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

Malaria Burden through routine reporting: relationships between incidence estimates

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

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

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  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  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

Quantifying HIV transmission flow between high-prevalence hotspots and surrounding communities: a population-based study in Rakai, Uganda

Background International and global organisations advocate targeting interventions to areas of high HIV prevalence (ie, hotspots). To better understand the potential benefits of geo-targeted control, we assessed the extent to which HIV hotspots along Lake Victoria sustain transmission in neighbouring populations in south-central Uganda. Methods We did a population-based survey in Rakai, Uganda, using data from the Rakai Community Cohort Study. The study surveyed all individuals aged 15–49 years in four high-prevalence Lake Victoria fishing communities and 36 neighbouring inland communities. Viral RNA was deep sequenced from participants infected with HIV who were antiretroviral therapy-naive during the observation period. Phylogenetic analysis was used to infer partial HIV transmission networks, including direction of transmission. Reconstructed networks were interpreted through data for current residence and migration history. HIV transmission flows within and between high-prevalence and low-prevalence areas were quantified adjusting for incomplete sampling of the population. Findings Between Aug 10, 2011, and Jan 30, 2015, data were collected for the Rakai Community Cohort Study. 25 882 individuals participated, including an estimated 75·7% of the lakeside population and 16·2% of the inland population in the Rakai region of Uganda. 5142 participants were HIV-positive (2703 [13·7%] in inland and 2439 [40·1%] in fishing communities). 3878 (75·4%) people who were HIV-positive did not report antiretroviral therapy use, of whom 2652 (68·4%) had virus deep-sequenced at sufficient quality for phylogenetic analysis. 446 transmission networks were reconstructed, including 293 linked pairs with inferred direction of transmission. Adjusting for incomplete sampling, an estimated 5·7% (95% credibility interval 4·4–7·3) of transmissions occurred within lakeside areas, 89·2% (86·0–91·8) within inland areas, 1·3% (0·6–2·6) from lakeside to inland areas, and 3·7% (2·3–5·8) from inland to lakeside areas. Interpretation Cross-community HIV transmissions between Lake Victoria hotspots and surrounding inland populations are infrequent and when they occur, virus more commonly flows into rather than out of hotspots. This result suggests that targeted interventions to these hotspots will not alone control the epidemic in inland populations, where most transmissions occur. Thus, geographical targeting of high prevalence areas might not be effective for broader epidemic control depending on underlying epidemic dynamics. Funding The Bill & Melinda Gates Foundation, the National Institute of Allergy and Infectious Diseases, the National Institute of Mental Health, the National Institute of Child Health and Development, the Division of Intramural Research of the National Institute for Allergy and Infectious Diseases, the World Bank, the Doris Duke Charitable Foundation, the Johns Hopkins University Center for AIDS Research, and the President's Emergency Plan for AIDS Relief through the Centers for Disease Control and Prevention

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

Contains fulltext : 226128.pdf (Publisher’s version ) (Open Access