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Evaluation of malaria outbreak detection methods, Uganda, 2022
by
Kwesiga, Benon
, Nanziri, Carol
, Ario, Alex Riolexus
, Harris, Julie R.
, Zalwango, Marie Gorreti
, Kadobera, Daniel
, Zalwango, Jane F.
, Bulage, Lilian
, Agaba, Bosco B.
, Opigo, Jimmy
, Migisha, Richard
in
Analysis
/ Biomedical and Life Sciences
/ Biomedicine
/ Detection
/ Disease transmission
/ Entomology
/ Epidemic thresholds
/ Epidemics
/ Epidemiology
/ Health care management
/ Health care policy
/ Health facilities
/ Health surveillance
/ Human diseases
/ Infectious Diseases
/ Information systems
/ Malaria
/ Malaria outbreak
/ Methods
/ Microbiology
/ Outbreaks
/ Parasitology
/ Prevention
/ Public Health
/ Regions
/ Risk factors
/ Seasonal variations (Diseases)
/ Sensitivity
/ Standard deviation
/ Towards malaria elimination
/ Tropical Medicine
/ Uganda
/ Vector-borne diseases
2024
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Evaluation of malaria outbreak detection methods, Uganda, 2022
by
Kwesiga, Benon
, Nanziri, Carol
, Ario, Alex Riolexus
, Harris, Julie R.
, Zalwango, Marie Gorreti
, Kadobera, Daniel
, Zalwango, Jane F.
, Bulage, Lilian
, Agaba, Bosco B.
, Opigo, Jimmy
, Migisha, Richard
in
Analysis
/ Biomedical and Life Sciences
/ Biomedicine
/ Detection
/ Disease transmission
/ Entomology
/ Epidemic thresholds
/ Epidemics
/ Epidemiology
/ Health care management
/ Health care policy
/ Health facilities
/ Health surveillance
/ Human diseases
/ Infectious Diseases
/ Information systems
/ Malaria
/ Malaria outbreak
/ Methods
/ Microbiology
/ Outbreaks
/ Parasitology
/ Prevention
/ Public Health
/ Regions
/ Risk factors
/ Seasonal variations (Diseases)
/ Sensitivity
/ Standard deviation
/ Towards malaria elimination
/ Tropical Medicine
/ Uganda
/ Vector-borne diseases
2024
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Evaluation of malaria outbreak detection methods, Uganda, 2022
by
Kwesiga, Benon
, Nanziri, Carol
, Ario, Alex Riolexus
, Harris, Julie R.
, Zalwango, Marie Gorreti
, Kadobera, Daniel
, Zalwango, Jane F.
, Bulage, Lilian
, Agaba, Bosco B.
, Opigo, Jimmy
, Migisha, Richard
in
Analysis
/ Biomedical and Life Sciences
/ Biomedicine
/ Detection
/ Disease transmission
/ Entomology
/ Epidemic thresholds
/ Epidemics
/ Epidemiology
/ Health care management
/ Health care policy
/ Health facilities
/ Health surveillance
/ Human diseases
/ Infectious Diseases
/ Information systems
/ Malaria
/ Malaria outbreak
/ Methods
/ Microbiology
/ Outbreaks
/ Parasitology
/ Prevention
/ Public Health
/ Regions
/ Risk factors
/ Seasonal variations (Diseases)
/ Sensitivity
/ Standard deviation
/ Towards malaria elimination
/ Tropical Medicine
/ Uganda
/ Vector-borne diseases
2024
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Evaluation of malaria outbreak detection methods, Uganda, 2022
Journal Article
Evaluation of malaria outbreak detection methods, Uganda, 2022
2024
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Overview
Background
Malaria outbreaks are detected by applying the World Health Organization (WHO)-recommended thresholds (the less sensitive 75th percentile or mean + 2 standard deviations [2SD] for medium-to high-transmission areas, and the more sensitive cumulative sum [C-SUM] method for low and very low-transmission areas). During 2022, > 50% of districts in Uganda were in an epidemic mode according to the 75th percentile method used, resulting in a need to restrict national response to districts with the highest rates of complicated malaria. The three threshold approaches were evaluated to compare their outbreak-signaling outputs and help identify prioritization approaches and method appropriateness across Uganda.
Methods
The three methods were applied as well as adjusted approaches (85th percentile and C-SUM + 2SD) for all weeks in 2022 for 16 districts with good reporting rates ( ≥ 80%). Districts were selected from regions originally categorized as very low, low, medium, and high transmission; district thresholds were calculated based on 2017–2021 data and re-categorized them for this analysis.
Results
Using district-level data to categorize transmission levels resulted in re-categorization of 8/16 districts from their original transmission level categories. In all districts, more outbreak weeks were detected by the 75th percentile than the mean + 2SD method (p < 0.001). For all 9 very low or low-transmission districts, the number of outbreak weeks detected by C-SUM were similar to those detected by the 75th percentile. On adjustment of the 75th percentile method to the 85th percentile, there was no significant difference in the number of outbreak weeks detected for medium and low transmission districts. The number of outbreak weeks detected by C-SUM + 2SD was similar to those detected by the mean + 2SD method for all districts across all transmission intensities.
Conclusion
District data may be more appropriate than regional data to categorize malaria transmission and choose epidemic threshold approaches. The 75th percentile method, meant for medium- to high-transmission areas, was as sensitive as C-SUM for low- and very low-transmission areas. For medium and high-transmission areas, more outbreak weeks were detected with the 75th percentile than the mean + 2SD method. Using the 75th percentile method for outbreak detection in all areas and the mean + 2SD for prioritization of medium- and high-transmission areas in response may be helpful.
Publisher
BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
Subject
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