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Practical Implications of a Relationship between Health Management Information System and Community Cohort–Based Malaria Incidence Rates

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Practical Implications of a Relationship between Health Management Information System and Community Cohort–Based Malaria Incidence Rates. / Kigozi, Simon P.; Giorgi, Emanuele; Mpimbaza, Arthur et al.
In: American Journal of Tropical Medicine and Hygiene, Vol. 103, No. 1, 08.07.2020, p. 404-414.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Kigozi, SP, Giorgi, E, Mpimbaza, A, Kigozi, R, Bousema, T, Arinaitwe, E, Nankabirwa, J, Sebuguzi, CM, Kamya, MR, Staedke, SG, Dorsey, G & Pullan, RL 2020, 'Practical Implications of a Relationship between Health Management Information System and Community Cohort–Based Malaria Incidence Rates', American Journal of Tropical Medicine and Hygiene, vol. 103, no. 1, pp. 404-414. https://doi.org/10.4269/ajtmh.19-0950

APA

Kigozi, S. P., Giorgi, E., Mpimbaza, A., Kigozi, R., Bousema, T., Arinaitwe, E., Nankabirwa, J., Sebuguzi, C. M., Kamya, M. R., Staedke, S. G., Dorsey, G., & Pullan, R. L. (2020). Practical Implications of a Relationship between Health Management Information System and Community Cohort–Based Malaria Incidence Rates. American Journal of Tropical Medicine and Hygiene, 103(1), 404-414. https://doi.org/10.4269/ajtmh.19-0950

Vancouver

Kigozi SP, Giorgi E, Mpimbaza A, Kigozi R, Bousema T, Arinaitwe E et al. Practical Implications of a Relationship between Health Management Information System and Community Cohort–Based Malaria Incidence Rates. American Journal of Tropical Medicine and Hygiene. 2020 Jul 8;103(1):404-414. Epub 2020 Apr 4. doi: 10.4269/ajtmh.19-0950

Author

Kigozi, Simon P. ; Giorgi, Emanuele ; Mpimbaza, Arthur et al. / Practical Implications of a Relationship between Health Management Information System and Community Cohort–Based Malaria Incidence Rates. In: American Journal of Tropical Medicine and Hygiene. 2020 ; Vol. 103, No. 1. pp. 404-414.

Bibtex

@article{3210f9e9e9aa41238f6b9f64e318190f,
title = "Practical Implications of a Relationship between Health Management Information System and Community Cohort–Based Malaria Incidence Rates",
abstract = "Global malaria burden is reducing with effective control interventions, and surveillance is vital to maintain progress. Health management information system (HMIS) data provide a powerful surveillance tool; however, its estimates of burden need to be better understood for effectiveness. We aimed to investigate the relationship between HMIS and cohort incidence rates and identify sources of bias in HMIS-based incidence. Malaria incidence was estimated using HMIS data from 15 health facilities in three subcounties in Uganda. This was compared with a gold standard of representative cohort studies conducted in children aged 0.5 to < 11 years, followed concurrently in these sites. Between October 2011 and September 2014, 153,079 children were captured through HMISs and 995 followed up through enhanced community cohorts in Walukuba, Kihihi, and Nagongera subcounties. Although HMISs substantially underestimated malaria incidence in all sites compared with data from the cohort studies, there was a strong linear relationship between these rates in the lower transmission settings (Walukuba and Kihihi), but not the lowest HMIS performance highest transmission site (Nagongera), with calendar year as a significant modifier. Although health facility accessibility, availability, and recording completeness were associated with HMIS incidence, they were not significantly associated with bias in estimates from any site. Health management information systems still require improvements; however, its strong predictive power of unbiased malaria burden when improved highlights the important role it could play as a cost-effective tool for monitoring trends and estimating impact of control interventions. This has important implications for malaria control in low-resource, high-burden countries.",
author = "Kigozi, {Simon P.} and Emanuele Giorgi and Arthur Mpimbaza and Ruth Kigozi and Teun Bousema and Emmanuel Arinaitwe and Joaniter Nankabirwa and Sebuguzi, {Catherine M.} and Kamya, {Moses R.} and Staedke, {Sarah G.} and Grant Dorsey and Pullan, {Rachel L.}",
year = "2020",
month = jul,
day = "8",
doi = "10.4269/ajtmh.19-0950",
language = "English",
volume = "103",
pages = "404--414",
journal = "American Journal of Tropical Medicine and Hygiene",
issn = "0002-9637",
publisher = "American Society of Tropical Medicine and Hygiene",
number = "1",

}

RIS

TY - JOUR

T1 - Practical Implications of a Relationship between Health Management Information System and Community Cohort–Based Malaria Incidence Rates

AU - Kigozi, Simon P.

AU - Giorgi, Emanuele

AU - Mpimbaza, Arthur

AU - Kigozi, Ruth

AU - Bousema, Teun

AU - Arinaitwe, Emmanuel

AU - Nankabirwa, Joaniter

AU - Sebuguzi, Catherine M.

AU - Kamya, Moses R.

AU - Staedke, Sarah G.

AU - Dorsey, Grant

AU - Pullan, Rachel L.

PY - 2020/7/8

Y1 - 2020/7/8

N2 - Global malaria burden is reducing with effective control interventions, and surveillance is vital to maintain progress. Health management information system (HMIS) data provide a powerful surveillance tool; however, its estimates of burden need to be better understood for effectiveness. We aimed to investigate the relationship between HMIS and cohort incidence rates and identify sources of bias in HMIS-based incidence. Malaria incidence was estimated using HMIS data from 15 health facilities in three subcounties in Uganda. This was compared with a gold standard of representative cohort studies conducted in children aged 0.5 to < 11 years, followed concurrently in these sites. Between October 2011 and September 2014, 153,079 children were captured through HMISs and 995 followed up through enhanced community cohorts in Walukuba, Kihihi, and Nagongera subcounties. Although HMISs substantially underestimated malaria incidence in all sites compared with data from the cohort studies, there was a strong linear relationship between these rates in the lower transmission settings (Walukuba and Kihihi), but not the lowest HMIS performance highest transmission site (Nagongera), with calendar year as a significant modifier. Although health facility accessibility, availability, and recording completeness were associated with HMIS incidence, they were not significantly associated with bias in estimates from any site. Health management information systems still require improvements; however, its strong predictive power of unbiased malaria burden when improved highlights the important role it could play as a cost-effective tool for monitoring trends and estimating impact of control interventions. This has important implications for malaria control in low-resource, high-burden countries.

AB - Global malaria burden is reducing with effective control interventions, and surveillance is vital to maintain progress. Health management information system (HMIS) data provide a powerful surveillance tool; however, its estimates of burden need to be better understood for effectiveness. We aimed to investigate the relationship between HMIS and cohort incidence rates and identify sources of bias in HMIS-based incidence. Malaria incidence was estimated using HMIS data from 15 health facilities in three subcounties in Uganda. This was compared with a gold standard of representative cohort studies conducted in children aged 0.5 to < 11 years, followed concurrently in these sites. Between October 2011 and September 2014, 153,079 children were captured through HMISs and 995 followed up through enhanced community cohorts in Walukuba, Kihihi, and Nagongera subcounties. Although HMISs substantially underestimated malaria incidence in all sites compared with data from the cohort studies, there was a strong linear relationship between these rates in the lower transmission settings (Walukuba and Kihihi), but not the lowest HMIS performance highest transmission site (Nagongera), with calendar year as a significant modifier. Although health facility accessibility, availability, and recording completeness were associated with HMIS incidence, they were not significantly associated with bias in estimates from any site. Health management information systems still require improvements; however, its strong predictive power of unbiased malaria burden when improved highlights the important role it could play as a cost-effective tool for monitoring trends and estimating impact of control interventions. This has important implications for malaria control in low-resource, high-burden countries.

U2 - 10.4269/ajtmh.19-0950

DO - 10.4269/ajtmh.19-0950

M3 - Journal article

VL - 103

SP - 404

EP - 414

JO - American Journal of Tropical Medicine and Hygiene

JF - American Journal of Tropical Medicine and Hygiene

SN - 0002-9637

IS - 1

ER -