Temporal, spatial and household dynamics of Typhoid fever in Kasese district, Uganda

Abstract

Typhoid fever affects 21 million people globally, 1% of whom succumb to the disease. The social, economic and public health consequences of this disease disproportionately affect people in Africa and Asia. In order to design context specific prevention strategies, we need to holistically characterise outbreaks in these settings. In this study, we used retrospective data (2013–2016) at national and district level to characterise temporal and spatial dynamics of Typhoid fever outbreaks using time series and spatial analysis. We then selected cases matched with controls to investigate household socio-economic drivers using a conditional logistic regression model, and also developed a Typhoid fever outbreak-forecasting framework. The incidence rate of Typhoid fever at national and district level was ~ 160 and 60 cases per 100,000 persons per year, respectively, predominantly in urban areas. In Kasese district, Bwera sub-county registered the highest incidence rate, followed by Kisinga, Kitholhu and Nyakiyumbu sub-counties. The male-female case ratio at district level was at 1.68 and outbreaks occurred between the 20 th and 40 th week (May and October) each year following by seven weeks of precipitation. Our forecasting framework predicted outbreaks better at the district level rather than national. We identified a temporal window associated with Typhoid fever outbreaks in Kasese district, which is preceded by precipitation, flooding and displacement of people. We also observed that areas with high incidence of Typhoid fever also had high environmental contamination with limited water treatment. Taken together with the forecasting framework, this knowledge can inform the development of specific control and preparedness strategies at district and national level.

Spatio-temporal trends and distribution patterns of typhoid disease in Uganda from 2012 to 2017

“…As research generally associates environmental conditions with typhoid disease (Walters et al, 2014;Gu et al, 2017;Mirembe et al, 2019), we explored its association with the average rainfall during the study period. It was observed that the number of typhoid cases always started to raise during the rainy season of March to May, when some places with poor drainage and risk of flooding created conducive environments for bacterial survival and growth.…”

“…In Uganda, over 80% of the districts are endemic for typhoid with those in the central and western regions topping the list (Agwu, 2012;Walters, et al, 2014), making Uganda only second to Nigeria in Africa with respect to typhoid (Jong-Hoon, 2017). New cases continue to be registered every day in many hospitals and clinics, and outbreaks are reported in different parts of the country (Kabwama et al, 2015;Mirembe et al, 2019). The disease is caused by a Gramnegative bacterium called Salmonella enterica serovar typhi, whose main route into the body is with drinking unsafe water or eating contaminated food as a result of poor sanitation and hygiene (Tiwari and Nayak, 2017;Mirembe et al, 2019).…”

“…New cases continue to be registered every day in many hospitals and clinics, and outbreaks are reported in different parts of the country (Kabwama et al, 2015;Mirembe et al, 2019). The disease is caused by a Gramnegative bacterium called Salmonella enterica serovar typhi, whose main route into the body is with drinking unsafe water or eating contaminated food as a result of poor sanitation and hygiene (Tiwari and Nayak, 2017;Mirembe et al, 2019).…”

“…Knowledge that can be revealed by spatio-temporal studies include incidence rates, trends, disease distributions as well as attributes, such susceptible population groups including low and high-risk areas. This knowledge is essential in disease surveillance (Ahangarcani et al, 2019), as it enables the study of populations at risk through the use of surveillance teams that can i) plan and enforce preventive measures; ii) effectively prepare for outbreaks; iii) make targeted interventions for resource optimization; iv) explore possible factors influencing disease occurrence; and v) evaluate effectiveness of intervention methods (Mirembe et al, 2019).…”

Environmental surveillance for Salmonella Typhi as a tool to estimate the incidence of typhoid fever in low-income populations

“…Incidence varies geographically with >90% of cases in south and southeast Asia and sub-Saharan Africa, where access to water, sanitation and hygiene (WASH) infrastructure is limited [2,3]. Typhoid is especially prevalent in urban, low socio-economic settings where overcrowding and unhygienic living conditions are common [4,5]. Incidence is typically lower in rural areas, although recent findings in rural or peri-urban sites describe a higher burden than previously appreciated [6,7].…”