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.

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.

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.

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.

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.