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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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  • Simon P. Kigozi
  • Emanuele Giorgi
  • Arthur Mpimbaza
  • Ruth Kigozi
  • Teun Bousema
  • Emmanuel Arinaitwe
  • Joaniter Nankabirwa
  • Catherine M. Sebuguzi
  • Moses R. Kamya
  • Sarah G. Staedke
  • Grant Dorsey
  • Rachel L. Pullan
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<mark>Journal publication date</mark>8/07/2020
<mark>Journal</mark>American Journal of Tropical Medicine and Hygiene
Issue number1
Volume103
Number of pages11
Pages (from-to)404-414
Publication statusPublished
Early online date4/04/20
Original languageEnglish

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.