Outcome of Pregnancy Oral Glucose Tolerance Test and Preterm Birth.

BACKGROUND: Gestational diabetes is associated with adverse outcomes such as preterm birth (<37 weeks). However, there is no international consensus on screening criteria or diagnostic levels for gestational diabetes, and it is unknown whether body mass index (BMI) or obesity modifies the relation between glucose level and preterm birth. METHODS: We studied a pregnancy cohort restricted to two Danish regions from the linked Danish Medical Birth Register to study associations between glucose measurements from the 2-hour postload 75-g oral glucose tolerance test (one-step approach) and preterm birth from 2004 to 2018. In Denmark, gestational diabetes screening is a targeted strategy for mothers with identified risk factors. We used Poisson regression to estimate rate ratios (RR) of preterm birth with z-standardized glucose measurements. We assessed effect measure modification by stratifying analyses and testing for heterogeneity. RESULTS: Among 11,337 pregnancies (6.2% delivered preterm), we observed an adjusted preterm birth RR of 1.2 (95% confidence interval [CI] = 1.1, 1.3) for a one-standard deviation glucose increase of 1.4 mmol/l from the mean of 6.7 mmol/l. There was evidence for effect measure modification by obesity, for example, adjusted RR for nonobese (BMI, <30): 1.2 (95% CI = 1.1, 1.3) versus obese (BMI, ≥30): 1.3 (95% CI = 1.2-1.5), P = 0.05 for heterogeneity. CONCLUSION: Among mothers screened for gestational diabetes, increased glucose levels, even those below the diagnostic level for gestational diabetes in Denmark, were associated with increased preterm birth risk. Obesity (BMI, ≥30) may be an effect measure modifier, not just a confounder, of the relation between blood glucose and preterm birth risk.

Post-COVID Conditions in US Primary Care: A PRIME Registry Comparison of Patients With COVID-19, Influenza-Like Illness, and Wellness Visits.

PURPOSE: COVID-19 is a condition that can lead to other chronic conditions. These conditions are frequently diagnosed in the primary care setting. We used a novel primary care registry to quantify the burden of post-COVID conditions among adult patients with a COVID-19 diagnosis across the United States. METHODS: We used the American Family Cohort, a national primary care registry, to identify study patients. After propensity score matching, we assessed the prevalence of 17 condition categories individually and cumulatively, comparing patients having COVID-19 in 2020-2021 with (1) historical control patients having influenza-like illness in 2018 and (2) contemporaneous control patients seen for wellness or preventive visits in 2020-2021. RESULTS: We identified 28,215 patients with a COVID-19 diagnosis and 235,953 historical control patients with influenza-like illness. The COVID-19 group had higher prevalences of breathing difficulties (4.2% vs 1.9%), type 2 diabetes (12.0% vs 10.2%), fatigue (3.9% vs 2.2%), and sleep disturbances (3.5% vs 2.4%). There were no differences, however, in the postdiagnosis monthly trend in cumulative morbidity between the COVID-19 patients (trend = 0.026; 95% CI, 0.025-0.027) and the patients with influenza-like illness (trend = 0.026; 95% CI, 0.023-0.027). Relative to contemporaneous wellness control patients, COVID-19 patients had higher prevalences of breathing difficulties and type 2 diabetes. CONCLUSIONS: Our findings show a moderate burden of post-COVID conditions in primary care, including breathing difficulties, fatigue, and sleep disturbances. Based on clinical registry data, the prevalence of post-COVID conditions in primary care practices is lower than that reported in subspecialty and hospital settings.

Trends and inequities in the diagnosis and treatment of poststroke depression: a retrospective cohort study of privately insured patients in the USA, 2003-2020.

BACKGROUND: Depression is a common neuropsychiatric consequence of stroke, but there is little empiric evidence regarding clinical diagnosis and management of poststroke depression. METHODS: Retrospective cohort study among 831 471 privately insured patients with first stroke in the USA from 2003 to 2020. We identified diagnoses of poststroke depression using codes from the International Classification of Diseases. We identified treatment based on prescriptions for antidepressants. We used Cox proportional hazards regression analysis to examine rates of poststroke depression diagnosis by gender, age and race/ethnicity. Among individuals who received a diagnosis of poststroke depression, we estimated treatment rates by gender, race/ethnicity and age using negative binomial regression analysis. RESULTS: Annual diagnosis and treatment rates for poststroke depression increased from 2003 to 2020 (both p for trend<0.001). Diagnosis rates were higher in women than men (HR 1.53, 95% CI 1.51 to 1.55), lower among members of racial/ethnic minorities (vs white patients: Asian HR 0.63, 95% CI 0.60 to 0.66; Black HR 0.76, 95% CI 0.74 to 0.78; Hispanic HR 0.88, 95% CI 0.86 to 0.90) and varied by age. Among individuals diagnosed with poststroke depression, 69.8% were prescribed an antidepressant. Rates of treatment were higher in women vs men (rate ratio, RR=1.19, 95% CI: 1.17 to 1.21), lower among members of racial/ethnic minorities (vs white patients: Asian RR 0.85, 95% CI 0.80 to 0.90; Black RR 0.92, 95% CI 0.89 to 0.94; Hispanic RR 0.96, 95% CI 0.93 to 0.99) and higher among older patients. CONCLUSIONS: In this insured population, we identify potential inequities in clinical management of poststroke depression by gender, race/ethnicity and age that may reflect barriers other than access to healthcare.

Predicting mortality from 57 economic, behavioral, social, and psychological factors.

Behavioral and social scientists have identified many nonbiological predictors of mortality. An important limitation of much of this research, however, is that risk factors are not studied in comparison with one another or from across different fields of research. It therefore remains unclear which factors should be prioritized for interventions and policy to reduce mortality risk. In the current investigation, we compare 57 factors within a multidisciplinary framework. These include (i) adverse socioeconomic and psychosocial experiences during childhood and (ii) socioeconomic conditions, (iii) health behaviors, (iv) social connections, (v) psychological characteristics, and (vi) adverse experiences during adulthood. The current prospective cohort investigation with 13,611 adults from 52 to 104 y of age (mean age 69.3 y) from the nationally representative Health and Retirement Study used weighted traditional (i.e., multivariate Cox regressions) and machine-learning (i.e., lasso, random forest analysis) statistical approaches to identify the leading predictors of mortality over 6 y of follow-up time. We demonstrate that, in addition to the well-established behavioral risk factors of smoking, alcohol abuse, and lack of physical activity, economic (e.g., recent financial difficulties, unemployment history), social (e.g., childhood adversity, divorce history), and psychological (e.g., negative affectivity) factors were also among the strongest predictors of mortality among older American adults. The strength of these predictors should be used to guide future transdisciplinary investigations and intervention studies across the fields of epidemiology, psychology, sociology, economics, and medicine to understand how changes in these factors alter individual mortality risk.

Explaining the Variance in Cardiovascular Disease Risk Factors: A Comparison of Demographic, Socioeconomic, and Genetic Predictors.

BACKGROUND: Efforts to explain the burden of cardiovascular disease (CVD) often focus on genetic factors or social determinants of health. There is little evidence on the comparative predictive value of each, which could guide clinical and public health investments in measuring genetic versus social information. We compared the variance in CVD-related outcomes explained by genetic versus socioeconomic predictors. METHODS: Data were drawn from the Health and Retirement Study (N = 8,720). We examined self-reported diabetes, heart disease, depression, smoking, and body mass index, and objectively measured total and high-density lipoprotein cholesterol. For each outcome, we compared the variance explained by demographic characteristics, socioeconomic position (SEP), and genetic characteristics including a polygenic score for each outcome and principal components (PCs) for genetic ancestry. We used R-squared values derived from race-stratified multivariable linear regressions to evaluate the variance explained. RESULTS: The variance explained by models including all predictors ranged from 3.7% to 14.3%. Demographic characteristics explained more than half this variance for most outcomes. SEP explained comparable or greater variance relative to the combination of the polygenic score and PCs for most conditions among both white and Black participants. The combination of SEP, polygenic score, and PCs performed substantially better, suggesting that each set of characteristics may independently contribute to the prediction of CVD-related outcomes. Philip R. Lee Institute for Health Policy Studies, Department of Family & Community Medicine, UCSF. CONCLUSIONS: Focusing on genetic inputs into personalized medicine predictive models, without considering measures of social context that have clear predictive value, needlessly ignores relevant information that is more feasible and affordable to collect on patients in clinical settings. See video abstract at, http://links.lww.com/EDE/B879.

A US State Index of Successful Aging: Differences Between States and Over Time.

Policy Points The focus of successful aging is on the social contexts that enable individuals to be productively engaged and secure, with an emphasis on equity. There is currently no index to measure progress towards this goal at the US state level. We developed an empirical index for the evaluation of US state adaptation to societal aging across five critical domains that support successful population aging: (1) productivity and engagement, (2) security, (3) equity, (4) cohesion, and (5) well-being. Our index shows substantial variability over time and is not overly influenced by the performance of an individual domain. This suggests that it can be used to monitor state progress over time toward the goal of supporting successful aging. Rather than a major national trend, there are large between-state differences and changes in our index over time. This suggests individual US state policies and programs, as well as local economic conditions, may have a substantial impact on adaptations to societal aging. CONTEXT: Although it is recognized that aspects of US state environments impact the likelihood that older adults age successfully, there is currently no reliable and comprehensive measure of contexts that best support successful aging at a state level. The current project adapts a multidimensional index previously used to assess adaptation to successful aging in developed countries and applies it to the 50 US states and the District of Columbia. METHODS: We obtained data from multiple sources for all 50 US states and the District of Columbia from 2003 to 2017 in order to measure five distinct domains that define successful population aging: (1) productivity and engagement, (2) security, (3) equity, (4) cohesion, and (5) well-being. We created a ranking of states for the year 2017 based on these domains, and also examined how individual US state rankings changed over time from 2003 to 2017. FINDINGS: The level of adaptation to successful aging varied substantially between states and over time. The highest-ranked states in 2017 were Vermont, Hawaii, Iowa, Colorado, and New Hampshire, and the lowest-ranked states were Louisiana, Arkansas, Kentucky, West Virginia, and Mississippi. Mississippi, South Carolina, Iowa, Arizona, and Delaware had the greatest improvement in their ranking over the period of 2003 to 2017. Our findings were generally robust to the weighting scheme used and were not overly influenced by any particular domain. CONCLUSIONS: The US State Index of Successful Aging can be used to monitor US state progress in promoting the well-being and health of aging populations. Factors driving the changes in the index remain to be elucidated.

Anomalously warm weather and acute care visits in patients with multiple sclerosis: A retrospective study of privately insured individuals in the US.

BACKGROUND: As the global climate changes in response to anthropogenic greenhouse gas emissions, weather and temperature are expected to become increasingly variable. Although heat sensitivity is a recognized clinical feature of multiple sclerosis (MS), a chronic demyelinating disorder of the central nervous system, few studies have examined the implications of climate change for patients with this disease. METHODS AND FINDINGS: We conducted a retrospective cohort study of individuals with MS ages 18-64 years in a nationwide United States patient-level commercial and Medicare Advantage claims database from 2003 to 2017. We defined anomalously warm weather as any month in which local average temperatures exceeded the long-term average by ≥1.5°C. We estimated the association between anomalously warm weather and MS-related inpatient, outpatient, and emergency department visits using generalized log-linear models. From 75,395,334 individuals, we identified 106,225 with MS. The majority were women (76.6%) aged 36-55 years (59.0%). Anomalously warm weather was associated with increased risk for emergency department visits (risk ratio [RR] = 1.043, 95% CI: 1.025-1.063) and inpatient visits (RR = 1.032, 95% CI: 1.010-1.054). There was limited evidence of an association between anomalously warm weather and MS-related outpatient visits (RR = 1.010, 95% CI: 1.005-1.015). Estimates were similar for men and women, strongest among older individuals, and exhibited substantial variation by season, region, and climate zone. Limitations of the present study include the absence of key individual-level measures of socioeconomic position (i.e., race/ethnicity, occupational status, and housing quality) that may determine where individuals live-and therefore the extent of their exposure to anomalously warm weather-as well as their propensity to seek treatment for neurologic symptoms. CONCLUSIONS: Our findings suggest that as global temperatures rise, individuals with MS may represent a particularly susceptible subpopulation, a finding with implications for both healthcare providers and systems.

Leisure time activities and biomarkers of chronic stress: The mediating roles of alcohol consumption and smoking.

AIMS: Previous research found an association between leisure time activities such as arts and cultural activities and self-reported health over the life course-a measure prone to response bias. This study tested the relationship between arts and cultural activities and allostatic load, a biomarker of chronic stress, and examined risky health behaviors, including alcohol consumption and smoking, as possible mediators. METHODS: The sample consists of 8948 adults from the second wave of the United Kingdom Household Longitudinal Study, which is representative of the British population. The cross-sectional association between arts and cultural activities and allostatic load was tested with negative binomial models, and the mediation roles of alcohol consumption and smoking in the association was tested with the Karlson-Holm-Breen (KHB) decomposition method. RESULTS: Frequent participation in arts, frequent attendance of cultural events, visits to museums or galleries, and visits to historical sites have negative associations with allostatic load. The associations are mediated by lower frequency of alcohol drinking and smoking. CONCLUSIONS: Cultural capital may promote health by reducing the frequency of health risk behaviors such as drinking alcohol and smoking. Future research and public health policies should consider whether cultural capital acts as a social determinant of health to promote healthy leisure activities over the life course.

Short-term effects of the earned income tax credit on mental health and health behaviors.

Poverty has consistently been linked to poor mental health and risky health behaviors, yet few studies evaluate the effectiveness of programs and policies to address these outcomes by targeting poverty itself. We test the hypothesis that the earned income tax credit (EITC)-the largest U.S. poverty alleviation program-improves short-term mental health and health behaviors in the months immediately after income receipt. We conducted parallel analyses in two large longitudinal national data sets: the National Health Interview Survey (NHIS, 1997-2016, N = 379,603) and the Panel Study of Income Dynamics (PSID, 1985-2015, N = 29,808). Outcomes included self-rated health, psychological distress, tobacco use, and alcohol consumption. We employed difference-in-differences analysis, a quasi-experimental technique. We exploited seasonal variation in disbursement of the EITC, which is distributed as a tax refund every spring: we compared outcomes among EITC-eligible individuals interviewed immediately after refund receipt (Feb-Apr) with those interviewed in other months more distant from refund receipt (May-Jan), "differencing out" seasonal trends among non-eligible individuals. For most outcomes, we were unable to rule out the null hypothesis that there was no short-term effect of the EITC. Findings were cross-validated in both data sets. The exception was an increase in smoking in PSID, although this finding was not robust to sensitivity analyses. While we found no short-term "check effect" of the EITC on mental health and health behaviors, others have found long-term effects on these outcomes. This may be because recipients anticipate EITC receipt and smooth their income accordingly.

Income, inflammation and cancer mortality: a study of U.S. National Health and Nutrition Examination Survey mortality follow-up cohorts.

BACKGROUND: To estimate the relationship between inflammatory biomarkers and cancer mortality in a nationally representative sample of the U.S. population while controlling for education, occupation, and income. METHODS: Data were obtained from the U.S. National Health and Nutrition Examination Survey from 1988 to 1994 (N = 7817) and 1999-2002 (N = 2344). We fit Cox proportional hazard models to examine the relationship between C-reactive protein (CRP) and fibrinogen with cancer mortality. RESULTS: In the full Cox multivariate model, clinically raised CRP was associated with cancer mortality in NHANES 1988-1994 (> 0.99 mg/dL: 95%CI: 1.04-2.13). However, across two inflammatory biomarkers (CRP and Fibrinogen), two NHANES time periods (1998-1994 and 1999-2002) and three income levels (12 strata in total), Hazard ratio confidence intervals did not include the null only for one association: CRP and cancer mortality among low income participants from 1988 to 1994 (HR = 1.83, 95% CI: 1.10-3.04). CONCLUSIONS: We find evidence that only in one unique stratum is earlier life CRP, and not fibrinogen, associated with prospective cancer mortality. After more complete control for socioeconomic confounding, CRP and fibrinogen do not predict cancer mortality in most subpopulations.

Educational attainment and cardiovascular disease in the United States: A quasi-experimental instrumental variables analysis.

BACKGROUND: There is ongoing debate about whether education or socioeconomic status (SES) should be inputs into cardiovascular disease (CVD) prediction algorithms and clinical risk adjustment models. It is also unclear whether intervening on education will affect CVD, in part because there is controversy regarding whether education is a determinant of CVD or merely correlated due to confounding or reverse causation. We took advantage of a natural experiment to estimate the population-level effects of educational attainment on CVD and related risk factors. METHODS AND FINDINGS: We took advantage of variation in United States state-level compulsory schooling laws (CSLs), a natural experiment that was associated with geographic and temporal differences in the minimum number of years that children were required to attend school. We linked census data on educational attainment (N = approximately 5.4 million) during childhood with outcomes in adulthood, using cohort data from the 1992-2012 waves of the Health and Retirement Study (HRS; N = 30,853) and serial cross-sectional data from 1971-2012 waves of the National Health and Nutrition Examination Survey (NHANES; N = 44,732). We examined self-reported CVD outcomes and related risk factors, as well as relevant serum biomarkers. Using instrumental variables (IV) analysis, we found that increased educational attainment was associated with reduced smoking (HRS ß -0.036, 95%CI: -0.06, -0.02, p < 0.01; NHANES ß -0.032, 95%CI: -0.05, -0.02, p < 0.01), depression (HRS ß -0.049, 95%CI: -0.07, -0.03, p < 0.01), triglycerides (NHANES ß -0.039, 95%CI: -0.06, -0.01, p < 0.01), and heart disease (HRS ß -0.025, 95%CI: -0.04, -0.002, p = 0.01), and improvements in high-density lipoprotein (HDL) cholesterol (HRS ß 1.50, 95%CI: 0.34, 2.49, p < 0.01; NHANES ß 0.86, 95%CI: 0.32, 1.48, p < 0.01), but increased BMI (HRS ß 0.20, 95%CI: 0.002, 0.40, p = 0.05; NHANES ß 0.13, 95%CI: 0.01, 0.32, p = 0.05) and total cholesterol (HRS ß 2.73, 95%CI: 0.09, 4.97, p = 0.03). While most findings were cross-validated across both data sets, they were not robust to the inclusion of state fixed effects. Limitations included residual confounding, use of self-reported outcomes for some analyses, and possibly limited generalizability to more recent cohorts. CONCLUSIONS: This study provides rigorous population-level estimates of the association of educational attainment with CVD. These findings may guide future implementation of interventions to address the social determinants of CVD and strengthen the argument for including educational attainment in prediction algorithms and primary prevention guidelines for CVD.

Socioeconomic gradients in cancer incidence by race and ethnicity in California, 2008-2012: the influence of tobacco use or screening detectable cancers.

PURPOSE: There are clearly documented inequalities in cancer incidence by socioeconomic position, but it is unclear whether this is due primarily to differences in tobacco exposure and screening practices or to other factors. METHODS: Our study included 741,373 incident cases of invasive cancer from 2008 to 2012 in California. We calculated age-standardized incidence rates across twelve categories of census tract poverty as a measure of socioeconomic position (SEP) for (1) all cancer sites combined, (2) sites not strongly related to tobacco use, (3) sites not related to screening, and (4) sites not related to tobacco use or screening. RESULTS: There was higher cancer incidence among those living in areas with higher levels of poverty for sites not strongly related to tobacco use or screening, among Whites, Blacks, and Asians, but not among Latinos. Among Whites there was no relationship with census tract poverty at lower levels of poverty-the relationship with cancer incidence was primarily among those in higher poverty. For Blacks and Asians, there is a more linear relationship with cancer incidence across levels of poverty. CONCLUSIONS: SEP gradients in cancer incidence remain after exclusion of cancer sites strongly related to tobacco use and screening. Our findings demonstrate a need for research on other environmental and social causes of cancer where exposures are differentially distributed by SEP.

Gender, Depression, and Blue-collar Work: A Retrospective Cohort Study of US Aluminum Manufacturers.

BACKGROUND: Industrial blue-collar workers face multiple work-related stressors, but evidence regarding the burden of mental illness among today's blue-collar men and women remains limited. METHODS: In this retrospective cohort study, we examined health and employment records for 37,183 blue- and white-collar workers employed by a single US aluminum manufacturer from 2003 to 2013. Using Cox proportional hazards regression, we modeled time to first episode of treated depression by gender and occupational class. Among cases, we modeled rates of depression-related service utilization with generalized gamma regression. RESULTS: Compared with their white-collar counterparts, blue-collar men were more likely to be treated for depression (hazard ratio [HR] = 1.3; 95% confidence interval [CI] = 1.1, 1.4) as were blue-collar women (HR = 1.4; 1.2, 1.6). Blue-collar women were most likely to be treated for depression as compared with white-collar men (HR = 3.2; 95% CI = 2.1, 5.0). However, blue-collar workers used depression-related services less frequently than their white-collar counterparts among both men (rate ratio = 0.91; 95% CI = 0.84, 0.98) and women (rate ratio = 0.82; 95% CI = 0.77, 0.88). CONCLUSIONS: Blue-collar women were more likely to be treated for depression than white-collar workers, and blue-collar women were most likely to be treated for depression compared with white-collar men. However, blue-collar men and women used depression-related healthcare services less frequently than white-collar workers. These findings underscore that blue-collar women may be uniquely susceptible to depression, and suggest that blue-collar workers may encounter barriers to care-seeking related mental illness other than their insurance status.

Quality and quantity: The association of state-level educational policies with later life cardiovascular disease.

Education is a powerful predictor of cardiovascular morbidity and mortality. While the majority of the literature has focused on years of educational attainment or degree attainment, fewer studies examine the role of educational quality in the prevention of cardiovascular disease (CVD). We tested the hypothesis that average state-level educational quality was associated with CVD, linking state-level data on educational quality with individual demographic and health data from multiple waves of the National Health and Nutrition Examination Survey (N = 34,770). We examined thirteen CVD-related outcomes-including blood pressure, cholesterol, and heart attack-to understand the multiple pathways through which educational quality may influence CVD. The primary predictor was a composite index of educational quality, combining state-level measures of student-teacher ratios, per-pupil expenditures, and school term length. We fit multivariable models, regressing each outcome on the educational quality composite index and adjusting for individual- and state-level covariates. We also assessed whether the association between state educational quality and CVD differed for less educated individuals. Overall, higher educational quality was associated with less smoking (OR = 0.86, 95%CI: 0.77, 0.97), but there was no statistically significant association for the other 12 outcomes. Interaction tests indicated that less educated individuals benefited less from higher educational quality relative to those with more education for several outcomes. Our study suggests that state-level educational quality is not strongly associated with CVD, and that this null association overall may mask heterogeneous benefits that accrue disproportionately to those with higher levels of education.

Life-course BMI and biomarkers in persons aged 60 years or older: a comparison of the USA and Costa Rica.

OBJECTIVE: There is a large literature linking current BMI to levels of cardiovascular risk biomarkers, but it is unknown whether measures of BMI earlier in the life course and maximum BMI are predictive of current levels of biomarkers. The objective of the current study was to determine how current, maximum and age-25 BMI among individuals over the age of 60 years are associated with their current levels of cardiovascular risk biomarkers. DESIGN: Cross-sectional study with retrospective recall. SETTING: Costa Rica (n 821) and the USA (n 4110). SUBJECTS: Nationally representative samples of adults aged 60 years or over. RESULTS: We used regression models to examine the relationship between multiple meaures of BMI with four established cardiovascular risk biomarkers. The most consistent predictor of current levels of systolic blood pressure, TAG and HDL-cholesterol was current BMI. However, maximum BMI was the strongest predictor of glycosylated Hb (HbA1c) and was also related to HDL-cholesterol and TAG. HbA1c was independent of current BMI. We found that these relationships are consistent between Costa Rica and the USA for HbA1c and for HDL-cholesterol. CONCLUSIONS: Current levels of cardiovascular risk biomarkers are not only the product of current levels of BMI, but also of maximum lifetime BMI, particularly for levels of HbA1c and for HDL-cholesterol. Managing maximum obtained BMI over the life course may be most critical for maintaining the healthiest levels of cardiovascular risk.

Similarities in Maternal Weight and Birth Weight Across Pregnancies and Across Sisters.

Objectives The current study examined how prepregnancy body mass index (BMI), gestational weight gain, and birth weight cluster between births within women and between women who are sisters. Methods Using data from the National Longitudinal Survey of Youth 1979 cohort, we utilized nested, multivariable hierarchical linear models to examine the correlation of these three outcomes between births (n = 6006) to women (n = 3605) and sisters (n = 3170) so that we can quantify the clustering by sibship and by woman for these three pregnancy-related outcomes. Results After controlling for confounding covariates, prepregnancy BMI (intraclass correlation (ICC) 0.24, 95% CI 0.16, 0.32), gestational weight gain (ICC 0.23, 95% CI 0.16, 0.31), and infant's birthweight (ICC 0.07, 95% CI 0.003, 0.13) were correlated between sisters. Additionally, all three outcomes were significantly correlated between births for each sister, suggesting that prepregnancy BMI (ICC 0.82, 95% CI 0.81, 0.83), gestational weight gain (ICC 0.45, 95% CI 0.42, 0.49), and birth weight (ICC 0.31, 95% CI 0.28, 0.35) track between pregnancies in the same woman. Conclusions for Practice The observed clustering both within women and between sisters suggests that shared genetic and environmental factors among sisters play a role in pregnancy outcomes above and beyond that of women's own genetic and environmental factors. Findings suggest that asking a woman about her sisters' pregnancy outcomes could provide insight into the possible outcomes for her current pregnancy. Future research should test if collecting such a family history and providing tailored clinical recommendations accordingly would be useful.

Limitations of GCTA as a solution to the missing heritability problem.

Genome-wide association studies (GWASs) seek to understand the relationship between complex phenotype(s) (e.g., height) and up to millions of single-nucleotide polymorphisms (SNPs). Early analyses of GWASs are commonly believed to have "missed" much of the additive genetic variance estimated from correlations between relatives. A more recent method, genome-wide complex trait analysis (GCTA), obtains much higher estimates of heritability using a model of random SNP effects correlated between genotypically similar individuals. GCTA has now been applied to many phenotypes from schizophrenia to scholastic achievement. However, recent studies question GCTA's estimates of heritability. Here, we show that GCTA applied to current SNP data cannot produce reliable or stable estimates of heritability. We show first that GCTA depends sensitively on all singular values of a high-dimensional genetic relatedness matrix (GRM). When the assumptions in GCTA are satisfied exactly, we show that the heritability estimates produced by GCTA will be biased and the standard errors will likely be inaccurate. When the population is stratified, we find that GRMs typically have highly skewed singular values, and we prove that the many small singular values cannot be estimated reliably. Hence, GWAS data are necessarily overfit by GCTA which, as a result, produces high estimates of heritability. We also show that GCTA's heritability estimates are sensitive to the chosen sample and to measurement errors in the phenotype. We illustrate our results using the Framingham dataset. Our analysis suggests that results obtained using GCTA, and the results' qualitative interpretations, should be interpreted with great caution.

Socioeconomic Differences in the Epidemiologic Transition From Heart Disease to Cancer as the Leading Cause of Death in the United States, 2003 to 2015: An Observational Study.

Background: Recent data suggest that the United States is in the midst of an epidemiologic transition in the leading cause of death. Objective: To examine county-level sociodemographic differences in the transition from heart disease to cancer as the leading cause of death in the United States. Design: Observational study. Setting: U.S. death records, 2003 to 2015. Participants: Decedents aged 25 years or older, classified by racial/ethnic group. Measurements: All-cause, heart disease, and cancer mortality stratified by quintiles of county median household income. Age- and sex-adjusted mortality rates and average annual percentage of change were calculated. Results: Heart disease was the leading cause of death in 79% of counties in 2003 and 59% in 2015. Cancer was the leading cause of death in 21% of counties in 2003 and 41% in 2015. The shift to cancer as the leading cause of death was greatest in the highest-income counties. Overall, heart disease mortality rates decreased by 28% (30% in high-income counties vs. 22% in low-income counties) from 2003 to 2015, and cancer mortality rates decreased by 16% (18% in high-income counties vs. 11% in low-income counties). In the lowest-income counties, heart disease remained the leading cause of death among all racial/ethnic groups, and improvements were smaller for both heart disease and cancer. Limitation: Use of county median household income as a proxy for socioeconomic status. Conclusion: Data show that heart disease is more likely to be the leading cause of death in low-income counties. Low-income counties have not experienced the same decrease in mortality rates as high-income counties, which suggests a later transition to cancer as the leading cause of death in low-income counties. Primary Funding Source: National Institute on Minority Health and Health Disparities.

Estimating the Short-Term Effects of the Earned Income Tax Credit on Child Health.

The Earned Income Tax Credit (EITC) is the largest US poverty-alleviation program, yet few studies examine its effects on the health of recipients' children. We employed quasiexperimental techniques to test the hypothesis that EITC refund receipt is associated with short-term improvements in child health. The data set included children in families surveyed in the Third National Health and Nutrition Examination Survey (n = 7,444). We employed a difference-in-differences approach, exploiting the seasonal nature of EITC refund receipt. We compared children of EITC-eligible families interviewed immediately after refund receipt (February to April) with those interviewed during other months (May to January), differencing out seasonal variation among non-EITC-eligible families. We examined outcomes that were likely to be affected immediately after refund receipt, including general health, nutrition, metabolic and inflammatory biomarkers, and test scores. There were improvements in physician-reported overall health after refund receipt but no changes in infection, serum metabolic or inflammatory markers, or test scores, and there were contradictory findings for food insufficiency. In summary, EITC refunds are not strongly associated with most short-term health outcomes among recipients' children, although numerous previous studies have demonstrated impacts on longer-term outcomes. This highlights the importance of examining the effects of public policies on beneficiaries and their children using varying study designs.

A Multiple-Imputation "Forward Bridging" Approach to Address Changes in the Classification of Asian Race/Ethnicity on the US Death Certificate.

The incomparability of old and new classification systems for describing the same data can be seen as a missing-data problem, and, under certain assumptions, multiple imputation may be used to "bridge" 2 classification systems. One example of such a change is the introduction of detailed Asian-American race/ethnicity classifications on the 2003 version of the US national death certificate, which was adopted for use by 38 states between 2003 and 2011. Using county- and decedent-level data from 3 different national sources for pre- and postadoption years, we fitted within-state multiple-imputation models to impute ethnicities for decedents classified as "other Asian" during preadoption years. We present mortality rates derived using 3 different methods of calculation: 1) including all states but ignoring the gradual adoption of the new death certificate over time, 2) including only the 7 states with complete reporting of all ethnicities, and 3) including all states and applying multiple imputation. Estimates from our imputation model were consistently in the middle of the other 2 estimates, and trend results demonstrated that the year-by-year estimates of the imputation model were more similar to those of the 7-state model. This work demonstrates how multiple imputation can provide a "forward bridging" approach to make more accurate estimates over time in newly categorized populations.