ABSTRACT
Freshwater pollution is a major concern in Ghana, directly impacting human health. However, the underlying drivers of exposure and risks are not comprehensively understood, emphasizing the severity and impact of these diseases. This study assessed the interaction between water and human health, specifically focusing on the risk factors for waterborne diseases and the drivers of water pollution among residents near the Tano River Basin, Ghana. A sample size of 400 households was selected from five communities within the basin based on their proximity to the Tano River. In addition, the study combined both spatial and non-spatial data sources to map potential flood zones for the basin. The study found that inadequate sanitation, poor hygiene practices, and contamination from illegal mining were the primary causative factors of waterborne diseases. Additionally, floods and improper waste management significantly contributed to disease outbreaks. The flood susceptibility analysis indicated that areas highly susceptible to flooding cover 21.2% of the basin, predominantly in the southern part. The results highlight the urgent need for comprehensive interventions to address the drivers of waterborne diseases. This study will contribute to the local authorities in developing plans to prevent waterborne diseases and mitigate their economic and public health impacts.
HIGHLIGHTS
Identified primary contributors: inadequate sanitation, poor hygiene, and illegal mining contamination.
High flood risk zones cover 21.2%, mainly in the southern region.
Integrated spatial and non-spatial data for flood zone mapping.
Floods and waste mismanagement exacerbate waterborne disease incidence.
Urgent need for comprehensive interventions highlighted; policy recommendations for authorities provided.
INTRODUCTION
Water is a crucial resource for aquatic ecosystems and provides numerous health benefits. However, water pollution can destroy ecosystems and spread waterborne diseases, resulting in significant economic consequences. Natural factors like climate change and natural disasters, as well as human activities, influence the state of water resources (Charnley et al. 2022). Waterborne diseases are often associated with communities that have limited access to clean water and sanitation facilities. Despite advancements in access to clean water and sanitation in some regions, waterborne diseases continue to be a major burden, particularly in vulnerable areas of Africa and Asia (Mabhaudhi et al. 2019).
Global trends like rapid population growth, urbanization, and environmental changes such as climate change and land use alterations exacerbate the scarcity and pollution of freshwater resources worldwide. Waterborne diseases remain a significant burden globally, mainly due to the inadequate investment in clean water and sanitation infrastructure despite the growing threats posed by climate change disasters, conflicts between transboundary countries and among competitive users, drought, and frequent flooding (Ntajal et al. 2020). In 2019 alone, diarrhea claimed the lives of over 370,000 children under five, with water, sanitation, and hygiene-related infections contributing to an estimated 21,000–143,000 deaths worldwide (Charnley et al. 2022).
In addition to water access and sanitation, land use, waste management, and water pollution are significant contributors to waterborne disease exposure and risks (Ntajal et al. 2020; Brattig et al. 2021). Moreover, changes in land use patterns, driven by urbanization and agricultural expansion, alter hydrological dynamics and increase the likelihood of surface water contamination (McGrane 2016).
Natural disasters, particularly floods, pose additional threats by disrupting water and sanitation infrastructure and contaminating water sources with sewage and other pollutants (Islam et al. 2021). For instance, flood events can contaminate surface water and sewage-disposal systems, leading to outbreaks of waterborne diseases when the contaminated water sources are ingested (Walika et al. 2023). The rapid spread of waterborne pathogens during floods underscores the vulnerability of communities dependent on surface water sources and inadequate sanitation facilities. However, there is limited research on how these factors interact to influence pathogen levels in surface water, highlighting the need for further investigation to mitigate health risks.
In Ghana, despite improvements in access to drinking water and sanitation, communities in flood-prone areas, such as the Tano River catchment in Bono, Bono East, Western Ghana, and parts of Côte d'Ivoire, experience frequent outbreaks of diarrheal diseases (Honlah et al. 2019). In 2016, the incidence of diseases like cholera, typhoid, and dysentery in these regions was approximately 25.6 and 26.5% (Honlah et al. 2019). Inadequate sanitation, poor hygiene, and water contamination are some of the underlying causative factors. Waterborne diseases generally have different causes and underlying drivers influencing the outbreaks.
Despite advancements in understanding the direct links between water quality, sanitation, and hygiene (WASH) and waterborne diseases, gaps remain in comprehending how complex interactions among these factors, alongside broader environmental changes, influence disease dynamics (UNICEF & WHO 2022). This study aims to address these gaps by investigating the drivers of waterborne diseases in the Tano River catchment area. Specifically, it examines the interaction between water quality and human health, focusing on identifying and understanding the risk factors for waterborne diseases among residents near the Tano River Basin, Ghana. The findings will support local authorities in formulating effective strategies to prevent disease outbreaks and mitigate their adverse impacts on public health and the economy.
MATERIALS AND METHODS
Descriptions of the study area
The Tano River Basin is a crucial water resource in Ghana, holding significant socioeconomic value. The Ghana Water Company Limited (GWCL) has constructed dams at Tanoso, Sefwi Wiawso, and Elubo along the river to treat and distribute drinking water to over 2.4 million people in the Bono, Bono East, Ahafo, and Western Regions (Water Resources Commission & Ghana Country Water 2019; Obiri et al. 2021). The Tano River has a basin area of approximately 15,000 km2, shared between Ghana and Cote D'Ivoire (Larbi et al. 2022). This river supports various activities such as irrigation, bathing, and drinking and also serves as a recipient of raw pollutants.
Over the past 37 years (1981–2019), the Tano basin has experienced annual rainfall ranging from 1,136.7 to 2,156.0 mm (Nasirudeen et al. 2021). Peak rainfall seasons occur in May/June and October/November. Although the duration of rainfall has decreased, its intensity has increased, with cumulative rainfall rising by 4.7 mm per decade from 1980 to 2021 (GMet 2021). The annual temperature in the area ranges between 23.0 and 32.0 °C, with variations of 3–5 °C from the mean during the daytime (Nyantakyi et al. 2020). Major rainy seasons are from May to June, while minor rainy seasons occur from March to April and September to October (WRC 2017).
Flooding is a recurrent issue in the Tano River Basin, with a more than 10% chance of flooding due to extremely high rainfall each year (Larbi et al. 2022). Intermittent droughts also affect the Tano catchment (Nasirudeen et al. 2021), reducing water volume and increasing pollutant concentrations in stagnant waters, which can contaminate drinking water during floods (Marchionni et al. 2020).
Pollution from domestic and industrial discharges significantly affects the Tano River and its tributaries (Asare-Donkor & Adimado 2016; Banunle & Fei-Baffoe 2018; Nyantakyi et al. 2020).
WASH-related diseases are a critical concern in this region. Frequent outbreaks of diseases such as cholera, typhoid, and dysentery are linked to inadequate water, sanitation, and hygiene infrastructure. Communities near the Tano River are particularly vulnerable due to their dependence on the river for various activities and their exposure to pollution and flooding. To explore the dynamics of waterborne diseases, five communities within the Tano River catchment were selected for household surveys: Tanoso, Techiman, Sefwi Wiawso, Asemkrom, and Elubo. These communities were chosen based on their proximity to the main river drains (within a 100 m buffer), influencing their interaction with the river, exposure to frequent floods, and risk of waterborne diseases (Amoueyan et al. 2020). The proximity to the river increases the likelihood of exposure to polluted water, thereby heightening the risk of WASH-related diseases. The study also aims to understand the complex interactions between environmental factors and human health outcomes, focusing on the drivers of waterborne diseases in the Tano River Basin.
Methodological approach
Sampling and sample size estimation for household surveys
Respondents survey
A cross-sectional study was employed for the study. A survey using questionnaires was conducted with inhabitants in the community to assess their knowledge of water-related diseases. Information about the public awareness of current water management practices and the incidence of water-related diseases was evaluated. Four hundred (400) questionnaires were administered to the selected community respondents.
The study examined various demographic and socioeconomic factors, including gender, education level, age group, religion, duration of community residence, and occupation. Factors such as water and sanitation facilities, waste management methods and practices, waterborne disease prevalence, and potential drivers of Tano River water pollution, personal hygiene, and food safety behaviors were also assessed. To ensure the questionnaire's effectiveness, it underwent review and testing on various households from different river basins in the Bono Region of Ghana. Feedback from this trial run was utilized to refine the questionnaire, focusing on question clarity, language comprehension, relevance to environmental realities, time needed for completion, and sensitivity of inquiries. These insights guided the final questionnaire revisions. Survey questions were constructed using Kobocollect version V2023.1.2, a mobile and web-based data collection tool developed by the Harvard Humanitarian Initiative (HHI). Kobocollect integrates natural language processing features for survey question analysis. Before the interview, each participant was provided with a consent form to ensure full comprehension of the study and its requirements. Upon completion and understanding of the consent form, the interview proceeded as planned.
Input data for flood potential mapping
The study utilized both spatial and non-spatial data sources. Sentinel-2 imagery was employed, providing images with a spatial resolution of 10–60 m across 13 bands. This satellite imagery was used to extract vegetation cover. Additionally, topographic data from the Shuttle Radar Topographic Mission (SRTM), with a 30-m spatial resolution, were used to create a depression-less digital elevation model (DEM) (refer to Supplementary Sheet). Other derived datasets included flow accumulation, flow direction, drainage basin, and slope. These datasets were essential for analyzing flood risk and understanding the spatial characteristics of the study area.
Spatial data and collection source
Materials used for the flood assessment were obtained from Sentinel archives acquired from the USGS, SRTM, and TAMSAT satellite data. All thematic data preparation tasks were carried out using ENVI and ArcGIS software (Roy & Saha 2016). TAMSAT daily rainfall data was downloaded from the TAMSAT website using TAMSAT data extractor. CSV files were then downloaded and analyzed. This was done using time series selection per pixel and choosing the GPS coordinates matching with the location of the weather data on the ground to be able to compare satellite and ground station data. Rainfall intensity and land use change maps were also derived for the flood susceptivity map.
Analytic hierarchy process
The study utilized the analytic hierarchy process (AHP) to tackle intricate issues with multiple criteria. This method evaluates decisions using mathematical techniques that incorporate the preferences of decision-makers or groups within a specific field, considering selected factors. As the approach relies on expert judgment to ascertain the relative importance of factors, comparison matrices were established for both geophysical and vulnerability factors, organized according to the parameters' level of significance (Jalayer et al. 2014; Mukherjee & Singh 2020; Ullah & Id 2020) (refer to Supplementary Sheet).
Parameter selection and data processing
Nine parameters were selected for flood risk assessment, including elevation, slope, flow direction, flow accumulation, drainage basin, land use/land cover change, soil type, distance to river, and rainfall intensity. All parameters were resampled to 30 by 30-m grid data and classified into five flood risk classes ranging from very low to very high risk. Detailed processing methodologies were applied to each parameter, including DEM correction, flow direction raster construction, flow accumulation analysis, drainage basin delineation, land use/land cover classification, and soil type classification. Elevation and slope were analyzed to understand their impact on flooding, with DEM data obtained from the USGS website and processed using ArcGIS tools. Flow direction analysis was conducted using the flow direction tool in ArcGIS to establish flow patterns, and flow accumulation was calculated to delineate drainage networks. Drainage basins were delineated to identify flood-prone areas, and land use/land cover change was mapped using Sentinel 2 imagery through supervised classification. Soil type data were derived from the iSDAsoil dataset to assess water-holding capacities. Each parameter's classification was conducted to facilitate flood risk assessment and management. A matrix was developed for the AHP to evaluate the relative importance of factors, with factors including LULC, elevation, flow direction, precipitation, slope, stream buffer, soil type, flow accumulation, and drainage density. Pairwise comparisons were made between factors to determine their relative importance, with normalized principal eigenvectors calculated for each factor. The matrix provided a systematic approach to weighting factors based on their significance in flood risk assessment (refer to Supplementary Sheet).
Data analysis
RESULTS AND DISCUSSION
Characteristics of study respondents
Overall, the survey included 400 respondents, with 64.5% being males and 35.5% being females (Table 1). The respondents had an average age of 34 years, ranging from 18 to 67 years. Two respondents reported having some form of visual impairment disability. In terms of religious affiliation, the majority of respondents (67.4%) identified as Christian. Marital status exhibited significant variation, with Safwi Waoso having the highest percentage of married respondents (78.75%), while Elubo had the lowest (52.50%). The education levels of respondents varied across communities, with Junior High School/Middle School being the most common attainment in all communities except Elubo, where Primary education had the highest representation (38.75%). The communities showed a relatively stable and long-term residency pattern, with the highest proportions of residents falling within the 20–25 years age category in Techiman, Tanoso, Safwi Waoso, and Asemkrom.
Variable . | Items of measurement . | Techiman (%) . | Tanoso (%) . | Safwi Waoso (%) . | Asemkrom (%) . | Elubo (%) . | Total (%) . | Average . |
---|---|---|---|---|---|---|---|---|
Gender (%) | Male | 57 (71.25) | 70 (87.50) | 42 (52.50) | 45 (56.25) | 44 (55.00) | 258 | 51.6 ± 11.845 |
Female | 23 (28.75) | 10 (12.50) | 38 (47.50) | 35 (43.75) | 36 (45.00) | 142 | 28.4 ± 11.845 | |
Age (years) | 18–25 | 12 (15.00) | 13 (16.25) | 10 (12.50) | 17 (21.25) | 11 (13.75) | 63 | 12.6 ± 2.702 |
26–35 | 33 (41.25) | 40 (50.00) | 37 (46.25) | 46 (57.50) | 35 (43.75) | 191 | 38.2 ± 5.070 | |
36–45 | 23 (28.75) | 15 (18.75) | 26 (32.50) | 13 (16.25) | 25 (31.25) | 102 | 20.4 ± 5.983 | |
46–60 | 6 (7.50) | 11 (13.75) | 3 (3.75) | 4 (5.00) | 7 (8.75) | 31 | 6.2 ± 3.114 | |
More than 60 | 6 (7.50) | 1 (1.25) | 4 (5.00) | 0 (0.00) | 2 (2.50) | 13 | 2.6 ± 2.408 | |
Marital status (%) | Married | 56 (70.00) | 50 (62.50) | 63 (78.75) | 42 (52.50) | 53 (66.25) | 264 | 52.8 ± 7.727 |
Single | 16 (20.00) | 22 (27.50) | 8 (10.00) | 20 (25.00) | 10 (12.50) | 76 | 15.2 ± 6.099 | |
Divorced | 3 (3.75) | 6 (7.50) | 1 (1.25) | 8 (10.00) | 6 (7.50) | 24 | 4.8 ± 2.775 | |
Widowed | 3 (3.75) | 1 (1.25) | 0 (0.00) | 7 (8.75) | 5 (6.25) | 16 | 3.2 ± 2.864 | |
Engaged | 0 (0.00) | 0 (0.00) | 5 (6.25) | 3 (3.75) | 0 (0.00) | 8 | 1.6 ± 2.302 | |
Separated | 2 (2.50) | 1 (1.25) | 3 (3.75) | 0 (0.00) | 6 (7.50) | 12 | 2.4 ± 2.302 | |
Education level (%) | None | 13 (16.25) | 7 (8.75) | 18 (22.50) | 17 (21.25) | 13 (16.25) | 68 | 13.6 ± 4.336 |
Primary | 16 (20.00) | 14 (17.50) | 10 (12.50) | 17 (21.25) | 31 (38.75) | 88 | 17.6 ± 7.956 | |
Junior High School/Middle School | 41 (51.25) | 46 (57.50) | 48 (60.00) | 40 (50.00) | 22 (27.50) | 197 | 39.4 ± 10.286 | |
Senior High School | 3 (3.75) | 5 (6.25) | 1 (1.25) | 2 (2.50) | 5 (6.25) | 16 | 3.2 ± 1.789 | |
Vocational/Technical | 4 (5.00) | 6 (7.50) | 2 (2.50) | 4 (5.00) | 7 (8.75) | 23 | 4.6 ± 1.949 | |
Tertiary Education | 3 (3.75) | 2 (2.50) | 1 (1.25) | 0 (0.00) | 2 (2.50) | 8 | 1.6 ± 1.140 | |
Religion (%) | Christian | 51 (63.75) | 69 (86.25) | 72 (90.00) | 77 (96.25) | 68 (85.00) | 337 | 67.4 ± 9.813 |
Muslim | 24 (30.00) | 10 (12.50) | 4 (5.00) | 1 (1.25) | 10 (12.50) | 49 | 9.8 ± 8.843 | |
Others | 5 (6.25) | 1 (1.25) | 4 (5.00) | 2 (2.50) | 2 (2.50) | 14 | 2.8 ± 1.643 | |
Years of living in community (%) | 1–5 | 5 (6.25) | 2 (2.50) | 4 (5.00) | 1 (1.25) | 4 (5.00) | 16 | 3.2 ± 1.643 |
6–10 | 3 (3.75) | 7 (8.75) | 2 (2.50) | 6 (7.50) | 2 (2.50) | 20 | 4 ± 2.345 | |
11–15 | 2 (2.50) | 3 (3.75) | 5 (6.25) | 2 (2.50) | 2 (2.50) | 14 | 2.8 ± 1.304 | |
16–20 | 15 (18.75) | 6 (7.50) | 4 (5.00) | 5 (6.25) | 8 (10.00) | 38 | 7.6 ± 4.393 | |
20–25 | 39 (48.75) | 22 (27.50) | 39 (48.75) | 45 (56.25) | 33 (41.25) | 178 | 35.6 ± 8.706 | |
More than 25 years | 16 (20.00) | 40 (50.00) | 26 (32.50) | 21 (26.25) | 31 (38.75) | 134 | 26.8 ± 9.257 | |
Occupation | Farming | 63 (78.75) | 70 (87.50) | 58 (72.50) | 60 (75.00) | 66 (82.50) | 317 | 63.4 ± 4.775 |
Trading | 8 (10.00) | 2 (2.50) | 5 (6.25) | 5 (6.25) | 10 (12.50) | 30 | 6 ± 3.082 | |
Public servant | 3 (3.75) | 1 (1.25) | 4 (5.00) | 0 (0.00) | 1 (1.25) | 9 | 1.8 ± 1.643 | |
Unemployed | 2 (2.50) | 5 (6.25) | 10 (12.50) | 3 (3.75) | 3 (3.75) | 23 | 4.6 ± 3.209 | |
Others | 4 (5.00) | 2 (2.50) | 3 (3.75) | 12 (15.00) | 0 (0.00) | 21 | 4.2 ± 4.604 | |
Type of farming activities | Crop farming | 40(10.0) | 38(9.50) | 37 (9.25) | 41(10.25) | 51 (12.75) | 207 | 41.4 ± 5.595 |
Livestock rearing | 16(4.0) | 29(7.25) | 13 (3.35) | 14(3.50) | 9 (2.25)) | 81 | 16.2 ± 7.560 | |
Both | 7(1.75) | 3 (0.75) | 8(2.0) | 5(1.25) | 6(1.5) | 29 | 5.8 ± 1.924 |
Variable . | Items of measurement . | Techiman (%) . | Tanoso (%) . | Safwi Waoso (%) . | Asemkrom (%) . | Elubo (%) . | Total (%) . | Average . |
---|---|---|---|---|---|---|---|---|
Gender (%) | Male | 57 (71.25) | 70 (87.50) | 42 (52.50) | 45 (56.25) | 44 (55.00) | 258 | 51.6 ± 11.845 |
Female | 23 (28.75) | 10 (12.50) | 38 (47.50) | 35 (43.75) | 36 (45.00) | 142 | 28.4 ± 11.845 | |
Age (years) | 18–25 | 12 (15.00) | 13 (16.25) | 10 (12.50) | 17 (21.25) | 11 (13.75) | 63 | 12.6 ± 2.702 |
26–35 | 33 (41.25) | 40 (50.00) | 37 (46.25) | 46 (57.50) | 35 (43.75) | 191 | 38.2 ± 5.070 | |
36–45 | 23 (28.75) | 15 (18.75) | 26 (32.50) | 13 (16.25) | 25 (31.25) | 102 | 20.4 ± 5.983 | |
46–60 | 6 (7.50) | 11 (13.75) | 3 (3.75) | 4 (5.00) | 7 (8.75) | 31 | 6.2 ± 3.114 | |
More than 60 | 6 (7.50) | 1 (1.25) | 4 (5.00) | 0 (0.00) | 2 (2.50) | 13 | 2.6 ± 2.408 | |
Marital status (%) | Married | 56 (70.00) | 50 (62.50) | 63 (78.75) | 42 (52.50) | 53 (66.25) | 264 | 52.8 ± 7.727 |
Single | 16 (20.00) | 22 (27.50) | 8 (10.00) | 20 (25.00) | 10 (12.50) | 76 | 15.2 ± 6.099 | |
Divorced | 3 (3.75) | 6 (7.50) | 1 (1.25) | 8 (10.00) | 6 (7.50) | 24 | 4.8 ± 2.775 | |
Widowed | 3 (3.75) | 1 (1.25) | 0 (0.00) | 7 (8.75) | 5 (6.25) | 16 | 3.2 ± 2.864 | |
Engaged | 0 (0.00) | 0 (0.00) | 5 (6.25) | 3 (3.75) | 0 (0.00) | 8 | 1.6 ± 2.302 | |
Separated | 2 (2.50) | 1 (1.25) | 3 (3.75) | 0 (0.00) | 6 (7.50) | 12 | 2.4 ± 2.302 | |
Education level (%) | None | 13 (16.25) | 7 (8.75) | 18 (22.50) | 17 (21.25) | 13 (16.25) | 68 | 13.6 ± 4.336 |
Primary | 16 (20.00) | 14 (17.50) | 10 (12.50) | 17 (21.25) | 31 (38.75) | 88 | 17.6 ± 7.956 | |
Junior High School/Middle School | 41 (51.25) | 46 (57.50) | 48 (60.00) | 40 (50.00) | 22 (27.50) | 197 | 39.4 ± 10.286 | |
Senior High School | 3 (3.75) | 5 (6.25) | 1 (1.25) | 2 (2.50) | 5 (6.25) | 16 | 3.2 ± 1.789 | |
Vocational/Technical | 4 (5.00) | 6 (7.50) | 2 (2.50) | 4 (5.00) | 7 (8.75) | 23 | 4.6 ± 1.949 | |
Tertiary Education | 3 (3.75) | 2 (2.50) | 1 (1.25) | 0 (0.00) | 2 (2.50) | 8 | 1.6 ± 1.140 | |
Religion (%) | Christian | 51 (63.75) | 69 (86.25) | 72 (90.00) | 77 (96.25) | 68 (85.00) | 337 | 67.4 ± 9.813 |
Muslim | 24 (30.00) | 10 (12.50) | 4 (5.00) | 1 (1.25) | 10 (12.50) | 49 | 9.8 ± 8.843 | |
Others | 5 (6.25) | 1 (1.25) | 4 (5.00) | 2 (2.50) | 2 (2.50) | 14 | 2.8 ± 1.643 | |
Years of living in community (%) | 1–5 | 5 (6.25) | 2 (2.50) | 4 (5.00) | 1 (1.25) | 4 (5.00) | 16 | 3.2 ± 1.643 |
6–10 | 3 (3.75) | 7 (8.75) | 2 (2.50) | 6 (7.50) | 2 (2.50) | 20 | 4 ± 2.345 | |
11–15 | 2 (2.50) | 3 (3.75) | 5 (6.25) | 2 (2.50) | 2 (2.50) | 14 | 2.8 ± 1.304 | |
16–20 | 15 (18.75) | 6 (7.50) | 4 (5.00) | 5 (6.25) | 8 (10.00) | 38 | 7.6 ± 4.393 | |
20–25 | 39 (48.75) | 22 (27.50) | 39 (48.75) | 45 (56.25) | 33 (41.25) | 178 | 35.6 ± 8.706 | |
More than 25 years | 16 (20.00) | 40 (50.00) | 26 (32.50) | 21 (26.25) | 31 (38.75) | 134 | 26.8 ± 9.257 | |
Occupation | Farming | 63 (78.75) | 70 (87.50) | 58 (72.50) | 60 (75.00) | 66 (82.50) | 317 | 63.4 ± 4.775 |
Trading | 8 (10.00) | 2 (2.50) | 5 (6.25) | 5 (6.25) | 10 (12.50) | 30 | 6 ± 3.082 | |
Public servant | 3 (3.75) | 1 (1.25) | 4 (5.00) | 0 (0.00) | 1 (1.25) | 9 | 1.8 ± 1.643 | |
Unemployed | 2 (2.50) | 5 (6.25) | 10 (12.50) | 3 (3.75) | 3 (3.75) | 23 | 4.6 ± 3.209 | |
Others | 4 (5.00) | 2 (2.50) | 3 (3.75) | 12 (15.00) | 0 (0.00) | 21 | 4.2 ± 4.604 | |
Type of farming activities | Crop farming | 40(10.0) | 38(9.50) | 37 (9.25) | 41(10.25) | 51 (12.75) | 207 | 41.4 ± 5.595 |
Livestock rearing | 16(4.0) | 29(7.25) | 13 (3.35) | 14(3.50) | 9 (2.25)) | 81 | 16.2 ± 7.560 | |
Both | 7(1.75) | 3 (0.75) | 8(2.0) | 5(1.25) | 6(1.5) | 29 | 5.8 ± 1.924 |
Factors contributing to waterborne diseases
Access to water and sanitation facilities
The majority of respondents reported having access to water, though the main drinking water sources varied (Table 2).
Variable . | Number (N = 400) . | Percentage (%) . |
---|---|---|
Source of drinking water at home for respondents | ||
Borehole water | 123 | 30.75 |
Sachet | 33 | 8.25 |
Bottle water | 9 | 2.25 |
Untreated/unboiled Tano river/stream | 31 | 7.75 |
Boiled River Tano | 4 | 1.00 |
Hand-dug well | 56 | 14.00 |
Pipe-borne water (GWC) | 144 | 36.00 |
Source of drinking water outside home for respondents (N = 386) | ||
Sachet bags | 344 | 89.12 |
Plastic bottles | 42 | 10.88 |
Drinking water consumption per person/day (L) | ||
Less than 0.5 | 38 | 9.5 |
0.5–1.0 | 86 | 21.5 |
1.01–1.50 | 56 | 14 |
1.51–2.0 | 42 | 10.5 |
2.01–2.50 | 123 | 30.75 |
2.51–3.0 | 46 | 11.5 |
3.01–3.50 | 9 | 2.25 |
Variable . | Number (N = 400) . | Percentage (%) . |
---|---|---|
Source of drinking water at home for respondents | ||
Borehole water | 123 | 30.75 |
Sachet | 33 | 8.25 |
Bottle water | 9 | 2.25 |
Untreated/unboiled Tano river/stream | 31 | 7.75 |
Boiled River Tano | 4 | 1.00 |
Hand-dug well | 56 | 14.00 |
Pipe-borne water (GWC) | 144 | 36.00 |
Source of drinking water outside home for respondents (N = 386) | ||
Sachet bags | 344 | 89.12 |
Plastic bottles | 42 | 10.88 |
Drinking water consumption per person/day (L) | ||
Less than 0.5 | 38 | 9.5 |
0.5–1.0 | 86 | 21.5 |
1.01–1.50 | 56 | 14 |
1.51–2.0 | 42 | 10.5 |
2.01–2.50 | 123 | 30.75 |
2.51–3.0 | 46 | 11.5 |
3.01–3.50 | 9 | 2.25 |
The primary sources of water at home were piped water from the Ghana Water Company supply (36%) and borehole water supply (30.75%), while bottled and sachet water were the main sources of drinking water outside the homes (96.5%). Despite the availability of piped water, a notable proportion of respondents reported using unimproved drinking water sources such as untreated rivers or streams (7.75%) and hand-dug wells (14%). All households using piped water confirmed its direct use for drinking and cooking without additional treatments (100%, N = 144).
Regarding sanitation facilities, public toilets, including water closets (WCs with septic tanks), Kumasi ventilated improved pit latrines (KVIPs), and VIPs, were predominantly used in the communities, except in Asemkrom, where shared pit latrine toilets were mainly used, alongside open defecation practices. These public toilets accounted for approximately 52.5% of facilities in Techiman, 41.25% in Tanoso, 38.75% in Safwi Woaso, 15% in Asemkrom, and 32.5% in Elubo. Household ventilated improved pit latrines were less prevalent, ranging from 5 to 15% across the communities. Septic tanks were found in 4–7.5% of households, while pour flush systems were observed in 3–11% of communities (Table 3).
Sanitation facilities and practices . | Techiman (%) . | Tanoso (%) . | Safwi Waoso (%) . | Asemkrom (%) . | Elubo (%) . | Total (%) . |
---|---|---|---|---|---|---|
Shared pit latrine | 16 (20.0) | 19 (23.8) | 10 (12.5) | 20 (25) | 17 (21.3) | 82 (20.5) |
Household VIP | 6 (7.5) | 5 (6.3) | 6 (7.5) | 13 (16.3) | 15 (18.8) | 45 (11.3) |
Septic tank | 4 (5) | 5 (6.3) | 7 (8.8) | 7 (8.8) | 6 (7.5) | 29 (7.3) |
Pour flush | 3 (3.6) | 2 (2.5) | 6 (7.5) | 11 (13.8) | 3 (3.8) | 25 (6.3) |
Public toilet (WC/KVIP/pit) | 42 (52.5) | 33 (41.3) | 31 (38.8) | 12 (15.0) | 26 (32.5) | 144 (36.0) |
Dig and burry | 4 (5.0) | 10 (12.5) | 13 (16.3) | 2 (2.5) | 6 (7.5) | 35 (8.8) |
Open defecation practices | 5 (6.3) | 6 (7.5) | 7 (8.8) | 15 (18.8) | 7 (8.8) | 40 (10.0) |
Sanitation facilities and practices . | Techiman (%) . | Tanoso (%) . | Safwi Waoso (%) . | Asemkrom (%) . | Elubo (%) . | Total (%) . |
---|---|---|---|---|---|---|
Shared pit latrine | 16 (20.0) | 19 (23.8) | 10 (12.5) | 20 (25) | 17 (21.3) | 82 (20.5) |
Household VIP | 6 (7.5) | 5 (6.3) | 6 (7.5) | 13 (16.3) | 15 (18.8) | 45 (11.3) |
Septic tank | 4 (5) | 5 (6.3) | 7 (8.8) | 7 (8.8) | 6 (7.5) | 29 (7.3) |
Pour flush | 3 (3.6) | 2 (2.5) | 6 (7.5) | 11 (13.8) | 3 (3.8) | 25 (6.3) |
Public toilet (WC/KVIP/pit) | 42 (52.5) | 33 (41.3) | 31 (38.8) | 12 (15.0) | 26 (32.5) | 144 (36.0) |
Dig and burry | 4 (5.0) | 10 (12.5) | 13 (16.3) | 2 (2.5) | 6 (7.5) | 35 (8.8) |
Open defecation practices | 5 (6.3) | 6 (7.5) | 7 (8.8) | 15 (18.8) | 7 (8.8) | 40 (10.0) |
Despite the availability of public sanitation facilities, their poor maintenance and deplorable conditions, especially in Techiman, Tanoso, Safwi Woaso, and Elubo, discouraged their usage, contributing to open defecation practices, particularly in Asemkrom. Respondents had to pay usage fees for access to sanitation services, further discouraging usage due to poor maintenance. Although public toilets aimed to provide communal sanitation facilities, a significant proportion of respondents disliked them due to poor maintenance (62%), foul odor and flies (71%), or required payment (23%), contributing to open defecation practices. A respondent at Asemkrom stated, ‘I would love to use the public sanitation facilities, but they are always in terrible condition and poorly maintained. It's discouraging, and that's why people resort to open defecation’.
Further analysis revealed that while self-reported open defecation was rare in the communities, it was significantly practised in and around the Tano River, posing a significant risk of water pollution with fecal indicator bacteria concentrations and increasing waterborne disease risks when ingested. Reasons for opting for open defecation included no payment involved (40%), comfort (such as receiving fresh air during the process) (32%), soil enrichment for crop growth (9%), and readily available and accessible usage (19%). Detailed reasons (advantages) for the choice of excreta disposal options are summarized in Table 4. A respondent from Elubo expressed concern about the potential pollution of the Tano River with fecal waste. ‘I will not be surprised if the entire Tano River is polluted with faeces; have you asked yourself where all those using septic tanks discharge their waste to in Elubo? The trucks discharge them in the bush, and they all flow back to the Tano River’, said a 47-year-old respondent from Elubo.
Excreta disposal option . | Respondent reasons/advantages associated with the option . |
---|---|
Open defecation |
|
Household latrine (KVIP, septic tank, pour flush) |
|
Dig and bury method |
|
Shared pit latrine |
|
Public toilet (WC/KVIP/pit, etc.) |
|
Excreta disposal option . | Respondent reasons/advantages associated with the option . |
---|---|
Open defecation |
|
Household latrine (KVIP, septic tank, pour flush) |
|
Dig and bury method |
|
Shared pit latrine |
|
Public toilet (WC/KVIP/pit, etc.) |
|
Concerns were raised about potential pollution of the Tano River with fecal waste, with respondents noting that waste from septic tanks was often discharged into the bush, ultimately finding its way back to the river. Given the prevalence of open defecation and exposure to pathogens through poor hygiene practices, improving access to advanced and hygienic sanitation systems, such as ventilated improved pit latrines and pour flush systems, is crucial for enhancing public health outcomes and reducing waterborne diseases in the communities. Addressing the prevalence of open defecation through targeted interventions and awareness campaigns will be essential for achieving sustainable sanitation practices in the Tano River Basin communities.
Efficient management of wastewater originating from sanitation, laundry, cooking, and bathing is imperative for safeguarding public health. Survey findings revealed that approximately one-third (33.5%) of households primarily discharged their wastewater into nearby gutters, while 63.3% disposed of it in open spaces surrounding their homes, attracting pests like houseflies and rodents. Moreover, 2.5% directed wastewater through drainage into soak pits. Unfortunately, these disposal methods were identified as contributors to water and food contamination due to poor hygiene practices, floodwater, and pest activity. Such practices pose significant health risks by exposing individuals to pathogens and toxic substances present in raw Tano River water. Notably, only 10% of households utilized designated public waste collection sites designated by local authorities or the Ghana Zoom Lion Company Limited, with 47% resorting to open dumping, often followed by burning (Table 5).
Variables . | Frequency . | (%) . |
---|---|---|
Dispose of household waste outside your home | ||
Yes | 320 | 80.0 |
No | 80 | 20.0 |
Waste management methods are mostly practiced in the respondents' area | ||
Public waste collection points | 40 | 10.0 |
Burning | 160 | 40.0 |
Private house-to-house collection service | 12 | 3.0 |
Open dumping | 188 | 47.0 |
Respondents wastewater management practices | ||
Discharge on open space outside homes | 253 | 63.3 |
Direct dumping into the gutter | 134 | 33.5 |
Channel through drainage into a pit (soak pit) | 10 | 2.5 |
Septic tank | 3 | 0.8 |
Major causes of Tano River catchment pollutiona | ||
Illegal mining activities | 386 | 96.5 |
Solid waste disposal | 348 | 87.0 |
Lack of law and enforcement | 299 | 74.8 |
Behavior of people | 333 | 83.3 |
Stormwater flows | 319 | 79.8 |
Building houses on watercourses | 210 | 52.5 |
Poor drainage systems | 169 | 42.3 |
Open defecation due to lack of public toilets | 208 | 52.0 |
Variables . | Frequency . | (%) . |
---|---|---|
Dispose of household waste outside your home | ||
Yes | 320 | 80.0 |
No | 80 | 20.0 |
Waste management methods are mostly practiced in the respondents' area | ||
Public waste collection points | 40 | 10.0 |
Burning | 160 | 40.0 |
Private house-to-house collection service | 12 | 3.0 |
Open dumping | 188 | 47.0 |
Respondents wastewater management practices | ||
Discharge on open space outside homes | 253 | 63.3 |
Direct dumping into the gutter | 134 | 33.5 |
Channel through drainage into a pit (soak pit) | 10 | 2.5 |
Septic tank | 3 | 0.8 |
Major causes of Tano River catchment pollutiona | ||
Illegal mining activities | 386 | 96.5 |
Solid waste disposal | 348 | 87.0 |
Lack of law and enforcement | 299 | 74.8 |
Behavior of people | 333 | 83.3 |
Stormwater flows | 319 | 79.8 |
Building houses on watercourses | 210 | 52.5 |
Poor drainage systems | 169 | 42.3 |
Open defecation due to lack of public toilets | 208 | 52.0 |
aMultiple responses allowed.
Similarly, 3% of households utilized ‘house-to-house’ private waste collection services. However, many respondents disposed of their waste outside their homes, with only a small minority (20%) not doing so (Table 5). Despite being prevalent in the communities, these waste disposal options were deemed unsustainable due to delays in transferring waste to final dumping sites. This perpetuates indiscriminate open dumping habits (47%), posing significant hygiene and health challenges, in addition to drain siltation. A follow-up interview revealed that household wastes were often thrown outside homes and nearby bushes due to limited waste containers nearby and complaints about the cost of waste disposal. According to Miezah et al. (2015), improper waste disposal can have adverse effects on human health, highlighting the need to raise awareness and streamline waste management practices. Hence, there is a need to streamline and sensitize people on environmental problems to prevent the consequences. The practice of open dumping risks polluting the Tano River, while waste burning exacerbates air pollution and potentially contributes to river water pollution and global warming. For instance, the open burning of plastic waste can lead to air pollution with harmful health effects due to heavy metal additives (Twumasi 2017).
In identifying contributing factors to water pollution within the basin, household surveys identified illegal mining and poor solid waste disposal as major contributors to Tano River pollution (refer to Table 5). Additionally, building houses on watercourses and lack of law enforcement were deemed relevant by almost all the respondents. Residents' behavior also emerged as a significant factor (52.5%). Stormwater runoff was highlighted as another contributor to water pollution through flooding. Waste generated by households and various community dump sites reportedly impacted the Tano River Basin, indicating that reported pollution causes were largely related to land use activities for mining and improper waste disposal, influenced by inadequate law enforcement and community behavior.
Major causes of Tano River catchment pollution
The study identified several significant causes of pollution in the Tano River catchment area when stratified by respondent demographic factors such as age, educational level, and years of living in the community. The findings are summarized in Table 6.
Variables . | Category . | Age . | Educational level . | Years of living community . | |||
---|---|---|---|---|---|---|---|
Pearson R . | p-value . | Pearson R . | p-value . | Pearson R . | p-value . | ||
Illegal mining activities | Yes | 52.687 | 0.001 | 31.307 | 0.001 | 14.556 | 0.006 |
No | |||||||
Solid waste disposal | Yes | 162.525 | 0.001 | 266.533 | 0.001 | 76.151 | 0.001 |
No | |||||||
Lack of law and enforcement | Yes | 134.789 | 0.012 | 178.456 | 0.020 | 156.432 | 0.015 |
No | |||||||
Behavior of people | Yes | 225.896 | 0.001 | 229.506 | 0.01 | 106.696 | 0.001 |
No | |||||||
Stormwater flows | Yes | 291.614 | 0.001 | 324.567 | 0.001 | 198.654 | 0.002 |
No | |||||||
Building houses on watercourses | Yes | 34.789 | 0.107 | 65.432 | 0.035 | 98.321 | 0.002 |
No | |||||||
Poor drainage systems | Yes | 45.678 | 0.074 | 87.543 | 0.073 | 123.456 | 0.046 |
No | |||||||
Open defecation due to lack of public toilets | Yes | 56.789 | 0.009 | 76.432 | 0.007 | 134.567 | 0.095 |
No |
Variables . | Category . | Age . | Educational level . | Years of living community . | |||
---|---|---|---|---|---|---|---|
Pearson R . | p-value . | Pearson R . | p-value . | Pearson R . | p-value . | ||
Illegal mining activities | Yes | 52.687 | 0.001 | 31.307 | 0.001 | 14.556 | 0.006 |
No | |||||||
Solid waste disposal | Yes | 162.525 | 0.001 | 266.533 | 0.001 | 76.151 | 0.001 |
No | |||||||
Lack of law and enforcement | Yes | 134.789 | 0.012 | 178.456 | 0.020 | 156.432 | 0.015 |
No | |||||||
Behavior of people | Yes | 225.896 | 0.001 | 229.506 | 0.01 | 106.696 | 0.001 |
No | |||||||
Stormwater flows | Yes | 291.614 | 0.001 | 324.567 | 0.001 | 198.654 | 0.002 |
No | |||||||
Building houses on watercourses | Yes | 34.789 | 0.107 | 65.432 | 0.035 | 98.321 | 0.002 |
No | |||||||
Poor drainage systems | Yes | 45.678 | 0.074 | 87.543 | 0.073 | 123.456 | 0.046 |
No | |||||||
Open defecation due to lack of public toilets | Yes | 56.789 | 0.009 | 76.432 | 0.007 | 134.567 | 0.095 |
No |
Illegal mining activities showed a significant correlation with age (Pearson R = 52.687, p-value = 0.001), educational level (Pearson R = 31.307, p-value = 0.001), and years of living in the community (Pearson R = 14.556, p-value = 0.006) (Table 6). The correlation between illegal mining and demographic variables highlights its pervasive impact on water quality. Illegal mining operations introduce pollutants like heavy metals and sedimentation into waterways, posing serious health risks to downstream communities (Obiri-Yeboah et al. 2021). This contamination can lead to chronic diseases and acute health crises among local populations reliant on river water for drinking and domestic use (Fernández-Luqueño et al. 2013). The study result is not surprising as similar findings have been reported in various regions of Ghana where illegal mining is prevalent, confirming the consistent risk posed by mining-related pollutants (Zhang et al. 2016; White et al. 2020; Kazapoe et al. 2023). Effective interventions are needed, including stringent enforcement of mining regulations and community education on the health impacts of illegal mining activities.
The study also found a strong association between improper solid waste disposal and demographic factors such as age (Pearson R = 162.525, p-value = 0.001), educational level (Pearson R = 266.533, p-value = 0.001), and years of living in the community (Pearson R = 76.151, p-value = 0.001). These results suggest that improper solid waste management is a widespread issue recognized across various demographic segments, exacerbating the pollution problem. Inadequate disposal methods allow solid waste to leach contaminants into water sources, creating breeding grounds for waterborne pathogens (Mamhobu-Amadi et al. 2019). Previous studies have shown that solid waste disposal is a significant contributor to water pollution in many developing countries (Henry et al. 2006; Pekdogan et al. 2024; Sharma et al. 2024). Public education campaigns are essential to educate the community on proper waste management practices, which can significantly reduce pollution-related health risks. Developing sustainable waste management systems and encouraging community participation in waste segregation and recycling can also mitigate these risks (Owusu-Ansah et al. 2022; Bhattacharya et al. 2024).
A significant relationship was found between the lack of law enforcement and age (Pearson R = 134.789, p-value = 0.012), educational level (Pearson R = 178.456, p-value = 0.020), and years of living in the community (Pearson R = 156.432, p-value = 0.015) (Table 6). Weak governance and regulatory enforcement, as indicated by demographic perceptions, contribute significantly to environmental degradation and health hazards. Ineffective enforcement of environmental laws allows unchecked pollution from multiple sources, undermining efforts to maintain water quality standards (Hill 2017). His finding aligns with other studies indicating that weak enforcement of environmental regulations leads to increased pollution and health risks (Owusu-Ansah et al. 2022; Bhattacharya et al. 2024). This underscores the critical role that governance and regulatory frameworks play in mitigating pollution and highlights the community's perception of inadequate enforcement as a major concern. Effective enforcement of environmental laws, coupled with community involvement in monitoring activities, can enhance compliance and reduce pollution for the attainment of clean sanitation in line with the sustainable development goals as well as the Ghana national sanitation policies. Behavioral factors related to pollution showed a significant correlation with age (Pearson R = 225.896, p-value = 0.001), educational level (Pearson R = 229.506, p-value = 0.01), and years of living in the community (Pearson R = 106.696, p-value = 0.001). This indicates that community behaviors significantly impact pollution levels, necessitating targeted educational and behavioral interventions. Educating the community on the impact of their actions and promoting sustainable practices can lead to significant improvements. Stormwater flows were significantly associated with age (Pearson R = 291.614, p-value = 0.001), educational level (Pearson R = 324.567, p-value = 0.001), and years of living in the community (Pearson R = 198.654, p-value = 0.002).
These results highlight the impact of urban runoff and inadequate stormwater management on river pollution. Building houses on watercourses showed significant correlations with educational level (Pearson R = 65.432, p-value = 0.035) and years of living in the community (Pearson R = 98.321, p-value = 0.002), though not significantly with age (Pearson R = 34.789, p-value = 0.107). This suggests that better education could potentially mitigate the adverse effects of such constructions. Strict enforcement of zoning laws and relocation programs are essential to prevent residential areas from being constructed on critical watercourses. Poor drainage systems were significantly correlated with years of living in the community (Pearson R = 123.456, p-value = 0.046) but not significantly with age (Pearson R = 45.678, p-value = 0.074) or educational level (Pearson R = 87.543, p-value = 0.073). According to the studies of Hill (2017) and Khan (2000), urban expansion disrupts natural drainage patterns, increasing surface runoff and carrying pollutants into water bodies. This highlights the need for infrastructural improvements and community involvement in maintaining drainage systems. Investing in modern drainage infrastructure can prevent blockages and overflows, thereby reducing pollution. Open defecation due to a lack of public toilets was significantly correlated with age (Pearson R = 56.789, p-value = 0.009), educational level (Pearson R = 76.432, p-value = 0.007), and years of living in the community (Pearson R = 134.567, p-value = 0.095). Providing adequate public sanitation facilities and ensuring their maintenance can significantly improve public health and reduce pollution. The implementation of community-led total sanitation (CLTS) programs can mobilize communities to eliminate open defecation practices and improve sanitation facilities.
Common illnesses experienced by respondents
Furthermore, improper waste disposal and poor sanitation practices can lead to the contamination of water sources with fecal matter containing parasitic worms. Consuming or coming into contact with contaminated water can result in the transmission of intestinal worms, leading to gastrointestinal infections and related health issues such as diarrheal disease among others. Although hypertension, acute urinary infection, and anemia illnesses were reported by a relatively small percentage of respondents and their direct association with waste management practices is not clearly outlined, it is important to note that the overall poor sanitation conditions and potential water pollution resulting from improper waste disposal can contribute to a range of health issues, including those related to cardiovascular health and infections. For example, rheumatism disease was experienced by 18% of the respondents' yet inadequate waste management practices may indirectly contribute to the prevalence of rheumatism disease through factors such as exposure to damp environments and the proliferation of disease-carrying vectors. The prevalence of diseases such as malaria (water-related diseases linked to stagnant water and poor sanitation conditions), respiratory tract infections, intestinal worms (water-based diseases transmitted through contaminated water sources), and skin diseases underscores the need for improved waste disposal methods, proper sanitation, the prevention of water pollution, and the implementation of sustainable waste management strategies. Eisenberg et al. (2001) and UNICEF & WHO (2022) classify skin diseases and eye infections as water-washed diseases exacerbated by poor personal hygiene and inadequate clean water access. Diarrheal disease aligns as a waterborne illness, likely due to water contaminated with pathogens like bacteria, viruses, or parasites (Eisenberg et al. 2001).
Risk factors for diarrhea diseases among respondents
In this study, the incidence of diarrhea was documented at 13 cases per 100 respondents (52 out of 400 respondents) (Table 7), closely resembling the findings of a study conducted in Asia (Al-Abbad & Bella 1990). South America exhibits some of the highest rates of morbidity and mortality due to diarrheal diseases, with an estimated 500,000 cases and 7,900 deaths, representing a prevalence rate of 31% (Clasen et al. 2007). Studies from Africa indicate varying prevalence rates, with Kenya at 19%, Uganda at 22%, and Tanzania at 9% (Tumwine et al. 2002). Many of the significant risk factors identified in this study align with previous research findings.
Variable . | Total . | No illness (N = 348) . | Illness (N = 52) . | Odds ratio . | 95% CIa . | p-value . |
---|---|---|---|---|---|---|
Source of drinking water at home | ||||||
Borehole water | 123 | 119 | 4 | 0.45 | 0.14–1.47 | 0.283 |
Sachet | 33 | 30 | 3 | 1.34 | 0.35–5.17 | 0.955 |
Bottle water | 9 | 8 | 1 | 1.68 | 0.19–14.76 | 0.958 |
Untreated/unboiled Tano river/stream | 31 | 12 | 19 | 21.22 | 8.07–55.80 | <0.001 |
Boiled River Tano | 4 | 2 | 2 | 13.4 | 1.70–105.41 | 0.029 |
Hand-dug well | 56 | 43 | 13 | 4.05 | 1.66–9.90 | 0.003 |
Pipe-borne water (GWC) | 144 | 134 | 10 | 1.0 | 0.40–2.48 | 0.985 |
Drinking water consumption per person/day (L) | ||||||
Less than 0.5 | 38 | 30 | 8 | 1.78 | 0.70–4.57 | 0.340 |
0.5–1.0 | 86 | 77 | 9 | 0.78 | 0.33–1.86 | 0.731 |
1.01–1.50 | 56 | 50 | 6 | 0.8 | 0.30–2.17 | 0.853 |
1.51–2.0 | 42 | 35 | 7 | 1.34 | 0.51–3.52 | 0.744 |
2.01–2.50 | 123 | 107 | 16 | 1 | 0.48–2.10 | 0.891 |
2.51–3.0 | 46 | 42 | 4 | 0.64 | 0.20–2.02 | 0.612 |
3.01–3.50 | 9 | 7 | 2 | 1.91 | 0.36–10.02 | 0.78 |
Sanitation facilities and practices | ||||||
Shared pit latrine | 82 | 73 | 9 | 1.14 | 0.47–2.77 | 0.941 |
Ventilated improved pit latrine | 45 | 40 | 5 | 1.16 | 0.39–3.42 | 0.899 |
Public toilet (WC/KVIP/pit) | 144 | 130 | 14 | 1 | 0.46–2.18 | 0.876 |
Septic tank | 29 | 24 | 5 | 1.93 | 0.64–5.87 | 0.395 |
Pour flush | 25 | 22 | 3 | 1.27 | 0.34–4.77 | 0.799 |
Dig and bury | 35 | 29 | 6 | 1.92 | 0.68–5.42 | 0.34 |
Open defecation practices | 40 | 30 | 10 | 3.1 | 1.25–7.64 | 0.020 |
Dispose of household waste outside your home | ||||||
Yes | 320 | 302 | 18 | 1 | 0.51–1.96 | 0.769 |
No | 80 | 46 | 34 | 12.4 | 6.47–23.76 | <0.001 |
Waste management methods mostly practiced in the respondents’ area | ||||||
Public waste collection points | 40 | 33 | 7 | 1.45 | 0.58–3.64 | 0.590 |
Burning | 160 | 141 | 19 | 0.92 | 0.48–1.75 | 0.930 |
Private house-to-house collection service | 12 | 10 | 2 | 1.37 | 0.28–6.62 | 0.932 |
Open dumping | 188 | 164 | 24 | 1 | 0.55–1.83 | 0.912 |
Respondents’ wastewater management practices | ||||||
Discharge on open space outside homes | 253 | 227 | 26 | 1 | 0.56–1.78 | 0.946 |
Direct dumping into the gutter | 134 | 108 | 25 | 2 | 1.10–3.63 | 0.03 |
Channel through drainage into a pit (soak pit) | 10 | 9 | 1 | 0.97 | 0.12–7.97 | 0.867 |
Septic tank | 3 | 1 | 0 | 0.64 | 0.20–2.02 | 0.564 |
Wash hands before eating with soap | ||||||
Always | 315 | 302 | 13 | <0.001 | ||
Mostly | 60 | 36 | 24 | 12 | 5.79–24.84 | |
Sometimes | 23 | 10 | 13 | 27 | 7.32–99.23 | |
Never/rarely | 2 | 0 | 2 | 1 | 0.03–34.78 | |
Wash hands after toilet with soap | ||||||
Always | 83 | 80 | 3 | 0.002 | ||
Mostly | 55 | 47 | 8 | 10.67 | 3.19–35.61 | |
Sometimes | 145 | 121 | 24 | 16.67 | 8.12–34.29 | |
Never/rarely | 117 | 100 | 17 | 8.5 | 3.86–18.72 | |
Wash hands when coming from outside | ||||||
Always | 31 | 25 | 6 | 0.001 | ||
Mostly | 69 | 56 | 13 | 8.67 | 2.78–27.08 | |
Sometimes | 100 | 83 | 17 | 9.83 | 3.42–28.25 | |
Never/rarely | 200 | 184 | 16 | 7.75 | 2.81–21.41 | |
Buy from street vendors | ||||||
Rarely | 34 | 29 | 5 | <0.001 | ||
Sometimes | 87 | 68 | 19 | 9.5 | 3.28–27.49 | |
Mostly | 119 | 103 | 16 | 11.5 | 3.84–34.67 | |
Always | 160 | 148 | 12 | 19 | 5.98–60.24 |
Variable . | Total . | No illness (N = 348) . | Illness (N = 52) . | Odds ratio . | 95% CIa . | p-value . |
---|---|---|---|---|---|---|
Source of drinking water at home | ||||||
Borehole water | 123 | 119 | 4 | 0.45 | 0.14–1.47 | 0.283 |
Sachet | 33 | 30 | 3 | 1.34 | 0.35–5.17 | 0.955 |
Bottle water | 9 | 8 | 1 | 1.68 | 0.19–14.76 | 0.958 |
Untreated/unboiled Tano river/stream | 31 | 12 | 19 | 21.22 | 8.07–55.80 | <0.001 |
Boiled River Tano | 4 | 2 | 2 | 13.4 | 1.70–105.41 | 0.029 |
Hand-dug well | 56 | 43 | 13 | 4.05 | 1.66–9.90 | 0.003 |
Pipe-borne water (GWC) | 144 | 134 | 10 | 1.0 | 0.40–2.48 | 0.985 |
Drinking water consumption per person/day (L) | ||||||
Less than 0.5 | 38 | 30 | 8 | 1.78 | 0.70–4.57 | 0.340 |
0.5–1.0 | 86 | 77 | 9 | 0.78 | 0.33–1.86 | 0.731 |
1.01–1.50 | 56 | 50 | 6 | 0.8 | 0.30–2.17 | 0.853 |
1.51–2.0 | 42 | 35 | 7 | 1.34 | 0.51–3.52 | 0.744 |
2.01–2.50 | 123 | 107 | 16 | 1 | 0.48–2.10 | 0.891 |
2.51–3.0 | 46 | 42 | 4 | 0.64 | 0.20–2.02 | 0.612 |
3.01–3.50 | 9 | 7 | 2 | 1.91 | 0.36–10.02 | 0.78 |
Sanitation facilities and practices | ||||||
Shared pit latrine | 82 | 73 | 9 | 1.14 | 0.47–2.77 | 0.941 |
Ventilated improved pit latrine | 45 | 40 | 5 | 1.16 | 0.39–3.42 | 0.899 |
Public toilet (WC/KVIP/pit) | 144 | 130 | 14 | 1 | 0.46–2.18 | 0.876 |
Septic tank | 29 | 24 | 5 | 1.93 | 0.64–5.87 | 0.395 |
Pour flush | 25 | 22 | 3 | 1.27 | 0.34–4.77 | 0.799 |
Dig and bury | 35 | 29 | 6 | 1.92 | 0.68–5.42 | 0.34 |
Open defecation practices | 40 | 30 | 10 | 3.1 | 1.25–7.64 | 0.020 |
Dispose of household waste outside your home | ||||||
Yes | 320 | 302 | 18 | 1 | 0.51–1.96 | 0.769 |
No | 80 | 46 | 34 | 12.4 | 6.47–23.76 | <0.001 |
Waste management methods mostly practiced in the respondents’ area | ||||||
Public waste collection points | 40 | 33 | 7 | 1.45 | 0.58–3.64 | 0.590 |
Burning | 160 | 141 | 19 | 0.92 | 0.48–1.75 | 0.930 |
Private house-to-house collection service | 12 | 10 | 2 | 1.37 | 0.28–6.62 | 0.932 |
Open dumping | 188 | 164 | 24 | 1 | 0.55–1.83 | 0.912 |
Respondents’ wastewater management practices | ||||||
Discharge on open space outside homes | 253 | 227 | 26 | 1 | 0.56–1.78 | 0.946 |
Direct dumping into the gutter | 134 | 108 | 25 | 2 | 1.10–3.63 | 0.03 |
Channel through drainage into a pit (soak pit) | 10 | 9 | 1 | 0.97 | 0.12–7.97 | 0.867 |
Septic tank | 3 | 1 | 0 | 0.64 | 0.20–2.02 | 0.564 |
Wash hands before eating with soap | ||||||
Always | 315 | 302 | 13 | <0.001 | ||
Mostly | 60 | 36 | 24 | 12 | 5.79–24.84 | |
Sometimes | 23 | 10 | 13 | 27 | 7.32–99.23 | |
Never/rarely | 2 | 0 | 2 | 1 | 0.03–34.78 | |
Wash hands after toilet with soap | ||||||
Always | 83 | 80 | 3 | 0.002 | ||
Mostly | 55 | 47 | 8 | 10.67 | 3.19–35.61 | |
Sometimes | 145 | 121 | 24 | 16.67 | 8.12–34.29 | |
Never/rarely | 117 | 100 | 17 | 8.5 | 3.86–18.72 | |
Wash hands when coming from outside | ||||||
Always | 31 | 25 | 6 | 0.001 | ||
Mostly | 69 | 56 | 13 | 8.67 | 2.78–27.08 | |
Sometimes | 100 | 83 | 17 | 9.83 | 3.42–28.25 | |
Never/rarely | 200 | 184 | 16 | 7.75 | 2.81–21.41 | |
Buy from street vendors | ||||||
Rarely | 34 | 29 | 5 | <0.001 | ||
Sometimes | 87 | 68 | 19 | 9.5 | 3.28–27.49 | |
Mostly | 119 | 103 | 16 | 11.5 | 3.84–34.67 | |
Always | 160 | 148 | 12 | 19 | 5.98–60.24 |
a=95% Confidence Interval
The analysis highlights several significant associations between various factors and health outcomes. Among the key findings, individuals relying on untreated or unboiled water from the Tano River or stream exhibit notably higher odds of illness (OR 21.22, 95% CI: 8.07–55.80) compared with other sources of water such as with access to pipe-borne water (GWC).
Moreover, sanitation practices play a pivotal role, with open defecation showing a substantially higher odds ratio for illness (OR 3.1, 95% CI: 1.25–7.64), underscoring the importance of adequate sanitation infrastructure. Additionally, handwashing habits significantly influence health outcomes, with consistent handwashing before eating associated with reduced odds of illness (OR 0.001, 95% CI: <0.001–0.002), while inconsistent handwashing after toilet use correlates with elevated risks (OR 16.67, 95% CI: 8.12–34.29). Many of the most significant risk factor findings in this study are consistent with results from other studies.
The association between the use of untreated or unboiled water from rivers or streams and a higher risk of diarrheal diseases is well-documented. Previous research in regions with similar water sources has shown that untreated surface water often contains pathogens that lead to gastrointestinal diseases (Mourad 2004; Mara 2017). The boiled Tano River water, although safer than untreated water, still presented a significantly elevated risk in the study communities. This could be due to inconsistent boiling practices, as noted in studies by Cohen & Colford (2017) and Liu et al. (2020), who highlight that improper boiling may not eliminate all pathogens. According to Cohen & Colford (2017), pathogens such as E. coli O157:H7 cells can survive boiling or microwaving at viable temperatures in a study of the effects of boiling drinking water on diarrhea and pathogens. The analysis also suggests that factors beyond water quantity, such as water quality and hygiene practices, play significant roles in determining disease risk. This finding supports previous studies that emphasize the need for reliable and consistent water treatment methods in these communities (Yoada et al. 2014).
Sanitation practices were another significant factor. Open defecation, identified as a major risk in our study (OR = 3.1, p = 0.02), has been repeatedly linked to higher incidences of diarrheal diseases. The increased risk of diarrheal diseases associated with open defecation is consistent with findings from Ogundele et al. (2018) who identified poor sanitation as a major contributor to the spread of diarrheal diseases. This finding also aligns with global research recording the impact of poor sanitation on public health (Owoeye & Adedeji 2013; Andrés et al. 2021). However, the lack of significant associations with other sanitation facilities might indicate variability in the quality and maintenance of these facilities, which is consistent with findings from Prüss-Ustün et al. (2014) who noted that the effectiveness of sanitation facilities heavily depends on their proper use and maintenance.
Handwashing habits also showed significant associations with health outcomes. The protective effect of consistent handwashing before eating (OR 0.001, 95% CI: <0.001–0.002) and the increased risk associated with inconsistent handwashing after toilet use (OR 16.67, 95% CI: 8.12–34.29) are in line with studies by Zhang et al. (2016). White et al. (2020) and Zhang et al. (2016) demonstrated that handwashing is a critical intervention for preventing over 30% of diarrheal diseases. These findings suggest that hygiene education and the availability of handwashing facilities are crucial for improving health outcomes.
Furthermore, our study found that improper waste disposal was associated with a higher risk of illness (OR 12.4, 95% CI: 6.47–23.76), consistent with findings by Yoada et al. (2014). That improper waste disposal can contaminate the environment and serve as a breeding ground for disease vectors. Similarly, direct dumping of wastewater into gutters, which increases the risk of illness (OR 2, 95% CI: 1.10–3.63), has been shown to contaminate water sources and spread waterborne diseases (Ogundele et al. 2018). Previous research, as highlighted by Clasen et al. (2007), has established a correlation between diarrhea and exposure to wastewater or inadequate sanitation facilities. Areas with stagnant water accumulation provide conducive environments for microbial proliferation, potentially serving as infection reservoirs, as noted by Adhikari et al. (2023). Furthermore, studies indicate that microorganisms thriving in such conditions pose a heightened risk to vulnerable demographics, including children, pregnant women, the elderly, and individuals with compromised immune systems, compared with other household members (Clasen et al. 2007).
In contrast to previous studies where handwashing showed significant associations with reduced illness risk, our study yielded consistent results. The association between handwashing and reduced risk of illness was robust, with statistically significant findings observed across all analyses. This underscores the importance of proper hand hygiene practices, particularly the use of soap, in mitigating the risk of diarrheal diseases. The significant association persisted even after adjusting for potential confounding factors, affirming the substantial role of handwashing in preventing waterborne illnesses in our study population. Several factors may contribute to the strong association observed in our study. Firstly, the emphasis on handwashing with soap as a key hygiene intervention may have led to higher compliance rates among our participants. Cultural norms and public health campaigns promoting hand hygiene practices could have influenced behavior, resulting in better adherence to recommended handwashing techniques. Additionally, the availability of soap and water may have facilitated consistent handwashing behaviors among our study population, further enhancing the protective effect against diarrheal diseases. Our findings underscore the effectiveness of handwashing with soap as a simple yet powerful preventive measure against waterborne illnesses. Public health interventions aimed at promoting hand hygiene should prioritize the provision of soap and water access, along with educational efforts to encourage proper handwashing practices. By addressing barriers to hand hygiene and promoting behavioral change, we can significantly reduce the burden of diarrheal diseases and improve overall community health outcomes (Adhikari et al. 2023). Further research is warranted to explore the long-term impact of sustained handwashing interventions and their implications for reducing the transmission of waterborne pathogens.
The association between consuming food from outside sources and increased illness risk (OR 1.7) observed in our study is consistent with findings from previous research. Epidemiological data indicate that a significant proportion of foodborne illness outbreaks, approximately 44%, are linked to restaurants, hotels, and other catering establishments. This association may be attributed to various factors, including inadequate food handling practices and insufficient hygiene standards, particularly in establishments operated by immigrants from developing countries who may lack adequate training in food safety protocols (Angulo et al. 2006). Despite the prevalence of foodborne illness outbreaks associated with eating from outside sources, limited research has been conducted on this topic. For instance, a local study identified a popular fast-food outlet as the source of numerous salmonellosis outbreaks linked to chicken shawarma, highlighting the potential for food poisoning due to suboptimal cooking practices. Our findings suggest that individuals who consume food from outside sources may face an increased risk of foodborne illnesses, emphasizing the importance of food safety measures in commercial food establishments. The findings presented in this study are based on self-reported instances of diarrhea, which, while providing valuable insights, lack the advantage of microbiological confirmation. Conversely, alternative study methodologies may offer more precise diagnostic capabilities but often suffer from significant underreporting of illness. This issue is particularly evident in approaches reliant on physician-reported surveillance and is compounded by a steep decline in reporting over time in prospective studies. Moreover, the necessity of collecting stool samples in many cultural contexts may serve as a disincentive for individuals to report diarrheal episodes. Consequently, we contend that the study design employed in our research yields a more accurate estimate of the population prevalence of diarrheal disease compared with alternative methodologies.
Susceptibility map
The spatial distribution of geophysical-based flood risk and vulnerability maps was generated for the Tano River Basin at a 30-m resolution. Generally, high- and very-high-risk flood susceptibility zones are situated in areas near the river banks with lower elevations. In the geophysical-based flood risk maps, high-risk areas are predominantly found in water bodies and around water resources, covering approximately 21.2% of the area. A land cover map provides spatial information on various types/classes of physical coverage of the earth's surface, such as forests and lakes. This map was created by identifying and classifying the pixels in the Landsat 8 image. Each pixel in the image was assigned to a specific class based on the statistical characteristics of the pixel brightness values using supervised classification. This classification method relies on selecting sample pixels in an image that are representative of specific classes.
Exposure pathways and risks of waterborne diseases
The study shed light on crucial insights provided by respondents, highlighting that the consumption of contaminated water, especially among those who consume untreated Tano River water due to inadequate hygiene and sanitation practices, contributes significantly to the prevalence of diseases like diarrheal illness. Scarce access to water and sanitation facilities, compounded by substandard hygiene habits among households, was identified as a key factor leading to water contamination through unsafe wastewater disposal and open defecation practices. Moreover, a majority of respondents disclosed that household wastewater is occasionally utilized for backyard irrigation to fulfill the growing food demand. However, these irrigation practices, alongside the use of livestock manure and fertilizers, result in wastewater laden with nutrients and chemical residues, consequently polluting the Tano River. Additionally, the influx of urban migrants into vacant areas bordering the river has led to the establishment of informal settlements. Proximity to the river heightens their exposure to polluted water, particularly during floods, exacerbating drinking water contamination and the prevalence of waterborne diseases. Industrial waste discharge from factories along the river introduces harmful pollutants and toxic substances, further degrading water quality. Construction activities disturb the soil and lead to increased sedimentation in the river, affecting its clarity and ecosystem health. Illegal mining operations, often unregulated, result in heavy metal contamination and siltation, severely impacting the river's health. Lastly, open defecation along the riverbanks introduces pathogens and fecal matter directly into the water, posing immediate health risks to the communities relying on the river for their water supply.
Limited access to healthcare services, especially in rural settings, may exacerbate the prevalence of diseases among infected individuals (Honlah et al. 2019). Long-term repercussions may encompass a reduction in population growth, albeit interventions by the government and time-related factors could disrupt this trajectory. Insights gleaned from the literature indicate that the actual number of unreported cases of waterborne and flood-related fatalities in Ghana surpasses documented statistics due to inadequate record-keeping practices and social stigmatization at both household and district levels.
CONCLUSION
The study investigated the water–health nexus in the Tano River Basin, considering land use dynamics, flooding, and waterborne diseases using a questionnaire survey and GIS application to determine flood-prone areas through the generation of a flood map. The study identified inadequate sanitation, poor hygiene practices, and contamination from illegal mining activities as the primary contributors to waterborne diseases. Additionally, flooding and improper waste management were found to exacerbate these issues. The incidence of diarrhea was recorded at 13 cases per 100 respondents with significant risk factors including untreated water from the Tano River, inadequate sanitation leading to open defecation practices, and inconsistent handwashing after toilet use. Again, the study found that illegal mining, waste disposal, and lack of law enforcement in the Tano River catchment correlated significantly with demographic factors such as age, educational level, and years of living in the community (p < 0.05). Furthermore, geospatial analysis revealed that areas highly susceptible to flooding cover 21.2% of the entire area and predominantly in the southernmost part of the basin. This highlights the urgent need for flood protection measures and sustainable soil conservation practices, including the installation of rock berms, rip-raps, and enhanced drainage systems. In conclusion, the study recommends immediate action to mitigate these identified risk factors. Stakeholders and local authorities must prioritize the development and implementation of comprehensive strategies aimed at improving water quality and public health. Effective measures include enhancing WASH facilities, implementing stringent waste management practices, and fortifying flood resilience through infrastructure improvements. These findings are instrumental for local authorities in formulating evidence-based policies and interventions to mitigate the economic and public health impacts of waterborne diseases in the Tano River Basin.
DATA AVAILABILITY STATEMENT
All relevant data are included in the paper or its Supplementary Information.
CONFLICT OF INTEREST
The authors declare there is no conflict.