BACKGROUND: Malaria surveillance is critical for monitoring changes in malaria morbidity over time. National Malaria Control Programmes often rely on surrogate measures of malaria incidence, including the test positivity rate (TPR) and total laboratory confirmed cases of malaria (TCM), to monitor trends in malaria morbidity. However, there are limited data on the accuracy of TPR and TCM for predicting temporal changes in malaria incidence, especially in high burden settings. METHODS: This study leveraged data from 5 malaria reference centres (MRCs) located in high burden settings over a 15-month period from November 2018 through January 2020 as part of an enhanced health facility-based surveillance system established in Uganda. Individual level data were collected from all outpatients including demographics, laboratory test results, and village of residence. Estimates of malaria incidence were derived from catchment areas around the MRCs. Temporal relationships between monthly aggregate measures of TPR and TCM relative to estimates of malaria incidence were examined using linear and exponential regression models. RESULTS: A total of 149,739 outpatient visits to the 5 MRCs were recorded. Overall, malaria was suspected in 73.4% of visits, 99.1% of patients with suspected malaria received a diagnostic test, and 69.7% of those tested for malaria were positive. Temporal correlations between monthly measures of TPR and malaria incidence using linear and exponential regression models were relatively poor, with small changes in TPR frequently associated with large changes in malaria incidence. Linear regression models of temporal changes in TCM provided the most parsimonious and accurate predictor of changes in malaria incidence, with adjusted R2 values ranging from 0.81 to 0.98 across the 5 MRCs. However, the slope of the regression lines indicating the change in malaria incidence per unit change in TCM varied from 0.57 to 2.13 across the 5 MRCs, and when combining data across all 5 sites, the R2 value reduced to 0.38. CONCLUSIONS: In high malaria burden areas of Uganda, site-specific temporal changes in TCM had a strong linear relationship with malaria incidence and were a more useful metric than TPR. However, caution should be taken when comparing changes in TCM across sites.
BACKGROUND:Malaria surveillance is critical for monitoring changes in malaria morbidity over time. National Malaria Control Programmes often rely on surrogate measures of malaria incidence, including the test positivity rate (TPR) and total laboratory confirmed cases of malaria (TCM), to monitor trends in malaria morbidity. However, there are limited data on the accuracy of TPR and TCM for predicting temporal changes in malaria incidence, especially in high burden settings. METHODS: This study leveraged data from 5 malaria reference centres (MRCs) located in high burden settings over a 15-month period from November 2018 through January 2020 as part of an enhanced health facility-based surveillance system established in Uganda. Individual level data were collected from all outpatients including demographics, laboratory test results, and village of residence. Estimates of malaria incidence were derived from catchment areas around the MRCs. Temporal relationships between monthly aggregate measures of TPR and TCM relative to estimates of malaria incidence were examined using linear and exponential regression models. RESULTS: A total of 149,739 outpatient visits to the 5 MRCs were recorded. Overall, malaria was suspected in 73.4% of visits, 99.1% of patients with suspected malaria received a diagnostic test, and 69.7% of those tested for malaria were positive. Temporal correlations between monthly measures of TPR and malaria incidence using linear and exponential regression models were relatively poor, with small changes in TPR frequently associated with large changes in malaria incidence. Linear regression models of temporal changes in TCM provided the most parsimonious and accurate predictor of changes in malaria incidence, with adjusted R2 values ranging from 0.81 to 0.98 across the 5 MRCs. However, the slope of the regression lines indicating the change in malaria incidence per unit change in TCM varied from 0.57 to 2.13 across the 5 MRCs, and when combining data across all 5 sites, the R2 value reduced to 0.38. CONCLUSIONS: In high malaria burden areas of Uganda, site-specific temporal changes in TCM had a strong linear relationship with malaria incidence and were a more useful metric than TPR. However, caution should be taken when comparing changes in TCM across sites.
Entities:
Keywords:
Cases; Incidence; Malaria; Metrics; Surveillance; Test positivity rate
Authors: Saned Raouf; Arthur Mpimbaza; Ruth Kigozi; Asadu Sserwanga; Denis Rubahika; Henry Katamba; Steve W Lindsay; Bryan K Kapella; Kassahun A Belay; Moses R Kamya; Sarah G Staedke; Grant Dorsey Journal: Clin Infect Dis Date: 2017-08-01 Impact factor: 9.079
Authors: Jaffer Okiring; Peter Olwoch; Abel Kakuru; Joseph Okou; Harriet Ochokoru; Tedy Andra Ochieng; Richard Kajubi; Moses R Kamya; Grant Dorsey; Lucy S Tusting Journal: Malar J Date: 2019-04-23 Impact factor: 2.979
Authors: Jaffer Okiring; Isobel Routledge; Adrienne Epstein; Jane F Namuganga; Emmanuel V Kamya; Gloria Odei Obeng-Amoako; Catherine Maiteki Sebuguzi; Damian Rutazaana; Joan N Kalyango; Moses R Kamya; Grant Dorsey; Ronald Wesonga; Steven M Kiwuwa; Joaniter I Nankabirwa Journal: BMC Public Health Date: 2021-10-30 Impact factor: 3.295
Authors: Jaffer Okiring; Adrienne Epstein; Jane F Namuganga; Emmanuel V Kamya; Isaiah Nabende; Martha Nassali; Asadu Sserwanga; Samuel Gonahasa; Mercy Muwema; Steven M Kiwuwa; Sarah G Staedke; Moses R Kamya; Joaniter I Nankabirwa; Jessica Briggs; Prasanna Jagannathan; Grant Dorsey Journal: Malar J Date: 2022-01-21 Impact factor: 3.469