Studies on factors of low birth weight in Malawi have neglected the flexible approach of using smooth functions for some covariates in models. Such flexible approach reveals detailed relationship of covariates with the response. The study aimed at investigating risk factors of low birth weight in Malawi by assuming a flexible approach for continuous covariates and geographical random effect. A Bayesian geo-additive model for birth weight in kilograms and size of the child at birth (less than average or average and higher) with district as a spatial effect using the 2010 Malawi demographic and health survey data was adopted. A Gaussian model for birth weight in kilograms and a binary logistic model for the binary outcome (size of child at birth) were fitted. Continuous covariates were modelled by the penalized (p) splines and spatial effects were smoothed by the two dimensional p-spline. The study found that child birth order, mother weight and height are significant predictors of birth weight. Secondary education for mother, birth order categories 2-3 and 4-5, wealth index of richer family and mother height were significant predictors of child size at birth. The area associated with low birth weight was Chitipa and areas with increased risk to less than average size at birth were Chitipa and Mchinji. The study found support for the flexible modelling of some covariates that clearly have nonlinear influences. Nevertheless there is no strong support for inclusion of geographical spatial analysis. The spatial patterns though point to the influence of omitted variables with some spatial structure or possibly epidemiological processes that account for this spatial structure and the maps generated could be used for targeting development efforts at a glance.

Epidemiological studies in Malawi on child anaemia have neglected the community spatial effect to childhood anaemia. Neglecting the community spatial effect in the model ignores the influence of unobserved or unmeasured contextual variables, and at the same time the resultant model may under estimate model parameter standard errors which can result in erroneous significance of covariates. We aimed at investigating risk factors of childhood anaemia in Malawi with focus on geographical spatial effect. We adopted a Bayesian random effect model for child anaemia with district as spatial effect using the 2010 Malawi demographic healthy survey data. We fitted the binary logistic model for the two categories outcome (anaemia (Hb < 11), and no anaemia (Hb ≥ 11)). Continuous covariates were modelled by the penalized splines and spatial effects were smoothed by the two dimensional spline. Residual spatial patterns reveal Nsanje, Chikhwawa, Salima, Nkhota-kota, Mangochi and Machinga increasing the risk of childhood anaemia. Karonga, Chitipa, Rumphi, Mzimba, Ntchisi, and Chiradzulu reduce the risk of childhood anaemia. Known determinants such as maternal anaemia, child stunting, and child fever, have a positive effect on child anaemia. Furthermore childhood anaemia decreases with child age. It also decreases with wealth index. There is a U relationship between child anaemia and mother age. Strategies in childhood anaemia control should be tailored to local conditions, taking into account the specific etiology and prevalence of anaemia.

Childhood undernutrition is an important public health problem. Many studies have investigated the factors of childhood undernutrition, but not the association between the undernutrition indicators. This study aimed at investigating the association between the childhood undernutrition indicators. A loglinear model of cell counts of a three way table of stunting, wasting, and underweight was fitted based on the 2010 Malawi demographic health survey data. Interaction terms in the model depicted deviations from independence. A multiple correspondence analysis of undernutrition indicators was also plotted to have a visual impression of association of the undernutrition variables. A loglinear model showed that underweight was associated with both stunting (P<0.001), and wasting (P<0.001). There was no association between stunting and wasting (P=1). Furthermore there was no three way association of stunting, wasting and underweight (P=1). Lack of three way interaction of stunting, wasting and underweight means that childhood undernutrition multidimensional nature is still valid, and no each indicator can represent the other.

The aim of this study was to investigate the effects of climate and location while using the multivariate model of malnutrition. The joint semiparametric model of stunting, wasting, and overweight was fitted to 2015 child Malawi demographic health survey data with 5149 records. The MDHS was a cross-sectional study. The smooth functions for the non-parametric terms were the regression splines and the effect of location was smoothed by the Markov random field (MRF). Rainfall had a positive effect on stunting (β = 0.076, P = 0.044) and overweight (β = 0.854, P = 0.039). Mean temperature (β = 1.220, P = 0.031) and distance to water body (β = 0.009, P = 0.049) also had a positive effect on wasting. Increased length of rainy season was associated with reduced overweight (β = -0.163, P = 0.042). Location was not a significant predictor of all malnutrition indicators, although there was observable spatial variation regarding overweight and wasting. There was significant positive correlation between stunting and overweight (ρ = 0.234; 95% confidence interval, 0.135-0.324). The findings on socioeconomic determinants are consistent with the literature. Nutrition interventions may target hot spot areas that have shown increased risk for overweight and wasting. The strategies to minimize malnutrition should focus on consequences of climate change like high rainfall, length of season, and temperature.

Tuberculosis (TB) in cattle negatively affects the cattle economy in Africa, with zoonotic TB posing drug-resistance issues in humans. The burden of TB in cattle and zoonotic TB in humans in sub-Saharan Africa (SSA) is not well understood. This study aimed to determine the prevalence of both TB in cattle and zoonotic TB in humans in SSA through meta-analysis. Research on TB prevalence was sourced from multiple databases. A random effects meta-analysis model estimated TB prevalence in SSA and its regions, while meta-regression identified risk factors. The analysis included 114 studies for cattle and 59 for humans. The estimated TB prevalence in cattle was 5.06% (95% CI: 3.76-6.78), with a higher burden in West Africa. The prevalence was greater on farms than at abattoirs. Among humans, M. bovis prevalence was 0.73% (95% CI: 0.53-1.01), increasing to 1.56% (95% CI: 1.04-2.33) in TB incident cases, especially in the West and East Africa. Higher prevalence was noted among livestock workers, and in drug-resistant cases. Significant factors influencing TB prevalence varied for cattle and humans, including country, diagnostic methods, and study populations. Focusing interventions on farms and livestock workers could help reduce the disease burden.

Background COVID-19 has been one of the greatest challenges the world has faced since the second world war. This study aimed at investigating the distribution of COVID-19 in both space and time in Malawi. Methods The study used publicly available data of COVID-19 cases for the period from 2 April 2020 to 28 October 2020. Semiparametric spatial temporal models were fitted to the number of monthly confirmed cases as an outcome data, with time and district as independent variables, where district was the spatial unit, while accounting for sociodemographic factors. Results The study found significant effects of location and time, with the two interacting. The spatial distribution of COVID-19 risk showed major cities being at greater risk than rural areas. Over time, the COVID-19 risk was increasing then decreasing in most districts with the rural districts being consistently at lower risk. High proportion of elderly people was positively associated with COVID-19 risk (β = 1.272, 95% CI [0.171, 2.370]) than low proportion of elderly people. There was negative association between poverty incidence and COVID-19 risk (β = −0.100, 95% CI [−0.136, −0.065]). Conclusion Future or present strategies to limit the spread of COVID-19 should target major cities and the focus should be on time periods that had shown high risk. Furthermore, the focus should be on elderly and rich people.