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Poverty continues to be one of the biggest challenges facing many economies worldwide, and its incidences and intensities are very high in developing economies. This paper utilized the Alkire-Foster (AF) method to compute the multidimensional poverty index (MPI) and analyze the incidence and intensity of multidimensional poverty among urban households in Kenya. The findings indicated that 8.7 % of urban households are multidimensionally deprived in 33.3 % of the selected dimensional indicators. Also, the results showed that over 50 % of urban households are deprived of drinking water and sanitation services. In addition, the findings revealed that higher poverty incidence, intensity, and urban multidimensional poverty exist among female-headed households, old household headships, and households residing in peri-urban regions. The Probit regression analysis indicated that large household size, number of children under five years, household head age, gender, marital status, urban food insecurity, health status, and residing in Malaria endemic zone are significant positive predictors of urban multidimensional poverty. On the other hand, an increase in the number of educated women, owners of insurance coverage, agricultural land, and wealthy and home-owning households is linked to a decline in urban multidimensional poverty. The paper professes that policymakers should cautiously consider household socioeconomic differences while designing poverty alleviation policies.

Poverty continues to be one of the biggest challenges facing many economies worldwide, and its incidences and intensities are very high in developing economies. This paper utilized the Alkire-Foster (AF) method to compute the multidimensional poverty index (MPI) and analyze the incidence and intensity of multidimensional poverty among urban households in Kenya. The findings indicated that 8.7 % of urban households are multidimensionally deprived in 33.3 % of the selected dimensional indicators. Also, the results showed that over 50 % of urban households are deprived of drinking water and sanitation services. In addition, the findings revealed that higher poverty incidence, intensity, and urban multidimensional poverty exist among female-headed households, old household headships, and households residing in peri-urban regions. The Probit regression analysis indicated that large household size, number of children under five years, household head age, gender, marital status, urban food insecurity, health status, and residing in Malaria endemic zone are significant positive predictors of urban multidimensional poverty. On the other hand, an increase in the number of educated women, owners of insurance coverage, agricultural land, and wealthy and home-owning households is linked to a decline in urban multidimensional poverty. The paper professes that policymakers should cautiously consider household socioeconomic differences while designing poverty alleviation policies., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: No additional relationships. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Author.)

Limited evidence is available concerning the household-level costs of prevailing diseases and the potential cost of climate adaptation in Nepal. This study estimates these costs and assesses the relationships between prevalent diseases and climate adaptation at the household level using survey data from 420 households. An ingredients-based approach was used to estimate the cost of health and adaptation, and a Probit regression model was used to analyze the relationship between prevalent diseases and climate adaptation costs. Household direct curative costs are the highest among health cost components. Two-thirds of total health costs are direct costs for households. On average, 15.90% of household income is used for direct cost of health care. The climate hazard cost among afflicted households is estimated to be high. In addition, diseases like malaria, typhoid and jaundice, their costs, climate awareness program, droughts, family size and loss of per capita income are more likely to raise the cost of climate adaptation. The occurrence of gastritis, prevalence of diarrhea and cold waves are less likely to affect the cost. Policymakers should implement health financing schemes and adaptation strategies to prevent the loss of human health in western Nepal.

Limited evidence is available concerning the household-level costs of prevailing diseases and the potential cost of climate adaptation in Nepal. This study estimates these costs and assesses the relationships between prevalent diseases and climate adaptation at the household level using survey data from 420 households. An ingredients-based approach was used to estimate the cost of health and adaptation, and a Probit regression model was used to analyze the relationship between prevalent diseases and climate adaptation costs. Household direct curative costs are the highest among health cost components. Two-thirds of total health costs are direct costs for households. On average, 15.90% of household income is used for direct cost of health care. The climate hazard cost among afflicted households is estimated to be high. In addition, diseases like malaria, typhoid and jaundice, their costs, climate awareness program, droughts, family size and loss of per capita income are more likely to raise the cost of climate adaptation. The occurrence of gastritis, prevalence of diarrhea and cold waves are less likely to affect the cost. Policymakers should implement health financing schemes and adaptation strategies to prevent the loss of human health in western Nepal., Competing Interests: The authors declare no conflict of interest., (© 2020 The Authors. Published by Elsevier Ltd.)