Author response: A micro-epidemiological analysis of febrile malaria in Coastal Kenya showing hotspots within hotspots
2014-03-06

Full text Figures and data Side by side Abstract eLife digest Introduction Results Discussion Materials and methods References Decision letter Author response Article and author information Metrics Abstract Malaria transmission is spatially heterogeneous. This reduces the efficacy of control strategies, but focusing control strategies on clusters or 'hotspots' of transmission may be highly effective. Among 1500 homesteads in coastal Kenya we calculated (a) the fraction of febrile children with positive malaria smears per homestead, and (b) the mean age of children with malaria per homestead. These two measures were inversely correlated, indicating that children in homesteads at higher transmission acquire immunity more rapidly. This inverse correlation increased gradually with increasing spatial scale of analysis, and hotspots of febrile malaria were identified at every scale. We found hotspots within hotspots, down to the level of an individual homestead. Febrile malaria hotspots were temporally unstable, but 4 km radius hotspots could be targeted for 1 month following 1 month periods of surveillance. https://doi.org/10.7554/eLife.02130.001 eLife digest Malaria remains a formidable threat to public health in tropical regions. The parasite that causes the disease is transmitted to humans by bites from infected mosquitoes, and the complicated lifecycle of the parasite makes developing vaccines difficult. However, preventive strategies are effective at reducing the spread of malaria. The two most widely used and effective strategies are the use of pesticide-treated bed nets to create a barrier between sleeping families and biting mosquitoes, and indoor residual spraying to reduce the numbers of mosquitoes biting sleeping families in homesteads. Other potential preventive strategies include killing mosquito larvae in breeding sites and mass anti-malarial drug treatment for infected humans. Targeting preventive efforts to malaria hotspots—the areas where the risk of malaria transmission is greatest—may help to eliminate malaria more efficiently. Unfortunately, identifying hotspots is complicated as there are many different factors that affect how malaria spreads. These factors range from ecological conditions such as rainfall and soil type, to human effects like population density and migration. Bejon et al. have examined the patterns of malaria transmission in Kenya over 9 years. Over this period, 54% of children who went to health clinics with a fever tested positive for the parasite that causes malaria. Infected children from areas with the highest rate of malaria infection were, on average, younger than those from less infected regions. This makes sense as in highly affected areas children have a greater chance of encountering the parasite at an early age. They are therefore more likely to get malaria when younger and, as exposure to the parasite can provide some immunity to a child, they are also less likely to get infected again when older. In addition, mapping the spread of malaria reveals hotspots at different geographical scales. Bejon et al. could see hotspots within hotspots, and in some cases could go as far as identifying the individual homesteads most at risk of malaria. Public health workers could potentially use these analyses to identify areas that are likely to be hotspots and then target preventive measures there for the next month. However, the constantly changing locations of the hotspots means workers would have to reanalyse the data and retarget their interventions at the end of each month. https://doi.org/10.7554/eLife.02130.002 Introduction The transmission of infectious disease often shows substantial heterogeneity (Woolhouse et al., 1997). Malaria transmission is determined by mosquito ecology and behavior, which is in turn determined by rainfall, hydrology, soils, human behavior and population distributions, and a range of other social, biotic and abiotic factors. Heterogeneity of malaria transmission is apparent at global scale (Gething et al., 2011), regional scale (Kleinschmidt et al., 2001a; Noor et al., 2009), and at fine scale in, for instance, Mali (Gaudart et al., 2006), Ghana (Kreuels et al., 2008), Ethiopia (Yeshiwondim et al., 2009) Kenya (Brooker et al., 2004; Ernst et al., 2006; Bejon et al., 2010), and Tanzania (Bousema et al., 2010). This spatial heterogeneity makes transmission relatively resilient to indiscriminate control efforts, but also provides an opportunity to engage in targeted malaria control on clusters of transmission (or 'hotspots'), a strategy that is predicted to be highly effective (Dye and Hasibeder 1986; Woolhouse et al., 1997). We have previously identified hotspots of malaria using active surveillance (Bejon et al., 2010). Others have identified hotspots using passive surveillance in health facilities linked to demographic surveillance systems (Ernst et al., 2006). Passive surveillance is more readily scaled up, but may be biased by variations in access to health care facilities and socially-determined health-seeking behavior (Sumba et al., 2008; Franckel and Lalou 2009). The incidence of febrile malaria presenting to health care is thus biased by access to care. This bias may be countered by using the malaria positive fraction (MPF) among children with fever (also termed 'slide positivity rate' in some publications [Jensen et al., 2009]). The MPF includes all febrile children presenting to the dispensary as the denominator, hence controlling for access to health care, in contrast to incidence for which all children in the community are included in the denominator. The MPF is less likely to show systematic spatial bias with distance from the health facility since parental accounts of illness have not been found to discriminate malaria from non-malarial fever (Luxemburger et al., 1998; Mwangi et al., 2005), and diagnostic testing is not available outside the dispensary. We present data from demographic surveillance linked to passive case detection in Pingilikani dispensary in Kilifi District, coastal Kenya. Data are collected from 1500 homesteads within an 8 km radius followed for 9 years. We analyse the spatial heterogeneity of malaria cases in order to determine the temporal and spatial scales of case clustering so as to inform targeting in malaria control programmes. We also excluded visits with specific symptoms such as skin infections or cutaneous abscesses, otitis media, and gastroenteritis (>4 episodes diarrhoea per day) that might have been the primary motivation for seeking health care rather than fever per se. Results Among ∼20,000 remaining febrile presentations from ∼1500 different residences, 54% were positive for Plasmodium falciparum on blood smear examination. Using homestead as our unit of analysis, we found that the incidence of dispensary attendance declined with distance from the dispensary (on average −0.040 (95%CI 0.036–0.044) and −0.041 (95%CI 0.037–0.046) episodes per child year for each km for malaria smear positive and negative attendees, respectively). MPF was not found to vary significantly by distance of residence from the dispensary (from MPF = 0.50, 95%CI 0.47 to 0.54 at <2 km distance to MPF = 0.52, 95%CI 0.47 to 0.57 at 6–7 km, p=0.7). The spatio-temporal distribution of MPF by homestead is shown in Video 1 (slow speed) and Video 2 (fast speed). The visual impression from these clips suggests marked spatial variation, with some geographical areas showing persistently high MPFs, and other areas showing more marked temporal variation. Temporally stable spatial heterogeneity would be expected to lead to spatial heterogeneity in the acquisition of immunity, which may be evidenced by variation in the age profiles of children with febrile malaria. We therefore tested this hypothesis as below. Video 1 Download asset This video cannot be played in place because your browser does support HTML5 video. You may still download the video for offline viewing. Download as MPEG-4 Download as WebM Download as Ogg Each plotted point represents an individual homestead, where the colour shading indicates the malaria positive fraction (MPF), with red shading for high MPF and blue shading for low MPF. Points change colour each year. https://doi.org/10.7554/eLife.02130.003 Video 2 Download asset This video cannot be played in place because your browser does support HTML5 video. You may still download the video for offline viewing. Download as MPEG-4 Download as WebM Download as Ogg Each plotted point represents an individual homestead, where the colour shading indicates the malaria positive fraction (MPF), with red shading for high MPF and blue shading for low MPF. Points change color each year. The frames are identical to those in Video 1, but move more rapidly. https://doi.org/10.7554/eLife.02130.004 Spatial heterogeneity in malaria risk and acquisition of immunity MPF was inversely correlated with the average age of children with malaria, Spearman's rank correlation (rs) = −0.16, p<0.0001 (Figure 1A–C). This suggests that greater exposure to malaria (i.e., high MPF) leads to more rapid acquisition of immunity as children grow up, hence predominantly younger children visiting the dispensary with febrile malaria. There was no evidence that this relationship was confounded by spatial clustering of age: the average age of children with non-malarial fever did not show spatial clustering (Moran's I = 0.01, p=0.5 within 1 km and Moran's I = 0.02, p=0.5 within 5 km) and was not associated with MPF (rs = −0.02, p=0.4). We examined the effect of spatial scale at which this correlation occurred by imposing grids of increasing cell size on the study area, calculating rs within each cell of the grid, and then estimating the mean rs at each scale of grid (Figure 1D, blue lines). The mean rs trended gradually away from 0 as the grid divisions became larger in scale. This pattern suggests gradual differentiation in transmission characteristics as the distance between homesteads included within a cell of the grid increases. We then examined the patterns seen on applying this analysis to simulated data. In order to exclude that this trend was a result of cells at fine-scale containing fewer homesteads, we ran permutations of the data using after randomly re-assigning spatial coordinates to the homesteads. These permutations show that a consistent correlation at rs = −0.16 throughout the range of grid sizes, albeit with greater uncertainty with smaller cell size (Figure 1D, red lines). Hence, the trend of a gradually increasing inverse correlation as the grid size increases does not appear to be explained simply by having fewer homesteads in each cell at fine scale. In order to determine the pattern that might be seen with specific spatial scales of clustering, we conducted further simulations by imposed patterns with specific scales on the spatial coordinates of the homesteads, in varying proportions with random noise using a gamma distribution. These simulations show that a specific scale of clustering produces 'spikes' in rs as the cell size varies, with the position of the spike coinciding with scale of the clustering (Figure 1—figure supplement 1). Reducing the Signal:Noise ratio eventually obscured the 'spikes' due to a characteristic pattern, but only at the point where the overall correlation was no longer discernible (Figure 1—figure supplement 2). Adding a gradient to the simulated characteristic scale attenuated but did not obscure the 'spikes' (Figure 1—figure supplement 3). Figure 1 with 3 supplements see all Download asset Open asset Geographical distribution of malaria positive fraction and average age of febrile malaria. Each plotted point represents an individual homestead, where the colour shading indicates the malaria positive fraction (MPF) in panel A, or the average age of children who test positive for malaria in panel B. Panel C shows the scatter plot for MPF vs average age (Spearman's rank correlation coefficient (rs) = −0.16, p<0.0001). Panel D shows rs (y axis) plotted against scale of analysis (x axis), where a grid with varying cell size is imposed on the study area, rs is calculated within each cell and then the mean rs presented, with 95% confidence intervals produced by boot-strap (blue solid and dashed lines, respectively), and the results of analysis of spatially-random permutations of the data with equivalent cell size are shown for comparison (red solid and dashed lines, respectively). The analysis shown in panel D was compared on simulations with varying simulated characteristic scales, Signal:Noise ratios and with added gradients (Figure 1—figure supplements 1–3, respectively). https://doi.org/10.7554/eLife.02130.005 Hotspots within hotspots Using the Bernoulli model in SaTScan (Kulldorff, 1997), we identified a hotspot with a radius of 5.8 km at p<0.00001 (Figure 2A) using the full data set (for which n = 20,702). However, on re-analysis of the children within this hotspot (in which n = 5300), we identified a further hotspot (with a radius of 0.76 km) within the 5.8 km hotspot (p<0.00001, Figure 2B). Then on further re-analysis of the homesteads within that 0.76 km hotspot (within which n = 1406), we identified a third significant hotspot (p=0.016) which comprised a single homestead, in which there were 36 episodes of malaria compared with 3 malaria negative fevers (Figure 2D). When we selected a random 5-km square area outside the original 5.8 km radius hotspot, we identified a hotspot within this area a fourth hotspot with a 1.32 km radius (p<0.00001, Figure 2C). Figure 2 with 2 supplements see all Download asset Open asset Hotspots within hotspots. Each plotted point represents an individual homestead, where the colour shading indicates the malaria positive fraction (MPF). Hotspots are identified using SATScan, using the whole study area (panel A), then repeated within the hotspot (panel B), within the hotspot of panel B (panel D), and then within a randomly chosen area outside the hotspot (panel C). The semi-variogram and log–log semi-variogram plot are shown in Figure 2—figure supplements 1 and 2, respectively. https://doi.org/10.7554/eLife.02130.009 To further explore the scale of spatial clustering, we plotted the semivariogram (Figure 2—figure supplement 1) and the log–log transformed semivariogram (Figure 2—figure supplement 2). These plots suggested linear fits for the semivariogram, suggesting that spatial clustering occurred over a range of spatial scales. Temporal trends of spatial heterogeneity We also examined temporal trends for individual homesteads (Figure 3). There was an inverse correlation between the mean MPF and the variance in MPF over the 10-year study period (rs = −0.61, p<0.0001, Figure 3A). The temporal trends for two subsets of homestead can be seen in Figure 3B (stable high MPF) and Figure 3C (unstable low MPF), suggesting that homesteads can be characterized as stable high transmission homesteads or unstable low transmission homesteads. Infant parasite rates have been proposed as a measure of transmission intensity that minimizes the offsetting of acquired immunity in macro-epidemiological studies (Snow et al., 1996). We therefore hypothesized that the malaria positive fractions in children <1 year of age (hereafter 'MPF<1yr') would measure transmission intensity without the offsetting of acquired immunity, and that unstable transmission would result in higher risk of malaria in older children. To test this hypothesis, we calculated the mean MPF<1yr and the variance in MPF<1yr for each homestead over the 9 years of follow up and tested the relationships between these metrics and risk of malaria in older children in multivariable linear regression models. Figure 3 Download asset Open asset Temporal variations in malaria positive fraction. (Panel A) shows the scatter plot of individual homesteads by mean malaria positive fraction (MPF) on the x axis vs variance in MPF on the y axis (rs = −0.61, p<0.0001). A labelled blue circle indicates subset q (homesteads with high variance but low mean MPF) and subset p (homesteads with low variance and high mean MPF). The temporal trends for these two subsets are shown on panels (B and C), respectively. The median trend for the study area is shown in red. (Panel D) shows the regression coefficients (y axis) for the malaria positive fractions (MPF) in older children when regressed on; (i) the mean MPF in children <1 year of age (MPF<1y) and (ii) MPF in older children when regressed on the variance in MPF<1y over the 9 years of the study. Separate multivariable regression models (i.e., with mean MPF<1y and variance in MPF<1y as explanatory variables) are fit for each age group as shown on the x axis (excluding children <1 year of age, whose data are used to calculate MPF<1y). https://doi.org/10.7554/eLife.02130.012 In multivariable linear regression models, MPF<1yr was strongly correlated with MPFs in children in the 1- to 2-year-old and 2- to 3-year-old age group, but progressively less strongly correlated with MPF in older children (Figure 3Di). The regression coefficient was ∼0.4 for 1–2 year olds, meaning that each unit increase in MPF<1yr is associated with a 0.4 increase in the MPF for 1- to 2-year-old children. On the other hand, the variance in MPF<1yr was not correlated with MPFs in 1- to 2- or 2- to 3-year-old children, but was progressively more strongly correlated with MPF in older children (Figure 3Dii). Hence there were high stable transmission homesteads, with predominantly younger children getting febrile malaria, and low unstable transmission homesteads, with increasing risk to older children. This pattern of high stable vs low unstable transmission also occurs between regions or countries, and demonstrates a similarity between the micro- and macro-epidemiology of malaria (Hay et al., 2008). Theoretical accuracy of targeted control undertaken at varying temporal and spatial scales We then used our data set to simulate the accuracy of targeting cases that a malaria control programme might achieve on conducting surveillance over a defined period of time followed by targeted control. We assumed that malaria control programmes would need to define a priori the period of time to use for surveillance, and also to select a spatial scale at which to define hotspots. For varying time periods and spatial scales, we determined the % of excess malaria cases within the targeted hotspots compared with the surrounding area in the period of time immediately following the simulated surveillance. One week periods of surveillance (top left panel of Figure 4) did not identify hotspots that are still present the following week at fine spatial scales (i.e., the plotted line indicates that the accuracy of targeting is 0% at scales of less than 1 km). On the other hand, at larger spatial scales we found that 1 week periods of surveillance were more accurate, resulting in the targeting of areas with a 60% excess of new malaria cases compared with the surrounding area at a scale of an 8 km diameter. A similar pattern was seen for monthly periods of surveillance. Longer surveillance periods (e.g., 6 months) resulted in targeting areas with an excess of 20% malaria cases compared with the surrounding area over the range of spatial scales examined. Figure 4 Download asset Open asset Theoretical accuracy of targeted control undertaken at varying temporal and spatial scales. The accuracy of varying strategies of hotspot identification is shown. Each panel is labelled with the time period of surveillance data used. The x axis shows the diameter of hotspot defined. In each case hotspots were selected to account for 20% of the homesteads in the area. The y axis shows the increase that would have been present assuming that they were targeted in the time period following their identification. https://doi.org/10.7554/eLife.02130.013 ITN use and spatial variation in risk Mass distributions of Insecticide Treated Nets (ITNs) in the area began in 2006. ITN use was surveyed in 2009 and 2010. We found that children using ITNs had a reduced risk of malaria by logistic regression (i.e., OR = 0.69, 95%CI 0.67 to 0.8, p<0.001), in keeping with previous literature on the personal protection provided by ITN use (Lim et al., 2011). On the other hand, we did not identify significant evidence that ITN use was clustered spatially (Moran's I = 0.02, p=0.5). Furthermore, ITN use as a in SaTScan analysis to hotspots had effect on the of ITN use as a the of the hotspot by and the predicted radius of the hotspot from km to On re-analysis of the homesteads within the km hotspot, a further km hotspot was identified the position and radius of which were not by the of ITN use as a within this km hotspot the homesteads were identified as a hotspot of the of ITN use as a We did not identify significant evidence that ITN use correlated mean MPF<1yr (rs = or with the variance in MPF<1yr (rs = p=0.7). Hence, ITNs provided personal protection from malaria, but we were to show that they explained the spatial Discussion We found that malaria cases were spatially in an radius area of coastal Kenya. The strongly significant inverse correlation between the malaria positive fraction (MPF) and average age of children presenting with malaria suggests acquisition of immunity between homesteads. at high transmission intensity have a high MPF and a average age of malaria (with older children and therefore not presenting to the homesteads at low transmission intensity have a low MPF but an older average age of malaria since older children are not as rapidly. In this inverse correlation might have because of heterogeneity at spatial scales. For instance, there might have been a of homesteads all at high transmission in of the study area with high MPF and low average and a of homesteads at low transmission in the other (with low MPF and high average On the other hand, the inverse correlation might have because of a random distribution of and transmission intensity homesteads throughout the study area. To determine at which spatial scale transmission was we conducted an analysis where correlation coefficient was within each cell of a grid on the study area. The mean correlation coefficient of all cells was then as the cell size of the grid used was increased (Figure This analysis was to identify the most geographical scale at which the inverse correlation was In simulated we 'spikes' where the inverse correlation was when the size of cells in the grid with the size of the geographical of homesteads that the inverse as seen in Figure 1—figure supplement were seen after simulated noise and gradients in over which the correlation (Figure 1—figure supplements 1, 2 and 3). data would more of variation than we have and hence may not the analysis of these simulations suggests that in the correlation between MPF and average age of malaria over cell size might be expected when clustering is at a specific spatial scale. In there was no such in the shown in Figure 1D, indicating that the inverse correlation was present at every geographical scale examined within our study. is likely that this pattern would at greater geographical scales, since a similar inverse correlation between the age distributions of malaria cases and transmission intensity can be seen on and regions et al., 2009). The pattern of spatial heterogeneity is to malaria since targeted disease control is predicted to be highly effective (Woolhouse et al., 1997). Spatial targeting is for malaria 'hotspots' et al., et al., et al., et al., and many malaria control programmes are in et al., et al., data showing clustering at varying spatial scales that malaria control can to identify hotspots at many different geographical scales. We that hotspots within hotspots, down to the level of a single homestead, and also that hotspots can be identified on on random areas outside the hotspot (Figure 2C). These hotspots were on analysis of a with and were strongly significant on the permutations in suggesting that I is an for our The of presenting within to a malaria control programme is further by the temporal of the spatial pattern (Figure 3). We therefore simulated the accuracy with which hotspots could be targeted using varying spatial scales and varying time periods of surveillance. We found that using data over 1 month of surveillance to define 4 to 8 km diameter hotspots would provide but this information is only for 1 month temporal further surveillance. One might therefore a programme of surveillance and where the surveillance data are examined at the end of each month to determine the to be targeted for the following month. surveillance would targeting of hotspots for the following month. a strategy might be all year or for a period of the year on et al., Targeting at this spatial scale the added that could be with data and would not fine-scale data. There are some to this are from a single Other sites their data to determine a similar targeting strategy is Furthermore, some hotspots did show temporal For instance, we identified a 6 km diameter hotspot of the dispensary that a increase in MPF compared with the surrounding area throughout the surveillance. with positive for malaria presenting at the dispensary may have febrile malaria, or may have with non-malarial studies estimating malaria fractions in the of the children in our analysis would have malaria as the of their with the other having with fever from et al., 2011). We have previously that spatial heterogeneity is more temporally stable when for rather than febrile malaria (Bejon et al., 2010). Targeting hotspots of would community rather than dispensary which may need to be less than of febrile malaria MPF is not a of transmission with low average of febrile malaria are likely to be stable high transmission homesteads as those in subset p of Figure which transmission in the areas surrounding Targeting such high transmission homesteads to transmission may be highly effective (Woolhouse et al., 1997). The inverse correlation between MPF and average age of febrile malaria as spatial scale increases (Figure 1) suggests that the spatial heterogeneity of transmission is progressively more stable at more spatial scales. Malaria transmission is determined by mosquito ecology and ecology may be determined by geographical such as et al., 2005), et al., and et al., et al., and mosquito for et al., models on such have been using et al., 2005), et al., and (Snow et al., However, the ecological may in different geographical areas (Kleinschmidt et al., et al., 2006; Noor et al., 2008), and the effect of ecological factors is by fine-scale and et al., data suggests that the factors malaria transmission at a range of spatial scales. We might that