| dc.description.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, their
strong predictive power of unbiased malaria burden when improved highlights the important role they 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. | en_US |
