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Article

Disparities in Obesity Rates among Adults: Analysis of 514 Districts in Indonesia

1
Health Policy and Administration Department, Faculty of Public Health, Universitas Indonesia, Depok 16424, Indonesia
2
Centre for Health Economics & Policy Innovation, Imperial College Business School, London SW7 2AZ, UK
3
Research Center for Public Health and Nutrition, National Research and Innovation Agency, Bogor 16915, Indonesia
4
Research Center for Pre-Clinical and Clinical Medicine, National Research and Innovation Agency, Bogor 16915, Indonesia
*
Author to whom correspondence should be addressed.
Nutrients 2022, 14(16), 3332; https://doi.org/10.3390/nu14163332
Submission received: 8 July 2022 / Revised: 6 August 2022 / Accepted: 10 August 2022 / Published: 14 August 2022
(This article belongs to the Section Nutrition and Obesity)

Abstract

:
Background: Globally, it was estimated that over 650 million adults 18 years old and older were obese in 2016. It is an increasing global health challenge with a significant health and economic impact. Thus, understanding geographic and socioeconomic disparities in obesity among adults is crucial. Methods: We combined geospatial and quantitative analyses to assess the disparity in obesity across 514 districts in Indonesia. We used the Basic Health Survey (Riskesdas) 2018 for obesity data and the World Bank database for socioeconomic data. Dependent variables included obesity prevalence among all adults (18+ years), males, females, young adults (18–24 years), adults (25–59 years), and older adults (60+ years). Results: We found significant geographic and socioeconomic disparities in adult obesity in Indonesia. In terms of region, districts in Java and Bali had a significantly higher prevalence of obesity than those in Papua, Maluku, and Nusa Tenggara. Districts in Java had 29%, 32%, 60%, and 28% higher prevalence of obesity among all adults, female adults, young adults, and adults. By income, compared to the poorest ones, most affluent districts had a significantly higher prevalence of obesity; they had a 36%, 39%, 34%, 42%, 33%, and 73% higher prevalence of obesity among all adults, males, females, young adults, adults, and older adults. Similarly, by education, compared to the least educated ones, the most educated districts had a significantly higher prevalence of obesity; they had a 34%, 42%, 29%, 36%, and 80% higher prevalence of obesity among all adults, males, females, adults, and older adults. Conclusions: There are significant disparities in adult obesity among 514 districts in Indonesia. Efforts by policymakers and stakeholders are needed to reduce obesity among adults, especially within districts with high prevalence.

1. Background

Globally, the World Health Organization (WHO) estimated that over 650 million adults 18 years old and older, or 13% of the world’s adults, were obese in 2016 [1]. It is an increasing global health challenge with significant health and economic impact [2]. Obesity is among the main risk factors for non-communicable diseases, such as cardiovascular diseases and diabetes mellitus, and the leading causes of death and disability in 2019 [1,3]. It is also a major risk factor for musculoskeletal disorders, such as osteoarthritis, and cancers, such as breast, ovarian, and prostate [1]. Economically, recent estimates of eight countries showed the costs of obesity per capita ranged from US$17 in India to US$940 in Australia in 2019—comparable to 1.8% of the gross domestic product (GDP) on average. This economic impacts of obesity are projected to grow to 3.6% of GDP on average by 2060 if there are no significant changes to the status quo [2].
In Indonesia, a lower-middle-income country with a population of over 273 million, the burden of obesity is increasing. The latest Basic Health Survey (Riskesdas) showed the prevalence of obesity (using body mass index of 27 and over) among adults aged 18 years and older more than doubled from 10.5% in 2007 to 21.8% in 2018 [4,5]. Moreover, the latest Global Burden of Study 2019 showed that high body mass index (including obesity) was among the top five risk factors driving the most death and disability in Indonesia, along with other main risk factors for non-communicable diseases such as hypertension, tobacco, dietary risks, and high blood glucose [6].
The linkage between socioeconomic indicators and obesity among adults has been well-studied. For instance, Jaacks et al. [7] proposed the obesity transition framework (using a body mass index of 30 and over) and characterized many countries in Asia, Sub-Saharan Africa, and Latin America in Stages 1 and 2, with a higher prevalence of obesity in women than in men and those with higher socioeconomic status than in those with lower socioeconomic status. On the contrary, most countries in Europe and North America are in Stages 3, with a higher prevalence of obesity among those with lower socioeconomic status [7]. The linkages align with previous studies from low- and middle-income countries (LMICs) [8,9,10,11,12] and high-income countries [13,14,15]. Moreover, previous studies have provided some evidence on geographic disparity in obesity among adults. Slack et al. analyzed data from 3109 counties in the United States and showed that high-obesity regions were concentrated in disadvantaged areas and low-obesity regions were located in more affluent areas [16]. Another study in the United States analyzed data across 74 ZIP code areas within King County (Washington state) and found that obesity rates by ZIP code and median house values were inversely associated [17]. While income and education have been the focus of health disparities research, there is a growing emphasis to better understand geographic disparities to improve health policies that target the population groups that are most vulnerable and with the greatest need [8].
Effective responses to help reduce disparities in obesity are crucial in achieving reduced premature deaths from non-communicable diseases, one of the Sustainable Development Goals [18]. However, current literature on geographic and socioeconomic disparities in obesity among adults have at least two limitations. First, most analyses to examine the socioeconomic disparity used data at the individual level. They include analyses from Nepal and Indonesia in Asia, from Chad and South Africa in Africa, and from Mexico and Colombia in Latin America [8,9,10,11,12]. While such evidence is essential, analyses using locality-level data (such as counties and districts) are also crucial for policies. This is especially relevant in decentralized settings where many health sector policies are transferred to the local level. Second, previous studies on geographic disparity are mainly from high-income countries, especially the United States [16,17]. Such studies in LMICs are limited to analysis using urban/rural instead of other indicators, such as area-level income and education [8,10]. Thus, our analysis aims to assess geographic and socioeconomic disparities in obesity among adults across 514 Indonesian districts.

2. Methods

2.1. Study Design

We conducted a cross-sectional study to assess geographic and socioeconomic disparities in obesity among adults (18 years and over) across 514 Indonesian districts. For obesity data, we analyzed the latest national health survey RISKESDAS 2018 that was representative at the district level. In terms of sampling, RISKESDAS used two-stage sampling and included 30,000 census blocks and 300,000 households. The first stage selected 30,000 census blocks out of a total of 720,000 census blocks in the country, proportional by urban and rural. The second stage included selecting ten families by employing household head education implicit stratification. For adults, RISKESDAS included 624,563 individuals 18+ years old. Further details on RISKESDAS are provided elsewhere [19,20].

2.2. Dependent Variables

We used six indicators of obesity as dependent variables: obesity among all adults (18 years and over), male adults, female adults, young adults (18–24 years), adults (25–59 years), and elderly or older adults (60 years and over). Obesity was defined as a body mass index of 27.0 and above, per the Ministry of Health Regulation 41/2014. We assessed the prevalence by sex to see different patterning for males and females, as characterized in the obesity transition framework [7]. We evaluated the prevalence by age category to see the patterning among young adults, adults, and older adults, which is essential for NCD control and prevention and for designing effective health system responses [18].

2.3. Independent Variables

For geographic and socioeconomic data, we analyzed data on urban/rural, region, income, and education at the district level available from the World Bank [21]. We defined cities as urban and regencies as rural. We divided the country into five regions, including Sumatera, Java (and Bali), Kalimantan, Sulawesi, and Papua (and Nusa Tenggara and Maluku)—see Figure 1. In terms of development, the western part of the country, especially Java and Bali, is generally the most developed, while the eastern part, especially Papua, Nusa Tenggara, and Maluku, is the least developed [20,22,23]. Using poverty rates at the district level, we divided the districts into five quintiles, with the poorest districts having the highest poverty rates in the first quintile. Using the senior secondary net enrollment ratio, we divided the districts into five quintiles, with the least educated districts having the lowest net enrollment ratio in the first quintile [20,22,23].

2.4. Data Analysis

We performed geospatial analysis by analyzing the obesity prevalence by provinces and districts. We employed multivariate analysis using Ordinary Least Square to assess the associations between the independent variables (including urban/rural, region, income, and education) and dependent variables (including obesity among all adults, male adults, female adults, young adults, adults, and older adults). We compared the absolute and relative differences between the most developed and least developed regions, poorest and wealthiest districts, and least and most educated districts. We considered the 5% level or lower as statistically significant. Geospatial analysis and multivariate analysis were performed in ArcMap 10 and STATA 15, respectively.

3. Results

3.1. Provincial Level Analysis

For geographic disparities, Figure 2 provides the prevalence of obesity by quintile at the province level. In panel (a), the prevalence of obesity among all adults ranged from 9.1% to 28.2%; that among male adults ranged from 6.4% to 22.1%; that among female adults ranged from 11.8% to 34.9%; that among young adults ranged from 2.0% to 13.0%; that among adults ranged from 12.3% to 33.9%; that among older adults ranged from 7.0% to 26.2%. For all adults, the prevalence of obesity was highest (quintile 5) in Jakarta, Riau Islands, North Kalimantan, East Kalimantan, North Sulawesi, and West Papua. For males, obesity prevalence was highest in Jakarta, Riau Islands, North Kalimantan, East Kalimantan, North Sulawesi, and Bali. For females, the prevalence was highest in Jakarta, Bangka Belitung, East Kalimantan, North Sulawesi, Gorontalo, and West Papua. For young adults, the prevalence was highest in Jakarta, East Kalimantan, North Sulawesi, Yogyakarta, and Bali. For adults, the prevalence was highest in Jakarta, North Sumatera, North Kalimantan, East Kalimantan, North Sulawesi, and West Papua. For older adults, the prevalence was highest in Jakarta, Riau Islands, North Sumatera, North Kalimantan, North Sulawesi, and North Maluku. Note that obesity prevalence in Jakarta and North Sulawesi was highest for all adults, by sex, and by age groups.
For socioeconomic disparities, the prevalence of obesity by income at the province level is shown in Table 1. The top box shows the wealthiest provinces (including Bali, Jakarta, North Kalimantan, and Riau Islands) and the bottom box shows the poorest provinces (including Maluku, East Nusa Tenggara, and West Papua). The grey-shaded prevalence shows higher than the national average in each column. Among the ten wealthiest provinces, five had consistently higher than the national average for every obesity indicator (i.e., Jakarta, North Kalimantan, East Kalimantan, and Riau Islands). In contrast, among the ten poorest provinces, only two did (i.e., Gorontalo and West Papua).

3.2. District Level Analysis

The characteristics of districts and obesity among adults are shown in Table 2. Among 514 districts, 97 were cities and 417 were regencies. Out of 514 districts, 30.0% were in Sumatera, while 24.9% were in Java and Bali. Moreover, 79% of urban districts (cities) were wealthier districts in quintiles 4 and 5, and 47.2% of rural districts (regencies) were poorer districts in quintiles 1 and 2. In terms of education, 71.1% of urban districts were the most educated districts in quintiles 4 and 5, and 46.8% of rural districts were the least educated districts in quintiles 1 and 2. Regarding dependent variables (panel b), the prevalence of obesity among all adults was 19.0%; that among males and females was 12.8% and 25.6%; and that among young adults, adults, and older adults was 7.6%, 23.8%, and 14.8%, respectively. The prevalence of obesity was significantly higher in urban areas than in rural areas. Obesity prevalence among all adults in urban areas was higher by 1.35 (i.e., 24.2% divided by 17.9%) compared to that in rural areas. By sex, obesity prevalence among male and female adults in urban areas was higher by 1.54 and 1.25 times, respectively. By age group, obesity prevalence among young adults, adults, and older adults in urban areas was higher by 1.32, 1.33, and 1.82 times, respectively.
For geographic disparities, Figure 3 provides the district-level disparity in obesity by prevalence quintile. Many districts in Aceh, North Sumatera, Riau, East Java, and Papua provinces had the highest prevalence of obesity among all adults. Additionally, many districts in the provinces of Bangka Belitung, West Java, East Java, and Papua had the highest obesity among female adults. Many districts in the provinces of West Papua, Papua, Aceh, and Riau had the highest obesity among young adults.
For socioeconomic disparities, ten districts with the lowest and highest burden of obesity among adults are shown in Table 3 and Table 4, respectively. For all adults, obesity prevalence ranged from 3.3% in the Sumba Tengah regency (East Nusa Tenggara) to 40.3% in Yalimo (Papua). By sex, obesity among males ranged from 2.0% in Sumba Tengah and Sabu Raijua (East Nusa Tenggara) to 41.6% in Yalimo (Papua); obesity among females ranged from 4.0% in Sumba Barat Daya (East Nusa Tenggara) to 44.1% in Kep. Seribu (Jakarta). By age group, obesity among young adults ranged from 0% in Manggarai Timur and Belu (East Nusa Tenggara) to 38.0% in Yalimo (Papua); that among adults ranged from 4.8% in Sumba Tengah (East Nusa Tenggara) to 42.1% in Padang Sidempuan (North Sumatera); that among older adults ranged from 0% in nine regencies in Papua province to 38.1% in Kota Banda Aceh (Aceh). By urbanicity, almost all districts with the lowest obesity for all adults, by sex, and by age groups are rural, but about half of those with the highest obesity were urban. By income level, poverty rates among the ten districts with the highest obesity averaged up to 18%, while that among the ten districts with the lowest obesity averaged up to 37%.
We provide the associations between geographic and socioeconomic measures and obesity in Table 5. In terms of region, districts in Java and Bali had a significantly higher obesity prevalence among all adults, female adults, young adults, and adults than those in Papua, Nusa Tenggara, and Maluku. Additionally, districts in Java and Bali had 29%, 32%, 60%, and 28% higher obesity prevalence among all adults, female adults, young adults, and adults, respectively. By income, the wealthiest districts had a significantly higher prevalence of obesity among adults, males, females, young adults, adults, and older adults than the wealthiest districts. Wealthiest districts had 36%, 39%, 34%, 42%, 33%, and 73% higher prevalence of obesity among adults, males, females, young adults, adults, and older adults, respectively. By education, the most educated districts had a significantly higher prevalence of obesity among adults, males, females, adults, and older adults than the least educated districts. Most educated districts had 34%, 42%, 29%, 36%, and 80% higher prevalence of obesity among adults, males, females, adults, and older adults, respectively. These results were similar among rural districts.

4. Discussion

We found a high prevalence of obesity among adults 18 years and older in Indonesia. Using the national obesity cut-off of body mass index 27 and over, the prevalence among all adults, males, and females was 19.0%, 12.8%, and 25.6%, respectively. These findings characterize the country being in Stage 1 of the obesity transition, although further along within the stage, together with other countries with a higher per capita GNI in Southeast Asia, such as the Philippines and Thailand [7]. Moreover, by age, the prevalence among young adults (18–24 years), adults (25–59 years), and older adults (60 years and over) was 7.6%, 23.8%, and 14.8%, respectively.
We also found significant and large geographic and socioeconomic disparities in obesity among adults across 514 Indonesian districts. By urban/rural, obesity prevalence among all adults, males, females, young adults, adults, and older adults was significantly higher among urban districts (i.e., cities) compared to rural ones (i.e., regencies). These findings align with previous studies in other LMICs, including Nepal, Iran, Chad, and South Africa [8,10,11]. A review by Ford et al. [24] asserted that diet and physical activity are the two main drivers of obesity in LMICs. Urban areas tend to have higher availability of calorie-dense and cheap foods. Additionally, people in urban areas tend to have reduced physical activity through changes in infrastructure, transportation, and occupational activities [24]. However, our analysis also found that while all districts in the bottom ten districts with the lowest prevalence of all obesity indicators were rural, nearly half of the districts in the top ten districts with the highest prevalence were also rural. This may be because some rural districts (e.g., Kab. Karo in North Sumatera or Kab. Minahasa in North Sulawesi) have already had a similar economic development (e.g., income and education level) to nearby urban districts (e.g., Kota Tomohon and Kota Padang Sidempuan). All this indicates that, given limited resources, effective responses to reduce disparity in obesity may prioritize urban districts and rural districts with high obesity prevalence [25,26,27].
By region, the prevalence of all obesity indicators was higher in the most developed region (i.e., the Java region including Bali) than in the least developed region (e.g., the Papua region including Maluku and Nusa Tenggara). This finding aligns with previous studies. A study across 3109 counties in the United States found that high-obesity regions were concentrated in disadvantaged areas and low-obesity regions were located in more affluent areas [16]. Globally, obesity prevalence among adults was higher among LMICs with lower national income in Stage 1 of the obesity transition compared to LMICs with higher income in Stage 2 and high-income countries in Stage 3 [7].
By income, the wealthiest districts had a significantly higher prevalence of obesity by up to 73% (among older adults) than the poorest districts. By education, the most educated districts had a significantly higher prevalence of obesity by up to 80% (among older adults) than the least educated districts. This finding aligns with previous studies in Nepal, Iran, Chad, South Africa, and other LMICs [8,10,11]. Additionally, it aligns with Stages 1 and 2 of the obesity transition with a higher prevalence of obesity in those with higher socioeconomic status than in those with lower socioeconomic status [7].
For policy, obesity is already very high among young adults and adults as the primary working population, which may have an economic impact from lower productivity and increased cost of illness due to obesity-related health issues [2]. Moreover, the high burden of obesity among older adults may indicate the need to reorient the health system to better prevent and control obesity and other risk factors throughout the care continuum, from the community to primary care to secondary/tertiary care, potentially through integration with infectious disease platforms [28,29,30]. By sex, a much higher prevalence among women in Indonesia may be related to maternal obesity, indicating that effective intervention at the population and health systems levels are needed at each stage (e.g., pregnancy and post-partum) [31]. By region and socioeconomic status, given limited resources, effective responses to reduce disparity in obesity may prioritize more affluent urban districts and rural districts with higher burden of obesity and other non-communicable disease risk factors [32,33,34,35].
To our knowledge, our analysis is the first on the geographic and socioeconomic disparity in obesity among all adults, males, females, young adults, adults, and older adults using a relatively large number of subnational units (e.g., 514 districts) in LMICs. However, our study has at least two limitations. First, our dataset did not have information on ethnicity and migration, so we were not able to explore sub-group analysis by those variables [36]. Second, using cross-sectional data, our study was limited to assess the trends the obesity prevalence over time. Nonetheless, our findings have important policy implications, especially on the prevention and control of non-communicable diseases in Indonesia and other LMICs.

5. Conclusions

In Indonesia, the prevalence of obesity was highest among females (26.4%) and adults aged 25–59 years (24.8%). There were significant geographic and socioeconomic disparities in adult obesity. Compared to districts in the least developed region, those in the most developed region had 29%, 32%, 60%, and 28% higher prevalence of obesity among all adults, females, young adults aged 18–24 years, and adults. Compared to the poorest districts, the most affluent ones had a 36%, 39%, 34%, 42%, 33%, and 73% higher prevalence of obesity among all adults, males, females, young adults, adults, and older adults aged 60+ years. Compared to the least educated districts, the most educated ones had a 34%, 42%, 29%, 36%, and 80% higher prevalence of obesity among all adults, males, females, adults, and older adults. Efforts are needed to reduce obesity among adults, especially within districts with high prevalence.

Author Contributions

Conceptualization, D.K., V.A. and D.A.; data curation, D.H.T. and P.A.; formal analysis, D.K., V.A. and P.A.; writing-original draft preparation, D.K.; writing-review and editing, D.K., D.A., V.A., D.H.T. and P.A.; funding acquisition, D.A., D.K. and V.A. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Universitas Indonesia, Directorate of Research and Community Service grant number NKB-460/UN2.RST/HKP.05.00/2022. The funder had no role in study design, data collection, data analysis, interpretation, or writing of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available on request from the corresponding author. The data are not publicly available due to local policy.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Table A1. Regression outputs for urban/rural differences.
Table A1. Regression outputs for urban/rural differences.
AllMalesFemalesYoung AdultsAdultsOlder Adults
CoefCoefCoefCoefCoefCoef
RuralReference
Urban6.31 **6.32 **6.16 **2.29 **7.51 **10.49 **
Constant17.85 **11.59 **24.41 **7.15 **22.36 **12.79 **
Observations514514514514514514
R-squared0.200.230.120.060.200.32
Note: Coef = OLS Coefficient; Significance level ** p < 0.01
Table A2. Regression outputs for geographic and socioeconomic disparity in obesity.
Table A2. Regression outputs for geographic and socioeconomic disparity in obesity.
AllMalesFemalesYoung AdultsAdultsOlder Adults
CoefCoefCoefCoefCoefCoef
(a) All districts (N = 514)
PapuaReference
Java1.94 *0.123.53 **2.85 **2.14 *−0.72
Sumatera0.92−1.343.27 **0.760.980.59
Kalimantan−0.73−1.90 *0.811.24−1.12−4.72 **
Sulawesi2.01 *0.513.33 **1.65 **2.32 *1.61
Income
Quintile 1 poorReference
Quintile 2−0.54−1.120.06−0.93−0.350.82
Quintile 32.09 **1.173.03 **0.362.68 **4.03 **
Quintile 42.66 **2.07 **3.30 **0.163.44 **5.48 **
Quintile 5 rich4.62 **4.10 **5.23 **1.39 *5.31 **8.34 **
Education
Quintile 1 leastReference
Quintile 20.820.491.110.311.351.09
Quintile 32.51 **2.03 **2.94 **0.523.50 **3.29 **
Quintile 43.16 **2.55 **3.70 **0.834.09 **4.89 **
Quintile 5 most3.96 **3.88 **3.81 **0.975.33 **5.23 **
(b) Urban (N = 97)
PapuaReference
Java2.393.121.422.171.230.05
Sumatera0.941.100.630.530.101.21
Kalimantan0.642.04−0.542.14−0.36−5.47
Sulawesi1.022.75−0.931.220.800.66
Income
Quintile 1 poorReference
Quintile 25.092.138.56 *2.265.98−1.39
Quintile 34.513.455.732.205.57−1.28
Quintile 44.973.256.78 *2.056.32 *2.15
Quintile 5 rich6.03 *3.668.54 **3.556.77 *1.73
Education
Quintile 1 leastn/an/an/an/an/an/a
Quintile 2Reference
Quintile 3−0.70−1.390.07−0.97−0.721.47
Quintile 40.50−0.341.450.360.421.55
Quintile 5 most1.381.231.51−0.092.062.18
(c) Rural (N = 417)
PapuaReference
Java2.00 *−0.143.91 **3.05 **2.64 *−0.73
Sumatera1.35−1.284.12 **0.911.691.21
Kalimantan0.29−1.142.081.550.24−2.28
Sulawesi2.44 **0.514.22 **1.74 **2.91 **2.22 *
Income
Quintile 1 poorReference
Quintile 2−0.85−1.18−0.55−1.09−0.760.77
Quintile 31.66 *0.812.53 *0.161.96 *3.53 **
Quintile 41.81 *1.232.43 *−0.092.27 *3.72 **
Quintile 5 rich2.21 *1.852.75 *0.252.53 *4.29 **
Education
Quintile 1 leastReference
Quintile 20.600.190.960.231.060.90
Quintile 32.26 **1.80 **2.69 **0.533.23 **2.47 **
Quintile 42.44 **1.75 *3.08 **0.463.27 **3.50 **
Quintile 5 most2.68 **2.54 **2.58 *0.693.60 **2.81 **
Note: Coef = OLS Coefficient. In each panel (a–c), each column provides each multivariate OLS regression output (command-regress-in STATA 15). Significance level ** p < 0.01, * p < 0.05.

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Figure 1. Map of Indonesia by province. Note: Suma = Sumatera, Kepri = Riau Islands, Sula = Sulawesi, Kali = Kalimantan, NTB = West Nusa Tenggara, NTT = East Nusa Tenggara. We divided the provinces into five regions, including Sumatera, Java/Bali, Kalimantan, Sulawesi, and Papua/Maluku/Nusa Tenggara. Java/Bali is the most developed and Papua/Maluku/Nusa Tenggara is the least developed. We obtained the shapefile from the Indonesian Information and Geospatial Agency and created the map in ArcMap 10.
Figure 1. Map of Indonesia by province. Note: Suma = Sumatera, Kepri = Riau Islands, Sula = Sulawesi, Kali = Kalimantan, NTB = West Nusa Tenggara, NTT = East Nusa Tenggara. We divided the provinces into five regions, including Sumatera, Java/Bali, Kalimantan, Sulawesi, and Papua/Maluku/Nusa Tenggara. Java/Bali is the most developed and Papua/Maluku/Nusa Tenggara is the least developed. We obtained the shapefile from the Indonesian Information and Geospatial Agency and created the map in ArcMap 10.
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Figure 2. Disparity of obesity among adults by province in Indonesia, 2018. Note: Numbers show prevalence of obesity among all adults, males, females, young adults, adults, and older adults.
Figure 2. Disparity of obesity among adults by province in Indonesia, 2018. Note: Numbers show prevalence of obesity among all adults, males, females, young adults, adults, and older adults.
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Figure 3. Disparity of obesity among adults by district in Indonesia, 2018. Note: Numbers show prevalence of obesity among all adults, males, females, young adults, adults, and older adults.
Figure 3. Disparity of obesity among adults by district in Indonesia, 2018. Note: Numbers show prevalence of obesity among all adults, males, females, young adults, adults, and older adults.
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Table 1. Prevalence of obesity among adults by province in Indonesia, 2018.
Table 1. Prevalence of obesity among adults by province in Indonesia, 2018.
Obesity Prevalence
Poverty Young
RatesAllMalesFemalesAdultsAdultsOlder Adults
(1)(2)(3)(4)(5)(6)(7)
Bali4.5%21.9%19.2%24.6%11.4%26.7%14.2%
South Kalimantan4.8%17.9%11.8%24.3%8.6%21.9%12.0%
Central Kalimantan5.0%17.2%11.2%24.0%7.3%21.3%10.3%
Jakarta5.0%28.2%22.1%34.4%12.3%32.7%26.2%
Banten5.3%20.3%13.4%27.7%7.6%25.2%15.4%
Bangka Belitung5.4%22.0%14.0%31.1%9.9%26.6%18.5%
West Sumatera6.6%18.7%11.6%25.8%6.9%23.8%15.3%
North Kalimantan7.0%23.8%17.9%30.6%9.2%29.5%20.5%
East Kalimantan7.1%26.6%20.3%33.8%13.1%31.8%19.4%
Riau Islands7.6%24.2%19.0%29.9%8.6%29.0%20.9%
Jambi7.8%16.1%10.7%21.9%5.7%19.8%13.8%
North Maluku7.9%22.1%14.3%30.3%6.6%28.2%21.1%
West Java7.9%21.1%13.1%29.5%9.0%26.4%15.7%
West Kalimantan8.1%15.6%10.2%21.3%6.6%19.6%10.7%
North Sulawesi8.5%27.9%21.3%34.9%11.4%33.9%25.9%
Riau8.8%22.1%15.0%29.8%8.0%27.7%17.0%
South Sulawesi9.8%17.4%11.2%23.4%7.8%22.1%12.8%
West Sulawesi10.3%16.8%10.7%23.1%7.6%21.3%11.6%
East Java10.9%20.9%13.9%27.9%10.0%26.1%13.6%
Central Java10.9%18.9%12.2%25.4%8.5%23.9%12.5%
North Sumatera11.3%23.2%17.1%29.4%8.4%29.3%23.3%
Lampung12.6%15.9%8.7%23.6%6.1%19.8%11.5%
Yogyakarta12.7%20.3%16.5%24.0%10.8%24.8%14.5%
Southeast Sulawesi13.0%17.3%11.8%22.8%5.2%22.4%15.3%
South Sumatera13.1%15.9%9.9%22.3%5.5%20.0%12.6%
Central Sulawesi14.6%19.0%12.5%25.9%7.5%23.4%15.4%
West Nusa Tenggara14.8%13.5%7.0%19.5%4.4%17.9%8.3%
Bengkulu15.0%18.3%10.3%26.8%6.8%22.3%15.4%
Aceh16.4%22.2%13.9%30.6%8.0%28.3%17.3%
Gorontalo16.8%22.3%14.1%30.7%9.6%28.0%17.4%
Maluku21.8%17.8%12.3%23.4%4.1%23.9%16.0%
East Nusa Tenggara22.0%9.1%6.4%11.8%2.5%12.3%7.0%
West Papua26.5%24.0%17.4%31.6%8.4%30.1%18.8%
Papua29.4%18.9%15.1%23.1%8.8%21.7%17.8%
AVERAGE 19.9%13.7%26.4%8.0%24.8%15.8%
Note: Ordered by the average poverty rates (column 1), the provinces in the top box are richest and those in the bottom box are poorest. Shaded values are higher than the national average for each group.
Table 2. Characteristics of districts and obesity among adults.
Table 2. Characteristics of districts and obesity among adults.
AllUrbanRuralDifference
n%n%n%%
(1)(2)(3)(4)(5)(6)(7) = (4–6)
(a) Characteristics
Sample size district514100%97100%417100%0%
Region
 Papua9518.5%99.3%8620.6%11.3%
 Java12824.9%3536.1%9322.3%−13.8%
 Sumatera15430.0%3334.0%12129.0%−5.0%
 Kalimantan5610.9%99.3%4711.3%2.0%
 Sulawesi8115.8%1111.3%7016.8%5.4%
514 97 417
Income/poverty
 Q1 poor10219.8%33.1%9923.7%20.6%
 Q210320.0%55.2%9823.5%18.3%
 Q310320.0%1313.4%9021.6%8.2%
 Q410320.0%2222.7%8119.4%−3.3%
 Q5 rich10320.0%5455.7%4911.8%−43.9%
514 97 417
Education
 Q1 least10320.0%00.0%10324.7%24.7%
 Q210320.0%1111.3%9222.1%10.7%
 Q310320.0%1717.5%8620.6%3.1%
 Q410320.0%2929.9%7417.7%−12.2%
 Q5 most10219.8%4041.2%6214.9%−26.4%
514 97 417
(b) Obesity prevalence
All adultsn/a19.0%n/a24.2%n/a17.9%6.3%*
Male adultsn/a12.8%n/a17.9%n/a11.6%6.3%*
Female adultsn/a25.6%n/a30.6%n/a24.4%6.2%*
Young adultsn/a7.6%n/a9.4%n/a7.1%2.3%*
Adultsn/a23.8%n/a29.9%n/a22.4%7.5%*
Older adultsn/a14.8%n/a23.3%n/a12.8%10.5%*
Note: Q = Quintile, n = number, % = proportion of column total, Urban = City, Rural = Regency. Data on district characteristics are from the World Bank and obesity data are from Basic Health Survey 2018. Bold numbers with asterisk (*) show statistically significance at 5% level (see Table A1 for the regression outputs).
Table 3. Ten districts with LOWEST prevalence of obesity among adults in Indonesia.
Table 3. Ten districts with LOWEST prevalence of obesity among adults in Indonesia.
PrevalenceProvinceRegionUrbanPovertyEducationPop (000)
(a) All adults
Kab. Sumba Tengah3.3%East Nusa TenggaraPapuaRural35%44%68
Kab. Sumba Barat Daya3.4%East Nusa TenggaraPapuaRural29%42%319
Kab. Sabu Raijua4.0%East Nusa TenggaraPapuaRural31%69%86
Kab. Timor Tengah Selatan4.3%East Nusa TenggaraPapuaRural28%52%459
Kab. Manggarai Timur5.0%East Nusa TenggaraPapuaRural27%43%272
Kab. Nias5.1%North SumatraSumateraRural16%62%136
Kab. Belu5.3%East Nusa TenggaraPapuaRural16%54%206
Kab. Sumba Barat5.5%East Nusa TenggaraPapuaRural29%55%122
Kab. Jayawijaya5.7%PapuaPapuaRural39%67%206
Kab. Yahukimo6.4%PapuaPapuaRural39%12%181
AVERAGE 29%50%206
(b) Male adults
Kab. Sumba Tengah2%East Nusa TenggaraPapuaRural35%44%68
Kab. Sabu Raijua2%East Nusa TenggaraPapuaRural31%69%86
Kab. Manggarai Timur3%East Nusa TenggaraPapuaRural27%43%272
Kab. Yahukimo3%PapuaPapuaRural39%12%181
Kab. Sumba Barat Daya3%East Nusa TenggaraPapuaRural29%42%319
Kab. Jayawijaya4%PapuaPapuaRural39%67%206
Kab Pesisir Barat4%LampungSumateraRural15%72%150
Kab. Belu3.9%East Nusa TenggaraPapuaRural16%54%206
Kab. Nias4.0%North SumatraSumateraRural16%62%136
Kab. Timor Tengah Selatan4.0%East Nusa TenggaraPapuaRural28%52%459
AVERAGE 27%52%208
(c) Female adults
Kab. Sumba Barat Daya4%East Nusa TenggaraPapuaRural29%42%319
Kab. Timor Tengah Selatan5%East Nusa TenggaraPapuaRural28%52%459
Kab. Sumba Tengah5%East Nusa TenggaraPapuaRural35%44%68
Kab. Sabu Raijua6%East Nusa TenggaraPapuaRural31%69%86
Kab. Nias6.1%North SumatraSumateraRural16%62%136
Kab. Sintang6.4%West KalimantanKalimantanRural10%45%396
Kab. Asmat6.6%PapuaPapuaRural27%21%88
Kab. Sumba Barat6.6%East Nusa TenggaraPapuaRural29%55%122
Kab. Belu6.7%East Nusa TenggaraPapuaRural16%54%206
Kab. Manggarai Timur7.1%East Nusa TenggaraPapuaRural27%43%272
AVERAGE 25%49%215
(d) Young adults
Kab. Manggarai Timur0%East Nusa TenggaraPapuaRural27%43%272
Kab. Belu0%East Nusa TenggaraPapuaRural16%54%206
Kab. Sumba Tengah1%East Nusa TenggaraPapuaRural35%44%68
Kab. Jayawijaya1%PapuaPapuaRural39%67%206
Kab. Timor Tengah Selatan1%East Nusa TenggaraPapuaRural28%52%459
Kab. Sumba Barat Daya1%East Nusa TenggaraPapuaRural29%42%319
Kab. Lanny Jaya1%PapuaPapuaRural40%46%172
Kb. Manggarai1%East Nusa TenggaraPapuaRural21%51%319
Kab. Kupang1%East Nusa TenggaraPapuaRural23%58%347
Kab. Sabu Raijua1%East Nusa TenggaraPapuaRural31%69%86
AVERAGE 29%53%246
(e) Adults
Kab. Sumba Tengah4.8%East Nusa TenggaraPapuaRural35%44%68
Kab. Sumba Barat Daya4.9%East Nusa TenggaraPapuaRural29%42%319
Kab. Sabu Raijua5.1%East Nusa TenggaraPapuaRural31%69%86
Kab. Timor Tengah Selatan5.4%East Nusa TenggaraPapuaRural28%52%459
Kab. Jayawijaya6.4%PapuaPapuaRural39%67%206
Kab. Nias6.7%North SumatraSumateraRural16%62%136
Kab. Manggarai Timur6.7%East Nusa TenggaraPapuaRural27%43%272
Kab. Yahukimo6.9%PapuaPapuaRural39%12%181
Kab. Asmat7.0%PapuaPapuaRural27%21%88
Kab. Sumba Barat7.0%East Nusa TenggaraPapuaRural29%55%122
AVERAGE 30%47%194
(f) Older adults
Kab. Diyai0.0%PapuaPapuaRural43%51%69
Kab. Mambramo Tengah0.0%PapuaPapuaRural37%54%46
Kab. Nduga0.0%PapuaPapuaRural38%9%94
Kab. Puncak Jaya0.0%PapuaPapuaRural36%21%115
Kab. Intan Jaya0.0%PapuaPapuaRural43%9%46
Kab. Lanny Jaya0.0%PapuaPapuaRural40%46%172
Kab. Dogiyai0.0%PapuaPapuaRural30%39%92
Kab. Paniayi0.0%PapuaPapuaRural37%25%164
Kab. Yalimo0.0%PapuaPapuaRural35%28%59
Kab. Waropen0.3%PapuaPapuaRural31%61%28
AVERAGE 37%34%89
Note: Urban = City, Rural = Regency; Pop = Population. The districts are ordered by prevalence (column 1). Boldface values show the average.
Table 4. Ten districts with HIGHEST prevalence of obesity among adults in Indonesia, 2018.
Table 4. Ten districts with HIGHEST prevalence of obesity among adults in Indonesia, 2018.
PrevalenceProvinceRegionUrbanPovertyEducationPop (000)
(a) All adults
Kab. Yalimo40.3%PapuaPapuaRural35%28%59
Kab. Karo34.1%North SumateraSumateraRural9%74%389
Kota Tomohon33.8%North SulawesiSulawesiUrban6%71%100
Kota Jakarta Pusat32.1%JakartaJawaUrban4%55%914
Kab. Minahasa 31.7%North SulawesiSulawesiRural7%65%329
Kota Padang Sidempuan31.6%North SumateraSumateraUrban8%77%210
Kota Jakarta Timur30.7%JakartaJawaUrban3%67%2827
Kota Pematang Siantar30.1%North SumateraSumateraUrban9%77%247
Kab. Minahasa Selatan30.0%North SulawesiSulawesiRural9%62%205
Kota Bitung29.9%North SulawesiSulawesiUrban7%57%205
AVERAGE 10%63%548
(b) Male adults
Kab. Yalimo41.6%PapuaPapuaRural35%28%59
Kab. Puncak30.9%PapuaPapuaRural38%9%103
Kota Tomohon27.9%North SulawesiSulawesiUrban6%71%100
Kab. Minahasa 26.8%North SulawesiSulawesiRural7%65%329
Kota Jakarta Pusat25.8%JakartaJawaUrban4%55%914
Kota Padang Sidempuan25.6%North SumateraSumateraUrban8%77%210
Kota Manado25.5%North SulawesiSulawesiUrban5%66%425
Kota Denpasar25.5%BaliJawaUrban2%63%879
Kota Banda Aceh25.2%AcehSumateraUrban7%82%250
Kab. Karo24.9%North SumateraSumateraRural9%74%389
AVERAGE 12%59%366
(c) Female adults
Kep Seribu44.1%JakartaJawaRural12%71%23
Kab. Karo43.2%North SumateraSumateraRural9%74%389
Kab Bener Meriah 42.7%AcehSumateraRural20%67%137
Kab Aceh Tengah 41.2%AcehSumateraRural16%73%196
Kota Tomohon39.7%North SulawesiSulawesiUrban6%71%100
Kab. Kep Talaud39.7%North SulawesiSulawesiRural10%71%89
Kab. Minahasa Selatan39.5%North SulawesiSulawesiRural9%62%205
Kota. Tidore Kepulauan39.5%North MalukuPapuaUrban6%74%97
Kab. Yalimo38.8%PapuaPapuaRural35%28%59
Kab. Manowari Selatan38.6%West PapuaPapuaRural31%47%22
AVERAGE 15%64%132
(d) Young adults
Kab. Yalimo38.0%PapuaPapuaRural35%28%59
Kab. Pegunungan Bintang25.3%PapuaPapuaRural31%21%72
Kab. Waropen22.9%PapuaPapuaRural31%61%28
Kab. Paniayi18.7%PapuaPapuaRural37%25%164
Kota Samarinda18.0%East KalimantanKalimantanUrban5%66%811
Kab Tabanan17.3%BaliJawaRural4%81%436
Kab. Boven Digul16.6%PapuaPapuaRural20%35%63
Kota Tomohon16.6%North SulawesiSulawesiUrban6%71%100
Kota Balikpapan16.3%East KalimantanKalimantanUrban3%69%615
Kota Madiun16.3%East JavaJawaUrban4%80%175
AVERAGE 18%54%252
(e) Adults
Kota Padang Sidempuan42.1%North SumateraSumateraUrban8%77%210
Kab. Yalimo41.1%PapuaPapuaRural35%28%59
Kab. Karo40.8%North SumateraSumateraRural9%74%389
Kota Tomohon40.0%North SulawesiSulawesiUrban6%71%100
Kota Lhokseumawe 38.2%AcehSumateraUrban12%76%191
Kota Pematang Siantar38.1%North SumateraSumateraUrban9%77%247
Kota Blitar37.9%East JavaJawaUrban7%84%138
Kota Manado37.8%North SulawesiSulawesiUrban5%66%425
Kab. Mahakam Ulu37.7%East KalimantanKalimantanRural12%52%26
Kab. Minahasa 37.1%North SulawesiSulawesiRural7%65%329
AVERAGE 11%67%211
(f) Older adults
Kota Banda Aceh38.1%AcehSumateraUrban7%82%250
Kep Seribu37.9%JakartaJawaRural12%71%23
Kota Ternate36.3%North MalukuPapuaUrban3%63%213
Kota Padang Sidempuan35.5%North SumateraSumateraUrban8%77%210
Kota Bekasi35.3%West JavaJawaUrban4%71%2709
Kota Tomohon34.9%North SulawesiSulawesiUrban6%71%100
Kota Medan34.4%North SumateraSumateraUrban8%62%2209
Kab. Karo33.3%North SumateraSumateraRural9%74%389
Kab. Minahasa Utara33.1%North SulawesiSulawesiRural7%61%198
Kota Jayapura32.6%PapuaPapuaUrban11%62%283
AVERAGE 8%69%658
Note: Urban = City, Rural = Regency; Pop = Population. The districts are ordered by prevalence (column 1). Boldface values show the average.
Table 5. Geographic and socioeconomic disparity in obesity among adults.
Table 5. Geographic and socioeconomic disparity in obesity among adults.
All Districts (n = 514)Urban (n = 97)Rural (n = 417)
AllMaleFemaleYoung OlderAllMaleFemaleYoung OlderAllMaleFemaleYoung Older
AdultsAdultsAdultsAdultsAdultsAdultsAdultsAdultsAdultsAdultsAdultsAdultsAdults AdultsAdultsAdultsAdultsAdults
(1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)
Region
Papua16.0%11.6%20.7%5.8%20.1%11.5%22.2%15.7%29.1%7.6%28.8%22.6%15.4%11.2%19.9%5.6%19.2%10.3%
Sulawesi19.4%13.0%25.8%7.6%24.3%15.8%23.9%18.6%29.2%9.6%30.1%24.1%18.6%12.1%25.3%7.2%23.3%14.5%
Kalimantan18.6%12.6%25.3%8.0%22.9%12.5%23.5%17.6%29.9%10.7%28.7%18.0%17.6%11.6%24.4%7.5%21.8%11.5%
Sumatera19.5%12.5%27.0%7.1%24.5%16.5%23.6%17.1%30.3%8.4%29.4%24.1%18.4%11.2%26.1%6.7%23.2%14.4%
Java20.7%14.0%27.4%9.3%25.7%15.5%25.4%19.1%31.8%10.5%30.8%23.8%18.9%12.1%25.8%8.9%23.8%12.3%
Absolute4.7%2.4%6.7%3.5%5.6%4.0%3.2%3.4%2.7%2.9%2.0%1.2%3.5%0.9%5.9%3.3%4.6%2.0%
Relative1.291.211.321.601.281.351.141.221.091.381.071.051.231.081.301.591.241.19
Income
Q1 poor16.3%11.3%21.5%6.5%20.3%10.8%18.3%13.3%23.3%6.0%23.3%23.0%16.2%11.2%21.5%6.5%20.3%10.4%
Q217.1%10.7%23.6%6.4%21.7%12.7%23.9%16.5%31.8%8.4%29.8%21.4%16.7%10.4%23.2%6.3%21.2%12.3%
Q319.6%12.7%26.8%8.1%24.5%15.0%22.8%17.0%28.9%8.0%28.9%21.8%19.2%12.1%26.5%8.1%23.9%14.0%
Q420.1%13.5%27.0%7.7%25.2%16.5%24.1%18.2%30.0%8.8%30.2%24.5%19.1%12.3%26.2%7.5%23.9%14.4%
Q5 rich22.1%15.7%28.8%9.2%27.1%18.7%24.9%18.4%31.5%10.3%30.3%23.3%19.0%12.7%25.9%7.9%23.6%13.7%
Absolute5.8%4.4%7.3%2.7%6.8%7.9%6.6%5.1%8.2%4.3%7.0%0.3%2.8%1.5%4.4%1.4%3.3%3.3%
Relative1.361.391.341.421.331.731.361.381.351.721.301.011.171.131.201.221.161.32
Education
Q1 least16.0%10.6%21.7%6.7%19.8%10.1%n/an/an/an/an/an/a16.0%10.6%21.7%6.7%19.8%10.1%
Q218.0%11.7%24.6%7.5%22.5%13.2%24.1%18.5%29.8%10.2%29.5%21.3%17.2%10.8%23.9%7.1%21.7%12.3%
Q319.4%13.0%26.2%7.5%24.4%15.2%23.1%16.6%29.8%8.9%28.5%22.6%18.7%12.2%25.5%7.2%23.6%13.8%
Q420.3%13.6%27.3%7.9%25.2%17.1%23.9%17.4%30.6%9.8%29.2%23.1%18.9%12.2%26.0%7.2%23.7%14.7%
Q5 most21.5%15.1%28.1%8.2%27.0%18.2%24.8%18.7%31.1%9.2%31.0%24.2%19.4%12.7%26.2%7.6%24.4%14.3%
Absolute5.5%4.5%6.4%1.5%7.2%8.1%0.7%0.2%1.3%−1.0%1.5%2.9%3.4%2.1%4.5%0.9%4.6%4.2%
Relative1.341.421.291.221.361.801.031.011.040.901.051.141.211.201.211.131.231.42
Note: Q = Quintile; Java region includes Bali; Papua region includes Maluku and Nusa Tenggara. Income quintile used district-level poverty rate (e.g., Q1 = 20% of districts with highest poverty rate). Absolute (Relative) = Difference (Ratio) between Papua and Java as well as Q1 and Q5. For education, absolute (relative) was between Q1 and Q5 except among urban (Q2 and Q5). Boldface values show statistically significance at 5% level (see Table A2 for the regression outputs).
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Ayuningtyas, D.; Kusuma, D.; Amir, V.; Tjandrarini, D.H.; Andarwati, P. Disparities in Obesity Rates among Adults: Analysis of 514 Districts in Indonesia. Nutrients 2022, 14, 3332. https://doi.org/10.3390/nu14163332

AMA Style

Ayuningtyas D, Kusuma D, Amir V, Tjandrarini DH, Andarwati P. Disparities in Obesity Rates among Adults: Analysis of 514 Districts in Indonesia. Nutrients. 2022; 14(16):3332. https://doi.org/10.3390/nu14163332

Chicago/Turabian Style

Ayuningtyas, Dumilah, Dian Kusuma, Vilda Amir, Dwi Hapsari Tjandrarini, and Pramita Andarwati. 2022. "Disparities in Obesity Rates among Adults: Analysis of 514 Districts in Indonesia" Nutrients 14, no. 16: 3332. https://doi.org/10.3390/nu14163332

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