Systematic review of analytical methods applied to longitudinal studies of malaria

Modeling clinical malaria episodes in different ecological settings in Mali, 2018-2022

Objectives

Following the scaling-up of malaria control strategies in Mali, understanding the changes in age-specific prevalence of infection and risk factors associated with remains necessary to determine new priorities to progress toward disease elimination. This study aimed to estimate the risk of clinical malaria using longitudinal data across three different transmission settings in Mali.

Methods

Cohort-based longitudinal studies were performed from April 2018 to December 2022. Incidence of malaria was measured through community health center-based passive case detection. Generalized estimation equation model was used to assess risk factors for clinical malaria.

Results

A total of 21,453 clinical presentations were reported from 4500 participants, mainly from July to November. Data shows a significant association between malaria episodes, sex, age group, season, and year. Women had lower risk, the risk of clinical episode increased with age up to 14 years then declined, and in both sites, the dry-season risk of clinical episode was significantly lower compared to the rainy season.

Conclusion

Determining factors associated with the occurrence of clinical malaria across different ecological settings across the country could help in the development of new strategies aiming to accelerate malaria elimination in an area where malaria transmission remains intense.

Methodological approaches for analysing data from therapeutic efficacy studies

Several anti-malarial drugs have been evaluated in randomized clinical trials to treat acute uncomplicated Plasmodium falciparum malaria. The outcome of anti-malarial drug efficacy studies is classified into one of four possible outcomes defined by the World Health Organization: adequate clinical and parasitological response, late parasitological failure, late clinical failure, early treatment failure. These four ordered categories are ordinal data, which are reduced to either a binary outcome (i.e., treatment success and treatment failure) to calculate the proportions of treatment failure or to time-to-event outcome for KaplanMeier survival analysis. The arbitrary transition from 4-level ordered categories to 2-level type categories results in a loss of statistical power. In the opinion of the authors, this outcome can be considered as ordinal at a fixed endpoint or at longitudinal endpoints. Alternative statistical methods can be applied to 4-level ordinal categories of therapeutic response to optimize data exploitation. Furthermore, network meta-analysis is useful not only for direct comparison of drugs which were evaluated together in a randomized design, but also for indirect comparison of different artemisinin-based combinations across different clinical studies using a common drug comparator, with the aim to determine the ranking order of drug efficacy. Previous works conducted in Cameroonian children served as data source to illustrate the feasibility of these novel statistical approaches. Data analysis based on ordinal end-point may be helpful to gain further insight into anti-malarial drug efficacy.

Longitudinal Studies 1: Determinants of Risk

Longitudinal or observational study designs are important methodologies to investigate potential associations that may not be amenable to randomized controlled trials. In many cases, they may be performed using existing data and are often cost-effective ways of addressing important questions. The major disadvantage of observational studies is the potential for bias. The absence of randomization means that one can never be certain that unknown confounders are present, and specific studies designs have their own inherent forms of bias. Careful study design may minimize bias. Establishing casual association based on observational methods requires due consideration of the quality of the individual study and knowledge of their limitations.