Causal Mediation Analysis for Standardized Mortality Ratios

Prognostic score-based methods for estimating center effects based on survival probability: Application to post-kidney transplant survival

In evaluating the performance of different facilities or centers on survival outcomes, the standardized mortality ratio (SMR), which compares the observed to expected mortality has been widely used, particularly in the evaluation of kidney transplant centers. Despite its utility, the SMR may exaggerate center effects in settings where survival probability is relatively high. An example is one-year graft survival among U.S. kidney transplant recipients. We propose a novel approach to estimate center effects in terms of differences in survival probability (ie, each center versus a reference population). An essential component of the method is a prognostic score weighting technique, which permits accurately evaluating centers without necessarily specifying a correct survival model. Advantages of our approach over existing facility-profiling methods include a metric based on survival probability (greater clinical relevance than ratios of counts/rates); direct standardization (valid to compare between centers, unlike indirect standardization based methods, such as the SMR); and less reliance on correct model specification (since the assumed model is used to generate risk classes as opposed to fitted-value based 'expected' counts). We establish the asymptotic properties of the proposed weighted estimator and evaluate its finite-sample performance under a diverse set of simulation settings. The method is then applied to evaluate U.S. kidney transplant centers with respect to graft survival probability.

Maternal folic acid supplementation mediates the associations between maternal socioeconomic status and congenital heart diseases in offspring

Low maternal socioeconomic status (SES) is considered as a risk factor of congenital heart diseases (CHDs) in offspring. However, the pathways underpinning the SES-CHDs associations are unclear. We assessed if first trimester maternal folic acid supplementation (FAS) is a mediator of the SES-CHDs associations. This case-control study included 8379 CHD cases and 6918 CHD-free controls from 40 participating centers in Guangdong, Southern China, 2004-2016. All fetuses were screened for CHDs using ultrasound and cases were confirmed by echocardiogram. We collected SES and FAS information during face-to-face interview by obstetricians using a structured questionnaire. Low SES was defined as education attainment <12 years, household individual income <3000 Chinese Yuan/person/month or unemployment. FAS referred to at least 0.4 mg of daily folic acid intake over 5 days/week continuously. We used causal mediation analysis to estimate the direct, indirect and proportion mediated by FAS on the SES-CHDs associations adjusted for confounders. Both low maternal income and education were significantly associated with increased risks of CHDs and lower prevalence of FAS. Low maternal FAS prevalence mediated 10% [95%CI:5%,13%] and 3% [95%CI:1%,5%] of the maternal low income-CHDs and the maternal low education-CHDs associations, respectively. In addition, FAS mediated the highest proportion of the associations between income and multiple critical CHDs [46.9%, 95%CI:24.7%,77%] and conotruncal defects [31.5%, 95%CI:17.1%,52.0%], respectively. Maternal FAS partially mediated the SES-CHDs associations, especially among the most critical and common CHDs. Promoting FAS in low SES women of childbearing age may be a feasible intervention to help prevent CHDs.

Constructing inverse probability weights for institutional comparisons in healthcare

In comparing quality of care between hospitals, disease-specific quality indicators measure structural, process, or outcome elements related to the care of a particular condition. Such comparisons can be framed in terms of causal contrasts, answering the question of whether a patient (or a population of patients on average) would receive different care if treated at the care level of a different hospital. Fair comparisons have to be adjusted for patient case-mix, which is equivalent to controlling for confounding by the patient-level factors, including demographic factors, comorbidities, and disease progression. The methodological choice for such comparisons is usually between direct and indirect standardization methods. In this article, we discuss the alternative of inverse probability weighting as a tool for standardization in hospital comparisons. This involves fitting multinomial logistic hospital assignment models and using these to construct the inverse probability weights. The challenge in the present context is the presence of large number of hospitals being compared, many of which have a small patient volume. We propose methods to include small categories in the weighted analysis, as well as metrics and visualizations for checking the positivity/overlap and covariate balance in constructing such weights. The methods are illustrated in a running example using linked administrative data on surgical treatment of kidney cancer patients in Ontario.