Malaria Prevalence, Spatial Clustering and Risk Factors in a Low Endemic Area of Eastern Rwanda: A Cross Sectional Study

Abstract

BackgroundRwanda reported significant reductions in malaria burden following scale up of control intervention from 2005 to 2010. This study sought to; measure malaria prevalence, describe spatial malaria clustering and investigate for malaria risk factors among health-centre-presumed malaria cases and their household members in Eastern Rwanda.MethodsA two-stage health centre and household-based survey was conducted in Ruhuha sector, Eastern Rwanda from April to October 2011. At the health centre, data, including malaria diagnosis and individual level malaria risk factors, was collected. At households of these Index cases, a follow-up survey, including malaria screening for all household members and collecting household level malaria risk factor data, was conducted.ResultsMalaria prevalence among health centre attendees was 22.8%. At the household level, 90 households (out of 520) had at least one malaria-infected member and the overall malaria prevalence for the 2634 household members screened was 5.1%. Among health centre attendees, the age group 5–15 years was significantly associated with an increased malaria risk and a reported ownership of ≥4 bednets was significantly associated with a reduced malaria risk. At the household level, age groups 5–15 and >15 years and being associated with a malaria positive index case were associated with an increased malaria risk, while an observed ownership of ≥4 bednets was associated with a malaria risk-protective effect. Significant spatial malaria clustering among household cases with clusters located close to water- based agro-ecosystems was observed.ConclusionsMalaria prevalence was significantly higher among health centre attendees and their household members in an area with significant household spatial malaria clustering. Circle surveillance involving passive case finding at health centres and proactive case detection in households can be a powerful tool for identifying household level malaria burden, risk factors and clustering.

Assessing the effects of air temperature and rainfall on malaria incidence: an epidemiological study across Rwanda and Uganda

“…shows a highly non-linear relationship with these two drivers (Craig et al, 1999;Lunde et al, 2013;Tompkins and Ermert, 2013), and so both high temperatures and heavy rainfall may increase vector mortality resulting in reduced transmission (Thomson et al, 2005). The observed spatial heterogeneity in malaria incidence agrees well with previous research in the country (Karema et al, 2012;Rulisa et al, 2013), and is likely due to the interplay of several factors including the local environment, bed net use, and host genetic polymorphism (Clark et al, 2008). It is noted that child anaemia shows a somewhat similar geographical distribution to that of malaria incidence with a greater proportion of anaemic children in the eastern and south-western parts of the country.…”

“…Previous reports indicate a greater rate of change in the number of malaria cases in the highlands compared against mid or low altitudes in Rwanda (Loevinsohn, 1994). However, the absolute number of malaria cases is highest in the eastern, southern, and south-western parts of the country, characterised for mid to low altitudes as reported by Henninger (2013); USAID (2013a) and Rulisa et al (2013). It is noted that these patterns are highly similar to those of air temperature and rainfall, with greater mean temperature and lower rainfall in those districts where malaria incidence is greater.…”

“…In regions 1 and 3 (Figure 6) on the other hand, the significant decrease in incidence observed in 2006 was followed by a significant rise in incidence with a peak in late 2010, and a further decrease thereafter. Previous research suggested that these decreases in malaria incidence were related to a scale-up of intervention measures implemented by the government over the same period (Karema et al, 2012;Rulisa et al, 2013). Also, the conspicuous increase observed in malaria incidence has been attributed to a temporary decrease in the distribution of insecticide-treated mosquito nets (Karema et al, 2012).…”

“…These regions are intrinsically related to malaria occurrence. Thus, malaria is mesoendemic in the lowlands, and epidemic-prone in the highlands and hills (Rulisa et al, 2013;USAID, 2013a). In the endemic regions malaria transmission happens all year, and shows two peaks (in May-June, and December-January) following two distinct rainy seasons (USAID, 2013a).…”

“…Trends in malaria incidence could be attributed to the complex interplay of a range of determinants including climatic, environmental, and socioeconomic factors (Kazembe et al, 2006;Lowe et al, 2013;Rulisa et al, 2013). Statistical models are useful tools that allow us: i) to understand how disease outcomes change as a function of variations in their key driver; and ii) to predict disease outcomes based on the dynamics of such drivers (James et al, 2013).…”