Journal of Preventive Medicine and Public Health

Original Article

Classification of Healthy Family Indicators in Indonesia Based on a K-means Cluster Analysis: Herti Maryani, Anissa Rizkianti, Nailul Izza; J Prev Med Public Health. 2024;57(3):234-241. Published online May 5, 2024; DOI: https://doi.org/10.3961/jpmph.23.497

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Abstract Summary PDF: Objectives
Health development is a key element of national development. The goal of improving health development at the societal level will be readily achieved if it is directed from the smallest social unit, namely the family. This was the goal of the Healthy Indonesia Program with a Family Approach. The objective of the study was to analyze variables of family health indicators across all provinces in Indonesia to identify provincial disparities based on the status of healthy families.
Methods
This study examined secondary data for 2021 from the Indonesia Health Profile, provided by the Ministry of Health of the Republic of Indonesia, and from the 2021 welfare statistics by Statistics Indonesia (BPS). From these sources, we identified 10 variables for analysis using the k-means method, a non-hierarchical method of cluster analysis.
Results
The results of the cluster analysis of healthy family indicators yielded 5 clusters. In general, cluster 1 (Papua and West Papua Provinces) had the lowest average achievements for healthy family indicators, while cluster 5 (Jakarta Province) had the highest indicator scores.
Conclusions
In Indonesia, disparities in healthy family indicators persist. Nutrition, maternal health, and child health are among the indicators that require government attention.; Summary

Key Message
This paper explores disparities in family health status across provinces using cluster analysis with the K-means approach applied to 10 family health indicator variables. Secondary data analysis was used from the 2021 Indonesian Health Profile and statistics on people’s welfare. The study identified five distinct clusters, each with different characteristics. Papua and West Papua demonstrate the lowest average level in terms of healthy family indicators, whereas DKI Jakarta shows the greatest indicator level. To eliminate the disparities, the government should prioritize the following indicators: maternal health, infant health, and nutrition.

English Abstract

High Risk Groups in Health Behavior Defined by Clustering of Smoking, Alcohol, and Exercise Habits: National Heath and Nutrition Examination Survey.: Kiwon Kang, Joohon Sung, Chang yup Kim; J Prev Med Public Health. 2010;43(1):73-83.; DOI: https://doi.org/10.3961/jpmph.2010.43.1.73

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OBJECTIVES
We investigated the clustering of selected lifestyle factors (cigarette smoking, heavy alcohol consumption, lack of physical exercise) and identified the population characteristics associated with increasing lifestyle risks. METHODS: Data on lifestyle risk factors, sociodemographic characteristics, and history of chronic diseases were obtained from 7,694 individuals > or =20 years of age who participated in the 2005 Korea National Health and Nutrition Examination Survey (KNHANES). Clustering of lifestyle risks involved the observed prevalence of multiple risks and those expected from marginal exposure prevalence of the three selected risk factors. Prevalence odds ratio was adopted as a measurement of clustering. Multiple correspondence analysis, Kendall tau correlation, Man-Whitney analysis, and ordinal logistic regression analysis were conducted to identify variables increasing lifestyle risks. RESULTS: In both men and women, increased lifestyle risks were associated with clustering of: (1) cigarette smoking and excessive alcohol consumption, and (2) smoking, excessive alcohol consumption, and lack of physical exercise. Patterns of clustering for physical exercise were different from those for cigarette smoking and alcohol consumption. The increased unhealthy clustering was found among men 20-64 years of age with mild or moderate stress, and among women 35-49 years of age who were never-married, with mild stress, and increased body mass index (>30 kg/m2). CONCLUSIONS: Addressing a lack of physical exercise considering individual characteristics including gender, age, employment activity, and stress levels should be a focus of health promotion efforts.

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Original Article

Health Behavior Patterns of Korean.: Soon Young Lee, Seon Woo Kim, Ju Won Park; Korean J Prev Med. 1997;30(1):181-194.

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Abstract PDF: The purpose of this study was to identify population subgroups with similar patterns of diet quality, physical activity, alcohol consumption and cigarette smoking of Korean. The cluster analysis was conducted using the data from Korea National Health Survey(KNHS) in 1995, which consisted of 5,805 persons. We identified six health behavior typologies: 32.9% of the sample had a good diet but sedentary activity level(good diet lifestyle), 7.2% had high activity level but less diet quality(fitness lifestyle). Individuals in the passive lifestyle cluster(39.1%) had no active health promoting activities but tended to avoid risk taking health behavior such as cigarette smoking and alcohol drinking. 1.1% of the sample were in a drinking cluster, 17.2% in a smoking cluster and 2.5% had a hedonic lifestyle characterized by heavy drinking and smoking. The other characteristics of these lifestyle clusters could be presented by demographic and socioeconomic factors.; Summary

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