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

Ayuningtyas, Dumilah; Kusuma, Dian; Amir, Vilda; Tjandrarini, Dwi Hapsari; Andarwati, Pramita

doi:10.3390/nu14163332

Cited by 11 publications

(15 citation statements)

References 33 publications

(59 reference statements)

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“…It is possible that these factors are common among Indonesian women and do not effectively differentiate between the SUI and non-SUI groups. 23 These findings highlight the importance of considering multiple factors simultaneously when evaluating the predictive ability of a model. By incorporating various relevant risk factors into the analysis, the study was able to identify other factors that played a more significant role in predicting SUI during pregnancy in the Indonesian pregnant women population.…”

Section: Discussionmentioning

confidence: 99%

“…Therefore, the study suggests that the number of pregnancies and higher BMI during pregnancy may not be reliable predictors of SUI. It is possible that these factors are common among Indonesian women and do not effectively differentiate between the SUI and non‐SUI groups 23 . These findings highlight the importance of considering multiple factors simultaneously when evaluating the predictive ability of a model.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Development and internal validation of a risk prediction model for stress urinary incontinence throughout pregnancy: A multicenter retrospective longitudinal study in Indonesia

Liang,

Huang,

Andarini

et al. 2023

Neurourology and Urodynamics

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BackgroundThis study aimed to develop a risk prediction model for stress urinary incontinence (SUI) throughout pregnancy in Indonesian women.MethodsWe conducted a multicenter retrospective longitudinal study involving pregnant women in Indonesia, who sought care at obstetrics clinics from January 2023 to March 2023, encompassing all stages of pregnancy. We collected data on their predictive factors and SUI outcome. SUI was diagnosed based on responses to the “leaks when you are physically active/exercising” criterion in the ICIQ‐UI‐SF questionnaire during our investigation of the participants. The models underwent internal validation using a bootstrapping method with 1000 resampling iterations to assess discrimination and calibration.ResultsA total of 660 eligible pregnant women were recruited from the two study centers, with an overall SUI prevalence of 39% (258/660). The final model incorporated three predictive factors: BMI during pregnancy, constipation, and previous delivery mode. The area under the curve (AUROC) was 0.787 (95% CI: 0.751−0.823). According to the max Youden index, the optimal cut‐off point was 44.6%, with a sensitivity of 79.9% and specificity of 65.9%. A discrimination slope of 0.213 was found.ConclusionThe developed risk prediction model for SUI in pregnant women offers a valuable tool for early identification and intervention among high‐risk SUI populations in Indonesian pregnant women throughout their pregnancies. These findings challenge the assumption that a high BMI and multiple previous deliveries are predictors of SUI in Indonesian women. Further research is recommended to validate the model in diverse populations and settings.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Development and internal validation of a risk prediction model for stress urinary incontinence throughout pregnancy: A multicenter retrospective longitudinal study in Indonesia

Liang,

Huang,

Andarini

et al. 2023

Neurourology and Urodynamics

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“…However, our study also has at least two limitations. First, we did not have information on ethnicity in our dataset, which limits our sub-group analysis by that variable [ 34 , 35 ]. Secondly, we used cross-sectional data and could not assess trends over time.…”

Section: Discussionmentioning

confidence: 99%

District-Level Inequalities in Hypertension among Adults in Indonesia: A Cross-Sectional Analysis by Sex and Age Group

Oktamianti

Kusuma

Amir

et al. 2022

IJERPH

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Background: An estimated 1.28 billion adults 30–79 years old had hypertension globally in 2021, of which two-thirds lived in low- and middle-income countries (LMICs). Previous studies on geographic and socioeconomic inequalities in hypertension among adults have limitations: (a) most studies used individual-level data, while evidence from locality-level data is also crucial for policymaking; (b) studies from LMICs are limited. Thus, our study examines geographic and socioeconomic inequalities in hypertension among adults across districts in Indonesia. Methods: We combined geospatial and quantitative analyses to assess the inequalities in hypertension across 514 districts in Indonesia. Hypertension data were obtained from the Indonesian Basic Health Survey (Riskesdas) 2018. Socioeconomic data were obtained from the World Bank. Six dependent variables included hypertension prevalence among all adults (18+ years), male adults, female adults, young adults (18–24 years), adults (25–59 years), and older adults (60+ years). Results: We also found significant geographic and socioeconomic inequalities in hypertension among adults across 514 districts. All hypertension indicators were higher in the most developed region than in the least developed region. Districts in the Java region had up to 50% higher prevalence of hypertension among all adults, males, females, young adults, adults, and older adults. Notably, districts in the Kalimantan region had the highest prevalence of hypertension, even compared to those in Java. Moreover, income level was positively associated with hypertension; the wealthiest districts had higher hypertension than the poorest districts by up to 30%, but only among males and older adults were statistically significant. Conclusions: There were significant inequalities in hypertension among adults across 514 districts in the country. Policies to reduce such inequalities may need to prioritize more affluent urban areas and rural areas with a higher burden.

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“…For the income variable, we used the poverty rates information at the district level, which we then grouped into quintiles. For the education variable, we used net enrollment ratios of senior secondary information, which we grouped into quintiles as well [ 22 , 23 , 24 ].…”

Section: Methodsmentioning

confidence: 99%

Does the Disparity Patterning Differ between Diagnosed and Undiagnosed Hypertension among Adults? Evidence from Indonesia

et al. 2023

Self Cite

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Background: Healthcare systems in many low- and middle-income countries (LMICs) are not yet designed to tackle the high and increasing burden of non-communicable diseases (NCDs), including hypertension. As a result, a large proportion of people with disease or risk factors are undiagnosed. Policymakers need to understand the disparity better to act. However, previous analyses on the disparity in undiagnosed hypertension, especially from LMICs, are lacking. Our study assessed the geographic and socioeconomic disparity in undiagnosed hypertension and compared it with diagnosed hypertension. Methods: We used the Basic Health Survey (Riskesdas) 2018 and performed geospatial and quantitative analyses across 514 districts in Indonesia. Dependent variables included diagnosed and undiagnosed hypertension among adults (18+ years) and by gender. Results: A high prevalence of undiagnosed hypertension at 76.3% was found, with different patterns of disparity observed between diagnosed and undiagnosed hypertension. Diagnosed hypertension was 1.87 times higher in females compared with males, while undiagnosed hypertension rates were similar between genders. Urban areas had up to 22.6% higher rates of diagnosed hypertension, while undiagnosed hypertension was 11.4% more prevalent among females in rural areas. Districts with higher education rates had up to 25% higher diagnosed hypertension rates, while districts with lower education rates had 6% higher rates of undiagnosed hypertension among females. The most developed regions had up to 76% and 40% higher prevalence of both diagnosed and undiagnosed hypertension compared with the least developed regions. Conclusion: The disparity patterning differs between diagnosed and undiagnosed hypertension among adults in Indonesia. This highlights the need for effective measures, including healthcare system reforms to tackle NCDs in LMICs.

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

Cited by 11 publications

References 33 publications

Development and internal validation of a risk prediction model for stress urinary incontinence throughout pregnancy: A multicenter retrospective longitudinal study in Indonesia

Development and internal validation of a risk prediction model for stress urinary incontinence throughout pregnancy: A multicenter retrospective longitudinal study in Indonesia

District-Level Inequalities in Hypertension among Adults in Indonesia: A Cross-Sectional Analysis by Sex and Age Group

Does the Disparity Patterning Differ between Diagnosed and Undiagnosed Hypertension among Adults? Evidence from Indonesia

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