Assessment of need for elderly in Community in Hang Dong District, Chiang Mai province, Thailand using Camberwell Assessment of Need for the Elderly questionnaire (CANE)

Abstract

Background: The Population Ageing is poised to become an inevitable and predominant demographic changes occurring in every country around the world including Thailand. The country is facing challenges of the coming demographic shifts and enacting policies proactively to adapt to an ageing population. Therefore, the actual need of the elderly should be evaluated properly in order to manage the continuing growth of the ageing society. Method: This study is a cross-sectional description for quantitative data study which aimed to access the needs and identify the met need and un-met need of the elderly living Hang Dong District, Chiang Mai Province, Thailand using Camberwell Assessment of Need for the Elderly questionnaire (CANE). The CANE questionnaire was translated to Thai and the validity and reliability test were performed. The IOC (Index of Consistency) score of the questionnaire was 0.76 and the Cronbach’s Alpha coefficient was 0.82. The Chi-square analysis was used to explore the association between and sociodemographic, living status and health status and need identification. The regression analysis was also performed to evaluate association of independent factors which can explain the number (level) of need, met need and unmet need Results: Among 330 elderly participants who age 60 years old or above, 66.4 percent of them had at least one need per 24 items of need and 22.7% had at least one unmet need. The average need score of the sample was 3.09 per person out of 24 CANE topics (s.d.= 3.33) which 0.4 were unmet need (s.d.=1.05). The areas which had high percentage of need were physical health, eyesight/hearing/communication and memory respectively, while the area with the high percentage of unmet need (per total need) were the self-care, caring for other, benefit and accommodation. The age, number of disease and type of insurance had significant association with the number of total needs whereas, the monthly income and living environment had relationship with the unmet needs determination (p-value < 0.05). Conclusions: Although the most frequent identified need was in physical need, the highest proportion of unmet need was found in function category. Even age, number of disease and type of insurance had association with the number of total needs, the monthly income and living environment were the factors determining the unmet need.