Changing malaria intervention coverage, transmission and hospitalization in Kenya

Abstract

Background: Reports of declining incidence of malaria disease burden across several countries in Africa suggest that the epidemiology of malaria across the continent is in transition. Whether this transition is directly related to the scaling of intervention coverage remains a moot point.

Methods: Paediatric admission data from eight Kenyan hospitals and their catchments have been assembled across two three-year time periods: September 2003 to August 2006 (pre-scaled intervention) and September 2006 to August 2009 (post-scaled intervention). Interrupted time series (ITS) models were developed adjusting for variations in rainfall and hospital use by surrounding communities to show changes in malaria hospitalization over the two periods. The temporal changes in factors that might explain changes in disease incidence were examined sequentially for each hospital setting, compared between hospital settings and ranked according to plausible explanatory factors.

Results: In six out of eight sites there was a decline in Malaria admission rates with declines between 18% and 69%. At two sites malaria admissions rates increased by 55% and 35%. Results from the ITS models indicate that before scaled intervention in September 2006, there was a significant month-to-month decline in the mean malaria admission rates at four hospitals (trend P < 0.05). At the point of scaled intervention, the estimated mean admission rates for malaria was significantly less at four sites compared to the pre-scaled period baseline. Following scaled intervention there was a significant change in the month-to-month trend in the mean malaria admission rates in some but not all of the sites. Plausibility assessment of possible drivers of change pre- versus post-scaled intervention showed inconsistent patterns however, allowing for the increase in rainfall in the second period, there is a suggestion that starting transmission intensity and the scale of change in ITN coverage might explain some but not all of the variation in effect size. At most sites where declines between observation periods were documented admission rates were changing before free mass ITN distribution and prior to the implementation of ACT across Kenya.

Conclusion: This study provides evidence of significant within and between location heterogeneity in temporal trends of malaria disease burden. Plausible drivers for changing disease incidence suggest a complex combination of mechanisms, not easily measured retrospectively.

Figure 1

Graph panels show changing paediatric hospitalizations rates due to malaria per 1 000 children 0-14 years before and after scaling up of interventions. The data represents paediatric malaria admission rates by month (black solid line); model predictions of paediatric malaria hospitalization rates controlling for non-malaria case rates, rainfall and controlling for autoregressive and moving average effects (dashed blue line). Fitted lines illustrate the linear trends from model predictions (dashed line) for each segment of the period separated by a break point (red dashed vertical line).

Figure 2

Relationship between changes in the incidence rate of malaria admissions between period one and two (Incidence rate ratio) and equivalent absoulte changes in transmission intensity across 8 hospital sites in Kenya (Left-starting PfPR; Middle - PfPR at the end of the study and Right - absolute difference in prevalence between period one and two).

Figure 3

Relationship between changes in the incidence rate of malaria admissions between period one and two (Incidence rate ratio) and equivalent absolute changes in ITN coverage across 8 hospital catchments (Left- absolute difference in ITN coverage between period one and two; Right - ITN coverage in the follow-up period).