Skip to main content

Table 1 Summary of included studies

From: Epidemiology of floods in sub-Saharan Africa: a systematic review of health outcomes

Study Country and Year of Flooda Study Design Data Type Study Participants Sample Size Outcome Effect Measures Mechanisms of Disease Transmission
Water-borne Diseases
Rieckmann et al., 2018 [39] 40 Sub-Saharan African countriesb Register-based, country-level longitudinal study of cholera outbreaks and flood events in sub-Saharan Africa between 1990 and 2010. Disease surveillance data Not applicable 276 cholera outbreaks Cholera outbreaks Incidence rates of cholera outbreaks were elevated during flood periods when compared to periods not affected by floods (IRR = 144; 95% CI: 101–208, p-value not discussed). − Overflowing of the sanitation systems
− Contamination of the environment and water sources
− Displacement and influx of aid workers facilitates transmission of diseases
− Overcrowding, which exacerbates hygiene and sanitation concerns
Vector-borne Diseases
Oyekale, 2015 [41] Cameroon Cross-sectional study of 2011 Demographic and Health Survey (DHS) data. Clinical malaria cases and households dwelling characteristics, such as living in a flood-prone area, were analysed. Clinical data and survey-based data. Children, aged 6 to 59 months. 6623 children Malaria infections Children that resided in flood-prone areas compared to those who do not, had a 8.9 percentage points lower likelihood of a malaria infection (p-value < 0.01). Not applicable
Mboera et al., 2011, Mboera et al., 2010 [37, 38] Tanzania Cross-sectional study. Clinical malaria cases in flooded and non-flooded ecosystems were investigated in 2005. Clinical data Schoolchildren in classes 1 to 4 578 schoolchildren Malaria infections The prevalence of plasmodium falciparum was significantly higher in a flooding rice irrigation environment than in a non-flooded sugarcane farming environment (OR = 10.14; CI: 4.58 - 22.42, p-value < 0.05). Not applicable
Elsanousi et al., 2018 [42] Sudan, 2013 Observational retrospective study of malaria data sets between 2011 and 2013. Comparison of data sets during the year of flooding (2013) with those of corresponding non-flood years (2011, 2012). Clinical data Children, adolescents and adults. 2011: 5069 malaria cases, 2012: 5549 malaria cases, 2013: 7262 malaria cases Malaria infections People exposed to floods in 2013 had a significantly higher slide positivity rate (%) than people not exposed to floods in 2011 (SPR = 2.39%; 95% CI: 2.27-2.51, p-value < 0.0001) − Increased growth of Anopheles mosquito population through formation of new breeding sites and favourable conditions for mosquito development and survival.
− Displacement, damage to private houses, and destruction of the infrastructure, may decrease the reduces accessibility of healthcare services.
Boyce et al., 2016 [43] Uganda, 2013 Quasi-experimental design. Difference-in-difference approach to investigate the causal relationship between laboratory- confirmed malaria cases and different environmental factors in a pre- and a post-flood period. Clinical data Children, adolescents and adults. 7596 individuals Malaria infections The likelihood of receiving a positive test result was significantly higher in the post-flood period than in the pre-flood period (ARR = 1.47; 95% CI: 1.36-1.58, p-value < 0.001).
The presence of a flood-affected river near the studied villages was associated with a significantly higher test positivity rate compared to villages farther from a river (ARR = 1.30; 95% CI: 1.16–1.46, p-value < 0.001).
− Creation of stagnant pools as ideal breeding habitats for the Anopheles mosquito
Chirebvu et al., 2016 [44] Botswana 5-year retrospective time series analysis of clinical malaria cases and climate variables between flood and non-flood periods. Clinical data Children, adolescents and adults. Not applicable. Malaria infections At a lag period of six month, the incidence of clinical malaria cases correlates most strongly with flood extent (ρ = 0.467, p-value < 0.05).
When setting the lag period to zero months, the incidence of clinical malaria cases is most strongly associated with flood discharge (ρ = 0.396, p-value < 0.05).
− Emergence of suitable breeding habitats and thus influence growth of the Anopheles mosquito population.
Sara et al., 2018 [45] Ethiopia Unmatched case-control study (1:2 ratio). Analysis of scabies infections and individuals dwelling characteristics, such as home being affected by flooding. Clinical data and survey-based data. Individuals, aged 8 months to 70 years for the line-listed scabies cases. Individuals, aged 3 months to 65 years for the case-control analysis. 4532 line-listed scabies cases. 55 scabies cases and 110 controls for the case-control analysis. Scabies infections The odds of a scabies infection were approximately 22 times higher among people who lived in homes affected by flooding compared to people not affected by flooding (aOR = 22.32; 95% CI: 8.46–58.90, p-value < 0.0001). − Displacement, overcrowding, and worsened personal hygiene
Grossi-Soyster et al., 2017 [46] Kenya Cross-sectional study. Analysis of links between serological samples and demographic data, information on lifestyle, previous state of health and recent experience of village flooding. Clinical data and survey-based data. Children and adults, aged 5-75. 250 children, 250 adults Alphaviruses and flaviviruses seroprevalence Recent experience of village flooding significantly increased the likelihood of alpha- or flavivirus infection (OR = 2.49; 95% CI: 1.31–4.73, p-value < 0.005). − Emergence of potential environments for mosquito breeding.
Wardrop et al., 2013 [47] Uganda Matched case-control study design with 1:1 matching based on age group. Correlation between distribution of Rhodesian sleeping sickness and environmental factors, such as residence in a flooded environment, is investigated. Clinical data Children, adolescents and adults. 233 Rhodesian sleeping sickness cases and 233 controls. Rhodesian sleeping sickness infections Higher proportions of seasonally flooded grassland significantly increased the likelihood of Rhodesian sleeping sickness (OR = 1.18; 95% CI: 1.04-1.33, p-value   =  0.01). − Emergence of more favourable habitat for tsetse survival and reproduction.
Zoonotic Diseases
Wardrop et al., 2015 [48] Kenya Cross-sectional study. Clinical laboratory diagnostics are used to determine the presence of a taeniasis infection and are linked to survey-based and environmental data on flooding. Clinical data and survey-based data. Individuals older than five years and not in the third trimester of pregnancy. 416 households, comprising 2113 individuals Taeniasis infections The presence of the antigen in the study population is significantly higher within a 1 km distance to flooded agricultural land and flooded grassland (OR = 1.09; 95% CI: 1.01-1.17, p-value = 0.03). − Surface moisture and humidity increase likelihood of survival of Taenia spp. eggs
− Floodwaters transport the Taenia spp. eggs to other areas
  1. a if applicable
  2. b Angola, Burundi, Djibouti, Eritrea, Ethiopia, Kenya, Malawi, Mozambique, Rwanda, Somalia, Tanzania, Uganda, Zambia, Cameroon, Central African Republic, Chad, Congo, Democratic Republic of Congo, Equatorial Guinea, Gabon, Botswana, Lesotho, Namibia, South Africa, Swaziland, Zimbabwe, Benin, Burkina Faso, Cote d’Ivoire, Gambia, Ghana, Guinea, Guinea Bissau, Liberia, Mali, Mauritania, Niger, Nigeria, Senegal, Sierra Leone, Togo