Urban-rural difference in the determinants of dietary and energy intake patterns: A case study in West Java, Indonesia

Kosaka, Shogo; Suda, Kazuhiro; Gunawan, Budhi; Raksanagara, Ardini; Watanabe, Chiho; Umezaki, Masahiro

doi:10.1371/journal.pone.0197626

Cited by 24 publications

(27 citation statements)

References 23 publications

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“…Suprisingly, LDL level ≥ 130 mg/dl only accounted for prediabetes in rural group while remaining lipid profiles (total cholesterol, HDL) significantly contributed in urban citizens. Variation of risky diet pattern seemed to be the main culprit for this discrepancies [47,48]. This is not surprising since other variations of urban-rural risk factor differences have also been reported in other developing countries [13,15,16,30,47,48].…”

Section: Discussionmentioning

confidence: 75%

“…Variation of risky diet pattern seemed to be the main culprit for this discrepancies [47,48]. This is not surprising since other variations of urban-rural risk factor differences have also been reported in other developing countries [13,15,16,30,47,48]. Yet, the probability of other key factors such as rural-urban migration and physical stress associated with sleep disturbance cannot be excluded and might contribute considerably to higher proportion of prediabetes in rural people [9,15,49,50].…”

Section: Discussionmentioning

confidence: 95%

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Urban-rural distinction of potential determinants for prediabetes in Indonesian population aged ≥15 years: a cross-sectional analysis of Indonesian Basic Health Research 2018 among normoglycemic and prediabetic individuals

et al. 2020

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Background Based on Basic Health Research (RISKESDAS) conducted by Ministry of Health, Indonesia, prediabetes prevalence tends to increase from 2007 until 2018. The numbers are relatively higher in rural than those in urban area despite of small discrepancies between the two (~ 2–4%). The purpose of this study was to identify urban-rural differences in potential determinants for prediabetes in Indonesia. Methods This analysis used secondary data collected from nationwide Health Survey in 2018. Respondents were aged ≥15 years who met inclusion criteria of analysis with no history of diabetes mellitus. Prediabetes criteria followed American Diabetes Association 2019. Multiple logistic regression was also employed to assess the transition probability of potential determinants for prediabetes in urban and rural Indonesia. Results Up to 44.8% of rural respondents were prediabetics versus their urban counterparts at 34.9%, yet non-response bias was observed in the two. Young adults aged 30 years were already at risk of prediabetes. Urban-rural distinction for marital status and triglyceride level was observed while other determinants tended to overlap across residence. Several modifiable factors might contribute differently in both population with careful interpretation. Conclusions The minimum age limit for early prediabetes screening may start from 30 years old in Indonesia. Urban-rural distinction for marital status and triglyceride level was observed, yet non-response bias between the two groups could not be excluded. A proper model for early prediabetes screening need to be developed from a cohort study with adequate sample size.

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Section: Discussionmentioning

confidence: 75%

Section: Discussionmentioning

confidence: 95%

Urban-rural distinction of potential determinants for prediabetes in Indonesian population aged ≥15 years: a cross-sectional analysis of Indonesian Basic Health Research 2018 among normoglycemic and prediabetic individuals

et al. 2020

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“…Apart from the social and environmental determinants of health, variations in individual behavioural factors such as self-efficacy, hygiene, dietary, and healthcare seeking behaviour are also worthy of mentioning. Findings on urban-rural differences generated by epidemiological studies are usually designed to capture objective measures of health such as burden of morbidity and mortality, distribution of risk factors, use of healthcare facilities, as well as the subjective measures such as self-reported health, quality of life, and well-being [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Determinants of Subjective Health, Happiness, and Life Satisfaction among Young Adults (18-24 Years) in Guyana

Wang

Liu

et al. 2020

BioMed Research International

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Abstract. Persistent urban-rural disparity in subjective health and quality of life is a growing concern for healthcare systems across the world. In general, urban population performs better on most health indicators compared with their rural counterparts. However, research evidence on the urban-rural disparity on perceived health, happiness, and quality of life among the young adult population is scarce in South American countries like Guyana. Therefore, in the present study we aimed to investigate whether subjective health, happiness, and quality of life differ according to place of residence among the young adult population in Guyana. Methods. Cross-sectional data on 2,434 men and women aging between 15 and 24 years were collected from the most recent Guyana Multiple Indicator Cluster Survey conducted in 2014. Outcome variables were perceived: satisfaction about health, life, and happiness, as well as life satisfaction before and after one year from the time of the survey. The urban-rural disparity in reporting satisfaction for these indicators was assessed by multivariate regression methods and by adjusting for relevant sociodemographic factors. Results. More than four-fifth of the respondents reported satisfaction with health (82.4%) and life (81.4%) and 77.9% reported being happy. A vast majority expressed improvement in life situation compared with a year ago (81.4%), and nearly all of the participants (95.4%) expect to have better life situation a year later. Multivariate analysis revealed an inverse association between rural residence and subjective health among men [OR = 0.518, 95%CI = 0.297, 0.901], and happiness [OR = 0.662, 95%CI = 0.381, 0.845] and life satisfaction [OR = 3.722, 95%CI = 1.502, 9.227] among women. Women having secondary [OR = 2.219, 95%CI = 1.209, 3.720] and higher [OR = 1.600, 95%CI = 1.041, 3.302] education also had higher odds of satisfaction with happiness. Conclusions. Our findings demonstrate the existence of significant urban–rural disparities in perceived health and quality of life among the young adult population in Guyana, especially among women. National health promotion projects should therefore take proper policy actions to address the underlying factors contributing to the urban–rural gaps in order to establish a more equitable healthcare system. Further researches are necessary to explore the underlying causes behind such disparities.

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“…The previous study showed the prevalence of stunting in Indonesia was 28.4 % and severe stunting was 6.7 % [7]. Children with stunting majority live in rural areas than urban areas [8] [5].…”

Section: Introductionmentioning

confidence: 94%

“…The living environment has a very strong influence in fulfilling the nutrition of 6-59 months children [8]. Socialeconomic differences between urban and rural areas lie behind the differences in getting quality of health services [9].…”

Section: Introductionmentioning

confidence: 99%

The Prevalence and Associated Factors of Stunting Children in Rural Area, Yogyakarta, Indonesia

Huriah¹,

Fitriami²,

Erviana³

et al. 2019

Proceedings of the Third International Conference on Sustainable Innovation 2019 – Health Science and Nursing (IcoSIHSN 2019)

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The case of stunting in Indonesia was higher and still becomes an important issue in poor rural areas. Research objective is to examine the prevalence rate and associated factors with stunting in children aged 6-59 months in rural areas of Bantul, Yogyakarta, Indonesia. This study applied a case-control design. The cases group were stunted children and the control group was not stunted children from October to December 2018. The data was collected by interviewing selected mothers of 6-59 months children and measuring the length/height of 23 children as a case group and 91 children as a control group. Research location used two rural areas in Bantul Yogyakarta. This study used logistic regression analysis to identify the best model of factors leading to stunting in rural areas. Analysis to measure of association used 95% CIs and "Adjusted" ORs. The prevalence of stunting was 20.8% out of which 52.2% were mild, 4.3% were moderate and 43.5% were severe. The mean of children age in the case and control group were 31.78 16.8 and 29.92 15.96 months with the mean of height for age Z score were-3.23 1.03 and-0.14 1.22 respectively. Diarrhea was found significantly as the determinant factor of stunted children in rural areas (AOR=7.79, 95% CI 2.59-23.33, p = 0.001). The results showed diarrhea as determinants factor of stunting in children under five in rural areas, it is important to assist the Indonesian government to develop a program to address the problem of stunting associated with the environment

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Urban-rural difference in the determinants of dietary and energy intake patterns: A case study in West Java, Indonesia

Cited by 24 publications

References 23 publications

Urban-rural distinction of potential determinants for prediabetes in Indonesian population aged ≥15 years: a cross-sectional analysis of Indonesian Basic Health Research 2018 among normoglycemic and prediabetic individuals

Urban-rural distinction of potential determinants for prediabetes in Indonesian population aged ≥15 years: a cross-sectional analysis of Indonesian Basic Health Research 2018 among normoglycemic and prediabetic individuals

Determinants of Subjective Health, Happiness, and Life Satisfaction among Young Adults (18-24 Years) in Guyana

The Prevalence and Associated Factors of Stunting Children in Rural Area, Yogyakarta, Indonesia

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