Since 1970, cholera has become endemic in many countries of sub-Saharan Africa and remains a recurring problem [1]. During 2007 and 2011, annual Case-Fatality Ratios (CFRs) for cholera within this region ranged from 2.22% to 2.95%, and exceeded 5% in a country in each year [2–6]. It is evident that in spite of continued scientific advancement in prevention and treatment of cholera, it remains a major public health issue in many parts of sub-Saharan Africa.

Cholera incidence and mortality, and diarrheal diseases, can be reduced through increased access to safe water, sanitation and hygiene (WaSH) and through behavioral change resulting from education and training in communities [7] as was done in Latin America [8, 9] and in industrialized countries [10]. However, improvements in WaSH infrastructure have been slow in many cholera affected African countries [11, 12], resulting in repeated cholera outbreaks.

According to WHO, cholera prevention and control should be a priority in areas at risk for cholera, and vaccines should be included in conjunction with other cholera prevention and control strategies [13]. Safe WHO prequalified cholera vaccines (OCV) [14] provide 65% protection at least for 3 to 5 years [15]. Studies have also demonstrated that large-scale vaccination campaigns are feasible and acceptable, reflected by high acceptance rate and high coverage in both urban and rural settings [16–20]. To ensure access to OCV for cholera affected countries, a global stockpile is now available for both emergency use to control outbreaks and areas with recurrent cholera outbreaks. However, the supply of OCV is limited compared to the large number of people living in countries at risk [21]. Thus, countries with cholera need to identify specific areas at increased risk in order to focus their control efforts using an integrated approach with WaSH improvements and vaccination.

In Uganda, epidemics of cholera have occurred regularly since the disease first appeared in 1971 [22]. The Government of Uganda instituted preventive and control measures that included promotion of access to safe water, sanitation and hygiene; health education and community mobilization; disease surveillance; and case management [23]. However cholera cases continues to be reported annually [24]. It is important for the country to understand the extent of the problem, identify the areas of high risk, and use this information to plan for an effective intervention strategy. In most areas, during cholera outbreaks, cases tend to be clustered in specific areas and among certain population groups [25, 26], which can be demonstrated by the spatial epidemiology of the disease. A study that describes the spatial distribution of disease, its incidence using geographic information system (GIS) and its association to potential risk factors should help guide interventions to control cholera [27]. The aim of this study was to identify cholera high risk districts (hotspots) in Uganda in order to provide insights and guidance for prevention, control and ultimately elimination of cholera in Uganda.

A total of 11,030 cases of cholera were reported during 2011–2016. The highest number of cases were 6,226 in 2012 and the lowest were 229 in 2011 (Fig 1). In 37 of 112 districts (33%) cholera was reported in at least one of the study years. These districts made up 40% of the total population of Uganda. During the study period, the highest burden of cholera was in Nebbi district (2,320 cases) followed by Hoima (1,731 cases), and Buliisa (1,129 cases). The districts that had multiple outbreaks (at least two years) during study period had a population of about 7.8 millions.

The results of the SatScan for cluster detection based on the centers of the districts as the geographic coordinates yielded 16 significantly high-risk clusters of different sizes. Cholera hotspots were defined as districts whose center fell within a significantly high risk cluster or where significantly high risk cluster was completely superimposed onto a district. There were 22 districts whose centroids fell within the cluster detected by SatScan; these were defined as high risk districts. The risk of having cholera in these districts was 1.26 to 21.50 times compared to that elsewhere in the country (Table 1, Fig 2). About 7 million people live in these districts. Of the 22 districts, 13 of them (4.8 million people) are near the border of DRC and 9 of them (2.2 million people) are near the border of Kenya.

We observed a distinct seasonal pattern of cholera between eastern and western region of Uganda. The seasonal pattern also varied by year. There was no cholera outbreak in the eastern region in 2011 and 2013. In 2012, the peak was between March and May in the eastern region, while in the western region the peak was between April and August (Fig 3). Apparently, there was only a little link with rainfall in a specific year and specific region (Fig 3). Fig 4 shows geographic distribution of the rates of cholera which varied in the study area, illustrating that cholera affected the people living near a lake or river. The coefficient of variations for the year-wise rates indicates that border districts experienced cholera more frequently than the inner part of the country (S2 Fig).

The basic statistics of the study variables show a high rate of improved water (82%) and sanitation (75%) coverage at the household level (Table 2). An average of 15% of the population lived in the urban areas. There were 300 hospitals/clinics for Uganda in the world health facility database, and these health facilities were heterogeneously distributed (visual perception) in space (Fig 4). The average distance to the nearest health facility, river of lake, and border of DRC or Kenya from the center of the district was 33, 47, and 91 kilometers, respectively. The average weighted cholera incidence rate (cumulative over the six year period) of the 1^st order of neighbors of a district was 28 per 100,000 persons. Average annual rainfall during 2011–2015 was 1324 mm.

The result of the bivariate analysis of the ZINB are presented in Table 3. The NB component of the model shows that higher incidence rate in a district was significantly associated (p < .05) with improved water sources, longer distance to the nearest heath facility, shorter distance to a lake or river, shorter distance to DRC or Kenya border, and a higher cholera incidence rate of the neighbor districts. On the other, although a significant relationship of population density was observed with reporting of cholera case by a district in the zero-inflated model, the point estimate does not indicate an increase or decrease of having cholera by the district. Likewise the NB model, distance to DRC or Kenya border and cholera incidence rate of the neighbor districts yielded a significant relationship (p < .05) with reporting of cholera cases in the zero-inflated model.

We created multivariable model using the variables associated with the outcome at p<0.20. The results of the NB component of the multivariable model show that the distance to the nearest lake or river was significantly associated with the incidence of cholera (incidence rate ratio = 0.98, 95% CI: 0.97–0.99, p<0.01) (Table 4). This indicates that a one kilometer decrease in the distance to lake or river was associated with 2 percentage point of increase of cholera cases. Shorter distance to the DRC or Kenya border and higher cholera incidence of the neighbor districts were significantly associated (p < .05) with higher incidence rate of cholera in the district. On the other hand, the zero-inflated component of the multivariable model shows a one kilometer increase of distance to DRC or Kenya border was associated with 2 percentage point decrease in the odds of having cholera in the district. The zero-inflated model also shows that a one percentage point increase in the cholera incidence rate in the neighbor districts was associated with 3 percentage point increase in the odds of having cholera. Higher annual rainfall in the district was significantly associated (p = .03) with the risk of having cholera in the district in the zero-inflated model.

This results of our study show that most of the high risk districts for cholera were near the border with DRC and Kenya. These high risk districts with a population of about 7 million, make up about 20% of the population of Uganda. Those with a very high risk districts (relative risk was more than 10 compared to that elsewhere in the country) have a population of about 2.4 million (7% of the population of Uganda) from six districts. All of these districts are located near the DRC. Studies [36, 37] indicated that cross-border cholera outbreaks could be a major contributor to the recorded outbreaks in Uganda. Since a majority of the hotspot districts are near DRC or Kenya border, it suggests that a close collaboration with these countries would be an effective strategy for controlling cholera in that part of the world.

Our study also shows that proximity to a lake, specifically Lake Albert and Lake Victoria or to the Nile River, creates increased risk of cholera for the people in Uganda, as found in an earlier study [38]. Interestingly, rates of cholera are low near Lake Kyoga and along the Nile River as the water flows between Lake Kyoga and Lake Albert, but the rates are higher along the Nile River as the water flows north from Lake Albert. The finding in our study is consistent with earlier studies, which indicated that people are continuously being affected by cholera in lakeside areas [39]. Several other studies have also reported that Lake Albert and Lake Victoria could be a source of Vibrio cholerae [40, 41]. However, there is no definitive evidence to support the hypothesis. The link between high incidence of cholera and presence of lakes has also been noted in DRC [40, 42]. Bompangue et al [39] believe that in the absence of lakeside areas, the disease would have disappeared from the country. This may be true as we find cholera was less likely to create a threat to the people living far from the lakes. However, it is not clear if the lake is the major risk factor, or the behaviors or occupations of the people who live along the lake [43]. It should be noted that these lake areas also are borders with the neighboring countries with cholera; thus, it is difficult to separate the risk associated with the lake environment and the risk associated with cross border spread. The association between cholera outbreak and rainfall, as observed in our zero-inflated model, is consistent with earlier findings [39].

The seasonal pattern of cholera varied by year in Uganda suggesting exact timing of the outbreaks in the different regions of the country may not be predicted. A spike of cholera was observed in 2012, which was associated with a large outbreak in the African region that mostly affected six nations including Uganda (http://www.who.int/hac/crises/cholera_afro_22august2012.pdf?ua=1). In 2016, cholera showed a two distinct seasonal pattern between eastern and western region of the country. The peak season for cholera in eastern region started in March and continued until May, and the peak season in the western region started in June and lasted until August. Since Uganda experiences heavy rainfall from March to June, it seems that cholera outbreaks are associated with the rainfall in the country. Association of rainfall with the risk for cholera in Africa as well as in the neighboring country Kenya has already been observed [44, 45].

We did not find an association of water and sanitation with the risk for cholera in Uganda. This could be due to a reported high coverage of improved water sources and improved sanitation in the country. Also, the indicator used for the district which is an average for the district, may not represent the water and sanitation status of those who are vulnerable to cholera. With such high coverage for these WaSH indicators, one may want to reassess whether these indicators are providing an accurate reflection of the true situation for those at risk. The sustainability of water services in the small towns is also reported to be high (92%). This high coverage of access to safe water is largely due to back-up support for operations and maintenance which is being provided by regionally based Umbrella Organizations [33]. On the other hand, access to improved sanitation is reported to be high (85%) in the urban areas, and open defecation is reported to be rare in the both urban and rural areas of the country, except slums [33].

The strength of our study is that the cases of cholera were collected from a surveillance system which has been maintained systematically throughout the country and was done according to the WHO standard. Water and sanitation data were obtained from the report prepared by the Ministry of Water and Environment, Uganda, which are being updated on a yearly basis ensuring greater reliability of the data. The population data were obtained from a recent report prepared by the Uganda Bureau of Statistics [31] providing national population and housing census 2014, which is consistent with the period of our study. Additionally, since the data came from the national surveillance conducted by the Uganda Ministry of Health and was systematically executed throughout the country, we believe the burden of cholera across the districts are comparable.

The main limitation in our study is that cholera data were not population-based, which precluded calculating the absolute risk of the disease. Conducting a national level population-based disease surveillance in this setting is probably unrealistic. However, the data used in this study provided a basis for understanding relative burden of cholera across the districts in Uganda.

Our study identified cholera hotspots in Uganda that should be prioritized to accelerate reduction of cholera by implementation of targeted interventions. The intervention program includes provision of safe water, hygiene and sanitation, provision healthcare service and health education, and should be complemented with OCV and strong cross-border collaboration in outbreak prevention. The findings of our study could be used as a guide to strengthen the cholera control program in Uganda.