Investigating the upsurge of malaria prevalence in Zambia between 2010 and 2015: a decomposition of determinants

Malaria is endemic to Zambia and has stable transmission throughout the year [1], the burden of the disease follows a seasonal pattern that is dictated by environmental factors including rainfall, vegetation and temperature among others [2]. It is lowest during the cold and dry season around June to August and highest during the warm and rainy season from November to April [3].

Apart from following the seasonal pattern based on temperature and rainfall season, the malaria burden also follows regional trends. The northern parts of Zambia namely Luapula, Northern, North-western and Muchinga provinces receive the highest rainfall of up to 1200 mm annually and similarly have the highest malaria prevalence of above 20% in children aged below 5 years at the peak of the transmission season. The middle parts of Zambia from Eastern, Central, Copper-belt and Western provinces which receive between 800 and 1000 mm of rainfall annually also have low to moderate burden of malaria with prevalence from 10 to 20% whilst the southernmost parts of the country including Lusaka, Southern and the southern parts of Western provinces receive less than 800 mm of rainfall annually similarly have malaria prevalence rates of less than 10% malaria in under five children [4].

Interventions against malaria can alter the natural transmission cycle of malaria from the vector to the human host and vice versa and be able to change and/or eliminate malaria transmission altogether. A meta-analysis of 13 studies done in sub-Saharan Africa on indoor residual spraying (IRS) effectiveness indicated a relative risk in malaria prevalence of 0.38 [5]. A study in Zambia on the Copper-belt province reported an IRS protective incident rate ratio of 0.65 [6]. Use of insecticide-treated nets (ITNs) is another effective strategy for reducing malaria burden; one systematic review of over 10 studies in sub-Saharan Africa estimated an incident rate ratio of 0.49 [7]; in another systematic review which included 19 studies worldwide, ITNs were found to have a protective effect of 17% compared to no nets (Relative Rate 0.83) and to reduce the incidence of uncomplicated malaria episodes by 50% [8]. Another systematic review attributed 79% of the reduction in malaria burden to vector control interventions [9].

Prompt treatment of malaria cases with effective antimalarial medication and environmental management also reduces malaria morbidity and mortality; one study showed an odds ratio of 0.55 between 2003 and 2005 after the deployment of artemisinin-based combination therapy (ACT) and ITNs [10]. Another study, a systematic review in West Africa involving seven studies found that intermittent preventive therapy in children (IPT_c) prevented three-quarters of all clinical malaria episodes [11]. At the time of the literature search, studies in Zambia which linked treatment to the burden of disease were not found.

Financial and programmatic coverage figures compiled by the World Health Organization (WHO) indicate that prevention and treatment interventions for malaria were implemented in Zambia at a larger scale during the period 2010 to 2015 than before [12]. Zambia implemented mass distribution of ITNs in 2012 and 2015 throughout the country [13] and routine ITN distribution to pregnant women and children below 5 years old, IRS, prompt diagnosis and treatment of malaria cases, intermittent prophylactic therapy for pregnant women and mass drug administration among other activities. Despite these interventions, the prevalence of malaria in Zambia increased during the same period from 16.0 to 19.4% [4], yet globally, the malaria incidence reduced on average by more than 21% between 2010 and 2015. Zambia is among the only 13 countries that recorded increased incidence out of the 91 countries and territories with malaria transmission in 2015 [12]. During the previous strategic planning cycle (2006–2010), malaria incidence in Zambia reduced by about 40.3% from 412/1000 persons to 246/1000 persons in 2009 [14]. This study investigated factors that could have contributed to malaria burden increase despite the high impact interventions implemented by government and cooperating partners in Zambia. The findings would inform malaria programme implementers, policymakers and donors alike on how to further strengthen the fight against malaria in Zambia and possibly plan to avoid future resurgences in malaria.

Changes in malaria prevalence by province showed that seven out of ten provinces in Zambia recorded increases in malaria prevalence in 2015 compared to the baseline of 2010; however, this does not explain why there were increases where there were increases. Our findings suggest that significant predictors of malaria prevalence include age, the location of residence, altitude, housing, IRS, and temperature. Increase in age was significantly associated with the increase in the risk of malaria prevalence while increases in the percentage of children who reside in standard houses, urban location, IRS, and altitude were significantly associated with reduced risk of malaria prevalence while gender, ITN use and rainfall were not significantly associated with malaria prevalence.

There were conflicting results among studies conducted in Zambia on the determinants of malaria prevalence; some studies found ITNs and IRS as effective [25, 26], but another study found that both IRS and ITNs were not significant predictors of malaria prevalence among children [27]. This study may have detected significance because MIS was highly powered and nationally representative compared to other studies. The study also agreed with similar studies in Sub-Saharan Africa using MIS datasets which found that IRS was effective [28, 29], in meta-analyses [30] and those targeting specific interventions, such as IRS [5], and climatic factors such as rainfall and temperature [2, 31].

Whilst this study found temperature to be a significant predictor but not rainfall, it is probably because only 3 months of rainfall prior to the surveys were considered in the model so this is likely not to have factored in seasonal variability of malaria where rainfall is an established significant predictor of malaria [31‐33].

Decomposition of malaria prevalence by determinants showed that the increases in the two mainstream malaria interventions ITN and IRS between 2010 and 2015 could have possibly contributed to the reduction in the overall malaria prevalence by − 0.2% and − 0.3%, respectively, however, these positive contributions were overridden by changes in other factors which contributed to the increase namely location of residence in rural areas and changed composition of under-five children by age groups which contributed + 2.2% and + 2.3%, respectively.

Residing in urban areas carried less risk of malaria prevalence compared to rural areas with adjusted odds ratio of 0.23 (0.14–0.37) implying that the 10% replacement of less at risk urban residents in 2010 with 10% more at risk rural residents in 2015 contributed to the overall increase in malaria prevalence in 2015, similarly there were 6.6% more children in the age group 12–59 months with an adjusted odds ratio of 4.0 compared to the less-at-risk children in the age group 0–11 months. Other than poor housing structures and wealth status in rural areas compared to urban areas which were adjusted for in this study, other factors still make rural residents more at risk of malaria than their urban counterparts such as outdoor activities for example farming, fishing, animal rearing which have been shown in literature to increase vector-human contact [34]. Under five children are generally more at risk of malaria than the general population [35], however, infants (age group 0–11 months) were less at risk of malaria positivity in this study compared to older under five children aged 12–59 months, this was similar to what was found in Uganda [28] and the Democratic Republic of Congo [36]. It is documented that babies acquire passive antibodies from their mothers in utero and during breastfeeding against malaria which gives some protection in early infancy; however, these wane off around 6 months of age [37]. Secondly, as the children grow older, they become more independent of their caregivers and may move outdoors more frequently and wear less protective clothing against invading mosquito compared to infants [38].

This study was of a cross-sectional design in nature and, therefore, was not meant to explain population dynamics such as changes in urban–rural migration or age differences among under-five children, however it was noted that the sampling of clusters in 2015 MIS had more clusters in rural areas (77%) compared to the cluster sampling in 2010 which had 58% of clusters in rural areas. The 2015 MIS cluster sampling was based on the 2010 Census of Population and Housing population structure while the 2010 MIS cluster sampling was based on the 2000 Census of Population and Housing structure. Whilst urbanization was projected to increase in Zambia from 39.5 in 2010 to 41.8% in 2015 [16], the fertility rates in rural areas (6.6%) were higher than in urban areas (3.7%) [39] so this could also explain why there were more children in rural areas than urban areas. Further, birth rates were reducing over the years in Zambia, for example, the crude birth rate was 44.3/1000 in 2011, but decreased to 42.4/1000 in 2015 [16]; this could explain why there was a lower proportion of under-five children aged below 1 year in 2015 compared to 2010.

The ‘upsurge’ in malaria prevalence noted in the 2015 MIS compared to 2010 MIS may not truly represent a rise in prevalence in Zambia; the true 2015 MIS prevalence may have been over-estimated because of over-sampling the rural areas where there was a higher burden of malaria. Whilst sampling weights were applied in both 2010 and 2015 because of oversampling in rural areas, 2010 reverted the oversampling to true rural population proportion from 70.8 to 68.3% which was not statistically different from 65.3% reported in the 2000 census; however, 2015 did not adequately revert the rural strata proportion to the truly representative rural proportion. Before weights were applied, the rural percentage among under-five children was 81.2%, after weights were applied, the rural stratum percentage reduced to 79.0% which was statistically different from the 60.5% reported in the 2010 census of population and housing. In this case, there would have needed to standardize the findings to the 2010 population structure, and if this were done, the malaria prevalence for 2015 would have been about 17.6%. The 17.6% is not statistically different from the 16.0%-point prevalence reported in the 2010 MIS (P-value = 0.398). Though there were changes in climatic indicators such as rainfall and temperature, this study did not elicit sufficient evidence based on their marginal effects to suggest that they influenced the upsurge in malaria between 2010 and 2015. Rainfall, temperature and altitude are well documented factors that influence malaria [31, 33] as they are known to affect vector dynamics, such as vector population density and biting behaviour driven by reproduction needs [32, 40] and also influence human behaviour that predisposes them to bites by the vector, for example, when it is hot, some people in villages sleep outside and avoid to cover themselves with mosquito nets because of the heat [41, 42].

The determinants that were found to be significantly associated with malaria include age, the location of residence, altitude, housing, IRS and temperature. Malaria prevalence recorded in Zambia increased in 2015 by 21% of the 2010 baseline despite overall increases in mainstream interventions such as IRS and ITN utilization partly due to increases in a high-risk group of children aged between 12 and 59 months and increases in more at risk rural residents. The findings of this study demonstrate the complexity of the drivers of malaria; while interventions like ITNs and IRS have been demonstrated to bring down malaria, counter changes in demographics such as increases in more at risk groups among under-five children and increases in the ratio of rural–urban residence may override the impact of these interventions. The apparent increase in rural residents in the 2015 sample population may not have been a reflection of the true population proportion of rural areas but due to oversampling of the rural strata that was not adequately adjusted for using weights. The true malaria point prevalence may, therefore, have been overestimated.

Though there was an overall ITN utilization increase of 10% between 2010 and 2015 among under-five children, the overall ITN utilization rate of 58.9% was still below the national target of 80%. Similarly, the overall coverage of IRS was low at below 30% at national level despite some provinces having coverage as high as 60%; however, this is below desired coverage of 80% or more.

Standard housing which consists of a solid roof, brick walls with closable windows and concrete floor has been shown to be an effective way of addressing malaria after adjusting for all other factors like wealth status, urban/rural residence, IRS, ITN utilization among others.