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Background: Childhood stunting is a major indicator of child malnutrition and a focus area of Global Nutrition Targets for 2025 and Sustainable Development Goals. Risk factors for childhood stunting are well studied and well known and could be used in a risk prediction model for assessing whether a child is stunted or not. However, the selection of child stunting predictor variables is a critical step in the development and performance of any such prediction model. This paper compares the performance of child stunting diagnostic predictive models based on predictor variables selected using a set of variable selection methods., Methods: Firstly, we conducted a subjective review of the literature to identify determinants of child stunting in Sub-Saharan Africa. Secondly, a multivariate logistic regression model of child stunting was fitted using the identified predictors on stunting data among children aged 0-59 months in the Malawi Demographic Health Survey (MDHS 2015-16) data. Thirdly, several reduced multivariable logistic regression models were fitted depending on the predictor variables selected using seven variable selection algorithms, namely backward, forward, stepwise, random forest, Least Absolute Shrinkage and Selection Operator (LASSO), and judgmental. Lastly, for each reduced model, a diagnostic predictive model for the childhood stunting risk score, defined as the child propensity score based on derived coefficients, was calculated for each child. The prediction risk models were assessed using discrimination measures, including area under-receiver operator curve (AUROC), sensitivity and specificity., Results: The review identified 68 predictor variables of child stunting, of which 27 were available in the MDHS 2016-16 data. The common risk factors selected by all the variable selection models include household wealth index, age of the child, household size, type of birth (singleton/multiple births), and birth weight. The best cut-off point on the child stunting risk prediction model was 0.37 based on risk factors determined by the judgmental variable selection method. The model's accuracy was estimated with an AUROC value of 64% (95% CI: 60%-67%) in the test data. For children residing in urban areas, the corresponding AUROC was AUC = 67% (95% CI: 58-76%), as opposed to those in rural areas, AUC = 63% (95% CI: 59-67%)., Conclusion: The derived child stunting diagnostic prediction model could be useful as a first screening tool to identify children more likely to be stunted. The identified children could then receive necessary nutritional interventions., (© 2024. The Author(s).)

To determine early COVID-19 burden in Malawi, we conducted a multistage cluster survey in 5 districts. During October-December 2020, we recruited 5,010 community members (median age 32 years, interquartile range 21-43 years) and 1,021 health facility staff (HFS) (median age 35 years, interquartile range 28-43 years). Real-time PCR-confirmed SARS-CoV-2 infection prevalence was 0.3% (95% CI 0.2%-0.5%) among community and 0.5% (95% CI 0.1%-1.2%) among HFS participants; seroprevalence was 7.8% (95% CI 6.3%-9.6%) among community and 9.7% (95% CI 6.4%-14.5%) among HFS participants. Most seropositive community (84.7%) and HFS (76.0%) participants were asymptomatic. Seroprevalence was higher among urban community (12.6% vs. 3.1%) and HFS (14.5% vs. 7.4%) than among rural community participants. Cumulative infection findings 113-fold higher from this survey than national statistics (486,771 vs. 4,319) and predominantly asymptomatic infections highlight a need to identify alternative surveillance approaches and predictors of severe disease to inform national response.

Background: Information on HIV drug resistance (HIVDR) prevalence in people newly diagnosed with HIV is limited. We implemented a cross-sectional study to estimate HIVDR prevalence among pregnant women recently infected with HIV in Malawi., Methods: The HIVDR study was nested within a routine antenatal clinic (ANC) sentinel surveillance survey. Dried blood spot samples were tested for recent infection using a limiting antigen antibody assay together with HIV viral load testing. HIV-1 protease and reverse transcriptase were sequenced using Sanger sequencing. Drug susceptibility was predicted using Stanford HIVdb algorithm (version 8.9). Weighted analysis was performed in Stata 15.1., Results: Of the 21,642 pregnant women enrolled in the ANC survey, 8.4% (1826/21,642) tested HIV positive. Of these, 5.0% (92/1826) had recent HIV infection, and 90.2% (83/92) were tested by PCR. The amplification and sequencing success rate was 57.8% (48/83). The prevalence of any HIVDR was 14.6% (5/45) (95% CI: 4.7-36.8%), all of which indicated HIVDR to nonnucleoside reverse transcriptase inhibitors (NNRTIs). HIVDR to nucleoside reverse transcriptase inhibitors was 7.9% (2/45) (95% CI: 1.4-34.6%). Resistance to protease inhibitors currently in use in Malawi was not observed., Conclusions: Despite the low number of cases with presumed TDR, our study hints that resistance to NNRTIs was high, above the 10% target for regimen change. Further investigation is needed to establish the exact magnitude of presumed TDR among women recently infected with HIV. These findings support the transition to an integrase inhibitor-based first-line regimen for patients initiating or on ART.