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Title: Health facility data to describe the epidemiology of malaria in sub-Saharan Africa
Author: Kamau, Alice
ISNI:       0000 0005 0291 9667
Awarding Body: University of Oxford
Current Institution: University of Oxford
Date of Award: 2020
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The current understanding of malaria burden in Africa relies on modelled estimates based on incomplete and outdated data. The WHO defines malaria disease surveillance as a central pillar of its Global Technical Strategy. The thesis examines the precision of existing models to predict malaria burden changes and interrogation of malaria metrics from health facility and community surveys on the Kenyan coast. Geostatistical, epidemiological models provide approximations of malaria disease burden but are rarely tested against empirical data. A systematic review included 93 health facility sites across Africa with a minimum of 5 complete years of temporal data. There was a broad congruence in the matched changes at 70 sites, but significant discordance at 23 sites which showed stagnated or upward trends. Data on the pathway from infection to death, necessary to parametrise models of morbidity and mortality risk are rare. Prospective 12-month surveillance at six health facilities, the county hospital and repeat surveys among 36 matched communities was used to describe the epidemiology of malaria in all age groups. Despite conditions of declining transmission intensity, immunity to disease and the fatal consequences of infection continue to be acquired in early childhood, faster than anti-parasitic immunity and without evidence of emerging burdens of severe malaria or mortality among young-older non-pregnant adults. The value of routinely collected data from health facilities to define malaria risk was explored at 36 health facility-community pairs. There was a direct non-linear polynomial relationship between passively detected fever test-positivity and traditional community-based parasite prevalence. Information obtained through routine testing of febrile patients for malaria was able to identify spatial and temporal heterogeneities of malaria risk. Routine data offers important insights into local malaria transmission patterns, disease burdens and changing patterns of disease. An increased effort is required to replace malaria burden models with empirical data.
Supervisor: Bejon, Philip ; Snow, Robert Sponsor: Wellcome Trust
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Malaria epidemiology