Search

The disease risk score (DRS) is a summary score that is a function of a potentially large set of covariates. The DRS can be used to control for confounding by the covariates that went into estimation of the DRS and obtain a standardized estimate of an exposure's effect on disease. However, to date, literature on the DRS has not addressed analyses that focus on estimation of survival or hazard functions, which are common in epidemiologic analyses of cohort data. Here, we propose a method for standardization of hazard ratios using the DRS in longitudinal analyses of the association between a binary exposure and an outcome. This approach to handling a potentially large set of covariates through a model-based approach to standardization may provide a useful tool for cohort analyses of hazard ratios and may be particularly well-suited to settings where an exposure propensity score is difficult to model. Simulations are used in this paper to illustrate the approach, and an empirical example is provided. [ABSTRACT FROM AUTHOR]

In 2019, the National Institutes of Health combined the Multicenter AIDS Cohort Study (MACS) and the Women's Interagency HIV Study (WIHS) into the MACS/WIHS Combined Cohort Study (MWCCS). In this paper, participants who made a study visit during October 2018–September 2019 (targeted for MWCCS enrollment) are described by human immunodeficiency virus (HIV) serostatus and compared with people living with HIV (PLWH) in the United States. Participants include 2,115 women and 1,901 men with a median age of 56 years (interquartile range, 48–63); 62% are PLWH. Study sites encompass the South (18%), the Mid-Atlantic/Northeast (45%), the West Coast (22%), and the Midwest (15%). Participant race/ethnicity approximates that of PLWH throughout the United States. Longitudinal data and specimens collected for 35 years (men) and 25 years (women) were combined. Differences in data collection and coding were reviewed, and key risk factor and comorbidity data were harmonized. For example, recent use of alcohol (62%) and tobacco (28%) are common, as are dyslipidemia (64%), hypertension (56%), obesity (42%), mildly or severely impaired daily activities (31%), depressive symptoms (28%), and diabetes (22%). The MWCCS repository includes serum, plasma, peripheral blood mononuclear cells, cell pellets, urine, cervicovaginal lavage samples, oral samples, B-cell lines, stool, and semen specimens. Demographic differences between the MACS and WIHS can confound analyses by sex. The merged MWCCS is both an ongoing observational cohort study and a valuable resource for harmonized longitudinal data and specimens for HIV-related research. [ABSTRACT FROM AUTHOR]

Epidemiologists often compare the observed number of deaths in a cohort with the expected number of deaths, obtained by multiplying person-time accrued in the cohort by mortality rates for a reference population (ideally, a reference that represents the mortality rate in the cohort in the absence of exposure). However, if exposure is hazardous (or salutary), this calculation will not consistently estimate the number of deaths expected in the absence of exposure because exposure will have affected the distribution of person-time observed in the study cohort. While problems with interpretation of this standard calculation of expected counts were discussed more than 2 decades ago, these discussions had little impact on epidemiologic practice. The logic of counterfactuals may help clarify this topic as we revisit these issues. In this paper, we describe a simple way to consistently estimate the expected number of deaths in such settings, and we illustrate the approach using data from a cohort study of mortality among underground miners. [ABSTRACT FROM AUTHOR]

In cohort mortality studies, there often is interest in associations between an exposure of primary interest and mortality due to a range of different causes. A standard approach to such analyses involves fitting a separate regression model for each type of outcome. However, the statistical precision of some estimated associations may be poor because of sparse data. In this paper, we describe a hierarchical regression model for estimation of parameters describing outcome-specific relative rate functions and associated credible intervals. The proposed model uses background stratification to provide flexible control for the outcome-specific associations of potential confounders, and it employs a hierarchical “shrinkage” approach to stabilize estimates of an exposure’s associations with mortality due to different causes of death. The approach is illustrated in analyses of cancer mortality in 2 cohorts: a cohort of dioxin-exposed US chemical workers and a cohort of radiation-exposed Japanese atomic bomb survivors. Compared with standard regression estimates of associations, hierarchical regression yielded estimates with improved precision that tended to have less extreme values. The hierarchical regression approach also allowed the fitting of models with effect-measure modification. The proposed hierarchical approach can yield estimates of association that are more precise than conventional estimates when one wishes to estimate associations with multiple outcomes. [ABSTRACT FROM AUTHOR]

Suppose that an investigator is interested in quantifying an exposure-disease causal association in a setting where the exposure, disease, and some potential confounders of the association of interest have been measured. However, there remains concern about residual confounding of the association of interest by unmeasured confounders. We propose an approach to account for residual bias due to unmeasured confounders. The proposed approach uses a measured confounder to derive a "bespoke" instrumental variable that is tailored to the study population and is used to control for bias due to residual confounding. The approach may provide a useful tool for assessing and accounting for bias due to residual confounding. We provide a formal description of the conditions for identification of causal effects, illustrate the method using simulations, and provide an empirical example concerning mortality among Japanese atomic bomb survivors. [ABSTRACT FROM AUTHOR]

Observational epidemiologic studies typically face challenges of exposure measurement error and confounding. Consider an observational study of the association between a continuous exposure and an outcome, where the exposure variable of primary interest suffers from classical measurement error (i.e. the measured exposures are distributed around the true exposure with independent error). In the absence of exposure measurement error, it is widely recognized that one should control for confounders of the association of interest to obtain an unbiased estimate of the effect of that exposure on the outcome of interest. However, here we show that, in the presence of classical exposure measurement error, the net bias in an estimate of the association of interest may increase upon adjustment for confounders. We offer an analytical expression for calculating the change in net bias in an estimate of the association of interest upon adjustment for a confounder in the presence of classical exposure measurement error, and we illustrate this problem using simulations. [ABSTRACT FROM AUTHOR]

Suppose that an investigator wants to estimate an association between a continuous exposure variable and an outcome, adjusting for a set of confounders. If the exposure variable suffers classical measurement error, in which the measured exposures are distributed with independent error around the true exposure, then an estimate of the covariate-adjusted exposure-outcome association may be biased. We propose an approach to estimate a marginal exposure-outcome association in the setting of classical exposure measurement error using a disease score–based approach to standardization to the exposed sample. First, we show that the proposed marginal estimate of the exposure-outcome association will suffer less bias due to classical measurement error than the covariate-conditional estimate of association when the covariates are predictors of exposure. Second, we show that if an exposure validation study is available with which to assess exposure measurement error, then the proposed marginal estimate of the exposure-outcome association can be corrected for measurement error more efficiently than the covariate-conditional estimate of association. We illustrate both of these points using simulations and an empirical example using data from the Orinda Longitudinal Study of Myopia (California, 1989–2001). [ABSTRACT FROM AUTHOR]

Meta-analyses are undertaken to combine information from a set of studies, often in settings where some of the individual study-specific estimates are based on relatively small study samples. Finite sample bias may occur when maximum likelihood estimates of associations are obtained by fitting logistic regression models to sparse data sets. Here we show that combining information from small studies by undertaking a meta-analytical summary of logistic regression estimates can propagate such sparse-data bias. In simulations, we illustrate 2 challenges encountered in meta-analyses of logistic regression results in settings of sparse data: 1) bias in the summary meta-analytical result and 2) confidence interval coverage that can worsen rather than improve, in terms of being less than nominal, as the number of studies in the meta-analysis increases. [ABSTRACT FROM AUTHOR]

Race/ethnicity is associated with intrauterine growth restriction (IUGR) and small-for-gestational age (SGA) birth. We evaluated the extent to which this association is mediated by adequacy of prenatal care (PNC). A retrospective cohort study was conducted using US National Center for Health Statistics natality files for the years 2011–2017. We performed mediation analyses using a statistical approach that allows for exposure-mediator interaction, and we estimated natural direct effects, natural indirect effects, and proportions mediated. All effects were estimated as risk ratios. Among 23,118,656 singleton live births, the excess risk of IUGR among Black women, Hispanic women, and women of other race/ethnicity as compared with White women was partly mediated by PNC adequacy: 13% of the association between non-Hispanic Black race/ethnicity and IUGR, 12% of the association in Hispanic women, and 10% in other women was attributable to PNC inadequacy. The percentage of excess risk of SGA birth that was mediated was 7% in Black women, 6% in Hispanic women, and 5% in other women. Our findings suggest that PNC adequacy may partly mediate the association between race/ethnicity and fetal growth restriction. In future research, investigators should employ causal mediation frameworks to consider additional factors and mediators that could help us better understand this association. [ABSTRACT FROM AUTHOR]

The purpose of this study was to examine whether the increased risk of colorectal cancer due to cigarette smoking differed by anatomical subsite or sex. We analyzed data from 188,052 participants aged 45–75 years (45% men) who were enrolled in the Multiethnic Cohort Study in 1993–1996. During a mean follow-up period of 16.7 years, we identified 4,879 incident cases of invasive colorectal adenocarcinoma. In multivariate Cox regression models, as compared with never smokers of the same sex, male ever smokers had a 39% higher risk (hazard ratio (HR) = 1.39, 95% confidence interval (CI): 1.16, 1.67) of cancer of the left (distal or descending) colon but not of the right (proximal or ascending) colon (HR = 1.03, 95% CI: 0.89, 1.18), while female ever smokers had a 20% higher risk (HR = 1.20, 95% CI: 1.06, 1.36) of cancer of the right colon but not of the left colon (HR = 0.96, 95% CI: 0.80, 1.15). Compared with male smokers, female smokers had a greater increase in risk of rectal cancer with number of pack-years of smoking (P for heterogeneity = 0.03). Our results suggest that male smokers are at increased risk of left colon cancer and female smokers are at increased risk of right colon cancer. Our study also suggests that females who smoke may have a higher risk of rectal cancer due to smoking than their male counterparts. [ABSTRACT FROM AUTHOR]

The US Centers for Disease Control and Prevention has estimated human immunodeficiency virus (HIV) viral suppression (VS) using 2 data sources. The National HIV Surveillance System estimate (50% of HIV-diagnosed persons in 2012) is derived from viral load reporting from a subset of jurisdictions that vary yearly. The Medical Monitoring Project (MMP) estimate (42% of HIV-diagnosed persons in 2012) is based on a sample of persons receiving HIV care during the first 4 months of each year. We developed the cohort-adjustment method to reconstruct VS estimates, accounting for persons receiving care later in the year. Using the HIV Outpatient Study cohort, we assessed timing of care receipt, demographics, and VS at last test (<200 vs. ≥200 copies/mL), standardizing MMP to HIV Outpatient Study data using multivariable regression models and yielding adjusted VS estimates. We estimated that 52% (95% CI: 48, 56) of HIV-diagnosed persons achieved VS in 2012. Differences from previously published estimates were due to: 1) 23% underestimation of persons receiving HIV care, and 2) lower VS rates among persons receiving care outside versus inside the 4-month MMP sampling period (79% vs. 88%). This methodology yielded VS estimates closer to the National HIV Surveillance System estimate than previously published. Use of more, geographically diverse cohort data may enable assessment of temporal trends. [ABSTRACT FROM AUTHOR]

Dietary protein may help prevent age-related declines in strength and functional capacity. This study examines the independent relationship between dietary protein and longitudinal changes in physical functioning among adults participating in the Framingham Offspring Study from examination 5 (1991-1995) to examination 8 (2005-2008). Protein intakes were derived from 3-day diet records during examinations 3 and 5; functional status was determined over 12 years using 7 items selected from standardized questionnaires. Multivariable models adjusted for age, sex, education, physical activity, smoking, height, and energy intake. Functional tasks that benefitted most from a higher-protein diet (≥1.2 g/kg/day vs. <0.8 g/kg/day) were doing heavy work at home, walking 1/2 mile (0.8 km), going up and down stairs, stooping/kneeling/crouching, and lifting heavy items. Those with higher protein intakes were 41% less likely (95% CI: 0.43, 0.82) to become dependent in 1 or more of the functional tasks over follow-up. Higher physical activity and lower body mass index were both independently associated with less functional decline. The greatest risk reductions were found among those with higher protein intakes combined with either higher physical activity, more skeletal muscle mass, or lower body mass index. This study demonstrates that dietary protein intakes above the current US Recommended Daily Allowance may slow functional decline in older adults. [ABSTRACT FROM AUTHOR]

Prolonged exposures can have complex relationships with health outcomes, as timing, duration, and intensity of exposure are all potentially relevant. Summary measures such as cumulative exposure or average intensity of exposure may not fully capture these relationships. We applied penalized and unpenalized distributed-lag nonlinear models (DLNMs) with flexible exposure-response and lag-response functions in order to examine the association between crystalline silica exposure and mortality from lung cancer and nonmalignant respiratory disease in a cohort study of 2,342 California diatomaceous earth workers followed during 1942-2011. We also assessed associations using simple measures of cumulative exposure assuming linear exposure-response and constant lag-response. Measures of association from DLNMs were generally higher than those from simpler models. Rate ratios from penalized DLNMs corresponding to average daily exposures of 0.4 mg/m3 during lag years 31-50 prior to the age of observed cases were 1.47 (95% confidence interval (CI): 0.92, 2.35) for lung cancer mortality and 1.80 (95% CI: 1.14, 2.85) for nonmalignant respiratory disease mortality. Rate ratios from the simpler models for the same exposure scenario were 1.15 (95% CI: 0.89, 1.48) and 1.23 (95% CI: 1.03, 1.46), respectively. Longitudinal cohort studies of prolonged exposures and chronic health outcomes should explore methods allowing for flexibility and nonlinearities in the exposure-lag-response. [ABSTRACT FROM AUTHOR]

The Diesel Exhaust in Miners Study (DEMS) (United States, 1947-1997) reported positive associations between diesel engine exhaust exposure, estimated as respirable elemental carbon (REC), and lung cancer mortality. This reanalysis of the DEMS cohort used an alternative estimate of REC exposure incorporating historical data on diesel equipment, engine horsepower, ventilation rates, and declines in particulate matter emissions per horsepower. Associations with cumulative REC and average REC intensity using the alternative REC estimate and other exposure estimates were generally attenuated compared with original DEMS REC estimates. Most findings were statistically nonsignificant; control for radon exposure substantially weakened associations with the original and alternative REC estimates. No association with original or alternative REC estimates was detected among miners who worked exclusively underground. Positive associations were detected among limestone workers, whereas no association with REC or radon was found among workers in the other 7 mines. The differences in results based on alternative exposure estimates, control for radon, and stratification by worker location or mine type highlight areas of uncertainty in the DEMS data. [ABSTRACT FROM AUTHOR]

There is concern that sickle cell trait (SCT) increases risk of exertional collapse, a primary cause of which is heat injury. However, to our knowledge, no population-based studies among active individuals have addressed this, representing a critical evidence gap. We conducted a retrospective cohort study of SCT-tested African-American soldiers who were on active duty in the US Army anytime between January 2011 and December 2014. Using Cox proportional hazards models and adjusting for demographic and medical factors, we observed no significant associations between SCT and either mild heat injury (hazard ratio (HR) = 1.15, 95% confidence interval (CI): 0.84, 1.56; n = 45,999) or heat stroke (HR = 1.11, 95% CI: 0.44, 2.79; n = 46,183). Risk of mild heat injury was substantially higher among soldiers with recent prescriptions for antipsychotic agents (HR = 3.25, 95% CI: 1.33, 7.90). Risk of heat stroke was elevated among those with a prior mild heat injury (HR = 17.7, 95% CI: 8.50, 36.7) and among overweight and obese individuals (HR = 2.91 (95% CI: 1.38, 6.17) and HR = 4.04 (95% CI: 1.72, 9.45), respectively). In a setting where universal precautions are utilized to mitigate risk of exertion-related illnesses, SCT is not associated with either mild heat injury or heat stroke. [ABSTRACT FROM AUTHOR]

The association between low-level crystalline silica (silica) exposure and mortality risk is not well understood. We investigated a cohort of 44,807 Chinese workers who had worked in metal mines or pottery factories for at least 1 year from January 1, 1960, to December 31, 1974, and were followed through 2003. Low-level silica exposure was defined as having a lifetime highest annual mean silica exposure at or under a permissible exposure limit (PEL). We considered 3 widely used PELs, including 0.05 mg/m³, 0.10 mg/m³, and 0.35 mg/m³. Cumulative silica exposure was estimated by linking a job exposure matrix with each participant's work history. For the 0.10-mg/m³ exposure level, Cox proportional hazards models showed significantly increased risk of mortality from all diseases (for each 1-ln mg/m³-years increase in logged cumulative silica exposure, hazard ratio (HR) = 1.05, 95% confidence interval (CI): 1.03, 1.07), malignant neoplasms (HR = 1.06, 95% CI: 1.03, 1.09), lung cancer (HR = 1.08, 95% CI: 1.02,1.14), ischemic heart disease (HR = 1.09, 95% CI: 1.02,1.16), pulmonary heart disease (HR = 1.08, 95% CI: 1.00,1.16), and respiratory disease (HR = 1.20, 95% CI: 1.14,1.26). The 0.05-mg/m³ and 0.35-mg/m³ exposure levels yielded similar associations. Long-term exposure to low levels (PELs ≤0.05 mg/m³, ≤0.10 mg/m³, or ≤0.35 mg/m³) of silica is associated with increased total and certain cause-specific mortality risk. Control of ambient silica levels and use of personal protective equipment should be emphasized in practice. [ABSTRACT FROM AUTHOR]

Prenatal exposure to fever and infections has been linked to various neurodevelopmental disorders, but it is not yet known whether more subtle effects on neurodevelopment may exist as well. Therefore, we aimed to investigate whether these early-life exposures were associated with academic performance in childhood and early adolescence. Children and mothers who were enrolled in the Danish National Birth Cohort during 1996-2002 were included in this study. Information on fever and infections common in pregnancy was prospectively collected in 2 pregnancy interviews and linked with assessments of academic performance from the 2010-2013 Danish National Tests. Hierarchical multilevel linear regression of 216,350 assessments made in 71,850 children born to 67,528 mothers revealed no differences in academic performance among the children according to prenatal exposure to fever (odds ratio (OR) = 1.01, 95% confidence interval (CI): 1.00,1.03), any infection (OR = 1.00, 95% CI: 0.99, 1.01), genitourinary infection (OR = 1.01, 95% CI: 0.99, 1.02), prolonged cough (OR = 1.00, 95% CI: 0.99, 1.02), or diarrhea (OR = 0.99, 95% CI: 0.97, 1.00). The findings were supported in different types of academic as-sessments, with different timings of exposure, and in sibling comparisons. This large population-based study suggested that prenatal exposure to fever and common infections does not affect the child's basic school performance. [ABSTRACT FROM AUTHOR]

Initially submitted February 1, 2016; accepted for publication August 4, 2016. The ability to classify people according to their underlying genetic susceptibility to a disease is increasing with new knowledge, better family data, and more sophisticated risk prediction models, allowing for more effective prevention and screening. To do so, however, we need to know whether risk associations are the same for people with different genetic susceptibilities. To illustrate one way to estimate such gene-environment interactions, we used prospective data from 3 Australian family cancer cohort studies, 2 enriched for familial risk of breast cancer. There were 288 incident breast cancers in 9,126 participants from 3,222 families. We used Cox proportional hazards models to investigate whether associations of breast cancer with body mass index (BMI; weight (kg)/height (m)2) at age 18-21 years, BMI at baseline, and change in BMI differed according to genetic risk based on lifetime breast cancer risk from birth, as estimated by BOADICEA (Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm) software, adjusted for age at baseline data collection. Although no interactions were statistically significant, we have demonstrated the power with which gene-environment interactions can be investigated using a cohort enriched for persons with increased genetic risk and a continuous measure of genetic risk based on family history. [ABSTRACT FROM AUTHOR]

Sickness absence is associated with adverse health, organizational, and societal outcomes. Using data from a longitudinal cohort study of working Australians (the Household, Income and Labour Dynamics in Australia (HILDA) Survey), we examined the relationship between changes in individuals' overall psychosocial job quality and variation in sickness absence. The outcome variables were paid sickness absence (yes/no) and number of days of paid sickness absence in the past year (2005-2012). The main exposure variable was psychosocial job quality, measured using a psychosocial job quality index (levels of job control, demands and complexity, insecurity, and perceptions of unfair pay). Analysis was conducted using longitudinal fixed-effects logistic regression models and negative binomial regression models. There was a dose-response relationship between the number of psychosocial job stressors reported by an individual and the odds of paid sickness absence (1 adversity: odds ratio (OR) = 1.26, 95% confidence interval (CI): 1.09, 1.45 (P = 0.002); 2 adversities: OR = 1.28, 95% CI: 1.09, 1.51 (P = 0.002); ⩾3 adversities: OR = 1.58, 95% CI: 1.29, 1.94 (P < 0.001)). The negative binomial regression models also indicated that respondents reported a greater number of days of sickness absence in response to worsening psychosocial job quality. These results suggest that workplace interventions aiming to improve the quality of work could help reduce sickness absence. [ABSTRACT FROM AUTHOR]

To assess the relationship between antioxidant vitamin intake and all-cause mortality in older adults, we examined these associations using data from the Leisure World Cohort Study, a prospective study of residents of the Leisure World retirement community in Laguna Hills, California. In the early 1980s, participants (who were aged 44–101 years) completed a postal survey, which included details on use of vitamin supplements and dietary intake of foods containing vitamins A and C. Age-adjusted and multivariate-adjusted (for factors related to mortality in this cohort—smoking, alcohol intake, caffeine consumption, exercise, body mass index, and histories of hypertension, angina, heart attack, stroke, diabetes, rheumatoid arthritis, and cancer) hazard ratios for death were calculated using Cox regression for 8,640 women and 4,983 men (median age at entry, 74 years). During follow-up (1981–2013), 13,104 participants died (median age at death, 88 years). Neither dietary nor supplemental intake of vitamin A or vitamin C nor supplemental intake of vitamin E was significantly associated with mortality after multivariate adjustment. A compendium that summarizes previous findings of cohort studies evaluating vitamin intake and mortality is provided. Attenuation in the observed associations between mortality and antioxidant vitamin use after adjustment for confounders in our study and in previous studies suggests that such consumption identifies persons with other mortality-associated lifestyle and health risk factors. [ABSTRACT FROM PUBLISHER]

Workers' smoking histories are not measured in many occupational cohort studies. Here we discuss the use of negative control outcomes to detect and adjust for confounding in analyses that lack information on smoking. We clarify the assumptions necessary to detect confounding by smoking and the additional assumptions necessary to indirectly adjust for such bias. We illustrate these methods using data from 2 studies of radiation and lung cancer: the Colorado Plateau cohort study (1950–2005) of underground uranium miners (in which smoking was measured) and a French cohort study (1950–2004) of nuclear industry workers (in which smoking was unmeasured). A cause-specific relative hazards model is proposed for estimation of indirectly adjusted associations. Among the miners, the proposed method suggests no confounding by smoking of the association between radon and lung cancer—a conclusion supported by adjustment for measured smoking. Among the nuclear workers, the proposed method suggests substantial confounding by smoking of the association between radiation and lung cancer. Indirect adjustment for confounding by smoking resulted in an 18% decrease in the adjusted estimated hazard ratio, yet this cannot be verified because smoking was unmeasured. Assumptions underlying this method are described, and a cause-specific proportional hazards model that allows easy implementation using standard software is presented. [ABSTRACT FROM PUBLISHER]