See the published version of this preprint at Malaria Journal.
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Jaffer Okiring
Infectious Diseases Research Collaboration
Corresponding Author
ORCiD: https://orcid.org/0000-0003-3668-8997
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background: Malaria surveillance is critical for monitoring changes in malaria morbidity over time. National Malaria Control Programs 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 centers (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.
Keywords
Malaria, surveillance, metrics, test positivity rate, cases, incidence
Figure 1
Figure 2
Figure 3
Figure 4
This is a list of supplementary files associated with this preprint. Click to download.
- Appendix1.pdf
Appendix 1. Data captured on the HMIS 031 standardised form.
- Appendix2.pdf
Appendix 2. Maps of villages and parishes surrounding each MRC. Catchment area around each MRC used to estimate malaria incidence surrounded by black border.
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View the published article at Malaria Journal.
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On 05 Jan, 2021
Editorial decision: Accept
On 05 Jan, 2021
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On 05 Jan, 2021
Editor invited
On 05 Jan, 2021
Version 4
Posted 05 Jan, 2021
No comments provided
Editorial decision: Minor Revision
On 03 Jan, 2021
Reviewer #1 agreed
On 26 Dec, 2020
Review #1 received
Received 26 Dec, 2020
Reviewers invited
Invitations sent on 20 Dec, 2020
Editor assigned
On 19 Dec, 2020
Submission checks complete
On 19 Dec, 2020
Editor invited
On 19 Dec, 2020
No comments provided
Editorial decision: Minor Revision
On 15 Dec, 2020
Reviewers invited
Invitations sent on 26 Nov, 2020
Reviewer #1 agreed
On 26 Nov, 2020
Review #1 received
Received 26 Nov, 2020
Editor assigned
On 10 Nov, 2020
Submission checks complete
On 10 Nov, 2020
Editor invited
On 10 Nov, 2020
No comments provided
Editorial decision: Minor Revision
On 07 Nov, 2020
Review #2 received
Received 01 Nov, 2020
Reviewer #2 agreed
On 27 Oct, 2020
Reviewers invited
Invitations sent on 26 Oct, 2020
Reviewer #1 agreed
On 26 Oct, 2020
Review #1 received
Received 26 Oct, 2020
Editor assigned
On 23 Oct, 2020
Submission checks complete
On 22 Oct, 2020
Editor invited
On 22 Oct, 2020
No comments provided
Editorial decision: Major Revision
On 12 Oct, 2020
Review #2 received
Received 09 Oct, 2020
Review #1 received
Received 06 Oct, 2020
Reviewer #3 agreed
On 25 Sep, 2020
Reviewer #2 agreed
On 24 Sep, 2020
Reviewers invited
Invitations sent on 14 Sep, 2020
Reviewer #1 agreed
On 14 Sep, 2020
Editor assigned
On 30 Aug, 2020
Submission checks complete
On 29 Aug, 2020
Editor invited
On 29 Aug, 2020
First submitted
On 24 Aug, 2020
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See the published version of this preprint at Malaria Journal.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research
Jaffer Okiring
Infectious Diseases Research Collaboration
Corresponding Author
ORCiD: https://orcid.org/0000-0003-3668-8997
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
View the published article at Malaria Journal.
No community comments so far
Editor assigned
On 05 Jan, 2021
Editorial decision: Accept
On 05 Jan, 2021
Submission checks complete
On 05 Jan, 2021
Editor invited
On 05 Jan, 2021
Version 4
Posted 05 Jan, 2021
No comments provided
Editorial decision: Minor Revision
On 03 Jan, 2021
Reviewer #1 agreed
On 26 Dec, 2020
Review #1 received
Received 26 Dec, 2020
Reviewers invited
Invitations sent on 20 Dec, 2020
Editor assigned
On 19 Dec, 2020
Submission checks complete
On 19 Dec, 2020
Editor invited
On 19 Dec, 2020
No comments provided
Editorial decision: Minor Revision
On 15 Dec, 2020
Reviewers invited
Invitations sent on 26 Nov, 2020
Reviewer #1 agreed
On 26 Nov, 2020
Review #1 received
Received 26 Nov, 2020
Editor assigned
On 10 Nov, 2020
Submission checks complete
On 10 Nov, 2020
Editor invited
On 10 Nov, 2020
No comments provided
Editorial decision: Minor Revision
On 07 Nov, 2020
Review #2 received
Received 01 Nov, 2020
Reviewer #2 agreed
On 27 Oct, 2020
Reviewers invited
Invitations sent on 26 Oct, 2020
Reviewer #1 agreed
On 26 Oct, 2020
Review #1 received
Received 26 Oct, 2020
Editor assigned
On 23 Oct, 2020
Submission checks complete
On 22 Oct, 2020
Editor invited
On 22 Oct, 2020
No comments provided
Editorial decision: Major Revision
On 12 Oct, 2020
Review #2 received
Received 09 Oct, 2020
Review #1 received
Received 06 Oct, 2020
Reviewer #3 agreed
On 25 Sep, 2020
Reviewer #2 agreed
On 24 Sep, 2020
Reviewers invited
Invitations sent on 14 Sep, 2020
Reviewer #1 agreed
On 14 Sep, 2020
Editor assigned
On 30 Aug, 2020
Submission checks complete
On 29 Aug, 2020
Editor invited
On 29 Aug, 2020
First submitted
On 24 Aug, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Infectious Diseases
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at Malaria Journal.
Background: Malaria surveillance is critical for monitoring changes in malaria morbidity over time. National Malaria Control Programs 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 centers (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.
Figure 1
Figure 2
Figure 3
Figure 4
This is a list of supplementary files associated with this preprint. Click to download.
- Appendix1.pdf
Appendix 1. Data captured on the HMIS 031 standardised form.
- Appendix2.pdf
Appendix 2. Maps of villages and parishes surrounding each MRC. Catchment area around each MRC used to estimate malaria incidence surrounded by black border.
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