Abstract
Objectives
This study aims to investigate whether there is a differential association between socioeconomic status (SES) and adherence to hypertension medication among older adults in rural and urban areas in Myanmar and assess what type of SES is associated with a difference.
Design
Cross-sectional study using baseline data from the Japan Gerontological Evaluation Study in Myanmar prospective cohort study. A multistage random sampling method was applied in each region.
Setting
An urban and a rural area in Myanmar.
Participants
A total of 1200 older adults over 60 years old in Myanmar were randomly selected in 2018 (600 each from rural and urban areas). Of them, 573 had hypertension and were eligible for the analysis (urban: 317, rural: 256).
Outcome
Adherence to hypertension medication (yes/no) is the outcome of interest. Three types of SES (wealth, education and current employment status) were the independent variables.
Results
We found that 21.5% of urban residents and 48.4% of rural residents were non-adherent in the study population. Poisson regression modelling stratified by area was performed to estimate the prevalence ratios (PRs) of not following treatment instructions. Demographic information and complications of hypertension were adjusted for in all models as possible confounders. In terms of SES, middle level of wealth compared with low level was significantly associated with poor adherence (PR 2.68, 95% CI 1.28 to 5.59) in the urban area, but education and employment status did not show similar associations. Lower education compared with middle/high school or higher was significantly associated with poor adherence in the rural area (no school: PR 3.22, 1.37–7.58; monastic: 3.42, 1.16–5.07; primary school: 2.41, 1.18–4.95), but wealth and income did not show similar associations.
Conclusions
SES and adherence to hypertension medication were differently associated among older adults in rural and urban areas in Myanmar. To ensure healthcare access to hypertension treatment for every citizen, the differential association between SES and adherence in urban/rural areas needs to be recognised.
Keywords: hypertension, epidemiology, public health, social medicine
STRENGTHS AND LIMITATIONS OF THIS STUDY.
Since a population-proportionate random sampling method was applied to the study in urban and rural areas, regional difference can be compared in the data.
A relatively low response rate of the study may affect the study result (although the response rate was not statistically different between urban and rural areas).
The result may not represent the whole older population in Myanmar because only 2 regions out of 14 regions/states in Myanmar were surveyed in the study.
With regards to the calculation of wealth, rural assets may not be accurately reflected, as some items that may be relevant to rural asset status, such as the presence of livestock, were not included in the survey instrument.
As a measure of adherence, scores such as the MMAS-8 could not be used in the study, so adherence-related factors may not be fully taken into account.
Introduction
According to WHO, cardiovascular disease (CVD) is the number one cause of death worldwide.1 Hypertension is one of the chronic diseases strongly associated with cardiovascular morbidity and mortality. It affects more than 1.1 billion people worldwide; 1 in 4 men and 1 in 5 women.2 Furthermore, hypertension has been the top CVD burden attributable to modifiable risk factors in at least the past three decades.3 In Asia, the prevalence of poor adherence to antihypertensive medications was reported to be 48%, which was relatively high compared with other regions.4 The rapid ageing of the population also contributes to this situation through a decrease in income due to retirement, and difficulty in consulting a doctor, for example, due to a decline in physical and cognitive functions.5 When considering the target of universal health coverage, it is crucial to identify the factors associated with access to treatment and maintenance of adherence in each country.
Treatment of hypertension is a long-term process, often lasting until the end of life. While ensuring continuity is very important, many social factors can hamper this effort. In 2003, the first official definition of adherence was published by WHO. The definition was ‘the extent to which a person’s behavior – taking medication, following a diet, and executing lifestyle changes, corresponds with agreed recommendations from a health care provider’.6 Although the concept of adherence has evolved gradually, the five categories that affect adherence and their details based on this definition, that is, sociodemographic, healthcare team/healthcare system, therapy-related, condition-related and patient-related, are still relevant today.6 7
The sociodemographic factors related to poor adherence to antihypertensives, which is the main focus of this study, were as follows: younger age,6 8 male sex,9 minority,7 10 low income,11 12 low wealth,13 14 low education,15 not favourable job,16 being employed,17 unstable residence,6 few social support,18 high co-payments19 and war.20 However, the association between residential regions, such as urban-rural difference, and non-adherence is controversial. Some studies showed that urban residents have lower adherence than rural residents (Brazil, USA),8 9 while others showed the opposite (India, Pakistan and Bangladesh).21 Furthermore, one study showed a null result (USA).22 Although we know that the prevalence of hypertension is higher in urban than in rural areas in low-income and middle-income countries (LMICs),21 23 it is not clear what makes the urban-rural difference of adherence in LMICs. As extracted from the literature review mentioned above, socioeconomic status (SES), such as wealth, education and working status, is one of the most important determinants of adherence globally. However, it has yet to be clarified whether the association between SES and adherence to hypertensive medications is different in rural and urban areas. It is important to investigate the type of SES that could be differently related in both urban and rural areas to gain more information about the appropriate interventions.
Myanmar is now considered one of the least developed countries in the world,24 but as other south-east Asian countries,5 it is one of the countries that is likely to face the same challenges of dealing with chronic diseases. In Myanmar, the prevalence of hypertension was 20%–30% in 2014,25 a percentage similar to that of other LMICs.26 Although the risk factors for hypertension in Myanmar, such as being female, living in urban areas and lacking physical activity at work,25 are gradually becoming more evident, adherence to hypertension treatment and related factors have not been investigated.
According to the United Nations Population Fund, in Myanmar in 2015, 30% of the population lived in urban areas while 70% in rural areas. Like other LMICs, the disparity between the poor and the rich could be wider in urban areas than in rural areas.27 Conversely, the amount of information and the opportunities to learn that could influence adherence are more affluent in urban areas than in rural areas. Therefore, the potential relationship between SES and adherence may differ in urban and rural areas.
The purpose of this study, which used data collected from the rural and urban regions of Myanmar, is to determine (1) Whether there is a differential association between SES and adherence to hypertension medication in rural and urban areas and (2) Which type of SES is associated with such a difference.
Methods
Study design and participants
The study is an analysis of cross-sectional baseline data from the longitudinal study, the Healthy and Active Ageing in Myanmar (Japan Gerontological Evaluation Study (JAGES) in Myanmar 2018) prospective cohort study.28 The study aims to clarify SES affecting active ageing in Myanmar. The study participants comprised 1200 community-dwelling older people in Myanmar aged 60 years and over. Of the 1200 participants, 600 were recruited from urban Yangon (population density was 716/km2),29 and the remaining 600 were from Bago, a rural area located about 90 km north-west of Yangon (population density was 124/km2).29
A multistage random sampling method was applied in each region. Six townships, each from Yangon with 35 townships and Bago with 28 townships, were randomly selected by population-proportionated sampling. Then, 10 wards were randomly selected from Yangon, and 10 village tracts were randomly selected from Bago. In the rural areas of Myanmar, sometimes there is more than one village in a single village tract, so in such cases, one of them was further randomly selected. Ward is the smallest unit of residential area in the urban areas in Myanmar, and village tract is the smallest unit in rural areas. In this study, we treated Yangon as an urban area and Bago as a rural area.
Trained surveyors with public health nurses visited the study participants’ homes and conducted interviews based on the survey questionnaire. In Yangon, they visited the homes of 1083 older adults. Of these, 610 were at home (contact rate=56.3%). Among them, 10 were excluded because of refusal to participate (n=6) or severely cognitively impaired or bedridden to the extent that they could not respond to the survey (n=4). Thus, the cooperation rate was 98.5% and the participation rate was 55.5% (0.563 × 0.985=0.555) in Yangon. In Bago, the surveyors visited 1044 people, and 694 were at home (contact rate=66.5%). Of these, 94 were cognitively impaired or bedridden and were excluded from the study. Therefore, the cooperation rate was 86.5% and the participation rate was 57.5% (0.665 × 0.865=0.575) in Bago.
Among the total participants, 573 were diagnosed with hypertension and were receiving a prescription. They were included in the analysis of this study since the outcome of the analysis was adherence to hypertension treatment (Yangon: 317; Bago: 256).
Questionnaire
We developed a structured questionnaire for face-to-face interviews based on the JAGES Questionnaire. The JAGES is a nationwide epidemiological survey conducted since 1999 to analyse the factors and social determinants of active ageing in Japan among older people who have not been certified as requiring care. The questionnaire was first translated from Japanese to English, from English to the local language, Burmese, and then back-translated to English to check for any discrepancies in the content. The validation of the questionnaire was conducted based on the Linguistic Validation Manual.30
Dependent variable
As the outcome measure, we used the question, ‘Do you take antihypertensive medication as instructed?’. The answer was a binary variable defining ‘No’ as 1, ‘Yes’ as 0.
Independent variable
We used self-reported wealth, educational attainment and current employment status as SES for independent variables. As for wealth, we calculated the wealth index from household asset items following the previous report.31 Principal component analysis was performed using the possession of the items, for example, radio, black and white television, colour television, video/Digital Versatile Disc (DVD) player, electric fan, refrigerator, computer, store-bought furniture, personal music player, washing machine, gas cooker, electric cooker or rice cooker, air conditioner, bicycle, motorcycle, van/truck, microwave oven, mobile telephone, and internet. The calculated score was categorised into tertiles in each area (high, middle, low). Education was categorised as ‘middle/high school or higher’, ‘some/all primary school’, ‘monastic’ and ‘no school’, following the educational context in Myanmar. Current employment status was divided into three groups such as ‘employed’, ‘retired’ and ‘never’.
Covariates
Potential confounding factors included age, sex, marital status and hypertension complications. Marital status was categorised as ‘married’, ‘widowed’, ‘divorced’ and ‘never married’. Renal disease, stroke, retinopathy and CVDs were defined as hypertension complications. Those who had one or more complications were categorised as ‘Yes’, those with no complications as ‘No’, and those who did not know whether they had complications or not as ‘Do not know’.
Statistical analysis
Poisson regression modelling was performed to estimate prevalence ratios (PRs) and 95% CIs. We defined those who lived in Yangon as urban residents and those in Bago as rural residents. Model 1 assessed the association between the wealth category and adherence by adjusting for demographic factors such as age, sex, marital status and hypertension complication. Model 2 was adjusted for educational attainment and other covariates. In Model 3, current employment status and other covariates were adjusted. In the final Model 4, all three SES factors were mutually adjusted. The same analysis was performed for the Yangon and Bago populations. The value of p for trend was also evaluated if the association showed a dose-response relation. All study participants answered all questions. Thus, there were no missing values for the used data. Analysis was conducted using Stata/SE V.17 (StataCorp, College Station, Texas, USA).
Patient and public involvement
None.
Results
The overall characteristics of the study participants are shown in table 1. Among the sample, 61.7% of Yangon residents and 56.3% of Bago residents had hypertension (p=0.06). The non-adherence rates were 21.5% in Yangon and 48.4% in Bago (p<0.001). Material wealth was divided into tertiles based on their respective distributions in urban and rural areas. For education attainment, 52.1% of Yangon and 14.5% of Bago residents had ‘middle/high school’ level of higher education (p<0.001). For employment status, 14.8% of Yangon and 26.6% of Bago residents were ‘employed’ (p<0.001, table 1).
Table 1.
Characteristics of the participants
| Region | All | Yangon | Bago | P value* |
| Overall participants (n=1200) | 1200 | 600 | 600 | |
| Hypertension | ||||
| Yes | 708 (59.0%) | 370 (61.7%) | 338 (56.3%) | 0.06 |
| No | 492 (41.0%) | 230 (38.3%) | 262 (43.7%) | |
| Study participants | 573 | 317 | 256 | |
| Taking hypertension medications | ||||
| As instructed | 381 (66.5%) | 249 (78.5%) | 132 (51.6%) | <0.001 |
| Not as instructed | 192 (33.5%) | 68 (21.5%) | 124 (48.4%) | |
| Age group, years | ||||
| 60–64 | 169 (29.5%) | 94 (29.7%) | 75 (29.3%) | 0.89 |
| 65–69 | 142 (24.8%) | 79 (24.9%) | 63 (24.6%) | |
| 70–74 | 117 (20.4%) | 65 (20.5%) | 52 (20.3%) | |
| 75–79 | 75 (13.1%) | 44 (13.9%) | 31 (12.1%) | |
| 80+ | 70 (12.2%) | 35 (11.0%) | 35 (13.7%) | |
| Sex | ||||
| Male | 196 (34.2%) | 106 (33.4%) | 90 (35.2%) | 0.67 |
| Female | 377 (65.8%) | 211 (66.6%) | 166 (64.8%) | |
| Marital status | ||||
| Married | 290 (50.6%) | 160 (50.5%) | 130 (50.8%) | 0.85 |
| Widowed | 246 (42.9%) | 134 (42.3%) | 112 (43.8%) | |
| Divorced | 5 (0.9%) | 3 (0.9%) | 2 (0.8%) | |
| Never married | 32 (5.6%) | 20 (6.3%) | 12 (4.7%) | |
| Hypertension complications (one or more: yes, none: no) | ||||
| No | 324 (56.5%) | 182 (57.4%) | 142 (55.5%) | 0.03 |
| Yes | 157 (27.4%) | 95 (30.0%) | 62 (24.2%) | |
| Do not know | 92 (16.1%) | 40 (12.6%) | 52 (20.3%) | |
| Wealth index category | ||||
| Low | 198 (34.6%) | 112 (35.3%) | 86 (33.6%) | 0.75 |
| Middle | 183 (31.9%) | 97 (30.6%) | 86 (33.4%) | |
| High | 192 (33.5%) | 108 (34.1%) | 84 (32.8%) | |
| Educational attainment | ||||
| No school | 50 (8.7%) | 23 (7.3%) | 27 (10.5%) | <0.001 |
| Monastic | 132 (23.0%) | 46 (14.5%) | 86 (33.6%) | |
| Some/all primary school | 189 (33.0%) | 83 (26.2%) | 106 (41.4%) | |
| Middle/high school or higher | 202 (35.3%) | 165 (52.1%) | 37 (14.5%) | |
| Employment status | ||||
| Employed | 115 (20.1%) | 47 (14.8%) | 68 (26.6%) | <0.001 |
| Retired | 409 (71.4%) | 223 (70.3%) | 186 (72.7%) | |
| Never | 49 (8.6%) | 47 (14.8%) | 2 (0.8%) |
*χ2 test
Table 2 shows the association between SES and non-adherence in Yangon. In the urban population, wealth was significantly associated with non-adherence (Model 1); that is, PR of ‘Low’ was 2.39 (95% CI 1.16 to 4.93), and the PR of ‘Middle’ was 2.99 (95% CI 1.48 to 6.05) compared with ‘High’. After mutual adjustment of the three types of SES, the association remained. Educational attainment was marginally significantly associated with non-adherence in ‘Primary school’ and ‘Monastic’. The association was dose-response according to p for trend (p=0.049) in Model 2, but after adjusting all three types of SES, the trend disappeared. Current employment status was not significantly associated.
Table 2.
Prevalence ratios (95% CIs) of not taking medicines as instructed in Yangon (n=317)
| Model 1 | Model 2 | Model 3 | Model 4 | |
| Wealth index category | ||||
| Low | 2.39 (1.16–4.93)* | 2.04 (0.94–4.42) | ||
| Middle | 2.99 (1.48–6.05)** | 2.68 (1.28–5.59)** | ||
| High | 1 (referent) | 1 (referent) | ||
| Educational attainment | ||||
| No school | 1.98 (0.75–5.21) | 1.63 (0.59–4.46) | ||
| Monastic | 1.80 (0.91–3.59) | 1.38 (0.67–2.83) | ||
| Primary school | 1.71 (0.94–3.11) | 1.40 (0.76–2.60) | ||
| Middle/high school or higher | 1 (referent) | 1 (referent) | ||
| Trend p | 0.049 | 0.264 | ||
| Current employment status | ||||
| Never | 0.48 (0.18–1.25) | 0.57 (0.31–1.07) | ||
| Retired | 0.62 (0.34–1.16) | 0.52 (0.19–1.38) | ||
| Employed | 1 (referent) | 1 (referent) |
Model 1 was adjusted for wealth index, age, sex, marital status and hypertension complication.
Model 2 was adjusted for educational attainment, age, sex, marital status and hypertension complication.
Model 3 was adjusted for current employment status, age, sex, marital status and hypertension complication.
Model 4 was mutually adjusted for wealth index, educational attainment, current employment status and other covariates.
* p<0.05
** p<0.01
Table 3 shows the result of the same analysis as in table 2 in Bago. In the rural population, only educational attainment showed a significant relationship with non-adherence. PR of ‘No school’, ‘Monastic’ and ‘Primary school’ was 3.06 (95% CI 1.32 to 7.09), 2.32 (95% CI 1.11 to 4.84) and 2.31 (95% CI 1.13 to 4.72), respectively. Even after mutually adjusting for the other two SES factors, the association clearly appeared. The association seemed dose-response and p for trend was also significant both before and after mutually adjusting all SES types (Model 2 and Model 4). With regards to wealth, although the association itself was not statistically significant, the direction of the association was the opposite compared with urban areas. Current employment status had no significant relation in the rural areas as well.
Table 3.
Prevalence ratios (95% CIs) of not taking medicines as instructed in Bago (n=256)
| Model 1 | Model 2 | Model 3 | Model 4 | |
| Wealth index category | ||||
| Low | 0.92 (0.58–1.44) | 0.80 (0.50–1.27) | ||
| Middle | 0.86 (0.56–1.32) | 0.79 (0.51–1.22) | ||
| High | 1 (referent) | 1 (referent) | ||
| Educational attainment | ||||
| No school | 3.06 (1.32–7.09)** | 3.22 (1.37–7.58)** | ||
| Monastic | 2.32 (1.11–4.84)* | 2.42 (1.16–5.07)* | ||
| Primary school | 2.31 (1.13–4.72)* | 2.41 (1.18–4.95)* | ||
| Middle/high school or higher | 1 (referent) | 1 (referent) | ||
| Trend p | 0.024 | 0.021 | ||
| Current employment status | ||||
| Never | 0.94 (0.13–7.08) | 1.30 (0.17–10.03) | ||
| Retired | 0.83 (0.52–1.32) | 0.86 (0.54–1.38) | ||
| Employed | 1 (referent) | 1 (referent) |
Model 1 was adjusted for wealth index, age, sex, marital status and hypertension complication.
Model 2 was adjusted for educational attainment, age, sex, marital status and hypertension complication.
Model 3 was adjusted for current employment status, age, sex, marital status and hypertension complication.
Model 4 was mutually adjusted for wealth index, educational attainment, current employment status and other covariates.
* p<0.05
** p<0.01
Discussion
The present study elucidated some aspects of the association between the area of residence and hypertension adherence among the older population in Myanmar. The results of the study indicated three things. First, in Myanmar, living in rural areas was associated with poor hypertension adherence. Second, stratified analysis showed that low wealth was significantly associated with poor adherence only in urban areas, indicating that the association’s direction may be opposite in urban and rural areas. Finally, low educational attainment was associated with poor adherence in rural areas and marginally associated in urban areas. Current employment status was not associated.
The study results showed that adherence is dependent on whether a person lives in an urban or rural area. In previous studies in LMICs, the incidence of hypertension was higher in urban areas, with mixed results for poor adherence.8 9 21 22 Previous studies have also found that adherence disparities among urban and rural residents were higher in low-income countries than in high-income countries.13 In Myanmar, the magnitude was almost twice as high (non-adherence rate: 21.5% in Yangon vs 48.4% in Bago). In terms of direction, it was also found that adherence was poorer in rural areas than in urban areas. For example, in China, the same LMIC studies have shown that risk factors for hypertension differ between urban and rural areas.32 The present results may suggest that there is more room for intervention in rural areas than in urban areas in Myanmar.
In urban Yangon, people in the moderate or low-wealth category had poorer adherence than those in the high-wealth category. Educational background was not as strongly associated as in rural areas. As wealth is strongly related to income, a possible explanation for the association of wealth with adherence in urban areas is the disparity in access to healthcare facilities related to income. There is a wide range of healthcare facilities in urban areas, including the public and private sectors. In the context of LMICs, such as Myanmar, while there is free access to public healthcare facilities, there is no option for choice of medicines coupled with very long waiting times. Also, in urban areas, private healthcare facilities provide an attractive working environment for healthcare professionals. That leads to the low number of healthcare professionals in public hospitals relative to the number of patients,33 and this may be partly related to the situation in which those with low incomes are more likely to drop out, as it takes more time and effort to receive medication for high blood pressure.34 This situation may also allow the affluent to secure their medications easily, as they can visit private healthcare facilities with a wide choice of medications and with shorter waiting times.
One possible explanation for why educational attainment was not as strongly associated with adherence in the urban areas as in the rural areas is that adherence may be mainly sustained independent of education in urban areas. This is so since information on the chronic nature of hypertension, the importance of regular oral medication, and the fear of complications of hypertension could be routinely received from the media and other sources.
Educational attainment was associated with adherence in rural areas but not wealth or current working status. Several explanations could be raised. First, access to medication could be easier in rural areas than in urban areas in some LMIC contexts. Considering income is one of the determinants of wealth, a reason for this phenomenon is that access to medications is less determined by wealth in rural areas than in urban areas due to support from Non-Governmental Organizations (NGOs) and other organisations. Since rural areas do not have as many options for healthcare facilities as urban areas, public hospitals usually cover patients' prescriptions. That may allow people living in rural areas to have relatively easier access to obtain medications. In contrast, the fact that adherence itself is lower in rural areas than in urban areas may be related to the unstable supply of drugs in rural areas. Second, in rural areas, determinants of adherence may, therefore, purely be education-related factors, such as understanding lowering blood pressure and following instructions from healthcare providers.35 Third, it is also possible that self-control ability could explain the association between education and adherence, with the latter requiring patience.36 Since self-control depends on educational attainment,36 37 the dose-response relationship between educational attainment and adherence could be observed.
However, in rural China, educational history was not associated with adherence,11 but low household income was associated with poor adherence. That may indicate that even in countries with the same income level, the factors creating the urban-rural adherence gap may differ by region. Lastly, since an association between low educational attainment and poor adherence was found even in urban areas, education generally influenced an individual’s adherence status regardless of living place.
The limitations of this study should be mentioned. First, a relatively low participation rate could be pointed out. Since our survey was conducted in the daytime, older people working during the day were less likely to be at home. However, the response rate was almost the same between the urban and rural areas in the study (55.5% vs 57.5%), so it could be assumed that regional differences were unlikely to be affected by the response rate. Second, with regard to the calculation of wealth, rural assets may not be accurately reflected, as some items that may be relevant to rural asset status, such as the presence of livestock, were not included in the survey instrument. However, as tertiles were calculated for each region, the gradations of asset status could be extracted to some extent. Also, in online supplemental appendix 1, 92.7% of study respondents selected ‘I only take them when I feel that I need them’ as the reason. For the question, 94.1% of the Yangon non-adherent group and 91.9% of the non-adherent group in Bago ticked ‘Yes’ (p=0.578). The cost or the availability of hypertension medication was not the main reason. Third, as a measure of adherence, scores such as the 8-item Morisky Medication Adherence Scale (MMAS-8)38 39 could not be used in the study, so adherence-related factors such as the complexity of medication and satisfaction with care at the healthcare organisation were not taken into account.
bmjopen-2022-065370supp001.pdf (109.1KB, pdf)
Conclusions
Our analysis indicates that the risk of poor adherence is higher in rural areas than in urban areas in Myanmar. As for intervening SES, educational attainment was associated with non-adherence in both urban and rural areas. Wealth was only associated with adherence in urban areas. The factors associated with maintaining adherence differ depending on the country’s urban-rural context. Therefore, effective interventions for each area need to be considered.
Supplementary Material
Acknowledgments
The authors thank the staff of the University of Medicine 1, Yangon; Yangon General Hospital; and the Ministry of Health and Sports for their collaboration. The authors also thank members of the Infectious Diseases Research Centre of Niigata University, especially Professor Reiko Saito and Professor Hisami Watanabe for their support to this project and Ms Saw Thu Nander, Mr Ye Min Kyaw and members of the Myanmar Perfect Research team for their involvement in conducting the survey. The authors also thank JAGES (principal investigator: Professor Katsunori Kondo; core members: Dr Naoki Kondo, Dr Jun Aida, Dr Toshiyuki Ojima and Dr Masashige Saito) members for providing helpful advice for the project. The authors thank Dr Hiroshi Murayama from the University of Tokyo for advice to the authors based on his professional experience. The authors also thank Ms Akiko Tomita and Ms Naoko Ito from the Japan International Cooperation Agency for assistance in the conducting of the survey. The authors thank Ms Tomoko Manabe for providing excellent secretarial support throughout the survey process and Dr Reiko Hayashi from the National Institute of Population and Social Security Research, Japan, for helpful advice. The authors thank other group members for their assistance. The authors also thank Ms Julian W Tang for editing their paper.
Footnotes
Contributors: YN established the research question, analysed the data and drafted the manuscript. YugoS is the guarantor of the study and assumed all responsibility for the survey and research as the principal investigator of the project. HHW was the principal investigator in Myanmar. TF and YPK intensively edited the paper. KTL, PEZ and TZB performed data collection, questionnaire development and survey management. YuriS, YukiS, IN and DT contributed to developing the questionnaire and the study design and intensively edited the manuscript.
Funding: This work was supported by the Japan Agency for Medical Research and Development, under the project title: 'Development of a health-equity assessment tool based on a social epidemiological survey of older adults in Myanmar and Malaysia' (Grant Number 17934739). This research was supported by the WHO Centre for Health Development (WHO Kobe Centre—WKC: K18015). Japan Society for the Promotion of Science (JSPS) KAKEN Grants (Grant Number JP19K19472) was also used for research processing. The Ministry of Health Labour and Welfare (MHLW) Program (Grant Number JPMH20BA2002) also supported this work.
Competing interests: None declared.
Patient and public involvement: Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.
Provenance and peer review: Not commissioned; externally peer reviewed.
Supplemental material: This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.
Data availability statement
No data are available.
Ethics statements
Patient consent for publication
Not applicable.
Ethics approval
This study involves human participants. The ethical review committee of the Department of Medical Research at the Ministry of Health and Sports, the Republic of the Union of Myanmar (approval no. Ethics/DMR/2018/038), the WHO ethics committee (protocol no. ERC.0003072), the ethics board of Niigata University (2018－0096), and the National Institute of Public Health in Japan (IBRA#12279) approved the research protocol. At the start of face-to-face questions, written informed consent was obtained from all participants. Participants were informed that participation was voluntary and were assured that they could withdraw from the survey at any time. This study was conducted following the principles of the Declaration of Helsinki. Participants gave informed consent to participate in the study before taking part.
References
- 1.World Health Organization . The top 10 causes of death, 2020. Available: https://www.who.int/news-room/fact-sheets/detail/the-top-10-causes-of-death [Accessed 31 Jan 2022].
- 2.World Health Organization . Social determinants of health. Available: https://www.who.int/health-topics/social-determinants-of-health#tab=tab_1 [Accessed 5 Jan 2021].
- 3.Roth GA, Mensah GA, Johnson CO, et al. Global burden of cardiovascular diseases and risk factors, 1990-2019: update from the GBD 2019 study. J Am Coll Cardiol 2020;76:2982–3021. 10.1016/j.jacc.2020.11.010 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Mahmood S, Jalal Z, Hadi MA, et al. Prevalence of non-adherence to antihypertensive medication in Asia: a systematic review and meta-analysis. Int J Clin Pharm 2021;43:486–501. 10.1007/s11096-021-01236-z [DOI] [PubMed] [Google Scholar]
- 5.UNESCAP . Ageing in Asia and the Pacific: overview: social development division; 2017.
- 6.Sabaté E. Adherence to long-term therapies: evidence for action World Health Organization; 2003. [PubMed] [Google Scholar]
- 7.Burnier M, Egan BM. Adherence in hypertension. Circ Res 2019;124:1124–40. 10.1161/CIRCRESAHA.118.313220 [DOI] [PubMed] [Google Scholar]
- 8.Magnabosco P, Teraoka EC, de Oliveira EM, et al. Comparative analysis of non-adherence to medication treatment for systemic arterial hypertension in urban and rural populations. Rev Lat Am Enfermagem 2015;23:20–7. 10.1590/0104-1169.0144.2520 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.King DE, Crisp JR. Rural-Urban differences in factors associated with poor blood pressure control among outpatients. South Med J 2006;99:1221–3. 10.1097/01.smj.0000223736.99072.22 [DOI] [PubMed] [Google Scholar]
- 10.Li W-W, Wallhagen MI, Froelicher ES. Hypertension control, predictors for medication adherence and gender differences in older Chinese immigrants. J Adv Nurs 2008;61:326–35. 10.1111/j.1365-2648.2007.04537.x [DOI] [PubMed] [Google Scholar]
- 11.Ma C. A cross-sectional survey of medication adherence and associated factors for rural patients with hypertension. Appl Nurs Res 2016;31:94–9. 10.1016/j.apnr.2016.01.004 [DOI] [PubMed] [Google Scholar]
- 12.Grotto I, Huerta M, Sharabi Y. Hypertension and socioeconomic status. Curr Opin Cardiol 2008;23:335–9. 10.1097/HCO.0b013e3283021c70 [DOI] [PubMed] [Google Scholar]
- 13.Palafox B, McKee M, Balabanova D, et al. Wealth and cardiovascular health: a cross-sectional study of wealth-related inequalities in the awareness, treatment and control of hypertension in high-, middle- and low-income countries. Int J Equity Health 2016;15:199. 10.1186/s12939-016-0478-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Lloyd-Sherlock P, Beard J, Minicuci N, et al. Hypertension among older adults in low- and middle-income countries: prevalence, awareness and control. Int J Epidemiol 2014;43:116–28. 10.1093/ije/dyt215 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Baroletti S, Dell'Orfano H. Medication adherence in cardiovascular disease. Circulation 2010;121:1455–8. 10.1161/CIRCULATIONAHA.109.904003 [DOI] [PubMed] [Google Scholar]
- 16.Asgari MR, Bouraghi H, Mohammadpour A, et al. The role of psychosocial determinants in predicting adherence to treatment in patient with hypertension. Interv Med Appl Sci 2019;11:8–16. 10.1556/1646.10.2018.43 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Lee GKY, Wang HHX, Liu KQL, et al. Determinants of medication adherence to antihypertensive medications among a Chinese population using Morisky medication adherence scale. PLoS One 2013;8:e62775. 10.1371/journal.pone.0062775 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Magrin ME, D'Addario M, Greco A, et al. Social support and adherence to treatment in hypertensive patients: a meta-analysis. Ann Behav Med 2015;49:307–18. 10.1007/s12160-014-9663-2 [DOI] [PubMed] [Google Scholar]
- 19.Maciejewski ML, Bryson CL, Perkins M, et al. Increasing copayments and adherence to diabetes, hypertension, and hyperlipidemic medications. Am J Manag Care 2010;16:e20–34. [PubMed] [Google Scholar]
- 20.DU S. Effects of war on compliance. Journal of Medical Anthropology 1997;20:101–7. [Google Scholar]
- 21.Gupta R, Kaur M, Islam S, et al. Association of household wealth index, educational status, and social capital with hypertension awareness, treatment, and control in South Asia. Am J Hypertens 2017;30:hpw169–81. 10.1093/ajh/hpw169 [DOI] [PubMed] [Google Scholar]
- 22.Arbuckle C, Tomaszewski D, Aronson BD, et al. Evaluating factors impacting medication adherence among rural, urban, and suburban populations. J Rural Health 2018;34:339–46. 10.1111/jrh.12291 [DOI] [PubMed] [Google Scholar]
- 23.Bjertness MB, Htet AS, Meyer HE, et al. Prevalence and determinants of hypertension in Myanmar - a nationwide cross-sectional study. BMC Public Health 2016;16:590. 10.1186/s12889-016-3275-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.UNCTAD . UN list of least developed countries, 2019. Available: https://unctad.org/topic/least-developed-countries/list
- 25.Naing C, Aung K. Prevalence and risk factors of hypertension in Myanmar: a systematic review and meta-analysis. Medicine 2014;93:e100–e00. 10.1097/MD.0000000000000100 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.World Health Organization . Hypertension [Available from. Available: https://www.who.int/health-topics/hypertension#tab=tab_1 [Accessed 1 Jun 2021].
- 27.Kristian B. Do cities widen the gap between rich and poor?: World economic forum, 2014. Available: https://www.weforum.org/agenda/2014/07/cities-urbanization-rich-poor-inequality/ [Accessed 11 Mar 2022].
- 28.Win HH, Nyunt TW, Lwin KT, et al. Cohort profile: healthy and active ageing in Myanmar (JAGES in Myanmar 2018): a prospective population-based cohort study of the long-term care risks and health status of older adults in Myanmar. BMJ Open 2020;10:e042877. 10.1136/bmjopen-2020-042877 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Ministry of Labor IaP . Census atlas Myanmar: the 2014 Myanmar population and housing census Ministry of Labor, Immigration and Population; 2017. [Google Scholar]
- 30.Acquadro C, Conway K, Giroudet C. Linguistic validation manual for health outcome. Lyon, France: Assessments, 2012. [Google Scholar]
- 31.Filmer D, Pritchett LH. Estimating wealth effects without expenditure data--or tears: an application to educational enrollments in states of India. Demography 2001;38:115–32. 10.1353/dem.2001.0003 [DOI] [PubMed] [Google Scholar]
- 32.Song H, Feng D, Wang R, et al. The urban-rural disparity in the prevalence and risk factors of hypertension among the elderly in China-a cross-sectional study. PeerJ 2019;7:e8015–e15. 10.7717/peerj.8015 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Latt NN, Myat Cho S, Htun NMM, et al. Healthcare in Myanmar. Nagoya J Med Sci 2016;78:123–34. [PMC free article] [PubMed] [Google Scholar]
- 34.Cameron A, Roubos I, Ewen M, et al. Differences in the availability of medicines for chronic and acute conditions in the public and private sectors of developing countries. Bull World Health Organ 2011;89:412–21. 10.2471/BLT.10.084327 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Gee ME, Campbell NRC, Gwadry-Sridhar F, et al. Antihypertensive medication use, adherence, stops, and starts in Canadians with hypertension. Can J Cardiol 2012;28:383–9. 10.1016/j.cjca.2012.01.014 [DOI] [PubMed] [Google Scholar]
- 36.Duckworth AL, Taxer JL, Eskreis-Winkler L, et al. Self-Control and academic achievement. Annu Rev Psychol 2019;70:373–99. 10.1146/annurev-psych-010418-103230 [DOI] [PubMed] [Google Scholar]
- 37.Moghadari-Koosha M, Moghadasi-Amiri M, Cheraghi F, et al. Self-Efficacy, self-regulated learning, and motivation as factors influencing academic achievement among paramedical students: a correlation study. J Allied Health 2020;49:e145–52. [PubMed] [Google Scholar]
- 38.Morisky DE, Ang A, Krousel-Wood M, et al. Predictive validity of a medication adherence measure in an outpatient setting. J Clin Hypertens 2008;10:348–54. 10.1111/j.1751-7176.2008.07572.x [DOI] [PMC free article] [PubMed] [Google Scholar] [Retracted]
- 39.Oliveira-Filho AD, Barreto-Filho JA, Neves SJF, et al. Association between the 8-item Morisky medication adherence scale (MMAS-8) and blood pressure control. Arq Bras Cardiol 2012;99:649–58. 10.1590/s0066-782x2012005000053 [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
bmjopen-2022-065370supp001.pdf (109.1KB, pdf)
Data Availability Statement
No data are available.