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Malaria micro-stratification using routine surveillance data in Western Kenya

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Malaria micro-stratification using routine surveillance data in Western Kenya. / Alegana, V.A.; Suiyanka, L.; Macharia, P.M. et al.
In: Malaria Journal, Vol. 20, No. 1, 22, 07.01.2021.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Alegana, VA, Suiyanka, L, Macharia, PM, Ikahu-Muchangi, G & Snow, RW 2021, 'Malaria micro-stratification using routine surveillance data in Western Kenya', Malaria Journal, vol. 20, no. 1, 22. https://doi.org/10.1186/s12936-020-03529-6

APA

Alegana, V. A., Suiyanka, L., Macharia, P. M., Ikahu-Muchangi, G., & Snow, R. W. (2021). Malaria micro-stratification using routine surveillance data in Western Kenya. Malaria Journal, 20(1), Article 22. https://doi.org/10.1186/s12936-020-03529-6

Vancouver

Alegana VA, Suiyanka L, Macharia PM, Ikahu-Muchangi G, Snow RW. Malaria micro-stratification using routine surveillance data in Western Kenya. Malaria Journal. 2021 Jan 7;20(1):22. doi: 10.1186/s12936-020-03529-6

Author

Alegana, V.A. ; Suiyanka, L. ; Macharia, P.M. et al. / Malaria micro-stratification using routine surveillance data in Western Kenya. In: Malaria Journal. 2021 ; Vol. 20, No. 1.

Bibtex

@article{cc63b7711c1d48ad8dd9c2f14d9a630b,
title = "Malaria micro-stratification using routine surveillance data in Western Kenya",
abstract = "Background: There is an increasing need for finer spatial resolution data on malaria risk to provide micro-stratification to guide sub-national strategic plans. Here, spatial-statistical techniques are used to exploit routine data to depict sub-national heterogeneities in test positivity rate (TPR) for malaria among patients attending health facilities in Kenya. Methods: Routine data from health facilities (n = 1804) representing all ages over 24 months (2018–2019) were assembled across 8 counties (62 sub-counties) in Western Kenya. Statistical model-based approaches were used to quantify heterogeneities in TPR and uncertainty at fine spatial resolution adjusting for missingness, population distribution, spatial data structure, month, and type of health facility. Results: The overall monthly reporting rate was 78.7% (IQR 75.0–100.0) and public-based health facilities were more likely than private facilities to report ≥ 12 months (OR 5.7, 95% CI 4.3–7.5). There was marked heterogeneity in population-weighted TPR with sub-counties in the north of the lake-endemic region exhibiting the highest rates (exceedance probability > 70% with 90% certainty) where approximately 2.7 million (28.5%) people reside. At micro-level the lowest rates were in 14 sub-counties (exceedance probability <30% with 90% certainty) where approximately 2.2 million (23.1%) people lived and indoor residual spraying had been conducted since 2017. Conclusion: The value of routine health data on TPR can be enhanced when adjusting for underlying population and spatial structures of the data, highlighting small-scale heterogeneities in malaria risk often masked in broad national stratifications. Future research should aim at relating these heterogeneities in TPR with traditional community-level prevalence to improve tailoring malaria control activities at sub-national levels. {\textcopyright} 2021, The Author(s).",
keywords = "Malaria, Routine data, Test positivity rate, article, building, controlled study, health care facility, human, indoor residual spraying, Kenya, major clinical study, malaria control, population distribution, prevalence, probability, quantitative analysis, uncertainty",
author = "V.A. Alegana and L. Suiyanka and P.M. Macharia and G. Ikahu-Muchangi and R.W. Snow",
year = "2021",
month = jan,
day = "7",
doi = "10.1186/s12936-020-03529-6",
language = "English",
volume = "20",
journal = "Malaria Journal",
issn = "1475-2875",
publisher = "BioMed Central",
number = "1",

}

RIS

TY - JOUR

T1 - Malaria micro-stratification using routine surveillance data in Western Kenya

AU - Alegana, V.A.

AU - Suiyanka, L.

AU - Macharia, P.M.

AU - Ikahu-Muchangi, G.

AU - Snow, R.W.

PY - 2021/1/7

Y1 - 2021/1/7

N2 - Background: There is an increasing need for finer spatial resolution data on malaria risk to provide micro-stratification to guide sub-national strategic plans. Here, spatial-statistical techniques are used to exploit routine data to depict sub-national heterogeneities in test positivity rate (TPR) for malaria among patients attending health facilities in Kenya. Methods: Routine data from health facilities (n = 1804) representing all ages over 24 months (2018–2019) were assembled across 8 counties (62 sub-counties) in Western Kenya. Statistical model-based approaches were used to quantify heterogeneities in TPR and uncertainty at fine spatial resolution adjusting for missingness, population distribution, spatial data structure, month, and type of health facility. Results: The overall monthly reporting rate was 78.7% (IQR 75.0–100.0) and public-based health facilities were more likely than private facilities to report ≥ 12 months (OR 5.7, 95% CI 4.3–7.5). There was marked heterogeneity in population-weighted TPR with sub-counties in the north of the lake-endemic region exhibiting the highest rates (exceedance probability > 70% with 90% certainty) where approximately 2.7 million (28.5%) people reside. At micro-level the lowest rates were in 14 sub-counties (exceedance probability <30% with 90% certainty) where approximately 2.2 million (23.1%) people lived and indoor residual spraying had been conducted since 2017. Conclusion: The value of routine health data on TPR can be enhanced when adjusting for underlying population and spatial structures of the data, highlighting small-scale heterogeneities in malaria risk often masked in broad national stratifications. Future research should aim at relating these heterogeneities in TPR with traditional community-level prevalence to improve tailoring malaria control activities at sub-national levels. © 2021, The Author(s).

AB - Background: There is an increasing need for finer spatial resolution data on malaria risk to provide micro-stratification to guide sub-national strategic plans. Here, spatial-statistical techniques are used to exploit routine data to depict sub-national heterogeneities in test positivity rate (TPR) for malaria among patients attending health facilities in Kenya. Methods: Routine data from health facilities (n = 1804) representing all ages over 24 months (2018–2019) were assembled across 8 counties (62 sub-counties) in Western Kenya. Statistical model-based approaches were used to quantify heterogeneities in TPR and uncertainty at fine spatial resolution adjusting for missingness, population distribution, spatial data structure, month, and type of health facility. Results: The overall monthly reporting rate was 78.7% (IQR 75.0–100.0) and public-based health facilities were more likely than private facilities to report ≥ 12 months (OR 5.7, 95% CI 4.3–7.5). There was marked heterogeneity in population-weighted TPR with sub-counties in the north of the lake-endemic region exhibiting the highest rates (exceedance probability > 70% with 90% certainty) where approximately 2.7 million (28.5%) people reside. At micro-level the lowest rates were in 14 sub-counties (exceedance probability <30% with 90% certainty) where approximately 2.2 million (23.1%) people lived and indoor residual spraying had been conducted since 2017. Conclusion: The value of routine health data on TPR can be enhanced when adjusting for underlying population and spatial structures of the data, highlighting small-scale heterogeneities in malaria risk often masked in broad national stratifications. Future research should aim at relating these heterogeneities in TPR with traditional community-level prevalence to improve tailoring malaria control activities at sub-national levels. © 2021, The Author(s).

KW - Malaria

KW - Routine data

KW - Test positivity rate

KW - article

KW - building

KW - controlled study

KW - health care facility

KW - human

KW - indoor residual spraying

KW - Kenya

KW - major clinical study

KW - malaria control

KW - population distribution

KW - prevalence

KW - probability

KW - quantitative analysis

KW - uncertainty

U2 - 10.1186/s12936-020-03529-6

DO - 10.1186/s12936-020-03529-6

M3 - Journal article

VL - 20

JO - Malaria Journal

JF - Malaria Journal

SN - 1475-2875

IS - 1

M1 - 22

ER -