Determinants of self-reported hypertension among women in South Africa: evidence from the population-based survey

Clinical Hypertension, Volume 28, No. 1, Year 2022

Interpretation

Background: Hypertension (HTN), characterized by an elevation of blood pressure, is a serious public health chronic condition that significantly raises the risks of heart, brain, kidney, and other diseases. In South Africa, the prevalence of HTN (measured objectively) was reported at 46.0% in females, nonetheless little is known regarding the prevalence and risks factors of self-reported HTN among the same population. Therefore, the aim of this study was to examine determinants of self-reported HTN among women in South Africa. Methods: The study used data obtained from the 2016 South African Demographic and Health Survey. In total, 6,027 women aged ≥ 20 years were analyzed in this study. Self-reported HTN was defined as a case in which an individual has not been clinically diagnosed with this chronic condition by a medical doctor, nurse, or health worker. Multiple logistic regression models were employed to examine the independent factors of self-reported HTN while considering the complex survey design. Results: Overall, self-reported HTN was reported in 23.6% (95% confidence interval [CI], 23.1–24.1) of South African women. Being younger (adjusted odds ratio [aOR], 0.04; 95% CI, 0.03–0.06), never married (aOR, 0.69; 95% CI, 0.56–0.85), and not covered by health insurance (aOR, 0.74; 95% CI, 0.58–0.95) reduced the odds of self-reported HTN. On the other hand, being black/African (aOR, 1.73; 95% CI, 1.17–2.54), perception of being overweight (aOR, 1.72; 95% CI, 1.40–2.11), and perception of having poor health status (aOR, 3.53; 95% CI, 2.53–5.21) and the presence of other comorbidities (aOR, 7.92; 95% CI, 3.63–17.29) increased the odds of self-reported HTN. Conclusions: Self-reported HTN was largely associated with multiple sociodemographic, health, and lifestyle factors and the presence of other chronic conditions. Health promotion and services aiming at reducing the burden of HTN in South Africa should consider the associated factors reported in this study to ensure healthy aging and quality of life among women. This study was approved by the South Africa Medical Research Council and the Institutional Review Board of International Classification of Functioning (ICF). All methods were conducted in accordance with the Declaration of Helsinki principles. The data were de-identified to prevent the respondents’ identity from being revealed. All individuals who agreed to take part in the survey were asked to provide either written or verbal consent for the interview. Particularly, verbal consent was sought from participants who could not write or read. The Centre for Disease Control and Prevention granted a waiver of written consent per 45CFR46 for respondents who were unable to provide written consent but consented verbally. The study used cross-sectional data from SADHS 2016. The SADHS 2016 was conducted to provide up-to-date estimates of basic demographic and measures of population health such as fertility levels, awareness and use of contraceptives, childhood and maternal mortality, immunization coverage, and prevalence and treatment of acute respiratory infection, fever, diarrhea, nutrition, etc. [29]. Additionally, the survey was also conducted to provide estimates of health and behavior measures for adults aged 15 years and older. The sample for the SADHS 2016 was designed to produce estimates of vital measures of the country as a whole: urban and nonurban areas. Administratively, South Africa is divided into nine provinces namely Eastern Cape, Free State, Gauteng, KwaZulu-Natal, Limpopo, Mpumalanga, Northern Cape, North West, and Western Cape, which vary considerable in size. The Statistics South Africa Master Sample Frame was created using Census 2011 data. Enumeration areas (EAs) was used as the sampling frame for the SADHS 2016. In the Master Sample Frame, EAs of manageable size were treated as primary sampling units (PSUs), whereas small neighboring EAs were combined to form new PSUs, and large EAs were split into conceptual PSUs. Using a two-stage stratified sampling design with a probability proportional to size, PSUs were selected at the first stage while the systematic sampling of residential dwelling units (DUs) was performed at the second stage. Thus, a total of 750 PSUs were selected from the 26 sampling strata. From each PSU, a fixed number of 20 residential DUs were selected using a systematic sampling technique. Of the 15,292 households selected, 13,288 households were occupied, and interviews were successfully conducted in 83% of the occupied households. Data collection was conducted between June 2016 and November 2016 at the request of the National Department of Health. Data were collected using questionnaires administered by conducting face-to-face interviews. All households were eligible for interviews using the Household Questionnaire where basic demographic indicators on the characteristics of each person listed were collected, including age, sex, marital status, education, and relationship to the head of the household were collected. Further, information on characteristics of the household’s DU, such as the source of drinking water, type of sanitation facility, materials used for the floor, walls, and roof of the DU, and ownership of various durable goods were also collected. Secondly, the woman’s questionnaire was used to collect information from all eligible women aged 15 years and older on background characteristics such as age, education, media, exposure, etc. Additionally, the woman’s questionnaires also included a module on adult health where information on the use of tobacco, alcohol, consumption of fat, salt, sugar, fruit, and vegetables, health care-seeking behaviors, and self-reported prevalence of a variety of noncommunicable diseases including HTN were captured. The present study considered self-reported HTN (high blood pressure) as the dependent variable. Specifically, respondents were asked, “Has a medical doctor, nurse, or health professional told you that you have high blood pressure?” If the response was “yes” then respondents were classified as having self-rated HTN, and if the response was “no,” then the respondents were considered having no self-reported HTN. Independent variables of this study were selected after a review of the relevant literature and their availability in the SADHS 2016 dataset [11, 17, 31]. In total, 10 sociodemographic variables, seven health and lifestyle-related factors, and three other comorbidities of chronic diseases were included. Sociodemographic variables included the age of the respondents, education level, ethnicity, household wealth, marital status, employment status, exposure to mass media, place of residence, and geographical region. Lifestyle-related factors included self-reported body mass index (BMI) and perception of own health, type of fruits eaten yesterday, type of vegetables eaten yesterday, frequency of eating processed meat, frequency of eating fried foods, and sugar-sweetened drinks in the last 24 h. Other chronic diseases included comorbidities of self-reported HTN, comorbidities of self-reported diabetes, and comorbidities of self-reported hypercholesterolemia. These variables were categorized as follows: age of the respondents in years (20–24, 25–29, 30–34, 35–39, 40–44, 45–49, 50 + years), educational level (no education/primary, secondary, tertiary education), ethnicity (black/African, white, colored and other), household wealth (poorest, poorer, middle, richer, richest), marital status (never married, currently married, divorced/widowed), current employment (not employed or employed), covered by health insurance (yes or no), the amount of mass media exposure (0, 1, 2, 3), place of residence (urban or rural), and geographical region (Western Cape, Eastern Cape, Northern Cape, Free State, KwaZulu-Natal, North West, Gauteng, Mpumalanga, Limpopo). Household wealth was calculated using household items such as such as televisions and bicycles, materials used for housing construction, and types of water access and sanitation facilities and principal component analysis was used to create scores and these were further divided into quintiles. Health and lifestyle-related factors included perception of own weight—BMI (underweight, normal weight, overweight/obese), perception of own health, also known as self-rated health status (SRHS) was measured using a single question: “In general, how do would you rate your health?” with the following response options: excellent, good, moderate, or poor; type of fruits eaten yesterday (none, one type, two or more types); type of vegetables eaten yesterday (none, one type, two or more types); frequency of eating processed meat (never, every day, at least once a week, occasionally); frequency of eating fried foods (never, every day, at least once a week, occasionally); and sugar-sweetened drinks yesterday (no or yes). Objectively BMI is calculated by dividing the person’s weight in kilograms by their height in meters squared (kg/m2) [32]. For adults over 20 years old, BMI falls into one of the following categories: underweight (below 18.5 kg/m2), normal weight (18.5–24.9 kg/m2), overweight (25.0–29.9 kg/m2), and obesity (30 kg/m2 and above). Comorbidities for self-reported HTN included self-reported diabetes and self-reported hypercholesterolemia. Particularly, respondents were asked; “Has a medical doctor, nurse, or health provider told you that you have high blood pressure?” If the response was “yes” then respondents were classified as having self-reported diabetes/blood sugar or high blood cholesterol/fats in the blood (hypercholesterolemia), and if the response was “no”, then the respondents were considered having no self-reported diabetes or hypercholesterolemia. All data analyses were conducted using SAS ver. 9.4 (SAS Institute Inc., Cary, NC, USA). Characteristics of the study population were presented with numbers and percentages. Group comparisons between respondents that reported to have HTN and those that did not have HTN were made with Rao-Scott chi-square test. To assess the relationship between the selected variables and self-reported HTN, multivariate logistic analyses were constructed using a generalized estimating equation. The generalized estimating equation models accounted for the clustering effects of the hierarchical SADHS data. The results of the multivariate analysis were reported as adjusted odds ratios (aORs) with their P-values and 95% confidence intervals (CIs). A P-value of less than 0.05 was considered statistically significant.

AI Digest

Study Justification:

This study aimed to examine the determinants of self-reported hypertension (HTN) among women in South Africa. While the prevalence of objectively measured HTN in females has been reported, little is known about the prevalence and risk factors of self-reported HTN in the same population. Understanding the determinants of self-reported HTN is crucial for developing effective health promotion and services to reduce the burden of HTN in South Africa.

Highlights:

– The study found that 23.6% of South African women self-reported HTN.
– Factors associated with reduced odds of self-reported HTN included being younger, never married, and not covered by health insurance.
– Factors associated with increased odds of self-reported HTN included being black/African, perceiving oneself as overweight, perceiving poor health status, and having other comorbidities.
– The study highlights the importance of considering sociodemographic, health, and lifestyle factors in addressing the burden of HTN among women in South Africa.

Recommendations:

– Health promotion efforts should focus on raising awareness about HTN and its risk factors, particularly among black/African women.
– Access to healthcare services, including health insurance coverage, should be improved to ensure early detection and management of HTN.
– Interventions should address lifestyle factors such as healthy eating habits and weight management to reduce the risk of HTN.
– Integrated care models that consider the presence of other chronic conditions should be implemented to provide comprehensive healthcare for women with self-reported HTN.

Key Role Players:

– South Africa Medical Research Council
– Institutional Review Board of International Classification of Functioning (ICF)
– National Department of Health
– Centre for Disease Control and Prevention

Cost Items:

– Research funding for data collection, analysis, and publication
– Training and compensation for interviewers and data collectors
– Administrative costs for survey coordination and logistics
– Data management and analysis software
– Dissemination of study findings through conferences and publications

Strength of Evidence

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 population-based survey with a large sample size. The study used multiple logistic regression models to examine the independent factors of self-reported hypertension, considering the complex survey design. The results provide adjusted odds ratios and confidence intervals for the identified factors. To improve the evidence, the abstract could include information on the response rate and any potential limitations of the study, such as selection bias or measurement error.

Abstract

This study was approved by the South Africa Medical Research Council and the Institutional Review Board of International Classification of Functioning (ICF). All methods were conducted in accordance with the Declaration of Helsinki principles. The data were de-identified to prevent the respondents’ identity from being revealed. All individuals who agreed to take part in the survey were asked to provide either written or verbal consent for the interview. Particularly, verbal consent was sought from participants who could not write or read. The Centre for Disease Control and Prevention granted a waiver of written consent per 45CFR46 for respondents who were unable to provide written consent but consented verbally. The study used cross-sectional data from SADHS 2016. The SADHS 2016 was conducted to provide up-to-date estimates of basic demographic and measures of population health such as fertility levels, awareness and use of contraceptives, childhood and maternal mortality, immunization coverage, and prevalence and treatment of acute respiratory infection, fever, diarrhea, nutrition, etc. [29]. Additionally, the survey was also conducted to provide estimates of health and behavior measures for adults aged 15 years and older. The sample for the SADHS 2016 was designed to produce estimates of vital measures of the country as a whole: urban and nonurban areas. Administratively, South Africa is divided into nine provinces namely Eastern Cape, Free State, Gauteng, KwaZulu-Natal, Limpopo, Mpumalanga, Northern Cape, North West, and Western Cape, which vary considerable in size. The Statistics South Africa Master Sample Frame was created using Census 2011 data. Enumeration areas (EAs) was used as the sampling frame for the SADHS 2016. In the Master Sample Frame, EAs of manageable size were treated as primary sampling units (PSUs), whereas small neighboring EAs were combined to form new PSUs, and large EAs were split into conceptual PSUs. Using a two-stage stratified sampling design with a probability proportional to size, PSUs were selected at the first stage while the systematic sampling of residential dwelling units (DUs) was performed at the second stage. Thus, a total of 750 PSUs were selected from the 26 sampling strata. From each PSU, a fixed number of 20 residential DUs were selected using a systematic sampling technique. Of the 15,292 households selected, 13,288 households were occupied, and interviews were successfully conducted in 83% of the occupied households. Data collection was conducted between June 2016 and November 2016 at the request of the National Department of Health. Data were collected using questionnaires administered by conducting face-to-face interviews. All households were eligible for interviews using the Household Questionnaire where basic demographic indicators on the characteristics of each person listed were collected, including age, sex, marital status, education, and relationship to the head of the household were collected. Further, information on characteristics of the household’s DU, such as the source of drinking water, type of sanitation facility, materials used for the floor, walls, and roof of the DU, and ownership of various durable goods were also collected. Secondly, the woman’s questionnaire was used to collect information from all eligible women aged 15 years and older on background characteristics such as age, education, media, exposure, etc. Additionally, the woman’s questionnaires also included a module on adult health where information on the use of tobacco, alcohol, consumption of fat, salt, sugar, fruit, and vegetables, health care-seeking behaviors, and self-reported prevalence of a variety of noncommunicable diseases including HTN were captured. The present study considered self-reported HTN (high blood pressure) as the dependent variable. Specifically, respondents were asked, “Has a medical doctor, nurse, or health professional told you that you have high blood pressure?” If the response was “yes” then respondents were classified as having self-rated HTN, and if the response was “no,” then the respondents were considered having no self-reported HTN. Independent variables of this study were selected after a review of the relevant literature and their availability in the SADHS 2016 dataset [11, 17, 31]. In total, 10 sociodemographic variables, seven health and lifestyle-related factors, and three other comorbidities of chronic diseases were included. Sociodemographic variables included the age of the respondents, education level, ethnicity, household wealth, marital status, employment status, exposure to mass media, place of residence, and geographical region. Lifestyle-related factors included self-reported body mass index (BMI) and perception of own health, type of fruits eaten yesterday, type of vegetables eaten yesterday, frequency of eating processed meat, frequency of eating fried foods, and sugar-sweetened drinks in the last 24 h. Other chronic diseases included comorbidities of self-reported HTN, comorbidities of self-reported diabetes, and comorbidities of self-reported hypercholesterolemia. These variables were categorized as follows: age of the respondents in years (20–24, 25–29, 30–34, 35–39, 40–44, 45–49, 50 + years), educational level (no education/primary, secondary, tertiary education), ethnicity (black/African, white, colored and other), household wealth (poorest, poorer, middle, richer, richest), marital status (never married, currently married, divorced/widowed), current employment (not employed or employed), covered by health insurance (yes or no), the amount of mass media exposure (0, 1, 2, 3), place of residence (urban or rural), and geographical region (Western Cape, Eastern Cape, Northern Cape, Free State, KwaZulu-Natal, North West, Gauteng, Mpumalanga, Limpopo). Household wealth was calculated using household items such as such as televisions and bicycles, materials used for housing construction, and types of water access and sanitation facilities and principal component analysis was used to create scores and these were further divided into quintiles. Health and lifestyle-related factors included perception of own weight—BMI (underweight, normal weight, overweight/obese), perception of own health, also known as self-rated health status (SRHS) was measured using a single question: “In general, how do would you rate your health?” with the following response options: excellent, good, moderate, or poor; type of fruits eaten yesterday (none, one type, two or more types); type of vegetables eaten yesterday (none, one type, two or more types); frequency of eating processed meat (never, every day, at least once a week, occasionally); frequency of eating fried foods (never, every day, at least once a week, occasionally); and sugar-sweetened drinks yesterday (no or yes). Objectively BMI is calculated by dividing the person’s weight in kilograms by their height in meters squared (kg/m2) [32]. For adults over 20 years old, BMI falls into one of the following categories: underweight (below 18.5 kg/m2), normal weight (18.5–24.9 kg/m2), overweight (25.0–29.9 kg/m2), and obesity (30 kg/m2 and above). Comorbidities for self-reported HTN included self-reported diabetes and self-reported hypercholesterolemia. Particularly, respondents were asked; “Has a medical doctor, nurse, or health provider told you that you have high blood pressure?” If the response was “yes” then respondents were classified as having self-reported diabetes/blood sugar or high blood cholesterol/fats in the blood (hypercholesterolemia), and if the response was “no”, then the respondents were considered having no self-reported diabetes or hypercholesterolemia. All data analyses were conducted using SAS ver. 9.4 (SAS Institute Inc., Cary, NC, USA). Characteristics of the study population were presented with numbers and percentages. Group comparisons between respondents that reported to have HTN and those that did not have HTN were made with Rao-Scott chi-square test. To assess the relationship between the selected variables and self-reported HTN, multivariate logistic analyses were constructed using a generalized estimating equation. The generalized estimating equation models accounted for the clustering effects of the hierarchical SADHS data. The results of the multivariate analysis were reported as adjusted odds ratios (aORs) with their P-values and 95% confidence intervals (CIs). A P-value of less than 0.05 was considered statistically significant.

Materials

https://efashare.b-cdn.net/materials/cd9a54e7a62e6badaee9fbeba8223acc372e4ce936bfe0ab63032c62e9f5e27a/40885_2022_222_MOESM1_ESM.pdf

Innovations

Based on the provided information, here are some potential innovations that could improve access to maternal health:

1. Telemedicine: Implementing telemedicine services can provide remote access to healthcare professionals, allowing pregnant women to receive medical advice and consultations without having to travel long distances.

2. Mobile health (mHealth) applications: Developing mobile applications that provide information and resources related to maternal health can empower women with knowledge and support throughout their pregnancy journey.

3. Community health workers: Training and deploying community health workers who can provide basic prenatal care, education, and support to pregnant women in rural or underserved areas can help improve access to maternal health services.

4. Transportation services: Establishing transportation services specifically for pregnant women in remote areas can help overcome geographical barriers and ensure that they can access prenatal care and emergency obstetric services when needed.

5. Maternal health clinics: Setting up dedicated maternal health clinics in underserved areas can provide comprehensive prenatal care, including regular check-ups, screenings, and access to essential medications.

6. Health education programs: Implementing targeted health education programs that focus on maternal health and pregnancy-related issues can increase awareness and empower women to make informed decisions about their health.

7. Mobile clinics: Utilizing mobile clinics that travel to remote areas can bring essential maternal health services, such as prenatal care and vaccinations, directly to women who may not have easy access to healthcare facilities.

8. Partnerships with local organizations: Collaborating with local organizations, such as community-based groups or non-profit organizations, can help leverage existing resources and networks to improve access to maternal health services.

9. Maternal health vouchers: Introducing voucher programs that provide financial assistance for maternal health services can help reduce the financial burden on pregnant women and increase their access to quality care.

10. Maternal health hotlines: Establishing dedicated hotlines staffed by trained healthcare professionals can provide pregnant women with immediate access to medical advice, support, and referrals.

It is important to note that the specific implementation and effectiveness of these innovations may vary depending on the local context and resources available.

AI Innovations Description

Based on the provided description, the study aims to examine the determinants of self-reported hypertension (HTN) among women in South Africa. The study found that self-reported HTN was associated with various sociodemographic, health, and lifestyle factors, as well as the presence of other chronic conditions.

To develop this study into an innovation to improve access to maternal health, the following recommendations can be considered:

1. Increase awareness and education: Develop targeted health promotion campaigns to raise awareness about the risks and consequences of hypertension during pregnancy. This can include educating women about the importance of regular blood pressure monitoring and seeking medical care if they experience symptoms of hypertension.

2. Improve access to healthcare services: Enhance access to prenatal care services by expanding the availability of healthcare facilities, particularly in rural areas. This can involve increasing the number of healthcare providers, improving transportation infrastructure, and implementing telemedicine solutions to reach remote areas.

3. Strengthen health insurance coverage: Address the finding that not being covered by health insurance reduced the odds of self-reported HTN. Explore options to expand health insurance coverage for women, especially those from low-income backgrounds, to ensure they have access to affordable and comprehensive maternal healthcare services.

4. Promote healthy lifestyle behaviors: Implement interventions that encourage healthy lifestyle behaviors, such as regular physical activity, balanced diet, and weight management, to reduce the risk of hypertension among women. This can involve community-based programs, educational campaigns, and support groups.

5. Integrate hypertension screening into routine maternal healthcare: Incorporate routine blood pressure monitoring and hypertension screening into prenatal care visits to ensure early detection and management of hypertension during pregnancy. This can help prevent complications and improve maternal and fetal outcomes.

6. Strengthen healthcare infrastructure: Invest in improving healthcare infrastructure, including facilities, equipment, and resources, to ensure adequate and quality maternal healthcare services. This can involve upgrading existing healthcare facilities, training healthcare providers, and ensuring the availability of essential medications and equipment.

By implementing these recommendations, it is possible to improve access to maternal health and reduce the burden of hypertension among women in South Africa.

AI Innovations Methodology

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

1. Increase awareness and education: Implement comprehensive health education programs that focus on maternal health, including the importance of regular check-ups, proper nutrition, and healthy lifestyle choices during pregnancy.

2. Strengthen healthcare infrastructure: Invest in improving healthcare facilities, especially in rural areas, by providing necessary equipment, trained healthcare professionals, and adequate resources to ensure quality maternal healthcare services.

3. Enhance community-based care: Establish community health centers or mobile clinics that can provide accessible and affordable maternal healthcare services, particularly in remote areas where access to healthcare facilities is limited.

4. Improve transportation services: Develop transportation systems or initiatives that can facilitate the transportation of pregnant women to healthcare facilities, especially in areas with limited transportation options.

5. Promote telemedicine: Utilize technology to provide remote healthcare consultations and monitoring for pregnant women, enabling them to access healthcare services without the need for physical travel.

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

1. Define the indicators: Identify key indicators that reflect access to maternal health, such as the number of prenatal visits, percentage of women receiving skilled birth attendance, or maternal mortality rates.

2. Collect baseline data: Gather data on the current status of the selected indicators in the target population or region.

3. Model the impact: Use statistical modeling techniques, such as regression analysis or simulation models, to estimate the potential impact of the recommended interventions on the selected indicators. This can involve analyzing historical data, conducting surveys, or using existing literature to inform the model.

4. Sensitivity analysis: Assess the sensitivity of the model by varying key parameters or assumptions to understand the potential range of outcomes and identify potential limitations or uncertainties.

5. Evaluate the results: Analyze the simulated impact of the recommendations and assess their feasibility, cost-effectiveness, and potential challenges or barriers to implementation.

6. Refine and prioritize recommendations: Based on the simulation results, refine the recommendations and prioritize those that are expected to have the greatest impact on improving access to maternal health.

7. Implement and monitor: Implement the recommended interventions and establish a monitoring and evaluation system to track progress and measure the actual impact on access to maternal health over time.

It is important to note that the specific methodology for simulating the impact may vary depending on the available data, resources, and context of the study.

Author

Dima Health

Identifiers:

DOI: 10.1186/s40885-022-00222-5

Research Areas:

Community Interventions, Disability, Environmental, Food Security, Health System and Policy, Infectious Diseases, Maternal Access, Maternal and Child Health, Noncommunicable Diseases, Quality of Care, Sexual and Reproductive Health, Social Determinants, Substance Abuse

Study Countries:

South Africa

Study Design:

Case-Control Study, Cross Sectional Study, Grounded Theory

Study Approach:

Quantitative

Participants Gender:

Female