Infant sugar sweetened beverage and 100% juice consumption: Racial/ethnic differences and links with fathers' consumption in a longitudinal cohort.

The consumption of sugar-sweetened beverages (SSB) and 100% juice before age 12 months is discouraged. We examine racial/ethnic differences in SSB and 100% juice consumption when infants were 6- and 12-months old and examine links between fathers' and infants' beverage consumption. Participants were from a longitudinal cohort of infants and their parents (recruited 2016-2018), followed from birth until the child was 24 months. In 2020, we analyzed data collected when infants were 6- (N = 352 infants and 168 fathers) and 12-months (N = 340 infants and 152 fathers) old. Based on maternal report, 13% of infants consumed 100% juice at 6 months and 31% at 12 months. Two percent of infants consumed SSB at 6 months and 7% at 12 months. In models adjusting for income and education, Black/African American (Black/AA) and Hispanic infants were 5-6 times as likely at 6 months and 3 times as likely at 12 months to consume 100% juice compared with non-Hispanic white and Asian infants. At 12 months, Black/AA and Hispanic infants were 6-7 times as likely to consume SSB than non-Hispanic white and Asian infants after adjusting for covariates. In unadjusted models, infants were more likely to consume 100% juice and SSB at 12 months when their fathers were high consumers (>12times/month) of the beverage; effects were no longer significant after adjusting for income, race/ethnicity, education and maternal beverage consumption. Results highlight the need to implement culturally responsive interventions promoting healthy beverage consumption in infants prior to birth and should concurrently target fathers, in addition to mothers.

On the analysis of two-phase designs in cluster-correlated data settings.

In public health research, information that is readily available may be insufficient to address the primary question(s) of interest. One cost-efficient way forward, especially in resource-limited settings, is to conduct a two-phase study in which the population is initially stratified, at phase I, by the outcome and/or some categorical risk factor(s). At phase II detailed covariate data is ascertained on a subsample within each phase I strata. While analysis methods for two-phase designs are well established, they have focused exclusively on settings in which participants are assumed to be independent. As such, when participants are naturally clustered (eg, patients within clinics) these methods may yield invalid inference. To address this, we develop a novel analysis approach based on inverse-probability weighting that permits researchers to specify some working covariance structure and appropriately accounts for the sampling design and ensures valid inference via a robust sandwich estimator for which a closed-form expression is provided. To enhance statistical efficiency, we propose a calibrated inverse-probability weighting estimator that makes use of information available at phase I but not used in the design. In addition to describing the technique, practical guidance is provided for the cluster-correlated data settings that we consider. A comprehensive simulation study is conducted to evaluate small-sample operating characteristics, including the impact of using naïve methods that ignore correlation due to clustering, as well as to investigate design considerations. Finally, the methods are illustrated using data from a one-time survey of the national antiretroviral treatment program in Malawi.

On the analysis of hybrid designs that combine group- and individual-level data.

Ecological studies that make use of data on groups of individuals, rather than on the individuals themselves, are subject to numerous biases that cannot be resolved without some individual-level data. In the context of a rare outcome, the hybrid design for ecological inference efficiently combines group-level data with individual-level case-control data. Unfortunately, except in relatively simple settings, use of the design in practice is limited since evaluation of the hybrid likelihood is computationally prohibitively expensive. In this article we first propose and develop an alternative representation of the hybrid likelihood. Second, based on this new representation, a series of approximations are proposed that drastically reduce computational burden. A comprehensive simulation shows that, in a broad range of scenarios, estimators based on the approximate hybrid likelihood exhibit the same operating characteristics as the exact hybrid likelihood, without any penalty in terms of increased bias or reduced efficiency. Third, in settings where the approximations may not hold, a pragmatic estimation and inference strategy is developed that uses the approximate form for some likelihood contributions and the exact form for others. The strategy gives researchers the ability to balance computational tractability with accuracy in their own settings. Finally, as a by-product of the development, we provide the first explicit characterization of the hybrid aggregate data design which combines data from an aggregate data study (Prentice and Sheppard, 1995, Biometrika 82, 113-125) with case-control samples. The methods are illustrated using data from North Carolina on births between 2007 and 2009.

Estimation of the effect of interventions that modify the received treatment.

Motivated by a study of surgical operating time and post-operative outcomes for lung cancer, we consider the estimation of causal effects of continuous point-exposure treatments. To investigate causality, the standard paradigm postulates a series of treatment-specific counterfactual outcomes and establishes conditions under which we may learn about them from observational study data. While many choices are possible, causal effects are typically defined in terms of variation of the mean of counterfactual outcomes in hypothetical worlds in which specific treatment strategies are 'applied' to all individuals. For example, one might compare two worlds: one where each individual receives some specific dose and a second where each individual receives some other dose. For our motivating study, defining causal effects in this way corresponds to (hypothetical) interventions that could not conceivably be implemented in the real world. In this work, we consider an alternative, complimentary framework that investigates variation in the mean of counterfactual outcomes under hypothetical treatment strategies where each individual receives a treatment dose corresponding to that actually received but modified in some pre-specified way. Quantification of this variation is defined in terms of contrasts for specific interventions as well as in terms of the parameters of a new class of marginal structural mean models. Within this framework, we propose three estimators: an outcome regression estimator, an inverse probability of treatment weighted estimator and a doubly robust estimator. We illustrate the methods with an analysis of the motivating data.

A multiphase design strategy for dealing with participation bias.

A recently funded study of the impact of oral contraceptive use on the risk of bone fracture employed the randomized recruitment scheme of Weinberg and Wacholder (1990, Biometrics 46, 963-975). One potential complication in the bone fracture study is the potential for differential response rates between cases and controls; participation rates in previous, related studies have been around 70%. Although data from randomized recruitment schemes may be analyzed within the two-phase study framework, ignoring potential differential participation may lead to biased estimates of association. To overcome this, we build on the two-phase framework and propose an extension by introducing an additional stage of data collection aimed specifically at addressing potential differential participation. Four estimators that correct for both sampling and participation bias are proposed; two are general purpose and two are for the special case where covariates underlying the participation mechanism are discrete. Because the fracture study is ongoing, we illustrate the methods using infant mortality data from North Carolina.

Nonsteroidal anti-inflammatory drugs are associated with increased neuritic plaques.

OBJECTIVES: Observational and experimental studies suggest that nonsteroidal anti-inflammatory drugs (NSAIDs) may protect against Alzheimer disease (AD); however, clinical trials and other observational studies, including the Adult Changes in Thought (ACT) study, show no protection or promotion of AD. The objective of this study is to determine the relationship between common dementia-associated pathologies and mid- to late-life NSAID exposure. METHODS: We examined the association of mid- to late-life NSAID use with neuropathologic findings on 257 autopsies from ACT, a population-based study of brain aging and incident dementia. Cumulative standard daily doses (SDD) of nonselective NSAIDs were determined from ≥10 years of computerized pharmacy dispensing data. Analyses were adjusted for selection bias to broaden generalizability of results to 3,026 eligible participants in the ACT cohort. Seven pathologic indices were evaluated: intermediate or frequent score for neuritic plaques, Braak stages V or VI for neurofibrillary tangles, >2 cerebral microinfarcts, the presence of any neocortical Lewy bodies, any macroscopic infarcts, any amyloid angiopathy, and moderate or severe atherosclerosis. RESULTS: Of the neuropathologic indices evaluated, only neuritic plaque score was significantly increased in participants with greater use of nonselective NSAIDs (p = 0.065), specifically in those with high levels of cumulative use: 1,000-2,000 SDD (adjusted relative risk [RR] 2.16, 95% confidence interval [CI] 1.02-4.25, compared to light/nonuse [<60 SDD]) and >2,000 SDD (adjusted RR 2.37, 95% CI 1.24-4.67). CONCLUSIONS: Increased neuritic plaque accumulation may explain the association between heavy use of nonselective NSAIDs and increased risk of dementia among ACT participants.

Risk of dementia and AD with prior exposure to NSAIDs in an elderly community-based cohort.

BACKGROUND: Nonsteroidal anti-inflammatory drugs (NSAIDs) may prevent Alzheimer dementia (AD). METHODS: We analyzed the association of prior NSAID exposure with incident dementia and AD in the Adult Changes in Thought population-based cohort aged > or = 65 years (median 74.8) at enrollment. Participants were members of Group Health, which provided computerized pharmacy dispensing records from 1977 onward. We studied 2,736 dementia-free enrollees with extensive prior pharmacy data, following them biennially for up to 12 years to identify dementia and AD. Cox proportional hazards regression assessed association of dementia or AD with NSAID use graded in standard daily doses (SDD) dispensed over 2 years (e.g., heavy use = 500 + SDD), with some analyses also adding consecutive biennial self-reports of NSAID use. RESULTS: Pharmacy records identified 351 participants (12.8%) with history of heavy NSAID use at enrollment. Another 107 became heavy users during follow-up. Some 476 individuals developed incident dementia, 356 with AD (median onset ages 83.5 and 83.8 years). Contrary to the hypothesis that NSAIDs protect against AD, pharmacy-defined heavy NSAID users showed increased incidence of dementia and AD, with adjusted hazard ratios of 1.66 (95% confidence interval, 1.24-2.24) and 1.57 (95% confidence interval, 1.10-2.23). Addition of self-reported exposure data did not alter these results. CONCLUSIONS: These findings differ from those of other studies with younger cohorts. The results observed elsewhere may reflect delayed onset of Alzheimer dementia (AD) in nonsteroidal anti-inflammatory drug (NSAID) users. Conceivably, such delay could result in increased AD incidence in late old age. The relation of NSAID use and AD pathogenesis needs further investigation.

Adjustment for selection bias in observational studies with application to the analysis of autopsy data.

BACKGROUND: The interpretation of neuropathological studies of dementia and Alzheimer's disease is complicated by potential selection mechanisms that can drive whether or not a study participant is observed to undergo autopsy. Notwithstanding this, there appears to have been little emphasis placed on potential selection bias in published reports from population-based neuropathological studies of dementia. METHODS: We provide an overview of methodological issues relating to the identification of and adjustment for selection bias. When information is available on factors that govern selection, inverse-probability weighting provides an analytic approach to adjust for selection bias. The weights help alleviate bias by serving to bridge differences between the population from which the observed data may be viewed as a representative sample and the target population, identified as being of scientific interest. RESULTS: We illustrate the methods with data obtained from the Adult Changes in Thought study. Adjustment for potential selection bias yields substantially strengthened association between neuropathological measurements and risk of dementia. CONCLUSIONS: Armed with analytic techniques to adjust for selection bias and to ensure generalizability of results from population-based neuropathological studies, researchers should consider incorporating information related to selection into their data collection schemes.

Geographic-based ecological correlation studies using supplemental case-control data.

It is well known that the ecological study design suffers from a variety of biases that render the interpretation of its results difficult. Despite its limitations, however, the ecological study design is still widely used in a range of disciplines. The only solution to the ecological inference problem is to supplement the aggregate data with individual-level data and, to this end, Haneuse and Wakefield (Biometrics 2007; 63:128-136) recently proposed a hybrid study design in which an ecological study is supplemented with a sample of case-control data. The latter provides the basis for the control of bias, while the former may provide efficiency gains. Building on that work, we illustrate the use of the hybrid design in the context of a geographical correlation study of lung cancer mortality from the state of Ohio. Focusing on epidemiological applications, we initially provide an overview of the use of ecological studies in scientific research, highlighting the breadth of current application as well as advantages and drawbacks of the design. We consider the interplay between the two sources of information in the design: ecological and case-control, and then provide details on a Bayesian spatial random effects model in the setting of the hybrid design. Issues of specification are addressed, as well as sensitivity to modeling assumptions. Further, an interesting feature of these data is that they provide an example of how the proposed design may be used to resolve the ecological fallacy.

The interpretation of exposure effect estimates in chronic air pollution studies.

In this article we consider the interpretation of regression parameters used to represent 'chronic' or 'long-term' air pollution exposure effects. Although scientific interest typically lies in understanding such effects at the level of the individual, studies have generally employed a semi-ecological design; outcomes and confounder information are collected on individuals while exposure is only available at the aggregate-or group-level. A precise interpretation of results from a semi-ecological design must take into account the aggregated nature, both spatial and temporal, of the exposure measure. The most common analysis approach for assessing chronic exposure effects has been within the Cox proportional hazards model framework; specific analyses are tailored to accommodate the shortcomings of the available exposure information. We revisit the underlying assumptions of the Cox model and discuss the implications of two common aspects of chronic effects studies: time-dependent exposures and time-varying effects. Focusing on the consequences of temporal aggregation of exposure, we show that an estimate obtained from a time-aggregated semi-ecological design can correspond to very different underlying time-varying exposure and risk scenarios. Further, distinguishing which of these is correct is not possible from the semi-ecological data alone. Our goal is to highlight some statistical issues faced by existing studies of chronic air pollution effects, and aid in the development and planning of future studies.