Diabetes, Hypertension, and Comorbidity among Bangladeshi Adults: Associated Factors and Socio-Economic Inequalities

Journal of Cardiovascular Development and Disease, Volume 10, No. 1, Year 2023

Interprétation

Diabetes, hypertension, and comorbidity are still crucial public health challenges that Bangladeshis face. Nonetheless, very few studies have been conducted to examine the associated factors, especially the socioeconomic inequalities in diabetes, hypertension, and comorbidity in Bangladesh. This study explored the prevalence of, factors connected with, and socioeconomic inequalities in diabetes, hypertension, and comorbidity among Bangladeshi adults. We used the Bangladesh Demographic and Health Survey (BDHS) data set of 2017–2018. A total of 12,136 (weighted) Bangladeshi adults with a mean age of 39.5 years (±16.2) participated in this study. Multilevel (mixed-effect) logistic regression analysis was employed to ascertain the determinants of diabetes, hypertension, and comorbidity, where clusters were considered as a level-2 factor. The concentration curve (CC) and concentration index (CIX) were utilized to investigate the inequalities in diabetes, hypertension, and comorbidity. The weighted prevalence of diabetes, hypertension, and comorbidity was 10.04%, 25.70%, and 4.47%, respectively. Age, body mass index, physical activity, household wealth status, and diverse administrative divisions were significantly associated with diabetes, hypertension, and comorbidity among the participants. Moreover, participants’ smoking statuses were associated with hypertension. Women were more prone to hypertension and comorbidity than men. Diabetes (CIX: 0.251, p < 0.001), hypertension (CIX: 0.071, p < 0.001), and comorbidity (CIX: 0.340, p < 0.001) were higher among high household wealth groups. A pro-wealth disparity in diabetes, hypertension, and comorbidity was found. These inequalities in diabetes, hypertension, and comorbidity emphasize the necessity of designing intervention schemes geared towards addressing the rising burden of these diseases. We utilized the BDHS 2017-18 data in this study. The survey was conducted by the National Institute of Population Research and Training (NIPORT) and the Ministry of Health and Family Welfare of Bangladesh [32]. This survey’s main goals were to assess the population’s general health, maternal and child health, and sexual and reproductive health, and to collect information on chronic non-communicable diseases such as diabetes, hypertension, etc. A double-stage stratified sampling technique was employed in BDHS 2017–2018 to choose households from various enumeration areas (EAs). Primarily, 250 and 425 EAs were selected from urban and rural areas, respectively, and these EAs were regarded as the primary sampling unit (PSU), with a total number of 20,250 households. One third of these households was chosen randomly to assess fasting plasma glucose levels. All adults in these households were asked to participate, and approximately 90% took part [32]. Only data from the adult participants aged ≥ 18 years were included in this study. Data from 12,136 (weighted) Bangladeshi adults with a mean age of 39.5 years (±16.2) were included in the final analysis. Diabetes, hypertension, and comorbidity were the outcome variables of this study. To measure the fasting plasma glucose level (FPG), HemoCue 201 RT was used [32]. An individual was considered to have diabetes if his/her FPG ≥ 7 mmol/l and/or if he/she was taking any approved medicines to reduce glucose in the blood [29,32]. For measuring the blood pressure (BP) level, a LIFE SOURCE R UA-767 Plus BP monitor was used by qualified health experts to measure BP three times at around ten-minute intervals. The average of the second and third measurements was then used to report participants’ last BP [32]. Participants who recorded an SBP of ≥140 mmHg and/or a DBP of ≥90 mmHg were regarded as hypertensive [33], and those who were placed on antihypertensive medicines to regulate their BP were also considered hypertensive [32]. Respondents who suffered from both hypertension and diabetes were regarded as having comorbidity, yielding a dichotomous variable (yes/no). The three dependent variables were dichotomized and analyzed. Explanatory variables were chosen depending on the previous literature on diabetes, hypertension, and comorbidity in LMICs [13,26,27,28,29,30]. The individual-level factors included BMI, sex, age, employment status, educational level, smoking status, physical activity level, and marital status; household-level factors included household wealth status, media access, place of residence, and the administrative region; and the community-level factors were wealth status, employment status, educational level, and physical activity at the community level. WHO (2013) classifies BMI as follows: underweight (<18.5 kg/m2), normal (18.5–24.9 kg/m2), overweight (25.0–29.9 kg/m2), and obese (≥30.0 kg/m2) [34]. The smoking status was measured based on information on whether participants had smoked within the last 30 min before measuring their blood glucose level and blood pressure [32]. Information on physical activity was not directly available in the BDHS 2017-18 data. Thus, occupation was adopted as a substitute variable to measure the physical activity level [27]. Any respondent whose work responsibilities involved physical activities were regarded as ‘involved in an occupation with high physical activity’; otherwise, they were considered to ‘involve less physical activity’ [27]. The highly physically active occupation group comprised fishermen, farmers, cattle raisers, agricultural workers, poultry raisers, rickshaw drivers, home-based manufacturers, road builders, brick breakers, domestic servants, construction workers, and factory workers. Contrarily, the occupations related to low physical activity included nurses, those not working, carpenters, dentists, land owners, doctors, tailors, lawyers, teachers, accountants, retired persons, businessmen, and unemployed individuals/students [35]. Household wealth status (wealth quintiles) was constructed using principal component analysis, relying on the household characteristics and different household assets with five wealth quintiles (poorest, poorer, middle, richer, richest) [32]. The media exposure of each household was measured based on access to television, radio, and audio. Households that had access to any of the three media were considered as having access to media [32]. Due to the intricate survey design, data were prepared using the survey weights before the analysis. The “svy” command was applied to assign the weight of the sample to regulate the clustering effect and sample stratification in STATA 16.0 (StataCorp., College Station, TX, USA). In the bivariate arrangement, the chi-square test was employed based on the distribution of the data to identify the relationship between dependent and independent variables. Since a double-stage stratified cluster sampling with a hierarchical composition was utilized for the BDHS 2017–2018, a single-level analysis model would not be appropriate for analyzing such data [36]. Thus, multi-level (mixed-effect) binary logistic regression analysis was used to identify the factors related to diabetes, hypertension, and comorbidity, where clusters were considered as a level-2 factor. The intra-class correlation coefficient (ICC) was also calculated after applying the two-level models [37]. The concentration curve (CC) and concentration index (CIX) were used to examine the inequalities in either having diabetes, hypertension, or comorbidity across different socioeconomic groups [38]. The CIX calculated represented a horizontal imbalance, as each participant was assumed to be equally prone to contracting diabetes, hypertension, or comorbidity. While creating the CC, the aggregated fraction of participants rated according to the wealth index score (poorest first) was plotted against the aggregated proportion with diabetes, hypertension, or comorbidity on the y-axis. The 45-degree slope from the origin indicated perfect similarity, while a CC that overlapped with the similarity line showed that the presence of diabetes, hypertension, and comorbidity was equal among participants. The further the CC subtends from the equality line, the larger the dissimilarity. To assess wealth-related disparity, CIX was determined. CIX is broadened as twice the point between the similarity line and CC [38]. A positive concentration index value, or a CC that lay below the line of equality, specified that diabetes, hypertension, and comorbidity were higher among high wealth-indexed groups (high household wealth groups). Contrarily, a negative CIX value or a CC that lay above the line of equality indicated that diabetes, hypertension, and comorbidity were higher among low wealth-indexed groups [39,40]. Within the CC, greater inequality was established by how strongly the curves deviated from the equality line. CIXs were applied to compute the contrast in having diabetes, hypertension, and comorbidity [41]. CIX takes values between − 1 and + 1 [42]. When diabetes, hypertension, and comorbidity were similar across socioeconomic groups, CIX became 0. A positive CIX value implied that having diabetes, hypertension, or comorbidity was centered among the higher household wealth group. Conversely, a negative CIX value revealed that having diabetes, hypertension, or comorbidity was centered among the lower household wealth group [42]. Stata version 16.0 (StataCorp., College Station, TX, USA) was applied to analyze the CC and concentration index. The statistical significance was indicated at p < 0.05. The relative CIX was disintegrated to ascertain the portion of inequality owing to the inequality in the fundamental determinants. The results were analyzed and reported using the technique defined by Wagstaff et al. [38] and Bilger et al. [43]. The impact of each determinant of contracting diabetes, hypertension, or comorbidity to overall wealth-related disparity was established as the result of the determinant’s sensitivity to diabetes, hypertension, comorbidity, and the amount of wealth-related disparity (CIX of determinant). The remaining was the percentage of the CIX unexplained by the determinants. A secondary data set from the publicly available Demographic and Health Surveys (DHS) Program was used for the current study; therefore, no further ethical approval was required. The detailed ethical procedures followed by the DHS Program can be found in the BDHS report [32].

Résumé de l’IA

Study Justification:

– Diabetes, hypertension, and comorbidity are significant public health challenges in Bangladesh.
– Limited studies have examined the associated factors and socioeconomic inequalities in these conditions.
– This study aims to fill the gap in knowledge and provide insights for designing effective intervention strategies.

Study Highlights:

– The study utilized data from the Bangladesh Demographic and Health Survey (BDHS) 2017-2018.
– A total of 12,136 Bangladeshi adults participated, with a weighted prevalence of 10.04% for diabetes, 25.70% for hypertension, and 4.47% for comorbidity.
– Factors such as age, body mass index, physical activity, household wealth status, and administrative divisions were significantly associated with diabetes, hypertension, and comorbidity.
– Women were more prone to hypertension and comorbidity than men.
– Socioeconomic inequalities were found, with higher prevalence of diabetes, hypertension, and comorbidity among high household wealth groups.

Recommendations for Lay Reader and Policy Maker:

– Design intervention schemes to address the rising burden of diabetes, hypertension, and comorbidity in Bangladesh.
– Focus on factors such as age, body mass index, physical activity, and household wealth status in intervention strategies.
– Pay special attention to women, as they are more prone to hypertension and comorbidity.
– Address socioeconomic inequalities by targeting high household wealth groups and promoting equity in access to healthcare services.

Key Role Players:

– National Institute of Population Research and Training (NIPORT)
– Ministry of Health and Family Welfare of Bangladesh
– Qualified health experts
– Researchers and analysts

Cost Items for Planning Recommendations:

– Research and data collection expenses
– Training and capacity building for health experts
– Development and implementation of intervention schemes
– Healthcare infrastructure and facilities
– Awareness campaigns and health education materials
– Monitoring and evaluation of intervention programs

Force de la preuve

The strength of evidence for this abstract is 8 out of 10.
The evidence in the abstract is strong, as it is based on a large sample size and utilizes a nationally representative survey dataset. The study employs multilevel logistic regression analysis to identify determinants and investigate socioeconomic inequalities. The use of concentration curve and concentration index further strengthens the evidence. However, to improve the rating, the abstract could provide more information on the statistical significance of the associations found and the magnitude of the socioeconomic inequalities. Additionally, it would be helpful to include information on the limitations of the study and potential implications for public health interventions.

Abstrait

We utilized the BDHS 2017-18 data in this study. The survey was conducted by the National Institute of Population Research and Training (NIPORT) and the Ministry of Health and Family Welfare of Bangladesh [32]. This survey’s main goals were to assess the population’s general health, maternal and child health, and sexual and reproductive health, and to collect information on chronic non-communicable diseases such as diabetes, hypertension, etc. A double-stage stratified sampling technique was employed in BDHS 2017–2018 to choose households from various enumeration areas (EAs). Primarily, 250 and 425 EAs were selected from urban and rural areas, respectively, and these EAs were regarded as the primary sampling unit (PSU), with a total number of 20,250 households. One third of these households was chosen randomly to assess fasting plasma glucose levels. All adults in these households were asked to participate, and approximately 90% took part [32]. Only data from the adult participants aged ≥ 18 years were included in this study. Data from 12,136 (weighted) Bangladeshi adults with a mean age of 39.5 years (±16.2) were included in the final analysis. Diabetes, hypertension, and comorbidity were the outcome variables of this study. To measure the fasting plasma glucose level (FPG), HemoCue 201 RT was used [32]. An individual was considered to have diabetes if his/her FPG ≥ 7 mmol/l and/or if he/she was taking any approved medicines to reduce glucose in the blood [29,32]. For measuring the blood pressure (BP) level, a LIFE SOURCE R UA-767 Plus BP monitor was used by qualified health experts to measure BP three times at around ten-minute intervals. The average of the second and third measurements was then used to report participants’ last BP [32]. Participants who recorded an SBP of ≥140 mmHg and/or a DBP of ≥90 mmHg were regarded as hypertensive [33], and those who were placed on antihypertensive medicines to regulate their BP were also considered hypertensive [32]. Respondents who suffered from both hypertension and diabetes were regarded as having comorbidity, yielding a dichotomous variable (yes/no). The three dependent variables were dichotomized and analyzed. Explanatory variables were chosen depending on the previous literature on diabetes, hypertension, and comorbidity in LMICs [13,26,27,28,29,30]. The individual-level factors included BMI, sex, age, employment status, educational level, smoking status, physical activity level, and marital status; household-level factors included household wealth status, media access, place of residence, and the administrative region; and the community-level factors were wealth status, employment status, educational level, and physical activity at the community level. WHO (2013) classifies BMI as follows: underweight (<18.5 kg/m2), normal (18.5–24.9 kg/m2), overweight (25.0–29.9 kg/m2), and obese (≥30.0 kg/m2) [34]. The smoking status was measured based on information on whether participants had smoked within the last 30 min before measuring their blood glucose level and blood pressure [32]. Information on physical activity was not directly available in the BDHS 2017-18 data. Thus, occupation was adopted as a substitute variable to measure the physical activity level [27]. Any respondent whose work responsibilities involved physical activities were regarded as ‘involved in an occupation with high physical activity’; otherwise, they were considered to ‘involve less physical activity’ [27]. The highly physically active occupation group comprised fishermen, farmers, cattle raisers, agricultural workers, poultry raisers, rickshaw drivers, home-based manufacturers, road builders, brick breakers, domestic servants, construction workers, and factory workers. Contrarily, the occupations related to low physical activity included nurses, those not working, carpenters, dentists, land owners, doctors, tailors, lawyers, teachers, accountants, retired persons, businessmen, and unemployed individuals/students [35]. Household wealth status (wealth quintiles) was constructed using principal component analysis, relying on the household characteristics and different household assets with five wealth quintiles (poorest, poorer, middle, richer, richest) [32]. The media exposure of each household was measured based on access to television, radio, and audio. Households that had access to any of the three media were considered as having access to media [32]. Due to the intricate survey design, data were prepared using the survey weights before the analysis. The “svy” command was applied to assign the weight of the sample to regulate the clustering effect and sample stratification in STATA 16.0 (StataCorp., College Station, TX, USA). In the bivariate arrangement, the chi-square test was employed based on the distribution of the data to identify the relationship between dependent and independent variables. Since a double-stage stratified cluster sampling with a hierarchical composition was utilized for the BDHS 2017–2018, a single-level analysis model would not be appropriate for analyzing such data [36]. Thus, multi-level (mixed-effect) binary logistic regression analysis was used to identify the factors related to diabetes, hypertension, and comorbidity, where clusters were considered as a level-2 factor. The intra-class correlation coefficient (ICC) was also calculated after applying the two-level models [37]. The concentration curve (CC) and concentration index (CIX) were used to examine the inequalities in either having diabetes, hypertension, or comorbidity across different socioeconomic groups [38]. The CIX calculated represented a horizontal imbalance, as each participant was assumed to be equally prone to contracting diabetes, hypertension, or comorbidity. While creating the CC, the aggregated fraction of participants rated according to the wealth index score (poorest first) was plotted against the aggregated proportion with diabetes, hypertension, or comorbidity on the y-axis. The 45-degree slope from the origin indicated perfect similarity, while a CC that overlapped with the similarity line showed that the presence of diabetes, hypertension, and comorbidity was equal among participants. The further the CC subtends from the equality line, the larger the dissimilarity. To assess wealth-related disparity, CIX was determined. CIX is broadened as twice the point between the similarity line and CC [38]. A positive concentration index value, or a CC that lay below the line of equality, specified that diabetes, hypertension, and comorbidity were higher among high wealth-indexed groups (high household wealth groups). Contrarily, a negative CIX value or a CC that lay above the line of equality indicated that diabetes, hypertension, and comorbidity were higher among low wealth-indexed groups [39,40]. Within the CC, greater inequality was established by how strongly the curves deviated from the equality line. CIXs were applied to compute the contrast in having diabetes, hypertension, and comorbidity [41]. CIX takes values between − 1 and + 1 [42]. When diabetes, hypertension, and comorbidity were similar across socioeconomic groups, CIX became 0. A positive CIX value implied that having diabetes, hypertension, or comorbidity was centered among the higher household wealth group. Conversely, a negative CIX value revealed that having diabetes, hypertension, or comorbidity was centered among the lower household wealth group [42]. Stata version 16.0 (StataCorp., College Station, TX, USA) was applied to analyze the CC and concentration index. The statistical significance was indicated at p < 0.05. The relative CIX was disintegrated to ascertain the portion of inequality owing to the inequality in the fundamental determinants. The results were analyzed and reported using the technique defined by Wagstaff et al. [38] and Bilger et al. [43]. The impact of each determinant of contracting diabetes, hypertension, or comorbidity to overall wealth-related disparity was established as the result of the determinant’s sensitivity to diabetes, hypertension, comorbidity, and the amount of wealth-related disparity (CIX of determinant). The remaining was the percentage of the CIX unexplained by the determinants. A secondary data set from the publicly available Demographic and Health Surveys (DHS) Program was used for the current study; therefore, no further ethical approval was required. The detailed ethical procedures followed by the DHS Program can be found in the BDHS report [32].

Matériaux

N/A

Innovations

Based on the provided information, it appears that the study focuses on the prevalence of diabetes, hypertension, and comorbidity among Bangladeshi adults, as well as the associated factors and socioeconomic inequalities. The study utilized data from the Bangladesh Demographic and Health Survey (BDHS) 2017-2018.

To improve access to maternal health, some potential innovations and recommendations could include:

1. Mobile Health (mHealth) Interventions: Utilizing mobile phones and technology to provide maternal health information, reminders for prenatal care appointments, and access to telemedicine consultations.

2. Community Health Workers: Training and deploying community health workers to provide maternal health education, antenatal care, and postnatal support in rural and underserved areas.

3. Telemedicine: Expanding access to maternal health services through telemedicine platforms, allowing pregnant women to consult with healthcare providers remotely.

4. Maternal Health Vouchers: Implementing voucher programs that provide financial assistance to pregnant women, enabling them to access quality maternal health services.

5. Transportation Support: Establishing transportation networks or subsidies to ensure pregnant women can easily access healthcare facilities for prenatal care, delivery, and postnatal care.

6. Maternal Health Clinics: Establishing specialized clinics that focus on providing comprehensive maternal health services, including prenatal care, delivery, and postnatal care.

7. Maternal Health Education Programs: Developing and implementing educational programs that focus on raising awareness about maternal health, pregnancy complications, and the importance of antenatal care.

8. Maternal Health Hotlines: Setting up helplines or hotlines that pregnant women can call to seek advice, ask questions, or report any concerns related to their maternal health.

9. Public-Private Partnerships: Collaborating with private healthcare providers and organizations to expand access to maternal health services, particularly in areas with limited public healthcare facilities.

10. Maternal Health Insurance: Introducing or expanding insurance schemes that cover the costs of maternal health services, reducing financial barriers for pregnant women.

These are just a few potential innovations and recommendations that could be considered to improve access to maternal health based on the information provided. It is important to conduct further research and feasibility studies to determine the effectiveness and suitability of these interventions in the context of Bangladesh.

AI Innovations Description

The description provided is a research study that examines the prevalence, associated factors, and socioeconomic inequalities in diabetes, hypertension, and comorbidity among Bangladeshi adults. The study utilized data from the Bangladesh Demographic and Health Survey (BDHS) conducted in 2017-2018.

Based on the findings of the study, the following recommendations can be developed into an innovation to improve access to maternal health:

1. Increase awareness and education: Develop targeted health education programs to raise awareness about the risk factors and prevention of diabetes, hypertension, and comorbidity among pregnant women. This can be done through community health workers, antenatal care clinics, and mass media campaigns.

2. Strengthen antenatal care services: Enhance the capacity of healthcare facilities to provide comprehensive antenatal care services that include screening and management of diabetes, hypertension, and comorbidity. This can involve training healthcare providers, improving infrastructure, and ensuring the availability of necessary medications and equipment.

3. Implement regular screening programs: Establish routine screening programs for diabetes, hypertension, and comorbidity during pregnancy to identify high-risk women early and provide appropriate interventions. This can be integrated into existing antenatal care visits or conducted separately.

4. Improve access to healthcare: Address socioeconomic inequalities in access to healthcare by implementing targeted interventions for vulnerable populations, such as low-income individuals and those living in remote areas. This can involve mobile health clinics, telemedicine services, and financial support programs to reduce barriers to care.

5. Strengthen health information systems: Enhance the collection, analysis, and utilization of health data to monitor the prevalence and trends of diabetes, hypertension, and comorbidity among pregnant women. This can inform evidence-based decision-making and resource allocation for maternal health services.

By implementing these recommendations, it is possible to improve access to maternal health and reduce the burden of diabetes, hypertension, and comorbidity among pregnant women in Bangladesh.

AI Innovations Methodology

Based on the provided description, here are some potential recommendations for improving access to maternal health:

1. Strengthening healthcare infrastructure: Invest in improving healthcare facilities, including hospitals, clinics, and maternity centers, particularly in rural and underserved areas. This can involve increasing the number of healthcare professionals, ensuring the availability of essential medical equipment and supplies, and improving the quality of healthcare services.

2. Enhancing community-based healthcare: Implement community-based healthcare programs that focus on maternal health education, awareness, and support. This can involve training community health workers to provide basic prenatal and postnatal care, conducting health education sessions for pregnant women and their families, and establishing support groups for mothers.

3. Improving transportation and logistics: Address transportation barriers by improving access to reliable and affordable transportation for pregnant women, especially in remote areas. This can include providing transportation vouchers or subsidies, establishing emergency transportation systems, and improving road infrastructure to facilitate safe and timely access to healthcare facilities.

4. Promoting telemedicine and digital health solutions: Utilize technology to overcome geographical barriers and improve access to maternal health services. This can involve implementing telemedicine programs that allow pregnant women to consult healthcare professionals remotely, using mobile health applications to provide health information and reminders, and leveraging digital platforms for appointment scheduling and follow-up care.

To simulate the impact of these recommendations on improving access to maternal health, a methodology could involve the following steps:

1. Define the indicators: Identify key indicators that measure access to maternal health, such as the number of prenatal care visits, the percentage of deliveries attended by skilled birth attendants, and the availability of emergency obstetric care.

2. Collect baseline data: Gather data on the current status of these indicators before implementing the recommendations. This can involve conducting surveys, analyzing existing health records, and consulting relevant stakeholders.

3. Implement the recommendations: Roll out the recommended interventions and initiatives to improve access to maternal health. Ensure proper implementation and monitor the progress of each intervention.

4. Collect post-intervention data: After a sufficient period of time, collect data on the indicators again to assess the impact of the recommendations. This can involve conducting follow-up surveys, analyzing updated health records, and evaluating the feedback from healthcare providers and beneficiaries.

5. Analyze and compare the data: Compare the baseline data with the post-intervention data to determine the impact of the recommendations on improving access to maternal health. Use statistical analysis techniques to identify any significant changes and quantify the improvements.

6. Evaluate the results: Assess the effectiveness of each recommendation and identify any challenges or limitations encountered during the implementation process. This evaluation can help refine and optimize the interventions for future implementation.

By following this methodology, policymakers and healthcare professionals can gain insights into the potential impact of various recommendations on improving access to maternal health and make informed decisions to prioritize and implement the most effective interventions.

Auteurs et co-auteurs

Statistics:

Authors: 7

Identifiers:

DOI: 10.3390/jcdd10010007

Research Areas:

Community Interventions, Disparities, Health System and Policy, Maternal Access, Maternal and Child Health, Noncommunicable Diseases, Sexual and Reproductive Health, Social Determinants, Workforce

Study Design:

Cross Sectional Study, randomised-control-trial

Study Approach:

Quantitative

Participants Gender:

Male & Female

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