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Background: As global progress to reduce malaria transmission continues, it is increasingly important to track changes in malaria incidence rather than prevalence. Risk estimates for Africa have largely underutilized available health management information systems (HMIS) data to monitor trends. This study uses national HMIS data, together with environmental and geographical data, to assess spatial-temporal patterns of malaria incidence at facility catchment level in Uganda, over a recent 5-year period., Methods: Data reported by 3446 health facilities in Uganda, between July 2015 and September 2019, was analysed. To assess the geographic accessibility of the health facilities network, AccessMod was employed to determine a three-hour cost-distance catchment around each facility. Using confirmed malaria cases and total catchment population by facility, an ecological Bayesian conditional autoregressive spatial-temporal Poisson model was fitted to generate monthly posterior incidence rate estimates, adjusted for caregiver education, rainfall, land surface temperature, night-time light (an indicator of urbanicity), and vegetation index., Results: An estimated 38.8 million (95% Credible Interval [CI]: 37.9-40.9) confirmed cases of malaria occurred over the period, with a national mean monthly incidence rate of 20.4 (95% CI: 19.9-21.5) cases per 1000, ranging from 8.9 (95% CI: 8.7-9.4) to 36.6 (95% CI: 35.7-38.5) across the study period. Strong seasonality was observed, with June-July experiencing highest peaks and February-March the lowest peaks. There was also considerable geographic heterogeneity in incidence, with health facility catchment relative risk during peak transmission months ranging from 0 to 50.5 (95% CI: 49.0-50.8) times higher than national average. Both districts and health facility catchments showed significant positive spatial autocorrelation; health facility catchments had global Moran's I = 0.3 (p < 0.001) and districts Moran's I = 0.4 (p < 0.001). Notably, significant clusters of high-risk health facility catchments were concentrated in Acholi, West Nile, Karamoja, and East Central - Busoga regions., Conclusion: Findings showed clear countrywide spatial-temporal patterns with clustering of malaria risk across districts and health facility catchments within high risk regions, which can facilitate targeting of interventions to those areas at highest risk. Moreover, despite high and perennial transmission, seasonality for malaria incidence highlights the potential for optimal and timely implementation of targeted interventions.

Global malaria burden is reducing with effective control interventions, and surveillance is vital to maintain progress. Health management information system (HMIS) data provide a powerful surveillance tool; however, its estimates of burden need to be better understood for effectiveness. We aimed to investigate the relationship between HMIS and cohort incidence rates and identify sources of bias in HMIS-based incidence. Malaria incidence was estimated using HMIS data from 15 health facilities in three subcounties in Uganda. This was compared with a gold standard of representative cohort studies conducted in children aged 0.5 to < 11 years, followed concurrently in these sites. Between October 2011 and September 2014, 153,079 children were captured through HMISs and 995 followed up through enhanced community cohorts in Walukuba, Kihihi, and Nagongera subcounties. Although HMISs substantially underestimated malaria incidence in all sites compared with data from the cohort studies, there was a strong linear relationship between these rates in the lower transmission settings (Walukuba and Kihihi), but not the lowest HMIS performance highest transmission site (Nagongera), with calendar year as a significant modifier. Although health facility accessibility, availability, and recording completeness were associated with HMIS incidence, they were not significantly associated with bias in estimates from any site. Health management information systems still require improvements; however, their strong predictive power of unbiased malaria burden when improved highlights the important role they could play as a cost-effective tool for monitoring trends and estimating impact of control interventions. This has important implications for malaria control in low-resource, high-burden countries.

Background: Malaria control using long-lasting insecticidal nets (LLINs) and indoor residual spraying of insecticide (IRS) has been associated with reduced transmission throughout Africa. However, the impact of transmission reduction on the age distribution of malaria cases remains unclear., Methods: Over a 10-year period (January 2009 to July 2018), outpatient surveillance data from four health facilities in Uganda were used to estimate the impact of control interventions on temporal changes in the age distribution of malaria cases using multinomial regression. Interventions included mass distribution of LLINs at all sites and IRS at two sites., Results: Overall, 896,550 patient visits were included in the study; 211,632 aged < 5 years, 171,166 aged 5-15 years and 513,752 > 15 years. Over time, the age distribution of patients not suspected of malaria and those malaria negative either declined or remained the same across all sites. In contrast, the age distribution of suspected and confirmed malaria cases increased across all four sites. In the two LLINs-only sites, the proportion of malaria cases in < 5 years decreased from 31 to 16% and 35 to 25%, respectively. In the two sites receiving LLINs plus IRS, these proportions decreased from 58 to 30% and 64 to 47%, respectively. Similarly, in the LLINs-only sites, the proportion of malaria cases > 15 years increased from 40 to 61% and 29 to 39%, respectively. In the sites receiving LLINs plus IRS, these proportions increased from 19 to 44% and 18 to 31%, respectively., Conclusions: These findings demonstrate a shift in the burden of malaria from younger to older individuals following implementation of successful control interventions, which has important implications for malaria prevention, surveillance, case management and control strategies.

Test positivity rate (TPR)-confirmed cases per 100 suspected cases tested, and test-confirmed malaria case rate (IR)-cases per 1,000 population, are common indicators used routinely for malaria surveillance. However, few studies have explored relationships between these indicators over time and space. We studied the relationship between these indicators in children aged < 11 years presenting with suspected malaria to the outpatient departments of level IV health centers in Nagongera, Kihihi, and Walukuba in Uganda from October 2011 to June 2016. We evaluated trends in indicators over time and space, and explored associations using multivariable regression models. Overall, 65,710 participants visited the three clinics. Pairwise comparisons of TPR and IR by month showed similar trends, particularly for TPRs < 50% and during low-transmission seasons, but by village, the relationship was complex. Village mean annual TPRs remained constant, whereas IRs drastically declined with increasing distance from the health center. Villages that were furthest away from the health centers (fourth quartile for distance) had significantly lower IRs than nearby villages (first quartile), with an incidence rate ratio of 0.40 in Nagongera (95% CI: 0.23-0.63; P = 0.001), 0.55 in Kihihi (0.40-0.75; P < 0.001), and 0.25 in Walukuba (0.12-0.51; P < 0.001). Regression analysis results emphasized a nonlinear (cubic) relationship between TPR and IR, after accounting for month, village, season, and demographic factors. Results show that the two indicators are highly relevant for monitoring malaria burden. However, interpretation differs with TPR primarily indicating demand for malaria treatment resources and IR indicating malaria risk among health facility catchment populations.

Background: Previous studies have suggested that helminth infection exacerbates malaria, but few existing epidemiological studies adequately control for infection heterogeneities and confounding factors. In this study, we investigate spatial and household heterogeneities, predictors, and consequences of Plasmodium species and hookworm coinfection in rural communities in Uganda., Methods: A cross-sectional study was conducted among 1770 individuals aged 0-88 years in 4 villages. We recorded demographic, socioeconomic, and microgeographic factors during household surveys. We determined malaria parasitemia and hemoglobin concentration and collected stool samples on 2 consecutive days. For data analysis, we used a hierarchical, spatially explicit Bayesian framework., Results: Prevalence of Plasmodium-hookworm coinfection was 15.5% overall and highest among school-aged children. We found strong evidence of spatial and household clustering of coinfection and an enduring positive association between Plasmodium-species and hookworm infection among preschool-aged children (odds ratio [OR], 2.36; 95% Bayesian credible interval [BCI], 1.26-4.30) and adults (OR, 2.09; 95% BCI, 1.35-3.16) but not among school-aged children. Coinfection was associated with lower hemoglobin level only among school-aged children., Conclusions: Plasmodium-hookworm coinfection exhibits marked age dependency and significant spatial and household heterogeneity, and among preschool-aged children and adults, occurs more than would be expected by chance. Such heterogeneities provide insight into factors underlying observed patterns and the design of integrated control strategies.

Many factors influence variation in Plasmodium infection levels, including parasite/host genetics, immunity, and exposure. Here, we examine the roles of host genetics and exposure in determining parasite density, and test whether effects differ with age. Data for 1,711 residents of an eastern Ugandan community were used in pedigree-based variance component analysis. Heritability of parasite density was 13% (P < 0.001) but was not significant after controlling for shared household. Allowing variance components to vary between children (< 16 years) and adults (≥ 16 years) revealed striking age differences; 26% of variation could be explained by additively acting genes in children (P < 0.001), but there was no genetic involvement in adults. Domestic environment did not explain variation in children and explained 5% in adults (P = 0.09). Genetic effects are an important determinant of parasite density in children in this population, consistent with previous quantitative genetic studies of Plasmodium parasitaemia, although differences in environmental exposure play a lesser role.

Background: Malaria is a leading cause of disease burden in Uganda, although surprisingly few contemporary, age-stratified data exist on malaria epidemiology in the country. This report presents results from a total population survey of malaria infection and intervention coverage in a rural area of eastern Uganda, with a specific focus on how risk factors differ between demographic groups in this population., Methods: In 2008, a cross-sectional survey was conducted in four contiguous villages in Mulanda, sub-county in Tororo district, eastern Uganda, to investigate the epidemiology and risk factors of Plasmodium species infection. All permanent residents were invited to participate, with blood smears collected from 1,844 individuals aged between six months and 88 years (representing 78% of the population). Demographic, household and socio-economic characteristics were combined with environmental data using a Geographical Information System. Hierarchical models were used to explore patterns of malaria infection and identify individual, household and environmental risk factors., Results: Overall, 709 individuals were infected with Plasmodium, with prevalence highest among 5-9 year olds (63.5%). Thin films from a random sample of 20% of parasite positive participants showed that 94.0% of infections were Plasmodium falciparum and 6.0% were P. malariae; no other species or mixed infections were seen. In total, 68% of households owned at least one mosquito although only 27% of school-aged children reported sleeping under a net the previous night. In multivariate analysis, infection risk was highest amongst children aged 5-9 years and remained high in older children. Risk of infection was lower for those that reported sleeping under a bed net the previous night and living more than 750 m from a rice-growing area. After accounting for clustering within compounds, there was no evidence for an association between infection prevalence and socio-economic status, and no evidence for spatial clustering., Conclusion: These findings demonstrate that mosquito net usage remains inadequate and is strongly associated with risk of malaria among school-aged children. Infection risk amongst adults is influenced by proximity to potential mosquito breeding grounds. Taken together, these findings emphasize the importance of increasing net coverage, especially among school-aged children.