Does the Disparity Patterning Differ between Diagnosed and Undiagnosed Hypertension among Adults? Evidence from Indonesia
by
, , and
Puput Oktamianti
1, 
Dian Kusuma
2,*,
Vilda Amir
3,
Dwi Hapsari Tjandrarini
4 and
Astridya Paramita
4 1
Health Administration and Policy Department, Faculty of Public Health, Universitas Indonesia, Depok 16424, Indonesia
2
Department of Health Services Research and Management, School of Health & Psychological Sciences, City University of London, London EC1V 0HB, UK
3
Center for Health Administration and Policy Studies, Faculty of Public Health, Universitas Indonesia, Depok 16424, Indonesia
4
Research Center for Public Health and Nutrition, National Research and Innovation Agency, Bogor 16915, Indonesia
*
Author to whom correspondence should be addressed.
Healthcare 2023, 11(6), 816; https://doi.org/10.3390/healthcare11060816
Submission received: 4 January 2023
/
Revised: 5 March 2023
/
Accepted: 6 March 2023
/
Published: 10 March 2023
Abstract
: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.
1. Background
Hypertension, or high blood pressure, is linked with increased heart, brain, and kidney disease risks [1]. Globally, about 1.3 billion adults aged 30 years and over had hypertension in 2021. Most of those with hypertension (over 60%) are in low- and middle-income countries (LMICs) [1]. Moreover, data also showed that less than half (42%) of those with hypertension were diagnosed and treated [1]. In Indonesia, hypertension is also high and increasing. Analyses from the nationally representative survey (RISKESDAS) showed that hypertension prevalence among adults 18 years and over was 34.1% in 2018, which increased considerably from 25.8% in 2013 [2]. Moreover, a study of the Indonesian Family Life Survey 2016 found that the prevalence of hypertension among adults 40 years and over was 47.8%, of which almost 70% were undiagnosed [3].
The current literature provides some evidence of social determinants of cardiovascular diseases and risk factors including hypertension [4]. A comprehensive literature review and meta-analysis in 2014 found that income level was positively associated with hypertension, but education level was not. The study also found geographic variation in the association between education and hypertension, showing an inverse association in the East Asian region and a positive one in the South Asian region [5]. A study in 2017 using the South Korean National Health and Nutrition Examination Survey (NHNES) found sexual variation in the association between education and undiagnosed hypertension, showing an inverse association among women but not among men [6]. Recent analyses (2019–2021) of the Demographic & Health Survey data in Peru, Bangladesh, and Nepal also found that adults from lower socioeconomic and educational backgrounds had higher odds of undiagnosed hypertension [7,8,9,10]. Another study in 2016 showed that being in a deprived neighborhood increased the influence of individual socioeconomic status on mortality among newly diagnosed hypertension patients in South Korea [11]. Similarly, a study in Peru found that adult males living in the more remote and deprived areas (e.g., coasts and mountains) had a higher prevalence of undiagnosed hypertension [7]. A study in the United States showed that rural areas were most vulnerable to adverse chronic health outcomes and found a positive association between social vulnerability index and cardiometabolic indicators including hypertension [12].
To achieve the SDG target 3.4.1 to reduce premature mortality from NCDs by one-third by 2030, efforts need to aim at reducing the disparity in diagnosed and undiagnosed hypertension [1]. However, the current literature on such disparity is limited in three ways. First, while most of the current literature used data at the individual level (e.g., national surveys) [3,5,6,7,8,9,10], studies that employed data at the local level (such as districts) are lacking. Such evidence is also crucial, especially in countries with more local decision space, such as Indonesia. Second, because of the better availability of local level data, current geographic analyses are mainly from high-income countries such as the United States and South Korea [11,13,14]. Such analyses from LMICs (e.g., China, Thailand, and Peru) are limited to the urban/rural and provincial levels [15,16,17]. Third, previous studies focused on overall hypertension and lacked disaggregation between diagnosed and undiagnosed hypertension. Effective health system reforms and population-based interventions may be needed to reduce the undiagnosed population [18]. Our study aimed to assess the disparity (geographic and socioeconomic) in diagnosed and undiagnosed adult hypertension across over 500 Indonesian districts.
2. Methods
2.1. Study Design
This is a cross-sectional study comparing the disparity in diagnosed and undiagnosed hypertension among adults. We analyzed geographic and socioeconomic disparities across 514 districts within 34 provinces in Indonesia. We took advantage of the 2018 Basic Health Survey (Riskesdas) data that were representative at the district level for diagnosed and undiagnosed hypertension. The survey conducted interviews and physical examinations of about 300,000 households from a two-stage sampling procedure. The sampling first randomly selected 30,000 census blocks (out of a total of over 700,000 in Indonesia). Within each block, 10 households were systematically selected, which resulted in 624.563 adults (18+ years). The mean ages (standard deviation) were 41.0 (15.5) years, 40.8 (15.3) years, and 41.3 (15.7) years for all adults, males, and females, respectively [2].
2.2. Independent Variables
The main independent variables included geographic and socioeconomic indicators at the district level. The variables used in our analyses were region, urbanicity, income level, and education level. This information was taken from the World Bank. The regional variable includes five regions: Sumatera, Java (including Bali), Kalimantan, Sulawesi, and Papua (including Nusa Tenggara and Maluku). Generally, the eastern parts of the country are the least developed [19,20,21]. Appendix A provides the map reference. The urbanicity variable shows cities as urban and regencies as rural areas. 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].
2.3. Dependent Variables
There were six dependent variables used in our analysis, including diagnosed adults, diagnosed males, diagnosed females, undiagnosed adults, undiagnosed males, and undiagnosed females. Diagnosed hypertension was a binary variable with a value of 1 if one reported ever being told by a doctor that they have high blood pressure and 0 if otherwise. We defined undiagnosed hypertension as not diagnosed but meeting the criteria for hypertension based on the blood pressure measurement (i.e., either systolic blood pressure of at least 140 mmHg, diastolic blood pressure of at least 90 mmHg, or both) [25].
2.4. Data Analysis
We performed both geospatial analyses and multivariable regression analyses in this paper. In conducting the geospatial analyses, we grouped each dependent variable for 34 provinces and 514 districts by quintile. In conducting the regressions, we employed ordinary least squares and examined the relationship between independent and dependent variables. We compared the regional variations between the western and eastern parts of the country, and the income/education variations between the poorest/least educated and wealthiest/most educated. The geospatial analyses were conducted in ArcMap 10 and the statistical analyses were performed in STATA 15, using 5% as statistically significant.
3. Results
3.1. Analysis at the Provincial Level
Figure 1 and Table 1 show results at the provincial level. Figure 1 compares diagnosed hypertension (panels a–c) and undiagnosed hypertension (panels d–f) by quintile. At the provincial level, diagnosed hypertension among all adults ranged from 4.4% to 13.2%; males from 3.7% to 9.5%; and females from 5.2% to 17.0%. At that level, undiagnosed hypertension among all adults ranged from 19.4% to 35.5%; males from 18.7% to 35.6%; and females from 17.3% to 35.4%. Diagnosed hypertension among all adults was highest (quintiles four–five) in many provinces in the Java and Bali region (e.g., Jakarta, Banten, West Java, Yogyakarta, and Bali), several provinces in Kalimantan (e.g., East, North, and South Kalimantan) and Sulawesi (e.g., North Sulawesi, Central Sulawesi, and Gorontalo), and a province in Sumatera (i.e., Aceh). Undiagnosed hypertension among all adults was highest (quintiles four–five) in many provinces in Java (e.g., Jakarta, West Java, Central Java, East Java, and Bali) and Kalimantan (e.g., East, West, Central, and South Kalimantan), many provinces in Sulawesi (e.g., West, South, and Southeast Sulawesi), and two provinces in Sumatera and Papua. By sex, the patterning showed some differences. For instance, diagnosed hypertension among females was higher (quintile four) and that among males was lower (quintile two) in Bangka Belitung. In contrast, diagnosed hypertension among females was lower and that among males was higher in West Kalimantan. Similarly, undiagnosed hypertension among females was higher, and that among males was lower in North Sumatera, South Sumatera, and Lampung.
Moreover, Table 1 compares diagnosed hypertension and undiagnosed hypertension by the level of poverty rates at the provincial level. The top box and bottom box compare the ten richest and poorest provinces. The provincial prevalence higher than the national level is shown in grey in each column. Of the ten wealthiest provinces, six provinces (e.g., Jakarta, Bali, South, North, and East Kalimantan) had higher prevalence than average for at least four indicators, while none of the ten poorest provinces did.
3.2. Analysis at the District Level
Figure 2 and Table 2 and Table 3 show results at the district level. Table 2 shows the characteristics of districts in terms of geographic indicators, socioeconomic indicators, and dependent variables (i.e., diagnosed and undiagnosed hypertension). Of the total of 514 districts in our analysis, 97 (18.9%) and 417 (81.1%) were urban (cities) and rural (regencies). The two regions where urban districts were dominant included Java (36.1% of 97) and Sumatera (34.0%). For the income variable, most of the urban areas (78.4%) were considered wealthy (quintiles four–five), but fewer than a third of rural areas (31.2%) were. Similarly, for education, 71.1% of urban areas had higher education (quintiles four–five), while only a third (32.6%) of rural areas did. In terms of hypertension, diagnosed prevalence was 7.9%, 5.5%, and 10.3%, while that of undiagnosed hypertension was 25.4%, 24.9%, and 25.8% among adults, males, and females. Relative to rural areas, diagnosed hypertension was significantly higher, but undiagnosed hypertension among females was significantly lower in urban areas. Diagnosed hypertension among adults, males, and females was 8.9%, 6.5%, and 11.2% in urban areas and 7.6%, 5.3%, and 10.1% in rural areas. Undiagnosed hypertension among females was 23.4% and 26.4% in urban and rural areas, respectively.
Figure 2 compares the prevalence of diagnosed and undiagnosed hypertension by quintile at the district level. For diagnosed hypertension, many districts in the provinces of Jambi, Riau, Bengkulu, Central Java, East Java, West Kalimantan, Central Kalimantan, South Sulawesi, Papua, and West Papua had higher hypertension among all adults (quintiles four–five). For undiagnosed hypertension, many districts in all provinces in Sumatera and Papua had higher prevalence among adults (quintiles four–five).
For socioeconomic disparity analysis at the district level, Appendix C and Appendix D compare districts with the lowest and highest diagnosed and undiagnosed hypertension. For diagnosed hypertension, the prevalence among adults ranged from 0% in Buton Tengah regency (Central Sulawesi province) to 20.8% in Sitaro Kepulauan (North Sulawesi). By sex, diagnosed hypertension among males ranged from 0% in Yahukimo and Pegunungan Bintang (Papua) to 15.8% in Tomohon city (North Sulawesi); that among females ranged from 0% in Buton Tengah (Southeast Sulawesi), Yahukimo, Dogiyai, and Mambramo Raya (Papua) to 27.0% in Sitaro Kepulauan (North Sulawesi). For undiagnosed hypertension, the prevalence among adults ranged from 7% in Puncak Jaya (Papua) to 43.2% in Hulu Sungai Tengah (South Kalimantan). By sex, undiagnosed hypertension among males ranged from 6.8% in Puncak Jaya (Papua) to 44.9% in Madiun city (East Java); that among females ranged from 6.2% in Puncak Jaya (Papua) to 44.6% in Ciamis (West Java). By urbanicity, districts with the lowest prevalence of diagnosed and undiagnosed hypertension for all adults, males, and females were rural. Similarly, most districts with the highest prevalence of diagnosed and undiagnosed were rural. By income, poverty rates among ten districts with the highest diagnosed and undiagnosed hypertension were averaged up to 10%, while those with the lowest prevalence were averaged up to 33%.
Table 3 compares the associations between geographic/socioeconomic variables and diagnosed/undiagnosed hypertension. Districts in the least disadvantaged region had a significantly higher prevalence of both diagnosed and undiagnosed among all adults, males, and females, relative to the most disadvantaged region (e.g., Papua). Compared with Papua, districts in the Java region had 68%, 45%, and 76% higher diagnosed prevalence among adults, males, and females; they had 40%, 39%, and 40% higher undiagnosed prevalence (significant at 5% level). Moreover, results showed that districts in the Kalimantan region had the highest diagnosed and undiagnosed prevalence among all adults, males, and females in the country. For the income variable, results show that the richest districts had a higher diagnosed and undiagnosed prevalence among all adults, males, and females than the poorest ones (but not statistically significant in multivariable regressions). For the education variable, the relationships are mixed. Districts with the most education had 23%, 18%, and 25% significantly higher diagnosed prevalence among adults, males, and females than the least educated ones. However, districts with the least education had a 6% (i.e., 1/0.94 = 1.06) higher undiagnosed prevalence among females.
4. Discussion
Using nationally representative survey data of adults, we found the prevalence of overall hypertension was 33.3%, of which 76.3% were undiagnosed (i.e., 7.9% diagnosed and 10.3% undiagnosed). Global estimates showed similar hypertension prevalence in adults 30–79 years of age at 32% and 34% among women and men in 2019 [26]. In terms of undiagnosed hypertension, while considerably higher than in high-income countries such as the United States (19.7% in 2010), South Korea (33.4% in 2013), and Ireland (41.2% in 2011) [6,27], the prevalence in Indonesia was relatively similar to that in LMICs such as Nepal (50.4% in 2016), Bangladesh (59.9% in 2011 and 50.1% in 2017), and Peru (67.2% in 2019) [7,9,10].
By sex, diagnosed hypertension among females was 1.87 times higher compared with males (i.e., 5.5% males and 10.3% females), while undiagnosed hypertension was similar between both sexes (i.e., 24.9% and 25.8% among males and females). This finding aligns with evidence from other LMICs, such as Nepal, Bangladesh, and Peru, showing a significantly lower prevalence of undiagnosed hypertension among women [7,9,10]. This might be due to women having more interactions with the health systems (e.g., through antenatal, delivery, and postnatal care) and other population-based interventions more towards women (e.g., conditional cash transfers) [28,29].
We found significant disparities (geographic and socioeconomic) between the prevalence of diagnosed and undiagnosed hypertension across 514 districts. Diagnosed hypertension was higher by up to 22.6% in urban areas, while undiagnosed hypertension among females was higher by 11.4% in rural areas. Previous studies showed a higher prevalence of diagnosed hypertension in urban areas but a higher prevalence of undiagnosed hypertension in rural areas [7,8,9,10]. This is expected, as urban areas tend to have higher access to health facilities and healthcare professionals. However, many rural districts were among the top ten districts with the highest prevalence of diagnosed and undiagnosed hypertension, which may be due to similarities in economic development and mobility between rural and urban areas [30]. For example, the North Sulawesi, Minahasa and Minahasa Selatan regencies, which had similar income levels and were adjacent to Tomohon City, were found to have high rates of hypertension.
By region, the patterning is similar for diagnosed and undiagnosed hypertension. Districts in the most developed areas (i.e., Java and Bali) had up to a 76% and 40% higher prevalence of diagnosed and undiagnosed hypertension compared with the least developed areas (i.e., Papua, Nusa Tenggara, and Maluku). This is likely due to a higher burden of hypertension (diagnosed and undiagnosed) among higher socioeconomic levels of the population in more developed regions. By income, the richest districts had a higher prevalence of diagnosed and undiagnosed hypertension among all adults, males, and females than that of the poorest districts (although only statistically significant in bivariate analyses). By education, districts with the most education had up to a 25% higher prevalence of diagnosed hypertension, while those with the least had a 6% higher undiagnosed prevalence among females.
While evidence from LMICs are limited in the literature, our findings align with previous studies. Studies using provincial-level data in China showed a higher prevalence of hypertension in the least disadvantaged areas than that in the most disadvantaged ones [15,16]. Similar study at the provincial level in Thailand found a higher prevalence of hypertension in Bangkok and metropolitan areas than in less developed areas in the north and south regions [17]. On the contrary, studies from high-income countries found a higher prevalence of hypertension in the most disadvantaged areas [11,13,14]. Moreover, a higher prevalence of diagnosed hypertension among districts with the most education may be due to better health systems and access to health facilities [31]. In contrast, analyses at the individual level in Peru, Bangladesh, and Nepal found adults with low education had higher odds of undiagnosed hypertension [7,8,9,10]. Studies have also shown strong association between low education and cardiometabolic comorbidities and that education may be considered the best predictor of cardiovascular risk in people with hypertension [32,33].
Effective efforts are needed to reduce undiagnosed hypertension (and other NCD risk factors such as high cholesterol and diabetes) by sex, urbanicity, region, and socioeconomic status [34,35]. Efforts may include health system reforms such as enhanced primary health care in Malaysia or routine assessment national programs such as NHS Health Check in the United Kingdom [18,36]. Healthcare delivery reforms may also include integration with infectious disease platforms [37,38].
Our study is the first analysis from LMICs to compare the disparity (geographic and socioeconomic) in the prevalence of diagnosed and undiagnosed hypertension across over 500 localities. However, our study also has limitations. Because of the lack of information, our analysis could not conduct sub-group analysis by ethnicity [39]. Additionally, because of using cross-sectional data, our analysis could not conduct trend analysis. However, regardless of these limitations, our evidence is crucial for policymaking nationally and globally, especially in low-resource settings.
5. Conclusions
In Indonesia, 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 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 a 76% and 40% higher prevalence of both diagnosed and undiagnosed hypertension compared with the least developed regions. This study highlights the need for effective measures, including healthcare system reforms, to tackle NCDs in LMICs.
Author Contributions
D.K., V.A., and P.O. conceptualized the study. D.H.T., A.P., and D.K. collected and cleaned the data; D.K., V.A., and A.P. performed the formal analyses. D.K. drafted and P.O., V.A., D.H.T., and A.P. provided inputs. All authors have read and agreed to the published version of the manuscript.
Funding
Funding was from the Directorate of Research and Community Service, Universitas Indonesia (NKB-626/UN2.RST/HKP.05.00/2022). The funder had no role in study design, data collection and analysis/interpretation, or preparation of the manuscript.
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
Available from the corresponding author upon reasonable request.
Conflicts of Interest
The authors declare no conflict of interest.
Appendix A. Map of Provinces in Indonesia
Appendix B. Regression Outputs for Urban/Rural Differences
| Diagnosed All | Diagnosed Males | Diagnosed Females | Undiagnosed All | Undiagnosed Males | Undiagnosed Females | |
| Coef | Coef | Coef | Coef | Coef | Coef | |
| Rural | Reference | |||||
| Urban | 1.24 ** | 1.28 ** | 1.09 * | −0.80 | 1.39 | −2.97 ** |
| Constant | 7.63 ** | 5.27 ** | 10.07 ** | 25.54 ** | 24.68 ** | 26.37 ** |
| Observations | ||||||
| R-squared | 513 | 511 | 512 | 514 | 514 | 514 |
Note: Coef = OLS coefficient; significance level ** p < 0.01, * p < 0.05.
Appendix C. Ten Districts with LOWEST Prevalence of Diagnosed and Undiagnosed Hypertension among Adults in Indonesia
| Prevalence | Province | Region | Urban | Poverty | Education | Pop (000) | |
| (a) Diagnosed all | |||||||
| Kab. Buton Tengah | 0.0% | Central Sulawesi | Sulawesi | Rural | 15% | 80% | 89 |
| Kab. Yahukimo | 0.3% | Papua | Papua | Rural | 39% | 12% | 181 |
| Kab. Intan Jaya | 0.4% | Papua | Papua | Rural | 43% | 9% | 46 |
| Kab. Dogiyai | 0.5% | Papua | Papua | Rural | 30% | 39% | 92 |
| Kab. Pegunungan Bintang | 0.6% | Papua | Papua | Rural | 31% | 21% | 72 |
| Kab. Lanny Jaya | 0.7% | Papua | Papua | Rural | 40% | 46% | 172 |
| Kab. Mambramo Raya | 0.7% | Papua | Papua | Rural | 30% | 51% | 21 |
| Kab. Buru Selatan | 1.0% | Maluku | Papua | Rural | 16% | 67% | 59 |
| Kab. Muna Barat | 1.2% | Southeast Sulawesi | Sulawesi | Rural | 14% | 71% | 77 |
| Kab. Jayawijaya | 1.2% | Papua | Papua | Rural | 39% | 67% | 206 |
| AVERAGE | 30% | 46% | 102 | ||||
| (b) Diagnosed males | |||||||
| Kab. Yahukimo | 0% | Papua | Papua | Rural | 39% | 12% | 181 |
| Kab. Pegunungan Bintang | 0% | Papua | Papua | Rural | 31% | 21% | 72 |
| Kab. Tolikara | 1% | Papua | Papua | Rural | 33% | 34% | 131 |
| Kab. Dogiyai | 1% | Papua | Papua | Rural | 30% | 39% | 92 |
| Kab. Lanny Jaya | 1% | Papua | Papua | Rural | 40% | 46% | 172 |
| Kab. Mambramo Raya | 1% | Papua | Papua | Rural | 30% | 51% | 21 |
| Kab. Intan Jaya | 1% | Papua | Papua | Rural | 43% | 9% | 46 |
| Kab. Jayawijaya | 1.1% | Papua | Papua | Rural | 39% | 67% | 206 |
| Kab. Teluk Wondama | 1.1% | West Papua | Papua | Rural | 33% | 39% | 30 |
| Kab. Halmahera Barat | 1.2% | North Maluku | Papua | Rural | 9% | 70% | 111 |
| AVERAGE | 33% | 39% | 106 | ||||
| (c) Diagnosed females | |||||||
| Kab. Buton Tengah | 0% | Southeast Sulawesi | Sulawesi | Rural | 15% | 80% | 89 |
| Kab. Yahukimo | 0% | Papua | Papua | Rural | 39% | 12% | 181 |
| Kab. Dogiyai | 0% | Papua | Papua | Rural | 30% | 39% | 92 |
| Kab. Mambramo Raya | 0% | Papua | Papua | Rural | 30% | 51% | 21 |
| Kab. Diyai | 0.4% | Papua | Papua | Rural | 43% | 51% | 69 |
| Kab. Lanny Jaya | 0.6% | Papua | Papua | Rural | 40% | 46% | 172 |
| Kab. Buru Selatan | 0.6% | Maluku | Papua | Rural | 16% | 67% | 59 |
| Kab. Muna Barat | 0.7% | Southeast Sulawesi | Sulawesi | Rural | 14% | 71% | 77 |
| Kab. Pegunungan Bintang | 1.0% | Papua | Papua | Rural | 31% | 21% | 72 |
| Kab. Jayawijaya | 1.3% | Papua | Papua | Rural | 39% | 67% | 206 |
| AVERAGE | 30% | 51% | 104 | ||||
| (d) Undiagnosed all | |||||||
| Kab. Puncak Jaya | 7% | Papua | Papua | Rural | 36% | 21% | 115 |
| Kab. Nduga | 10% | Papua | Papua | Rural | 38% | 9% | 94 |
| Kab. Tolikara | 10% | Papua | Papua | Rural | 33% | 34% | 131 |
| Kab. Asmat | 11% | Papua | Papua | Rural | 27% | 21% | 88 |
| Kab. Halmahera Tengah | 11% | North Maluku | Papua | Rural | 14% | 63% | 50 |
| Kab. Mimika | 12% | Papua | Papua | Rural | 15% | 67% | 201 |
| Kab. Sorong Selatan | 12% | West Papua | Papua | Rural | 19% | 56% | 43 |
| Kab. Simeulue | 12% | Aceh | Sumatera | Rural | 20% | 81% | 89 |
| Kab. Aceh Jaya | 12% | Aceh | Sumatera | Rural | 14% | 74% | 86 |
| Kab. Teluk Wondama | 13% | West Papua | Papua | Rural | 33% | 39% | 30 |
| AVERAGE | 25% | 46% | 93 | ||||
| (e) Undiagnosed males | |||||||
| Kab. Puncak Jaya | 6.8% | Papua | Papua | Rural | 36% | 21% | 115 |
| Kab. Halmahera Tengah | 9.4% | North Maluku | Papua | Rural | 14% | 63% | 50 |
| Kab. Keerom | 10.1% | Papua | Papua | Rural | 17% | 61% | 54 |
| Kab. Aceh Jaya | 10.1% | Aceh | Sumatera | Rural | 14% | 74% | 86 |
| Kab. Simeulue | 10.5% | Aceh | Sumatera | Rural | 20% | 81% | 89 |
| Kab. Dompu | 10.6% | West Nusa Tenggara | Papua | Rural | 12% | 70% | 238 |
| Kab. Nduga | 11.2% | Papua | Papua | Rural | 38% | 9% | 94 |
| Kab. Sumbawa | 11.6% | West Nusa Tenggara | Papua | Rural | 14% | 56% | 441 |
| Kab. Buton Tengah | 11.7% | Southeast Sulawesi | Sulawesi | Rural | 15% | 80% | 89 |
| Kab. Sorong Selatan | 11.8% | West Papua | Papua | Rural | 19% | 56% | 43 |
| AVERAGE | 20% | 57% | 130 | ||||
| (f) Undiagnosed females | |||||||
| Kab. Puncak Jaya | 6.2% | Papua | Papua | Rural | 36% | 21% | 115 |
| Kab. Nduga | 7.8% | Papua | Papua | Rural | 38% | 9% | 94 |
| Kab. Tolikara | 8.4% | Papua | Papua | Rural | 33% | 34% | 131 |
| Kab. Tambrauw | 9.6% | West Papua | Papua | Rural | 35% | 47% | 14 |
| Kab. Asmat | 10.0% | Papua | Papua | Rural | 27% | 21% | 88 |
| Kab. Mimika | 10.5% | Papua | Papua | Rural | 15% | 67% | 201 |
| Kab. Jayawijaya | 11.2% | Papua | Papua | Rural | 39% | 67% | 206 |
| Kab. Yahukimo | 11.3% | Papua | Papua | Rural | 39% | 12% | 181 |
| Kab. Mambramo Tengah | 11.4% | Papua | Papua | Rural | 37% | 54% | 46 |
| Kab. Boven Digul | 12.4% | Papua | Papua | Rural | 20% | 35% | 63 |
| AVERAGE | 32% | 37% | 114 |
Note: Urban = city, Rural = regency; Pop = population. The districts are ordered by prevalence (column 1).
Appendix D. Ten Districts with HIGHEST Prevalence of Diagnosed and Undiagnosed Hypertension among Adults in Indonesia
| Prevalence | Province | Region | Urban | Poverty | Education | Pop (000) | |
| (a) Diagnosed all | |||||||
| Kab. Sitaro Kepulauan | 20.8% | North Sulawesi | Sulawesi | Rural | 10% | 71% | 66 |
| Kota Tomohon | 17.7% | North Sulawesi | Sulawesi | Urban | 6% | 71% | 100 |
| Kab. Kep Talaud | 16.6% | North Sulawesi | Sulawesi | Rural | 10% | 71% | 89 |
| Kab. Natuna Kep | 16.5% | Riau Islands | Sumatera | Rural | 5% | 70% | 74 |
| Kab. Minahasa | 16.3% | North Sulawesi | Sulawesi | Rural | 7% | 65% | 329 |
| Kab. Anambas Kep | 15.6% | Riau Islands | Sumatera | Rural | 7% | 77% | 40 |
| Kab. Sumedang | 15.3% | West Java | Java | Rural | 10% | 43% | 1137 |
| Kab. Tanah Tidung | 14.4% | North Kalimantan | Kalimantan | Rural | 5% | 45% | 22 |
| Kab. Minahasa Selatan | 14.3% | North Sulawesi | Sulawesi | Rural | 9% | 62% | 205 |
| Kab. Karimun | 14.2% | Riau Islands | Sumatera | Rural | 7% | 70% | 225 |
| AVERAGE | 8% | 65% | 229 | ||||
| (b) Diagnosed males | |||||||
| Kota Tomohon | 15.8% | North Sulawesi | Sulawesi | Urban | 6% | 71% | 100 |
| Kab. Puncak Jaya | 15.2% | Papua | Papua | Rural | 36% | 21% | 115 |
| Kab. Sitaro Kepulauan | 14.2% | North Sulawesi | Sulawesi | Rural | 10% | 71% | 66 |
| Kab. Kep Talaud | 13.3% | North Sulawesi | Sulawesi | Rural | 10% | 71% | 89 |
| Kab. Mahakam Ulu | 13.2% | East Kalimantan | Kalimantan | Rural | 12% | 52% | 26 |
| Kab. Minahasa | 12.9% | North Sulawesi | Sulawesi | Rural | 7% | 65% | 329 |
| Kab. Gianyar | 11.5% | Bali | Java | Rural | 4% | 77% | 495 |
| Kab Klungkung | 11.2% | Bali | Java | Rural | 6% | 77% | 176 |
| Kab. Tanah Tidung | 11.0% | North Kalimantan | Kalimantan | Rural | 5% | 45% | 22 |
| Kab. Natuna Kep | 11.0% | Riau Islands | Sumatera | Rural | 5% | 70% | 74 |
| AVERAGE | 10% | 62% | 149 | ||||
| (c) Diagnosed females | |||||||
| Kab. Sitaro Kepulauan | 27.0% | North Sulawesi | Sulawesi | Rural | 10% | 71% | 66 |
| Kab. Anambas Kep | 22.4% | Riau Islands | Sumatera | Rural | 7% | 77% | 40 |
| Kab. Natuna Kep | 22.4% | Riau Islands | Sumatera | Rural | 5% | 70% | 74 |
| Kab. Sumedang | 20.9% | West Java | Java | Rural | 10% | 43% | 1137 |
| Kab. Minahasa Selatan | 20.3% | North Sulawesi | Sulawesi | Rural | 9% | 62% | 205 |
| Kab. Minahasa | 19.9% | North Sulawesi | Sulawesi | Rural | 7% | 65% | 329 |
| Kab. Kep Talaud | 19.7% | North Sulawesi | Sulawesi | Rural | 10% | 71% | 89 |
| Kota Tomohon | 19.6% | North Sulawesi | Sulawesi | Urban | 6% | 71% | 100 |
| Kab. Sangihe Kepulauan | 19.0% | North Sulawesi | Sulawesi | Rural | 12% | 55% | 130 |
| Kab. Hulu Sungai Utara | 19.0% | South Kalimantan | Kalimantan | Rural | 6% | 55% | 225 |
| AVERAGE | 8% | 64% | 239 | ||||
| (d) Undiagnosed all | |||||||
| Kab. Hulu Sungai Tengah | 43.2% | South Kalimantan | Kalimantan | Rural | 6% | 66% | 260 |
| Kab. Tabalong | 42.2% | South Kalimantan | Kalimantan | Rural | 6% | 61% | 239 |
| Kab. Mamasa | 40.8% | West Sulawesi | Sulawesi | Rural | 13% | 66% | 152 |
| Kab. Ciamis | 40.8% | West Java | Java | Rural | 7% | 51% | 1168 |
| Kota Madiun | 40.4% | East Java | Java | Urban | 4% | 80% | 175 |
| Kab. Cianjur | 40.2% | West Java | Java | Rural | 10% | 45% | 2243 |
| Kab. Barito Kuala | 39.1% | South Kalimantan | Kalimantan | Rural | 5% | 62% | 298 |
| Melawi | 38.6% | West Kalimantan | Kalimantan | Rural | 13% | 41% | 196 |
| Kab. Karo | 38.4% | North Sumatera | Sumatera | Rural | 9% | 74% | 389 |
| Kab. Kutai Barat | 38.4% | East Kalimantan | Kalimantan | Rural | 9% | 60% | 146 |
| AVERAGE | 8% | 61% | 527 | ||||
| (e) Undiagnosed males | |||||||
| Kota Madiun | 44.9% | East Java | Java | Urban | 4% | 80% | 175 |
| Kab. Tabalong | 43.6% | South Kalimantan | Kalimantan | Rural | 6% | 61% | 239 |
| Kab. Hulu Sungai Tengah | 42.5% | South Kalimantan | Kalimantan | Rural | 6% | 66% | 260 |
| Kab. Karo | 41.3% | North Sumatera | Sumatera | Rural | 9% | 74% | 389 |
| Kab. Kutai Barat | 41.1% | East Kalimantan | Kalimantan | Rural | 9% | 60% | 146 |
| Kota Banjarmasin | 40.5% | South Kalimantan | Kalimantan | Urban | 4% | 55% | 675 |
| Kota Singkawang | 40.4% | West Kalimantan | Kalimantan | Urban | 5% | 60% | 207 |
| Kab. Buton Selatan | 39.6% | Southeast Sulawesi | Sulawesi | Rural | 15% | 44% | 77 |
| Kab. Mamasa | 39.2% | West Sulawesi | Sulawesi | Rural | 13% | 66% | 152 |
| Kab. Barito Kuala | 39.2% | South Kalimantan | Kalimantan | Rural | 5% | 62% | 298 |
| AVERAGE | 8% | 63% | 262 | ||||
| (f) Undiagnosed females | |||||||
| Kab. Ciamis | 44.6% | West Java | Jawa | Rural | 7% | 51% | 1168 |
| Kab. Hulu Sungai Tengah | 43.9% | South Kalimantan | Kalimantan | Rural | 6% | 66% | 260 |
| Melawi | 43.0% | West Kalimantan | Kalimantan | Rural | 13% | 41% | 196 |
| Kab. Mamasa | 42.4% | West Sulawesi | Sulawesi | Rural | 13% | 66% | 152 |
| Kab. Cianjur | 41.9% | West Java | Jawa | Rural | 10% | 45% | 2243 |
| Kab. Tabalong | 40.7% | South Kalimantan | Kalimantan | Rural | 6% | 61% | 239 |
| Kab. Indramayu | 40.4% | West Java | Jawa | Rural | 12% | 56% | 1691 |
| Kab. Wonosobo | 40.4% | Central Java | Jawa | Rural | 18% | 45% | 777 |
| Kab. Nganjuk | 40.0% | East Java | Jawa | Rural | 12% | 63% | 1041 |
| Kota Batu | 39.8% | East Java | Jawa | Urban | 4% | 73% | 200 |
| AVERAGE | 10% | 57% | 797 |
Note: Urban = city, Rural = regency; Pop = population. The districts are ordered by prevalence (column 1).
Appendix E. Regression Outputs for Geographic and Socioeconomic Disparity in Diagnosed and Undiagnosed Hypertension
| Diagnosed All | Diagnosed Males | Diagnosed Females | Undiagnosed All | Undiagnosed Males | Undiagnosed Females | |
| Coef | Coef | Coef | Coef | Coef | Coef | |
| (a) All districts (N = 514) | ||||||
| Papua | Reference | |||||
| Java | 3.05 ** | 1.60 ** | 4.29 ** | 7.72 ** | 7.07 ** | 8.45 ** |
| Sumatera | 1.88 ** | 0.74 * | 2.95 ** | 1.32 | 0.06 | 2.70 ** |
| Kalimantan | 3.65 ** | 2.23 ** | 5.13 ** | 9.36 ** | 8.77 ** | 10.00 ** |
| Sulawesi | 2.77 ** | 1.53 ** | 3.93 ** | 3.91 ** | 3.49 ** | 4.36 ** |
| Income | ||||||
| Quintile 1 poor | Reference | |||||
| Quintile 2 | −0.20 | −0.41 | 0.03 | 2.10 ** | 1.76 * | 2.39 ** |
| Quintile 3 | 0.15 | −0.12 | 0.37 | 2.56 ** | 2.31 ** | 2.73 ** |
| Quintile 4 | 0.58 | 0.18 | 0.99 | 1.23 | 1.47 | 0.96 |
| Quintile 5 rich | 0.64 | 0.67 | 0.61 | 0.21 | 1.47 | −1.06 |
| Education | ||||||
| Quintile 1 least | Reference | |||||
| Quintile 2 | 0.59 | 0.09 | 1.00 * | −0.93 | −0.89 | −0.90 |
| Quintile 3 | 0.60 | 0.35 | 0.77 | −0.27 | 0.22 | −0.72 |
| Quintile 4 | 0.89 * | 0.54 | 1.16 * | −1.25 | −0.62 | −1.82 * |
| Quintile 5 most | 1.14 ** | 0.76 * | 1.44 ** | −0.65 | 0.45 | −1.73 * |
| (b) Urban (N = 97) | ||||||
| Papua | Reference | |||||
| Java | 3.47 ** | 2.30 ** | 4.51 ** | 7.42 ** | 7.38 ** | 7.68 ** |
| Sumatera | 1.42 | 0.84 | 1.94 * | 0.62 | −0.09 | 1.44 |
| Kalimantan | 3.99 ** | 3.11 ** | 4.87 ** | 8.44 ** | 9.12 ** | 7.81 ** |
| Sulawesi | 3.74 ** | 3.07 ** | 4.27 ** | 3.75 | 4.51 | 3.21 |
| Income | ||||||
| Quintile 1 poor | Reference | |||||
| Quintile 2 | 1.15 | 0.26 | 2.14 | −3.13 | −2.96 | −3.34 |
| Quintile 3 | 1.17 | 0.20 | 2.21 | −1.65 | −1.43 | −1.95 |
| Quintile 4 | −0.46 | −1.09 | 0.24 | −4.08 | −4.28 | −3.99 |
| Quintile 5 rich | 0.13 | −0.41 | 0.78 | −3.43 | −2.69 | −4.28 |
| Education | ||||||
| Quintile 1 least | n/a | n/a | n/a | n/a | n/a | n/a |
| Quintile 2 | Reference | |||||
| Quintile 3 | −0.79 | −0.42 | −1.13 | 0.70 | 1.38 | 0.05 |
| Quintile 4 | −0.15 | −0.13 | −0.12 | −0.52 | −0.08 | −0.90 |
| Quintile 5 most | −0.21 | −0.23 | −0.18 | 1.96 | 2.51 | 1.48 |
| (c) Rural (N = 417) | ||||||
| Papua | Reference | |||||
| Java | 3.07 ** | 1.56 ** | 4.34 ** | 7.89 ** | 7.26 ** | 8.57 ** |
| Sumatera | 2.00 ** | 0.78 * | 3.15 ** | 1.34 | 0.16 | 2.65 ** |
| Kalimantan | 3.78 ** | 2.33 ** | 5.28 ** | 9.31 ** | 9.18 ** | 9.51 ** |
| Sulawesi | 2.74 ** | 1.39 ** | 4.00 ** | 3.92 ** | 3.48 ** | 4.38 ** |
| Income | ||||||
| Quintile 1 poor | Reference | |||||
| Quintile 2 | −0.28 | −0.43 | −0.12 | 2.27 ** | 1.93 * | 2.57 ** |
| Quintile 3 | −0.03 | −0.24 | 0.15 | 2.72 ** | 2.25 * | 3.12 ** |
| Quintile 4 | 0.66 | 0.19 | 1.13 | 1.81 * | 1.76 | 1.81 |
| Quintile 5 rich | 0.38 | 0.33 | 0.49 | 0.71 | 0.75 | 0.67 |
| Education | ||||||
| Quintile 1 least | Reference | |||||
| Quintile 2 | 0.49 | −0.03 | 0.92 | −0.88 | −0.89 | −0.79 |
| Quintile 3 | 0.61 | 0.30 | 0.84 | −0.06 | 0.20 | −0.27 |
| Quintile 4 | 0.72 | 0.33 | 1.05 | −0.76 | −0.52 | −0.92 |
| Quintile 5 most | 1.15 * | 0.74 * | 1.54 * | −0.90 | −0.14 | −1.66 |
Note: Coef = OLS coefficient; significance level ** p < 0.01, * p < 0.05.
References
- WHO. Hypertension Fact Sheets; WHO: Geneva, Switzerland, 2021. [Google Scholar]
- NIHRD. Report of Riskesdas; NIHRD: Jakarta, Indonesia, 2018. [Google Scholar]
- Hussain, M.A.; Al Mamun, A.; Reid, C.; Huxley, R.R. Prevalence, Awareness, Treatment and Control of Hypertension in Indonesian Adults Aged ≥40 Years: Findings from the Indonesia Family Life Survey (IFLS). PLoS ONE 2016, 11, e0160922. [Google Scholar] [CrossRef] [Green Version]
- Lang, T.; Lepage, B.; Schieber, A.-C.; Lamy, S.; Kelly-Irving, M. Social Determinants of Cardiovascular Diseases. Public Health Rev. 2011, 33, 601–622. [Google Scholar] [CrossRef] [Green Version]
- Busingye, D.; Arabshahi, S.; Subasinghe, A.K.; Evans, R.G.; Riddell, M.A.; Thrift, A.G. Do the socioeconomic and hypertension gradients in rural populations of low- and middle-income countries differ by geographical region? A systematic review and meta-analysis. Int. J. Epidemiol. 2014, 43, 1563–1577. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Lee, H.Y. Socioeconomic Disparities in the Prevalence, Diagnosis, and Control of Hypertension in the Context of a Universal Health Insurance System. J. Korean Med. Sci. 2017, 32, 561–567. [Google Scholar] [CrossRef] [PubMed]
- Guerrero-Díaz, D.V.; Hernández-Vásquez, A.; Montoya-Rivera, W.C.; Rojas-Roque, C.; Chacón Díaz, M.A.; Bendezu-Quispe, G. Undiagnosed hypertension in Peru: Analysis of associated factors and socioeconomic inequalities, 2019. Heliyon 2021, 7, e07516. [Google Scholar] [CrossRef]
- Ahmed, S.; Tariqujjaman, M.; Rahman, M.A.; Hasan, M.Z.; Hasan, M.M. Inequalities in the prevalence of undiagnosed hypertension among Bangladeshi adults: Evidence from a nationwide survey. Int. J. Equity Health 2019, 18, 33. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Haider, M.R.; Gupta, R. Das Inequalities in Undiagnosed Hypertension Among Adult Population in Bangladesh: Evidence from a Nationally Representative Survey. High Blood Press. Cardiovasc. Prev. 2021, 29, 57–64. [Google Scholar] [CrossRef]
- Hasan, M.M.; Tasnim, F.; Tariqujjaman, M.; Ahmed, S.; Cleary, A.; Mamun, A. Examining the prevalence, correlates and inequalities of undiagnosed hypertension in Nepal: A population-based cross-sectional study. BMJ Open 2020, 10, e037592. [Google Scholar] [CrossRef] [PubMed]
- Cho, K.H.; Lee, S.G.; Nam, C.M.; Lee, E.J.; Jang, S.Y.; Lee, S.H.; Park, E.C. Disparities in socioeconomic status and neighborhood characteristics affect all-cause mortality in patients with newly diagnosed hypertension in Korea: A nationwide cohort study, 2002–2013. Int. J. Equity Health 2016, 15, 3. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Wild, L.E.; Walters, M.; Powell, A.; James, K.A.; Corlin, L.; Alderete, T.L. County-Level Social Vulnerability Is Positively Associated with Cardiometabolic Disease in Colorado. Int. J. Environ. Res. Public Health 2022, 19, 2202. [Google Scholar] [CrossRef]
- Kershaw, K.N.; Diez Roux, A.V.; Carnethon, M.; Darwin, C.; Goff, D.C.; Post, W.; Schreiner, P.J.; Watson, K. Geographic variation in hypertension prevalence among blacks and whites: The multi-ethnic study of atherosclerosis. Am. J. Hypertens. 2010, 23, 46–53. [Google Scholar] [CrossRef] [PubMed]
- Morenoff, J.D.; House, J.S.; Hansen, B.B.; Williams, D.R.; Kaplan, G.A.; Hunte, H.E. Understanding social disparities in hypertension prevalence, awareness, treatment, and control: The role of neighborhood context. Soc. Sci. Med. 2007, 65, 1853–1866. [Google Scholar] [CrossRef]
- Li, Y.; Wang, L.; Feng, X.; Zhang, M.; Huang, Z.; Deng, Q.; Zhou, M.; Astell-Burt, T.; Wang, L. Geographical variations in hypertension prevalence, awareness, treatment and control in China: Findings from a nationwide and provincially representative survey. J. Hypertens. 2018, 36, 178–187. [Google Scholar] [CrossRef]
- Yin, M.; Augustin, B.; Fu, Z.; Yan, M.; Fu, A.; Yin, P. Geographic Distributions in Hypertension Diagnosis, Measurement, Prevalence, Awareness, Treatment and Control Rates among Middle-aged and Older Adults in China. Sci. Rep. 2016, 6, 37020. [Google Scholar] [CrossRef] [PubMed]
- Laohasiriwong, W.; Puttanapong, N.; Singsalasang, A. Prevalence of hypertension in Thailand: Hotspot clustering detected by spatial analysis. Geospat. Health 2018, 13, 20–27. [Google Scholar] [CrossRef] [PubMed]
- Kusuma, D.; Kusumawardani, N.; Ahsan, A.; Sebayang, S.K.; Amir, V.; Ng, N. On the verge of a chronic disease epidemic: Comprehensive policies and actions are needed in Indonesia. Int. Health 2019, 11, 422–424. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Ayuningtyas, D.; Hapsari, D.; Rachmalina, R.; Amir, V.; Rachmawati, R.; Kusuma, D. Geographic and Socioeconomic Disparity in Child Undernutrition across 514 Districts in Indonesia. Nutrients 2022, 14, 843. [Google Scholar] [CrossRef] [PubMed]
- Bella, A.; Akbar, M.T.; Kusnadi, G.; Herlinda, O.; Regita, P.A.; Kusuma, D. Socioeconomic and Behavioral Correlates of COVID-19 Infections among Hospital Workers in the Greater Jakarta Area, Indonesia: A Cross-Sectional Study. Int. J. Environ. Res. Public Health 2021, 18, 5048. [Google Scholar] [CrossRef] [PubMed]
- Hapsari, D.; Nainggolan, O.; Kusuma, D. Hotspots and Regional Variation in Smoking Prevalence Among 514 Districts in Indonesia: Analysis of Basic Health Research 2018. Glob. J. Health Sci. 2020, 12, 32. [Google Scholar] [CrossRef]
- 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. [Google Scholar] [CrossRef] [PubMed]
- Megatsari, H.; Kusuma, D.; Ernawaty, E.; Putri, N.K. Geographic and Socioeconomic Inequalities in Delays in COVID-19 Vaccinations: A Cross-Sectional Study in Indonesia. Vaccines 2022, 10, 1857. [Google Scholar] [CrossRef] [PubMed]
- Oktamianti, P.; Kusuma, D.; Amir, V.; Tjandrarini, D.H.; Paramita, A. District-Level Inequalities in Hypertension among Adults in Indonesia: A Cross-Sectional Analysis by Sex and Age Group. Int. J. Environ. Res. Public Health 2022, 19, 13268. [Google Scholar] [CrossRef]
- Di Cesare, M.; Khang, Y.H.; Asaria, P.; Blakely, T.; Cowan, M.J.; Farzadfar, F.; Guerrero, R.; Ikeda, N.; Kyobutungi, C.; Msyamboza, K.P.; et al. Inequalities in non-communicable diseases and effective responses. Lancet 2013, 381, 585–597. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Zhou, B.; Carrillo-Larco, R.M.; Danaei, G.; Riley, L.M.; Paciorek, C.J.; Stevens, G.A.; Gregg, E.W.; Bennett, J.E.; Solomon, B.; Singleton, R.K.; et al. Worldwide trends in hypertension prevalence and progress in treatment and control from 1990 to 2019: A pooled analysis of 1201 population-representative studies with 104 million participants. Lancet 2021, 398, 957–980. [Google Scholar] [CrossRef]
- Mosca, I.; Kenny, R.A. Exploring differences in prevalence of diagnosed, measured and undiagnosed hypertension: The case of Ireland and the United States of America. Int. J. Public Health 2014, 59, 759–767. [Google Scholar] [CrossRef] [Green Version]
- Kusuma, D.; Cohen, J.; McConnell, M.; Berman, P. Can cash transfers improve determinants of maternal mortality? Evidence from the household and community programs in Indonesia. Soc. Sci. Med. 2016, 163, 10–20. [Google Scholar] [CrossRef]
- Kusuma, D.; Atanasova, P.; Pineda, E.; Anjana, R.M.; De Silva, L.; Hanif, A.A.M.; Hasan, M.; Hossain, M.M.; Indrawansa, S.; Jayamanne, D.; et al. Food environment and diabetes mellitus in South Asia: A geospatial analysis of health outcome data. PLoS Med. 2022, 19, e1003970. [Google Scholar] [CrossRef]
- Li, J.; Shi, L.; Li, S.; Xu, L.; Qin, W.; Wang, H. Urban-rural disparities in hypertension prevalence, detection, and medication use among Chinese Adults from 1993 to 2011. Int. J. Equity Health 2017, 16, 50. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Ibrahim, M.M.; Damasceno, A. Hypertension in developing countries. Lancet 2012, 380, 611–619. [Google Scholar] [CrossRef] [PubMed]
- Di Chiara, T.; Scaglione, A.; Corrao, S.; Argano, C.; Pinto, A.; Scaglione, R. Association Between Low Education and Higher Global Cardiovascular Risk. J. Clin. Hypertens. 2015, 17, 332–337. [Google Scholar] [CrossRef] [PubMed]
- Di Chiara, T.; Scaglione, A.; Corrao, S.; Argano, C.; Pinto, A.; Scaglione, R. Education and hypertension: Impact on global cardiovascular risk. Acta Cardiol. 2017, 72, 507–513. [Google Scholar] [CrossRef]
- Nasution, F.; Gurning, F.; Siregar, P.; Ahsan, A.; Kusuma, D. Implementation of the Smoke-Free Policy in Medan City, Indonesia: Compliance and Challenges. Int. J. Prev. Med. 2022, 13, 30. [Google Scholar] [CrossRef]
- Sebayang, S.K.; Dewi, D.M.S.K.; Puspikawati, S.I.; Astutik, E.; Melaniani, S.; Kusuma, D. Spatial analysis of outdoor tobacco advertisement around children and adolescents in Indonesia. Glob. Public Health 2021, 17, 420–430. [Google Scholar] [CrossRef] [PubMed]
- Sivasampu, S.; Teh, X.R.; Lim, Y.M.F.; Ong, S.M.; Ang, S.H.; Husin, M.; Khamis, N.; Jaafar, F.S.A.; Wong, W.J.; Shanmugam, S.; et al. Study protocol on Enhanced Primary Healthcare (EnPHC) interventions: A quasi-experimental controlled study on diabetes and hypertension management in primary healthcare clinics. Prim. Health Care Res. Dev. 2020, 21, E27. [Google Scholar] [CrossRef] [PubMed]
- Kusuma, D. Lessons from primary health care in the United Kingdom. Indones. J. Health Adm. J. Adm. Kesehat. Indones. 2021, 9, 4–8. [Google Scholar] [CrossRef]
- Adisasmito, W.; Amir, V.; Atin, A.; Megraini, A.; Kusuma, D. Geographic and socioeconomic disparity in cardiovascular risk factors in Indonesia: Analysis of the Basic Health Research 2018. BMC Public Health 2020, 20, 1004. [Google Scholar] [CrossRef] [PubMed]
- Atanasova, P.; Kusuma, D.; Pineda, E.; Anjana, R.M.; De Silva, L.; Hanif, A.A.M.; Hasan, M.; Hossain, M.M.; Indrawansa, S.; Jayamanne, D.; et al. Food environments and obesity: A geospatial analysis of the South Asia Biobank, income and sex inequalities. SSM—Popul. Health 2022, 17, 101055. [Google Scholar] [CrossRef] [PubMed]
Figure 1.
Disparity in diagnosed and undiagnosed hypertension among adults (18+ years) by province in Indonesia, 2018. Note: Numbers show prevalence of diagnosed and undiagnosed hypertension among all adults, males, and females.
Figure 1.
Disparity in diagnosed and undiagnosed hypertension among adults (18+ years) by province in Indonesia, 2018. Note: Numbers show prevalence of diagnosed and undiagnosed hypertension among all adults, males, and females.
Figure 2.
Disparity in diagnosed and undiagnosed hypertension among adults (18+ years) by district in Indonesia, 2018. Note: Numbers show prevalence of diagnosed and undiagnosed hypertension among all adults, males, and females.
Figure 2.
Disparity in diagnosed and undiagnosed hypertension among adults (18+ years) by district in Indonesia, 2018. Note: Numbers show prevalence of diagnosed and undiagnosed hypertension among all adults, males, and females.
Table 1.
Prevalence of diagnosed and undiagnosed hypertension among adults (18+ years) by province in Indonesia, 2018.
Table 1.
Prevalence of diagnosed and undiagnosed hypertension among adults (18+ years) by province in Indonesia, 2018.
| Poverty | Hypertension Prevalence (%) | ||||||
|---|---|---|---|---|---|---|---|
| Rates | Diagnosed | Undiagnosed | |||||
| (%) | All | Males | Females | All | Males | Females | |
| [1] | [2] | [3] | [4] | [5] | [6] | [7] | |
| Bali | 4.5 | 9.5 | 7.8 | 11.2 | 22.4 | 25.0 | 19.9 |
| South Kalimantan | 4.8 | 10.0 | 6.8 | 13.2 | 35.5 | 35.6 | 35.4 |
| Central Kalimantan | 5.0 | 8.4 | 5.8 | 11.3 | 27.6 | 26.9 | 28.4 |
| Jakarta | 5.0 | 10.1 | 7.7 | 12.5 | 25.3 | 27.1 | 23.5 |
| Banten | 5.3 | 8.6 | 5.9 | 11.4 | 22.8 | 22.4 | 23.2 |
| Bangka Belitung | 5.4 | 8.3 | 4.7 | 12.3 | 23.2 | 23.0 | 23.4 |
| West Sumatera | 6.6 | 7.2 | 5.2 | 9.2 | 19.8 | 18.7 | 20.9 |
| North Kalimantan | 7.0 | 10.5 | 7.3 | 14.1 | 24.8 | 26.3 | 23.0 |
| East Kalimantan | 7.1 | 10.6 | 8.2 | 13.2 | 30.6 | 31.8 | 29.3 |
| Riau Islands | 7.6 | 8.5 | 5.8 | 11.5 | 19.5 | 21.7 | 17.3 |
| Jambi | 7.8 | 7.4 | 5.1 | 9.8 | 22.7 | 21.5 | 23.9 |
| North Maluku | 7.9 | 5.7 | 4.0 | 7.5 | 20.7 | 20.3 | 21.2 |
| West Java | 7.9 | 9.7 | 6.3 | 13.1 | 31.2 | 30.5 | 32.0 |
| West Kalimantan | 8.1 | 8.1 | 6.1 | 10.3 | 30.2 | 30.0 | 30.4 |
| North Sulawesi | 8.5 | 13.2 | 9.5 | 17.0 | 23.7 | 25.5 | 21.7 |
| Riau | 8.8 | 8.4 | 5.9 | 11.0 | 22.6 | 21.9 | 23.4 |
| South Sulawesi | 9.8 | 7.1 | 4.5 | 9.4 | 26.1 | 24.9 | 27.3 |
| West Sulawesi | 10.3 | 6.6 | 5.1 | 8.1 | 29.7 | 28.6 | 30.7 |
| East Java | 10.9 | 8.0 | 5.2 | 10.6 | 29.7 | 28.6 | 30.7 |
| Central Java | 10.9 | 8.1 | 5.6 | 10.5 | 30.6 | 30.1 | 31.1 |
| North Sumatera | 11.3 | 5.5 | 3.8 | 7.1 | 24.8 | 24.7 | 25.0 |
| Lampung | 12.6 | 8.0 | 5.1 | 10.9 | 23.2 | 21.0 | 25.4 |
| Yogyakarta | 12.7 | 10.7 | 7.3 | 14.0 | 24.5 | 26.8 | 22.3 |
| Southeast Sulawesi | 13.0 | 6.2 | 3.9 | 8.4 | 24.9 | 25.6 | 24.2 |
| South Sumatera | 13.1 | 7.3 | 5.2 | 9.5 | 24.4 | 22.7 | 26.2 |
| Central Sulawesi | 14.6 | 8.7 | 6.3 | 11.2 | 23.5 | 22.2 | 24.9 |
| West Nusa Tenggara | 14.8 | 7.2 | 5.2 | 9.0 | 22.1 | 19.3 | 24.6 |
| Bengkulu | 15.0 | 8.4 | 5.5 | 11.3 | 21.5 | 20.4 | 22.6 |
| Aceh | 16.4 | 9.4 | 6.3 | 12.3 | 19.5 | 18.9 | 20.0 |
| Gorontalo | 16.8 | 10.0 | 7.2 | 12.8 | 22.7 | 21.0 | 24.4 |
| Maluku | 21.8 | 5.0 | 4.0 | 5.9 | 25.0 | 25.2 | 24.8 |
| East Nusa Tenggara | 22.0 | 5.4 | 4.0 | 6.7 | 23.6 | 23.3 | 23.9 |
| West Papua | 26.5 | 7.4 | 5.6 | 9.4 | 20.6 | 22.2 | 18.9 |
| Papua | 29.4 | 4.4 | 3.7 | 5.2 | 19.4 | 20.3 | 18.4 |
| Average | 8.2 | 5.8 | 10.6 | 24.7 | 24.5 | 24.8 | |
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 show higher than the national average prevalence for each group.
Table 2.
Characteristics of districts and hypertension (diagnosed and undiagnosed) among adults (18+ years) in Indonesia, 2018.
Table 2.
Characteristics of districts and hypertension (diagnosed and undiagnosed) among adults (18+ years) in Indonesia, 2018.
| All | Urban | Rural | Difference | |||||
|---|---|---|---|---|---|---|---|---|
| n | % | n | % | n | % | % | ||
| [1] | [2] | [3] | [4] | [5] | [6] | [7] = [4,5,6] | ||
| (a) Characteristics (#) | ||||||||
| Sample size district | 514 | 100% | 97 | 100% | 417 | 100% | 0% | |
| Region | ||||||||
| Papua | 95 | 18.5% | 9 | 9.3% | 86 | 20.6% | 11.3% | |
| Java | 128 | 24.9% | 35 | 36.1% | 93 | 22.3% | −13.8% | |
| Sumatera | 154 | 30.0% | 33 | 34.0% | 121 | 29.0% | −5.0% | |
| Kalimantan | 56 | 10.9% | 9 | 9.3% | 47 | 11.3% | 2.0% | |
| Sulawesi | 81 | 15.8% | 11 | 11.3% | 70 | 16.8% | 5.4% | |
| 514 | 97 | 417 | ||||||
| Income/poverty | ||||||||
| Q1 poor | 102 | 19.8% | 3 | 3.1% | 99 | 23.7% | 20.6% | |
| Q2 | 103 | 20.0% | 5 | 5.2% | 98 | 23.5% | 18.3% | |
| Q3 | 103 | 20.0% | 13 | 13.4% | 90 | 21.6% | 8.2% | |
| Q4 | 103 | 20.0% | 22 | 22.7% | 81 | 19.4% | −3.3% | |
| Q5 rich | 103 | 20.0% | 54 | 55.7% | 49 | 11.8% | −43.9% | |
| 514 | 97 | 417 | ||||||
| Education | ||||||||
| Q1 least | 103 | 20.0% | 0 | 0.0% | 103 | 24.7% | 24.7% | |
| Q2 | 103 | 20.0% | 11 | 11.3% | 92 | 22.1% | 10.7% | |
| Q3 | 103 | 20.0% | 17 | 17.5% | 86 | 20.6% | 3.1% | |
| Q4 | 103 | 20.0% | 29 | 29.9% | 74 | 17.7% | −12.2% | |
| Q5 most | 102 | 19.8% | 40 | 41.2% | 62 | 14.9% | −26.4% | |
| 514 | 97 | 417 | ||||||
| (b) Hypertension (%) | ||||||||
| Diagnosed all | n/a | 7.9% | n/a | 8.9% | n/a | 7.6% | 1.3% * | |
| Diagnosed males | n/a | 5.5% | n/a | 6.5% | n/a | 5.3% | 1.2% * | |
| Diagnosed females | n/a | 10.3% | n/a | 11.2% | n/a | 10.1% | 1.1% * | |
| Undiagnosed all | n/a | 25.4% | n/a | 24.7% | n/a | 25.5% | −0.8% | |
| Undiagnosed males | n/a | 24.9% | n/a | 26.1% | n/a | 24.7% | 1.4% | |
| Undiagnosed females | n/a | 25.8% | n/a | 23.4% | n/a | 26.4% | −3.0% * | |
Note: Q = quintile, n = number, % = proportion of column total, Urban = city, Rural = regency. Data on district characteristics are from the World Bank and hypertension data are from Basic Health Survey 2018. Bold numbers with asterisk (*) show statistical significance at 5% level (see Appendix B for the regression outputs).
Table 3.
Geographic and socioeconomic disparity in diagnosed and undiagnosed hypertension among adults (18+ years) in Indonesia, 2018.
Table 3.
Geographic and socioeconomic disparity in diagnosed and undiagnosed hypertension among adults (18+ years) in Indonesia, 2018.
| All Districts (n = 514) | Urban (n = 97) | Rural (n = 417) | |||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Diagnosed | Undiagnosed | Diagnosed | Undiagnosed | Diagnosed | Undiagnosed | ||||||||||||||
| All | Males | Females | All | Males | Females | All | Males | Females | All | Males | Females | All | Males | Females | All | Males | Females | ||
| [1] | [2] | [3] | [4] | [5] | [6] | [7] | [8] | [9] | [10] | [11] | [12] | [13] | [14] | [15] | [16] | [17] | [18] | ||
| Region | |||||||||||||||||||
| Papua | 5.3% | 4.2% | 6.6% | 21.0% | 20.9% | 21.1% | 6.6% | 5.0% | 8.2% | 21.7% | 23.2% | 20.2% | 5.2% | 4.1% | 6.4% | 20.9% | 20.6% | 21.2% | |
| Sulawesi | 8.4% | 5.8% | 11.0% | 25.6% | 25.2% | 26.0% | 9.9% | 7.7% | 12.0% | 23.4% | 25.4% | 21.4% | 8.1% | 5.4% | 10.8% | 25.9% | 25.1% | 26.7% | |
| Kalimantan | 9.5% | 6.8% | 12.3% | 30.6% | 30.6% | 30.5% | 10.1% | 7.8% | 12.5% | 28.3% | 30.6% | 25.9% | 9.3% | 6.7% | 12.3% | 31.0% | 30.6% | 31.4% | |
| Sumatera | 7.7% | 5.2% | 10.3% | 22.8% | 21.9% | 23.8% | 8.0% | 5.8% | 10.2% | 21.9% | 22.7% | 21.2% | 7.7% | 5.1% | 10.3% | 23.1% | 21.7% | 24.5% | |
| Java | 8.9% | 6.1% | 11.6% | 29.3% | 29.0% | 29.6% | 9.6% | 7.0% | 12.2% | 27.6% | 29.1% | 26.3% | 8.6% | 5.8% | 11.3% | 29.9% | 29.0% | 30.8% | |
| Absolute | 3.6% | 1.9% | 5.0% | 8.3% | 8.1% | 8.5% | 3.0% | 2.0% | 4.0% | 5.9% | 5.9% | 6.1% | 3.4% | 1.7% | 4.9% | 9.0% | 8.4% | 9.6% | |
| Relative | 1.68 | 1.45 | 1.76 | 1.40 | 1.39 | 1.40 | 1.45 | 1.40 | 1.49 | 1.27 | 1.25 | 1.30 | 1.65 | 1.41 | 1.77 | 1.43 | 1.41 | 1.45 | |
| Income | |||||||||||||||||||
| Q1 poor | 6.2% | 4.7% | 7.9% | 21.7% | 21.3% | 22.1% | 7.2% | 5.8% | 8.5% | 23.8% | 24.4% | 23.3% | 6.2% | 4.7% | 7.9% | 21.6% | 21.2% | 22.1% | |
| Q2 | 7.3% | 4.9% | 9.8% | 25.3% | 24.4% | 26.2% | 9.0% | 6.5% | 11.4% | 22.9% | 23.6% | 22.2% | 7.3% | 4.9% | 9.7% | 25.4% | 24.4% | 26.4% | |
| Q3 | 8.1% | 5.5% | 10.7% | 27.6% | 26.6% | 28.5% | 8.5% | 6.2% | 10.9% | 23.3% | 24.2% | 22.5% | 8.1% | 5.4% | 10.7% | 28.2% | 26.9% | 29.4% | |
| Q4 | 8.6% | 5.8% | 11.4% | 25.9% | 25.4% | 26.4% | 8.5% | 6.1% | 10.9% | 24.4% | 25.2% | 23.6% | 8.6% | 5.7% | 11.5% | 26.3% | 25.4% | 27.2% | |
| Q5 rich | 9.0% | 6.6% | 11.5% | 26.4% | 27.1% | 25.7% | 9.2% | 6.9% | 11.5% | 25.4% | 27.2% | 23.6% | 8.9% | 6.3% | 11.6% | 27.5% | 27.0% | 28.1% | |
| Absolute | 2.8% | 1.9% | 3.6% | 4.7% | 5.8% | 3.6% | 2.0% | 1.1% | 3.0% | 1.6% | 2.8% | 0.3% | 2.7% | 1.6% | 3.7% | 5.9% | 5.8% | 6.0% | |
| Relative | 1.45 | 1.40 | 1.46 | 1.22 | 1.27 | 1.16 | 1.28 | 1.19 | 1.35 | 1.07 | 1.11 | 1.01 | 1.44 | 1.34 | 1.47 | 1.27 | 1.27 | 1.27 | |
| Education | |||||||||||||||||||
| Q1 least | 6.9% | 5.0% | 8.9% | 25.8% | 25.1% | 26.6% | n/a | n/a | n/a | n/a | n/a | n/a | 6.9% | 5.0% | 8.9% | 25.8% | 25.1% | 26.6% | |
| Q2 | 8.0% | 5.3% | 10.6% | 25.6% | 24.8% | 26.4% | 10.0% | 7.6% | 12.4% | 25.2% | 26.6% | 23.8% | 7.7% | 5.1% | 10.4% | 25.7% | 24.6% | 26.7% | |
| Q3 | 7.8% | 5.5% | 10.2% | 25.8% | 25.4% | 26.2% | 8.5% | 6.5% | 10.6% | 25.5% | 27.2% | 23.7% | 7.7% | 5.3% | 10.1% | 25.9% | 25.0% | 26.7% | |
| Q4 | 8.2% | 5.7% | 10.6% | 24.6% | 24.3% | 24.8% | 9.1% | 6.7% | 11.5% | 23.9% | 25.2% | 22.6% | 7.8% | 5.3% | 10.3% | 24.8% | 23.9% | 25.7% | |
| Q5 most | 8.5% | 5.9% | 11.1% | 25.1% | 25.2% | 25.0% | 8.5% | 6.2% | 10.8% | 24.9% | 26.1% | 23.8% | 8.5% | 5.8% | 11.2% | 25.2% | 24.6% | 25.8% | |
| Absolute | 1.6% | 0.9% | 2.2% | −0.7% | 0.1% | −1.6% | −1.5% | −1.4% | −1.6% | −0.3% | −0.5% | 0.0% | 1.6% | 0.8% | 2.3% | −0.6% | −0.5% | −0.8% | |
| Relative | 1.23 | 1.18 | 1.25 | 0.97 | 1.00 | 0.94 | 0.85 | 0.82 | 0.87 | 0.99 | 0.98 | 1.00 | 1.23 | 1.16 | 1.26 | 0.98 | 0.98 | 0.97 | |
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 areas (Q2 and Q5). Boldface values show statistical significance at 5% level (see Appendix E for the regression outputs).
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
MDPI and ACS Style
Oktamianti, P.; Kusuma, D.; Amir, V.; Tjandrarini, D.H.; Paramita, A. Does the Disparity Patterning Differ between Diagnosed and Undiagnosed Hypertension among Adults? Evidence from Indonesia. Healthcare 2023, 11, 816. https://doi.org/10.3390/healthcare11060816
AMA Style
Oktamianti P, Kusuma D, Amir V, Tjandrarini DH, Paramita A. Does the Disparity Patterning Differ between Diagnosed and Undiagnosed Hypertension among Adults? Evidence from Indonesia. Healthcare. 2023; 11(6):816. https://doi.org/10.3390/healthcare11060816
Chicago/Turabian StyleOktamianti, Puput, Dian Kusuma, Vilda Amir, Dwi Hapsari Tjandrarini, and Astridya Paramita. 2023. "Does the Disparity Patterning Differ between Diagnosed and Undiagnosed Hypertension among Adults? Evidence from Indonesia" Healthcare 11, no. 6: 816. https://doi.org/10.3390/healthcare11060816
Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.
