Socioeconomic inequality in abdominal obesity among older people in Purworejo District, Central Java, Indonesia – a decomposition analysis approach

This study was conducted in Purworejo district, Central Java province, Indonesia. The district is located in the southern part of Java Island with a population of 712.686 inhabitants in an area of 1035 km², where 84% of the areas are agricultural land [29]. The district consists of 90% rural area and 10% small urban settlement, with the geographical terrains ranging from coastal in the south to hilly and mountainous in the north. A Health and Demographic Surveillance System (HDSS) site was established in Purworejo district in 1994, which became a member of the INDEPTH Network of HDSS sites in Africa and Asia in 1998. The Purworejo HDSS covers 55,000 individuals in 14,500 households in the district [30]. The HDSS collects the demographic data (birth, death, marital status, migration, etc.) on annual basis and household socioeconomic data every 5-year, plus ad-hoc surveys nested within the HDSS site.

We utilised data from the WHO-INDEPTH Study on global AGEing and adult health (WHO-INDEPTH SAGE) conducted in Purworejo HDSS in 2010. The survey included 14,235 individuals aged 50 years and older in the Purworejo HDSS (Fig. 1). Along with the WHO-INDEPTH SAGE survey, the socioeconomic census round was conducted among 12,321 households in the Purworejo HDSS. We linked the individual- and household-level data using the unique household identification number. In total, data from 13,941 individuals living in 9302 households were successfully merged. Due to missing data on key variables (mainly education and smoking status), a total of 1371 individuals were further excluded from the analysis; thus, the subsequent analyses were based on complete data from 12,570 individuals (88% of all respondents).

The individual and household-level WHO-INDEPTH SAGE questionnaires [31] were translated into Indonesian, pilot-tested, and back translated into English to ensure the equivalence of the translation. The individual WHO-INDEPTH SAGE questionnaire contained self-reported information on respondents’ socio-demographic characteristics, health status, quality of life, ownership of health insurance and health care utilisation, health behaviours and risk factors, self-reported chronic conditions, self-reported weight and height, and waist circumference measurement [31].

The household questionnaire contained information on housing condition, infrastructure facilities, and ownership of assets. We selected some of the key from the household questionnaire to create the wealth index as a proxy for household SES using principal component analysis (PCA) [32]. All three assumptions of PCA (Kaiser-Meyer-Olkin measure of sampling adequacy, Bartlett’s test, and determinants of matrix correlation) [33] were tested and fulfilled. PCA generated the weight for each chosen asset and then created an index based on the sum of all weights of variables included in the PCA for each household. The index was categorised into SES quintiles with the 1st quintile representing the poorest group and the 5^th quintile representing the richest group.

We used data on household and individual socio-demographic characteristics (i.e. age, education, occupation, marital status, residence, and SES quintiles), smoking habits and self-reported chronic conditions as independent variables. Waist circumference was used as the dependent variable to measure abdominal obesity (Table 1).

A total of 25 field surveyors performed face-to face interviews under the supervision of four supervisors. All field workers participated in several training sessions in December 2009, during which the study protocols were discussed in detail and role-plays of interviews were conducted. Following the interview, waist circumference was measured at the point midway of the last palpable rib and top of iliac crest using a non-elastic measuring tape (in centimetres) [34]. The data were collected on paper forms, which were later scanned optically. Two data operators were responsible for validating the digital data and ensuring the safe storage of the databases.

We used concentration index (CI) to show the concentration of abdominal obesity distribution in subgroups of populations across the wealth index. The CI ranges from −1 to +1. A negative CI indicates that abdominal obesity is concentrated among the disadvantaged (poor/deprived); a positive CI indicates that abdominal obesity is concentrated among the advantaged (rich/wealthy) and a zero CI suggests no inequality [35]. The concentration curve illustrates the CI visually. On the horizontal axis, the curve ranks the population from the most-disadvantaged to the most-advantaged group. The vertical axis indicates the cumulative percentage of abdominal obesity. The 45° diagonal line is called the line of equality [35], which represents perfect equality (i.e. CI = 0) in wealth distribution.

Decomposition analysis was performed to identify factors (covariates) contributing to the socioeconomic inequality in abdominal obesity [35]. The analysis decomposes the abdominal obesity CI into the explained component and the unexplained component. For the explained component, the analysis produces the elasticity, the concentration index (CI), and contribution to the CI for each covariate. The elasticity (frequency weighted coefficient) indicates the direction (positive or negative) and the degree of association (the impact) between the covariates and abdominal obesity [36]. Negative elasticity means the abdominal obesity would be lower in that category compared to the reference category (base). The contribution of CI was calculated by multiplying the elasticity and the CI. Thereafter, the percentage contribution of CI was calculated by dividing the contribution by the overall CI. Positive contribution in any covariate means the inequality would be less if that covariate were not present. The unexplained component remains as ‘residual’, which reflects the inequality that cannot be explained by the covariates included in the decomposition analysis [35, 36].

Analysis with binary health variables required an additional step of Wagstaff normalization in the CI and decomposition analysis as the minimum and maximum possible value of CI is not in the range of −1 and +1 [35, 37]. Normalization is needed to ensure that the CI is quantified in the range of −1 and +1, by dividing the CI by 1 minus the mean [35, 37]. To normalize the decomposition analysis, marginal effects from a probit regression were used in the elasticity calculation [35]. The affluent (advantaged) covariate was selected as the reference category (base). All analyses were conducted using Stata Version 13.

Our finding that abdominal obesity was concentrated more among the rich is consistent with the findings from other studies in Indonesia [17, 19, 20, 38, 39] and in other LMICs [13‐15]. Obesity inequalities and its determinants reported in this study may be partly explained by economic and nutrition transition in Indonesia. The economic transition in Indonesia brings about the nutrition transition with changing diet patterns from traditional healthier staple food to energy-dense food [19, 38, 40]. Populations with higher socio-economic status changed their food consumption patterns earlier and faster than their poorer counterparts, as they have better accessibility and affordability to the foods. Studies have shown that they are less prone to food scarcity, and on the contrary, might have over-consumed food, often foods with high calorie contents [19, 38, 40, 41]. Therefore, they are at a higher risk of developing obesity compared to their poorer counterparts.

At the same time, economic development and technology advances also promote sedentary life across all wealth groups [13, 15, 19]. Developments in technology render work tasks less laborious and physically less demanding. Household appliances for performing household chores are now widely available and affordable [23]. The increased use of motorised private transportation also leads to less energy expenditure during transfer from one place to another [17, 20, 23, 42]. This low level of physical activity combined with a shift in food consumption patterns have been identified as major contributors to obesity in Indonesia [19].

We observed a significant difference in abdominal obesity prevalence between older women and men. This finding is in line with other studies in Indonesia that showed a higher prevalence of obesity among adolescents and adults women [17], as well as in older adult women aged 45 years and older [24]. The gender difference could partly be explained by women’s physiology and metabolism during adolescence, pregnancy and menopause [6, 43]. Another potential explanation relates to studies of the long-term impact of early malnutrition that may affect energy intake and expenditure mechanism, appetite regulation and weight gain patterns differently in men and women. Studies showed that women who experienced childhood malnutrition were facing greater risk to be obese during their adulthood than men [16, 44‐47]. In the 1980s, the estimated prevalence of stunting in Asia exceeded 60% [48]. We believe the condition in Indonesia was worse 50 years ago (1960s), especially among the young girls, as the newly independent country struggled from problems of malnutrition (mainly under-nutrition) and related infectious diseases.

This study also confirms a more prominent socio-economic inequality gap in abdominal obesity among Indonesian men compared to their female counterparts. Gender differences in the degree of inequality in this study appear to be inversely related to the differences observed in the prevalence of obesity. Women in all socio-economic groups are consistently more obese than men. This may indicate that the obesity epidemic has affected women in the poorer groups. An Asian Development Bank Institute (ADBI) study among Indonesian men and women aged 20+ showed that the concentration index of obesity (BMI ≥ 25) has decreased during the period of 1993 to 2014 (from 0.324 to 0.175 among men and from 0.178 to 0.038 among women), which might indicate the shift of the obesity burden to the poorer group. The study also showed that the concentration index was consistently lower among women, indicating that obesity is rapidly becoming the problem of poor women [49]. The same patterns have been observed in another study conducted in LMICs, with a shift of obesity burden among women from the rich to the poor, mainly in LMICs with a gross national income (GNI) per capita > 1000 US$ and with medium HDI [15].

Beyond the gender difference shown in obesity inequality in our study, we believed that education might have different roles in influencing the levels of obesity in men and women [50, 51]. In high-income countries, highly-educated women usually engage in a healthier lifestyle (e.g. regular physical exercise and healthier diet) [50, 51], more than their male counterparts with the same level of education. Studies in the US also showed that highly-educated women, but not men, were more likely to be dissatisfied with their body image and they prefer a thinner silhouette [51]. Our findings showed contrasting results in which that highly-educated older women have higher level of obesity than their male counterpart. Older women (50+) might no longer be concerned about their body image and therefore do not engage in routine physical exercise. A study of physical inactivity prevalence in five Asian countries among adults age 25–64 years old showed that Indonesian women were more physically inactive compared to men (26% vs. 12% with physical inactivity lifestyle), and respondents with higher education tended to be less active compared to their counterparts with a lower education level [52]. Nevertheless, when we looked into the level of obesity within men and women in the current study, it is shown that the obesity prevalence of men with > 6 years education was twelve times greater than for those with no formal education. Meanwhile among women the level of obesity between the highest and lowest education was only higher by two folds.

Other factors that might be related to inequality in obesity are social relationships (i.e. marriage) and cultural factors [53]. Several studies have suggested that marriage predicts weight gain in both men and women [17, 19, 23, 53]. In our study, even though marital status showed small contributions to socioeconomic inequality in abdominal obesity in both men and women, higher abdominal obesity prevalence was observed among those in partnerships. Averett et al. proposed four mechanisms to elucidate the relationship between marriage and obesity [54]. These four hypotheses included: selection (where the leaner individual are most likely to be selected into marriage), protection (where marriage will improve one’s health as social support increases and risky behaviour decreases), social obligation (where meals with richer and denser food will be served regularly as a marriage social obligation) and marriage market hypothesis (where those who are married may not maintain a leaner figure as they are no longer on the market) [54]. We hypothesise that older couples in our setting might have more regular meals with richer and denser foods, hence contributing to the larger waist measurements among married couples.

Cultural factors and population perception on obesity might influence the burden of obesity. In some LMICs, the population believes that larger body size reflects higher social strata [12, 19, 20]. Our qualitative study of the community’s perception of diabetes confirms this observation, in that the community believe that diabetes, which is also caused by obesity, is a disease of wealthy people [42]. Furthermore, another study using IFLS data also found that obese people in Indonesia were satisfied and happy with their lives compared to the non-obese [20].

The Indonesian government has continuously prioritised health promotion program to deal with severe malnutrition (underweight or stunting) in the national agenda [40]. Obesity, on the other hand, has received less attention from the government, as it has not been perceived as a public health threat [17, 19, 20]. The IFLS data during 1993–2007 showed a decrease in the prevalence of stunting in Indonesia while at the same time, the prevalence of overweight and obesity in children and adult populations increased significantly in both urban and rural settings [19, 38, 40, 58]. The socio-economic patterns of obesity in Indonesia start resembling the patterns observed in the HICs, where obesity is more common among the poor and less among the rich and well-educated, particularly among women [4, 8]. As one of the main risk factors for chronic diseases, obesity epidemic can imminently lead to a larger burden of chronic diseases and health-care expenditure [6]. Hence, it is very important for the Indonesian government to develop health promotion programme to address the double burden of malnutrition, with a specific focus to tackle the imminent obesity epidemic in Indonesia [38‐40].

The Indonesian government has implemented several national community movements such as health promotion programmes to promote physical activity and healthy eating in order to decrease the prevalence of obesity [59]. Public awareness of obesity and knowledge of its health risks need to be raised through health promotion activities. The healthcare providers at primary health care units need to actively educate communities, especially women, regarding the benefit of consuming healthier food (more consumption of fruits and vegetable, healthy fats, reduction of sugar and salt intake) and promoting physical exercise during leisure time (for example the existing exercise program called ‘senam lansia’ targeting older people in neighbourhood). Furthermore, changing the socially positive perception of obesity among the lay communities is also important, as it would be difficult to address obesity as a public health problem when being fat is still perceived as a reflection of prosperity [20]. These activities could be achieved through multiple channels, including mass media campaigns, which could hopefully change the lay perception of the cultural value of obesity. It is also essential to maintain and improve the community health post’s programme for NCDs called POSBINDU PTM (‘pos pembinaan terpadu penyakit tidak menular’), to detect the NCD risk factors including obesity, and address them at earlier stages (through routine measurement of height, weight, waist circumference, blood pressure, etc.) [60]. As these community health post programmes have good coverage in Indonesia, they have the potential to reach larger population groups, especially the poorer population.

In 2010, the Indonesian government established a National Team for the Acceleration of Poverty Reduction called TNP2K (Tim Nasional Percepatan Penanggulangan Kemiskinan) aiming to close the socio-economic inequality gaps in the country. The poverty reduction programmes include among which equal and universal access to education and health care, cash transfers, subsidized rice to poor families, etc. These national programmes do not target the older population in particular. Recently, the High-Risk Elderly Social Assistance Program called ASLURETI (cash transfers scheme for older people i.e. social pension), which has been successfully implemented in Aceh Jaya district, Aceh Province, Indonesia [61, 62], was scaled-up at the national level. These national programmes, yet to be evaluated, could be an effective way to reduce health inequality among the older population in Indonesia. These programmes could serve as an entry point for national programme to reduce the burden of obesity, as nearly 50% of the cash transfers were spent on food (e.g. meat, rice) at household level [62].

Community education on the health effects of obesity and campaigns to increase community’s awareness on healthier food choice could piggyback in the national poverty reduction programmes. Government efforts to provide low-cost healthy meals and fresh and green products are also important to increase the availability of these healthier choices for low-income older peoples. Obesity prevention programmes at school-level might also be important and effective for instilling healthier lifestyle since early life in order to prevent obesity in adulthood.