Spatial, temporal, and spatiotemporal analysis of under-five diarrhea in Southern Ethiopia

Spatial, temporal, and spatiotemporal analysis of under-five diarrhea in Southern Ethiopia Background: Despite improvements in prevention efforts, childhood diarrhea remains a public health concern. However, there may be substantial variation influenced by place, time, and season. Description of diarrheal clusters in time and space and understanding seasonal patterns can improve surveillance and management. The present study investigated the spatial and seasonal distribution and purely spatial, purely temporal, and space-time clusters of childhood diarrhea in Southern Ethiopia. Methods: The study was a retrospective analysis of data from the Health Management Information System (HMIS) under-five diarrheal morbidity reports from July 2011 to June 2017 in Sidama Zone. Annual diarrhea incidence at district level was calculated. Incidence rate calculation and seasonal trend analysis were performed. The Kulldorff SaTScan software with a discrete Poisson model was used to identify statistically significant special, temporal, and space-time diarrhea clusters. ArcGIS 10.1 was used to plot the maps. Results: A total of 202,406 under-five diarrheal cases with an annual case of 5822 per 100,000 under-five population were reported. An increasing trend of diarrhea incidence was observed over the 6 years with seasonal variation picking between February and May. The highest incidence rate (135.8/1000) was observed in the year 2016/17 in Boricha district. One statistically significant most likely spatial cluster (Boricha district) and six secondary clusters (Malga, Hulla, Aleta Wondo, Shebedino, Loka Abaya, Dale, and Wondogenet) were identified. One statistically significant temporal cluster (LLR = 2109.93, p < 0.001) during December 2013 to May 2015 was observed in all districts. Statistically significant spatiotemporal primary hotspot was observed in December 2012 to January 2015 in Malga district with a likelihood ratio of 1214.67 and a relative risk of 2.03. First, second, third, and fourth secondary hotspots occurred from January 2012 to May 2012 in Loka Abaya, December 2011 in Bursa, from March to April 2014 in Gorchie, and March 2012 in Wonsho districts. Conclusion: Childhood diarrhea was not distributed randomly over space and time and showed an overall increasing trend of seasonal variation peaking between February and May. The health department and other stakeholders at various levels need to plan targeted interventional activities at hotspot seasons and areas to reduce morbidity and mortality. Keywords: Southern Ethiopia, Under-five diarrhea, SatScan, Cluster, Spatial, Temporal * Correspondence: hunachew@gmail.com College of Health Sciences, Hawassa University, P.O. Box 1560, Hawassa, Ethiopia School of Public Health, Addis Ababa University, P.O. Box 1176, Addis Ababa, Ethiopia Full list of author information is available at the end of the article © The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Beyene et al. Tropical Medicine and Health (2018) 46:18 Page 2 of 12 Background studies used different data sources and did not include Globally, diarrheal disease is the second leading cause of the current study area. Acute watery diarrhea (AWD) death in children under 5 years of age, and there are has been a public health threat since 2006 in the study nearly 1.7 billion cases of childhood diarrheal disease area and caused the morbidity of thousands of people. every year [1]. In 2015, diarrhea was a leading cause of These outbreaks were linked to lack of basic sanitation disability-adjusted life years (DALYs) on young children and safe water supply, as well as to the high sensitivity [2]. If not properly treated, diarrhea will be responsible of the pathogens to variations in climatic variability [34]. for dehydration and death in children [2–4]. Despite a In addition to this, the effect of climate change is re- decrease in the proportions of diarrheal morbidity ported in parts of the study area [35]. The present study, among under-five children, a growing trend of inequali- therefore, was conducted to investigate the seasonal dis- ties among neighborhoods and villages of countries has tribution, purely spatial, purely temporal, and space-time been observed [2, 5, 6]. clusters of childhood diarrhea in Sidama Zone, Southern Based on the 2016 Ethiopian Demographic and Health Ethiopia. The study adds to the already existing know- Survey (DHS) report, access to improved water supply ledge, and identifying the risk areas would help in de- and sanitation facilities have shown improvements. The signing effective intervention mechanisms to reduce percentage of children aged between 12 and 23 months, childhood diarrhea in these areas. who received all basic vaccinations, also increased from 14% in 2000 to 20% in 2005, 24% in 2011, and 39% in Methods 2016. In addition, the 2 weeks under-five diarrheal mor- Description of the study area bidity prevalence reduced from 24% in 2000 to 18% in The study was conducted in Sidama Administration 2005, 13% in 2011, and 12% in 2016 [7]. Studies which Zone, Southern Ethiopia (Fig. 1). It consists of 19 rural were conducted in different times and places in Ethiopia, districts and two administrative towns. however, indicated that diarrhea remains a public health Its geographic location lies between 6°14′ and 7°18′ problem with morbidity prevalence ranging from 18 to North latitude and 37°92′ and 39°14′ East longitude. 30.5% [8–12]. In the study area, high rate of reversion to The total area of the Sidama Administrative Zone is open defecation was reported [13]. about 6981.8 km . The administrative zone is bounded Several studies indicated that morbidity patterns of by Oromiya in North, East, and South East, with Gedieo childhood diarrhea showed spatial variation, with the oc- Zone in the South and Wolayta Zone in the West. The currence of clusters [14–16]. However, studies indicated altitude ranges between the highest peak of Garamba that under-five diarrhea disease did not differ signifi- mountains 3500 m above sea level (masl) to low lands cantly across different study locations [17, 18]. Seasonal (1190 m) around Bilate River in Loka-Abaya and Borcha patterns of childhood diarrheal morbidity have been re- districts. The climatic condition can be described as wet ported from different countries with different seasonal moist highland (27.7%), wet moist midland (45.4%), dry features [14, 18–21]. Diarrheal morbidity has also been midland (14.5%), dry lowland (8.6%), and wet moist low- observed to be influenced by metrological parameters land (3.8%) [36]. In 2017, the administrative zone had a such as precipitation and temperature anomalies in dif- total population of 3,668,304 with 1,849,128 male and ferent parts of the world including Ethiopia [14, 22–25]. 1,819,176 female [37]. Analysis of disease trends in space and time provides context which can be linked to possible risk factors in a Study design and population research environment [26]. Scan statistics has been used This is a retrospective longitudinal study design using widely in the field of epidemiology for investigation of the Sidama Zone HMIS under-five diarrheal morbidity spatial, temporal, and space-time clusters of infectious report from July 2011 to June 2017. In the study area, disease such as hemorrhagic fever [27], Clostridium the annual morbidity report is compiled based on the difficile infection clusters [28], healthcare-associated in- Ethiopian fiscal year which spans from July to June. The fections or colonizations with Pseudomonas aeruginosa study population was all under-five children of the 19 [29], visceral leishmaniasis [30], typhoid fever [31], districts who lived during the study period. Each district cholera [32], malaria [33], and diarrhea [14, 15]. was represented by a geographical point location by the Description of diarrheal clusters in time and space and geographic coordinates taken from a representative loca- understanding seasonal patterns is important for in- tion in the district. Coordinates were specified using the formed decision-making at various levels of the health standard Cartesian coordinate system. department and may lead to improvements in disease Population projection for the years 2011 to 2017 surveillance. However, studies in Ethiopia which assessed was made based on the 2007 census report as a ref- the seasonal trend, spatial, temporal, and space-time eree population, using the population projection for- clusters of diarrhea are lacking. The very few available mula: P = Po(1 + r),where P is the projected total Beyene et al. Tropical Medicine and Health (2018) 46:18 Page 3 of 12 Fig. 1 Map of the study area, Sidama Zone, Southern Ethiopia, 2017 population, Po the reference population, r the regional National Meteorology Agency (NMA) Climate Analysis annual population growth rate (2.9), and t thetimeof and Application (map room) [39]. The annual under-five the year when the projection was made. The diarrhea incidence per 1000 individuals in each district under-five population was calculated to be 15.6% of for the years between July 2011 and June 2017 and sea- the total population in Southern Nations, National- sonal trend were calculated using Excel . Smoothing of ities, and Peoples’ Region (SNNPR) [38]. The pro- the data was done by calculating the 12 months (1 year) jected under-five population data of each district was moving average followed by calculating the centered specified continuously over each month for the 6 years moving average (CMA). The trend of the monthly mor- from July 2011 to June 2017 and matched with its lo- bidity data of the 6 years was calculated by using the cation ID, monthly under-five diarrheal morbidity deseasonalized data and time. The incidence rate per data, and XY coordinate of each study location. 1000 under-five children, the CMA, and the trend com- Cases were defined as the number of under-five chil- ponent of the data were plotted to observe seasonal vari- dren who were diagnosed to have diarrhea in each health ations and trend of childhood diarrhea in the study areas facility of the study districts. Since July 2011, the SNNPR (Additional file 1). Health Bureau has adopted electronic Health Manage- The excess hazard (the ratio of observed to expected ment Information System (HMIS), where diarrheal cases cases greater than one) for each district was calculated have been compiled electronically at health facility level. by dividing the observed cases by the expected cases and The compiled data are reported monthly to the Zonal plotted using the geographic information system (GIS). Health Department and Regional Health Bureau. For The expected number of cases in each area under the this study, a 6-year monthly under-five diarrheal mor- null hypothesis was calculated using the following for- bidity data from July 2011 to June 2017 was collected mula: E[c] = p*C/P, where c is the observed number of from the e-HMIS database. The data was collected by cases and p the population in the location of interest, using a checklist. Data were collected by trained health while C and P are the total number of cases and popula- professionals who had knowledge of the HMIS data tion respectively [40]. management. Cluster analysis Data analysis The Kulldorff scan statistic, implemented in SaTScan The 30 years (1983–1984) average monthly rainfall, the software (SaTSCan v9.4.4), was used to detect if diarrhea maximum temperature, and the minimum temperature was randomly distributed over space, time, or space and of the administrative zone were calculated from the time and to evaluate the statistical significance of disease Beyene et al. Tropical Medicine and Health (2018) 46:18 Page 4 of 12 clusters [41]. SaTScan was preferred among software covariate-adjusted expected number of cases within the programs capable of space-time disease surveillance ana- window under the null-hypothesis, C-E(c) is the ex- lysis, as it was found to be the best-equipped package pected number of cases outside the window, and I() is for use in surveillance system [26]. an indicator function. In this study, since SaTScan is set The scan statistic technique detects and evaluates the to scan only for clusters with high rates, I() is equal to 1 statistical significance of spatial or space-time clusters when the window has more cases than expected under that cannot be explained by the assumption of spatial the null hypothesis, and 0 otherwise. or space-time randomness, noting the number of ob- served and expected observations inside the window at Purely temporal cluster each location. In the SaTScan software, the scanning A purely temporal cluster analysis scanning was per- window is an interval (in time), a circle or an ellipse formed to detect the temporal clusters of childhood (in space), or a cylinder with a circular or elliptic base diarrheal cases with high rates, representing the whole (in space-time). The discrete Poisson-based model was geographic area but a 1-month time aggregation length. used assuming that the reported monthly diarrheal The maximum time was specified to be the default 50% cases are Poisson-distributed in the study area with the within the study period. To identify clusters, a likelihood projected under-five underlying population at risk. The function was maximized across all locations and times. statistical principles behind the spatial and space-time The maximum likelihood indicates the cluster least scan statistics used in the SaTScan software for our likely to have occurred by chance (primary cluster). The specific analysis have been described in detail by p value is obtained through Monte Carlo hypothesis Martin Kulldorff [42]. testing. Secondary clusters are those that are in rank order after primary cluster by their likelihood ratio test Purely spatial clusters statistic. In this study, the maximum spatial cluster size of the population at risk was set to 50%. The observed diar- Space-time clusters rheal cases were compared with expected cases inside The space-time scan statistic was defined by a cylindrical and outside of each window, and the risk ratios were es- window with a circular (or elliptic) geographic base and timates on the basis of Poisson distribution. The null hy- with a height corresponding to time. The base is defined pothesis of the spatial scan statistic states that childhood exactly as for the purely spatial scan statistic, while the diarrhea is randomly distributed throughout the districts height reflects the time period of potential clusters. The of the administrative zone and that the expected event cylindrical window is then moved in space and time so count is proportional to the population at risk. For any that for each possible geographical location and size, it circular window, if the null hypothesis is statistically also visits each possible time period, where each cylinder rejected, then the geographic area defined by the scan reflects a possible cluster. A likelihood ratio was calcu- window can be considered as a spatial cluster. For each lated for each space-time window to indicate to what ex- circle, rejection of the null hypothesis is based on a like- tent the rate of cases inside the area is higher than lihood ratio statistic. The p value was calculated through expected. Monte Carlo hypothesis testing is then used to Monte Carlo hypothesis testing, by comparing the rank indicate the significance level of specific space-time of the maximum likelihood from the real data set with windows. the maximum likelihoods from random data sets. To evaluate the statistical significance of the primary cluster, Results the 999 random replications of the data set are gener- Monthly diarrheal morbidity data were collected from ated under the null hypothesis. all the 19 study districts from the 6 years HMIS data. For each location and size of the scanning window, There was no missing data from each district during the SaTScan uses a Monte Carlo simulation to test the null study period. A total of 202,406 under-five diarrheal hypothesis, that is, there is no an elevated risk within cases were reported, with an annual childhood diarrheal the window as compared to outside. Under the Poisson case of 5822 per 100,000 under-five population. The in- assumption, the likelihood function for a specific win- cidence rate varies from place to place and year to year, dow is proportional to: and an overall increasing trend of childhood diarrheal with seasonal variation peaking between February and May c C−c c C−c was observed. The highest incidence rate (135.8/1000) was IðÞ observed in Boricha district in the year 2016/17, and the Ec ðÞ C−Ec ðÞ lowest incidence rate (17.3 per 1000 under-five children) where C is the total number of cases, c is the observed was observed in Bensa district in 2016/17 and Chirie dis- number of cases within the window and E[c] is the trict in 2015/16 (Table 1). Beyene et al. Tropical Medicine and Health (2018) 46:18 Page 5 of 12 Table 1 Yearly diarrheal incidence rate under-five children of each district, Sidama Zone, Southern Ethiopia, 2017 SN District name Yearly incidence rate per 1000 under-five population 2011/12 2012/13 2013/14 2014/15 2015/16 2016/17 1 Aleta Chuko 56.0 44.9 57.9 56.6 61.2 54.7 2 Aleta Wondo 56.0 64.0 84.7 95.4 60.2 70.6 3 Arbegona 52.4 42.5 63.9 68.3 43.9 52.0 4 Aroresa 31.6 35.5 43.8 27.7 22.4 31.2 5 Bensa 24.5 18.3 26.1 27.9 17.5 17.3 6 Bona 26.1 49.1 51.2 48.1 40.2 65.2 7 Boricha 88.3 62.0 85.1 121.8 95.7 135.8 8 Bursa 50.7 31.4 33.9 26.7 24.2 17.5 9 Chirie 18.6 91.3 82.7 37.4 17.3 27.2 10 Dale 115.2 38.5 50.7 59.6 53.4 58.1 11 Dara 59.7 48.0 53.0 64.2 52.8 44.4 12 Gorchie 45.9 42.0 65.8 63.8 46.3 32.1 13 Hawassa Zuria 40.2 37.8 30.3 59.8 41.2 51.3 14 Hulla 35.9 39.9 94.4 129.2 111.1 107.1 15 Loka Abaya 94.7 61.2 55.8 64.9 40.3 63.7 16 Malga 71.4 99.2 115.7 106.7 76.3 85.9 17 Shebedino 55.3 60.9 77.3 90.6 62.6 79.3 18 Wonsho 44.2 20.8 55.6 52.9 50.1 60.9 19 Wondogenet 32.0 40.9 56.1 63.8 79.9 75.8 Average 52.6 48.9 62.3 66.6 52.5 58.6 The 6 years seasonal trend of the smoothed and desea- November, December, January, and February, between sonalized incidence rate of childhood diarrhea showed an 20 and 60 mm. The precipitation starts to increase in increasing trend, with an equation of Yt =0.015t +4.27, March (nearly 95 mm) and reaches its peak in the which starts to increase in January and reaches its peak in months of April and May (170–190 mm). A slight re- February. The incidence rate starts to slowly decline duction is observed in the months of July to August through time to reach its lowest peaks in the months of (100–120 mm), then increase in September (145 mm) July to November (Fig. 2). and October (140 mm). A significant reduction of pre- The 30 years average monthly precipitation, maximum cipitation was observed in November (nearly 60 mm) temperature, and minimum temperature of the study (Fig. 3). The 30 years monthly average maximum area are indicated in Fig. 3. The minimum average temperature of the study area showed that November, monthly rainfall was registered in the months of December, January, February, and March had a higher temperature, where its peak reaches in the months of February and March. It starts to drop in April and reaches its lowest in July and August and then starts to slowly increase again. The distribution of excess risk, which was defined as the ratio of the number of observed over the number of expected cases was indicated in Table 2 and Fig. 4. Eight districts (Boricha, Malga, Hulla, Aleta Wondo, Shebe- dion, Dale, Loka Abaya, and Wondogenet) had standard mortality ration (SMR) greater than one. Purely spatial cluster The purely spatial cluster analysis result indicated a Fig. 2 Trend and seasonal variation of under-five diarrhea rate in non-random distribution of under-five diarrhea inci- Southern Ethiopia, between July 2011 and June 2017 dence in Sidama Zone during July 2011–June 2017 Beyene et al. Tropical Medicine and Health (2018) 46:18 Page 6 of 12 Fig. 3 Thirty years historical (1983–2014) monthly data of precipitation, maximum temperature, and minimum temperature calculated from the National Meteorology Agency (NMA) Climate Analysis and Application (map room) (Table 3 and Fig. 5). Out of the 19 districts, eight of them (Boricha, Malga, Hulla, Aleta Wondo, Shebedino, Loka Abaya, Dale, and Wondogenet) had significantly Table 2 Values of excess risk and relative risk of diarrhea, July higher cases than expected (log likelihood ratio greater 2011 to June 2017, Southern Ethiopia, 2017 than 1). Using the maximum spatial cluster size of ≤ 50% SN District Obs.* Exp Obs./exp. RR of the total population, one most likely cluster and six 1 Boricha 29,153 17,146.87 1.70 1.82 secondary clusters were identified. The most likely clus- 2 Malga 11,952 7522.60 1.59 1.63 ter had a relative risk (RR) of 1.82 (p < 0.001), with an 3 Hulla 13,335 8856.59 1.51 1.54 observed number of cases of 29,153 and expected 4 Aleta Wondo 16,005 12,947.92 1.24 1.26 cases of 17,146.87. The RR of secondary clusters within a non-random distribution pattern was also 5 Shebedino 19,636 16,027.43 1.23 1.25 significant (p <0.001). 6 Loka Abaya 7351 6799.06 1.08 1.08 7 Dale 17,713 16,626 1.07 1.07 Purely temporal cluster 8 Wondogenet 10,792 10,668.99 1.01 1.01 The purely temporal cluster analysis indicated that 9 Aleta Chuko 10,883 11,462.75 0.95 0.95 one most likely cluster was identified in all districts 10 Arbegona 8606 9308.74 0.92 0.92 (LLR = 2109.93, p < 0.001) during December 2013 to May 2015 (01/12/2013 to 31/5/2015). The overall RR 11 Dara 9786 10,638.18 0.92 0.92 within the cluster was 1.37 (p < 0.001) with an observed 12 Gorchie 6102 7226.53 0.84 0.84 number of cases of 63,683 and 50,695.23 expected cases. 13 Wensho 5045 6143.31 0.82 0.82 There was no secondary cluster identified. 14 Bona 6713 8306.62 0.81 0.80 15 Chirie 6407 8252.70 0.78 0.77 Spatiotemporal clusters of childhood diarrhea 16 Hawassa Zuria 6391 8528.34 0.75 0.74 The space-time cluster analysis of cases of under-five diarrhea from July 2011 to June 2017 in Sidama Zone 17 Aroresa 6393 11,664.18 0.55 0.53 showed that diarrhea was not distributed randomly in 18 Bursa 3691 7100.39 0.52 0.51 space-time. Using the maximum spatial cluster size of 19 Bensa 6452 17,178.83 0.38 0.36 50% of the total population, and the maximum temporal RR relative risk, SN serial number cluster size of 50% of the total population, one most *Number of observed cases in a cluster Number of expected cases in a cluster likely cluster and four secondary clusters were identified Beyene et al. Tropical Medicine and Health (2018) 46:18 Page 7 of 12 Fig. 4 Excess risk map of under-five diarrhea from July 2011 to June 2017 in Southern Ethiopia. The areas with darker brown indicated areas with higher excess risk, and the lighter the color the lesser the excess risk of the districts on the map (Table 4). The overall RR within the most likely cluster under-five diarrhea report shows an overall increasing was 2.03 (p < 0.001) with an observed number of cases trend and seasonal variation in the study area. The high- of 6186 compared with expected cases of 3097.21. The est incidence rate peaked in Boricha district in the year RR of secondary clusters, within a non-random distribu- 2016/17. Spatial, temporal, and space-time hotspots of tion pattern, was also significant (p < 0.001). diarrhea were also observed in Boricha, Malga, Hulla, Loka Abaya, Bursa, Gorchie, and Wonsho districts of Discussion Southern Ethiopia. In this study, seasonal variation and hotspots of The current study showed that childhood diarrhea oc- under-five diarrhea are indicated. The 6-year monthly curred in a cyclical pattern over the months of the Table 3 Spatial clusters of under-five diarrhea in Southern Ethiopia between July 2011 and June 2017 Cluster number District Population Coordinates Obs.* Exp Annual Obs./exp RR LLR P value cases/100,000 Primary cluster Boricha 49,076 6.939005 N, 38.253064 E 29,153 17,146.87 9898.2 1.70 1.82 3864.33 < 0.001 1st secondary Malga 21,530 6.933700 N, 38.562860 E 11,952 7522.60 9249.8 1.59 1.65 1154.94 < 0.001 cluster 2nd secondary Hulla 25,348 6.487117 N, 38.522366 E 13,335 8856.59 8765.7 1.51 1.54 1030.89 < 0.001 cluster 3rd secondary Shebedino 45,872 6.874400 N, 38.441810 E 19,636 16,027.43 7132.6 1.23 1.25 413.94 < 0.001 cluster 4th secondary Aleta Wondo 37,058 6.596820 N, 38.422840 E 16,005 12,947.92 7196.4 1.24 1.26 360.24 < 0.001 cluster 5th secondary Dale 47,585 6.745428 N, 38.409888 E) 17,713 16,626.00 6202.4 1.07 1.07 37.97 < 0.001 cluster 6th secondary Loka Abaya 19,459 6.694306 N, 38.202427 E 7351 6799.06 6294.4 1.08 1.08 22.60 < 0.001 cluster RR relative risk, LLR log-likelihood ratio *Number of observed cases in a cluster Number of expected cases in a cluster Beyene et al. Tropical Medicine and Health (2018) 46:18 Page 8 of 12 Fig. 5 Most likely spatial cluster and secondary clusters of under-five diarrhea in Southern Ethiopia between July 2011 and June 2017. The primary cluster is found in Boricha district, and the secondary clusters were identified in Malga, Hulla, Shebedino, Aleta Wondo, Dale, and Loka Abaya districts. The order of the names of the districts is based on their likelihood ratio with decreasing order. Numerical identification of the clusters are in order of their likelihood ratio. Tuscan red color indicates the cluster with the likelihood ratio and labeled cluster 1 (most likely cluster or primary cluster), while cluster 2 (flame red), cluster 3 (fire red), cluster 4 (mars red), cluster 5 (seville orange), and cluster 6 (mango) are secondary clusters from the highest to lowest likelihood ratio. Olive color indicates no cluster districts (Table 3) 6 years study period. Previous reports found this. For over the years, the annual number of reported child- example, in Brazil, hospitalization rate caused by acute hood diarrheal cases showed an increasing trend. Our diarrhea in children under the age of one showed an- finding contradicts the previous report in Gojam, nual seasonal and 6-monthly patterns [20]. A study in Northwest Ethiopia, where childhood diarrhea showed Northwest Ethiopia also showed that peak childhood a decreasing trend [14]. The previous four DHS re- diarrheal cases showed a seasonal trend [14]. In China, ports of Ethiopia also showed a decreasing trend in diarrhea in children under 5 years showed a bimodal distri- childhood diarrhea in [44]. Another study based on bution, where it showed its peak in fall-winter seasons [43]. DHS data in Burkina Faso, Mali, Nigeria, and Niger Despite reported improvements in water, sanitation, during the period between 1990 and 2013 identified a hygiene, and vaccination coverage in the study area decrease in the proportions of diarrheal morbidity Table 4 Spatiotemporal clusters of under-five diarrhea in Southern Ethiopia between July 2011 and June 2017 Cluster number District Population Coordinates Time frame Obs.* Exp Annual Observed/ RR LLR P value cases/100,000 expected 1 Malga 21,530 6.933700 N, 2012/12/1 to 6186 3097.21 11,627.8 2.00 2.03 1214.67 < 0.001 38.562860 E 2015/5/31 2 Loka Abaya 19,459 6.694306 N, 2012/1/1 to 900 441.07 11,879.4 2.04 2.05 183.47 < 0.001 38.202427 E 2012/5/31 3 Bursa 20,322 6.590160 N, 2011/12/1 to 327 93.27 20,410.6 3.51 3.51 176.61 < 0.001 38.606330 E 2011/12/31 4 Gorchie 20,683 6.876740 N, 2014/3/1 to 479 199.43 13,983.0 2.40 2.41 140.34 < 0.001 38.584650 E 2014/4/30 5 Wensho 17,583 6.749010 N, 2012/3/1 to 216 81.28 15,471.9 2.66 2.66 76.44 < 0.001 38.517450 E 2012/3/31 RR relative risk, LLR log-likelihood ratio *Number of observed cases in a cluster Number of expected cases in a cluster Beyene et al. Tropical Medicine and Health (2018) 46:18 Page 9 of 12 among under-five children [45]. Thefactthatthe Spatial hotspots of diarrhea were also observed in childhood diarrhea morbidity showed an increasing Malga and Hulla districts. This might be because these trend over the years might be because newly built districts are located in Highlands, where most people health facilities started reporting the diarrheal mor- share their home with their domestic animals at night bidity and health extension workers, who previously because of fear of cold and theft. It is estimated that did not diagnose and treat diarrhea, have started diag- around 90% of rural households in Ethiopia own some nosing, treating, and reporting of diarrhea morbidity. farm animals [54]. Sharing of the dwellings with live- Our data suggest an annual diarrheal incidence of stock is quite common [55, 56]. This has been linked to around six cases per 100 while the DHS data implies an disease [55, 57]. One of the main routes of transmission annual incidence order of magnitude higher, suggesting of diarrheic agents is through domesticated animals as that the great majority of cases are not reported in the they serve as reservoirs for various zoonotic diseases HMIS.Thisisbecause theDHS studyisbased on a agents and also to other domestic and wild animals community-based survey, whereas the current study is [58–61]. The presence of domestic animals around the based on the report of cases who visited health institu- dwellings can compromise the sanitation of the house- tions seeking medical assistance. hold and their neighborhood environment, and thus, it The 6 years childhood diarrhea incidence starts to in- increases the chance of the dwellers come in contact with crease in January and reaches its peak in February. Accord- animal droppings; thereby, there are chances of vertical ing to the historical (1983–1984) monthly data of rainfall, transmission of the microbes to the owners [62, 63]. maximum temperature, and minimum temperature, this is Zoonotic diseases such as Campylobacter diarrhea, the transition from driest to the rainy season. During this Cryptosporidium diarrhea, and E. coli O157 infection have time, the average maximum temperature reaches its been reported following exposure to unhygienic environ- highest [39]. Similar studies also showed that increase in ments as a result of domestic animals living in and around temperature was positively associated with diarrhea inci- the human dwellings [61, 64, 65]. dence [46, 47]. The incidence rate starts to slowly decline The findings showed temporal variation in the overall through time to reach its lowest peaks in the months of risk of diarrhea, which indicated that childhood diar- July to November. Shortage of water in the dry season has rhea was not distributed randomly in time. This might been associated with increased prevalence of diarrhea [22]. be due to the influence of socio-economical, environ- This may be due to less availability of fresh water or con- mental, or climate-related factors. Statistically signifi- centration of contaminants in smaller volumes of water cant space-time hotspots (p < 0.001) were also observed [48]orlongerwater storage[49]. A large outbreak of (Table 4). This consisted of a primary hotspot and four diarrhea occurred following severe droughts due to de- secondary hotspots. The primary hotspot was observed creased water availability and worsened personal hygiene in December 2012 to January 2015 in Malga district [50]. Extreme rainfall days and associated flooding were with a likelihood ratio of 1214.67 and relative risk of also strongly related to diarrhea-associated morbidity 2.03. The first, second, third, and fourth secondary hot- [51, 52]. This is because flooding could result in the spots occurred from January 2012 to May 2012 in Loka breakdown of sanitary conditions and contamination of Abaya, December 2011 in Bursa, from March to April drinking water sources by washing nutrients, patho- 2014 in Gorchie, and March 2012 in Wonsho districts. gens, and toxins into water bodies [53]. The primary hotspot spanned for slightly more than The study also showed the existence of substantial 2 years, and the other secondary clusters existed from variation in the spatial distribution of diarrhea within 1 to 5 months only. This might be due to the occur- the study area. The finding was in agreement with an- rence of risk factors specific to the local areas and other national study [15]. However, there is the differ- time periods. ence in the nature of the data sources between the This study has strengths and limitations. The fact that studies in that the later used cross-sectional DHS data. complete monthly morbidity data were obtained from all The highest risk of diarrhea was found in Boricha dis- districts throughout the study period was a strength. trict. This might be due to the fact that most of the resi- The other strength was the existence of historical dents of the district relied heavily on pond water, which 30 years precipitation, maximum temperature, and mini- is open for both human and animal. In addition, the mum temperature data which were compared with the population is agro-pastoralist, where they move from seasonal variation of childhood diarrhea that could have place to place in search of food and water for their ani- been influenced by metrological parameters. The use of mals. These types of people would not have the chance the SaTScan software allowed us to both detect the loca- to construct and use their own latrines. It can also be tion of clusters and evaluate their statistical significance due to other factors such as differing levels of poverty, without problems with multiple testing. A limitation is education, and lifestyle. under-reporting of childhood diarrhea as the data Beyene et al. Tropical Medicine and Health (2018) 46:18 Page 10 of 12 reflects only those who sought healthcare setup level Authors’ contributions HB, WD, DG, and AK participated from the conception to the final write up treatment. As a result, the study might not be indicative of the study. DG also revised the English language. All authors read and of the true picture of diarrheal morbidity of the study approved the manuscript. area. However, this limitation might be minimal as the Authors’ information problem is assumed to be uniform across all districts HB is a lecturer of Environmental and Public Health at Hawassa University, over the study period. Ethiopia. WD is the associate professor and dean of the School of Public Health, College of Health Sciences, Addis Ababa University. He has been teaching several courses including biostatistics, epidemiology, and research Conclusions methodology. He has had supervised many masters and doctoral students. He also has more than 60 publications in the national and international This study assessed the seasonal variation and spatial, journals. AK is an associate professor, head of Environmental Health Unit, and temporal, and space-time clusters of under-five diarrhea PhD program coordinator at the School of Public Health, Addis Ababa using 6 years HMIS morbidity data (July 2011 to June University, Ethiopia. He has been teaching environmental health courses for various groups of health science students. He also has supervised several 2017) in Southern Ethiopia. An increasing trend with a masters and doctoral students. He has more than 60 publications in the seasonal variation of childhood diarrhea which peaks in peer-reviewed journals. DG is an epidemiologist and veterinarian with nearly the transition period from driest to the rainy season oc- 20 years of experience in developing countries. She is a senior researcher at the International Livestock Research Institute in Kenya and program manager curred. An excess risk of diarrhea was also observed in of agriculture-associated diseases in the new CGIAR Research Program on Boricha, Malga, and Hulla districts. Statistically signifi- Agriculture for Human Nutrition and Health. She is a prolific and effective cant space-time hotspots were also observed in five dis- author in a wide variety of media, including over 70 peer-reviewed journal articles, numerous book chapters, presentations, posters, policy briefs, films, tricts from December 2012 to January 2015 in Malga manuals, farmer diagnostic aids, and articles. district, from January 2012 to May 2012 in Loka Abaya, December 2011 in Bursa, from March to April 2014 in Ethics approval and consent to participate Gorchie, and March 2012 in Wonsho districts. Ethical clearance was granted by the ethical review board of Addis Ababa University, College of Health Sciences; permission to use the HMIS data was The spatial, temporal, and space-time clusters, gener- obtained from the SNNPR Health Bureau and Sidama Zone Health ated in this research, can be used by the various stake- department. All the information were kept confidential, and no individual holders to prioritize places of intervention. In addition, identifiers were collected. season-specific interventional strategies can be devel- Consent for publication oped with efficient resource use to reduce the childhood All information were obtained from secondary data. morbidity, mortality, and financial losses related to visit- ing health instructions as a result of diarrhea morbidity. Competing interests The authors declare that they have no competing interest. Further studies are required to clarify the effect of wea- ther variabilities on under-five diarrhea incidence and to Publisher’sNote investigate the specific risk factors of childhood diarrhea Springer Nature remains neutral with regard to jurisdictional claims in in hotspot areas. published maps and institutional affiliations. Author details Additional file College of Health Sciences, Hawassa University, P.O. Box 1560, Hawassa, Ethiopia. School of Public Health, Addis Ababa University, P.O. Box 1176, Addis Ababa, Ethiopia. International Livestock Research Institute, Box 30709, Additional file 1: Time series plot of under-five diarrhea incidence rate Nairobi, Kenya. per 1000. (XLSX 31 kb) Received: 8 March 2018 Accepted: 24 May 2018 Acknowledgements The authors would like to thank the funding organizations, the SNNPR Health Bureau, and the Sidama Zone Health Department people for References providing access to the HMIS data, the Central Statistical Agency of Ethiopia 1. World Health Organization (WHO). Diarrhoeal disease. 2017. Available from: Hawassa Branch Office, the SNNPR Finance and Economy Bureau, and the http://www.who.int/en/news-room/fact-sheets/detail/diarrhoeal-disease. Sidama Zone Finance and Economy Department for providing relevant updated 2 May 2017; cited 2017, November 7. information about the study area. 2. Troeger C, Forouzanfar M, Rao PC, Khalil I, Brown A, Reiner RC Jr, et al. Estimates of global, regional, and national morbidity, mortality, and aetiologies of diarrhoeal diseases: a systematic analysis for the Global Funding Burden of Disease Study 2015. Lancet Infect Dis. 2017;17(9):909–48. Financial support for this study was obtained from the German Academic 3. Ahmed M, Abedin J, Alam KF, Al Mamun A, Paul RC, Rahman M, et al. Exchange Program (DAAD), through the International Livestock Research Incidence of acute diarrhea-associated death among children <5 years of Institute, and from Addis Ababa and Hawassa Universities. age in Bangladesh, 2010-12. Am J Trop Med Hyg. 2018;98(1):281–6. 4. van der Westhuizen FP, Slogrove AL, Kunneke HM, Kruger M. Factors Availability of data and materials associated with severe dehydrating diarrhoea in the rural western cape, The data sets analyzed during the current study are not publicly available South Africa. J Trop Pediatr. 2018. https://doi.org/10.1093/tropej/fmy002. due to third party restrictions, but it can be obtained from the Southern [Epub ahead of print]. Nations, Nationalities, and Peoples’ Region Health Bureau by contacting 5. Bado AR, Susuman AS, Nebie EI. Trends and risk factors for childhood the HMIS officer, Melaku Getahun: melaku759@gmail.com, mobile phone diarrhea in sub-Saharan countries (1990–2013): assessing the neighborhood + 251911986024, Hawassa, Ethiopia. inequalities. Glob Health Action. 2016;9(1):30166. Beyene et al. Tropical Medicine and Health (2018) 46:18 Page 11 of 12 6. Stella S, Ajayi A. Viral causes of diarrhea in children in Africa: a literature statistic, in a community hospital in southern Ontario, Canada, 2006–2011. review. J Mole Biol Tech. 2017;1(1):1042. BMC Infect Dis. 2014;14(1):254. 7. Central Statistical Agency-CSA/Ethiopia, ICF. Ethiopia demographic and 29. Lefebvre A, Bertrand X, Vanhems P, Lucet JC, Chavanet P, Astruc K, et al. health survey 2016. Addis Ababa: CSA and ICF; 2017. Detection of temporal clusters of healthcare-associated infections or colonizations with Pseudomonas aeruginosa in two hospitals: comparison of 8. Dessalegn M, Kumie A, Tefera W. Predictors of under-five childhood SaTScan and WHONET software packages. PLoS One. 2015;10(10):e0139920. diarrhea: Mecha District, West Gojam. Ethiopia Ethiop J Health Dev. 2011; 30. Dewan A, Abdullah AYM, Shogib MRI, Karim R, Rahman MM. Exploring 25(3):192–200. spatial and temporal patterns of visceral leishmaniasis in endemic areas of 9. Eshete WB. A stepwise regression analysis on under-five diarrhoeal Bangladesh. Trop Med Health. 2017;45(1):29. morbidity prevalence in Nekemte town, western Ethiopia: maternal care giving and hygiene behavioral determinants. East African J Public Health. 31. Dewan AM, Corner R, Hashizume M, Ongee ET. Typhoid fever and its association 2008;5(3):193–8. with environmental factors in the Dhaka metropolitan area of Bangladesh: a 10. Mengistie B, Berhane Y, Worku A. Household water chlorination reduces spatial and time-series approach. PLoS Negl Trop Dis. 2013;7(1):e1998. incidence of diarrhea among under-five children in rural Ethiopia: a cluster 32. Osei FB, Stein A. Spatial variation and hot-spots of district level diarrhea randomized controlled trial. PLoS One. 2013;8(10):e77887. incidences in Ghana: 2010–2014. BMC Public Health. 2017;17(1):617. 11. Mengistie B, Berhane Y, Worku A. Prevalence of diarrhea and associated risk 33. Hundessa SH, Williams G, Li S, Guo J, Chen L, Zhang W, et al. Spatial and factors among children under-five years of age in eastern Ethiopia: a cross- space–time distribution of Plasmodium vivax and Plasmodium falciparum sectional study. Open J Prev Med. 2013;3(7):446–53. malaria in China, 2005–2014. Malar J. 2016;15(1):595. 34. FMoH. Acute Watery Diarrhea (AWD) prevention and control strategy. Addis 12. Tamiso A, Yitayal M, Awoke A. Prevalence and determinants of childhood Ababa: Federal Minstry of Health; 2011. Report No diarrhoea among graduated households, in rural area of Shebedino district, southern Ethiopia, 2013. Sci J Public Healtht. 2014;2(3):243–51. 35. Hameso SY. Development challenges in the age of climate change: the 13. Beyene H. Sanitation infrastructure sustainability challenges case study: case of Sidama. Economy of Southern Ethiopia, Ethiopian Economics Ethiopia. In: Bongartz P, Vernon N, Fox J, editors. Sustainable sanitation for Association, its Chapter at Hawassa University, Department of Economics; 1 all: experiences, challenges, and innovations. Rugby: Practical Action March 2012. Ethiopia: Hawassa University; 2012. Publishing Ltd; 2016. 36. Deqamo Y. In: Department SZFaE, editor. Socio-economical and 14. Azage M, Kumie A, Worku A, Bagtzoglou AC. Childhood diarrhea exhibits environmental characteristics of Sidama Zone. Hawassa: Sidama Zone spatiotemporal variation in Northwest Ethiopia: a SaTScan spatial statistical Finance & Economic Development Department, Ethiopia; 2011. p. 98. analysis. PLoS One. 2015;10(12):e0144690. 37. Central Statistical Agency (CSA). Population projection of Ethiopia for all 15. Bogale GG, Gelaye KA, Degefie DT, Gelaw YA. Spatial patterns of childhood regions at Wereda level from 2014–2017. Addis Ababa: Federal Democratic diarrhea in Ethiopia: data from Ethiopian demographic and health surveys Republic of Ethiopia, Central Statistical Agency; 2013. p. 118. (2000, 2005, and 2011). BMC Infect Dis. 2017;17(1):426. 38. Central Statistical Agency (CSA). National Census Report of Ethiopia. Addis Ababa: Central Statistical Agency of Ethiopia; 2007. 16. Osei FB, Duker AA. Spatial dependency of V. cholera prevalence on open space refuse dumps in Kumasi, Ghana: a spatial statistical modelling. Int J 39. National Meteorology Agency (NMA). Climate Analysis and Application Health Geogr. 2008;7(62). https://doi.org/10.1186/1476-072X-7-62. Addis Ababa, Ethiopia: National Metrological Agency of Ethiopia; 2017. 17. Thiam S, Diène AN, Fuhrimann S, Winkler MS, Sy I, Ndione JA, et al. Prevalence Available from: http://www.ethiometmaprooms.gov.et/. cited 2017 of diarrhoea and risk factors among children under five years old in Mbour, November 28. Senegal: a cross-sectional study. Infectious Dis Poverty. 2017;6:109. 40. Kulldorff M. SaTScanTM user guide. In: Boston; 2006. 18. Ahmed SF, Farheen A, Muzaffar A, Mattoo GM. Prevalence of diarrhoeal 41. Kulldorff M and Information Management Services, Inc. SaTScanTM v9.4.4: Software disease, its seasonal and age variation in under-fives in Kashmir, India. Int J for the spatial and space-time scan statistics. 2016. http://www.satscan.org/. Health Sci. 2008;2(2):126–33. August 2016. 19. Anyorikeya M, Ameme DK, Nyarko KM, Sackey SO, Afari E. Trends of 42. Kulldorff M. A spatial scan statistic. Commun Stat Theory Methods. 1997;26: 1481–96. diarrhoeal diseases in children under five years in the war memorial hospital-Navrongo, Ghana: 2010-2013. Pan African Med J. 2016;25(Suppl 1):8. 43. Xu Z, Hu W, Zhang Y, Wang X, Zhou M, Su H, et al. Exploration of diarrhoea 20. Masukawa MLT, EMd S, Gimenes E, Uchimura NS, Moriwaki AM, Uchimura seasonality and its drivers in China. Sci Rep. 2015;5:8241. TT. Time series investigation of changes in seasonality of acute diarrhea 44. CSA. Central Statistical Agency (CSA) [Ethiopia] and ICF. Ethiopia hospitalizations before and after rotavirus vaccine in Southern Brazil. Demographic and Health Survey 2016. Addis Ababa, Ethiopia, and Rockville, Cadernos de Saúde Pública; 2016;32(10):e00080515. Maryland: CSA and ICF; 2017. 21. Phung D, Huang C, Rutherford S, Chu C, Wang X, Nguyen M, et al. 45. Bado AR, Susuman AS, Nebie EI. Trends and risk factors for childhood diarrhea Temporal and spatial patterns of diarrhoea in the Mekong Delta area, in sub-Saharan countries (1990–2013): assessing the neighborhood Vietnam. Epidemiol Infect. 2015;143(16):3488–97. inequalities. Glob Health Action. 2016;9 https://doi.org/10.3402/gha.v9.30166. 22. Bandyopadhyay S, Kanji S, Wang L. The impact of rainfall and temperature 46. Checkley W, Epstein LD, Gilman RH, Figueroa D, Cama RI, Patz JA, et al. variation on diarrheal prevalence in sub-Saharan Africa. Appl Geogr. 2012; Effect of El Nino and ambient temperature on hospital admissions for 33(Supplement C):63–72. diarrhoeal diseases in Peruvian children. Lancet. 2000;355(9202):442–50. 23. Kulinkina AV, Mohan VR, Francis MR, Kattula D, Sarkar R, Plummer JD, et al. 47. Hashizume M, Armstrong B, Hajat S, Wagatsuma Y, Faruque AS, Hayashi T, Seasonality of water quality and diarrheal disease counts in urban and rural et al. Association between climate variability and hospital visits for non- settings in South India. Sci Rep. 2016;6:20521. cholera diarrhoea in Bangladesh: effects and vulnerable groups. Int J 24. Omore R, Tate JE, O’Reilly CE, Ayers T, Williamson J, Moke F, et al. Epidemiol. 2007;36(5):1030–7. Epidemiology, seasonality and factors associated with rotavirus infection 48. Leder K, Sinclair MI, McNeil JJ. Water and the environment: a natural among children with moderate-to-severe diarrhea in rural western Kenya, resource or a limited luxury? Med J Aust. 2002;177:609–13. 2008–2012: the Global Enteric Multicenter Study (GEMS). PLoS One. 2016; 49. Boelee E, McCartney M, Yohannes M, Hagos F, Lautze J, Kibret S. Climate 11(8):e0160060. change, water resource development and malaria in Ethiopia. Water and 25. Tornheim JA, Manya AS, Oyando N, Kabaka S, O’Reilly CE, Breiman RF, et al. Health: Where Science Meets Policy; October 25-26, 2010. The University of The epidemiology of hospitalization with diarrhea in rural Kenya: the utility North Carolina at Chapel Hill, USA; 2010. of existing health facility data in developing countries. Int J Infect Dis. 2010; 50. Jordan E. Drought as a climatic driver of an outbreak of diarrhea in Tuvalu, 14(6):e499–505. South Pacific: Public Health Theses; 2015. p. 1079. http://elischolar.library. 26. Robertson C, Nelson TA. Review of software for space-time disease yale.edu/ysphtdl/1079. Accessed on 26 Aug 2016 surveillance. Int J Health Geogr. 2010;9(16). 51. Chou W, Wu J, Wang Y, Huang H, Sung F, Chuang C. Modelling the impact 27. Wu W, Guo J, Guan P, Sun Y, Zhou B. Clusters of spatial, temporal, and of climate variability on diarrhea-associated diseases in Taiwan (1996–2007). space-time distribution of hemorrhagic fever with renal syndrome in Sci Total Environ. 2010;409(43–51):43–51. Liaoning Province, Northeastern China. BMC Infect Dis. 2011;11:229. 52. Hashizume M, Armstrong B, Wagatsuma Y, Faruque AS, Hayashi T, Sack DA. 28. Faires MC, Pearl DL, Ciccotelli WA, Berke O, Reid-Smith RJ, Weese JS. Rotavirus infections and climate variability in Dhaka, Bangladesh: a time- Detection of Clostridium difficile infection clusters, using the temporal scan series analysis. Epidemiol Infect. 2008;136(9):1281–9. Beyene et al. Tropical Medicine and Health (2018) 46:18 Page 12 of 12 53. Thomas MK, Charron DF, Waltner-Toews D, Schuster C, Maarouf AR, Holt JD. A role of high impact weather events in waterborne disease outbreaks in Canada, 1975–2001. Int J Environ Health Res. 2006;16:167–80. 54. EDHS. Central Statistical Agency [Ethiopia] and ICF International. Ethiopia Demographic and Health Survey (EDHS). Addis Ababa, Ethiopia and Calverton: Central statistical agency and ICF international; 2012. 55. Ayele WY, Neill SD, Zinsstag J, Weiss MG, Pavlik I. Bovine tuberculosis: an old disease but a new threat to Africa. Int J Tuberc Lung Dis. 2004;8(8):924–37. 56. Haile AA. Assessment of the impact of latrine utilization on idarrhoeal diseases in the rural community of Hulet Ejju Enessie District, Amhara Regional State. Addis Ababa: Addis Ababa University, Ethiopia; 2007. 57. Mulugeta T. Socio-economic, environmental, and behavioural factors associated with the occurrence of diarrhoeal disease among under-five children, Meskanena Mareko Woreda, southern Ethiopia. Addis Ababa: Addis Ababa University, Ethiopia; 2003. 58. Doorduyn Y, Van Den Brandhof WE, Van Duynhoven YT, Breukink BJ, Wagenaar JA, Van Pelt W. Risk factors for indigenous Campylobacter jejuni and Campylobacter coli infections in the Netherlands: a case-control study. Epidemiol Infect. 2010;138(10):1391–404. 59. Mahdi NK, Ali NH. Cryptosporidiosis among animal handlers and their livestock in Basrah, Iraq. East Afr Med J. 2002;79(10):550–3. 60. Moher D, Liberati A, Tetzlaff J, Altman DG. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Ann Intern Med. 2009;151(4):264–9. w64 61. Rao MR, Naficy AB, Savarino SJ, Abu-Elyazeed R, Wierzba TF, Peruski LF, et al. Pathogenicity and convalescent excretion of campylobacter in rural Egyptian children. Am J Epidemiol. 2001;154(2):166–73. 62. Fayehun OA. Household environmental health hazards and child survival in sub-Saharan Africa. DHS Working Papers No. 74. Calverton: ICF Macro; 2010. Contract No.: 74 63. Ramani SV, Frühauf T, Dutta A, Meijers H. Determinants of the prevalence of diarrhoea in adolescents attending school: a case study of an Indian village school. UNU-MERIT Working Papers. 2012;059:2012. 64. Locking ME, SJ O'BRIEN, Reilly WJ, Wright EM, Campbell DM, Coia JE, et al. Risk factors for sporadic cases of Escherichia coli O157 infection: the importance of contact with animal excreta. Epidemiol Infect. 2001;127:215–20. 65. Molbak K, Jensen H, Ingholt L, Aaby P. Risk factors for diarrheal disease incidence in early childhood: a community cohort study from Guinea- Bissau. Am J Epidemiol. 1997;146(3):273–82. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Tropical Medicine and Health Springer Journals

Spatial, temporal, and spatiotemporal analysis of under-five diarrhea in Southern Ethiopia

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Medicine & Public Health; Tropical Medicine; Infectious Diseases; Public Health; Vaccine
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Abstract

Background: Despite improvements in prevention efforts, childhood diarrhea remains a public health concern. However, there may be substantial variation influenced by place, time, and season. Description of diarrheal clusters in time and space and understanding seasonal patterns can improve surveillance and management. The present study investigated the spatial and seasonal distribution and purely spatial, purely temporal, and space-time clusters of childhood diarrhea in Southern Ethiopia. Methods: The study was a retrospective analysis of data from the Health Management Information System (HMIS) under-five diarrheal morbidity reports from July 2011 to June 2017 in Sidama Zone. Annual diarrhea incidence at district level was calculated. Incidence rate calculation and seasonal trend analysis were performed. The Kulldorff SaTScan software with a discrete Poisson model was used to identify statistically significant special, temporal, and space-time diarrhea clusters. ArcGIS 10.1 was used to plot the maps. Results: A total of 202,406 under-five diarrheal cases with an annual case of 5822 per 100,000 under-five population were reported. An increasing trend of diarrhea incidence was observed over the 6 years with seasonal variation picking between February and May. The highest incidence rate (135.8/1000) was observed in the year 2016/17 in Boricha district. One statistically significant most likely spatial cluster (Boricha district) and six secondary clusters (Malga, Hulla, Aleta Wondo, Shebedino, Loka Abaya, Dale, and Wondogenet) were identified. One statistically significant temporal cluster (LLR = 2109.93, p < 0.001) during December 2013 to May 2015 was observed in all districts. Statistically significant spatiotemporal primary hotspot was observed in December 2012 to January 2015 in Malga district with a likelihood ratio of 1214.67 and a relative risk of 2.03. First, second, third, and fourth secondary hotspots occurred from January 2012 to May 2012 in Loka Abaya, December 2011 in Bursa, from March to April 2014 in Gorchie, and March 2012 in Wonsho districts. Conclusion: Childhood diarrhea was not distributed randomly over space and time and showed an overall increasing trend of seasonal variation peaking between February and May. The health department and other stakeholders at various levels need to plan targeted interventional activities at hotspot seasons and areas to reduce morbidity and mortality. Keywords: Southern Ethiopia, Under-five diarrhea, SatScan, Cluster, Spatial, Temporal * Correspondence: hunachew@gmail.com College of Health Sciences, Hawassa University, P.O. Box 1560, Hawassa, Ethiopia School of Public Health, Addis Ababa University, P.O. Box 1176, Addis Ababa, Ethiopia Full list of author information is available at the end of the article © The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Beyene et al. Tropical Medicine and Health (2018) 46:18 Page 2 of 12 Background studies used different data sources and did not include Globally, diarrheal disease is the second leading cause of the current study area. Acute watery diarrhea (AWD) death in children under 5 years of age, and there are has been a public health threat since 2006 in the study nearly 1.7 billion cases of childhood diarrheal disease area and caused the morbidity of thousands of people. every year [1]. In 2015, diarrhea was a leading cause of These outbreaks were linked to lack of basic sanitation disability-adjusted life years (DALYs) on young children and safe water supply, as well as to the high sensitivity [2]. If not properly treated, diarrhea will be responsible of the pathogens to variations in climatic variability [34]. for dehydration and death in children [2–4]. Despite a In addition to this, the effect of climate change is re- decrease in the proportions of diarrheal morbidity ported in parts of the study area [35]. The present study, among under-five children, a growing trend of inequali- therefore, was conducted to investigate the seasonal dis- ties among neighborhoods and villages of countries has tribution, purely spatial, purely temporal, and space-time been observed [2, 5, 6]. clusters of childhood diarrhea in Sidama Zone, Southern Based on the 2016 Ethiopian Demographic and Health Ethiopia. The study adds to the already existing know- Survey (DHS) report, access to improved water supply ledge, and identifying the risk areas would help in de- and sanitation facilities have shown improvements. The signing effective intervention mechanisms to reduce percentage of children aged between 12 and 23 months, childhood diarrhea in these areas. who received all basic vaccinations, also increased from 14% in 2000 to 20% in 2005, 24% in 2011, and 39% in Methods 2016. In addition, the 2 weeks under-five diarrheal mor- Description of the study area bidity prevalence reduced from 24% in 2000 to 18% in The study was conducted in Sidama Administration 2005, 13% in 2011, and 12% in 2016 [7]. Studies which Zone, Southern Ethiopia (Fig. 1). It consists of 19 rural were conducted in different times and places in Ethiopia, districts and two administrative towns. however, indicated that diarrhea remains a public health Its geographic location lies between 6°14′ and 7°18′ problem with morbidity prevalence ranging from 18 to North latitude and 37°92′ and 39°14′ East longitude. 30.5% [8–12]. In the study area, high rate of reversion to The total area of the Sidama Administrative Zone is open defecation was reported [13]. about 6981.8 km . The administrative zone is bounded Several studies indicated that morbidity patterns of by Oromiya in North, East, and South East, with Gedieo childhood diarrhea showed spatial variation, with the oc- Zone in the South and Wolayta Zone in the West. The currence of clusters [14–16]. However, studies indicated altitude ranges between the highest peak of Garamba that under-five diarrhea disease did not differ signifi- mountains 3500 m above sea level (masl) to low lands cantly across different study locations [17, 18]. Seasonal (1190 m) around Bilate River in Loka-Abaya and Borcha patterns of childhood diarrheal morbidity have been re- districts. The climatic condition can be described as wet ported from different countries with different seasonal moist highland (27.7%), wet moist midland (45.4%), dry features [14, 18–21]. Diarrheal morbidity has also been midland (14.5%), dry lowland (8.6%), and wet moist low- observed to be influenced by metrological parameters land (3.8%) [36]. In 2017, the administrative zone had a such as precipitation and temperature anomalies in dif- total population of 3,668,304 with 1,849,128 male and ferent parts of the world including Ethiopia [14, 22–25]. 1,819,176 female [37]. Analysis of disease trends in space and time provides context which can be linked to possible risk factors in a Study design and population research environment [26]. Scan statistics has been used This is a retrospective longitudinal study design using widely in the field of epidemiology for investigation of the Sidama Zone HMIS under-five diarrheal morbidity spatial, temporal, and space-time clusters of infectious report from July 2011 to June 2017. In the study area, disease such as hemorrhagic fever [27], Clostridium the annual morbidity report is compiled based on the difficile infection clusters [28], healthcare-associated in- Ethiopian fiscal year which spans from July to June. The fections or colonizations with Pseudomonas aeruginosa study population was all under-five children of the 19 [29], visceral leishmaniasis [30], typhoid fever [31], districts who lived during the study period. Each district cholera [32], malaria [33], and diarrhea [14, 15]. was represented by a geographical point location by the Description of diarrheal clusters in time and space and geographic coordinates taken from a representative loca- understanding seasonal patterns is important for in- tion in the district. Coordinates were specified using the formed decision-making at various levels of the health standard Cartesian coordinate system. department and may lead to improvements in disease Population projection for the years 2011 to 2017 surveillance. However, studies in Ethiopia which assessed was made based on the 2007 census report as a ref- the seasonal trend, spatial, temporal, and space-time eree population, using the population projection for- clusters of diarrhea are lacking. The very few available mula: P = Po(1 + r),where P is the projected total Beyene et al. Tropical Medicine and Health (2018) 46:18 Page 3 of 12 Fig. 1 Map of the study area, Sidama Zone, Southern Ethiopia, 2017 population, Po the reference population, r the regional National Meteorology Agency (NMA) Climate Analysis annual population growth rate (2.9), and t thetimeof and Application (map room) [39]. The annual under-five the year when the projection was made. The diarrhea incidence per 1000 individuals in each district under-five population was calculated to be 15.6% of for the years between July 2011 and June 2017 and sea- the total population in Southern Nations, National- sonal trend were calculated using Excel . Smoothing of ities, and Peoples’ Region (SNNPR) [38]. The pro- the data was done by calculating the 12 months (1 year) jected under-five population data of each district was moving average followed by calculating the centered specified continuously over each month for the 6 years moving average (CMA). The trend of the monthly mor- from July 2011 to June 2017 and matched with its lo- bidity data of the 6 years was calculated by using the cation ID, monthly under-five diarrheal morbidity deseasonalized data and time. The incidence rate per data, and XY coordinate of each study location. 1000 under-five children, the CMA, and the trend com- Cases were defined as the number of under-five chil- ponent of the data were plotted to observe seasonal vari- dren who were diagnosed to have diarrhea in each health ations and trend of childhood diarrhea in the study areas facility of the study districts. Since July 2011, the SNNPR (Additional file 1). Health Bureau has adopted electronic Health Manage- The excess hazard (the ratio of observed to expected ment Information System (HMIS), where diarrheal cases cases greater than one) for each district was calculated have been compiled electronically at health facility level. by dividing the observed cases by the expected cases and The compiled data are reported monthly to the Zonal plotted using the geographic information system (GIS). Health Department and Regional Health Bureau. For The expected number of cases in each area under the this study, a 6-year monthly under-five diarrheal mor- null hypothesis was calculated using the following for- bidity data from July 2011 to June 2017 was collected mula: E[c] = p*C/P, where c is the observed number of from the e-HMIS database. The data was collected by cases and p the population in the location of interest, using a checklist. Data were collected by trained health while C and P are the total number of cases and popula- professionals who had knowledge of the HMIS data tion respectively [40]. management. Cluster analysis Data analysis The Kulldorff scan statistic, implemented in SaTScan The 30 years (1983–1984) average monthly rainfall, the software (SaTSCan v9.4.4), was used to detect if diarrhea maximum temperature, and the minimum temperature was randomly distributed over space, time, or space and of the administrative zone were calculated from the time and to evaluate the statistical significance of disease Beyene et al. Tropical Medicine and Health (2018) 46:18 Page 4 of 12 clusters [41]. SaTScan was preferred among software covariate-adjusted expected number of cases within the programs capable of space-time disease surveillance ana- window under the null-hypothesis, C-E(c) is the ex- lysis, as it was found to be the best-equipped package pected number of cases outside the window, and I() is for use in surveillance system [26]. an indicator function. In this study, since SaTScan is set The scan statistic technique detects and evaluates the to scan only for clusters with high rates, I() is equal to 1 statistical significance of spatial or space-time clusters when the window has more cases than expected under that cannot be explained by the assumption of spatial the null hypothesis, and 0 otherwise. or space-time randomness, noting the number of ob- served and expected observations inside the window at Purely temporal cluster each location. In the SaTScan software, the scanning A purely temporal cluster analysis scanning was per- window is an interval (in time), a circle or an ellipse formed to detect the temporal clusters of childhood (in space), or a cylinder with a circular or elliptic base diarrheal cases with high rates, representing the whole (in space-time). The discrete Poisson-based model was geographic area but a 1-month time aggregation length. used assuming that the reported monthly diarrheal The maximum time was specified to be the default 50% cases are Poisson-distributed in the study area with the within the study period. To identify clusters, a likelihood projected under-five underlying population at risk. The function was maximized across all locations and times. statistical principles behind the spatial and space-time The maximum likelihood indicates the cluster least scan statistics used in the SaTScan software for our likely to have occurred by chance (primary cluster). The specific analysis have been described in detail by p value is obtained through Monte Carlo hypothesis Martin Kulldorff [42]. testing. Secondary clusters are those that are in rank order after primary cluster by their likelihood ratio test Purely spatial clusters statistic. In this study, the maximum spatial cluster size of the population at risk was set to 50%. The observed diar- Space-time clusters rheal cases were compared with expected cases inside The space-time scan statistic was defined by a cylindrical and outside of each window, and the risk ratios were es- window with a circular (or elliptic) geographic base and timates on the basis of Poisson distribution. The null hy- with a height corresponding to time. The base is defined pothesis of the spatial scan statistic states that childhood exactly as for the purely spatial scan statistic, while the diarrhea is randomly distributed throughout the districts height reflects the time period of potential clusters. The of the administrative zone and that the expected event cylindrical window is then moved in space and time so count is proportional to the population at risk. For any that for each possible geographical location and size, it circular window, if the null hypothesis is statistically also visits each possible time period, where each cylinder rejected, then the geographic area defined by the scan reflects a possible cluster. A likelihood ratio was calcu- window can be considered as a spatial cluster. For each lated for each space-time window to indicate to what ex- circle, rejection of the null hypothesis is based on a like- tent the rate of cases inside the area is higher than lihood ratio statistic. The p value was calculated through expected. Monte Carlo hypothesis testing is then used to Monte Carlo hypothesis testing, by comparing the rank indicate the significance level of specific space-time of the maximum likelihood from the real data set with windows. the maximum likelihoods from random data sets. To evaluate the statistical significance of the primary cluster, Results the 999 random replications of the data set are gener- Monthly diarrheal morbidity data were collected from ated under the null hypothesis. all the 19 study districts from the 6 years HMIS data. For each location and size of the scanning window, There was no missing data from each district during the SaTScan uses a Monte Carlo simulation to test the null study period. A total of 202,406 under-five diarrheal hypothesis, that is, there is no an elevated risk within cases were reported, with an annual childhood diarrheal the window as compared to outside. Under the Poisson case of 5822 per 100,000 under-five population. The in- assumption, the likelihood function for a specific win- cidence rate varies from place to place and year to year, dow is proportional to: and an overall increasing trend of childhood diarrheal with seasonal variation peaking between February and May c C−c c C−c was observed. The highest incidence rate (135.8/1000) was IðÞ observed in Boricha district in the year 2016/17, and the Ec ðÞ C−Ec ðÞ lowest incidence rate (17.3 per 1000 under-five children) where C is the total number of cases, c is the observed was observed in Bensa district in 2016/17 and Chirie dis- number of cases within the window and E[c] is the trict in 2015/16 (Table 1). Beyene et al. Tropical Medicine and Health (2018) 46:18 Page 5 of 12 Table 1 Yearly diarrheal incidence rate under-five children of each district, Sidama Zone, Southern Ethiopia, 2017 SN District name Yearly incidence rate per 1000 under-five population 2011/12 2012/13 2013/14 2014/15 2015/16 2016/17 1 Aleta Chuko 56.0 44.9 57.9 56.6 61.2 54.7 2 Aleta Wondo 56.0 64.0 84.7 95.4 60.2 70.6 3 Arbegona 52.4 42.5 63.9 68.3 43.9 52.0 4 Aroresa 31.6 35.5 43.8 27.7 22.4 31.2 5 Bensa 24.5 18.3 26.1 27.9 17.5 17.3 6 Bona 26.1 49.1 51.2 48.1 40.2 65.2 7 Boricha 88.3 62.0 85.1 121.8 95.7 135.8 8 Bursa 50.7 31.4 33.9 26.7 24.2 17.5 9 Chirie 18.6 91.3 82.7 37.4 17.3 27.2 10 Dale 115.2 38.5 50.7 59.6 53.4 58.1 11 Dara 59.7 48.0 53.0 64.2 52.8 44.4 12 Gorchie 45.9 42.0 65.8 63.8 46.3 32.1 13 Hawassa Zuria 40.2 37.8 30.3 59.8 41.2 51.3 14 Hulla 35.9 39.9 94.4 129.2 111.1 107.1 15 Loka Abaya 94.7 61.2 55.8 64.9 40.3 63.7 16 Malga 71.4 99.2 115.7 106.7 76.3 85.9 17 Shebedino 55.3 60.9 77.3 90.6 62.6 79.3 18 Wonsho 44.2 20.8 55.6 52.9 50.1 60.9 19 Wondogenet 32.0 40.9 56.1 63.8 79.9 75.8 Average 52.6 48.9 62.3 66.6 52.5 58.6 The 6 years seasonal trend of the smoothed and desea- November, December, January, and February, between sonalized incidence rate of childhood diarrhea showed an 20 and 60 mm. The precipitation starts to increase in increasing trend, with an equation of Yt =0.015t +4.27, March (nearly 95 mm) and reaches its peak in the which starts to increase in January and reaches its peak in months of April and May (170–190 mm). A slight re- February. The incidence rate starts to slowly decline duction is observed in the months of July to August through time to reach its lowest peaks in the months of (100–120 mm), then increase in September (145 mm) July to November (Fig. 2). and October (140 mm). A significant reduction of pre- The 30 years average monthly precipitation, maximum cipitation was observed in November (nearly 60 mm) temperature, and minimum temperature of the study (Fig. 3). The 30 years monthly average maximum area are indicated in Fig. 3. The minimum average temperature of the study area showed that November, monthly rainfall was registered in the months of December, January, February, and March had a higher temperature, where its peak reaches in the months of February and March. It starts to drop in April and reaches its lowest in July and August and then starts to slowly increase again. The distribution of excess risk, which was defined as the ratio of the number of observed over the number of expected cases was indicated in Table 2 and Fig. 4. Eight districts (Boricha, Malga, Hulla, Aleta Wondo, Shebe- dion, Dale, Loka Abaya, and Wondogenet) had standard mortality ration (SMR) greater than one. Purely spatial cluster The purely spatial cluster analysis result indicated a Fig. 2 Trend and seasonal variation of under-five diarrhea rate in non-random distribution of under-five diarrhea inci- Southern Ethiopia, between July 2011 and June 2017 dence in Sidama Zone during July 2011–June 2017 Beyene et al. Tropical Medicine and Health (2018) 46:18 Page 6 of 12 Fig. 3 Thirty years historical (1983–2014) monthly data of precipitation, maximum temperature, and minimum temperature calculated from the National Meteorology Agency (NMA) Climate Analysis and Application (map room) (Table 3 and Fig. 5). Out of the 19 districts, eight of them (Boricha, Malga, Hulla, Aleta Wondo, Shebedino, Loka Abaya, Dale, and Wondogenet) had significantly Table 2 Values of excess risk and relative risk of diarrhea, July higher cases than expected (log likelihood ratio greater 2011 to June 2017, Southern Ethiopia, 2017 than 1). Using the maximum spatial cluster size of ≤ 50% SN District Obs.* Exp Obs./exp. RR of the total population, one most likely cluster and six 1 Boricha 29,153 17,146.87 1.70 1.82 secondary clusters were identified. The most likely clus- 2 Malga 11,952 7522.60 1.59 1.63 ter had a relative risk (RR) of 1.82 (p < 0.001), with an 3 Hulla 13,335 8856.59 1.51 1.54 observed number of cases of 29,153 and expected 4 Aleta Wondo 16,005 12,947.92 1.24 1.26 cases of 17,146.87. The RR of secondary clusters within a non-random distribution pattern was also 5 Shebedino 19,636 16,027.43 1.23 1.25 significant (p <0.001). 6 Loka Abaya 7351 6799.06 1.08 1.08 7 Dale 17,713 16,626 1.07 1.07 Purely temporal cluster 8 Wondogenet 10,792 10,668.99 1.01 1.01 The purely temporal cluster analysis indicated that 9 Aleta Chuko 10,883 11,462.75 0.95 0.95 one most likely cluster was identified in all districts 10 Arbegona 8606 9308.74 0.92 0.92 (LLR = 2109.93, p < 0.001) during December 2013 to May 2015 (01/12/2013 to 31/5/2015). The overall RR 11 Dara 9786 10,638.18 0.92 0.92 within the cluster was 1.37 (p < 0.001) with an observed 12 Gorchie 6102 7226.53 0.84 0.84 number of cases of 63,683 and 50,695.23 expected cases. 13 Wensho 5045 6143.31 0.82 0.82 There was no secondary cluster identified. 14 Bona 6713 8306.62 0.81 0.80 15 Chirie 6407 8252.70 0.78 0.77 Spatiotemporal clusters of childhood diarrhea 16 Hawassa Zuria 6391 8528.34 0.75 0.74 The space-time cluster analysis of cases of under-five diarrhea from July 2011 to June 2017 in Sidama Zone 17 Aroresa 6393 11,664.18 0.55 0.53 showed that diarrhea was not distributed randomly in 18 Bursa 3691 7100.39 0.52 0.51 space-time. Using the maximum spatial cluster size of 19 Bensa 6452 17,178.83 0.38 0.36 50% of the total population, and the maximum temporal RR relative risk, SN serial number cluster size of 50% of the total population, one most *Number of observed cases in a cluster Number of expected cases in a cluster likely cluster and four secondary clusters were identified Beyene et al. Tropical Medicine and Health (2018) 46:18 Page 7 of 12 Fig. 4 Excess risk map of under-five diarrhea from July 2011 to June 2017 in Southern Ethiopia. The areas with darker brown indicated areas with higher excess risk, and the lighter the color the lesser the excess risk of the districts on the map (Table 4). The overall RR within the most likely cluster under-five diarrhea report shows an overall increasing was 2.03 (p < 0.001) with an observed number of cases trend and seasonal variation in the study area. The high- of 6186 compared with expected cases of 3097.21. The est incidence rate peaked in Boricha district in the year RR of secondary clusters, within a non-random distribu- 2016/17. Spatial, temporal, and space-time hotspots of tion pattern, was also significant (p < 0.001). diarrhea were also observed in Boricha, Malga, Hulla, Loka Abaya, Bursa, Gorchie, and Wonsho districts of Discussion Southern Ethiopia. In this study, seasonal variation and hotspots of The current study showed that childhood diarrhea oc- under-five diarrhea are indicated. The 6-year monthly curred in a cyclical pattern over the months of the Table 3 Spatial clusters of under-five diarrhea in Southern Ethiopia between July 2011 and June 2017 Cluster number District Population Coordinates Obs.* Exp Annual Obs./exp RR LLR P value cases/100,000 Primary cluster Boricha 49,076 6.939005 N, 38.253064 E 29,153 17,146.87 9898.2 1.70 1.82 3864.33 < 0.001 1st secondary Malga 21,530 6.933700 N, 38.562860 E 11,952 7522.60 9249.8 1.59 1.65 1154.94 < 0.001 cluster 2nd secondary Hulla 25,348 6.487117 N, 38.522366 E 13,335 8856.59 8765.7 1.51 1.54 1030.89 < 0.001 cluster 3rd secondary Shebedino 45,872 6.874400 N, 38.441810 E 19,636 16,027.43 7132.6 1.23 1.25 413.94 < 0.001 cluster 4th secondary Aleta Wondo 37,058 6.596820 N, 38.422840 E 16,005 12,947.92 7196.4 1.24 1.26 360.24 < 0.001 cluster 5th secondary Dale 47,585 6.745428 N, 38.409888 E) 17,713 16,626.00 6202.4 1.07 1.07 37.97 < 0.001 cluster 6th secondary Loka Abaya 19,459 6.694306 N, 38.202427 E 7351 6799.06 6294.4 1.08 1.08 22.60 < 0.001 cluster RR relative risk, LLR log-likelihood ratio *Number of observed cases in a cluster Number of expected cases in a cluster Beyene et al. Tropical Medicine and Health (2018) 46:18 Page 8 of 12 Fig. 5 Most likely spatial cluster and secondary clusters of under-five diarrhea in Southern Ethiopia between July 2011 and June 2017. The primary cluster is found in Boricha district, and the secondary clusters were identified in Malga, Hulla, Shebedino, Aleta Wondo, Dale, and Loka Abaya districts. The order of the names of the districts is based on their likelihood ratio with decreasing order. Numerical identification of the clusters are in order of their likelihood ratio. Tuscan red color indicates the cluster with the likelihood ratio and labeled cluster 1 (most likely cluster or primary cluster), while cluster 2 (flame red), cluster 3 (fire red), cluster 4 (mars red), cluster 5 (seville orange), and cluster 6 (mango) are secondary clusters from the highest to lowest likelihood ratio. Olive color indicates no cluster districts (Table 3) 6 years study period. Previous reports found this. For over the years, the annual number of reported child- example, in Brazil, hospitalization rate caused by acute hood diarrheal cases showed an increasing trend. Our diarrhea in children under the age of one showed an- finding contradicts the previous report in Gojam, nual seasonal and 6-monthly patterns [20]. A study in Northwest Ethiopia, where childhood diarrhea showed Northwest Ethiopia also showed that peak childhood a decreasing trend [14]. The previous four DHS re- diarrheal cases showed a seasonal trend [14]. In China, ports of Ethiopia also showed a decreasing trend in diarrhea in children under 5 years showed a bimodal distri- childhood diarrhea in [44]. Another study based on bution, where it showed its peak in fall-winter seasons [43]. DHS data in Burkina Faso, Mali, Nigeria, and Niger Despite reported improvements in water, sanitation, during the period between 1990 and 2013 identified a hygiene, and vaccination coverage in the study area decrease in the proportions of diarrheal morbidity Table 4 Spatiotemporal clusters of under-five diarrhea in Southern Ethiopia between July 2011 and June 2017 Cluster number District Population Coordinates Time frame Obs.* Exp Annual Observed/ RR LLR P value cases/100,000 expected 1 Malga 21,530 6.933700 N, 2012/12/1 to 6186 3097.21 11,627.8 2.00 2.03 1214.67 < 0.001 38.562860 E 2015/5/31 2 Loka Abaya 19,459 6.694306 N, 2012/1/1 to 900 441.07 11,879.4 2.04 2.05 183.47 < 0.001 38.202427 E 2012/5/31 3 Bursa 20,322 6.590160 N, 2011/12/1 to 327 93.27 20,410.6 3.51 3.51 176.61 < 0.001 38.606330 E 2011/12/31 4 Gorchie 20,683 6.876740 N, 2014/3/1 to 479 199.43 13,983.0 2.40 2.41 140.34 < 0.001 38.584650 E 2014/4/30 5 Wensho 17,583 6.749010 N, 2012/3/1 to 216 81.28 15,471.9 2.66 2.66 76.44 < 0.001 38.517450 E 2012/3/31 RR relative risk, LLR log-likelihood ratio *Number of observed cases in a cluster Number of expected cases in a cluster Beyene et al. Tropical Medicine and Health (2018) 46:18 Page 9 of 12 among under-five children [45]. Thefactthatthe Spatial hotspots of diarrhea were also observed in childhood diarrhea morbidity showed an increasing Malga and Hulla districts. This might be because these trend over the years might be because newly built districts are located in Highlands, where most people health facilities started reporting the diarrheal mor- share their home with their domestic animals at night bidity and health extension workers, who previously because of fear of cold and theft. It is estimated that did not diagnose and treat diarrhea, have started diag- around 90% of rural households in Ethiopia own some nosing, treating, and reporting of diarrhea morbidity. farm animals [54]. Sharing of the dwellings with live- Our data suggest an annual diarrheal incidence of stock is quite common [55, 56]. This has been linked to around six cases per 100 while the DHS data implies an disease [55, 57]. One of the main routes of transmission annual incidence order of magnitude higher, suggesting of diarrheic agents is through domesticated animals as that the great majority of cases are not reported in the they serve as reservoirs for various zoonotic diseases HMIS.Thisisbecause theDHS studyisbased on a agents and also to other domestic and wild animals community-based survey, whereas the current study is [58–61]. The presence of domestic animals around the based on the report of cases who visited health institu- dwellings can compromise the sanitation of the house- tions seeking medical assistance. hold and their neighborhood environment, and thus, it The 6 years childhood diarrhea incidence starts to in- increases the chance of the dwellers come in contact with crease in January and reaches its peak in February. Accord- animal droppings; thereby, there are chances of vertical ing to the historical (1983–1984) monthly data of rainfall, transmission of the microbes to the owners [62, 63]. maximum temperature, and minimum temperature, this is Zoonotic diseases such as Campylobacter diarrhea, the transition from driest to the rainy season. During this Cryptosporidium diarrhea, and E. coli O157 infection have time, the average maximum temperature reaches its been reported following exposure to unhygienic environ- highest [39]. Similar studies also showed that increase in ments as a result of domestic animals living in and around temperature was positively associated with diarrhea inci- the human dwellings [61, 64, 65]. dence [46, 47]. The incidence rate starts to slowly decline The findings showed temporal variation in the overall through time to reach its lowest peaks in the months of risk of diarrhea, which indicated that childhood diar- July to November. Shortage of water in the dry season has rhea was not distributed randomly in time. This might been associated with increased prevalence of diarrhea [22]. be due to the influence of socio-economical, environ- This may be due to less availability of fresh water or con- mental, or climate-related factors. Statistically signifi- centration of contaminants in smaller volumes of water cant space-time hotspots (p < 0.001) were also observed [48]orlongerwater storage[49]. A large outbreak of (Table 4). This consisted of a primary hotspot and four diarrhea occurred following severe droughts due to de- secondary hotspots. The primary hotspot was observed creased water availability and worsened personal hygiene in December 2012 to January 2015 in Malga district [50]. Extreme rainfall days and associated flooding were with a likelihood ratio of 1214.67 and relative risk of also strongly related to diarrhea-associated morbidity 2.03. The first, second, third, and fourth secondary hot- [51, 52]. This is because flooding could result in the spots occurred from January 2012 to May 2012 in Loka breakdown of sanitary conditions and contamination of Abaya, December 2011 in Bursa, from March to April drinking water sources by washing nutrients, patho- 2014 in Gorchie, and March 2012 in Wonsho districts. gens, and toxins into water bodies [53]. The primary hotspot spanned for slightly more than The study also showed the existence of substantial 2 years, and the other secondary clusters existed from variation in the spatial distribution of diarrhea within 1 to 5 months only. This might be due to the occur- the study area. The finding was in agreement with an- rence of risk factors specific to the local areas and other national study [15]. However, there is the differ- time periods. ence in the nature of the data sources between the This study has strengths and limitations. The fact that studies in that the later used cross-sectional DHS data. complete monthly morbidity data were obtained from all The highest risk of diarrhea was found in Boricha dis- districts throughout the study period was a strength. trict. This might be due to the fact that most of the resi- The other strength was the existence of historical dents of the district relied heavily on pond water, which 30 years precipitation, maximum temperature, and mini- is open for both human and animal. In addition, the mum temperature data which were compared with the population is agro-pastoralist, where they move from seasonal variation of childhood diarrhea that could have place to place in search of food and water for their ani- been influenced by metrological parameters. The use of mals. These types of people would not have the chance the SaTScan software allowed us to both detect the loca- to construct and use their own latrines. It can also be tion of clusters and evaluate their statistical significance due to other factors such as differing levels of poverty, without problems with multiple testing. A limitation is education, and lifestyle. under-reporting of childhood diarrhea as the data Beyene et al. Tropical Medicine and Health (2018) 46:18 Page 10 of 12 reflects only those who sought healthcare setup level Authors’ contributions HB, WD, DG, and AK participated from the conception to the final write up treatment. As a result, the study might not be indicative of the study. DG also revised the English language. All authors read and of the true picture of diarrheal morbidity of the study approved the manuscript. area. However, this limitation might be minimal as the Authors’ information problem is assumed to be uniform across all districts HB is a lecturer of Environmental and Public Health at Hawassa University, over the study period. Ethiopia. WD is the associate professor and dean of the School of Public Health, College of Health Sciences, Addis Ababa University. He has been teaching several courses including biostatistics, epidemiology, and research Conclusions methodology. He has had supervised many masters and doctoral students. He also has more than 60 publications in the national and international This study assessed the seasonal variation and spatial, journals. AK is an associate professor, head of Environmental Health Unit, and temporal, and space-time clusters of under-five diarrhea PhD program coordinator at the School of Public Health, Addis Ababa using 6 years HMIS morbidity data (July 2011 to June University, Ethiopia. He has been teaching environmental health courses for various groups of health science students. He also has supervised several 2017) in Southern Ethiopia. An increasing trend with a masters and doctoral students. He has more than 60 publications in the seasonal variation of childhood diarrhea which peaks in peer-reviewed journals. DG is an epidemiologist and veterinarian with nearly the transition period from driest to the rainy season oc- 20 years of experience in developing countries. She is a senior researcher at the International Livestock Research Institute in Kenya and program manager curred. An excess risk of diarrhea was also observed in of agriculture-associated diseases in the new CGIAR Research Program on Boricha, Malga, and Hulla districts. Statistically signifi- Agriculture for Human Nutrition and Health. She is a prolific and effective cant space-time hotspots were also observed in five dis- author in a wide variety of media, including over 70 peer-reviewed journal articles, numerous book chapters, presentations, posters, policy briefs, films, tricts from December 2012 to January 2015 in Malga manuals, farmer diagnostic aids, and articles. district, from January 2012 to May 2012 in Loka Abaya, December 2011 in Bursa, from March to April 2014 in Ethics approval and consent to participate Gorchie, and March 2012 in Wonsho districts. Ethical clearance was granted by the ethical review board of Addis Ababa University, College of Health Sciences; permission to use the HMIS data was The spatial, temporal, and space-time clusters, gener- obtained from the SNNPR Health Bureau and Sidama Zone Health ated in this research, can be used by the various stake- department. All the information were kept confidential, and no individual holders to prioritize places of intervention. In addition, identifiers were collected. season-specific interventional strategies can be devel- Consent for publication oped with efficient resource use to reduce the childhood All information were obtained from secondary data. morbidity, mortality, and financial losses related to visit- ing health instructions as a result of diarrhea morbidity. Competing interests The authors declare that they have no competing interest. Further studies are required to clarify the effect of wea- ther variabilities on under-five diarrhea incidence and to Publisher’sNote investigate the specific risk factors of childhood diarrhea Springer Nature remains neutral with regard to jurisdictional claims in in hotspot areas. published maps and institutional affiliations. Author details Additional file College of Health Sciences, Hawassa University, P.O. Box 1560, Hawassa, Ethiopia. School of Public Health, Addis Ababa University, P.O. Box 1176, Addis Ababa, Ethiopia. International Livestock Research Institute, Box 30709, Additional file 1: Time series plot of under-five diarrhea incidence rate Nairobi, Kenya. per 1000. (XLSX 31 kb) Received: 8 March 2018 Accepted: 24 May 2018 Acknowledgements The authors would like to thank the funding organizations, the SNNPR Health Bureau, and the Sidama Zone Health Department people for References providing access to the HMIS data, the Central Statistical Agency of Ethiopia 1. World Health Organization (WHO). Diarrhoeal disease. 2017. Available from: Hawassa Branch Office, the SNNPR Finance and Economy Bureau, and the http://www.who.int/en/news-room/fact-sheets/detail/diarrhoeal-disease. Sidama Zone Finance and Economy Department for providing relevant updated 2 May 2017; cited 2017, November 7. information about the study area. 2. Troeger C, Forouzanfar M, Rao PC, Khalil I, Brown A, Reiner RC Jr, et al. Estimates of global, regional, and national morbidity, mortality, and aetiologies of diarrhoeal diseases: a systematic analysis for the Global Funding Burden of Disease Study 2015. Lancet Infect Dis. 2017;17(9):909–48. Financial support for this study was obtained from the German Academic 3. Ahmed M, Abedin J, Alam KF, Al Mamun A, Paul RC, Rahman M, et al. Exchange Program (DAAD), through the International Livestock Research Incidence of acute diarrhea-associated death among children <5 years of Institute, and from Addis Ababa and Hawassa Universities. age in Bangladesh, 2010-12. Am J Trop Med Hyg. 2018;98(1):281–6. 4. van der Westhuizen FP, Slogrove AL, Kunneke HM, Kruger M. Factors Availability of data and materials associated with severe dehydrating diarrhoea in the rural western cape, The data sets analyzed during the current study are not publicly available South Africa. J Trop Pediatr. 2018. https://doi.org/10.1093/tropej/fmy002. due to third party restrictions, but it can be obtained from the Southern [Epub ahead of print]. Nations, Nationalities, and Peoples’ Region Health Bureau by contacting 5. Bado AR, Susuman AS, Nebie EI. Trends and risk factors for childhood the HMIS officer, Melaku Getahun: melaku759@gmail.com, mobile phone diarrhea in sub-Saharan countries (1990–2013): assessing the neighborhood + 251911986024, Hawassa, Ethiopia. inequalities. Glob Health Action. 2016;9(1):30166. Beyene et al. Tropical Medicine and Health (2018) 46:18 Page 11 of 12 6. Stella S, Ajayi A. Viral causes of diarrhea in children in Africa: a literature statistic, in a community hospital in southern Ontario, Canada, 2006–2011. review. J Mole Biol Tech. 2017;1(1):1042. BMC Infect Dis. 2014;14(1):254. 7. Central Statistical Agency-CSA/Ethiopia, ICF. Ethiopia demographic and 29. Lefebvre A, Bertrand X, Vanhems P, Lucet JC, Chavanet P, Astruc K, et al. health survey 2016. Addis Ababa: CSA and ICF; 2017. Detection of temporal clusters of healthcare-associated infections or colonizations with Pseudomonas aeruginosa in two hospitals: comparison of 8. Dessalegn M, Kumie A, Tefera W. Predictors of under-five childhood SaTScan and WHONET software packages. PLoS One. 2015;10(10):e0139920. diarrhea: Mecha District, West Gojam. Ethiopia Ethiop J Health Dev. 2011; 30. Dewan A, Abdullah AYM, Shogib MRI, Karim R, Rahman MM. Exploring 25(3):192–200. spatial and temporal patterns of visceral leishmaniasis in endemic areas of 9. Eshete WB. A stepwise regression analysis on under-five diarrhoeal Bangladesh. Trop Med Health. 2017;45(1):29. morbidity prevalence in Nekemte town, western Ethiopia: maternal care giving and hygiene behavioral determinants. East African J Public Health. 31. Dewan AM, Corner R, Hashizume M, Ongee ET. Typhoid fever and its association 2008;5(3):193–8. with environmental factors in the Dhaka metropolitan area of Bangladesh: a 10. Mengistie B, Berhane Y, Worku A. Household water chlorination reduces spatial and time-series approach. PLoS Negl Trop Dis. 2013;7(1):e1998. incidence of diarrhea among under-five children in rural Ethiopia: a cluster 32. Osei FB, Stein A. Spatial variation and hot-spots of district level diarrhea randomized controlled trial. PLoS One. 2013;8(10):e77887. incidences in Ghana: 2010–2014. BMC Public Health. 2017;17(1):617. 11. Mengistie B, Berhane Y, Worku A. Prevalence of diarrhea and associated risk 33. Hundessa SH, Williams G, Li S, Guo J, Chen L, Zhang W, et al. Spatial and factors among children under-five years of age in eastern Ethiopia: a cross- space–time distribution of Plasmodium vivax and Plasmodium falciparum sectional study. Open J Prev Med. 2013;3(7):446–53. malaria in China, 2005–2014. Malar J. 2016;15(1):595. 34. FMoH. Acute Watery Diarrhea (AWD) prevention and control strategy. Addis 12. Tamiso A, Yitayal M, Awoke A. Prevalence and determinants of childhood Ababa: Federal Minstry of Health; 2011. Report No diarrhoea among graduated households, in rural area of Shebedino district, southern Ethiopia, 2013. Sci J Public Healtht. 2014;2(3):243–51. 35. Hameso SY. Development challenges in the age of climate change: the 13. Beyene H. Sanitation infrastructure sustainability challenges case study: case of Sidama. Economy of Southern Ethiopia, Ethiopian Economics Ethiopia. In: Bongartz P, Vernon N, Fox J, editors. Sustainable sanitation for Association, its Chapter at Hawassa University, Department of Economics; 1 all: experiences, challenges, and innovations. Rugby: Practical Action March 2012. Ethiopia: Hawassa University; 2012. Publishing Ltd; 2016. 36. Deqamo Y. In: Department SZFaE, editor. Socio-economical and 14. Azage M, Kumie A, Worku A, Bagtzoglou AC. Childhood diarrhea exhibits environmental characteristics of Sidama Zone. Hawassa: Sidama Zone spatiotemporal variation in Northwest Ethiopia: a SaTScan spatial statistical Finance & Economic Development Department, Ethiopia; 2011. p. 98. analysis. PLoS One. 2015;10(12):e0144690. 37. Central Statistical Agency (CSA). Population projection of Ethiopia for all 15. Bogale GG, Gelaye KA, Degefie DT, Gelaw YA. Spatial patterns of childhood regions at Wereda level from 2014–2017. Addis Ababa: Federal Democratic diarrhea in Ethiopia: data from Ethiopian demographic and health surveys Republic of Ethiopia, Central Statistical Agency; 2013. p. 118. (2000, 2005, and 2011). BMC Infect Dis. 2017;17(1):426. 38. Central Statistical Agency (CSA). National Census Report of Ethiopia. Addis Ababa: Central Statistical Agency of Ethiopia; 2007. 16. Osei FB, Duker AA. Spatial dependency of V. cholera prevalence on open space refuse dumps in Kumasi, Ghana: a spatial statistical modelling. Int J 39. National Meteorology Agency (NMA). Climate Analysis and Application Health Geogr. 2008;7(62). https://doi.org/10.1186/1476-072X-7-62. Addis Ababa, Ethiopia: National Metrological Agency of Ethiopia; 2017. 17. Thiam S, Diène AN, Fuhrimann S, Winkler MS, Sy I, Ndione JA, et al. Prevalence Available from: http://www.ethiometmaprooms.gov.et/. cited 2017 of diarrhoea and risk factors among children under five years old in Mbour, November 28. Senegal: a cross-sectional study. Infectious Dis Poverty. 2017;6:109. 40. Kulldorff M. SaTScanTM user guide. In: Boston; 2006. 18. Ahmed SF, Farheen A, Muzaffar A, Mattoo GM. Prevalence of diarrhoeal 41. Kulldorff M and Information Management Services, Inc. SaTScanTM v9.4.4: Software disease, its seasonal and age variation in under-fives in Kashmir, India. Int J for the spatial and space-time scan statistics. 2016. http://www.satscan.org/. Health Sci. 2008;2(2):126–33. August 2016. 19. Anyorikeya M, Ameme DK, Nyarko KM, Sackey SO, Afari E. Trends of 42. Kulldorff M. A spatial scan statistic. Commun Stat Theory Methods. 1997;26: 1481–96. diarrhoeal diseases in children under five years in the war memorial hospital-Navrongo, Ghana: 2010-2013. Pan African Med J. 2016;25(Suppl 1):8. 43. Xu Z, Hu W, Zhang Y, Wang X, Zhou M, Su H, et al. Exploration of diarrhoea 20. Masukawa MLT, EMd S, Gimenes E, Uchimura NS, Moriwaki AM, Uchimura seasonality and its drivers in China. Sci Rep. 2015;5:8241. TT. Time series investigation of changes in seasonality of acute diarrhea 44. CSA. Central Statistical Agency (CSA) [Ethiopia] and ICF. Ethiopia hospitalizations before and after rotavirus vaccine in Southern Brazil. Demographic and Health Survey 2016. Addis Ababa, Ethiopia, and Rockville, Cadernos de Saúde Pública; 2016;32(10):e00080515. Maryland: CSA and ICF; 2017. 21. Phung D, Huang C, Rutherford S, Chu C, Wang X, Nguyen M, et al. 45. Bado AR, Susuman AS, Nebie EI. Trends and risk factors for childhood diarrhea Temporal and spatial patterns of diarrhoea in the Mekong Delta area, in sub-Saharan countries (1990–2013): assessing the neighborhood Vietnam. Epidemiol Infect. 2015;143(16):3488–97. inequalities. Glob Health Action. 2016;9 https://doi.org/10.3402/gha.v9.30166. 22. Bandyopadhyay S, Kanji S, Wang L. The impact of rainfall and temperature 46. Checkley W, Epstein LD, Gilman RH, Figueroa D, Cama RI, Patz JA, et al. variation on diarrheal prevalence in sub-Saharan Africa. Appl Geogr. 2012; Effect of El Nino and ambient temperature on hospital admissions for 33(Supplement C):63–72. diarrhoeal diseases in Peruvian children. Lancet. 2000;355(9202):442–50. 23. Kulinkina AV, Mohan VR, Francis MR, Kattula D, Sarkar R, Plummer JD, et al. 47. Hashizume M, Armstrong B, Hajat S, Wagatsuma Y, Faruque AS, Hayashi T, Seasonality of water quality and diarrheal disease counts in urban and rural et al. Association between climate variability and hospital visits for non- settings in South India. Sci Rep. 2016;6:20521. cholera diarrhoea in Bangladesh: effects and vulnerable groups. Int J 24. Omore R, Tate JE, O’Reilly CE, Ayers T, Williamson J, Moke F, et al. Epidemiol. 2007;36(5):1030–7. Epidemiology, seasonality and factors associated with rotavirus infection 48. Leder K, Sinclair MI, McNeil JJ. Water and the environment: a natural among children with moderate-to-severe diarrhea in rural western Kenya, resource or a limited luxury? Med J Aust. 2002;177:609–13. 2008–2012: the Global Enteric Multicenter Study (GEMS). PLoS One. 2016; 49. Boelee E, McCartney M, Yohannes M, Hagos F, Lautze J, Kibret S. Climate 11(8):e0160060. change, water resource development and malaria in Ethiopia. Water and 25. Tornheim JA, Manya AS, Oyando N, Kabaka S, O’Reilly CE, Breiman RF, et al. Health: Where Science Meets Policy; October 25-26, 2010. The University of The epidemiology of hospitalization with diarrhea in rural Kenya: the utility North Carolina at Chapel Hill, USA; 2010. of existing health facility data in developing countries. Int J Infect Dis. 2010; 50. Jordan E. Drought as a climatic driver of an outbreak of diarrhea in Tuvalu, 14(6):e499–505. South Pacific: Public Health Theses; 2015. p. 1079. http://elischolar.library. 26. Robertson C, Nelson TA. Review of software for space-time disease yale.edu/ysphtdl/1079. Accessed on 26 Aug 2016 surveillance. Int J Health Geogr. 2010;9(16). 51. Chou W, Wu J, Wang Y, Huang H, Sung F, Chuang C. Modelling the impact 27. Wu W, Guo J, Guan P, Sun Y, Zhou B. Clusters of spatial, temporal, and of climate variability on diarrhea-associated diseases in Taiwan (1996–2007). space-time distribution of hemorrhagic fever with renal syndrome in Sci Total Environ. 2010;409(43–51):43–51. Liaoning Province, Northeastern China. BMC Infect Dis. 2011;11:229. 52. Hashizume M, Armstrong B, Wagatsuma Y, Faruque AS, Hayashi T, Sack DA. 28. Faires MC, Pearl DL, Ciccotelli WA, Berke O, Reid-Smith RJ, Weese JS. Rotavirus infections and climate variability in Dhaka, Bangladesh: a time- Detection of Clostridium difficile infection clusters, using the temporal scan series analysis. Epidemiol Infect. 2008;136(9):1281–9. Beyene et al. Tropical Medicine and Health (2018) 46:18 Page 12 of 12 53. Thomas MK, Charron DF, Waltner-Toews D, Schuster C, Maarouf AR, Holt JD. A role of high impact weather events in waterborne disease outbreaks in Canada, 1975–2001. Int J Environ Health Res. 2006;16:167–80. 54. EDHS. Central Statistical Agency [Ethiopia] and ICF International. Ethiopia Demographic and Health Survey (EDHS). Addis Ababa, Ethiopia and Calverton: Central statistical agency and ICF international; 2012. 55. Ayele WY, Neill SD, Zinsstag J, Weiss MG, Pavlik I. Bovine tuberculosis: an old disease but a new threat to Africa. Int J Tuberc Lung Dis. 2004;8(8):924–37. 56. Haile AA. Assessment of the impact of latrine utilization on idarrhoeal diseases in the rural community of Hulet Ejju Enessie District, Amhara Regional State. Addis Ababa: Addis Ababa University, Ethiopia; 2007. 57. Mulugeta T. Socio-economic, environmental, and behavioural factors associated with the occurrence of diarrhoeal disease among under-five children, Meskanena Mareko Woreda, southern Ethiopia. Addis Ababa: Addis Ababa University, Ethiopia; 2003. 58. Doorduyn Y, Van Den Brandhof WE, Van Duynhoven YT, Breukink BJ, Wagenaar JA, Van Pelt W. Risk factors for indigenous Campylobacter jejuni and Campylobacter coli infections in the Netherlands: a case-control study. Epidemiol Infect. 2010;138(10):1391–404. 59. Mahdi NK, Ali NH. Cryptosporidiosis among animal handlers and their livestock in Basrah, Iraq. East Afr Med J. 2002;79(10):550–3. 60. Moher D, Liberati A, Tetzlaff J, Altman DG. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Ann Intern Med. 2009;151(4):264–9. w64 61. Rao MR, Naficy AB, Savarino SJ, Abu-Elyazeed R, Wierzba TF, Peruski LF, et al. Pathogenicity and convalescent excretion of campylobacter in rural Egyptian children. Am J Epidemiol. 2001;154(2):166–73. 62. Fayehun OA. Household environmental health hazards and child survival in sub-Saharan Africa. DHS Working Papers No. 74. Calverton: ICF Macro; 2010. Contract No.: 74 63. Ramani SV, Frühauf T, Dutta A, Meijers H. Determinants of the prevalence of diarrhoea in adolescents attending school: a case study of an Indian village school. UNU-MERIT Working Papers. 2012;059:2012. 64. Locking ME, SJ O'BRIEN, Reilly WJ, Wright EM, Campbell DM, Coia JE, et al. Risk factors for sporadic cases of Escherichia coli O157 infection: the importance of contact with animal excreta. Epidemiol Infect. 2001;127:215–20. 65. Molbak K, Jensen H, Ingholt L, Aaby P. Risk factors for diarrheal disease incidence in early childhood: a community cohort study from Guinea- Bissau. Am J Epidemiol. 1997;146(3):273–82.

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Tropical Medicine and HealthSpringer Journals

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