Determinants of malaria testing at health facilities: the case of Uganda.

BACKGROUND: The World Health Organization (WHO) recommends prompt malaria diagnosis with either microscopy or malaria rapid diagnostic tests (RDTs) and treatment with an effective anti-malarial, as key interventions to control malaria. However, in sub-Saharan Africa, malaria diagnosis is still often influenced by clinical symptoms, with patients and care providers often interpreting all fevers as malaria. The Ministry of Health in Uganda defines suspected malaria cases as those with a fever. A target of conducting testing for at least 75% of those suspected to have malaria was established by the National Malaria Reduction Strategic Plan 2014-2020. METHODS: This study investigated factors that affect malaria testing at health facilities in Uganda using data collected in March/April 2017 in a cross-sectional survey of health facilities from the 52 districts that are supported by the US President's Malaria Initiative (PMI). The study assessed health facility capacity to provide quality malaria care and treatment. Data were collected from all 1085 public and private health facilities in the 52 districts. Factors assessed included supportive supervision, availability of malaria management guidelines, laboratory infrastructure, and training health workers in the use of malaria rapid diagnostic test (RDT). Survey data were matched with routinely collected health facility malaria data obtained from the district health information system Version-2 (DHIS2). Associations between testing at least 75% of suspect malaria cases with several factors were examined using multivariate logistic regression. RESULTS: Key malaria commodities were widely available; 92% and 85% of the health facilities reported availability of RDTs and artemether-lumefantrine, respectively. Overall, 933 (86%) of the facilities tested over 75% of patients suspected to have malaria. Predictors of meeting the testing target were: supervision in the last 6 months (OR: 1.72, 95% CI 1.04-2.85) and a health facility having at least one health worker trained in the use of RDTs (OR: 1.62, 95% CI 1.04-2.55). CONCLUSION: The study findings underscore the need for malaria control programmes to provide regular supportive supervision to health facilities and train health workers in the use of RDTs.

Efficacy and safety of artemether-lumefantrine and dihydroartemisinin-piperaquine for the treatment of uncomplicated Plasmodium falciparum malaria and prevalence of molecular markers associated with artemisinin and partner drug resistance in Uganda.

BACKGROUND: In Uganda, artemether-lumefantrine (AL) is first-line therapy and dihydroartemisinin-piperaquine (DP) second-line therapy for the treatment of uncomplicated malaria. This study evaluated the efficacy and safety of AL and DP in the management of uncomplicated falciparum malaria and measured the prevalence of molecular markers of resistance in three sentinel sites in Uganda from 2018 to 2019. METHODS: This was a randomized, open-label, phase IV clinical trial. Children aged 6 months to 10 years with uncomplicated falciparum malaria were randomly assigned to treatment with AL or DP and followed for 28 and 42 days, respectively. Genotyping was used to distinguish recrudescence from new infection, and a Bayesian algorithm was used to assign each treatment failure a posterior probability of recrudescence. For monitoring resistance, Pfk13 and Pfmdr1 genes were Sanger sequenced and plasmepsin-2 copy number was assessed by qPCR. RESULTS: There were no early treatment failures. The uncorrected 28-day cumulative efficacy of AL ranged from 41.2 to 71.2% and the PCR-corrected cumulative 28-day efficacy of AL ranged from 87.2 to 94.4%. The uncorrected 28-day cumulative efficacy of DP ranged from 95.8 to 97.9% and the PCR-corrected cumulative 28-day efficacy of DP ranged from 98.9 to 100%. The uncorrected 42-day efficacy of DP ranged from 73.5 to 87.4% and the PCR-corrected 42-day efficacy of DP ranged from 92.1 to 97.5%. There were no reported serious adverse events associated with any of the regimens. No resistance-associated mutations in the Pfk13 gene were found in the successfully sequenced samples. In the AL arm, the NFD haplotype (N86Y, Y184F, D1246Y) was the predominant Pfmdr1 haplotype, present in 78 of 127 (61%) and 76 of 110 (69%) of the day 0 and day of failure samples, respectively. All the day 0 samples in the DP arm had one copy of the plasmepsin-2 gene. CONCLUSIONS: DP remains highly effective and safe for the treatment of uncomplicated malaria in Uganda. Recurrent infections with AL were common. In Busia and Arua, the 95% confidence interval for PCR-corrected AL efficacy fell below 90%. Further efficacy monitoring for AL, including pharmacokinetic studies, is recommended. Trial registration The trail was also registered with the ISRCTN registry with study Trial No. PACTR201811640750761.

Rapid shifts in the age-specific burden of malaria following successful control interventions in four regions of Uganda.

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.

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.

Comparative Efficacy of Artemether-Lumefantrine and Dihydroartemisinin-Piperaquine for the Treatment of Uncomplicated Malaria in Ugandan Children.

BACKGROUND: In Uganda, artemether-lumefantrine (AL) and dihydroartemisinin-piperaquine (DHA-PQ) showed excellent treatment efficacy for uncomplicated malaria in prior trials. Because the frequency of resistance to artemisinins and piperaquine is increasing in Southeast Asia and the prevalence of Plasmodium falciparum polymorphisms associated with resistance has changed, we reassessed treatment efficacies at 3 sites in Uganda. METHODS: For this randomized, single-blinded clinical trial, children aged 6-59 months with uncomplicated falciparum malaria were assigned treatment with AL or DHA-PQ and followed for 42 days. Primary end points were risks of recurrent parasitemia, either unadjusted or adjusted to distinguish recrudescence from new infection. We assessed selection by study regimens of relevant P. falciparum genetic polymorphisms associated with drug resistance. RESULTS: Of 599 patients enrolled, 578 completed follow-up. There were no early treatment failures. The risk of recurrent parasitemia was lower with DHA-PQ as compared to AL at all 3 sites at 42 days (26.0% vs 47.0%; P < .001). Recrudescent infections were uncommon in both the DHA-PQ and AL arms (1.1% and 2.2%, respectively; P = .25). Neither regimen selected for pfcrt or pfmdr1 polymorphisms associated with drug resistance. CONCLUSIONS: AL and DHA-PQ remain effective for the treatment of malaria in Uganda. Neither regimen selected for genetic polymorphisms associated with drug resistance. CLINICAL TRIALS REGISTRATION: ISRCTN15793046.

Resurgence of Malaria Following Discontinuation of Indoor Residual Spraying of Insecticide in an Area of Uganda With Previously High-Transmission Intensity.

BACKGROUND: Indoor residual spraying (IRS) and long-lasting insecticidal nets (LLINs) are the primary tools for malaria prevention in Africa. It is not known whether reductions in malaria can be sustained after IRS is discontinued. Our aim in this study was to assess changes in malaria morbidity in an area of Uganda with historically high transmission where IRS was discontinued after a 4-year period followed by universal LLIN distribution. METHODS: Individual-level malaria surveillance data were collected from 1 outpatient department and 1 inpatient setting in Apac District, Uganda, from July 2009 through November 2015. Rounds of IRS were delivered approximately every 6 months from February 2010 through May 2014 followed by universal LLIN distribution in June 2014. Temporal changes in the malaria test positivity rate (TPR) were estimated during and after IRS using interrupted time series analyses, controlling for age, rainfall, and autocorrelation. RESULTS: Data include 65 421 outpatient visits and 13 955 pediatric inpatient admissions for which a diagnostic test for malaria was performed. In outpatients aged <5 years, baseline TPR was 60%-80% followed by a rapid and then sustained decrease to 15%-30%. During the 4-18 months following discontinuation of IRS, absolute TPR values increased by an average of 3.29% per month (95% confidence interval, 2.01%-4.57%), returning to baseline levels. Similar trends were seen in outpatients aged ≥5 years and pediatric admissions. CONCLUSIONS: Discontinuation of IRS in an area with historically high transmission intensity was associated with a rapid increase in malaria morbidity to pre-IRS levels.

Artesunate/Amodiaquine Versus Artemether/Lumefantrine for the Treatment of Uncomplicated Malaria in Uganda: A Randomized Trial.

BACKGROUND: In treating malaria in Uganda, artemether-lumefantrine (AL) has been associated with a lower risk of recurrent parasitemia, compared with artesunate-amodiaquine (AS/AQ), but changing treatment practices may have altered parasite susceptibility. METHODS: We enrolled 602 children aged 6-59 months with uncomplicated falciparum malaria from 3 health centers in 2013-2014 and randomly assigned them to receive treatment with AS/AQ or AL. Primary outcomes were risks of recurrent parasitemia within 28 days, with or without adjustment to distinguish recrudescence from new infection. Drug safety and tolerability and Plasmodium falciparum resistance-mediating polymorphisms were assessed. RESULTS: Of enrolled patients, 594 (98.7%) completed the 28-day study. Risks of recurrent parasitemia were lower with AS/AQ at all 3 sites (overall, 28.6% vs 44.6%; P < .001). Recrudescences were uncommon, and all occurred after AL treatment (0% vs 2.5%; P = .006). Recovery of the hemoglobin level was greater with AS/AQ (1.73 vs 1.39 g/dL; P = .04). Both regimens were well tolerated; serious adverse events were uncommon (1.7% in the AS/AQ group and 1.0% in the AL group). AS/AQ selected for mutant pfcrt/pfmdr1 polymorphisms and AL for wild-type pfcrt/pfmdr1 polymorphisms associated with altered drug susceptibility. CONCLUSIONS: AS/AQ treatment was followed by fewer recurrences than AL treatment, contrasting with older data. Each regimen selected for polymorphisms associated with decreased treatment response. Research should consider multiple or rotating regimens to maintain treatment efficacies.

Measures of Malaria Burden after Long-Lasting Insecticidal Net Distribution and Indoor Residual Spraying at Three Sites in Uganda: A Prospective Observational Study.

BACKGROUND: Long-lasting insecticidal nets (LLINs) and indoor residual spraying of insecticide (IRS) are the primary vector control interventions used to prevent malaria in Africa. Although both interventions are effective in some settings, high-quality evidence is rarely available to evaluate their effectiveness following deployment by a national malaria control program. In Uganda, we measured changes in key malaria indicators following universal LLIN distribution in three sites, with the addition of IRS at one of these sites. METHODS AND FINDINGS: Comprehensive malaria surveillance was conducted from October 1, 2011, to March 31, 2016, in three sub-counties with relatively low (Walukuba), moderate (Kihihi), and high transmission (Nagongera). Between 2013 and 2014, universal LLIN distribution campaigns were conducted in all sites, and in December 2014, IRS with the carbamate bendiocarb was initiated in Nagongera. High-quality surveillance evaluated malaria metrics and mosquito exposure before and after interventions through (a) enhanced health-facility-based surveillance to estimate malaria test positivity rate (TPR), expressed as the number testing positive for malaria/number tested for malaria (number of children tested for malaria: Walukuba = 42,833, Kihihi = 28,790, and Nagongera = 38,690); (b) cohort studies to estimate the incidence of malaria, expressed as the number of episodes per person-year [PPY] at risk (number of children observed: Walukuba = 340, Kihihi = 380, and Nagongera = 361); and (c) entomology surveys to estimate household-level human biting rate (HBR), expressed as the number of female Anopheles mosquitoes collected per house-night of collection (number of households observed: Walukuba = 117, Kihihi = 107, and Nagongera = 107). The LLIN distribution campaign substantially increased LLIN coverage levels at the three sites to between 65.0% and 95.5% of households with at least one LLIN. In Walukuba, over the 28-mo post-intervention period, universal LLIN distribution was associated with no change in the incidence of malaria (0.39 episodes PPY pre-intervention versus 0.20 post-intervention; adjusted rate ratio [aRR] = 1.02, 95% CI 0.36-2.91, p = 0.97) and non-significant reductions in the TPR (26.5% pre-intervention versus 26.2% post-intervention; aRR = 0.70, 95% CI 0.46-1.06, p = 0.09) and HBR (1.07 mosquitoes per house-night pre-intervention versus 0.71 post-intervention; aRR = 0.41, 95% CI 0.14-1.18, p = 0.10). In Kihihi, over the 21-mo post-intervention period, universal LLIN distribution was associated with a reduction in the incidence of malaria (1.77 pre-intervention versus 1.89 post-intervention; aRR = 0.65, 95% CI 0.43-0.98, p = 0.04) but no significant change in the TPR (49.3% pre-intervention versus 45.9% post-intervention; aRR = 0.83, 95% 0.58-1.18, p = 0.30) or HBR (4.06 pre-intervention versus 2.44 post-intervention; aRR = 0.71, 95% CI 0.30-1.64, p = 0.40). In Nagongera, over the 12-mo post-intervention period, universal LLIN distribution was associated with a reduction in the TPR (45.3% pre-intervention versus 36.5% post-intervention; aRR = 0.82, 95% CI 0.76-0.88, p < 0.001) but no significant change in the incidence of malaria (2.82 pre-intervention versus 3.28 post-intervention; aRR = 1.10, 95% 0.76-1.59, p = 0.60) or HBR (41.04 pre-intervention versus 20.15 post-intervention; aRR = 0.87, 95% CI 0.31-2.47, p = 0.80). The addition of three rounds of IRS at ~6-mo intervals in Nagongera was followed by clear decreases in all outcomes: incidence of malaria (3.25 pre-intervention versus 0.63 post-intervention; aRR = 0.13, 95% CI 0.07-0.27, p < 0.001), TPR (37.8% pre-intervention versus 15.0% post-intervention; aRR = 0.54, 95% CI 0.49-0.60, p < 0.001), and HBR (18.71 pre-intervention versus 3.23 post-intervention; aRR = 0.29, 95% CI 0.17-0.50, p < 0.001). High levels of pyrethroid resistance were documented at all three study sites. Limitations of the study included the observational study design, the lack of contemporaneous control groups, and that the interventions were implemented under programmatic conditions. CONCLUSIONS: Universal distribution of LLINs at three sites with varying transmission intensity was associated with modest declines in the burden of malaria for some indicators, but the addition of IRS at the highest transmission site was associated with a marked decline in the burden of malaria for all indicators. In highly endemic areas of Africa with widespread pyrethroid resistance, IRS using alternative insecticide formulations may be needed to achieve substantial gains in malaria control.

Assessing temporal associations between environmental factors and malaria morbidity at varying transmission settings in Uganda.

BACKGROUND: Environmental factors play a major role in transmission of malaria given their relationship to both the development and survival of the mosquito and parasite. The associations between environmental factors and malaria can be used to inform the development of early warning systems for increases in malaria burden. The objective of this study was to assess temporal relationships between rainfall, temperature and vegetation with malaria morbidity across three different transmission settings in Uganda. METHODS: Temporal relationships between environmental factors (weekly total rainfall, mean day time temperature and enhanced vegetation index series) and malaria morbidity (weekly malaria case count data and test positivity rate series) over the period January 2010-May 2013 in three sites located in varying malaria transmission settings in Uganda was explored using cross-correlation with pre-whitening. Sites included Kamwezi (low transmission), Kasambya (moderate transmission) and Nagongera (high transmission). RESULTS: Nagongera received the most rain (30.6 mm) and experienced, on average, the highest daytime temperatures (29.8 °C) per week. In the study period, weekly TPR and number of malaria cases were highest at Kasambya and lowest at Kamwezi. The largest cross-correlation coefficients between environmental factors and malaria morbidity for each site was 0.27 for Kamwezi (rainfall and cases), 0.21 for Kasambya (vegetation and TPR), and -0.27 for Nagongera (daytime temperature and TPR). Temporal associations between environmental factors (rainfall, temperature and vegetation) with malaria morbidity (number of malaria cases and TPR) varied by transmission setting. Longer time lags were observed at Kamwezi and Kasambya compared to Nagongera in the relationship between rainfall and number of malaria cases. Comparable time lags were observed at Kasambya and Nagongera in the relationship between temperature and malaria morbidity. Temporal analysis of vegetation with malaria morbidity revealed longer lags at Kasambya compared to those observed at the other two sites. CONCLUSIONS: This study showed that temporal associations between environmental factors with malaria morbidity vary by transmission setting in Uganda. This suggests the need to incorporate local transmission differences when developing malaria early warning systems that have environmental predictors in Uganda. This will result in development of more accurate early warning systems, which are a prerequisite for effective malaria control in such a setting.

Forecasting malaria in a highly endemic country using environmental and clinical predictors.

BACKGROUND: Malaria thrives in poor tropical and subtropical countries where local resources are limited. Accurate disease forecasts can provide public and clinical health services with the information needed to implement targeted approaches for malaria control that make effective use of limited resources. The objective of this study was to determine the relevance of environmental and clinical predictors of malaria across different settings in Uganda. METHODS: Forecasting models were based on health facility data collected by the Uganda Malaria Surveillance Project and satellite-derived rainfall, temperature, and vegetation estimates from 2006 to 2013. Facility-specific forecasting models of confirmed malaria were developed using multivariate autoregressive integrated moving average models and produced weekly forecast horizons over a 52-week forecasting period. RESULTS: The model with the most accurate forecasts varied by site and by forecast horizon. Clinical predictors were retained in the models with the highest predictive power for all facility sites. The average error over the 52 forecasting horizons ranged from 26 to 128% whereas the cumulative burden forecast error ranged from 2 to 22%. CONCLUSIONS: Clinical data, such as drug treatment, could be used to improve the accuracy of malaria predictions in endemic settings when coupled with environmental predictors. Further exploration of malaria forecasting is necessary to improve its accuracy and value in practice, including examining other environmental and intervention predictors, including insecticide-treated nets.

Estimating malaria parasite prevalence from community surveys in Uganda: a comparison of microscopy, rapid diagnostic tests and polymerase chain reaction.

BACKGROUND: Household surveys are important tools for monitoring the malaria disease burden and measuring impact of malaria control interventions with parasite prevalence as the primary metric. However, estimates of parasite prevalence are dependent on a number of factors including the method used to detect parasites, age of the population sampled, and level of immunity. To better understand the influence of diagnostics, age, and endemicity on estimates of parasite prevalence and how these change over time, community-based surveys were performed for two consecutive years in three settings and the sensitivities of microscopy and immunochromatographic rapid diagnostic tests (RDTs) were assessed, considering polymerase chain reaction (PCR) as the gold standard. METHODS: Surveys were conducted over the same two-month period in 2012 and 2013 in each of three sub-counties in Uganda: Nagongera in Tororo District (January-February), Walukuba in Jinja District (March-April), and Kihihi in Kanungu District (May-June). In each sub-county, 200 households were randomly enrolled and a household questionnaire capturing information on demographics, use of malaria prevention methods, and proxy indicators of wealth was administered to the head of the household. Finger-prick blood samples were obtained for RDTs, measurement of hemoglobin, thick and thin blood smears, and to store samples on filter paper. RESULTS: A total of 1200 households were surveyed and 4433 participants were included in the analysis. Compared to PCR, the sensitivity of microscopy was low (65.3% in Nagongera, 49.6% in Walukuba and 40.9% in Kihihi) and decreased with increasing age. The specificity of microscopy was over 98% at all sites and did not vary with age or year. Relative differences in parasite prevalence across different age groups, study sites, and years were similar for microscopy and PCR. The sensitivity of RDTs was similar across the three sites (range 77.2-82.8%), was consistently higher than microscopy (p < 0.001 for all pairwise comparisons), and decreased with increasing age. The specificity of RDTs was lower than microscopy (76.3% in Nagongera, 86.3% in Walukuba, and 83.5% in Kihihi) and varied significantly by year and age. Relative differences in parasite prevalence across age groups and study years differed for RDTs compared to microscopy and PCR. CONCLUSION: Malaria prevalence estimates varied with diagnostic test, age, and transmission intensity. It is important to consider the effects of these parameters when designing and interpreting community-based surveys.

Anti-malarial prescription practices among children admitted to six public hospitals in Uganda from 2011 to 2013.

BACKGROUND: In 2011, Uganda's Ministry of Health switched policy from presumptive treatment of malaria to recommending parasitological diagnosis prior to treatment, resulting in an expansion of diagnostic services at all levels of public health facilities including hospitals. Despite this change, anti-malarial drugs are often prescribed even when test results are negative. Presented is data on anti-malarial prescription practices among hospitalized children who underwent diagnostic testing after adoption of new treatment guidelines. METHODS: Anti-malarial prescription practices were collected as part of an inpatient malaria surveillance program generating high quality data among children admitted for any reason at government hospitals in six districts. A standardized medical record form was used to collect detailed patient information including presenting symptoms and signs, laboratory test results, admission and final diagnoses, treatments administered, and final outcome upon discharge. RESULTS: Between July 2011 and December 2013, 58,095 children were admitted to the six hospitals (hospital range 3294-20,426).A total of 56,282 (96.9 %) patients were tested for malaria, of which 26,072 (46.3 %) tested positive (hospital range 5.9-57.3 %). Among those testing positive, only 84 (0.3 %) were first tested after admission and 295 of 30,389 (1.0 %) patients who tested negative at admission later tested positive. Of 30,210 children with only negative test results, 11,977 (39.6 %) were prescribed an anti-malarial (hospital range 14.5-53.6 %). The proportion of children with a negative test result who were prescribed an anti-malarial fluctuated over time and did not show a significant trend at any site with the exception of one hospital where a steady decline was observed. Among those with only negative test results, children 6-12 months of age (aOR 3.78; p < 0.001) and those greater than 12 months of age (aOR 4.89; p < 0.001) were more likely to be prescribed an anti-malarial compared to children less than 6 months of age. Children with findings suggestive of severe malaria were also more likely to be prescribed an anti-malarial after a negative test result (aOR 1.98; p < 0.001). CONCLUSIONS: Despite high testing rates for malaria at all sites, prescription of anti-malarials to patients with negative test results remained high, with the exception of one site where a steady decline occurred.

Determining health-care facility catchment areas in Uganda using data on malaria-related visits.

OBJECTIVE: To illustrate the use of a new method for defining the catchment areas of health-care facilities based on their utilization. METHODS: The catchment areas of six health-care facilities in Uganda were determined using the cumulative case ratio: the ratio of the observed to expected utilization of a facility for a particular condition by patients from small administrative areas. The cumulative case ratio for malaria-related visits to these facilities was determined using data from the Uganda Malaria Surveillance Project. Catchment areas were also derived using various straight line and road network distances from the facility. Subsequently, the 1-year cumulative malaria case rate was calculated for each catchment area, as determined using the three methods. FINDINGS: The 1-year cumulative malaria case rate varied considerably with the method used to define the catchment areas. With the cumulative case ratio approach, the catchment area could include noncontiguous areas. With the distance approaches, the denominator increased substantially with distance, whereas the numerator increased only slightly. The largest cumulative case rate per 1000 population was for the Kamwezi facility: 234.9 (95% confidence interval, CI: 226.2-243.8) for a straight-line distance of 5 km, 193.1 (95% CI: 186.8-199.6) for the cumulative case ratio approach and 156.1 (95% CI: 150.9-161.4) for a road network distance of 5 km. CONCLUSION: Use of the cumulative case ratio for malaria-related visits to determine health-care facility catchment areas was feasible. Moreover, this approach took into account patients' actual addresses, whereas using distance from the facility did not.

Long-lasting insecticide-treated bed net ownership and use among children under five years of age following a targeted distribution in central Uganda.

BACKGROUND: Universal coverage of long-lasting insecticide-treated bed nets (LLINs) for prevention of malaria was adopted by the Uganda National Malaria Control Programme in 2007. The first mass distribution of LLINs was implemented in 2010. Initially, a campaign targeted to households with pregnant women and children aged

Estimating the annual entomological inoculation rate for Plasmodium falciparum transmitted by Anopheles gambiae s.l. using three sampling methods in three sites in Uganda.

BACKGROUND: The Plasmodium falciparum entomological inoculation rate (PfEIR) is a measure of exposure to infectious mosquitoes. It is usually interpreted as the number of P. falciparum infective bites received by an individual during a season or annually (aPfEIR). In an area of perennial transmission, the accuracy, precision and seasonal distribution (i.e., month by month) of aPfEIR were investigated. Data were drawn from three sites in Uganda with differing levels of transmission where falciparum malaria is transmitted mainly by Anopheles gambiae s.l. Estimates of aPfEIR derived from human-landing catches--the classic method for estimating biting rates--were compared with data from CDC light traps, and with catches of knock down and exit traps separately and combined. METHODS: Entomological surveillance was carried out over one year in 2011/12 in three settings: Jinja, a peri-urban area with low transmission; Kanungu, a rural area with moderate transmission; and Nagongera, Tororo District, a rural area with exceptionally high malaria transmission. Three sampling approaches were used from randomly selected houses with collections occurring once a month: human-landing collections (eight houses), CDC light traps (100 houses) and paired knock-down and exit traps each month (ten houses) for each setting. Up to 50 mosquitoes per month from each household were tested for sporozoites with P. falciparum by ELISA. Human biting rate (HBR) data were estimated month by month. P. falciparum Sporozoite rate (PfSR) for yearly and monthly data and confidence intervals were estimated using the binomial exact test. Monthly and yearly estimates of the HBR, the PfSR, and the PfEIR were estimated and compared. RESULTS: The estimated aPfEIR values using human-landing catch data were 3.8 (95% Confidence Intervals, CI 0-11.4) for Jinja, 26.6 (95% CI 7.6-49.4) for Kanungu, and 125 (95% CI 72.2-183.0) for Tororo. In general, the monthly PfEIR values showed strong seasonal signals with two peaks from May-June and October-December, although the precise timing of the peaks differed between sites. Estimated HBRs using human-landing catches were strongly correlated with those made using CDC light traps (r(2) = 0.67, p < 0.001), and with either knock-down catches (r(2) = 0.56, p < 0.001) and exit traps (r(2) = 0.82, p < 0.001) or the combined catches (r(2) = 0.73, p < 0.001). Using CDC light trap catch data, the PfSR in Tororo was strongly negatively correlated with monthly HBR (r(2) = 0.44, p = 0.01). In other sites, no patterns in the PfSR were discernible because either the number P. falciparum of sporozoite positive mosquitoes or the total number of mosquitoes caught was too low. CONCLUSIONS: In these settings, light traps provide an alternative method for sampling indoor-resting mosquitoes to human-landing catches and have the advantage that they protect individuals from being bitten during collection, are easy to use and are not subject to collector bias. Knock-down catches and exit traps could also be used to replace human-landing catches. Although these are cheaper, they are subject to collector bias.

Anti-malarial prescription practices among outpatients with laboratory-confirmed malaria in the setting of a health facility-based sentinel site surveillance system in Uganda.

BACKGROUND: Most African countries have adopted artemisinin-based combination therapy (ACT) as the first-line treatment for uncomplicated malaria. The World Health Organization now recommends limiting anti-malarial treatment to those with a positive malaria test result. Limited data exist on how these policies have affected ACT prescription practices. METHODS: Data were collected from all outpatients presenting to six public health facilities in Uganda as part of a sentinel site malaria surveillance programme. Training in case management, encouragement of laboratory-based diagnosis of malaria, and regular feedback were provided. Data for this report include patients with laboratory confirmed malaria who were prescribed anti-malarial therapy over a two-year period. Patient visits were analysed in two groups: those considered ACT candidates (defined as uncomplicated malaria with no referral for admission in patients ≥ 4 months of age and ≥ 5 kg in weight) and those who may not have been ACT candidates. Associations between variables of interest and failure to prescribe ACT to patients who were ACT candidates were estimated using multivariable logistic regression. RESULTS: A total of 51,355 patient visits were included in the analysis and 46,265 (90.1%) were classified as ACT candidates. In the ACT candidate group, 94.5% were correctly prescribed ACT. Artemether-lumefantrine made up 97.3% of ACT prescribed. There were significant differences across the sites in the proportion of patients for whom there was a failure to prescribe ACT, ranging from 3.0-9.3%. Young children and woman of childbearing age had higher odds of failure to receive an ACT prescription. Among patients who may not have been ACT candidates, the proportion prescribed quinine versus ACT differed based on if the patient had severe malaria or was referred for admission (93.4% vs 6.5%) or was below age or weight cutoffs for ACT (41.4% vs 57.2%). CONCLUSIONS: High rates of compliance with recommended ACT use can be achieved in resource-limited settings. The unique health facility-based malaria surveillance system operating at these clinical sites may provide a framework for improving appropriate ACT use at other sites in sub-Saharan Africa.

Health facility-based malaria surveillance: the effects of age, area of residence and diagnostics on test positivity rates.

BACKGROUND: The malaria test positivity rate (TPR) is increasingly used as an indicator of malaria morbidity because TPR is based on laboratory-confirmed cases and is simple to incorporate into existing surveillance systems. However, temporal trends in TPR may reflect changes in factors associated with malaria rather than true changes in malaria morbidity. This study examines the effects of age, area of residence and diagnostic test on TPR at two health facilities in regions of Uganda with differing malaria endemicity. METHODS: The analysis included data from diagnostic blood smears performed at health facilities in Walukuba and Aduku between January 2009 and December 2010. The associations between age and time and between age and TPR were evaluated independently to determine the potential for age to confound temporal trends in TPR. Subsequently, differences between observed TPR and TPR adjusted for age were compared to determine if confounding was present. A similar analysis was performed for area of residence. Temporal trends in observed TPR were compared to trends in TPR expected using rapid diagnostic tests, which were modelled based upon sensitivity and specificity in prior studies. RESULTS: Age was independently associated with both TPR and time at both sites. At Aduku, age-adjusted TPR increased relative to observed TPR due to the association between younger age and TPR and the gradual increase in age distribution. At Walukuba, there were no clear differences between observed and age-adjusted TPR. Area of residence was independently associated with both TPR and time at both sites, though there were no clear differences in temporal trends in area of residence-adjusted TPR and observed TPR at either site. Expected TPR with pLDH- and HRP-2-based rapid diagnostic tests (RDTs) was higher than observed TPR at all time points at both sites. CONCLUSIONS: Adjusting for potential confounders such as age and area of residence can ensure that temporal trends in TPR due to confounding are not mistakenly ascribed to true changes in malaria morbidity. The potentially large effect of diagnostic test on TPR can be accounted for by calculating and adjusting for the sensitivity and specificity of the test used.

Practical Implications of a Relationship between Health Management Information System and Community Cohort-Based Malaria Incidence Rates.

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.

Malaria Burden through Routine Reporting: Relationship between Incidence and Test Positivity Rates.

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.