Kathmandu Valley faces water scarcity from decades of the added burden of water storage and treatment, which imposed cost. We estimated the method-specific cost of in-house drinking water treatment (9 L/day used) based on equipment price and life, daily operation time, fuel used, and consumables replacement frequency, which were Nepalese Rupees (NRs) 23, 57, 392, 586 and 799 for chemicals, ceramic filter, boiling, Euro-Guard and reverse osmosis-ultraviolet (RO-UV) water purification, respectively. The monthly average water treatment cost was estimated based on these estimates and treatment methods used in households, obtained from a questionnaire survey of 1500 households, and its socioeconomic relationship in a Nepalese urban context was investigated. Of the households, 75% practiced at least one treatment method, (average, 1.4). The estimated monthly average cost per household was NRs 380. The fixed effects model showed that the cost was significantly higher in Lalitpur district, and in high school education households. Higher water insecurity perception of respondents was the main determinant of higher treatment cost, which was especially true in Lalitpur district. Water treatment added extra financial burden, especially for the poor households which should be averted or minimized by concerned authorities to provide adequate quantity, quality, and access to drinking water for all.

Objective To estimate the prevalence of internet addiction and its associated factors among secondary-level private school students in an urban municipality in Nepal. Design Cross sectional. Setting Private schools in Budhanilkantha Municipality, Kathmandu District in Nepal. Participants 376 secondary-level (grades 9–12) school students. Outcome measures Internet addiction prevalence was the primary outcome measure. We conducted a cross-sectional survey using a self-administered structured questionnaire containing information on participants’ sociodemographic profile, technological factors associated with internet use along with Young’s internet addiction test, insomnia severity index and depression anxiety stress scales to assess internet addiction and associated factors. Pearson’s χ2 tests and binary logistic regression were performed to examine the association between dependent and independent variables at a 5% level of significance. Result The prevalence of internet addiction was 51.10% (95% CI 46 to 55.60). Students who lived alone or with friends in an apartment had a higher likelihood of internet addiction compared with those living with their parents (adjusted odds ratio (AOR) 2.89, 95% CI 1.03 to 8.11). On the other hand, students residing in hostels were less likely to be affected (AOR 0.21, 95% CI 0.06 to 0.76). Additionally, spending 2 or more hours online per day was associated with a higher risk of internet addiction (AOR 2.09, 95% CI 1.20 to 3.63) compared with those who spent less than 2 hours per day online. Individuals with severe to extremely severe depression were more likely to have internet addiction (AOR 2.99, 95% CI 1.03 to 8.66) compared with individuals without depressive symptoms. Furthermore, a notable association was observed between internet addiction and insomnia. Individuals who experienced mild to moderate insomnia (AOR 5.19, 95% CI 2.93 to 9.19) and severe insomnia (AOR 21.09, 95% CI 2.09 to 212.91) were at significantly higher risk of internet addiction compared with those without clinically significant insomnia. Conclusion Prevalence of internet addiction among school-going adolescents in urban Nepal is high. Living situations, average time spent online, depression and insomnia are potential drivers of internet addiction.

Groundwater available in the Kathmandu Valley is not suitable for drinking due to chemical and microbial contamination. We installed a treatment system, which was made with locally available materials and was low-cost, and supplied drinking water to the intervention site where groundwater contains high amounts of ammonia, iron, and turbidity. This research aims to evaluate the socioeconomic impact of treated water distribution. One hundred households were randomly selected and asked to use treated water for drinking, and another 100 households in the nearby community were taken randomly as a control. We conducted questionnaire surveys with the enrolled households before and five months after starting water distribution to assess the water use patterns and quality perceptions. The socioeconomic impact of the intervention was evaluated by a pre-post comparison and by the difference-in-difference method. The intervention significantly enhanced most of the parameters of water quality perception, reduced the in-house water treatment, and improved the perceived water stress and quality of life. For the control site, these parameters generally became worse in the post-survey, which suggests that the survey might have affected people’s mindset regarding water security. The system is an option for sustainable management of drinking water in the water-scarce, hard-hit areas in the developing countries.