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 |
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 |