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

Abstract

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.

Plasmodium falciparum parasite prevalence in East Africa: Updating data for malaria stratification

“…Modelled national parasite prevalence predictions have been used to inform sub-national approaches to malaria control in Kenya [ 12 , 13 ], Uganda [ 14 ], Somalia [ 15 , 16 ], Namibia [ 17 ], Senegal [ 18 ], Cote D’Ivoire [ 19 , 20 ], Malawi [ 21 – 23 ], Angola [ 24 ], Madagascar [ 25 ], Ghana [ 26 ], Rwanda [ 27 , 28 ], Mozambique [ 29 , 30 ], Burkina Faso [ 31 ], Sudan [ 32 ] and Tanzania [ 33 – 35 ]. The use of routine health facility data sources to define malaria incidence has increased in recent years, in combination with parasite prevalence or independently, in Namibia [ 36 ], Zambia [ 37 ], Malawi [ 38 ], Tanzania [ 34 , 35 , 39 ], Madagascar [ 40 – 42 ], Zimbabwe [ 43 ], Ghana [ 44 ], Burkina Faso [ 45 ] and Uganda [ 46 , 47 ].…”

Integrated malaria prevention in rural communities in Uganda: a qualitative feasibility study for a randomised controlled trial

“…Country-specific studies in Uganda have shown that the use of ITNs [ 11 ] and IRS [ 12 ] have reduced the occurrence of malaria in areas where they have been used. However, despite all these efforts, the burden of malaria in many parts of the country is still high [ 1 , 5 , 13 ].…”

Hypoxaemia prevalence and management among children and adults presenting to primary care facilities in Uganda: a prospective cohort study

“…Data collection occurred over the warm, dry season months that are typically low-incidence for ARI and malaria and may underestimate the case numbers and hypoxaemia prevalence in other seasons. 47 At the time of this study (February to April 2021), Uganda was experiencing the COVID-19 pandemic, with data collection commencing shortly after a second major wave of infections had subsided and restrictions loosened in January 2021. While we observed similar service activity compared to previous years, changes in care-seeking, hygiene and social behaviour may have influenced participant characteristics and reduced the prevalence of ARI.…”