Patterns of malaria-related hospital admissions and mortality among Malawian children: an example of spatial modelling of hospital register data.
BACKGROUND
Malaria is a leading cause of hospitalization and in-hospital mortality among children in Africa, yet, few studies have described the spatial distribution of the two outcomes. Here spatial regression models were applied, aimed at quantifying spatial variation and risk factors associated with malaria hospitalization and in-hospital mortality.


METHODS
Paediatric ward register data from Zomba district, Malawi, between 2002 and 2003 were used, as a case study. Two spatial models were developed. The first was a Poisson model applied to analyse hospitalization and minimum mortality rates, with age and sex as covariates. The second was a logistic model applied to individual level data to analyse case-fatality rate, adjusting for individual covariates.


CONCLUSIONS
Rates of malaria hospitalization and in-hospital mortality decreased with age. Case fatality rate was associated with distance, age, wet season and increased if the patient was referred to the hospital. Furthermore, death rate was high on first day, followed by relatively low rate as length of hospital stay increased. Both outcomes showed substantial spatial heterogeneity, which may be attributed to the varying determinants of malaria risk, health services availability and accessibility, and health seeking behaviour. The increased risk of mortality of children referred from primary health facilities may imply inadequate care being available at the referring facility, or the referring facility are referring the more severe cases which are expected to have a higher case fatality rate. Improved prognosis as the length of hospital stay increased suggest that appropriate care when available can save lives. Reducing malaria burden may require integrated strategies encompassing availability of adequate care at primary facilities, introducing home or community case management as well as encouraging early referral, and reinforcing interventions to interrupt malaria transmission.
Kazembe
Lawrence N
LN
Applied Statistics and Epidemiology Research Unit, Mathematical Sciences Department, Chancellor College, University of Malawi, Zomba, Malawi. lkazembe@mrc.ac.za
Kleinschmidt
Immo
I
Sharp
Brian L
BL
D000293:Adolescent; D017677:Age Distribution; D002648:Child; D002675:Child, Preschool; D005260:Female; D017052:Hospital Mortality; D006760:Hospitalization / Q000706:statistics & numerical data*; D006767:Hospitals, District / Q000706:statistics & numerical data*; D006801:Humans; D015994:Incidence; D007223:Infant; D016015:Logistic Models; D008288:Malaria / Q000453:epidemiology / Q000401:mortality*; D008295:Malawi / Q000453:epidemiology; D008297:Male; D016012:Poisson Distribution; D012042:Registries / Q000706:statistics & numerical data*; D012307:Risk Factors; D012621:Seasons