Impact of family networks on uptake of health interventions: evidence from a community-randomized control trial aimed at increasing HIV testing in South Africa.

INTRODUCTION: While it is widely acknowledged that family relationships can influence health outcomes, their impact on the uptake of individual health interventions is unclear. In this study, we quantified how the efficacy of a randomized health intervention is shaped by its pattern of distribution in the family network. METHODS: The "Home-Based Intervention to Test and Start" (HITS) was a 2×2 factorial community-randomized controlled trial in Umkhanyakude, KwaZulu-Natal, South Africa, embedded in the Africa Health Research Institute's population-based demographic and HIV surveillance platform (ClinicalTrials.gov # NCT03757104). The study investigated the impact of two interventions: a financial micro-incentive and a male-targeted HIV-specific decision support programme. The surveillance area was divided into 45 community clusters. Individuals aged ≥15 years in 16 randomly selected communities were offered a micro-incentive (R50 [$3] food voucher) for rapid HIV testing (intervention arm). Those living in the remaining 29 communities were offered testing only (control arm). Study data were collected between February and November 2018. Using routinely collected data on parents, conjugal partners, and co-residents, a socio-centric family network was constructed among HITS-eligible individuals. Nodes in this network represent individuals and ties represent family relationships. We estimated the effect of offering the incentive to people with and without family members who also received the offer on the uptake of HIV testing. We fitted a linear probability model with robust standard errors, accounting for clustering at the community level. RESULTS: Overall, 15,675 people participated in the HITS trial. Among those with no family members who received the offer, the incentive's efficacy was a 6.5 percentage point increase (95% CI: 5.3-7.7). The efficacy was higher among those with at least one family member who received the offer (21.1 percentage point increase (95% CI: 19.9-22.3). The difference in efficacy was statistically significant (21.1-6.5 = 14.6%; 95% CI: 9.3-19.9). CONCLUSIONS: Micro-incentives appear to have synergistic effects when distributed within family networks. These effects support family network-based approaches for the design of health interventions.

The price of admission: does moving to a low-poverty neighborhood increase discriminatory experiences and influence mental health?

PURPOSE: The Moving to Opportunity (MTO) study is typically interpreted as a trial of changes in neighborhood poverty. However, the program may have also increased exposure to housing discrimination. Few prior studies have tested whether interpersonal and institutional forms of discrimination may have offsetting effects on mental health, particularly using intervention designs. METHODS: We evaluated the effects of MTO, which randomized public housing residents in 5 cities to rental vouchers, or to in-place controls (N = 4248, 1997-2002), which generated variation on neighborhood poverty (% of residents in poverty) and encounters with housing discrimination. Using instrumental variable analysis (IV), we derived two-stage least squares IV estimates of effects of neighborhood poverty and housing discrimination on adult psychological distress and major depressive disorder (MDD). RESULTS: Randomization to voucher group vs. control simultaneously decreased neighborhood % poverty and increased exposure to housing discrimination. Higher neighborhood % poverty was associated with increased psychological distress [BIV = 0.36, 95% confidence interval (CI) 0.03, 0.69] and MDD (BIV = 0.12, 95% CI - 0.005, 0.25). Effects of housing discrimination on mental health were harmful, but imprecise (distress BIV = 1.58, 95% CI - 0.83, 3.99; MDD BIV = 0.57, 95% CI - 0.43, 1.56). Because neighborhood poverty and housing discrimination had offsetting effects, omitting either mechanism from the IV model substantially biased the estimated effect of the other towards the null. CONCLUSIONS: Neighborhood poverty mediated MTO treatment on adult mental health, suggesting that greater neighborhood poverty contributes to mental health problems. Yet housing discrimination-mental health findings were inconclusive. Effects of neighborhood poverty on health may be underestimated when failing to account for discrimination.

Contribution of Socioeconomic Status at 3 Life-Course Periods to Late-Life Memory Function and Decline: Early and Late Predictors of Dementia Risk.

Both early life and adult socioeconomic status (SES) predict late-life level of memory; however, evidence is mixed on the relationship between SES and rate of memory decline. Further, the relative importance of different life-course periods for rate of late-life memory decline has not been evaluated. We examined associations between life-course SES and late-life memory function and decline. Health and Retirement Study participants (n = 10,781) were interviewed biennially from 1998-2012 (United States). SES measurements for childhood (composite score including parents' educational attainment), early adulthood (high-school or college completion), and older adulthood (income, mean age 66 years) were all dichotomized. Word-list memory was modeled via inverse-probability weighted longitudinal models accounting for differential attrition, survival, and time-varying confounding, with nonrespondents retained via proxy assessments. Compared to low SES at all 3 points (referent), stable, high SES predicted the best memory function and slowest decline. High-school completion had the largest estimated effect on memory (ß = 0.19; 95% confidence interval: 0.15, 0.22), but high late-life income had the largest estimated benefit for slowing declines (for 10-year memory change, ß = 0.35; 95% confidence interval: 0.24, 0.46). Both early and late-life interventions are potentially relevant for reducing dementia risk by improving memory function or slowing decline.

Theoretical Basis of the Test-Negative Study Design for Assessment of Influenza Vaccine Effectiveness.

Influenza viruses undergo frequent antigenic changes. As a result, the viruses circulating change within and between seasons, and the composition of the influenza vaccine is updated annually. Thus, estimation of the vaccine's effectiveness is not constant across seasons. In order to provide annual estimates of the influenza vaccine's effectiveness, health departments have increasingly adopted the "test-negative design," using enhanced data from routine surveillance systems. In this design, patients presenting to participating general practitioners with influenza-like illness are swabbed for laboratory testing; those testing positive for influenza virus are defined as cases, and those testing negative form the comparison group. Data on patients' vaccination histories and confounder profiles are also collected. Vaccine effectiveness is estimated from the odds ratio comparing the odds of testing positive for influenza among vaccinated patients and unvaccinated patients, adjusting for confounders. The test-negative design is purported to reduce bias associated with confounding by health-care-seeking behavior and misclassification of cases. In this paper, we use directed acyclic graphs to characterize potential biases in studies of influenza vaccine effectiveness using the test-negative design. We show how studies using this design can avoid or minimize bias and where bias may be introduced with particular study design variations.

Comparing Alternative Effect Decomposition Methods: The Role of Literacy in Mediating Educational Effects on Mortality.

BACKGROUND: Inverse odds ratio weighting, a newly proposed tool to evaluate mediation in exposure-disease associations, may be valuable for a host of research questions, but little is known about its performance in real data. We compare this approach to a more conventional Baron and Kenny type of decomposition on an additive hazards scale to estimate total, direct, and indirect effects using the example of the role of literacy in mediating the effects of education on mortality. METHODS: Health and Retirement Study participants born in the United States between 1900 and 1947 were interviewed biennially for up to 12 years (N = 17,054). Literacy was measured with a brief vocabulary assessment. Decomposition estimates were derived based on Aalen additive hazards models. RESULTS: A 1 standard deviation difference in educational attainment (3 years) was associated with 6.7 fewer deaths per 1000 person-years (ß = -6.7, 95% confidence interval [CI]: -7.9, -5.4). Of this decrease, 1.3 fewer deaths (ß = -1.3, 95% CI: -4.0, 1.2) were attributed to the literacy pathway (natural indirect), representing 19% of the total effect. Baron and Kenny estimates were consistent with inverse odds ratio weighting estimates but were less variable (natural indirect effect: -1.2 [95% CI: -1.7, -0.69], representing 18% of total effect). CONCLUSION: In a cohort of older Americans, literacy partially mediated the effect of education on mortality. See Video Abstract at http://links.lww.com/EDE/B78.

Assessment and indirect adjustment for confounding by smoking in cohort studies using relative hazards models.

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.

Accounting for selection bias in association studies with complex survey data.

Obtaining representative information from hidden and hard-to-reach populations is fundamental to describe the epidemiology of many sexually transmitted diseases, including HIV. Unfortunately, simple random sampling is impractical in these settings, as no registry of names exists from which to sample the population at random. However, complex sampling designs can be used, as members of these populations tend to congregate at known locations, which can be enumerated and sampled at random. For example, female sex workers may be found at brothels and street corners, whereas injection drug users often come together at shooting galleries. Despite the logistical appeal, complex sampling schemes lead to unequal probabilities of selection, and failure to account for this differential selection can result in biased estimates of population averages and relative risks. However, standard techniques to account for selection can lead to substantial losses in efficiency. Consequently, researchers implement a variety of strategies in an effort to balance validity and efficiency. Some researchers fully or partially account for the survey design, whereas others do nothing and treat the sample as a realization of the population of interest. We use directed acyclic graphs to show how certain survey sampling designs, combined with subject-matter considerations unique to individual exposure-outcome associations, can induce selection bias. Finally, we present a novel yet simple maximum likelihood approach for analyzing complex survey data; this approach optimizes statistical efficiency at no cost to validity. We use simulated data to illustrate this method and compare it with other analytic techniques.

Simulation from a known Cox MSM using standard parametric models for the g-formula.

It is routinely argued that, unlike standard regression-based estimates, inverse probability weighted (IPW) estimates of the parameters of a correctly specified Cox marginal structural model (MSM) may remain unbiased in the presence of a time-varying confounder affected by prior treatment. Previously proposed methods for simulating from a known Cox MSM lack knowledge of the law of the observed outcome conditional on the measured past. Although unbiased IPW estimation does not require this knowledge, standard regression-based estimates rely on correct specification of this law. Thus, in typical high-dimensional settings, such simulation methods cannot isolate bias due to complex time-varying confounding as it may be conflated with bias due to misspecification of the outcome regression model. In this paper, we describe an approach to Cox MSM data generation that allows for a comparison of the bias of IPW estimates versus that of standard regression-based estimates in the complete absence of model misspecification. This approach involves simulating data from a standard parametrization of the likelihood and solving for the underlying Cox MSM. We prove that solutions exist and computations are tractable under many data-generating mechanisms. We show analytically and confirm in simulations that, in the absence of model misspecification, the bias of standard regression-based estimates for the parameters of a Cox MSM is indeed a function of the coefficients in observed data models quantifying the presence of a time-varying confounder affected by prior treatment. We discuss limitations of this approach including that implied by the 'g-null paradox'.

Estimating rates of carriage acquisition and clearance and competitive ability for pneumococcal serotypes in Kenya with a Markov transition model.

BACKGROUND: There are more than 90 serotypes of Streptococcus pneumoniae, with varying biologic and epidemiologic properties. Animal studies suggest that carriage induces an acquired immune response that reduces duration of colonization in a nonserotype-specific fashion. METHODS: We studied pneumococcal nasopharyngeal carriage longitudinally in Kenyan children 3-59 months of age, following up positive swabs at days 2, 4, 8, 16, and 32 and then monthly thereafter until 2 swabs were negative for the original serotype. As previously reported, 1868/2840 (66%) of children swabbed at baseline were positive. We estimated acquisition, clearance, and competition parameters for 27 serotypes using a Markov transition model. RESULTS: Point estimates of type-specific acquisition rates ranged from 0.00025/d (type 1) to 0.0031/d (type 19F). Point estimates of time to clearance (inverse of type-specific immune clearance rate) ranged from 28 days (type 20) to 124 days (type 6A). For the serotype most resistant to competition (type 19F), acquisition of other serotypes was 52% less likely (95% confidence interval = 37%-63%) than in an uncolonized host. Fitness components (carriage duration, acquisition rate, lack of susceptibility to competition) were positively correlated with each other and with baseline prevalence, and were associated with biologic properties previously shown to associate with serotype. Duration of carriage declined with age for most serotypes. CONCLUSIONS: Common S. pneumoniae serotypes appear superior in many dimensions of fitness. Differences in rate of immune clearance are attenuated as children age and become capable of more rapid clearance of the longest-lived serotypes. These findings provide information for comparison after introduction of pneumococcal conjugate vaccine.

Genetic variants on 15q25.1, smoking, and lung cancer: an assessment of mediation and interaction.

Genome-wide association studies have identified variants on chromosome 15q25.1 that increase the risks of both lung cancer and nicotine dependence and associated smoking behavior. However, there remains debate as to whether the association with lung cancer is direct or is mediated by pathways related to smoking behavior. Here, the authors apply a novel method for mediation analysis, allowing for gene-environment interaction, to a lung cancer case-control study (1992-2004) conducted at Massachusetts General Hospital using 2 single nucleotide polymorphisms, rs8034191 and rs1051730, on 15q25.1. The results are validated using data from 3 other lung cancer studies. Tests for additive interaction (P = 2 × 10(-10) and P = 1 × 10(-9)) and multiplicative interaction (P = 0.01 and P = 0.01) were significant. Pooled analyses yielded a direct-effect odds ratio of 1.26 (95% confidence interval (CI): 1.19, 1.33; P = 2 × 10(-15)) for rs8034191 and an indirect-effect odds ratio of 1.01 (95% CI: 1.00, 1.01; P = 0.09); the proportion of increased risk mediated by smoking was 3.2%. For rs1051730, direct- and indirect-effect odds ratios were 1.26 (95% CI: 1.19, 1.33; P = 1 × 10(-15)) and 1.00 (95% CI: 0.99, 1.01; P = 0.22), respectively, with a proportion mediated of 2.3%. Adjustment for measurement error in smoking behavior allowing up to 75% measurement error increased the proportions mediated to 12.5% and 9.2%, respectively. These analyses indicate that the association of the variants with lung cancer operates primarily through other pathways.

Accounting for bias due to selective attrition: the example of smoking and cognitive decline.

BACKGROUND: Selective attrition may introduce bias into analyses of the determinants of cognitive decline. This is a concern especially for risk factors, such as smoking, that strongly influence mortality and dropout. Using inverse-probability-of-attrition weights, we examined the influence of selective attrition on the estimated association of current smoking (vs. never smoking) with cognitive decline. METHODS: Chicago Health and Aging Project participants (n = 3713), aged 65-109 years, who were current smokers or never- smokers, underwent cognitive assessments up to 5 times at 3-year interval. We used pooled logistic regression to fit predictive models of attrition due to death or study dropout across the follow-up waves. With these models, we computed inverse-probability-of-attrition weights for each observation. We fit unweighted and weighted, multivariable-adjusted generalized-estimating-equation models, contrasting rates of change in cognitive scores in current versus never-smokers. Estimates are expressed as rates of change in z score per decade. RESULTS: During the 12 years of follow-up, smokers had higher mortality than never-smokers (hazard ratio = 1.93 [95% confidence interval = 1.67 to 2.23]). Higher previous cognitive score was associated with increased likelihood of survival and continued participation. In unweighted analyses, current smokers' cognitive scores declined 0.11 standard units per decade more rapidly than never-smokers' (95% CI = -0.20 to -0.02). Weighting to account for attrition yielded estimates that were 56% to 86% larger, with smokers' estimated 10-year rate of decline up to 0.20 units faster than never-smokers' (95% CI = -0.36 to -0.04). CONCLUSIONS: Estimates of smoking's effects on cognitive decline may be underestimated due to differential attrition. Analyses that weight for the inverse probability of attrition help compensate for this attrition.

Neighborhood disadvantage and self-assessed health, disability, and depressive symptoms: longitudinal results from the health and retirement study.

PURPOSE: By using a longitudinal cohort, we assessed the association between neighborhood disadvantage and incidence of poor health and function in three domains. METHODS: More than 4,000 enrollees aged 55 to 65 years in the national Health and Retirement Study were assessed biennially from 1998 through 2006 for incidence of fair/poor self-rated health, elevated depressive symptoms, and limitations in six basic activities of daily living (disability). Each analysis was restricted to subjects without that condition in 1994 or 1996. Neighborhoods (census tracts, time-updated for moves), were considered disadvantaged if they fell below the 25th percentile in an index comprising six socioeconomic status indicators. Repeated measures logistic regressions, inverse probability weighted to account for individual confounders, selective survival, and loss to follow-up were used to estimate odds ratios (ORs) for incidence of each outcome in the wave after exposure to disadvantaged neighborhood. RESULTS: After covariate adjustment, neighborhood disadvantage predicted onset of fair/poor SRH (OR, 1.36; 95% confidence interval, 1.15-1.59) but not disability (OR, 0.97; 0.81-1.16) or elevated depressive symptoms (OR, 0.97; 0.81-1.16). CONCLUSIONS: Results confirmed previous findings that neighborhood disadvantage predicts self-rated health in a longitudinal context but did not support an association between neighborhood disadvantage and onset of disability or elevated depressive symptoms.