Evolution of Partial Resistance to Artemisinins in Malaria Parasites in Uganda.

BACKGROUND: Partial resistance of Plasmodium falciparum to the artemisinin component of artemisinin-based combination therapies, the most important malaria drugs, emerged in Southeast Asia and now threatens East Africa. Partial resistance, which manifests as delayed clearance after therapy, is mediated principally by mutations in the kelch protein K13 (PfK13). Limited longitudinal data are available on the emergence and spread of artemisinin resistance in Africa. METHODS: We performed annual surveillance among patients who presented with uncomplicated malaria at 10 to 16 sites across Uganda from 2016 through 2022. We sequenced the gene encoding kelch 13 (pfk13) and analyzed relatedness using molecular methods. We assessed malaria metrics longitudinally in eight Ugandan districts from 2014 through 2021. RESULTS: By 2021-2022, the prevalence of parasites with validated or candidate resistance markers reached more than 20% in 11 of the 16 districts where surveillance was conducted. The PfK13 469Y and 675V mutations were seen in far northern Uganda in 2016-2017 and increased and spread thereafter, reaching a combined prevalence of 10 to 54% across much of northern Uganda, with spread to other regions. The 469F mutation reached a prevalence of 38 to 40% in one district in southwestern Uganda in 2021-2022. The 561H mutation, previously described in Rwanda, was first seen in southwestern Uganda in 2021, reaching a prevalence of 23% by 2022. The 441L mutation reached a prevalence of 12 to 23% in three districts in western Uganda in 2022. Genetic analysis indicated local emergence of mutant parasites independent of those in Southeast Asia. The emergence of resistance was observed predominantly in areas where effective malaria control had been discontinued or transmission was unstable. CONCLUSIONS: Data from Uganda showed the emergence of partial resistance to artemisinins in multiple geographic locations, with increasing prevalence and regional spread over time. (Funded by the National Institutes of Health.).

East Africa International Center of Excellence for Malaria Research: Impact on Malaria Policy in Uganda.

Malaria is the leading cause of disease burden in sub-Saharan Africa. In 2010, the East Africa International Center of Excellence for Malaria Research, also known as the Program for Resistance, Immunology, Surveillance, and Modeling of Malaria (PRISM), was established to provide a comprehensive approach to malaria surveillance in Uganda. We instituted cohort studies and a robust malaria and entomological surveillance network at selected public health facilities that have provided a platform for monitoring trends in malaria morbidity and mortality, tracking the impact of malaria control interventions (indoor residual spraying of insecticide [IRS], use of long-lasting insecticidal nets [LLINs], and case management with artemisinin-based combination therapies [ACTs]), as well as monitoring of antimalarial drug and insecticide resistance. PRISM studies have informed Uganda's malaria treatment policies, guided selection of LLINs for national distribution campaigns, and revealed widespread pyrethroid resistance, which led to changes in insecticides delivered through IRS. Our continuous engagement and interaction with policy makers at the Ugandan Ministry of Health have enabled PRISM to share evidence, best practices, and lessons learned with key malaria stakeholders, participate in malaria control program reviews, and contribute to malaria policy and national guidelines. Here, we present an overview of interactions between PRISM team members and Ugandan policy makers to demonstrate how PRISM's research has influenced malaria policy and control in Uganda.

LLIN Evaluation in Uganda Project (LLINEUP2)-Factors associated with coverage and use of long­lasting insecticidal nets following the 2020-21 national mass distribution campaign: a cross-sectional survey of 12 districts.

BACKGROUND: In 2020-2021, long-lasting insecticidal nets (LLINs) were distributed nationwide in Uganda during the COVID-19 pandemic. A cross-sectional survey was conducted in 12 districts to evaluate the impact of the campaign 1-5 months after LLIN distribution. METHODS: During April-May 2021, households were randomly selected from target areas (1-7 villages) surrounding 12 government-run health facilities established as Malaria Reference Centres; at least 50 households were enrolled per cluster. Outcomes included household ownership of LLINs distributed through the universal coverage campaign (UCC) (at least one UCC LLIN), adequate coverage of UCC LLINs (at least one UCC LLIN per 2 residents), and use of LLINs (resident slept under a LLIN the previous night). Multivariate logistic regression models were used to identify household- and individual-level factors associated with outcomes, controlling for clustering around health facilities. RESULTS: In total, 634 households, with 3342 residents and 1631 bed-nets, were included. Most households (93.4%) owned at least 1 UCC LLIN, but only 56.8% were adequately covered by UCC LLINs. In an adjusted analysis, the factor most strongly associated with adequate coverage by UCC LLINs was fewer household residents (1-4 vs 7-14; adjusted odds ratio [aOR] 12.96, 95% CI 4.76-35.26, p < 0.001; 5-6 vs 7-14 residents; aOR 2.99, 95% CI 1.21-7.42, p = 0.018). Of the 3166 residents of households that owned at least one UCC LLIN, only 1684 (53.2%) lived in adequately covered households; 89.9% of these used an LLIN the previous night, compared to 1034 (69.8%) of 1482 residents living in inadequately covered households. In an adjusted analysis, restricted to residents of inadequately covered households, LLIN use was higher in children under-five than those aged 5-15 years (aOR 3.04, 95% CI 2.08-4.46, p < 0.001), and higher in household heads than distantly-related residents (aOR 3.94, 95% CI 2.38-6.51, p < 0.001). CONCLUSIONS: Uganda's 2021-21 campaign was successful, despite the COVID-19 pandemic. In future campaigns, strategies should be adopted to ensure high LLIN coverage, particularly for larger households. A better understanding of the drivers of LLIN use within households is needed to guide future interventions, educational messages, and behaviour change communication strategies; school-aged children and distantly-related residents appear vulnerable and could be targeted.

Gender difference in the incidence of malaria diagnosed at public health facilities in Uganda.

BACKGROUND: Routine malaria surveillance data in Africa primarily come from public health facilities reporting to national health management information systems. Although information on gender is routinely collected from patients presenting to these health facilities, stratification of malaria surveillance data by gender is rarely done. This study evaluated gender difference among patients diagnosed with parasitological confirmed malaria at public health facilities in Uganda. METHODS: This study utilized individual level patient data collected from January 2020 through April 2021 at 12 public health facilities in Uganda and cross-sectional surveys conducted in target areas around these facilities in April 2021. Associations between gender and the incidence of malaria and non-malarial visits captured at the health facilities from patients residing within the target areas were estimated using poisson regression models controlling for seasonality. Associations between gender and data on health-seeking behaviour from the cross-sectional surveys were estimated using poisson regression models controlling for seasonality. RESULTS: Overall, incidence of malaria diagnosed per 1000 person years was 735 among females and 449 among males (IRR = 1.72, 95% CI 1.68-1.77, p < 0.001), with larger differences among those 15-39 years (IRR = 2.46, 95% CI 2.34-2.58, p < 0.001) and over 39 years (IRR = 2.26, 95% CI 2.05-2.50, p < 0.001) compared to those under 15 years (IRR = 1.46, 95% CI 1.41-1.50, p < 0.001). Female gender was also associated with a higher incidence of visits where malaria was not suspected (IRR = 1.77, 95% CI 1.71-1.83, p < 0.001), with a similar pattern across age strata. These associations were consistent across the 12 individual health centres. From the cross-sectional surveys, females were more likely than males to report fever in the past 2 weeks and seek care at the local health centre (7.5% vs. 4.7%, p = 0.001) with these associations significant for those 15-39 years (RR = 2.49, 95% CI 1.17-5.31, p = 0.018) and over 39 years (RR = 2.56, 95% CI 1.00-6.54, p = 0.049). CONCLUSIONS: Females disproportionately contribute to the burden of malaria diagnosed at public health facilities in Uganda, especially once they reach childbearing age. Contributing factors included more frequent visits to these facilities independent of malaria and a higher reported risk of seeking care at these facilities for febrile illnesses.

Malaria hospitalisation in East Africa: age, phenotype and transmission intensity.

BACKGROUND: Understanding the age patterns of disease is necessary to target interventions to maximise cost-effective impact. New malaria chemoprevention and vaccine initiatives target young children attending routine immunisation services. Here we explore the relationships between age and severity of malaria hospitalisation versus malaria transmission intensity. METHODS: Clinical data from 21 surveillance hospitals in East Africa were reviewed. Malaria admissions aged 1 month to 14 years from discrete administrative areas since 2006 were identified. Each site-time period was matched to a model estimated community-based age-corrected parasite prevalence to provide predictions of prevalence in childhood (PfPR2-10). Admission with all-cause malaria, severe malaria anaemia (SMA), respiratory distress (RD) and cerebral malaria (CM) were analysed as means and predicted probabilities from Bayesian generalised mixed models. RESULTS: 52,684 malaria admissions aged 1 month to 14 years were described at 21 hospitals from 49 site-time locations where PfPR2-10 varied from < 1 to 48.7%. Twelve site-time periods were described as low transmission (PfPR2-10 < 5%), five low-moderate transmission (PfPR2-10 5-9%), 20 moderate transmission (PfPR2-10 10-29%) and 12 high transmission (PfPR2-10 ≥ 30%). The majority of malaria admissions were below 5 years of age (69-85%) and rare among children aged 10-14 years (0.7-5.4%) across all transmission settings. The mean age of all-cause malaria hospitalisation was 49.5 months (95% CI 45.1, 55.4) under low transmission compared with 34.1 months (95% CI 30.4, 38.3) at high transmission, with similar trends for each severe malaria phenotype. CM presented among older children at a mean of 48.7 months compared with 39.0 months and 33.7 months for SMA and RD, respectively. In moderate and high transmission settings, 34% and 42% of the children were aged between 2 and 23 months and so within the age range targeted by chemoprevention or vaccines. CONCLUSIONS: Targeting chemoprevention or vaccination programmes to areas where community-based parasite prevalence is ≥10% is likely to match the age ranges covered by interventions (e.g. intermittent presumptive treatment in infancy to children aged 2-23 months and current vaccine age eligibility and duration of efficacy) and the age ranges of highest disease burden.

Resurgence of malaria in Uganda despite sustained indoor residual spraying and repeated long lasting insecticidal net distributions.

Five years of sustained indoor residual spraying (IRS) of insecticide from 2014 to 2019, first using a carbamate followed by an organophosphate, was associated with a marked reduction in the incidence of malaria in five districts of Uganda. We assessed changes in malaria incidence over an additional 21 months, corresponding to a change in IRS formulations using clothianidin with and without deltamethrin. Using enhanced health facility surveillance data, our objectives were to 1) estimate the impact of IRS on monthly malaria case counts at five surveillance sites over a 6.75 year period, and 2) compare monthly case counts at five facilities receiving IRS to ten facilities in neighboring districts not receiving IRS. For both objectives, we specified mixed effects negative binomial regression models with random intercepts for surveillance site adjusting for rainfall, season, care-seeking, and malaria diagnostic. Following the implementation of IRS, cases were 84% lower in years 4-5 (adjusted incidence rate ratio [aIRR] = 0.16, 95% CI 0.12-0.22), 43% lower in year 6 (aIRR = 0.57, 95% CI 0.44-0.74), and 39% higher in the first 9 months of year 7 (aIRR = 1.39, 95% CI 0.97-1.97) compared to pre-IRS levels. Cases were 67% lower in IRS sites than non-IRS sites in year 6 (aIRR = 0.33, 95% CI 0.17-0.63) but 38% higher in the first 9 months of year 7 (aIRR = 1.38, 95% CI 0.90-2.11). We observed a resurgence in malaria to pre-IRS levels despite sustained IRS. The timing of this resurgence corresponded to a change of active ingredient. Further research is needed to determine causality.

Impact of COVID-19 on routine malaria indicators in rural Uganda: an interrupted time series analysis.

BACKGROUND: In March 2020, the government of Uganda implemented a strict lockdown policy in response to the COVID-19 pandemic. Interrupted time series analysis (ITSA) was performed to assess whether major changes in outpatient attendance, malaria burden, and case management occurred after the onset of the COVID-19 epidemic in rural Uganda. METHODS: Individual level data from all outpatient visits collected from April 2017 to March 2021 at 17 facilities were analysed. Outcomes included total outpatient visits, malaria cases, non-malarial visits, proportion of patients with suspected malaria, proportion of patients tested using rapid diagnostic tests (RDTs), and proportion of malaria cases prescribed artemether-lumefantrine (AL). Poisson regression with generalized estimating equations and fractional regression was used to model count and proportion outcomes, respectively. Pre-COVID trends (April 2017-March 2020) were used to predict the'expected' trend in the absence of COVID-19 introduction. Effects of COVID-19 were estimated over two six-month COVID-19 time periods (April 2020-September 2020 and October 2020-March 2021) by dividing observed values by expected values, and expressed as ratios. RESULTS: A total of 1,442,737 outpatient visits were recorded. Malaria was suspected in 55.3% of visits and 98.8% of these had a malaria diagnostic test performed. ITSA showed no differences between observed and expected total outpatient visits, malaria cases, non-malarial visits, or proportion of visits with suspected malaria after COVID-19 onset. However, in the second six months of the COVID-19 time period, there was a smaller mean proportion of patients tested with RDTs compared to expected (relative prevalence ratio (RPR) = 0.87, CI (0.78-0.97)) and a smaller mean proportion of malaria cases prescribed AL (RPR = 0.94, CI (0.90-0.99)). CONCLUSIONS: In the first year after the COVID-19 pandemic arrived in Uganda, there were no major effects on malaria disease burden and indicators of case management at these 17 rural health facilities, except for a modest decrease in the proportion of RDTs used for malaria diagnosis and the mean proportion of malaria cases prescribed AL in the second half of the COVID-19 pandemic year. Continued surveillance will be essential to monitor for changes in trends in malaria indicators so that Uganda can quickly and flexibly respond to challenges imposed by COVID-19.

Associations between environmental covariates and temporal changes in malaria incidence in high transmission settings of Uganda: a distributed lag nonlinear analysis.

BACKGROUND: Environmental factors such as temperature, rainfall, and vegetation cover play a critical role in malaria transmission. However, quantifying the relationships between environmental factors and measures of disease burden relevant for public health can be complex as effects are often non-linear and subject to temporal lags between when changes in environmental factors lead to changes in malaria incidence. The study investigated the effect of environmental covariates on malaria incidence in high transmission settings of Uganda. METHODS: This study leveraged data from seven malaria reference centres (MRCs) located in high transmission settings of Uganda over a 24-month period. Estimates of monthly malaria incidence (MI) were derived from MRCs' catchment areas. Environmental data including monthly temperature, rainfall, and normalized difference vegetation index (NDVI) were obtained from remote sensing sources. A distributed lag nonlinear model was used to investigate the effect of environmental covariates on malaria incidence. RESULTS: Overall, the median (range) monthly temperature was 30 °C (26-47), rainfall 133.0 mm (3.0-247), NDVI 0.66 (0.24-0.80) and MI was 790 per 1000 person-years (73-3973). Temperature of 35 °C was significantly associated with malaria incidence compared to the median observed temperature (30 °C) at month lag 2 (IRR: 2.00, 95% CI: 1.42-2.83) and the increased cumulative IRR of malaria at month lags 1-4, with the highest cumulative IRR of 8.16 (95% CI: 3.41-20.26) at lag-month 4. Rainfall of 200 mm significantly increased IRR of malaria compared to the median observed rainfall (133 mm) at lag-month 0 (IRR: 1.24, 95% CI: 1.01-1.52) and the increased cumulative IRR of malaria at month lags 1-4, with the highest cumulative IRR of 1.99(95% CI: 1.22-2.27) at lag-month 4. Average NVDI of 0.72 significantly increased the cumulative IRR of malaria compared to the median observed NDVI (0.66) at month lags 2-4, with the highest cumulative IRR of 1.57(95% CI: 1.09-2.25) at lag-month 4. CONCLUSIONS: In high-malaria transmission settings, high values of environmental covariates were associated with increased cumulative IRR of malaria, with IRR peaks at variable lag times. The complex associations identified are valuable for designing strategies for early warning, prevention, and control of seasonal malaria surges and epidemics.

The impact of stopping and starting indoor residual spraying on malaria burden in Uganda.

The scale-up of malaria control efforts has led to marked reductions in malaria burden over the past twenty years, but progress has slowed. Implementation of indoor residual spraying (IRS) of insecticide, a proven vector control intervention, has been limited and difficult to sustain partly because questions remain on its added impact over widely accepted interventions such as bed nets. Using data from 14 enhanced surveillance health facilities in Uganda, a country with high bed net coverage yet high malaria burden, we estimate the impact of starting and stopping IRS on changes in malaria incidence. We show that stopping IRS was associated with a 5-fold increase in malaria incidence within 10 months, but reinstating IRS was associated with an over 5-fold decrease within 8 months. In areas where IRS was initiated and sustained, malaria incidence dropped by 85% after year 4. IRS could play a critical role in achieving global malaria targets, particularly in areas where progress has stalled.

Relationships between test positivity rate, total laboratory confirmed cases of malaria, and malaria incidence in high burden settings of Uganda: an ecological analysis.

BACKGROUND: Malaria surveillance is critical for monitoring changes in malaria morbidity over time. National Malaria Control Programmes often rely on surrogate measures of malaria incidence, including the test positivity rate (TPR) and total laboratory confirmed cases of malaria (TCM), to monitor trends in malaria morbidity. However, there are limited data on the accuracy of TPR and TCM for predicting temporal changes in malaria incidence, especially in high burden settings. METHODS: This study leveraged data from 5 malaria reference centres (MRCs) located in high burden settings over a 15-month period from November 2018 through January 2020 as part of an enhanced health facility-based surveillance system established in Uganda. Individual level data were collected from all outpatients including demographics, laboratory test results, and village of residence. Estimates of malaria incidence were derived from catchment areas around the MRCs. Temporal relationships between monthly aggregate measures of TPR and TCM relative to estimates of malaria incidence were examined using linear and exponential regression models. RESULTS: A total of 149,739 outpatient visits to the 5 MRCs were recorded. Overall, malaria was suspected in 73.4% of visits, 99.1% of patients with suspected malaria received a diagnostic test, and 69.7% of those tested for malaria were positive. Temporal correlations between monthly measures of TPR and malaria incidence using linear and exponential regression models were relatively poor, with small changes in TPR frequently associated with large changes in malaria incidence. Linear regression models of temporal changes in TCM provided the most parsimonious and accurate predictor of changes in malaria incidence, with adjusted R2 values ranging from 0.81 to 0.98 across the 5 MRCs. However, the slope of the regression lines indicating the change in malaria incidence per unit change in TCM varied from 0.57 to 2.13 across the 5 MRCs, and when combining data across all 5 sites, the R2 value reduced to 0.38. CONCLUSIONS: In high malaria burden areas of Uganda, site-specific temporal changes in TCM had a strong linear relationship with malaria incidence and were a more useful metric than TPR. However, caution should be taken when comparing changes in TCM across sites.