Abstract
Background
Despite growing awareness of the health risks associated with climate change, how Nepalese people perceive these threats and their links to diarrheal diseases remains insufficiently understood in Nepal. This study examines and compares knowledge of climate change and diarrheal prevention practices among communities in four districts in Koshi and Karnali provinces.
Methods
We conducted a cross-sectional study from February 12 to June 28, 2023, surveying 882 households using a structured questionnaire. Data were collected on socio-demographic characteristics, climate change knowledge, perception of climate change and its impact on diarrhea, and practices for diarrhea prevention. Statistical comparisons between Koshi and Karnali provinces were performed to identify potential drivers of differences, and multiple logistic regression analyses were used to examine associations between socio-demographic factors and study outcomes, including climate knowledge and diarrheal prevention practices.
Results
Of the 882 participants, 70% were aware of climate change; among them, 66.1% strongly believed climate change increases diarrheal risk, with higher agreement in Koshi (P = 0.029); 60.9% linked rising temperatures to more diarrheal cases (P = 0.046), while only 24% associated it with increased precipitation. Regarding diarrheal prevention, 94.3% reported access to safe drinking water and 84.1% used improved sanitation facilities (P = 0.001). Only 11.3% of participants were vaccinated against rotavirus; 36.6% did not purify their water and 37.2% used cloth-filtered water. Despite access to care, 70.1% delayed treatment for diarrheal illness, varying by province (P < 0.001). In the multivariate analysis, females had significantly higher odds of lacking knowledge of climate change compared with males (AOR: 1.51; 95% CI: 1.14–2.08). A similar pattern was observed among participants engaged in agriculture (AOR: 6.16; 95% CI: 1.42–26.50) and labor occupations (AOR: 9.48; 95% CI: 1.81–49.71), indicating lower knowledge levels in these groups. Residents of the Karnali Province (AOR: 2.59; 95% CI: 1.83–3.66) had higher odds of not immediately visiting a health institution for diarrheal illness, indicating delayed healthcare-seeking behavior.
Conclusions
Although two-thirds of participants recognized the impact of climate change on diarrheal disease, our study identified low rotavirus vaccination coverage, suboptimal household water treatment practices, delayed care seeking, and limited recognition of rainfall as a risk factor. These findings highlight the need to strengthen education on climate-related risk factors, promote safe water and sanitation practices, and encourage timely healthcare seeking in the study districts to combat diarrhea in the context of climate change.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12889-026-27897-x.
Keywords: Climate change, Diarrheal disease, Districts, Provinces, Nepal
Introduction
Diarrheal illness remains a significant global health challenge, contributing to high morbidity and mortality, particularly in developing nations like Nepal. Just a decade ago, diarrheal diseases including cholera and dysentery ranked as the second most prevalent illness among the top ten diseases in Nepal [1]. Research indicates that climate change is expected to significantly influence the occurrence of diarrheal illnesses [2]. In addition, evidence suggests that Nepal’s maximum temperature is rising at a faster rate (0.05 °C/year) than its minimum temperature (0.03 °C/year) [3]. Furthermore, a recent report by the Department of Health Service indicates that rising temperatures are contributing to an increase in water and food-borne diseases such as diarrhea, dysentery, and typhoid [4]. Likewise, changes in precipitation patterns are leading to floods and landslides in areas with excessive rainfall, droughts in regions with low rainfall, and water scarcity in arid areas [5]. Shifting precipitation patterns can worsen diarrheal disease spread, especially in poor communities lacking clean water, sanitation, and healthcare.
Although the prevalence of diarrheal diseases in Nepal has been well documented over the years, with frequent outbreaks, including 425 cases in Khalanga in 2009, 188 in Gulmi in April 2019, more than 1,500 in Rautahat, and 310 in mid-western Nepal [6–9]. Nevertheless, few studies have also focused on the impact of climate change on diarrheal in children under five [10, 11]. In addition, some studies have also explored mothers’ knowledge, attitudes, and practices (KAP) regarding childhood diarrhea in different regions of Nepal [12, 13]. One study examined perceived causes and traditional treatments, while another, investigated the KAP of mothers regarding home management of acute diarrhea in Sunsari, Nepal.
Still, there is limited research on local communities’ understanding of climate change and its effects on diarrheal diseases in Nepal. To bridge this gap, our study assesses and compares the knowledge and preventive measures of people in Koshi and Karnali provinces.
Materials and methods
Study design
To gain a comprehensive understanding of the knowledge and preventive practices related to climate change and its impact on diarrheal in the Koshi and Karnali regions of Nepal. We conducted a descriptive cross-sectional household survey from February 12 to June 28, 2023. The study covered four administrative districts: Morang and Dhankhuta in Koshi province, Surkhet, and Jajarkot in Karnali province. The study area spanned a transect along a major highway, with an altitudinal range from 60 m to 8,848 m above mean sea level (asl).
Study site selection and description
The study sites included Karnali Province (Province 6) in western Nepal and Koshi Province, (Province 1) in eastern Nepal. The selected districts were Surkhet and Jajarkot in Karnali Province and Morang and Dhankuta in Koshi Province (Fig. 1). These regions represent extensive vertical cross-sections of Nepal’s two primary development zones. Koshi province spans 25,905 km2, with an elevation range from 70 m to 8,848 m asl and a subtropical climate. In contrast, Karnali is the largest province in Nepal covers 27,984 km2 and has an elevation range of 200 m to 7,348 m asl encompassing tundra, arctic, and subtropical climates. The study districts within the provinces exhibit marked variation in altitude. Morang district ranges from approximately 60 m above sea level in the southern Terai to about 2,410 m in the northern hilly areas. Dhankuta district lies mainly within the mid-hill region; with elevations, extending from roughly 300 m to around 2,500 m. Surkhet district spans a broad altitudinal gradient, from below 300 m in the inner Terai to nearly 3,000 m in the surrounding hills. Jajarkot district shows the widest elevation range, rising from about 300 m in the lower foothills to elevations exceeding 5,000 m in the high mountain zone. Study sites were selected based on the geographical differences. In addition, Koshi Province, in eastern Nepal, has a much larger and denser population than Karnali, with nearly 5 million people and 1.19 million households (2021 census). It spans fertile Tarai plains, hills, and lower mountains, with 36% urban, 41% peri‑urban, and 23% rural population. Roads, market towns, and basic piped water systems are common, though gaps remain in rural areas.In contrast, Karnali Province, in western Nepal, is the largest by area but least populated, with about 1.68 million people and a population density of 56/km². Most residents live in remote mountainous areas with limited road access, formal water treatment, and other services, relying mainly on subsistence agriculture and natural water sources.
Fig. 1.
Location map of study area (green and purple color) in Koshi Province and (yellow and blue color) in Karnali Province
Data collection tools and techniques
To assess variations in knowledge and preventive practices, a two-stage sampling strategy was employed. In the first stage, four districts (two from Koshi Province and two from Karnali Province) were selected based on geographic diversity and vulnerability to climate-related health risks. Within each selected district, a complete list of wards obtained from local government records served as the sampling frame. From this frame, eight wards were randomly selected using simple random sampling. The sampling focused exclusively on rural wards in both provinces; no urban wards were included, and comparisons are therefore limited to rural settings only.
In the second stage, households within each selected ward were sampled using systematic random sampling. Updated ward-level household lists maintained by local authorities were used as the sampling frame. From each ward, 20% of households were selected. In Koshi province from Morang District, Kanepokhari Rural Municipality Ward No. 3 has 1,275 households (255 sampled) and Kerabari Rural Municipality Ward No. 7 has 808 households (161 sampled). In Dhankuta District, Chaubise Rural Municipality Ward No. 4 and Sagurigadi Rural Municipality Ward No. 2 have 405 (81 sampled) and 403 households (80 sampled), respectively. In Karnali Province, from Surkhet District, Chingad Rural Municipality Ward No. 5 has 386 households (76 sampled), while Simta Rural Municipality Ward No. 4 has 404 households (80 sampled). In Jajarkot District, Shiwalaya Rural Municipality Ward No. 1 comprises 344 households (68 sampled), and Junichande Rural Municipality Ward No. 2 has 406 households (81 sampled). A random starting point was chosen from the first sampling interval, and subsequent households were selected at fixed intervals calculated by dividing the total number of households in the ward by the required sample size. Using this approach, 882 households were included, comprising 305 households from Karnali Province and 577 from Koshi Province.
In cases of non-response (e.g., households that were locked or where eligible respondents were unavailable after repeated visits), the next household in the sampling sequence was approached to maintain the predetermined sampling interval. Household heads or, in their absence, another adult household member (≥ 18 years) who was knowledgeable about household water, sanitation, hygiene, and childcare practices most often the primary caregiver was selected. When multiple eligible respondents were present, one respondent was chosen based on availability at the time of the visit to avoid duplicate responses from the same household. Individuals who were ill at the time of data collection or who had migrated out of the household within the past year were excluded from participation.
The survey questionnaire was developed by the principal investigator in consultation with the academic supervisor, guided by the study objectives and relevant literature. The questionnaire covered four domains: (1) socio-demographic characteristics, (2) knowledge of climate change, (3) knowledge of climate change–related impacts on diarrheal disease, and (4) household-level diarrheal disease prevention practices. The questionnaire was translated into Nepali and back translated into English to ensure semantic consistency. It was pre-tested in a community in Kavre district, which was not included in the final analysis, and minor revisions were made for clarity and contextual relevance.
Recent graduates from local universities were recruited and trained as enumerators. Training covered interview techniques, ethical considerations, and standardized administration of the questionnaire. Data collection was conducted by trained interviewers under the supervision of the lead investigators. To minimize interviewer bias, enumerators were not informed of the study hypotheses or expected responses.
Although some households were located near main roads or highways while others were in more remote settlements, all households within a ward had an equal probability of selection through the systematic sampling process. Investigators also conducted informal spot observations of sanitation and hygiene conditions inside and around households to verify reported practices; however, these observational data were used only to support field-level verification and were not analyzed separately or reported in the results.
Questionnaire validation
Internal consistency reliability of the questionnaire was assessed using Cronbach’s alpha, which yielded a coefficient of [0.81], indicating acceptable reliability of the instrument.
Survey data analysis
All completed questionnaires were reviewed daily for completeness and consistency. Data was organized, coded, and entered in Microsoft Excel before transferred to SPSS version 21.0 for statistical analysis. Pearson’s Chi-square test was used to compare the differences between two provinces.
To examine associations between dependent and independent variables, univariable and multivariable logistic regression analyses were performed. The analysis included participants from both provinces combined, with a total sample size of 882 households. In the univariable logistic regression analyses, each independent variable was examined separately, and crude odds ratios (ORs) were estimated. Multivariable logistic regression models were fitted to estimate adjusted odds ratios (aORs). Independent variables were selected based on theoretical relevance and prior evidence, including province, sex, age group, education level, and occupational status, and were retained in the models regardless of their statistical significance in univariable analyses. Univariable analyses were conducted to describe crude associations. All variables included in the multivariable models were mutually adjusted. Prior to multivariable logistic regression, relationships among independent variables were assessed to reduce the potential for multicollinearity. As all predictors were categorical, the assumption of linearity between continuous variables and the logit was not applicable. Diagnostic checks did not identify influential outliers.
The dependent variables were knowledge of climate change, perceived impact of climate change on diarrheal diseases, and diarrhea prevention practices. Potential confounding was assessed by comparing crude and adjusted odds ratios. All p-values were two-tailed, and statistical significance was set at p < 0.05.
Construction of dependent variables
Three main dependent variables were used in the logistic regression analyses: knowledge on climate change, knowledge of the impact of climate change on diarrheal diseases, and diarrheal-related preventive practices.
Knowledge on climate change was assessed using the question “Do you know about climate change?” Responses were dichotomized for analysis, where “Yes” was coded as 0 (has knowledge) and “No” was coded as 1 (does not have knowledge).
Knowledge of the impact of climate change on diarrheal diseases was assessed using the question “Do you believe that climate change increases diarrheal diseases?” Responses were dichotomized as 0 = Yes and 1 = No.
Preventive practice related to diarrheal diseases was assessed using the item “Do you immediately visit a health institution if you are suffering from diarrheal disease?” For regression analysis, responses were dichotomized as 0 = Yes (immediately visits a health institution) and 1 = No (does not immediately visit a health institution). This variable was used as a proxy indicator of healthcare-seeking behavior related to diarrhea prevention. Dichotomization of variables was performed to facilitate logistic regression analysis and to ensure consistency with the study objectives and survey design.
Results
Socio-demographic characteristics of the study population in Koshi and Karnali provinces of Nepal
Of the total 882 participants (Table 1), 34.6% were from Karnali Province, while 65.4% were from Koshi Province. Overall, 58.8% participants were women. The majority of participants (90.4%) were married, while 8.3% were single. Regarding education, 41.5% had completed primary education, whereas 18.8% were illiterate. Agriculture was the primary occupation (64.2%), followed by business (10.3%) and service (6.3%). Most participants (93.4%) identified as Hindu, while 4.6% were Buddhist. Significant differences between Koshi and Karnali provinces were observed in education level (P < 0.001), occupation (P = 0.001), and religion (P < 0.001).
Table 1.
Socio-demographic characteristics of the study population in Koshi (N = 577) and Karnali (N = 305) provinces of Nepal
| Socio-demographic characteristics | Koshi Province (N = 577) n (%) |
Karnali Province (N = 305) n (%) |
Total (N = 882) n (%) |
P-value |
|---|---|---|---|---|
| Age group | ||||
| 18–29 | 129 (22.4) | 81(26.6) | 210 (23.8) | 0.489 |
| 30–44 | 200 (34.7) | 104 (34.1) | 304 (34.5) | |
| 45–59 | 148 (25.6) | 68 (22.3) | 216 (24.5) | |
| > 60 | 100 (17.3) | 52 (17.0) | 152( 17.2) | |
| Sex | ||||
| Female | 346 (60.0) | 173 (56.7) | 519 (58.8) | 0.352 |
| Male | 231 (40.0) | 132 (43.3) | 363 (41.2) | |
| Marital status | ||||
| Married | 519 (89.9) | 278 (91.1) | 797(90.4) | 0.842 |
| Single | 50 (8.7) | 23 (7.5) | 73(8.3) | |
| Divorced | 0 | 0 | 0 | |
| Widowed | 8 (1.4) | 4 (1.3) | 12 (1.3) | |
| Education level | ||||
| Illiterate | 80 (13.9) | 86 (28.2) | 166(18.8) | < 0.001 |
| Primary | 247 (42.8) | 119 (39.0) | 366(41.5) | |
| Secondary | 203 (35.2) | 73 (23.9) | 276 (31.3) | |
| Higher education | 47 (8.1) | 27 (8.9) | 74 (8.4) | |
| Occupation | ||||
| Service | 39 (6.8) | 17 (5.6) | 56 (6.3) | 0.001 |
| Business | 63 (10.9) | 28 (9.2) | 91 (10.3) | |
| Agriculture | 344 (59.6) | 222 (72.8) | 566 (64.2) | |
| Labor | 19 (3.3) | 8 (2.6) | 27 (3.1) | |
| Others | 112 (19.4) | 30 (9.8) | 142 (16.1) | |
| Religion | ||||
| Hindu | 528 (91.5) | 296 (97.0) | 824 (93.4) | < 0.001 |
| Buddhist | 40 (6.9) | 1 (0.3) | 41 (4.6) | |
| Christianity | 5 (0.9) | 8 (2.6) | 13 (1.5) | |
| Others | 4 (0.7) | 0 (0) | 4 (0.5) | |
P values were obtained using chi-square tests to compare the distribution of socio-demographic characteristics between participants from Koshi and Karnali provinces. A two-tailed p value < 0.05 was considered statistically significant
Knowledge on climate change among the participants of Koshi and Karnali Provinces
Overall, 70.0% of participants were aware of climate change among those familiar with climate change, 77.7% identified rising temperatures with significant difference (P = 0.002). A higher proportion of participants from Karnali (77%) reported a decrease in precipitation compared to those from Koshi (56.1%), with the difference being statistically significant (P < 0.001). While 38.3% mentioned natural factors as causes of climate change, 20.3% attributed it to human activities. Additionally, 65.8% of participants linked climate change to recent floods, excessive heat, and unusual weather patterns, with a statistically significant difference between the two provinces (P = 0.028). Similarly, 56.6% of participants believed climate change could affect their lives or lifestyle (Table 2).
Table 2.
Knowledge on climate change among the participants of Koshi (N = 577) and Karnali (N = 305) Provinces
| Variables | Koshi Province (N = 577) n (%) |
Karnali Province (N = 305) n (%) |
Total (N = 882) n (%) |
P-value |
|---|---|---|---|---|
| Do you know about Climate change | ||||
| Yes | 416 (72.1) | 201 (65.9) | 617 (70.0) | 0.056 |
| No | 161 (26.9) | 104 (34.1) | 265 (30.0) | |
| If yes, what changes have you observed in temperature (n = 617) | ||||
| Rising/increasing | 329 (79.1) | 151 (75.1) | 480 (77.7) | 0.002 |
| Decreasing | 29 (7.0) | 31 (15.4) | 60 (9.7) | |
| Constant | 58 (13.9) | 19 (9.5) | 77 (12.5) | |
| If yes, what changes have you observed in precipitation (n = 617) | ||||
| Rising/increasing | 117 (28.7) | 36 (17.2) | 153 (24.8) | < 0.001 |
| Decreasing | 229 (56.1) | 161 (77.0) | 390 (63.2) | |
| Constant | 62 (15.2) | 12 (5.7) | 74 (12.0) | |
| Do you think climate change is happening because of | ||||
| Humans | 145 (25.1) | 34 (11.1) | 179 (20.3) | < 0.001 |
| Natural cause | 223 (38.6) | 115 (37.7) | 338 (38.3) | |
| Both | 69 (12.0) | 66 (21.6) | 135 (15.3) | |
| Don’t know | 140 (24.3) | 90 (29.5) | 230 (26.1) | |
| Do you believe recent flood, extreme heat and unusual weather patterns are due to climate change | ||||
| Yes | 388 (67.2) | 192 (63.0) | 580 (65.8) | 0.028 |
| No | 65 (11.3) | 54 (17.7) | 119 (13.5) | |
| Don’t know | 124 (21.5) | 59 (19.3) | 183 (20.7) | |
| Do you believe climate change could affect your way of life or lifestyle | ||||
| Yes | 349 (60.5) | 150 (49.2) | 499 (56.6) | 0.004 |
| No | 103 (17.9) | 79 (25.9) | 182 (20.6) | |
| Don’t Know | 125 (21.7) | 75 (24.6) | 200 (22.7) | |
| No response | 0 (0) | 1 (0.3) | 1 (0.1) | |
All P-values are based on chi-square test
Knowledge on the effect of climate change on diarrheal diseases among the participants of Koshi and Karnali provinces
Table 3, presents the participants understanding of the impact of climate change on diarrheal illness. The majority (66.1%) strongly believe that climate change plays an important role in the occurrence of diarrheal diseases, with a significant difference between Koshi and Karnali provinces (P = 0.029). Additionally, 60.9% of participants attributed the risk of diarrheal cases to increasing temperatures, with a statistically significant difference (P = 0.046). In contrast, only 24% mention diarrheal cases rises with increase in precipitation. A majority of participants (60.3%) perceived that diarrheal cases are most common during the summer months (April–June). This perception was significantly more prevalent among participants from Koshi compared with those from Karnali (P < 0.001).
Table 3.
Knowledge on the effect of climate change on diarrheal diseases among the participants of Koshi (N = 577) and Karnali (N = 305) provinces of Nepal
| Variables | Koshi Province (N = 577) n (%) |
Karnali Province (N = 305) n (%) |
Total (N = 882) n (%) |
P-value |
|---|---|---|---|---|
| Do you believe climatic change increases diarrheal disease | ||||
| Yes | 396 (68.6) | 187 (61.3) | 583 (66.1) | 0.029 |
| No | 181 (31.4) | 118 (38.7) | 299 (33.9) | |
| If, yes what climatic factors increase diarrheal disease | ||||
| Rise in temperature | 253 (63.9) | 102 (54.5) | 355 (60.9) | 0.046 |
| Increase in precipitation | 92 (23.2) | 48 (25.7) | 140 (24.0) | |
| Dry season | 51 (12.9) | 37 (19.8) | 88 (15.1) | |
| which season of the year influence the variability of diarrheal disease | ||||
| Increased cases throughout the year | 88 (15.3) | 52 (17.0) | 140 (15.9) | < 0.001 |
| Increased cases in the summer season (April-June) | 379 (65.7) | 153 (50.2) | 532 (60.3) | |
| Increased cases in the rainy seasons (June-Sept) | 68 (11.8) | 88 (28.9) | 156 (17.7) | |
| Increased cases in the winter season | 42 (7.3) | 12 (3.9) | 54 (6.1) | |
All P-values are based on chi-square test
Preventive practice against diarrheal disease among the participants of Koshi and Karnali Provinces
Table 4, shows that 94.3% of participants reported access to clean drinking water, with slightly higher access in Koshi Province (95.3%). In addition, 84.1% of participants reported having improved sanitation facilities. However, only 29.8% received health education on diarrheal spread, and just 11.3% received a childhood rotavirus vaccination. The majority (70.1%) did not visit a health facility immediately when suffering from diarrheal diseases. Both provinces practiced common preventive measures, including keeping the environment clean (94.2%) and covering drinking water containers (84.2%). The primary water sources were tube wells (44.2%), springs (30.5%) and rivers (25.3%). In Karnali province, spring (53.4%) and river (45.6%) were the main sources of drinking, whereas tube wells predominated in Koshi Province (67.1%). Most water sources (86.5%) were located on participants’ premises. Water purification methods included cloth filtration (37.2%), boiling (13.4%), and no method (36.4%). While 95.7% of participants washed their hands after defecation, 8.5% in Karnali used ash or mud to wash hands. The participants from Koshi province reported significantly higher engagement in several preventive practices, including covering water containers and visiting health facilities (P < 0.001). They also reported higher positive responses regarding water filtration methods (P < 0.001), home cleaning practices (P = 0.004), and having water sources on their premises (P = 0.014).
Table 4.
Preventive practice against diarrheal disease among the participants of Koshi (N = 577) and Karnali (N = 305) province of Nepal
| Variables | Koshi Province (N = 577) n (%) |
Karnali Province (N = 305) n (%) |
Total (N = 882) n (%) |
P-value |
|---|---|---|---|---|
| Access to safe drinking water | ||||
| Yes | 550 (95.3) | 282 (92.5) | 832 (94.3) | 0.080 |
| No | 27 (4.7) | 23 (7.5) | 50 (5.7) | |
| Use of improved sanitation facilities | ||||
| Yes | 502 (87.0) | 240 (78.7) | 742 (84.1) | 0.001 |
| No | 75 (13.0) | 65 (21.3) | 140 (15.9) | |
| Practice good personal and food hygiene | ||||
| Yes | 476 (82.5) | 231 (75.7) | 707 (80.2) | 0.017 |
| No | 101 (17.5) | 74 (24.3) | 175 (19.8) | |
| Obtain health education on diarrhea spread | ||||
| Yes | 175 (30.3) | 88(28.9) | 263(29.8) | 0.648 |
| No | 402 (69.7) | 217 (71.1) | 619 (70.2) | |
| Vaccinated rotavirus | ||||
| Yes | 73 (12.7) | 27 (8.9) | 100 (11.3) | 0.091 |
| No | 504 (87.3) | 278 (91.1) | 782 (88.7) | |
| Do you immediately visit health institution if you are suffering from diarrhea disease | ||||
| Yes | 209 (36.2) | 55 (18.0) | 264 (29.9) | < 0.001 |
| No | 368 (63.8) | 250 (82.0) | 618 (70.1) | |
| Do you make your household surrounding clean | ||||
| Yes | 552 (95.7) | 277 (90.8) | 829 (94.0) | 0.004 |
| No | 25 (4.3) | 28 (9.2) | 53 (6.0) | |
| Types of water sources used for drinking purposes | ||||
| Tube well | 387 (67.1) | 3 (1.0) | 390 (44.2) | < 0.001 |
| Spring | 106 (18.4) | 163 (53.4) | 269 (30.5) | |
| River | 84 (14.6) | 139 (45.6) | 223 (25.3) | |
| Distance of drinking water sources | ||||
| Within the premises | 511 (88.6) | 252 (82.6) | 763 (86.5) | 0.014 |
| Outside the premises | 66 (11.4) | 53 (17.4) | 119 (13.5) | |
| What method do you use for water purification | ||||
| Boiling | 75 (13.0) | 43 (14.1) | 118 (13.4) | < 0.001 |
| Cloth filtration | 256 (44.4) | 72 (23.6) | 328 (37.2) | |
| Steel filter | 14 (2.4) | 8 (2.6) | 22 (2.5) | |
| Sand filter | 87 (15.1) | 0 (0) | 87 (9.9) | |
| None | 139 (24.1) | 182 (59.7) | 321 (36.4) | |
| Other (chlorine tablet, Ro system) | 6 (1.0) | 0(0) | 6 (0.7) | |
| Do you cover the drinking water container | ||||
| Yes | 526 (91.2) | 217 (71.1) | 743 (84.2) | < 0.001 |
| No | 51 (8.8) | 88 (28.9) | 139 (15.8) | |
| What do you use to wash your hand after defecation | ||||
| Soap | 568 (98.4) | 276 (90.5) | 844 (95.7) | < 0.001 |
| Plain water | 9 (1.6) | 3 (1.0) | 12 (1.4) | |
| Ash, mud | 0 (0) | 26 (8.5) | 26 (2.9) | |
All P-values are based on chi-square test
Bivariate and multiple logistic regression analysis showing association between socio-demographic variables and knowledge of climate change and its impact on diarrhea
In the logistic regression analysis (Table 5), indicate that females had higher odds of lacking knowledge on climate change (P = 0.01). Similarly, education level was significantly associated with knowledge on climate change. Compared with illiterate participants, those with primary education had lower odds of lacking climate change knowledge (aOR = 0.66; 95% CI: 0.44–0.98; P = 0.04). The association was stronger among participants with secondary education (aOR = 0.20; 95% CI: 0.12–0.35; P < 0.001) and higher education (aOR = 0.16; 95% CI: 0.06–0.42; P < 0.001), indicating progressively better understanding of climate change with increasing educational attainment. Participants engaged in agriculture, labor, and other occupations had significantly higher odds of lacking knowledge of climate change compared with the reference group (P = 0.01, P < 0.001, and P < 0.001, respectively), indicating lower knowledge levels among these occupational groups. In addition, awareness of the perceived impact of climate changes on diarrheal diseases varied by age group. Participants aged ≥ 60 years had higher odds of not perceiving an impact of climate change on diarrheal diseases compared with younger participants (aOR = 1.7; 95% CI: 1.01–2.87, P = 0.04). In the bivariate logistic regression analysis, education level was significantly associated with knowledge of climate change. Participants with primary, secondary, and higher secondary education had significantly lower odds of lacking knowledge compared with illiterate participants (P < 0.001–0.002), indicating better knowledge among those with formal education.
Table 5.
Bivariate and multiple logistic regression analyses showing the association between socio-demographic variables and participants knowledge of climate change and its impact on diarrhea
| Dependent variable | Independent variable | Categories | OR (95% CI) | P-value | aOR (95% CI) | P-value |
|---|---|---|---|---|---|---|
| Knowledge on climate change | Province | Karnali | 1 | 1 | ||
| Koshi | 0.74(0.55-1.00) | 0.05 | 0.85 (0.61–1.20) | 0.37 | ||
| Sex | Male | 1 | 1 | |||
| Female | 1.54(1.14–2.08) | 0.005 | 1.51(1.08–2.12) | 0.01 | ||
| Age group | 18–29 | 1 | 1 | |||
| 30–44 | 1.10 (0.73–1.673) | 0.63 | 0.85 (0.53–1.35) | 0.50 | ||
| 45–59 | 1.45 (0.94–2.24) | 0.09 | 0.72(0.42–1.23) | 0.23 | ||
| > 60 | 3.46 (2.20–5.44) | < 0.001 | 1.57(0.90–2.73) | 0.10 | ||
| Education Level | Illiterate | 1 | 1 | |||
| Primary | 0.57(0.39–0.83) | 0.004 | 0.66(0.44–0.98) | 0.04 | ||
| Secondary | 0.15(0.09–0.24) | 0.001 | 0.20 (0.12–0.35) | < 0.001 | ||
| Higher Secondary | 0.10 (0.04–0.23) | 0.001 | 0.16 (0.06–0.42) | < 0.001 | ||
| Occupation | Service | 1 | 1 | |||
| Business | 4.10(0.88–19.06) | 0.07 | 2.93(0.60-14.14) | 0.18 | ||
| Agriculture | 14.0(3.39–58.36) | < 0.001 | 6.15(1.42–26.50) | 0.01 | ||
| Labor | 18.5(3.72–92.55) | < 0.001 | 9.48(1.81–49.71) | < 0.001 | ||
| Others | 12.9(3.02–55.39) | 0.001 | 7.19(1.62–31.79) | < 0.001 | ||
| Do you believe climatic change increases diarrhea disease? | Province | Karnali | 1 | 1 | ||
| Koshi | 0.72(0.54–0.96) | 0.02 | 0.74 (0.55–1.01) | 0.06 | ||
| Sex | Male | 1 | 1 | |||
| Female | 1.04(0.78–1.38) | 0.76 | 1.04(0.76–1.40) | 0.79 | ||
| Age group | 18–29 | 1 | 1 | |||
| 30–44 | 1.22 (0.83–1.79) | 0.29 | 1.23(0.82–1.84) | 0.31 | ||
| 45–59 | 1.27 (0.84–1.92) | 0.24 | 1.19(0.73–1.93) | 0.47 | ||
| > 60 | 1.91 (1.23–2.97) | 0.004 | 1.70(1.01–2.87) | 0.04 | ||
| Education Level | Illiterate | 1 | 1 | |||
| Primary | 0.48(0.33–0.70) | < 0.001 | 0.52(0.35–0.78) | 0.02 | ||
| Secondary | 0.47(0.32–0.71) | < 0.001 | 0.56(0.35–0.91) | 0.19 | ||
| Higher Secondary | 0.39(0.21–0.72) | 0.002 | 0.45(0.22–0.90) | 0.02 | ||
| Occupation | Service | 1 | 1 | |||
| Business | 1.35(0.65–2.79) | 0.40 | 1.22(0.58–2.59) | 0.59 | ||
| Agriculture | 1.27(0.69–2.33) | 043 | 0.91(0.47–1.76) | 0.78 | ||
| Labor | 1.25(0.46–3.35) | 0.65 | 1.01(0.36–2.85) | 0.97 | ||
| Others | 1.40(0.71–2.74) | 0.3 | 1.24(0.61–2.51) | 0.54 |
OR Odds ratio, aOR Adjusted odds ratio, CI Confidence intervals, 1-Reference category
Bivariate and multiple logistic regression analysis showing association between so-cio-demographic variables and preventive practice of diarrhea
In the multivariate analysis, Participants from Karnali Province had 2.59 times higher odds of not immediately visiting a health institution compared to Koshi Province. Females were less likely to immediately visit a health institution compared to males (aOR = 1.4, P = 0.01). No statistically significant association was found between education level and preventive practices. Although participants with higher secondary education had higher odds of reporting preventive practices (aOR = 1.34; 95% CI: 0.70–2.58), and those with primary education had similar odds (aOR = 0.95; 95% CI: 0.62–1.46) compared with illiterate participants, these associations were not statistically significant (Table 6).
Table 6.
Bivariate and Multiple logistic regression analysis showing association between socio-demographic variables and preventive practice of diarrhea
| Dependent variable | Independent variable | Categories | OR (95% CI) | P-value | aOR (95% CI) | P-value | |
|---|---|---|---|---|---|---|---|
| Immediate health facility visit for diarrhea disease? | Province | Karnali | 2.58(1.84–3.61) | < 0.001 | 2.59(1.83–3.66) | < 0.001 | |
| Koshi | 1 | 1 | |||||
| Sex | Male | 1 | 1 | ||||
| Female | 1.37(1.02–1.83) | 0.03 | 1.44 (1.07–1.59) | 0.01 | |||
| Education | Illiterate | 1 | 1 | ||||
| Primary | 0.78(0.51–1.18) | 0.24 | 0.95(0.62–1.46) | 0.83 | |||
| Secondary | 0.72(0.47–1.10) | 0.13 | 0.92(0.58–1.43) | 0.71 | |||
| Higher Secondary | 1.08(0.57–2.05) | 0.79 | 1.34(0.70–2.58) | 0.37 | |||
OR Odds ratio, aOR Adjusted odds ratio, CI Confidence intervals, 1-Reference category
Discussion
In this study, nearly three-fourths of the participants in the study districts reported being aware of climate change. This level of awareness is higher than that reported in previous studies conducted in least developed countries (LDCs), including Nepal (54%), Nigeria (51.3%), and Bangladesh (54%) [14–16]. The higher level of climate change awareness observed in this study may be partly explained by the increasing reach of mass media, particularly community radio and television, which are important sources of information in study districts. In the multivariate analysis, female participants had higher odds of lacking awareness of climate change compared with male participants, suggesting lower awareness of climate change among females. However, this finding is in consistent with evidence from a study conducted in Singapore, where men were reported to have higher level of climate change awareness than women [17]. This consistency across settings suggests that gender differences in climate change awareness may be a broader phenomenon influenced by structural and sociocultural factors. One possible explanation for this finding is that women in the study districts may have lower access to formal education and climate-related information compared with men, which could limit their awareness of climate change. In addition, sociocultural norms and gender roles may restrict women’s access to information channels such as media, training programs, and community decision-making spaces, thereby contributing to lower awareness. Male out-migration may increase women’s responsibilities at the household level [18]; however, this does not necessarily translate into improved access to climate-related knowledge or information. These interpretations are contextual and based on broader evidence rather than direct measurements from the present study. Participants engaged in agriculture and labor occupations were significantly less likely to be aware of climate change compared with the reference group, as reflected by higher adjusted odds of lack of awareness (aOR = 6.1 and 9.4, respectively).Our study findings contradict those of a previous study conducted in Nepal [19]. While our study cannot establish causality, this discrepancy may reflect differences in study populations or contextual factors such as the central role of agriculture in Nepal’s economy, dependence on monsoon rainfall, and limited irrigation infrastructure. Existing literature suggests that changing weather patterns and their impact on agriculture productivity may heighten climate change awareness among farmers and laborers. These explanations are offered as plausible interpretations based on external evidence rather than direct measurements from the present study. Additionally, over three-fourths of participants in the study districts reported noticing a rise in temperature, and 63.2% perceived a decrease in rainfall. These perceptions are broadly consistent with national climate trends reported by the Department of Hydrology and Meteorology (DHM), which documented an annual increase in maximum temperature by 0.056 °C and a yearly decline in precipitation of 1.3 mm during 1971–2014 [20]. While our findings reflect perceived changes rather than direct meteorological measurements, this alignment suggests that climate-related changes are increasingly noticeable at the community level within the study districts.
In this study, nearly two-thirds of participants perceived climate change as a key factor driving increased diarrheal outcomes, with more than half specifically attributing the rise in cases to increasing temperatures. This interpretation is consistent with findings from previous study in Nepal, which reported that a 1 °C increase in temperature is associated with an 8.1% increase in the incidence of diarrheal illness [11]. Similarly, evidence from other settings suggests that higher temperatures may promote the growth and survival of pathogenic microorganisms, thereby increasing the risk of diarrheal diseases caused by bacteria and parasites. For example, a study conducted in Bangladesh reported a positive correlation between ambient air temperature and non-cholera diarrheal cases [21]. While research from Peru observed increased hospital admissions for diarrheal diseases with rising outdoor temperatures [22]. These findings from diverse contexts provide supporting evidence for the potential role of temperature in influencing diarrheal risk, although causal mechanisms were not directly assessed in the present study. In contrast, only about one-fourth of participants in our study identified rainfall as a risk factor for diarrheal diseases. For example, a study conducted in India reported a positive association between increased rainfall and a higher risk of diarrheal illness [23]. Similarly, research from Ecuador found that heavy rainfall events following dry spells were associated with increased diarrhea incidence, whereas prolonged wet spells were linked to a reduction in diarrheal diseases [24]. Evidence from external studies highlights the complex relationship between rainfall patterns and diarrheal risk and suggests that rainfall-related transmission pathways may be under-recognized in the study districts rather than absent. While existing literature identifies heavy rainfall as an important risk factor for diarrheal diseases, our findings point to a gap between this documented risk and community perceptions. This disconnect has important implications for risk communication, community awareness, and water, sanitation, and hygiene (WASH) planning. Intense rainfall events can contaminate water sources, damage drinking water infrastructure, restrict access to safe water, and compromise sanitation facilities such as toilet pits, thereby increasing the transmission of diarrhea-causing pathogens. Addressing gaps between perceived and actual vulnerability may therefore be critical for improving public health preparedness in the study districts. Our study found that many participants reported adopting moderately effective measures to reduce diarrheal risk. More than three-fourths reported using improved sanitation facilities, which may reflect the influence of national sanitation initiatives that have promoted toilet use and sanitation awareness. However, these associations are based on self-reported practices and should be interpreted cautiously. Similarly, many were unaware that missing the vaccine increases diarrheal risk; additionally, only one-fourth had received education on diarrheal transmission. This indicates that high-risk populations may have overlooked due to inadequate program coverage [25, 26]. While our study did not directly assess the impact of health worker behavior, which may be associated with lower service use, several participants from our study also report, experiences of rude or unprofessional behavior from healthcare staff. Several studies identified potential association for example study from rural Vietnam, reported disparities in healthcare access and variations in health worker behavior based on patients’ income and ethnicity [25]. Additionally, other studies have reported that public medical staffs are sometimes perceived as uncommunicative or disrespectful, which may discourage people from seeking care and reduce overall utilization of health services [27]. These insights, drawn from external literature, suggest potential avenues for further research rather than definitive conclusions from our data. Access to clean water and sanitation is a fundamental human right recognized by the United Nations; however, our study found that 5.7% of participants in the study area still lack access to safe drinking water. In Karnali Province, water is primarily sourced from springs and rivers and distributed through communal tanks and pipes without any form of treatment. The reliance on unsafe and untreated water represents a persistent challenge in the study districts and may contribute to continued vulnerability to diarrheal diseases. Similar, findings have been reported in other studies, which identify inadequate access to treated drinking water as a major risk factors associated with diarrheal disease outbreaks [28, 29].
Studies have shown that proper home water filtration reduces diarrheal incidence and improves water quality [30, 31]. However, our study indicates limited awareness of water filtration practices among participants, which may contribute to increased vulnerability to diarrheal diseases. Seasonal changes may further exacerbate these challenges, as many households reported relying on rivers and springs that are more likely to become contaminated during the rainy season. In addition, our study observed limited and inconsistent use of water filtration devices distributed by various organizations. While these were not directly assessed in the present study, this pattern may reflect gaps in community engagement, training, or sustained support, as suggested in previous studies [32]. Similarly, nearly three-fourths of participants in our study reported not seeking treatment for diarrheal illness at formal health facilities, instead relying on local pharmacies and drug vendors. While the present study did not assess the quality of care provided by these sources, this pattern of health-seeking behavior raise concerns given existing evidence from other settings. This interpretation is consistent with studies from LDCs, which report that a substantial proportion of diarrheal cases are managed using medications obtained from pharmacies and informal drug vendors [33]. Previous, research conducted between 1986 and 2005 has documented that such providers may frequently dispense inappropriate or excessive medication and inadequate oral rehydration therapy (ORT) [34–37]. These observations underscore the importance of strengthening appropriate diarrhea management and health education, particularly in settings where reliance on informal providers is common. In this study, female participants had higher odds of not reporting diarrheal prevention practices compared with male participants. This finding is in consistent with a study conducted in Pakistan, which also reported lower engagement in preventive behaviors among females [38]. This similarity may reflect shared sociocultural factors in South Asian settings, where gender roles and access to health information can influence preventive health practices. However, in Nepal, women often play primary roles in household management and caregiving, which may increase their involvement in hygiene, and health related practices. While these mechanisms were not directly assessed in the present study, similar patterns have been reported elsewhere, including evidence that children of mothers with greater household decision-making autonomy experience lower rates of diarrheal illness [39].
Limitation
Our study had several key limitations. First, while we asked participants about their perceptions of climate-related risks to diarrheal disease, these perceptions may not fully reflect their actual understanding or behaviors in response to the risk. In addition, some participants may have provided socially desirable responses, particularly for questions about sanitation, hand washing, and care-seeking, which may have led to overestimation of “good” behaviors. Second, abstract concepts such as “climate change” may have been misunderstood or interpreted differently by participants, potentially affecting the accuracy of reported knowledge and attributions. Third, the study was conducted in only four districts across two provinces of Nepal, and findings may not be generalizable beyond the selected wards and districts, and certainly not to Nepal as a whole. Fourth, the cross-sectional study design and lack of temporality, reliance on self-reported practices without external validation, the possibility of residual confounding from unmeasured factors (such as household income, remoteness, and access to health services), and potential misclassification of knowledge and practices due to the use of dichotomous response items. Finally, a large number of statistical tests were performed, which increases the risk of type I error; some associations reported as statistically significant may therefore be due to chance. Despite these limitations, the study provides valuable insights into knowledge and preventive practices related to climate-sensitive diarrheal disease in study districts.
Conclusions
The results underscore the importance of awareness campaigns in the study districts that focus on the link between climate change and diarrheal diseases. Our findings indicate gaps in understanding the risks posed by climate change and limited knowledge of preventive measures within these districts. The results suggest concrete programmatic actions supported by our data, including improving communication about the role of rainfall and water treatment in diarrheal risk, addressing barriers to timely care seeking, and tailoring messages to groups with lower knowledge or preventive practices as identified in the regression analysis. Further community-level research across additional districts of Nepal is needed to better understand local variation and to develop effective, context- specific strategies for addressing climate change-related diarrheal risks. Efforts should particularly target high-risk, impoverished areas where diarrhea remains a major public health concern.
Supplementary Information
Acknowledgements
We thank the enumerators for their contributions during data collection.
Abbreviations
- asl
above sea level
- aOR
adjusted odds ratio
- CI
Confidence intervals
- °C
degree Celsius
- DHM
Department of Hydrology and Meteorology
- DHS
Department of Health Service
- ERB
Ethical Review Board
- KAP
knowledge, attitudes, and practices
- LDCs
Least developed countries
- mm
millimeter
- NHRC
Nepal Health Research Council
- ORT
oral rehydration treatment
- OR
odds ratio
- UGC
University of Grant Commission
- UN
United Nation
- WASH
Water, Sanitation, and Hygiene
Authors’ contributions
All authors (BD, RBK, and BK) made equal and substantial contributions to the conceptualization, methodology design, and proposal development. BD led the funding acquisition, carried out the investigation, managed data entry and analysis, interpreted the results, and played a major role in drafting the manuscript. RBK and BK provided critical supervision, manuscript review, and editorial input. All authors read and approved the final version of the manuscript.
Funding
“This research was funded by University of Grant Commission (UGC) of Nepal, grant number PhD-78/79 – S&T – 08”. We also wish to acknowledge the support we received for writing this paper from the University of California, San Francisco’s International Traineeships in AIDS Prevention Studies (ITAPS), U.S. NIMH, R25MH123256.
Data availability
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
Declarations
Ethics approval and consent to participate
The study protocol was approved by the Ethical Review Board (ERB) of the Nepal Health Research Council (NHRC) (Registration No. 574/2022). The study included only adult participants aged 18 years and above. Prior to data collection, all participants were thoroughly informed about the purpose of the study, their right to withdraw at any time, the importance of their participation, the confidentiality of their responses, and the absence of any financial incentives. Written informed consent was obtained from each participant before data collection commenced. For illiterate participants, the study information and consent form were explained verbally in the local language, and informed consent was obtained directly from participants using an appropriate consent procedure, including thumbprint/signature as applicable. All research procedures were conducted in accordance with relevant ethical guidelines and regulations, including the principles of the Declaration of Helsinki.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.