CVD across England, which can be partially explained by persistent inequity in GP supply. Disease management studies should consider the possible impact of under-diagnosis on population health outcomes. Compared to classical regression modelling, spatial analytic techniques can provide additional information on risk factors for under-diagnosis, and can suggest where healthcare resources may be most needed