The independent effect of living in malaria hotspots on future malaria infection: an observational study from Misungwi, Tanzania
© Mosha et al.; licensee BioMed Central Ltd. 2014
Received: 28 July 2014
Accepted: 10 November 2014
Published: 21 November 2014
As malaria transmission declines, continued improvements of prevention and control interventions will increasingly rely on accurate knowledge of risk factors and an ability to define high-risk areas and populations at risk for focal targeting of interventions. This paper explores the independent association between living in a hotspot and prospective risk of malaria infection.
Malaria infection status defined by nPCR and AMA-1 status in year 1 were used to define geographic hotspots using two geospatial statistical methods (SaTScan and Kernel density smoothing). Other malaria risk factors for malaria infection were explored by fitting a multivariable model.
This study demonstrated that residing in infection hotspot of malaria transmission is an independent predictor of malaria infection in the future.
It is likely that targeting such hotspots with better coverage and improved malaria control strategies will result in more cost-efficient uses of resources to move towards malaria elimination.
KeywordsMalaria Transmission Hotspots Risk factor Serology PCR Africa Plasmodium falciparum
Transmission of malaria is highly heterogeneous even in areas of moderate transmission with clusters of households that are at consistently high levels of risk of malaria. These clusters, termed hotspots, are responsible for spread of malaria infection in the wet season . As malaria transmission declines, prevention and control interventions will increasingly rely on accurate knowledge of risk factors and an ability to define high-risk areas and populations at risk for focal targeting of interventions. This could be useful in the allocation of limited resources to ensure areas that require them the most are given priority.
Several studies have documented individual and household risk factors associated with malaria infection. Some of the risk factors that have already been reported to be associated with malaria infection include type of housing [2–4], socio-economic status (SES) [5, 6], proximity to mosquito breeding sites [5–8], age, and sex [2, 5, 6, 9]. If the theory of malaria hotspots is true, that infection clusters in small spatial scales, then residing in a hotspot should be an important independent risk factor for individual level risk of malaria infection as local mosquitoes are more likely to be infectious. Identification of malaria transmission hotspots are important in order to focus the control and elimination activities to appropriate geographic areas and also to select the appropriate population level interventions, such as indoor residual spray in addition to interventions targeted towards high risk groups. This paper examines the independent association between living in a malaria hotspot and future risk of malaria infection.
Misungwi district (lat 2.85000 S, long 33.08333 E) is located 60 km from Mwanza town in the northwest of Tanzania at an altitude of 1,178 m above sea level. Details of the study site have been previously described . In brief, the district is rural with moderately intense malaria transmission; the overall prevalence of infection in the region is estimated to be 31.4% by microscopy in children six to 59 months of age .
This is a cross sectional study, which was conducted twice in year 1 in 2010 and year 2 in 2011. The exposure of interest was whether a person resided in a malaria hotspot or not in the first year of the study. Hotspots were defined by SaTScan and Kernal density method using infection status derived using nested polymerase chain reaction (nPCR) and AMA-1 sero status in year 1, as previously reported . The methods are explained briefly below. The outcome of interest for this analysis was infection status at the individual level by nPCR (infected/not infected) in the survey taken in the second year. In brief, the SaTScan software (SaTScan, version 8.2.1) used a spatial scan statistic using the Bernoulli model to identify clusters of significant high (hotspot) and low (coldspot) risk of infection . Using the SaTScan method, SaTScan cold spots were coded as 0, hotspots as 1 and everything else as 0.5. The kernel method of household clustering of both nPCR and AMA-1-positive individuals was estimated using Kernel density smoothing. Kernel density estimates, for any given point, the density of events within a predefined window, with the influence of events weighted according to the distance from the centre of the window. The weight assigned to each event is derived from the kernel function applied. Details of these methods have been described previously . Using the Kernel method, each household was assigned a value between 0 (least exposed households) and 1 (most exposed households). Households for which data were only available in the second year were assigned a hotspot score based on infection in neighboring households only.
A census of four villages in a single ward of the Misungwi district of Tanzania was carried out in the dry season, in two consecutive years 2010 and 2011 between the months of August and November. Trained interviewers administered a structured questionnaire to consenting household heads. Information gathered included morbidity, demographics and data on potential risk factors. Latitude and longitude of each household was taken using a GPS device. Data were recorded electronically using personalized digital assistants and were downloaded each evening of the survey onto a desktop computer to a master Microsoft access database. Also, every consenting individual was asked whether they slept under a long-lasting insecticidal-treated net (LLIN) the previous night. A finger-prick blood sample was collected and was spotted onto Whatman® standard 3 mm filter paper for parasite detection by nPCR and for serology analysis (AMA-1).
Statistical analysis was performed using STATA (version 12, College Station, TX, USA). Mixed effect logistic regression models were used to evaluate whether residing in a hotspot in the first year was predictive of subsequent malaria infection in the second year, controlling for other potential risk factors for malaria infection. The model adjusted for possible clustering of malaria cases within households.
Univariate analysis of potential risk factors for malaria infection in year 2, as measured by nPCR
N = 3,246
N (%) with malaria n = 1,683
Crude OR* (95% CI)
Wald test P-value
Age group inyears
Sleep under ITN
(Per additional increase in wealth quartile) 0.69 [0.58-0.82]
Indoor residual spraying
Number of cattle
Distance to health facility
(per additional increase in distance group to health facility) 2.01 [1.69-2.40]
Residence in a hotspot (SaTScan-nPCR)
Residence in a hotspot (SaTScan-AMA-1)
Residence in a hotspot (Kernel-nPCR)
Residence in a hotspot (Kernel-AMA-1)
All variables were analysed individually for an association with the outcome (malaria by nPCR infection in year 2) using logistic regression. A household-level random effect was included to account for correlation between individuals within the same household. All variables showing evidence for a possible association with malaria risk (p < 0.1) were included in the preliminary main effect multivariate logistic regression model. A forward stepwise approach was then followed to exclude any variable that showed a lack of effect on malaria risk (p > 0.05). Hotspots defined by SaTScan and kernal are fitted in different multivariate models.
A total of 3,246 individuals participated and provided a blood specimen in year 2. This represents 85.4% of individuals in the community who were eligible to participate. The median age of the study population was 13 years (IQR = 5–30 years; range <1-99 years) and 41.6% were male. The uptake of vector control measures was high in the study communities; 91% of the study participants reported to be sleeping under an LLIN the previous night, and 82% of households had received IRS within the six months before the survey.
Table 1 presents the results of the univariate associations with individual infection status in year 2. These univariate estimates were adjusted for possible household level clustering. Residing in hotspots defined by malaria infection and AMA-1 sero status were associated with higher odds of malaria infection. Children between the age of five and 15 years and males had significantly higher odds of malaria infection. Higher wealth status, use of LLIN and mother’s education were associated with lower odds of malaria infection. Households with poor quality of construction materials, presence of a breeding site near the household and greater distance to the health facility were associated with higher odds of malaria infection.
Multivariate models* estimating the independent risk of malaria infection associated with residing in a malaria hotspot
N = 3,246
Adjusted OR** (95% CI)
Wald test P-value
Residence in a hotspot (SaTScan-nPCR)
3.11 [1.57, 6.18]
Residence in a hotspot (SaTScan-AMA-1)
Residence in a hotspot (Kernel-nPCR)
0.88 [0.46 -1.66]
Residence in a hotspot (Kernel-AMA-1)
However, hotspots defined by both nPCR and AMA-1 using kernel method did not appear to be independent risk factors for future malaria infection after controlling for other factors in the multivariable analysis, OR 1.52; 95% CI 0.87-2.66 and OR 0.99; 95% CI 0.49-2.00, respectively.
SaTScan model of risk factors associated with malaria infection in year 2 in the multivariable analysis
Adjusted OR (95% CI)
Wald test P-value
Residence in a hotspot (nPCR)
3.11 [1.57, 6.18]
Residence in a hotspot (AMA-1)
Age group in years
Sleep under LLTN
Distance to health facility
Per additional increase in distance
Group to health facility
Individuals in the age group of five to 15 years had more than five times the odds of infection than those who were in younger age group (OR 5.04; 95% CI 3.82-6.64). There was borderline evidence that the risk of malaria infection was higher for males than females (OR 1.24; 95% CI 1.01-1.53).
Individuals living within 100 meters of a mosquito breeding site had increased odds of malaria infection compared to those not living near a mosquito breeding site (OR 1.59; 95% CI 1.11-2.29). Individuals living in poor-quality households had increased odds of malaria infection compared to those who were living in good-quality households (OR 1.53; 95% CI 1.01-2.32). Distance to the health facility was strongly associated with risk of having malaria infection. Individuals residing in households based further from health facilities had increased odds of malaria infection (OR 1.34; 95% CI 1.03-1.76).
There was strong evidence that sleeping under LLIN was associated with decreased odds of infection (OR 0.40; 95% CI 0.25-0.63). Individuals whose mother had primary education or more had decreased odds of malaria infection compared to those individuals whose mother had not gone to school (OR 0.66; 95% CI 0.45-0.95).
Wealth, IRS, number of cattle, and number people sleeping in the house were not significantly associated with the risk of malaria infection in multivariate analyses.
This study demonstrates that residing in an infection hotspot of malaria transmission is an independent predictor of getting malaria infection in the future, adjusting for known risk factors for malaria infection. This could be due to individuals’ proximity to other infections in the area, as mosquitoes tend to stay within the same area, puts others in the area at higher risk. Equally, there could be other spatially clustered risk factors which haven’t been accounted for, and there is therefore residual clustering of infections. The independent effect of residing in hotspots of malaria infection was only found when SaTScan was used to identify hotspots. SaTScan analysis identified a single big central cluster of malaria hotspots and kernel analysis identified the central cluster that was identified by the SaTScan method and also identified other smaller clusters of malaria hotspots. Possible explanation could be there are factors that are influencing risk in the central hotspot that are not adjusted for. Whereas, factors that are explaining risk of malaria infection in other hotspots identified by kernel are included in the model and they have been adjusted. The data support the hypothesis that residing in a malaria hotspot is an independent predictor of future malaria risk, controlling for other known risk factors for malaria.
The observed increased risk of malaria infection in older children (age group five to 15 years) compared to children under five years of age may possibly be due to the fact that older children are more exposed to infectious mosquitoes as this age group tend to be more active and hence spend more time outside the household in late evening and early night. In previous studies malaria risk was reported to be high in younger children [1, 13, 14]. However, the increased risk of malaria infection in older children in this study could also be as a result of overall increase in LLIN coverage in younger children in the study communities and also in other parts of Tanzania after universal distribution of LLINs . The same pattern of increased risk in older children has also been observed by other studies conducted in Tanzania [16, 17]. Likewise, the study used PCR for parasite detection rather than microscopy or RDT, therefore the likelihood of picking up more low density infections was much higher. It has been observed that decreasing transmission results in age escalation of infection to older children . This could be due to slower development of naturally acquired immunity.
Living far from health facility has been associated with increase in malaria risk . This study observed the same trend with malaria risk increasing with increasing distance from health facility. Individuals living near health facilities could be making more frequent trips to the health facilities and this might have resulted in more opportunities for health messages reinforcing proactive efforts to protect their health and of other family members and encourage early treatment, which is expected to clear infections completely. Individuals living far from health facilities could delay seeking prompt malaria treatment or easily choose to seek other alternative traditional treatment, which is ineffective and results in ongoing malaria transmission.
Environmental factors such as proximity of household to water bodies, bushes and stagnant water acting as breeding site for mosquitoes have been shown to be a major risk factor for malaria infection and transmission [2, 20–23]. Previous entomological studies have suggested that mosquitoes tend to have blood meals from humans that are in close proximity [9, 24]. Despite high coverage of IRS in the areas in this study, it was detected; a large number of infections and IRS was not associated with protection from malaria in univariate or multivariate models. This could be due to insecticide resistance as was documented by Kabula et al., whose national surveillance demonstrated widespread resistance to pyrethroids among Anopheles gambiae across Tanzania . It has also been reported that environmental management strategies to control breeding sites by either larval control or by other traditional methods have resulted in reduction of mosquito densities and malaria transmission [26, 27]. In Africa, larval source control is recommended where breeding sites are fixed, findable and few. This paper has shown that malaria risk is associated with proximity to known breeding sites, thus larval source control methods could be employed as an additional malaria control tool.
The finding that poor-quality housing is an independent risk factor for malaria infection in the second year agrees with previous studies [3, 28–32]. The presence of open eaves in house design and unscreened windows have been associated with increased risk for malaria infection, as the eaves are entry points to the household for malaria vectors [2, 4, 32, 33]. Quality of housing has been reported to influence the ease with which mosquitoes can enter and hide in a household and therefore contribute to malaria risk . Although interventions to address quality of household construction as a malaria risk factor are complex and difficult to achieve, it might be important to add this component as an intervention for malaria control in public health programmes. In recent years, Rwanda has started a campaign to improve household structures by replacing thatched roofs with iron sheets, as malaria control strategy . Improving everyone’s housing may be impossible but it might be cost effective to improve people’s housing within a hotspot.
This paper demonstrates that living in a geographical cluster of households at high risk of malaria is an important independent risk factor for future malaria infection. In this analysis, living within a malaria hotspot as defined by SaTScan, showed a strong association with malaria infection in the subsequent year (OR 3.11, 95% CI 1.57, 6.18) independent of housing quality, proximity to breeding site, maternal education, distance from the health facility, and the use of both IRS and LLIN for vector control. This suggests that targeting hotspots with better coverage and improved malaria control strategies will likely result in a more cost-efficient use of resources to achieve malaria control and elimination. A remaining challenge is how malaria control programmes can detect these hotspots without having to conduct PCR prevalence or serological surveys.
Other risk factors, such as residing in households built from poor-quality materials, households situated near breeding sites and households that are far from health facilities, should also be explored to see if they can be used to lead a malaria surveillance officer to a hotspot.
This study was supported by Malaria Capacity development consortium (MCDC), which is funded by Welcome Trust (Grant number WT084289MA) and Bill and Melinda Gates Foundation, Grand Challenge for Exploration no: 01916000035 supported the field work and OPP1013170 supported the analysis. We acknowledge support of John Changalucha and the management team of NIMR Mwanza Centre.
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