Application of Hierarchical/Multilevel Models and Quality of Reporting (2010-2020): A Systematic Review.

Introduction: Multilevel models have gained immense popularity across almost every discipline due to the presence of hierarchy in most data and phenomena. In this paper, we present a systematic review on the adoption and application of multilevel models and the important information reported on the results generated from the use of these models. Methods: The review was performed by searching Google Scholar for original research articles on the application of multilevel models published between 2010 and 2020. The search strategy involved topics such as "multilevel models," "hierarchical linear models," and "mixed models with hierarchy." The search placed more emphasis on the application of hierarchical models in any discipline but excluded software methodological development and related articles. Results: A total of 121 articles were initially obtained from the search results. However, 65 articles met the inclusion criteria for the review. Out of the 65 articles reviewed, 46.2% were related to health/epidemiology, 15.4% to education and psychology, and 16.9% to social life. The majority of the articles (78.5%) were two-level models, and most of these studies modelled univariate responses. However, the few that modelled more than one response modelled them separately. Moreover, 83.1% were cross-sectional design, and 9.2% and 6.2% were longitudinal and repeated measures, respectively. Moreover, a little over half (55.4%) of articles reported on the intraclass correlation measure, and all articles indicated the response variable distribution where most (47.7%) were normally distributed. Only 58.5% of articles reported on the estimation methods used as Bayesian (20%) and MLE (18.5%). Again, model validation measures and statistical software were reported in 70.8% and 90.8% articles, respectively. Conclusion: There is an increase in the utilization of multilevel modelling in the last decade, which could be attributed to the presence of clustered and hierarchically correlated data structures. There is a need for improvement in the area of measurement and reporting on the intraclass correlation, parameter estimation, and variable selection measures to further improve the quality of the application of multilevel models. The integration of spatial effects into multilevel models is very limited and needs to be explored in the future.

Vaccine value profile for invasive non-typhoidal Salmonella disease.

Invasive non-typhoidal Salmonella (iNTS) disease is an under-recognized high-burden disease causing major health and socioeconomic issues in sub-Saharan Africa (sSA), predominantly among immune-naïve infants and young children, including those with recognized comorbidities such as HIV infection. iNTS disease is primarily caused by Salmonella enterica serovar Typhimurium sequence type (ST) 313 and 'African-restricted clades' of Salmonella Enteritidis ST11 that have emerged across the African continent as a series of epidemics associated with acquisition of new antimicrobial resistance. Due to genotypes with a high prevalence of antimicrobial resistance and scarcity of therapeutic options, these NTS serovars are designated by the World Health Organization as a priority pathogen for research and development of interventions, including vaccines, to address and reduce NTS associated bacteremia and meningitis in sSA. Novel and traditional vaccine technologies are being applied to develop vaccines against iNTS disease, and the results of the first clinical trials in the infant target population should become available in the near future. The "Vaccine Value Profile" (VVP) addresses information related predominantly to invasive disease caused by Salmonella Enteritidis and Salmonella Typhimurium prevalent in sSA. Information is included on stand-alone iNTS disease candidate vaccines and candidate vaccines targeting iNTS disease combined with another invasive serotype, Salmonella Typhi, that is also common across sSA. Out of scope for the first version of this VVP is a wider discussion on either diarrheagenic NTS disease (dNTS) also associated with Salmonella Enteritidis and Salmonella Typhimurium or the development of a multivalent Salmonella vaccines targeting key serovars for use globally. This VVP for vaccines to prevent iNTS disease is intended to provide a high-level, holistic assessment of the information and data that are currently available to inform the potential public health, economic, and societal value of pipeline vaccines and vaccine-like products. Future versions of this VVP will be updated to reflect ongoing activities such as vaccine development strategies and a "Full Vaccine Value Assessment" that will inform the value proposition of an iNTS disease vaccine. This VVP was developed by a working group of subject matter experts from academia, non-profit organizations, public private partnerships, and multi-lateral organizations, and in collaboration with stakeholders from the World Health Organization African Region. All contributors have extensive expertise on various elements of the iNTS disease VVP and collectively aimed to identify current research and knowledge gaps. The VVP was developed using only existing and publicly available information.

Mapping the incidence rate of typhoid fever in sub-Saharan Africa.

BACKGROUND: With more than 1.2 million illnesses and 29,000 deaths in sub-Saharan Africa in 2017, typhoid fever continues to be a major public health problem. Effective control of the disease would benefit from an understanding of the subnational geospatial distribution of the disease incidence. METHOD: We collated records of the incidence rate of typhoid fever confirmed by culture of blood in Africa from 2000 to 2022. We estimated the typhoid incidence rate for sub-Saharan Africa on 20 km × 20 km grids by exploring the association with geospatial covariates representing access to improved water and sanitation, health conditions of the population, and environmental conditions. RESULTS: We identified six published articles and one pre-print representing incidence rate estimates in 22 sites in 2000-2022. Estimated incidence rates showed geospatial variation at sub-national, national, and regional levels. The incidence rate was high in Western and Eastern African subregions followed by Southern and Middle African subregions. By age, the incidence rate was highest among 5-14 yo followed by 2-4 yo, > 14 yo, and 0-1 yo. When aggregated across all age classes and grids that comprise each country, predicted incidence rates ranged from 43.7 (95% confidence interval: 0.6 to 591.2) in Zimbabwe to 2,957.8 (95% CI: 20.8 to 4,245.2) in South Sudan per 100,000 person-years. Sub-national heterogeneity was evident with the coefficient of variation at the 20 km × 20 km grid-level ranging from 0.7 to 3.3 and was generally lower in high-incidence countries and widely varying in low-incidence countries. CONCLUSION: Our study provides estimates of 20 km × 20 km incidence rate of typhoid fever across sub-Saharan Africa based on data collected from 2000 through 2020. Increased understanding of the subnational geospatial variation of typhoid fever in Africa may inform more effective intervention programs by better targeting resources to heterogeneously disturbed disease risk.