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 tren...
| Main Authors: | , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2020-12-01
|
| Series: | BMC Public Health |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12889-020-10007-w |
| _version_ | 1818363642464174080 |
|---|---|
| author | Simon P. Kigozi Ruth N. Kigozi Catherine M. Sebuguzi Jorge Cano Damian Rutazaana Jimmy Opigo Teun Bousema Adoke Yeka Anne Gasasira Benn Sartorius Rachel L. Pullan |
| author_facet | Simon P. Kigozi Ruth N. Kigozi Catherine M. Sebuguzi Jorge Cano Damian Rutazaana Jimmy Opigo Teun Bousema Adoke Yeka Anne Gasasira Benn Sartorius Rachel L. Pullan |
| author_sort | Simon P. Kigozi |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-13T21:51:43Z |
| format | Article |
| id | doaj.art-1d0069e7178b4f1497e006addb90e056 |
| institution | Directory Open Access Journal |
| issn | 1471-2458 |
| language | English |
| last_indexed | 2024-12-13T21:51:43Z |
| publishDate | 2020-12-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Public Health |
| spelling | doaj.art-1d0069e7178b4f1497e006addb90e0562022-12-21T23:30:14ZengBMCBMC Public Health1471-24582020-12-0120111410.1186/s12889-020-10007-wSpatial-temporal patterns of malaria incidence in Uganda using HMIS data from 2015 to 2019Simon P. Kigozi0Ruth N. Kigozi1Catherine M. Sebuguzi2Jorge Cano3Damian Rutazaana4Jimmy Opigo5Teun Bousema6Adoke Yeka7Anne Gasasira8Benn Sartorius9Rachel L. Pullan10Department of Disease Control, London School of Hygiene & Tropical MedicineUSAID’s Malaria Action Program for DistrictsInfectious Diseases Research CollaborationDepartment of Disease Control, London School of Hygiene & Tropical MedicineNational Malaria Control Division, Uganda Ministry of HealthNational Malaria Control Division, Uganda Ministry of HealthDepartment of Medical Microbiology, Radboud UniversityDepartment of Disease Control and Environmental Health, College of Health Sciences, School of Public Health, Makerere UniversityAfrican Leaders Malaria Alliance (ALMA)Department of Disease Control, London School of Hygiene & Tropical MedicineDepartment of Disease Control, London School of Hygiene & Tropical MedicineAbstract 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.https://doi.org/10.1186/s12889-020-10007-wUgandaMalariaIncidenceRelative riskRoutine surveillanceHMIS |
| spellingShingle | Simon P. Kigozi Ruth N. Kigozi Catherine M. Sebuguzi Jorge Cano Damian Rutazaana Jimmy Opigo Teun Bousema Adoke Yeka Anne Gasasira Benn Sartorius Rachel L. Pullan Spatial-temporal patterns of malaria incidence in Uganda using HMIS data from 2015 to 2019 BMC Public Health Uganda Malaria Incidence Relative risk Routine surveillance HMIS |
| title | Spatial-temporal patterns of malaria incidence in Uganda using HMIS data from 2015 to 2019 |
| title_full | Spatial-temporal patterns of malaria incidence in Uganda using HMIS data from 2015 to 2019 |
| title_fullStr | Spatial-temporal patterns of malaria incidence in Uganda using HMIS data from 2015 to 2019 |
| title_full_unstemmed | Spatial-temporal patterns of malaria incidence in Uganda using HMIS data from 2015 to 2019 |
| title_short | Spatial-temporal patterns of malaria incidence in Uganda using HMIS data from 2015 to 2019 |
| title_sort | spatial temporal patterns of malaria incidence in uganda using hmis data from 2015 to 2019 |
| topic | Uganda Malaria Incidence Relative risk Routine surveillance HMIS |
| url | https://doi.org/10.1186/s12889-020-10007-w |
| work_keys_str_mv | AT simonpkigozi spatialtemporalpatternsofmalariaincidenceinugandausinghmisdatafrom2015to2019 AT ruthnkigozi spatialtemporalpatternsofmalariaincidenceinugandausinghmisdatafrom2015to2019 AT catherinemsebuguzi spatialtemporalpatternsofmalariaincidenceinugandausinghmisdatafrom2015to2019 AT jorgecano spatialtemporalpatternsofmalariaincidenceinugandausinghmisdatafrom2015to2019 AT damianrutazaana spatialtemporalpatternsofmalariaincidenceinugandausinghmisdatafrom2015to2019 AT jimmyopigo spatialtemporalpatternsofmalariaincidenceinugandausinghmisdatafrom2015to2019 AT teunbousema spatialtemporalpatternsofmalariaincidenceinugandausinghmisdatafrom2015to2019 AT adokeyeka spatialtemporalpatternsofmalariaincidenceinugandausinghmisdatafrom2015to2019 AT annegasasira spatialtemporalpatternsofmalariaincidenceinugandausinghmisdatafrom2015to2019 AT bennsartorius spatialtemporalpatternsofmalariaincidenceinugandausinghmisdatafrom2015to2019 AT rachellpullan spatialtemporalpatternsofmalariaincidenceinugandausinghmisdatafrom2015to2019 |