Counterfactual Theory in Social Epidemiology: Reconciling Analysis and Action for the Social Determinants of Health

Causal Inference Challenges in the Relationship Between Social Determinants and Cardiovascular Outcomes

The effects of social determinants on cardiovascular outcomes are frequently estimated in epidemiologic analyses, but the profound causal and statistical challenges of this research program are not widely discussed. Here, we carefully review definitions and measures for social determinants of cardiovascular health and then examine the various assumptions required for valid causal inference in multivariable analyses of observational data, such as what one would typically encounter in cohorts, population surveys, health care databases, and vital statistics databases. We explain the necessity of the "well-defined exposure" and show how this goal relates to the "consistency assumption" that is necessary for valid causal inference. Well-defined exposure is especially challenging for social determinants of health because they are seldom simple atomistic interventions that are easily conceptualized and measured. We then review threats to valid inference that arise from confounding, selection bias, information bias, and positivity violations. Other causal considerations are reviewed and explained, such as correct model specification, absence of immortal time, and avoidance of the "Table 2 Fallacy," and their application to social determinants of cardiovascular outcomes are discussed. Fruitful approaches, including focusing on policy interventions and the "target trial" frameworks are proposed and provide a pathway for a more efficacious research program that can more reliably improve population health. Valid causal inference in this setting is quite challenging, but-with clever design and thoughtful analysis-the important role of social factors in patterning cardiovascular outcomes can be quantified and reported.

Taxing for a better life? The impact of environmental taxes on income distribution and inclusive education

This paper examines the impact of environmental taxes on economic and social inequalities using data from 38 OECD countries from 1994 to 2020. The results show that the introduction of an environmental tax can have unequal consequences on population groups due to differences in consumption behaviour and access to environmental alternatives. The results also indicate that environmental taxes with a progressive character (i.e. higher for higher income households) can reduce inequalities and improve environmental efficiency. The introduction of environmental taxes should therefore be done with care and with due regard to their impact on inequality. Tax policies must be designed to protect the most vulnerable households and promote equity while protecting the environment. Thus, environmental taxation should be accompanied by social and economic policies that reduce inequalities and support the most affected social groups. It is also important for governments to have better communication and awareness-raising on the impacts of environmental taxation on inequalities, in order to ensure a just transition towards sustainable lifestyles.

Visualizing the (Causal) Effect of a Continuous Variable on a Time-To-Event Outcome

Visualization is a key aspect of communicating the results of any study aiming to estimate causal effects. In studies with time-to-event outcomes, the most popular visualization approach is depicting survival curves stratified by the variable of interest. This approach cannot be used when the variable of interest is continuous. Simple workarounds, such as categorizing the continuous covariate and plotting survival curves for each category, can result in misleading depictions of the main effects. Instead, we propose a new graphic, the survival area plot, to directly depict the survival probability over time and as a function of a continuous covariate simultaneously. This plot utilizes g-computation based on a suitable time-to-event model to obtain the relevant estimates. Through the use of g-computation, those estimates can be adjusted for confounding without additional effort, allowing a causal interpretation under the standard causal identifiability assumptions. If those assumptions are not met, the proposed plot may still be used to depict noncausal associations. We illustrate and compare the proposed graphics to simpler alternatives using data from a large German observational study investigating the effect of the Ankle-Brachial Index on survival. To facilitate the usage of these plots, we additionally developed the contsurvplot R-package, which includes all methods discussed in this paper.

Socioeconomic position, bronchiolitis and asthma in children: counterfactual disparity measures from a national birth cohort study

BACKGROUND

The debated link between severe respiratory syncytial virus (RSV) infection in early life and asthma has yet to be investigated within a social inequity lens. We estimated the magnitude of socioeconomic disparity in childhood asthma which would remain if no child were admitted to hospital for bronchiolitis, commonly due to RSV, during infancy.

METHODS

The cohort, constructed from national administrative health datasets, comprised 83853 children born in Scotland between 1 January 2007 and 31 June 2008. Scottish Index for Multiple Deprivation (SIMD) was used to capture socioeconomic position. Emergency admissions for bronchiolitis before age 1 year were identified from hospital records. Yearly indicators of asthma/wheeze from ages 2 to 9 years were created using dispensing data and hospital admission records.

RESULTS

Using latent class growth analysis, we identified four trajectories of asthma/wheeze: early-transient (2.2% of the cohort), early-persistent (2.0%), intermediate-onset (1.8%) and no asthma/wheeze (94.0%). The estimated marginal risks of chronic asthma (combining early-persistent and intermediate-onset groups) varied by SIMD, with risk differences for the medium and high deprivation groups, relative to the low deprivation group, of 7.0% (95% confidence interval: 3.7-10.3) and 13.0% (9.6-16.4), respectively. Using counterfactual disparity measures, we estimated that the elimination of bronchiolitis requiring hospital admission could reduce these risk differences by 21.2% (4.9-37.5) and 17.9% (10.4-25.4), respectively.

CONCLUSIONS

The majority of disparity in chronic asthma prevalence by deprivation level remains unexplained. Our paper offers a guide to using causal inference methods to study other plausible pathways to inequities in asthma using complex, linked administrative data.

Marginal measures and causal effects using the relative survival framework

BACKGROUND

In population-based cancer survival studies, the event of interest is usually death due to cancer. However, other competing events may be present. Relative survival is a commonly used measure in cancer studies that circumvents problems caused by the inaccuracy of the cause of death information. A summary of the prognosis of the cancer population and potential differences between subgroups can be obtained using marginal estimates of relative survival.

METHODS

We utilize regression standardization to obtain marginal estimates of interest in a relative survival framework. Such measures include the standardized relative survival, standardized all-cause survival and standardized crude probabilities of death. Contrasts of these can be formed to explore differences between exposure groups and under certain assumptions are interpreted as causal effects. The difference in standardized all-cause survival can also provide an estimate for the impact of eliminating cancer-related differences between exposure groups. The potential avoidable deaths after such hypothetical scenarios can also be estimated. To illustrate the methods we use the example of survival differences across socio-economic groups for colon cancer.

RESULTS

Using relative survival, a range of marginal measures and contrasts were estimated. For these measures we either focused on cancer-related differences only or chose to incorporate both cancer and other cause differences. The impact of eliminating differences between groups was also estimated. Another useful way for quantifying that impact is the avoidable deaths under hypothetical scenarios.

CONCLUSIONS

Marginal estimates within the relative survival framework provide useful summary measures and can be applied to better understand differences across exposure groups.

To What Extent Is the Association Between Race/Ethnicity and Fetal Growth Restriction Explained by Adequacy of Prenatal Care? A Mediation Analysis of a Retrospectively Selected Cohort

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.

The mediating role of effective treatments in the relationship between income level and survival in patients with heart failure: a sex- and cohabitation-stratified study

AIMS 

Patients with heart failure and low income have a high mortality risk. We examined whether lower survival among low-income patients with heart failure could be explained by different use of β -blockers, renin-angiotensin system inhibitors (RASi), and implanted devices compared with high-income patients.

METHODS AND RESULTS 

We linked Danish national registries to identify patients with new-onset heart failure between 2005 and 2016. A total of 18 308 patients was included in the main analysis. We collected information on medical treatment and device therapy after discharge. We investigated the remaining income disparity if everybody had the same probability of treatment as the high-income patients. We used causal mediation analysis to examine to what extent treatment differences mediate the association between income and 1-year mortality in strata defined by sex and cohabitation status. If low-income patients had the same probability of initiating β-blockers and RASi treatment as high-income patients, low-income men who lived alone would increase initiation of treatment by 12.4% (CI: 10.0% to 14.9%) and as a result reduce their absolute 1-year mortality by 1.0% (CI: -1.4% to -0.5%). If low-income patients had the same probability of not having breaks in medical treatment and getting device therapy, as high-income patients, low-income patients would increase the probability of not having breaks in treatment between 1.8% and 5.8% and increase the probability of getting device therapy between 1.0% and 3.8%, across strata of sex and cohabitation status.

CONCLUSION 

Lower rates of treatment initiation appear to mediate the poorer survival seen among patients with heart failure and low income, but only in males living alone.

Commentary: Causal Inference for Social Exposures

Social epidemiology seeks to describe and quantify the causal effects of social institutions, interactions, and structures on human health. To accomplish this task, we define exposures as treatments and posit populations exposed or unexposed to these well-defined regimens. This inferential structure allows us to unambiguously estimate and interpret quantitative causal parameters and to investigate how these may be affected by biases such as confounding. This paradigm has been challenged recently by some critics who favor broadening the exposures that may be studied beyond treatments to also consider states. Defining the exposure protocol of an observational study is a continuum of specificity, and one may choose to loosen this definition, incurring the cost of causal parameters that become commensurately more vague. The advantages and disadvantages of broader versus narrower definitions of exposure are matters of continuing debate in social epidemiology as in other branches of epidemiology.

Exploring Causes of Depression and Anxiety Health Disparities (HD) by Examining Differences between 1:1 Matched Individuals

Poor comparability of social groups is one of the major methodological problems that threatens the validity of health disparities (HD) research findings. We illustrate a methodological solution that can additionally unpack the mechanisms behind differential effects on depression and anxiety. We describe racial/ethnic differences in the prevalence of depression and anxiety scores between Black and White women using classic methods, and then we illustrate a 1:1 matching procedure that allows for building of individual-level difference scores, i.e., actual HD difference score variables, for each pair of comparable participants. We compare the prevalence of depression disorder between Black and White young women after matching them 1:1 on common socio-economic characteristics (age, employment, education, and marital status). In essence, we follow matching or stratification methods, but make a step further and match cases 1:1 on propensity scores, i.e., we create Black–White ‘dyads’. Instead of concluding from plain comparisons that 11% more White young women (18–30 years old) report a depressive disorder than Black young women, the matched data confirms the trend, but provides more nuances. In 27% of the pairs of comparable pairs the White woman was depressed (and the comparable Black woman was not), while in 15% of the pairs the Black woman was depressed (and the comparable White woman was not). We find that Black-to-White disparities in neighborhood disorder do not predict depression differences (HDs), while such an effect is evident for anxiety HDs. The 1:1 matching approach allows us to examine more complex HD effects, like differential mediational or resilience mechanisms that appear to be protective of Black women’s mental health.

Differential impact of socioeconomic position across life on oral cancer risk in Kerala, India: An investigation of life-course models under a time-varying framework

OBJECTIVES

The incidence of oral cancer has been rapidly increasing in India, calling for evidence contributing to a deeper understanding of its determinants. Although disadvantageous life-course socioeconomic position (SEP) is independently associated with the risk of these cancers, the explanatory mechanisms remain unclear. Possible pathways may be better understood by testing which life-course model most influences oral cancer risk. We estimated the association between life-course SEP and oral cancer risk under three life-course models: critical period, accumulation and social mobility.

METHODS

We recruited incident oral cancer cases (N = 350) and controls (N = 371) frequency-matched by age and sex from two main referral hospitals in Kozhikode, Kerala, India, between 2008 and 2012. We collected information on childhood (0-16 years), early adulthood (17-30 years) and late adulthood (above 30 years) SEP and behavioural factors along the life span using interviews and a life-grid technique. Odds ratios (OR) and 95% confidence intervals (CI) were estimated for the association between life-course SEP and oral cancer risk using inverse probability weighted marginal structural models.

RESULTS

Relative to an advantageous SEP in childhood and early adulthood, a disadvantageous SEP was associated with oral cancer risk [(OR = 2.76, 95% CI: 1.99, 3.81) and (OR = 1.84, 95% CI: 1.21, 2.79), respectively]. In addition, participants who were in a disadvantageous (vs advantageous) SEP during all three periods of life had an increased oral cancer risk (OR = 4.86, 95% CI: 2.61, 9.06). The childhood to early adulthood social mobility model and overall life-course trajectories indicated strong influence of exposure to disadvantageous SEP in childhood on the risk for oral cancer.

CONCLUSIONS

Using novel approaches to existing methods, our study provides empirical evidence that disadvantageous childhood SEP is critical for oral cancer risk in this population from Kerala, India.

Multiple mediators approach to study environmental chemicals as determinants of health disparities

A major goal of health disparities research is to identify and intervene upon modifiable risk factors that help explain the observed associations between social factors and adverse health outcomes. To this end, statistical methods incorporating mediation analysis have shown promise, as they quantify the contribution of an intermediate variable in an exposure-outcome association. A growing body of literature suggests that environmental chemicals can contribute to health disparities. However, evaluating environmental chemicals as an important component of health disparities introduces methodological complexities that may make standard mediation approaches inadequate. Specific to environmental health is the issue of evaluating both the source and biomarker of the environmental toxicant in order to calculate the proportion of the disparity that would remain had we intervened on the modifiable factors. Recent methodological developments on multiple mediators can improve efforts to integrate both source and biomarker of exposure into epidemiological studies of health disparities. We illustrate a conceptual framework and present how mediation techniques can be used to address environmental health disparities questions. With this, we provide a methodological tool that has the potential to advance this growing field, while simultaneously informing public health prevention and policy surrounding the impact of environmental factors on health disparities.

Analyses of Sensitivity to the Missing-at-Random Assumption Using Multiple Imputation With Delta Adjustment: Application to a Tuberculosis/HIV Prevalence Survey With Incomplete HIV-Status Data

Multiple imputation with delta adjustment provides a flexible and transparent means to impute univariate missing data under general missing-not-at-random mechanisms. This facilitates the conduct of analyses assessing sensitivity to the missing-at-random (MAR) assumption. We review the delta-adjustment procedure and demonstrate how it can be used to assess sensitivity to departures from MAR, both when estimating the prevalence of a partially observed outcome and when performing parametric causal mediation analyses with a partially observed mediator. We illustrate the approach using data from 34,446 respondents to a tuberculosis and human immunodeficiency virus (HIV) prevalence survey that was conducted as part of the Zambia-South Africa TB and AIDS Reduction Study (2006-2010). In this study, information on partially observed HIV serological values was supplemented by additional information on self-reported HIV status. We present results from 2 types of sensitivity analysis: The first assumed that the degree of departure from MAR was the same for all individuals with missing HIV serological values; the second assumed that the degree of departure from MAR varied according to an individual's self-reported HIV status. Our analyses demonstrate that multiple imputation offers a principled approach by which to incorporate auxiliary information on self-reported HIV status into analyses based on partially observed HIV serological values.

Prepregnancy obesity and the racial disparity in infant mortality

OBJECTIVE

To estimate the extent to which prepregnancy obesity explains the Black-White disparity in stillbirth and infant mortality.

METHODS

A population-based study of linked Pennsylvania birth-infant death certificates (2003-2011; n = 1,055,359 births) and fetal death certificates (2006-2011; n = 3,102 stillbirths) for all singleton pregnancies in non-Hispanic (NH) White and NH Black women was conducted. Inverse probability weighted regression was used to estimate the role of prepregnancy obesity in explaining the race-infant/fetal death association.

RESULTS

Compared with NH White women, NH Black women were more likely to have obesity (≥30 kg/m² ) and experienced a higher rate of stillbirth (8.3 vs. 3.6 stillbirths per 1,000 live-born and stillborn infants) and infant death (8.5 vs. 3.0 infant deaths per 1,000 live births). When the contribution of prepregnancy obesity was removed, the difference in risk between NH Blacks and NH Whites decreased from 6.2 (95% CI: 5.6-6.7) to 5.5 (95% CI: 4.9-6.2) excess stillbirths per 1,000 and 5.8 (95% CI: 5.3-6.3) to 5.2 (95% CI: 4.7-5.7) excess infant deaths per 1,000.

CONCLUSIONS

For every 10,000 live births in Pennsylvania (2003-2011), 6 of the 61 excess infant deaths in NH Black women and 5 of the 44 excess stillbirths (2006-2011) were attributable to prepregnancy obesity.

Mediation Analysis for Health Disparities Research

Social epidemiologists often seek to determine the mechanisms that underlie health disparities. This work is typically based on mediation procedures that may not be justified with exposures of common interest in social epidemiology. In this analysis, we explored the consequences of using standard approaches, referred to as the difference and generalized product methods, when mediator-outcome confounders are associated with the exposure. We compared these with inverse probability-weighted marginal structural models, the structural transformation method, doubly robust g-estimation of a structural nested model, and doubly robust targeted minimum loss-based estimation. We used data on 900,726 births from 2003 to 2007 in the Penn Moms study, conducted in Pennsylvania, to assess the extent to which breastfeeding prior to hospital discharge explained the racial disparity in infant mortality. Overall, for every 1,000 births, 3.36 more infant deaths occurred among non-Hispanic black women relative to all other women (95% confidence interval: 2.78, 3.93). Using the difference and generalized product methods to assess the disparity that would remain if everyone breastfed prior to discharge suggested a complete elimination of the disparity (risk difference = -0.87 per 1,000 births; 95% confidence interval: -1.39, -0.35). In contrast, doubly robust methods suggested a reduction in the disparity to 2.45 (95% confidence interval: 2.20, 2.71) more infant deaths per 1,000 births among non-Hispanic black women. Standard approaches for mediation analysis in health disparities research can yield misleading results.