Excess Mortality by Multimorbidity, Socioeconomic, and Healthcare Factors, amongst Patients Diagnosed with Diffuse Large B-Cell or Follicular Lymphoma in England

(1) Background: Socioeconomic inequalities of survival in patients with lymphoma persist, which may be explained by patients’ comorbidities. We aimed to assess the association between comorbidities and the survival of patients diagnosed with diffuse large B-cell (DLBCL) or follicular lymphoma (FL) in England accounting for other socio-demographic characteristics. (2) Methods: Population-based cancer registry data were linked to Hospital Episode Statistics. We used a flexible multilevel excess hazard model to estimate excess mortality and net survival by patient’s comorbidity status, adjusted for sociodemographic, economic, and healthcare factors, and accounting for the patient’s area of residence. We used the latent normal joint modelling multiple imputation approach for missing data. (3) Results: Overall, 15,516 and 29,898 patients were diagnosed with FL and DLBCL in England between 2005 and 2013, respectively. Amongst DLBCL and FL patients, respectively, those in the most deprived areas showed 1.22 (95% confidence interval (CI): 1.18–1.27) and 1.45 (95% CI: 1.30–1.62) times higher excess mortality hazard compared to those in the least deprived areas, adjusted for comorbidity status, age at diagnosis, sex, ethnicity, and route to diagnosis. (4) Conclusions: Deprivation is consistently associated with poorer survival among patients diagnosed with DLBCL or FL, after adjusting for co/multimorbidities. Comorbidities and multimorbidities need to be considered when planning public health interventions targeting haematological malignancies in England.

Applied causal inference methods for sequential mediators

Background: Mediation analysis aims at estimating to what extent the effect of an exposure on an outcome is explained by a set of mediators on the causal pathway between the exposure and the outcome. The total effect of the exposure on the outcome can be decomposed into an indirect effect, i.e. the effect explained by the mediators jointly, and a direct effect, i.e. the effect unexplained by the mediators. However finer decompositions are possible in presence of independent or sequential mediators. Methods: We review four statistical methods to analyse multiple sequential mediators , the inverse odds ratio weighting approach, the inverse probability weighting approach, the imputation approach and the extended imputation approach. These approaches are compared and implemented using a case-study with the aim to investigate the role of adverse reproductive outcomes and infant respiratory infections on infant wheezing in the Ninfea birth cohort. Results: Using the inverse odds ratio weighting approach, the direct effect of maternal depression or anxiety in pregnancy is equal to a 59% (95% CI: 27%-94%) increased prevalence of infant wheezing and the mediated effect through adverse reproductive outcomes is equal to a 3% (95% CI:-6%-12%) increased prevalence of infant wheezing. When including infant lower respiratory infections in the mediation pathway, the direct effect decreases to 57% (95% CI: 25%-92%) and the indirect effect increases to 5% (95% CI:-5%,15%). The estimates of the effects obtained using the weighting and the imputation approaches are similar. The extended imputation approach suggests that the small joint indirect effect through adverse reproductive outcomes and lower respiratory infections is due entirely to the contribution of infant lower respiratory infections, independently from the increased prevalence of adverse reproductive outcomes. Conclusions: The use of these methods allows the study of multiple mechanisms underlying the association between an exposure and an outcome and provides a solution for the problem of intermediate confounding by considering the intermediate confounder as a sequential mediator. The choice of the method may depend on what is the effect of main interest, the nature of the variables involved in the analysis and the truthfulness of the underlying assumptions.

Applied causal inference methods for sequential mediators

Mediation analysis aims at estimating to what extent the effect of an exposure on an outcome is explained by a set of mediators on the causal pathway between the exposure and the outcome. In this context, the total effect of the exposure on the outcome can be decomposed into the natural indirect effect, i.e. the effect explained by the mediators jointly, and the natural direct effect, i.e. the effect unexplained by the mediators. However finer decompositions are also possible in presence of independent or sequential mediators. As sequential mediation analysis is increasingly common in epidemiology, applied researchers have to interface with difficulties related to the application, implementation, and interpretation of the methods pro- posed in literature. We review four statistical methods to analyse multiple sequential mediators, all based on the counterfactual framework: the inverse odds ratio weight- ing approach, the inverse probability weighting approach, the imputation approach and the extended imputation approach. These approaches are described, compared and implemented using a case-study with the aim to investigate the role of adverse reproductive outcomes and infant respiratory infections on infant wheezing in the Ninfea birth cohort.

A Novel Imputation Approach for Sharing Protected Public Health Data

Objectives. To develop an imputation method to produce estimates for suppressed values within a shared government administrative data set to facilitate accurate data sharing and statistical and spatial analyses. Methods. We developed an imputation approach that incorporated known features of suppressed Massachusetts surveillance data from 2011 to 2017 to predict missing values more precisely. Our methods for 35 de-identified opioid prescription data sets combined modified previous or next substitution followed by mean imputation and a count adjustment to estimate suppressed values before sharing. We modeled 4 methods and compared the results to baseline mean imputation. Results. We assessed performance by comparing root mean squared error (RMSE), mean absolute error (MAE), and proportional variance between imputed and suppressed values. Our method outperformed mean imputation; we retained 46% of the suppressed value’s proportional variance with better precision (22% lower RMSE and 26% lower MAE) than simple mean imputation. Conclusions. Our easy-to-implement imputation technique largely overcomes the adverse effects of low count value suppression with superior results to simple mean imputation. This novel method is generalizable to researchers sharing protected public health surveillance data. (Am J Public Health. Published online ahead of print September 16, 2021: e1–e9. https://doi.org/10.2105/AJPH.2021.306432 )