Influence of Climatic Factors on Malaria Epidemic in Gulu District, Northern Uganda: A 10-Year Retrospective Study

Climate Drivers of Malaria Transmission Seasonality and Their Relative Importance in Sub-Saharan Africa

A new database of the Entomological Inoculation Rate (EIR) was used to directly link the risk of infectious mosquito bites to climate in Sub-Saharan Africa. Applying a statistical mixed model framework to high-quality monthly EIR measurements collected from field campaigns in Sub-Saharan Africa, we analyzed the impact of rainfall and temperature seasonality on EIR seasonality and determined important climate drivers of malaria seasonality across varied climate settings in the region. We observed that seasonal malaria transmission was within a temperature window of 15°C-40°C and was sustained if average temperature was well above 15°C or below 40°C. Monthly maximum rainfall for seasonal malaria transmission did not exceed 600 in west Central Africa, and 400 mm in the Sahel, Guinea Savannah, and East Africa. Based on a multi-regression model approach, rainfall and temperature seasonality were found to be significantly associated with malaria seasonality in all parts of Sub-Saharan Africa except in west Central Africa. Topography was found to have significant influence on which climate variable is an important determinant of malaria seasonality in East Africa. Seasonal malaria transmission onset lags behind rainfall only at markedly seasonal rainfall areas such as Sahel and East Africa; elsewhere, malaria transmission is year-round. High-quality EIR measurements can usefully supplement established metrics for seasonal malaria. The study's outcome is important for the improvement and validation of weather-driven dynamical mathematical malaria models that directly simulate EIR. Our results can contribute to the development of fit-for-purpose weather-driven malaria models to support health decision-making in the fight to control or eliminate malaria in Sub-Saharan Africa.

Malaria outbreak facilitated by increased mosquito breeding sites near houses and cessation of indoor residual spraying, Kole district, Uganda, January-June 2019

BACKGROUND

In June 2019, surveillance data from the Uganda's District Health Information System revealed an outbreak of malaria in Kole District. Analysis revealed that cases had exceeded the outbreak threshold from January 2019. The Ministry of Health deployed our team to investigate the areas and people affected, identify risk factors for disease transmission, and recommend control and prevention measures.

METHODS

We conducted an outbreak investigation involving a matched case-control study. We defined a confirmed case as a positive malaria test in a resident of Aboke, Akalo, Alito, and Bala sub-counties of Kole District January-June 2019. We identified cases by reviewing outpatient health records. Exposures were assessed in a 1:1 matched case-control study (n = 282) in Aboke sub-county. We selected cases systematically from 10 villages using probability proportionate to size and identified age- and village-matched controls. We conducted entomological and environmental assessments to identify mosquito breeding sites. We plotted epidemic curves and overlaid rainfall, and indoor residual spraying (IRS). Case-control exposures were combined into: breeding site near house, proximity to swamp and breeding site, and proximity to swamp; these were compared to no exposure in a logistic regression analysis.

RESULTS

Of 18,737 confirmed case-patients (AR = 68/1,000), Aboke sub-county residents (AR = 180/1,000), children < 5 years (AR = 94/1,000), and females (AR = 90/1,000) were most affected. Longitudinal analysis of surveillance data showed decline in cases after an IRS campaign in 2017 but an increase after IRS cessation in 2018-2019. Overlay of rainfall and case data showed two malaria upsurges during 2019, occurring 35-42 days after rainfall increases. Among 141 case-patients and 141 controls, the combination of having mosquito breeding sites near the house and proximity to swamps increased the odds of malaria 6-fold (OR = 6.6, 95% CI = 2.24-19.7) compared to no exposures. Among 84 abandoned containers found near case-patients' and controls' houses, 14 (17%) had mosquito larvae. Adult Anopheles mosquitoes, larvae, pupae, and pupal exuviae were identified near affected houses.

CONCLUSION

Stagnant water formed by increased rainfall likely provided increased breeding sites that drove this outbreak. Cessation of IRS preceded the malaria upsurges. We recommend re-introduction of IRS and removal of mosquito breeding sites in Kole District.

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.

Validity of caregivers' reports on prior use of antibacterials in children under five years presenting to health facilities in Gulu, northern Uganda

INTRODUCTION

Given the frequent initiation of antibacterial treatment at home by caregivers of children under five years in low-income countries, there is a need to find out whether caregivers' reports of prior antibacterial intake by their children before being brought to the healthcare facility are accurate. The aim of this study was to describe and validate caregivers' reported use of antibacterials by their children prior to seeking care at the healthcare facility.

METHODS

A cross sectional study was conducted among children under five years seeking care at healthcare facilities in Gulu district, northern Uganda. Using a researcher administered questionnaire, data were obtained from caregivers regarding reported prior antibacterial intake in their children. These reports were validated by comparing them to common antibacterial agents detected in blood and urine samples from the children using liquid chromatography with tandem mass spectrometry (LC-MS/MS) methods.

RESULTS

A total of 355 study participants had a complete set of data on prior antibacterial use collected using both self-report and LC-MS/MS. Of the caregivers, 14.4% (51/355, CI: 10.9-18.5%) reported giving children antibacterials prior to visiting the healthcare facility. However, LC-MS/MS detected antibacterials in blood and urine samples in 63.7% (226/355, CI: 58.4-68.7%) of the children. The most common antibacterials detected from the laboratory analysis were cotrimoxazole (29%, 103/355), ciprofloxacin (13%, 46/355), and metronidazole (9.9%, 35/355). The sensitivity, specificity, positive predictive value (PPV), negative predictive value and agreement of self-reported antibacterial intake prior to healthcare facility visit were 17.3% (12.6-22.8), 90.7% (84.3-95.1), 76.5% (62.5-87.2), 38.5% (33.0-44.2) and 43.9% (k 0.06) respectively.

CONCLUSION

There is low validity of caregivers' reports on prior intake of antibacterials by these children. There is need for further research to understand the factors associated with under reporting of prior antibacterial use.

Spatial Modelling of Malaria in South of Iran in Line with the Implementation of the Malaria Elimination Program: A Bayesian Poisson-Gamma Random Field Model

Background:

Malaria is the third most important infectious disease in the world. WHO propose programs for controlling and elimination of the disease. Malaria elimination program has begun in first phase in Iran from 2010. Climate factors play an important role in transmission and occurrence of malaria infection. The main goal is to investigate the spatial distribution of incidence of malaria during April 2011 to March 2018 in Hormozgan Province and its association with climate covariates.

Methods:

The data included 882 confirmed cases gathered from CDC in Hormozgan University of Medical Sciences. A Poisson-Gamma Random field model with Bayesian approach was used for modeling the data and produces the smoothed standardized incidence rate (SIR).

Results:

The SIR for malaria ranged from 0 (Abu Musa and Haji Abad districts) to 280.57 (Bandar–e-Jask). Based on model, temperature (RR= 2.29; 95% credible interval: (1.92–2.78)) and humidity (RR= 1.04; 95% credible interval: (1.03–1.06)) had positive effect on malaria incidence, but rainfall (RR= 0.92; 95% credible interval: (0.90–0.95)) had negative impact. Also, smoothed map represent hot spots in the east of the province and in Qeshm Island.

Conclusion:

Based on the analysis of the study results, it was found that the ecological conditions of the region (temperature, humidity and rainfall) and population displacement play an important role in the incidence of malaria. Therefore, the malaria surveillance system should continue to be active in the region, focusing on high-risk areas of malaria.

Spatial-temporal patterns of malaria incidence in Uganda using HMIS data from 2015 to 2019

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.

Malaria Outbreak Facilitated by Appearance of Vector-Breeding Sites after Heavy Rainfall and Inadequate Preventive Measures: Nwoya District, Northern Uganda, February-May 2018

Background

Malaria is a leading cause of morbidity and mortality in Uganda. In April 2018, malaria cases surged in Nwoya District, Northern Uganda, exceeding expected limits and thereby requiring epidemic response. We investigated this outbreak to estimate its magnitude, identify exposure factors for transmission, and recommend evidence-based control measures.

Methods

We defined a malaria case as onset of fever in a resident of Anaka subcounty, Koch Goma subcounty, and Nwoya Town Council, Nwoya District, with a positive rapid diagnostic test or microscopy for malaria from 1 February to 25 May 2018. We reviewed medical records in all health facilities of affected subcounties to find cases. In a case-control study, we compared exposure factors between case-persons and asymptomatic controls matched by age and village. We also conducted entomological assessments on vector density and behavior.

Results

We identified 3,879 case-persons (attack rate [AR] = 6.5%) and two deaths (case-fatality rate = 5.2/10,000). Females (AR = 8.1%) were more affected than males (AR = 4.7%) (p < 0.0001). Of all age groups, 5–18 years (AR = 8.4%) were most affected. Heavy rain started in early March 2018, and a propagated outbreak followed in the first week of April 2018. In the case-control study, 55% (59/107) of case-persons and 18% (19/107) of controls had stagnant water around households for several days following rainfall (OR_M-H = 5.6, 95% CI = 3.0–11); 25% (27/107) of case-persons and 51% (55/107) of controls wore full extremity covering clothes during evening hours (OR_M-H = 0.30, 95% CI = 0.20–0.60); 71% (76/107) of case-persons and 85% (91/107) of controls slept under a long-lasting insecticide-treated net (LLIN) 14 days before symptom onset (OR_M-H = 0.43, 95% CI = 0.22–0.85); 37% (40/107) of case-persons and 52% (56/107) of controls had access to at least one LLIN per 2 household members (OR_M-H = 0.54, 95% CI = 0.30–0.97). Entomological assessment indicated active breeding sites in the entire study area; Anopheles gambiae sensu lato species were the predominant vector.

Conclusion

Increased vector-breeding sites after heavy rainfall and inadequate malaria preventive measures were found to have contributed to this outbreak. We recommended increasing coverage for LLINs and larviciding breeding sites in the area.

Recovery and stable persistence of chloroquine sensitivity in Plasmodium falciparum parasites after its discontinued use in Northern Uganda

Background

Usage of chloroquine was discontinued from the treatment of Plasmodium falciparum infection in almost all endemic regions because of global spread of resistant parasites. Since the first report in Malawi, numerous epidemiological studies have demonstrated that the discontinuance led to re-emergence of chloroquine-susceptible P. falciparum, suggesting a possible role in future malaria control. However, most studies were cross-sectional, with few studies looking at the persistence of chloroquine recovery in long term. This study fills the gap by providing, for a period of at least 6 years, proof of persistent re-emergence/stable recovery of susceptible parasite populations using both molecular and phenotypic methods.

Methods

Ex vivo drug-susceptibility assays to chloroquine (n = 319) and lumefantrine (n = 335) were performed from 2013 to 2018 in Gulu, Northern Uganda, where chloroquine had been removed from the official malaria treatment regimen since 2006. Genotyping of pfcrt and pfmdr1 was also performed.

Results

Chloroquine resistance (≥ 100 nM) was observed in only 3 (1.3%) samples. Average IC₅₀ values for chloroquine were persistently low throughout the study period (17.4–24.9 nM). Parasites harbouring pfcrt K76 alleles showed significantly lower IC₅₀s to chloroquine than the parasites harbouring K76T alleles (21.4 nM vs. 43.1 nM, p-value = 3.9 × 10⁻⁸). Prevalence of K76 alleles gradually increased from 71% in 2013 to 100% in 2018.

Conclusion

This study found evidence of stable persistence of chloroquine susceptibility with the fixation of pfcrt K76 in Northern Uganda after discontinuation of chloroquine in the region. Accumulation of similar evidence in other endemic areas in Uganda could open channels for possible future re-use of chloroquine as an option for malaria treatment or prevention.

Identification and analysis of vulnerable populations for malaria based on K-prototypes clustering

Malaria is a serious public health threat in Yunnan Province of China and has been frequently reported in some endemic regions, such as Tengchong County, with high morbidity. It is essential to analyze the characteristics of malaria cases and identify vulnerable populations. Previous studies about vulnerable populations have mostly used a statistical grouping method to count frequence from a single aspect rather than defined clustered groups. Based on descriptive analysis of the temporal variation and demographic structure of the populations with malaria infection, we used a k-prototypes clustering algorithm to cluster vulnerable populations in Tengchong County in three dimensions, according to sex, age, and occupation. The results indicated that a high incidence of malaria occurred mainly in young male farmers and young or middle-aged male migrant workers. Imported cases, low education level, lack of mosquito bite prevention, and risk behaviors contributed to the high malaria incidence in these groups. Double verification ensured the reliability of this method and reasonability of the results. In addition, we highlighted the importance of targeting prevention and control of malaria for vulnerable groups. We provided suggestions of policies and measures to be implemented by regional governments and at household and individual levels for farmers and migrant workers respectively. Using the k-prototypes clustering algorithm, we efficiently identified those populations at greatest risk of malaria infection. Our results may serve as scientific guidance for targeted malaria prevention and control in Yunnan Province.