Assessment Of Health Interventions Research Articles

Effect of important modifiers on harmful effects in evidence synthesis practice of adverse events were insufficiently investigated: an empirical investigation

BackgroundSafety is important in the assessment of health interventions, while the results of adverse events are often susceptive to potential effect modifiers since the event risk tends to be rare. In this study, we investigated whether the potential impact of the important effect modifiers on harmful effects was analyzed in meta-analyses of adverse events.MethodsSystematic reviews of healthcare interventions, had adverse events as the exclusive outcomes, had at least one meta-analysis, and published between 1st January 2015, and 1st January 2020 were collected. An adverse event was defined as any untoward medical occurrence in a patient or subject in healthcare practice. Six effect modifiers that are the most important for harmful effects were identified by a group discussion. The proportions of eligible systematic reviews that investigated the potential impact of the six effect modifiers on harmful effects were summarized.ResultsWe identified 279 systematic reviews eligible for this study. Except for the modifier of interventions/controls (70.61%, 197/279), most of the systematic reviews failed to investigate the potential impact of treatment duration (21.15%, 59/279), dosage (24.73%, 69/279), age (11.47%, 32/279), risk of bias (6.45%, 18/279), and source of funding (1.08%, 3/279) on harmful effects. Systematic reviews with meta-analyses containing more studies were more likely to investigate the potential impacts of these modifiers on the effects, but the proportion was still low (2.3% to 33.3%). Systematic reviews that developed a protocol were significantly more likely to investigate the potential impact of all these effect modifiers (e.g. treatment duration: odds ratio = 5.08, 95% CI: 2.76 to 9.35) on the results.ConclusionsCurrent systematic reviews rarely investigated the potential impact of the important effect modifiers on harmful effects. Methodological guidelines for meta-analysis of adverse events should consider “effect modifier” as one of the domains to help systematic review authors better investigate harmful effects.

A Guideline for Reporting and Assessment of Health Interventions Using Mobile Phones

A Guideline for Reporting and Assessment of Health Interventions Using Mobile Phones

Linear mixed models to handle missing at random data in trial-based economic evaluations.

Trial‐based cost‐effectiveness analyses (CEAs) are an important source of evidence in the assessment of health interventions. In these studies, cost and effectiveness outcomes are commonly measured at multiple time points, but some observations may be missing. Restricting the analysis to the participants with complete data can lead to biased and inefficient estimates. Methods, such as multiple imputation, have been recommended as they make better use of the data available and are valid under less restrictive Missing At Random (MAR) assumption. Linear mixed effects models (LMMs) offer a simple alternative to handle missing data under MAR without requiring imputations, and have not been very well explored in the CEA context. In this manuscript, we aim to familiarize readers with LMMs and demonstrate their implementation in CEA. We illustrate the approach on a randomized trial of antidepressants, and provide the implementation code in R and Stata. We hope that the more familiar statistical framework associated with LMMs, compared to other missing data approaches, will encourage their implementation and move practitioners away from inadequate methods.

De l’intégration des questions éthiques dans l’économie de la santé

L’économie de la santé comprise comme l’organisation des différents éléments de l’ensemble, du système « santé » intègre inévitablement des questions d’ordre éthique. Ce qui concerne la santé et les soins de santé nous invite à réfléchir à ce qui « doit » ou « devrait » être. Nous adoptons ici une posture philosophique favorable à l’explicitation des enjeux éthiques liés à l’économie de la santé et nous proposons deux exemples d’intégration de considérations éthiques dans les analyses classiques d’évaluation d’interventions de santé. JEL Classification : D61, D63, H51, I18

Building health research infrastructure in Rwanda.

Building health research infrastructure in Rwanda.

Profile: Nanoro Health and Demographic Surveillance System

The Nanoro Health and Demographic Surveillance System (HDSS), located in the rural centre of Burkina Faso, was established in 2009 by the Clinical Research Unit of Nanoro with the aim of providing a core framework for clinical trials and also to support the Burkina Faso health authorities in generating epidemiological data that can contribute to the setup and assessment of health interventions. In the baseline of initial census, 54 781 individuals were recorded of whom 56.1% are female. After the initial census, vital events such as pregnancies, births, migrations and deaths have been monitored, and data on individuals and household characteristics are updated during regular 4-monthly household visits. The available data are categorized into demographic, cultural, socio-economic and health information, and are used for monitoring and evaluation of population development issues. As a young site, our objective has been to strengthen our skills and knowledge and share new scientific experiences with INDEPTH and HDSS sites in Burkina Faso. In addition, all data produced by the Nanoro HDSS will be made publicly available through the INDEPTH data sharing system.