Rural-Urban Differences in Adult Life Expectancy in Indonesia: A Parametric g-formula-based Decomposition Approach

Gaps in the usage and reporting of multiple imputation for incomplete data: findings from a scoping review of observational studies addressing causal questions

BACKGROUND

Missing data are common in observational studies and often occur in several of the variables required when estimating a causal effect, i.e. the exposure, outcome and/or variables used to control for confounding. Analyses involving multiple incomplete variables are not as straightforward as analyses with a single incomplete variable. For example, in the context of multivariable missingness, the standard missing data assumptions ("missing completely at random", "missing at random" [MAR], "missing not at random") are difficult to interpret and assess. It is not clear how the complexities that arise due to multivariable missingness are being addressed in practice. The aim of this study was to review how missing data are managed and reported in observational studies that use multiple imputation (MI) for causal effect estimation, with a particular focus on missing data summaries, missing data assumptions, primary and sensitivity analyses, and MI implementation.

METHODS

We searched five top general epidemiology journals for observational studies that aimed to answer a causal research question and used MI, published between January 2019 and December 2021. Article screening and data extraction were performed systematically.

RESULTS

Of the 130 studies included in this review, 108 (83%) derived an analysis sample by excluding individuals with missing data in specific variables (e.g., outcome) and 114 (88%) had multivariable missingness within the analysis sample. Forty-four (34%) studies provided a statement about missing data assumptions, 35 of which stated the MAR assumption, but only 11/44 (25%) studies provided a justification for these assumptions. The number of imputations, MI method and MI software were generally well-reported (71%, 75% and 88% of studies, respectively), while aspects of the imputation model specification were not clear for more than half of the studies. A secondary analysis that used a different approach to handle the missing data was conducted in 69/130 (53%) studies. Of these 69 studies, 68 (99%) lacked a clear justification for the secondary analysis.

CONCLUSION

Effort is needed to clarify the rationale for and improve the reporting of MI for estimation of causal effects from observational data. We encourage greater transparency in making and reporting analytical decisions related to missing data.

Path-specific causal decomposition analysis with multiple correlated mediator variables

A causal decomposition analysis allows researchers to determine whether the difference in a health outcome between two groups can be attributed to a difference in each group's distribution of one or more modifiable mediator variables. With this knowledge, researchers and policymakers can focus on designing interventions that target these mediator variables. Existing methods for causal decomposition analysis either focus on one mediator variable or assume that each mediator variable is conditionally independent given the group label and the mediator-outcome confounders. In this article, we propose a flexible causal decomposition analysis method that can accommodate multiple correlated and interacting mediator variables, which are frequently seen in studies of health behaviors and studies of environmental pollutants. We extend a Monte Carlo-based causal decomposition analysis method to this setting by using a multivariate mediator model that can accommodate any combination of binary and continuous mediator variables. Furthermore, we state the causal assumptions needed to identify both joint and path-specific decomposition effects through each mediator variable. To illustrate the reduction in bias and confidence interval width of the decomposition effects under our proposed method, we perform a simulation study. We also apply our approach to examine whether differences in smoking status and dietary inflammation score explain any of the Black-White differences in incident diabetes using data from a national cohort study.

Risk of Adverse Perinatal Outcomes Among African-born Black Women in California, 2011-2020

BACKGROUND

African-born women have a lower risk of preterm birth and small for gestational age (SGA) birth compared with United States-born Black women, however variation by country of origin is overlooked. Additionally, the extent that nativity disparities in adverse perinatal outcomes to Black women are explained by individual-level factors remains unclear.

METHODS

We conducted a population-based study of nonanomalous singleton live births to United States- and African-born Black women in California from 2011 to 2020 (n = 194,320). We used age-adjusted Poisson regression models to estimate the risk of preterm birth and SGA and reported risk ratios (RR) and 95% confidence intervals (CI). Decomposition using Monte Carlo integration of the g-formula computed the percentage of disparities in adverse outcomes between United States- and African-born women explained by individual-level factors.

RESULTS

Eritrean women (RR = 0.4; 95% CI = 0.3, 0.5) had the largest differences in risk of preterm birth and Cameroonian women (RR = 0.5; 95% CI = 0.3, 0.6) in SGA birth, compared with United States-born Black women. Ghanaian women had smaller differences in risk of preterm birth (RR = 0.8; 95% CI = 0.7, 1.0) and SGA (RR = 0.9; 95% CI = 0.8, 1.1) compared with United States-born women. Overall, we estimate that absolute differences in socio-demographic and clinical factors contributed to 32% of nativity-based disparities in the risk of preterm birth and 26% of disparities in SGA.

CONCLUSIONS

We observed heterogeneity in risk of adverse perinatal outcomes for African- compared with United States-born Black women, suggesting that nativity disparities in adverse perinatal outcomes were not fully explained by differences in individual-level factors.

Causes of Outcome Learning: a causal inference-inspired machine learning approach to disentangling common combinations of potential causes of a health outcome

Nearly all diseases are caused by different combinations of exposures. Yet, most epidemiological studies focus on estimating the effect of a single exposure on a health outcome. We present the Causes of Outcome Learning approach (CoOL), which seeks to discover combinations of exposures that lead to an increased risk of a specific outcome in parts of the population. The approach allows for exposures acting alone and in synergy with others. The road map of CoOL involves (i) a pre-computational phase used to define a causal model; (ii) a computational phase with three steps, namely (a) fitting a non-negative model on an additive scale, (b) decomposing risk contributions and (c) clustering individuals based on the risk contributions into subgroups; and (iii) a post-computational phase on hypothesis development, validation and triangulation using new data before eventually updating the causal model. The computational phase uses a tailored neural network for the non-negative model on an additive scale and layer-wise relevance propagation for the risk decomposition through this model. We demonstrate the approach on simulated and real-life data using the R package 'CoOL'. The presentation focuses on binary exposures and outcomes but can also be extended to other measurement types. This approach encourages and enables researchers to identify combinations of exposures as potential causes of the health outcome of interest. Expanding our ability to discover complex causes could eventually result in more effective, targeted and informed interventions prioritized for their public health impact.

Educational note: causal decomposition of population health differences using Monte Carlo integration and the g-formula

One key objective of the population health sciences is to understand why one social group has different levels of health and well-being compared with another. Whereas several methods have been developed in economics, sociology, demography, and epidemiology to answer these types of questions, a recent method introduced by Jackson and VanderWeele (2018) provided an update to decompositions by anchoring them within causal inference theory. In this paper, we demonstrate how to implement the causal decomposition using Monte Carlo integration and the parametric g-formula. Causal decomposition can help to identify the sources of differences across populations and provide researchers with a way to move beyond estimating inequalities to explaining them and determining what can be done to reduce health disparities. Our implementation approach can easily and flexibly be applied for different types of outcome and explanatory variables without having to derive decomposition equations. We describe the concepts of the approach and the practical steps and considerations needed to implement it. We then walk through a worked example in which we investigate the contribution of smoking to sex differences in mortality in South Korea. For this example, we provide both pseudocode and R code using our package, cfdecomp. Ultimately, we outline how to implement a very general decomposition algorithm that is grounded in counterfactual theory but still easy to apply to a wide range of situations.

Systolic blood pressure and six-year mortality in South Africa: Evidence from a country-wide population-based cohort study

Background

Improving hypertension control is an important global health priority yet, to our knowledge, there is no direct evidence on the blood pressure (BP)-mortality relationship in sub-Saharan Africa. We investigate the BP-mortality relationship in South Africa and assess the comparative effectiveness of different care targets for clinical care and population-wide hypertension management efforts.

Methods

We use country-wide population-based longitudinal data from five waves (2008 - 2017) of the South African National Income Dynamics Study (N = 4,993). We estimate the relationship between systolic BP (SBP) and six-year all-cause mortality and compare the mortality reductions associated with lowering SBP to different targets. We then estimate the number needed to treat to avert one death (NNT) under different hypothetical population-wide scale up scenarios.

Findings

We found a weak, nonlinear, SBP-mortality relationship with larger incremental mortality benefits at higher SBP values: reducing SBP from 160 to 150 mmHg was associated with a mortality risk ratio of 0.95 (95% CI: 0.90, 0.99, p = 0.033), incrementally reducing SBP from 150 to 140 mmHg a risk ratio of 0.96 (95% CI: 0.91, 1.01, p = 0.12), with no evidence of incremental benefits of reducing BP below 140 mmHg. At the population level, reducing SBP to 150 mmHg among all those with an SBP > 150 mmHg had the lowest NNT (50) at 3.3 deaths averted (95% CI: -0.6, 0.3) per 1,000 population while requiring BP management for 16% (95% CI: 15.2, 17.3) of individuals.

Interpretation

The SBP-mortality association is weaker in South Africa than in high-income and many low- and middle-income countries. As such, we do not find compelling evidence in support of targets below 140 mmHg and find that scaling up management based on a 150 mmHg target is more efficient in terms of the NNT compared to strategies to reduce SBP to lower values.