Abstract

Many Cameroonian cities lack access to potable drinking water where populations rely on alternative water sources of doubtful quality. This study aimed at describing the trends and patterns of waterborne diseases (WBDs) reported in some health facilities in Bamboutos Division between 2013 and 2017 as baseline data towards understanding the profile of WBDs in this area. A retrospective review of clinical data kept on patients who visited the main health facilities in Bamboutos Division from January 2013 to December 2017 was conducted. Overall, 39.1% (n = 8,124) of total patients were positive for at least one WBD. Categories of WBDs were dysenteries (18.6%), gastroenteritis (4.2%), viral hepatitis (0.2%) and typhoid was the most preponderant (24.4%). The most affected age groups were those above 24 years but significant differences were observed only in 2013 and 2017. Distribution of potential WBDs was locality dependent. The highest prevalence of typhoid fever was recorded in Bameboro (35.4%), dysenteries in Bamedjinda (20.4%) and gastroenteritis (17.3%) in Bamekoumbou. The study shows very high overall prevalence of WBDs in some localities which could be considered as ‘hotspots’ of WBDs in Bamboutos. This suggests the urgent need for setting up measures to tackle the challenges of potable drinking water supply.

HIGHLIGHTS

  • The study shows an extremely high prevalence of potential waterborne diseases (WBDs). This can be used as baseline data to fight against WBDs.

  • High prevalence of Helicobacter pylori, an emerging WBDs is an issue of public health concern.

  • Spacial distribution of WBDS reveals some vulnerable localities that could be considered as ‘foci’ for waterborne diseases and used as a guide during epidemiological interventions.

Graphical Abstract

Graphical Abstract
Graphical Abstract

INTRODUCTION

The quality of water is an issue of public health worldwide. According to the World Health Organization (WHO), 3.4 million people die from waterborne diseases (WBDs) each year, especially children (WHO 2014). The global spatial distribution of WBDs shows that Asia and Africa are the most affected continents. In Cameroon, as in most developing countries with inefficient public health structures, there are challenges in the provision of clean drinking water and good sanitation where residents of rural areas are highly affected compared to their urban counterparts. The WHO states that over 82% and only 42% of the urban and rural populations, respectively, are covered in terms of water supply (WHO 2000). This implies that up to 58% of households in rural areas do not have direct access to drinking water. Due to the privatization of many technical companies, such as SNEC/Camwater in charge of providing good quality water to the population, the supply of drinking water has become a challenge, leaving the affected population at risk. In the absence of adequate public services, the population relies on alternative sources of drinking water, the quality of which is very doubtful (Fonyuy 2014; Nanfack et al. 2014; Lontuo-Fogang et al. 2020).

Waterborne diseases are diseases caused by direct consumption of water contaminated with pathogenic microorganisms. Contaminated drinking water can be the source of food-borne diseases by ingestion of the same pathogens when it is used in the preparation of food. Most waterborne diseases are accompanied by diarrhoea, involving excessive passing of stools which could lead to dehydration and possibly death. A variety of microorganisms are responsible for these diseases including bacteria, protozoa, viruses and intestinal parasites. Some of the organisms are responsible for numerous waterborne disease outbreaks including Vibrio cholarae (cholera), Entamoeba hystolitica (amoebic dysentery), Shigella sp. (bacillary dysentery), Cryptosporidium parvum (cryptosporidiosis), Salmonella typhi (typhoid fever), Giardia sp. (giardiasis), Balantidium coli (balantidiasis), Campylobacter (gastroenteritis), rotavirus (diarrhoea), Escherichia coli (diarrhoea), hepatitis A virus (liver diseases), hepatitis E virus (liver diseases), Leptospira interrogans (leptospirosis) and poliovirus (poliomyelitis) (Cheesbrough 2006; UNICEF 2008; Ako et al. 2009; Hulton & WHO 2012; Djomassi et al. 2013; Lendzele et al. 2017). Cholera has been designated as the most dangerous waterborne infection. Between 2010 and 2011, Cameroon registered outbreaks of cholera with high mortality and morbidity in the northern part (North and Far-North) of the country (Djomassi et al. 2013).

Waterborne diseases are well-established public health problems in the West Region of Cameroon, where it has been classified as the second cause of infant mortality and morbidity after malaria in Bafoussam, the capital city of this region (Institut National de la Statistique 2011). However, these diseases account for 40% of the top ten diseases affecting children under five years of age (WHO 2008). Some studies, conducted both in the country and in this region, have shown the low bacteriological quality of drinking water collected from different alternative sources (Ako et al. 2009; Temgoua 2011; Fonyuy 2014; Nanfack et al. 2014; Nchang 2014). Some studies have equally shown the low quality of water used in vegetable farming (Ntangmo et al. 2012), as most waterborne diseases can be transmitted through consumption of raw food or food prepared with contaminated water, while Djomassi et al. (2013) focused on the surveillance of cholera epidemics in the country. To the best of our knowledge, the only studies conducted so far on the trends of WBDs are that of Ako et al. (2009) in Douala and Nanfack et al. (2014) in Mbouda Sub-Division (with only one year's registers explored). Therefore, this paucity of information highlights the necessity for an epidemiological report on the trends of WBDs in the western part of Cameroon. As with many infectious diseases, the range of WBDs changes with demographics. Factors concerning household characteristics (sex, age and locality) in addition to the environment (season and geographical location) may contribute to the incidence of WBDs. As shown in a study recently carried out in Nigeria by Okpasuo et al. (2019), poor personal hygiene, poor sanitation, poor knowledge and practice towards drinking water may predispose residents to a high incidence of WBDs. However Lontuo-Fogang et al. (2020) reported a low level of knowledge and inappropriate practices in the prevention of WBDs in the study area.

It is in this light that we aimed at describing the trends and patterns of WBDs reported in different health facilities of Bamboutos Division, between 2013 and 2017 as baseline data towards understanding the profile of WBDs in this area. Data generated could play an important role in the evaluation of health care performances and interventions (Clarke et al. 2019). This study could equally provide a better understanding of WBDs' profile, such that interventions could be effectively targeted, when and where to anticipate outbreaks, and examine the manner in which health variables vary over time (Okpasuo et al. 2019).

MATERIALS AND METHODS

Study site

The study was conducted in four health facilities located within the Bamboutos Division in West Region of Cameroon. These health facilities included the Mbouda District Hospital (MDH), Ad-Lucem Hospital of Mbouda and Integrated Health Center (IHC) of Balepo and the District Medical Center (DMC) of Toumaka, both in the Babadjou Sub-Division. These health facilities receive patients from different communities such as Batcham, Bangang, Bamekoumbou, Bamedjinda, Bamessingue, Galim, Mboudacentre, Babete, Babadjou and others. Bamboutos Division covers a surface area of 1,173 km2 with about 318,848 residents. It is divided administratively into four councils (Babadjou, Batcham, Galim and Mbouda) and, in turn, into villages (Figure 1). The climate is equatorial with high elevations and moderate-to-high humidity (BUCREP 2010). Rainfall is moderated by the mountains where elevations might reach over 2,000 m with 1,000–2,000 mm rainfall per year with the highest rainfall around the Bamendjing Reservoir. Bamboutos Division experiences four major seasons: the long dry season (LDS) which runs from December to March, a short rainy season (SRS) from March to June, a period of little rainfall (SDS) from June to August and, finally, a long rainy season (LRS) from September to December (https://en.wikipedia.org/wiki/West_Region Cameroon). The main activities carried out in these communities are agriculture, livestock rearing and small-scale business (Lontuo-Fogang et al. 2020). This study was conducted in two of these councils: Mbouda and Babadjou.

Figure 1

Study site showing different neighbouring villages and hospitals included during the survey.

Figure 1

Study site showing different neighbouring villages and hospitals included during the survey.

Study population and design

The study involved a retrospective assessment of routine data kept on patients who visited some health facilities in Babadjou (IHC Balepo and DMC of Toumaka) and Mbouda (MDH Mbouda and Ad-Lucem Hospital) Sub-Divisions. The study population was made up of all patients who visited the health facilities from January 2013 to December 2017 for consultation and conducted any test concerning a WBD.

Data collection methods and tools

Registers from laboratories were reviewed to obtain clinical diagnosis of all cases during the period. This was done by serially reviewing all the records of patients from January 2013 to December 2017. The data collection device was designed as a listing in the form of rows and columns as described in Osei-Tutu & Anto (2016). Briefly, details such as record number, date (year/month) of visit, sex of patients, age, place of residence, reason for visits, clinical diagnosis, type of analysis and laboratory results (negative/positive or presence/absence) of pathogen tested were the headings of the columns and each row contained the names of patients, but this was not included in the study for reasons of confidentiality. A number was assigned to each patient to replace the names. We included patients that were tested for the following pathogens: S. typhi (Widal), Campylobacter sp., dysenteries (Shigella sp. and Entamoeba histolytica), E. coli, viral hepatitis (hepatitis A and E), gastroenteritis, Helicobacter pylori, Staphyloccocus aureus, Streptococcus sp., Giardia sp., Cryptosporidium sp. and gastrointestinal parasites (Ascaris sp., hookworms). The main diseases found were classified into categories of diseases using the International Classification of Diseases (ICD 2019). H. pylori was included in this study as an emerging WBD due to its classification by the WHO in the guidelines for drinking water under emerging pathogens in drinking water (WHO 2017). Due to the fact that data were collected from laboratory records, we cannot distinguish whether patients contracted diseases from contaminated drinking water, contaminated food, from person to person or faeco-orally. We therefore consider these routes as possible sources of infection, but more emphasis is laid on the waterborne route because there is the possibility of contracting diseases through the use of contaminated water in food preparation, hand washing and other chores.

Ethical issues

For the better enrolment of the study, permission was obtained from the Regional Delegate of Public Health (reference numbers 0051/L/MINSANTE/SG/DRSPO/CBF) and from the Directors of each health facility. Authorization was also obtained from the Senior Divisional Officer of Bamboutos with reference numbers 004/AR/F.31/SAAJP and an Ethical Clearance finally obtained from the Cameroon National Ethical Committee of Research for Human Health (2019/10/46/CE/CNERSH/SP).

Data processing and analysis

Data obtained from the registers of different health facilities were coded and entered into an Excel spreadsheet. After checking for typing errors and missing values, data were exported into Statistical Package for Social Sciences (SPSS) software version 20.0 for analysis. Pathogens diagnosed were classified into groups of diseases according to ICD10 (ICD 2019). Figures were drawn with Microsoft Excel 2010. Descriptive methods such as frequencies were used to explore characteristics of patients. Ages of participants were categorized in groups while patients who came from localities other than Bamboutos were classified as ‘out of Bamboutos’. In the same way, some villages with few patients (Bati, Bagam, Bamendjo, etc.) within Bamboutos were grouped together and referred to as ‘other villages in Bamboutos’. Prevalence of the four major categories of potential WBDs was expressed as a percentage of the total number of patients positive during the study period. Prevalence of WBDs was equally compared between localities (place of residence), years, seasons, age and gender over time. Statistical differences from the data were assessed using the χ2 (chi square) test for categorical variables and analysis of variance (ANOVA) for continuous variables (age of participants). All statistical tests were conducted at the two-sided P < 0.05 level of significance.

RESULTS

Demography and diagnostic characteristics of study population

In total, 20,755 patients attending the four health facilities during the period of 2013–2017 were reviewed for this study, consisting of 8,283 (39.91%) in MDH, 4,102 (19.76%) in Ad-Lucem Hospital of Mbouda, 1,771 (8.53%) in IHC Balepo and 6,599 (31.79%) in DMC of Toumaka. Out of the total patients, 10,294 (49.60%) had complete data (age, sex, year of attendance, place of residence and indication of laboratory test/diagnosis) but we included all the patients in the analysis. The place of residence was lacking in most registers of Ad-Lucem hospital and DMC Toumaka. Hospital records varied with the year of attendance (χ2 = 669.6; P = 0.0001). A progressive increase in hospital attendance was observed (2013: 2,804; 2014: 4,134; 2015: 5,365; 2016: 4,242 and 2017: 4,210). The highest attendance was recorded in 2015 where hospital visits reached their peak, then decreased in 2016 and 2017. For the five years, the rate of hospital attendance varied with months (P = 0.0001); cumulatively for the five years, the number of records reviewed varied between 1,492 and 1,895 patients per month (or 298–379 patients per month per year). Participants were made up of 8,204 males (39.53%) and 12,551 females (60.47%) with statistical difference (χ2 = 606.5; P = 0.0001). The age varied from less than 1 year to 102 years with the mean age of 39.89 ± 22.87 years. The mean age varied throughout the study period (ANOVA; F = 3.84; P = 0.004) (mean (SD); 2013: 41.41 (22.37); 2014: 39.69 (23.07); 2015: 40.00 (22.45); 2016: 39.26 (23.28) and 2017: 39.26 (23.28)) with those from 15 to 44 years recording the highest attendance rate followed by elderly people (>65 years). Most patients came from the Bamboutos Division and villages within the vicinity (Babete, Bamessingue, Balatchi, Galim, Batcham, Bangang, Babadjou, Bameboro, Bamekoumbou, Bamedjinda and Bafounda) (Table 1).

Table 1

Demographic characteristics of study population, 2013–2017

Characteristics of patients2013
2014
2015
2016
2017
Total
n(%)n(%)n(%)n(%)n(%)n(%)
Gender             
Females 1,629 58.1 2,413 58.4 3,238 60.4 2,628 62.0 2,643 62.8 1,2551 60.47 
Males 1,175 41.9 1,721 41.6 2,127 39.6 1,614 38.0 1,567 37.2 8,204 39.53 
Age (years)            
<5 112 4.4 164 6.2 88 2.1 97 2.5 137 3.3 598 3.4 
5–14. 113 4.4 139 5.2 296 7.2 368 9.5 399 9.6 1,315 7.6 
15–24 427 16.7 467 17.6 865 21.1 784 20.2 850 20.4 3,393 19.5 
25–34 469 18.3 506 19.1 721 17.5 656 16.9 674 16.2 3,026 17.4 
35–44 395 15.4 351 13.3 585 14.2 495 12.7 532 12.7 2,358 13.6 
45–54 253 9.9 266 10.0 404 9.8 382 9.8 426 10.2 1,731 10.0 
55–64 310 12.1 289 10.9 430 10.5 406 10.4 411 9.8 1,846 10.6 
>65 481 18.8 466 17.6 720 17.5 702 18.0 744 17.8 3,113 17.9 
Place of residence            
Babadjou 608 23.75 419 21.56 512 23.40 492 25.24 592 3,556 2,623 25.45 
Babete 49 1.91 86 4.43 163 7.45 170 8.72 75 4.50 543 5.27 
Bafounda 10 0.39 29 1.49 69 3.15 65 3.34 47 2.82 220 2.13 
Balatchi 27 1.05 26 1.34 36 1.65 36 1.85 28 1.68 153 1.48 
Bameboro 16 0.63 30 1.54 49 2.24 48 2.46 35 2.10 178 1.73 
Bamedjinda 24 0.94 35 1.80 45 2.06 62 3.18 55 3.30 221 2.14 
Bamekoumbou 0.31 60 3.09 85 3.88 73 3.75 40 2.40 266 2.58 
Bamessingue 158 6.17 205 10.55 291 13.30 265 13.60 206 12.37 1,125 10.92 
Bangang 18 0.70 50 2.57 31 1.42 38 1.95 29 1.74 166 1.61 
Batcham 26 1.02 54 2.78 65 2.97 39 2.00 41 2.46 225 2.18 
Galim 38 1.48 44 2.26 44 2.01 32 1.64 17 1.02 175 1.70 
Mbouda 1,400 54.69 821 42.25 667 30.48 494 25.35 336 20.18 3,718 36.08 
Other villages 72 2.81 10 0.51 15 0.69 36 1.85 68 4.08 201 1.95 
Out of Bamboutos 106 4.14 74 3.81 116 5.30 99 5.08 96 5.77 491 4.76 
Characteristics of patients2013
2014
2015
2016
2017
Total
n(%)n(%)n(%)n(%)n(%)n(%)
Gender             
Females 1,629 58.1 2,413 58.4 3,238 60.4 2,628 62.0 2,643 62.8 1,2551 60.47 
Males 1,175 41.9 1,721 41.6 2,127 39.6 1,614 38.0 1,567 37.2 8,204 39.53 
Age (years)            
<5 112 4.4 164 6.2 88 2.1 97 2.5 137 3.3 598 3.4 
5–14. 113 4.4 139 5.2 296 7.2 368 9.5 399 9.6 1,315 7.6 
15–24 427 16.7 467 17.6 865 21.1 784 20.2 850 20.4 3,393 19.5 
25–34 469 18.3 506 19.1 721 17.5 656 16.9 674 16.2 3,026 17.4 
35–44 395 15.4 351 13.3 585 14.2 495 12.7 532 12.7 2,358 13.6 
45–54 253 9.9 266 10.0 404 9.8 382 9.8 426 10.2 1,731 10.0 
55–64 310 12.1 289 10.9 430 10.5 406 10.4 411 9.8 1,846 10.6 
>65 481 18.8 466 17.6 720 17.5 702 18.0 744 17.8 3,113 17.9 
Place of residence            
Babadjou 608 23.75 419 21.56 512 23.40 492 25.24 592 3,556 2,623 25.45 
Babete 49 1.91 86 4.43 163 7.45 170 8.72 75 4.50 543 5.27 
Bafounda 10 0.39 29 1.49 69 3.15 65 3.34 47 2.82 220 2.13 
Balatchi 27 1.05 26 1.34 36 1.65 36 1.85 28 1.68 153 1.48 
Bameboro 16 0.63 30 1.54 49 2.24 48 2.46 35 2.10 178 1.73 
Bamedjinda 24 0.94 35 1.80 45 2.06 62 3.18 55 3.30 221 2.14 
Bamekoumbou 0.31 60 3.09 85 3.88 73 3.75 40 2.40 266 2.58 
Bamessingue 158 6.17 205 10.55 291 13.30 265 13.60 206 12.37 1,125 10.92 
Bangang 18 0.70 50 2.57 31 1.42 38 1.95 29 1.74 166 1.61 
Batcham 26 1.02 54 2.78 65 2.97 39 2.00 41 2.46 225 2.18 
Galim 38 1.48 44 2.26 44 2.01 32 1.64 17 1.02 175 1.70 
Mbouda 1,400 54.69 821 42.25 667 30.48 494 25.35 336 20.18 3,718 36.08 
Other villages 72 2.81 10 0.51 15 0.69 36 1.85 68 4.08 201 1.95 
Out of Bamboutos 106 4.14 74 3.81 116 5.30 99 5.08 96 5.77 491 4.76 

n, number of positive cases; %, prevalence.

Potential waterborne diseases reported

From 2013 to 2017, 39.1% (n = 8,124) of patients attending the four health facilities in Bamboutos were found positive for at least one WBD. The clinically diagnosed cases observed during this period were classified into four main groups. Typhoid fever took the lead with a prevalence of 24.4% (n = 5,064), followed by dysenteries (Shigella sp. and E. histolytica) with 18.6% (n = 3,859), then by gastroenteritis (Ascaris sp., hookworms, Campylobacter sp., Giardia lamblia and E. coli) with a prevalence of 4.2% (n = 870) and lastly viral hepatitis (hepatitis A and E) with 0.2% (n = 36). All these were laboratory confirmed cases. The overall prevalence of WBDs were year-dependent (χ2 = 16.55; df = 4; P = 0.002) with the highest (n = 1,964; 46.3%) prevalence recorded in 2016 while the lowest (n = 1,803; 42.8%) was in 2017. In addition, monthly variations were not observed in the cumulative prevalence of WBDs throughout the study period (χ2 = 10.27; df = 11; P = 0.51).

Prevalence of emerging waterborne diseases

Some pathogens have been enumerated by the WHO as emerging WBDs, among which, H. pylori is reported in this study with a prevalence of 8.4% (n = 1,753). However, because this pathogen falls under gastroenteritis in the International Classification of Diseases (ICD 2019), it was grouped together with gastroenteritis in other sections.

Prevalence of potential waterborne diseases by demographic characteristics

Table 2 highlights the variation of waterborne diseases in the study area by demographic characteristics. The prevalence of WBDs varied with gender, age and place of residence for some years. Significant differences were observed in gender-related prevalence only during the years 2015 and 2016. In 2015, more women (n = 1,439; 44.4%) than men (n = 869; 40.8%) were infected (χ2 = 6.9; P = 0.009). The pattern was similar in 2016 where we recorded a prevalence of 47.6% (n = 1,256) and 44.2% (n = 714), respectively, in women and men (χ2 = 4.45; P = 0.035). As for annual trends in age groups, significant differences were observed in 2013 (χ2 = 32.26; P = 0.001) and 2017 (χ2 = 30.42; P = 0.001) with the highest prevalence recorded in those older than 24 years. Prevalence of potential WBDs was locality dependent in 2013 (χ2 = 119.5 P = 0.001) and 2016 (χ2 = 31.23; P = 0.003) with the highest infection rates recorded in patients who consulted health facilities in Mbouda Sub-Division for both years.

Table 2

Prevalence of potential waterborne diseases by demographic characteristics per year

2013 (n = 2,408)
2014 (n = 4,134)
2015 (n = 5,365)
2016 (n = 4,242)
2017 (n = 4,210)
n(%)n(%)n(%)n(%)n(%)
Gender χ2 = 0.54; P = 0.46 χ2 = 3.41; P = 0.65 χ2 = 6.9; P = 0.009 χ2 = 4.45; P = 0.035 χ2 = 1.18; P = 0.28 
Female 730 44.8 1,075 44.6 1,439 44.4 1,250 47.6 1,115 42.2 
Males 543 46.2 717 41.7 869 40.8 714 44.2 688 43.9 
Age (years) χ2 = 32.26; P < 0.001 χ2 = 8.15; P = 0.32 χ2 = 7.3; P = 0.4 χ2 = 5.25; P = 0.63 χ2 = 30.42; P < 0.001 
< 5 34 30.4 69 42.1 37 42.0 49 50.5 37 27.0 
5–14 35 31.0 64 46.0 145 49.0 173 47.0 153 38.3 
15–24 207 48.5 237 50.7 432 49.9 380 48.5 362 42.6 
25–34 239 51.0 241 47.6 348 48.3 298 45.4 292 43.3 
35–44 187 47.3 169 48.1 270 46.2 242 48.9 271 49.1 
45–54 122 48.2 131 49.2 174 43.1 197 51.6 193 45.3 
55–64 156 50.3 157 54.3 201 46.7 185 45.6 163 39.7 
> 65 249 51.8 223 47.9 338 46.9 335 47.7 338 45.4 
Place of residence χ2 = 119.5 P < 0.001 χ2 = 15.82; P = 0.26 χ2 = 16.9; P = 0.20 χ2 = 31.23; P = 0.003 χ2 = 7.13; P = 0.89 
Babadjou 181 29.8 182 43.4 227 44.3 199 40.4 283 47.8 
Babete 17 34.7 48 55.8 84 51.5 91 53.5 30 40.0 
Bafounda 70.0 13 44.8 24 34.88 33 50.8 22 46.8 
Balatchi 14 51.9 30.8 18 50.0 16 44.4 11 39.3 
Bameboro 56.2 13 43.3 27 55.1 18 37.5 18 51.4 
Bamedjinda 11 45.8 14 40.0 23 51.1 29 46.8 26 47.3 
Bamekombou 50.0 30 50.0 44 51.8 41 56.2 18 45.0 
Bamessingue 84 53.2 101 49.3 146 50.2 135 50.9 103 50.0 
Bangang 10 55.6 25 50.0 11 35.5 17 44.7 16 55.2 
Batcham 15 57.7 27 50.0 29 44.6 15 38.5 17 41.5 
Galim 25 65.8 20 45.5 19 40.9 18 56.2 35.3 
Mbouda 764 54.6 429 52.3 287 43.0 271 54.9 159 47.3 
Other villages 34 47.2 50.0 53.3 20 55.6 37 54.4 
Out of Bamboutos 54 50.9 35 47.3 58 50.0 45 45.5 48 50.0 
2013 (n = 2,408)
2014 (n = 4,134)
2015 (n = 5,365)
2016 (n = 4,242)
2017 (n = 4,210)
n(%)n(%)n(%)n(%)n(%)
Gender χ2 = 0.54; P = 0.46 χ2 = 3.41; P = 0.65 χ2 = 6.9; P = 0.009 χ2 = 4.45; P = 0.035 χ2 = 1.18; P = 0.28 
Female 730 44.8 1,075 44.6 1,439 44.4 1,250 47.6 1,115 42.2 
Males 543 46.2 717 41.7 869 40.8 714 44.2 688 43.9 
Age (years) χ2 = 32.26; P < 0.001 χ2 = 8.15; P = 0.32 χ2 = 7.3; P = 0.4 χ2 = 5.25; P = 0.63 χ2 = 30.42; P < 0.001 
< 5 34 30.4 69 42.1 37 42.0 49 50.5 37 27.0 
5–14 35 31.0 64 46.0 145 49.0 173 47.0 153 38.3 
15–24 207 48.5 237 50.7 432 49.9 380 48.5 362 42.6 
25–34 239 51.0 241 47.6 348 48.3 298 45.4 292 43.3 
35–44 187 47.3 169 48.1 270 46.2 242 48.9 271 49.1 
45–54 122 48.2 131 49.2 174 43.1 197 51.6 193 45.3 
55–64 156 50.3 157 54.3 201 46.7 185 45.6 163 39.7 
> 65 249 51.8 223 47.9 338 46.9 335 47.7 338 45.4 
Place of residence χ2 = 119.5 P < 0.001 χ2 = 15.82; P = 0.26 χ2 = 16.9; P = 0.20 χ2 = 31.23; P = 0.003 χ2 = 7.13; P = 0.89 
Babadjou 181 29.8 182 43.4 227 44.3 199 40.4 283 47.8 
Babete 17 34.7 48 55.8 84 51.5 91 53.5 30 40.0 
Bafounda 70.0 13 44.8 24 34.88 33 50.8 22 46.8 
Balatchi 14 51.9 30.8 18 50.0 16 44.4 11 39.3 
Bameboro 56.2 13 43.3 27 55.1 18 37.5 18 51.4 
Bamedjinda 11 45.8 14 40.0 23 51.1 29 46.8 26 47.3 
Bamekombou 50.0 30 50.0 44 51.8 41 56.2 18 45.0 
Bamessingue 84 53.2 101 49.3 146 50.2 135 50.9 103 50.0 
Bangang 10 55.6 25 50.0 11 35.5 17 44.7 16 55.2 
Batcham 15 57.7 27 50.0 29 44.6 15 38.5 17 41.5 
Galim 25 65.8 20 45.5 19 40.9 18 56.2 35.3 
Mbouda 764 54.6 429 52.3 287 43.0 271 54.9 159 47.3 
Other villages 34 47.2 50.0 53.3 20 55.6 37 54.4 
Out of Bamboutos 54 50.9 35 47.3 58 50.0 45 45.5 48 50.0 

n, number of positive cases; %, prevalence.

Annual variation of potential waterborne diseases

Figure 2 shows temporal variation of the four categories of WBDs observed during the study. The yearly registered cases of typhoid fever were the highest compared to other diseases throughout the five years but a progressive decrease in clinical cases from 2013 (27%) to 2017 (22.4%) was observed. An opposite pattern was noticed for dysenteries with increased cases from 15.4% in 2013 to 21.4% in 2016 but with a slight drop in 2017 (20.5%). Reported cases of viral hepatitis also increased with time from 0% in 2013 to 0.3% in 2017. A fluctuation was observed in the number of laboratory confirmed cases of gastroenteritis throughout the five years, with the highest prevalence registered in 2013 (13.2%).

Figure 2

Prevalence of potential waterborne diseases per annum.

Figure 2

Prevalence of potential waterborne diseases per annum.

Diagnostic method of diseases per health facility

The same laboratory techniques were used in the different health facilities for analysis of WBDs in patients including microscopic examination of stool samples, coproculture, urine cyto-bacteriological test (UCBT), analysis of blood (Widal test), identification of H. pylori and hepatitis A and E. It was noticed in Ad-Lucem (a private hospital) that most of their diagnosis was done with antibiogram and it was the only hospital where coproculture was done, and some pathogens like Shigella sp,, Campylobacter sp,, G. lamblia, Streptococcus sp. and E. coli were recorded only in this health facility. The main pathogens recorded were Ascaris lumbricoides, E. histolytica, hookworms, H. pylori in addition to other pathogens like Campylobacter sp., Shigella sp., G. lamblia and E. coli reported in Ad-Lucem hospital (Table 3).

Table 3

Clinical detection method and pathogens found per hospital

HospitalSubdivisionDiagnostic methodPathogens found
Ad-Lucem Mbouda Widal, Hp test, stool, UCBT, coproculture (with antibiogram) E. histolytica, H. pylori, A. lumbricoides, hookworms, S. typhi, Shigella sp., Campylobacter sp., G. lamblia, Streptococcus sp. and E. coli 
MDH Mbouda Widal, Hp test, stool, UCBT E. histolytica, H. pylori, A. lumbricoides, hookworms, S. typhi, G. lamblia 
IHC Balepo Babadjou Widal, Hp test, Stool, UCBT E. histolytica, H. pylori, A. lumbricoides, hookworms, S. typhi 
DMC Toumaka Babadjou Widal, Hp test, stool, UCBT E. histolytica, H. pylori, A. lumbricoides, hookworms, S. typhi 
HospitalSubdivisionDiagnostic methodPathogens found
Ad-Lucem Mbouda Widal, Hp test, stool, UCBT, coproculture (with antibiogram) E. histolytica, H. pylori, A. lumbricoides, hookworms, S. typhi, Shigella sp., Campylobacter sp., G. lamblia, Streptococcus sp. and E. coli 
MDH Mbouda Widal, Hp test, stool, UCBT E. histolytica, H. pylori, A. lumbricoides, hookworms, S. typhi, G. lamblia 
IHC Balepo Babadjou Widal, Hp test, Stool, UCBT E. histolytica, H. pylori, A. lumbricoides, hookworms, S. typhi 
DMC Toumaka Babadjou Widal, Hp test, stool, UCBT E. histolytica, H. pylori, A. lumbricoides, hookworms, S. typhi 

DMC Toumaka, District Medical Center Toumaka; IHC: Balepo, Integrated Health Center Balepo; MDH, Mbouda District Hospital; UCBT, urine cyto-bacteriological test; Hp, Helicobacter pylori.

Seasonality of potential waterborne diseases

Figure 3(a) highlights the variation in the cumulative prevalence of WBDs according to seasons. After reviewing hospital records, it was revealed that the overall prevalence of WBDs was not season dependent (χ2 = 3.6; df = 3; P = 0.31) throughout the five years. Despite the fact that no significant variation was observed in seasons, the highest prevalence (n = 1,545; 45.5%) was recorded in the short dry season (from June to August) with proportions of hospital visits reaching their peak (32.9%) during the long dry season (from December to March) (Figure 3(b)).

Figure 3

Prevalence of potential waterborne diseases per season (a) and proportion of patients visiting hospitals per season (b).

Figure 3

Prevalence of potential waterborne diseases per season (a) and proportion of patients visiting hospitals per season (b).

Geographical pattern of each category of potential waterborne diseases

The prevalence of WBDs by locality is presented in Figure 4. Despite the high prevalence in all localities, prevalence of the different categories of WBDs varied significantly within these sites (χ2 = 79.17; P = 0.001). The highest prevalence of typhoid fever was recorded in Bameboro (n = 63; 35.4%) while dysenteries were more represented in Bamedjinda (n = 45; 20.4%) and gastroenteritis (n = 46; 17.3%) in Bamekoumbou. All the cases of viral hepatitis were observed in patients who did not declare their place of residence.

Figure 4

Spatial distribution of each category of diseases in Bamboutos Division.

Figure 4

Spatial distribution of each category of diseases in Bamboutos Division.

DISCUSSION

The current study aimed at describing the trends and patterns of WBDs reported in different health facilities of Mbouda and Babadjou Sub-Divisions between 2013 and 2017 as baseline data towards understanding the profile of WBDs in Bamboutos revealed important information which could be used to guide the control of WBDs in these localities. The overall prevalence of potential WBDs is extremely high in this study. It is clear that the proportion of the population involved in the study/visiting health facilities are those that are already sick or presenting signs and symptoms of diseases. In addition, other factors like poor personal hygiene, inappropriate food/water hygiene, lack of appropriate medical facilities, especially in rural areas, could be responsible for such high prevalence. However, this prevalence is lower than that obtained in Nigeria by Okpasuo et al. (2019) with up to 48.9% for typhoid fever. Review of clinical data revealed typhoid fever, dysenteries, gastroenteritis and viral hepatitis as the main groups of WBDs found in health facilities of the study area. These infections were found to vary with socio-demographic characteristics (gender, age group and locality) at different periods. The different WBDs found in this study are similar to the most common WBDs or food-borne diseases (FBDs) found in health facilities in Cameroon and other countries. A similar study was carried out in the economic capital of Cameroon by Ako et al. (2009) and revealed the same diseases in some hospitals in Douala. All the same, Osei-Tutu & Anto (2016) in Ghana observed that cholera, typhoid fever, viral hepatitis and dysentery (shigellosis) were the main diseases (food-borne/waterborne) in the Ridge Hospital in Accra. Okpasuo et al. (2019) and Oguntoke et al. (2009) in Nigeria also revealed typhoid fever, bacillary dysentery and cholera as the most commonly reported waterborne/food-borne diseases at the health facilities in Nigeria. The prevalence of these WBDs in Bamboutos may be due to lack of knowledge, poor attitude and practice towards the prevention of WBDs among residents, in addition to lack of access to safe drinking water sources (Lontuo-Fogang et al. 2020).

The temporal variation of WBDs during the years presented no regular pattern. Typhoid fever was the most reported WBD in this study. All the same, many studies in Cameroon and elsewhere revealed typhoid fever as the most rampant WBD (Oguntoke et al. 2009; Yusuff et al. 2014; Nanfack et al. 2014; Yusuff et al. 2014; Okpasuo et al. 2019) while Ako et al. (2009) identified gastroenteritis in the Doauala 4th District. However, the high prevalence of typhoid fever in this study may indicate that S. typhi is the most predominant aetiological agent of waterborne or food-borne diseases of public health importance in villages around the health facilities. In addition, the non-specificity of the Widal test and the possibility of producing positive results a few weeks after treatment (Beyene et al. 2008) could account also for its high prevalence as this was the only diagnostic method used in all the health facilities. Furthermore, the fight against this infection is becoming a challenge in sub-Saharan Africa due to its resistance to commonly used antibiotics (Slayton et al. 2013) and the common practice of self-medication (Lontuo-Fogang et al. 2020) and even drug dependence. The absence of cholera in the clinical records of these four health facilities may indicate their absence or scarcity in the population of Bamboutos Division. Nonetheless, cholera was reported in the West Region during the epidemics of 2010 and 2011, with peaks of up to 245 cases per week, although the Far North, North and Center Regions reported the greatest number of suspected cholera cases in 2011 (Djomassi et al. 2013).

Over the five years, more women than men were infected, with important prevalence recorded in adults (>24 years). There is no doubt that women and girls are the most exposed group due to their routine involvement in fetching water and domestic chores. However, to the best of our knowledge, infection with WBDs has not been proven to be gender dependent. High prevalence of WBDs was revealed in adults. Similar results were obtained in Zambia (Mweetwa 2015). This might be related to the fact that the population of Bamboutos Division is highly engaged in farm work and small-scale businesses as their daily activities (Lontuo-Fogang et al. 2020), implying that farmers rely on water of unknown quality from streams and other very doubtful sources on the farm while business men, on the other hand, rely either on sachet water or water and food from roadside vendors that might be contaminated. This behaviour is a risk factor for WBDs as Nchang (2014) reported very high bacterial loads in wells in the Bambui and Bambili residential areas while packaged water was reported to be very polluted in a study in Douala (Mananga et al. 2014). Surprisingly, children of less than five years old registered low WBD prevalence compared to other groups. This might be due to the poor organization of clinical records in some health facilities, with data on children less than five years old not well documented while the place of residence was completely absent for others. However, Bashiru & Asokan (2016) and Qureshi et al. (2011) reported children of less than five years old as the population at risk.

Among the different methods of diagnosis used for the detection of WBDs, AD-Lucem hospital was the only hospital that did coproculture and accompanied most of their tests with antibiograms. This could account for the fact that WBDs like Shigella sp., Campylobacter sp., Streptococcus sp. and E. coli were recorded only in this hospital. Despite the fact that the Widal test has been proven to be non-specific and gives positive results for a patient treated after a few weeks, this was the only method used in all the four health facilities for the diagnosis of S. typhi. This can be attributed to the fact that the Widal test is relatively cheaper, easy to perform and requires minimal training and equipment (Beyene et al. 2008). However, Nsutebu et al. (2002) and Wam et al. (2019) recommend ELISA technique as a more suitable test for the diagnosis of typhoid fever compared to Widal.

Of all the WBDs identified from hospital records there is no significant variation between seasons, but the highest prevalence was observed in the dry season (short dry season from June to August) and highest number of hospital attendance during the dry season (long dry season from December to March). Similar findings were reported in Nigeria where the occurrence of WBDs peaked during the drier periods of the year and was attributed to water scarcity and depletion of water reserves (Okpasuo et al. 2019). This scarcity of water during periods of no rainfall might have left the population with no other choice than to collect water of unknown quality from alternative sources (Lontuo-Fogang et al. 2020). This equally implies that water quality assessment should be pronounced in the dry season.

Without taking into account the different years, some localities were vulnerable to particular categories of WBDs. The highest percentage of typhoid fever was recorded in Bameboro, dysentery in Bamedjinda and gastroenteritis in Bamekombou. All these localities are rural areas. One of the possible reasons that could explain why disease prevalence is higher in rural areas could be due to the gap in health care access. However, further investigations are needed and, at the same time, these localities could be considered as foci for these particular WBDs in the study area (Subramanian et al. 2017).

CONCLUSION

It is very clear that the overall prevalence of potential WBDs is far too high in the Bamboutos Division, which is a call for concern given the importance of water for the human body. Residents suffer from four main categories of WBDs including typhoid fever, dysenteries, gastroenteritis and viral hepatitis with typhoid being the most preponderant. These infections vary by demographic characteristics for some years, with the highest number of hospital registered cases occurring during the drier periods of the year. Some rural areas were vulnerable to particular WBDs and could be used as ‘hotspots’ for these diseases during interventions or in guiding interventions to the areas in greatest need. Data gathered from this study could be used as baseline data for the fight against WBDs and understanding the profile of WBDs in the study area. However, because it is uncertain whether patients contracted these diseases through the consumption of contaminated water and food, from person to person, or faeco-orally, further studies are required to verify the quality of drinking water in the study area. As the saying goes, ‘prevention is better than cure’, we strongly recommend the dissemination of cheap, easy to apply water treatment techniques, such as boiling and filtering, hygienic storing and handling to households.

Limitations

Even though we considered all the data extracted from hospitals records, the poor presentation of data on children under five years, absence of place of residence and age of patients in some health facilities limited the study. The prevalence of reported WBDs in these health facilities may present only the severe cases because a previous study showed that residents of Bamboutos Division practise self-medication in the case of disease.

ACKNOWLEDGEMENTS

We would like to acknowledge the population of Bamboutos Division as a whole and, in particular, the health care personnel from the different health facilities for giving us the opportunity to explore their hospital records. The authors declare that they have no competing interests. This work did not receive financial support from anybody or agency. The authors' contributions were as follows: LFR, VKP, BR conceived and designed the study protocol; LFR, BR, MMS, BRM, NNA participated in data collection; LFR, BR, SM interpreted and analysed data; LFR and BR drafted the paper; NTH, VKP, MMS, BRM, NNA, SM critically revised the manuscript; all the authors read and approved the final version.

DATA AVAILABILITY STATEMENT

All relevant data are included in the paper or its Supplementary Information.

REFERENCES

Ako
A. A.
Nkeng
G. E.
Takem
G. E. E.
2009
Water quality and occurrence of water-borne diseases in the Douala 4th District, Cameroon
.
Water Science and Technology
59
(
12
),
2321
2329
.
Bashiru
I.
Asokan
C.
2016
Spatial and temporal distribution of waterborne diseases in Namanyonyi Sub-County, Mbale District, Uganda
.
International Journal of Scientific Research and Management
4
(
3
),
3967
3975
.
Beyene
G.
Asrat
D.
Mengistu
Y.
Aseffa
A.
Wain
J.
2008
Typhoid fever in Ethiopia
.
The Journal of Infection in Developing Countries
2
(
6
),
448
453
.
BUCREP
2010
Troisième recencement générale de la population et de l'habitat (Third General Population and Housing Census Cameroun) Rapport de présentation des résulstats définitifs République du Cameroun
. pp.
1
65
.
Cheesbrough
M.
2006
District Laboratory Practice in Tropical Countries
, 2nd edn.
Cambridge University Press
,
Cambridge
,
UK
.
Clarke
G. M.
Arne
T. W.
Adam
S.
2019
Evaluating the impact of healthcare interventions using routine data
.
British Medical Journal
365
(
l2239
),
1
7
.
Djomassi
L. D.
Gessner
B. D.
Andze
G. O.
Mballa
G. E.
2013
National surveillance data on the epidemiology of cholera in Cameroon
.
The Journal of Infectious Diseases
208
(
suppl. 1
),
92
97
.
Hulton
G.
&
World Health Organization
2012
Global Costs and Benefits of Drinking-water Supply and Sanitation Interventions to Reach the MDG Target and Universal Coverage
.
World Health Organization
,
Geneva
.
Available from: https://apps.who.int/iris/handle/10665/75140 (accessed 16 June 2021).
ICD
2019
International Statistical Classification of Diseases and Related Health Problems
.
Available from: http://www.who.int/classifications/icd/en/ (accessed 27 July 2020)
.
Institut National de la Statistique
2011
Ministère de l’Économie de la Planification et de l'Aménagement du Territoire, Ministère de la Santé Publique: Enquête Démographique et de Santé et à Indicateurs Multiples (EDS-MICS) 2011
.
Available from: http://www.measuredhs.com/pubs/pdf/FR260/FR260.pdf (accessed 26 February 2021)
.
Lendzele
S.
Abdoulmoumini
M.
Abdoulaye
M.
2017
Typhoid, malaria and their concurrent infections in Fondonera, West Region of Cameroon
.
Journal of Veterinary Science and Medical Diagnosis
6
(
3
),
61
69
.
Lontuo-Fogang
R.
Payne
V. K.
Cedsric
S. F.
Tsafack
H. N.
Bamou
R.
2020
A cross-sectional epidemiological survey on drinking water, sanitation and hygiene amongst residents of Bamboutos Division, West Region Cameroon: a knowledge, attitude and practice (KAP) study
.
International Journal of Tropical Disease & Health
41
(
16
),
1
14
.
Mananga
M. J.
Sop
M. M. K.
Nguidjol
E.
Gouado
I.
2014
Quality of packaged drinking water marketed in Douala-Cameroon
.
Journal of Water Resources and Ocean Science
3
(
6
),
74
79
.
Mweetwa
P.
2015
Typhoid Fever Outbreak Trends From 2009 to 2013, Assessment of Knowledge and Practices among Health Workers and Residents of Luanshya District, Zambia
.
Thesis
,
University of Zambia
,
Zambia
, p.
56
.
Nanfack
N.
Fonteh
F.
Vincent
K.
Katte
B.
Fogoh
J.
2014
Eaux non conventionnelles: un risque ou une solution aux problèmes d'eau pour les classes pauvres (non conventional waters: a risk or a solution to water problems for the poor?
LARHYSS Journal
17
(
2014
),
47
64
.
Nsutebu
E. F.
Ndumbe
P. M.
Koulla
S.
2002
The increase in occurrence of typhoid fever in Cameroon: overdiagnosis due to misuse of the Widal test?
Transactions of the Royal Society of Tropical Medicine and Hygiene
96
(
1
),
64
67
.
Ntangmo Tsafack
H.
Temgoua
E.
Njine
T.
2012
Physico-chemical and bacteriological quality of the vegetable watering water in the Dschang Town, Cameroon
.
Journal of Environmental Protection
3
,
949
955
.
Oguntoke
O.
Aboderin
O.
Bankole
A.
2009
Association of water-borne diseases morbidity pattern and water quality in parts of Ibadan City, Nigeria
.
Tanzania Journal of Health Research
11
(
4
),
189
195
.
Okpasuo
O. J.
Okafor
F. C.
Aguzie
I.
Ikele
C.
Anunobi
J.
2019
Spatiotemporal trend of waterborne disease in Enugu Urban, Nigeria: a retrospective study
.
International Journal of Tropical Disease and Health
38
(
3
),
1
13
.
Qureshi
E. M. A.
Khan
A. U.
Vehra
S.
2011
An investigation into the prevalence of water borne diseases in relation to microbial estimation of potable water in the community residing near River Ravi, Lahore, Pakistan
.
African Journal of Environmental Science and Technology
5
(
8
),
595
607
.
Slayton
R.
Date
K.
Mintz
E.
2013
Vaccination for typhoid fever in sub-Saharan Africa
.
Human Vaccines & Immunotherapeutics
9
(
4
),
903
906
.
Subramanian
V. S.
Cho
M. J.
Tan
S. Z.
Fayzieva
D.
Sebaly
C.
2017
Spatial distribution and trends of waterborne diseases in Tashkent Province
.
Central Asian Journal of Global Health
6
(
1
),
277
.
UNICEF
2008
Promotion of Household Water Treatment and Safe Storage in UNICEF/WASH Programme (online). Available from: http://www.unicef.org/wash/files/Scaling_up_HWTS_Jan_25th_with_comments.pdf (accessed 14 October 2020).
WHO
2000
Global Water Supply and Sanitation Assessment 2000. Report Section
.
World Health Organization
,
Geneva
,
Switzerland
.
WHO
2008
Guidelines for Drinking Water Quality
.
World Health Organization
,
Geneva
,
Switzerland
.
Available from: https://www.who.int/water_sanitation_health/dwq/fulltext.pdf (accessed 21 February 2021)
.
WHO
2014
Drug Resistance
.
World Health Organization
,
Geneva
,
Switzerland
.
Available from: www.who.int/drugresistance/diseases/en 2014 (accessed 3 July 2020)
.
WHO
2017
Guidelines for Drinking-Water Quality: Fourth Edition Incorporating the First Addendum
.
World Health Organization
,
Geneva
,
Switzerland
.
Yusuff
A. S.
John
W.
Oloruntoba
A. C.
2014
Review on prevalence of waterborne diseases in Nigeria
.
Journal of Advancement in Medical and Life Sciences
1
(
2
),
1
3
.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY-NC-ND 4.0), which permits copying and redistribution for non-commercial purposes with no derivatives, provided the original work is properly cited (http://creativecommons.org/licenses/by-nc-nd/4.0/).