Abstract
Background
Geographic disparities between urban and rural areas can impact healthcare access due to differences in infrastructure, availability of healthcare providers, and economic conditions. Investigating these disparities is essential for identifying gaps in treatment access and informing policies to improve medication adherence and health outcomes. This study aims to examine the prevalence of hypertension medication use among adults in Cape Verde and assess residential inequalities in adherence to medication use.
Methods
The study was based on the observations of 468 adults (18–69 years) living with hypertension who participated in the 2020 WHO STEPS. Descriptive analysis was done to assess the distribution of medication use across the participants’ characteristics. Two set of binary logistic regression were performed to determine the associated factors of medication use. Concentration index was used to assess the level of residential inequalities while a multivariate non-linear decomposition was done to identify factors that contributed to residential inequalities.
Results
Overall, less than half of persons living with hypertension in Cape Verde (40.9% [34.3–47.8]) take their medication. Urban residents were more likely to use medication than their rural counterparts [AOR = 1.88; 95%CI: 1.21–2.94]. Women were more likely to use medication than men [AOR = 1.70; 95%CI: 1.04–2.80]. Individuals who had diabetes [AOR = 2.12; 95%CI: 1.00-4.49] or were overweight/obese [AOR = 1.78; 95%CI: 1.09–2.90] were more likely than those without such co-morbidities to use medication. Persons with hypertension who consumed < 4 servings of fruits per day had lower odds of taking blood pressure medication [AOR = 0.52; 95%CI: 0.30–0.93]. The findings indicate that the rural-urban differences in characteristics explain only a small portion (8.78%) of the gap in hypertension medication use. Only age contributed significantly to the observed residential inequalities (p < 0.001, 2.75%).
Conclusion
There is a moderate level of residential inequality in medication use among persons living with hypertension. This inequality favors urban residents. Any interventions to improve antihypertensive medication uptake must prioritize rural residents.
Keywords: Hypertension, Inequalities, Medication use, Public health
Background
Hypertension is a major risk factor of cardiovascular disease, stroke, and premature mortality [1, 2]. Globally, hypertension is estimated to be responsible for 10.8 million deaths each year [3]. Given the high burden of hypertension, the World Health Assembly (WHA) adopted the NCD global monitoring framework, which set a target of a 25% relative reduction in raised blood pressure by 2025 [4]. However, the available evidence indicates that most countries did not meet that target. In the context of Cape Verde, the prevalence of hypertension increased from 29% in 2017 to 34% in 2019 [5, 6]. Given the increasing prevalence, it is impossible for Cape Verde to meet the target set by the WHO NCD global monitoring framework.
With the increasing prevalence of hypertension in Cape Verde, the country’s public health architecture must ensure that all persons living with hypertension have their condition under control. Literature shows that effective management of hypertension relies heavily on the consistent use of antihypertensive medication [7, 8]. The WHO [3] reports that only 42% of people living with hypertension use medication. In Cape Verde, it is estimated that 51.3% of adults living with hypertension use medication [6]. The underuse of hypertension medication can lead to poor health outcomes, increased hospitalizations, and a higher burden on healthcare systems [9, 10]. Despite improvements in hypertension awareness and treatment in many regions, medication non-adherence remains a significant barrier to effective blood pressure control [11].
Previous studies have shown that several factors influence medication use among persons living with hypertension [12–15]. These range from demographic factors (age, gender), socioeconomic status (income, education), availability of social support, health literacy, to the presence of comorbidities (e.g., diabetes) [12–14]. Additionally, healthcare system factors such as affordability, availability of medications, accessibility of healthcare facilities, and physician prescribing practices influence adherence to hypertension treatment [14, 16].
One critical yet underexplored determinant is residential location. Geographic disparities between urban and rural areas can impact healthcare access due to differences in infrastructure, availability of healthcare providers, and economic conditions [16, 17]. These residential inequalities may create barriers to obtaining and adhering to prescribed hypertension medication, exacerbating health disparities. Despite evidence from other regions demonstrating the link between residential factors and medication use [18–20], there is limited research examining how these inequalities affect medication use in Cape Verde. Given the country’s island geography and variations in healthcare resources across different regions, residential location may play a significant role in determining whether individuals have adequate access to antihypertension medication. Investigating these disparities is essential for identifying gaps in treatment access and informing policies to improve medication adherence and health outcomes. This study aims to examine the prevalence of hypertension medication use among adults in Cape Verde and assess residential inequalities in adherence to medication use.
Methods
Data source and design
In this study, we made use of the 2020 WHO NCD STEPS cross-sectional survey conducted in Cape Verde. The survey was conducted between February and March 2020, and was structured into three sequential phases [21]. In Step 1, socio-demographic and behavioral data were collected. Step 2 involved taking physical measurements, including height, weight, and blood pressure. Step 3 required the collection of blood and urine samples for biochemical analysis, assessing factors such as blood glucose levels, cholesterol levels, and salt intake [22]. The STEPS methodology, known for its standardized data collection procedures and focus on prevalence and risk factor analysis, made the dataset particularly suitable for this research.
The survey targeted adults aged 18 and above in Cape Verde. To ensure a representative sample within this age group, a multi-stage probability sampling design was implemented. A total of 4,563 adults participated in Steps 1 and 2, while a subsample of 2,436 individuals took part in Step 3 [21, 22]. The overall response rate for the survey was 64%, reflecting the proportion of eligible participants who actively engaged in the study. Ethical approval for the study was granted by the Data Protection Commission (CNPD) and the National Ethics Committee for Health Research (CNEPS) in Cape Verde. All participants provided written informed consent before their involvement [22].
Study variables
Outcome variable
The outcome variable, medication use, was derived from the question, ‘Have you taken medication for high blood pressure?’ This question was restricted to only persons who had been told they have hypertension. Responding ‘yes’ meant that the individual used medication, while a response of ‘no’ implied non-use of medication.
Explanatory variables
Based on the theoretical relevance and findings from previous studies [12–15], the following were selected as explanatory variables: place of residence (rural and urban), age, gender (men and women), educational level, and marital status. Additionally, health-related factors were included: alcohol consumption, diabetes status, overweight/obesity status, current smoking status, and fruit and vegetable consumption. Fruit and vegetable consumption was computed from the question about the servings of each eaten per day. This was categorized as < 4 servings and ≥ servings per day. Overweight/obesity status was computed from the body mass index (BMI) of the participants. Having a BMI of ≥ 25 was categorized as being overweight/obese.
Statistical analyses
All statistical analyses were conducted using Stata version 18 (StataCorp, College Station, TX, USA). Descriptive statistics were used to summarize the demographic and clinical characteristics of the study population. Categorical variables were presented as frequencies and percentages, while continuous variables were expressed as means and standard deviations. To assess the factors associated with hypertension medication use, we performed a two-step binary logistic regression analysis. The first model estimated the crude odds ratios (COR) for each explanatory variable, while the second model adjusted for potential confounders to obtain adjusted odds ratios (AOR) with 95% confidence intervals (CIs). Statistical significance was set at p < 0.05.
The extent of residential inequality in hypertension medication use was quantified using the concentration index (CI). A positive CI value indicated that medication use was concentrated among urban residents, while a negative value suggested a rural advantage. We further employed a multivariate non-linear decomposition analysis to decompose the observed residential inequality into two components: (1) differences in population characteristics (endowments) and (2) differences in the effects of these characteristics on medication use (coefficients). The percentage contribution of each factor to the observed inequality was also calculated. The decomposition analysis is expressed as:
 |
where D represents the total difference in medication use between urban (u) and rural (r) residents, X denotes the explanatory variables, and 
represents the estimated coefficients. The first term captures the explained portion (differences in characteristics), the second term accounts for the unexplained portion (differences in coefficients), and the third interaction term represents the simultaneous effect of both components. All analyses were conducted with appropriate survey weighting to account for the complex sampling design of the WHO NCD STEPS survey. Statistical significance was set at p < 0.05.
Results
Distribution of sample characteristics
Table 1 shows the distribution of the sample characteristics. Most participants were women (63.3%), aged 30–59 years (68.3%), had secondary education (70.9%), were never married (36.7%), and resided in urban areas (63%). In terms of the health indicators, 8.8% were living with diabetes, 49.2% were overweight/obese, 68.1% consumed alcohol, 5% were current smokers, while 88.1% and 70.4% consumed < 4 servings of fruits and vegetables per day, respectively.
Table 1.
Prevalence of medication use among people living with hypertension
| Characteristics | Weighted sample n (%) |
Proportion of persons with hypertension who take medications | p-values a | |
|---|---|---|---|---|
| Frequency (n) | % [95%CI] | |||
| Overall | 468 (100.0) | 216 | 40.9 [34.3–47.8] | |
| Living with diabetes | < 0.001 | |||
| No | 427 (91.2) | 168 | 40.8 [30.6, 44.3] | |
| Yes | 41 (8.8) | 48 | 84.2 [62.7, 89.1] | |
| Gender | 0.963 | |||
| Men | 172 (36.7) | 73 | 41.1 [29.8, 53.4] | |
| Women | 296 (63.3) | 142 | 40.8 [33.9, 48.0] | |
| Age Group | < 0.001 | |||
| < 30 years | 54 (11.4) | 2 | 5.1 [0.7, 28.7] | |
| 30–59 years | 320 (68.3) | 136 | 35.7 [28.3, 43.9] | |
| 60 years and older | 95 (20.3) | 77 | 78.6 [69.7, 85.5] | |
| Education Level | 0.238 | |||
| No formal education | 41 (8.7) | 29 | 61.7 [47.1, 74.4] | |
| Primary/basic | 25 (5.3) | 15 | 42.6 [19.4, 69.5] | |
| Secondary | 332 (70.9) | 137 | 36.9 [30.3, 44.1] | |
| Tertiary | 71 (15.1) | 34 | 48.3 [26.8, 70.4] | |
| Marital Status | 0.004 | |||
| Never married | 172 (36.7) | 53 | 27.8 [20.3, 36.8] | |
| Married | 111 (23.7) | 74 | 58.7 [40.1, 75.1] | |
| Previously married | 63 (13.5) | 36 | 53.8 [38.7, 68.3] | |
| Cohabiting | 122 (26.1) | 52 | 37.7 [25.2, 52.2] | |
| Residence | 0.011 | |||
| Rural | 173 (37.0) | 58 | 29.6 [21.5, 39.3] | |
| Urban | 295 (63.0) | 158 | 46.5 [37.8, 55.3] | |
| Alcohol Consumption | 0.438 | |||
| No | 149 (31.9) | 68 | 44.6 [35.3, 54.3] | |
| Yes | 319 (68.1) | 147 | 39.4 [30.9, 48.5] | |
| Fruit consumption | 0.077 | |||
| 4 or more servings per day | 56 (11.9) | 32 | 57.4 [37.0, 75.9] | |
| Less than four servings | 412 (88.1) | 184 | 38.2 [31.8, 45.1] | |
| Vegetable consumption | 0.480 | |||
| 4 or more servings per day | 139 (29.6) | 55 | 37.6 [28.1, 48.1] | |
| Less than four servings | 329 (70.4) | 160 | 42.3 [34.1, 50.9] | |
| Current Smoking Status | 0.867 | |||
| No | 445 (95.0) | 206 | 41.0 [34.2, 48.2] | |
| Yes | 23 (5.0) | 9 | 38.4 [15.1, 68.6] | |
| Overweight/obese | < 0.001 | |||
| No | 158 (33.8) | 48 | 24.2 [16.1, 34.7] | |
| Yes | 310 (66.2) | 167 | 49.2 [41.1, 57.4] | |
ap-values are computed from chi-square; Bold: statistically significant
Prevalence of medication use among people living with hypertension
Overall, less than half of persons living with hypertension in Cape Verde (40.9% [34.3–47.8]) take their medication. The prevalence was significantly high among those with diabetes (84.2%), older adults (78.6%), currently married (58.7%), urban residents (46.5%), and those who were overweight/obese (49.2%) (Table 1).
Factors associated with medication use among people living with hypertension
The results from the multivariable logistic regression show that increasing age is associated with higher odds of medication use [AOR = 1.10; 95%CI: 1.08–1.13] (Table 2). Urban residents were more likely to use medication than their rural counterparts [AOR = 1.88; 95%CI: 1.21–2.94]. Women were more likely to use medication than men [AOR = 1.70; 95%CI: 1.04–2.80]. Individuals who had diabetes [AOR = 2.12; 95%CI: 1.00–4.49.00.49] or were overweight/obese [AOR = 1.78; 95%CI: 1.09–2.90] were more likely than those without such co-morbidities to use medication. Persons with hypertension who consumed < 4 servings of fruits per day had lower odds of taking blood pressure medication [AOR = 0.52; 95%CI: 0.30–0.93].
Table 2.
Factors associated with medication use among people living with hypertension
| Characteristics | Model I COR (95%CI) |
Model II AOR (95%CI) |
|---|---|---|
| Living with diabetes | ||
| No | Ref. | Ref. |
| Yes | 3.07 (1.61, 5.85)** | 2.12 (1.00, 4.49)* |
| Gender | ||
| Men | Ref. | Ref. |
| Women | 1.52 (1.01, 2.29)* | 1.70 (1.04, 2.80)* |
| Age (continuous) | 1.10 (1.08, 1.12)*** | 1.10 (1.08, 1.13)*** |
| Education Level | ||
| No formal education | Ref. | - |
| Primary/basic | 0.79 (0.33, 1.89) | - |
| Secondary | 0.41 (0.23, 0.73) | - |
| Tertiary | 0.54 (0.24, 1.18) | - |
| Marital Status | ||
| Never married | Ref. | - |
| Married | 3.65 (2.21, 6.03)*** | - |
| Previously married | 2.45 (1.44, 4.16)** | - |
| Cohabiting | 1.41 (0.85, 2.34) | - |
| Residence | ||
| Rural | Ref. | Ref. |
| Urban | 1.69 (1.17, 2.46)** | 1.88 (1.21, 2.94)** |
| Alcohol Consumption | ||
| No | Ref. | - |
| Yes | 0.70 (0.48, 1.03) | - |
| Fruit consumption | ||
| 4 or more servings per day | Ref. | Ref. |
| Less than four servings | 0.56 (0.34, 0.94)* | 0.52 (0.30, 0.93)* |
| Vegetable consumption | ||
| 4 or more servings per day | Ref. | - |
| Less than four servings | 1.08 (0.74, 1.57) | - |
| Current Smoking Status | ||
| No | Ref. | - |
| Yes | 1.04 (0.44, 2.44) | - |
| Overweight/obese | ||
| No | Ref. | Ref. |
| Yes | 1.90 (1.27, 2.84)** | 1.78 (1.09, 2.90)* |
***p < 0.001, **p < 0.01, *p < 0.05; Ref: Reference category; AOR: Adjusted Odds Ratio; Bold: statistically significant
(-) variables excluded after application of a backward stepwise approach
Residential inequalities in hypertension medication use
The positive concentration index (0.091) means that hypertension medication use is skewed toward wealthier or more urban populations (Fig. 1). This suggests a significant but moderate level of residential inequality in medication use among adults living with hypertension in Cape Verde.
Fig. 1.
Residential inequalities in hypertension medication use
Decomposition analysis
Urban residents are 13.1% points more likely to use hypertension medication (p = 0.001) (Table 3). The findings indicate that the rural-urban differences in characteristics explain only a small portion (8.78%) of the gap in hypertension medication use. Only age contributed significantly to the observed residential inequalities (p < 0.001, 2.75%). The inequality is primarily driven by differences in how predictors affect medication use (91.22%). This means that even when rural residents have similar characteristics (e.g., same age, obesity, or health conditions), they are still less likely to take hypertension medication.
Table 3.
Results from the decomposition analysis
| Variable | Coef. | p-value | 95% CI | PCT. Contribution (%) |
|---|---|---|---|---|
| Total Decomposition | ||||
| E (Characteristics) | 0.0115 | 0.315 | [−0.0109, 0.0339] | 8.78 |
| C (Coefficients) | 0.11945 | 0.003 | [0.0405, 0.1984] | 91.22 |
| R (Total Difference) | 0.13095 | 0.001 | [0.0556, 0.2063] | 100.00 |
| Due to Difference in Characteristics (E) | ||||
| Diabetes (Yes vs. No) | 0.00664 | 0.366 | [−0.0078, 0.0210] | 5.07 |
| Age | 0.00360 | < 0.001 | [0.0020, 0.0052] | 2.75 |
| Gender (Women vs. Men) | −0.00539 | 0.118 | [−0.0121, 0.0014] | −4.11 |
| Marital Status (Married) | −0.00130 | 0.052 | [−0.0026, 0.0000] | −1.00 |
| Marital Status (Previously Married) | 0.00182 | 0.636 | [−0.0057, 0.0094] | 1.39 |
| Marital Status (Cohabiting) | 0.00041 | 0.356 | [−0.0005, 0.0013] | 0.32 |
| Alcohol Consumption (Yes vs. No) | 0.00041 | 0.951 | [−0.0127, 0.0135] | 0.31 |
| Smoking Status (Yes vs. No) | −0.00506 | 0.383 | [−0.0164, 0.0063] | −3.86 |
| Fruit Consumption (< 4 servings) | 0.00099 | 0.080 | [−0.0001, 0.0021] | 0.76 |
| Vegetable Consumption (< 4 servings) | 0.00228 | 0.467 | [−0.0039, 0.0084] | 1.74 |
| Obesity Status (Yes vs. No) | 0.00710 | 0.173 | [−0.0031, 0.0173] | 5.42 |
| Due to Difference in Coefficients (C) | ||||
| Diabetes (Yes vs. No) | −0.01565 | 0.123 | [−0.0356, 0.0042] | −11.95 |
| Age | −0.17351 | 0.405 | [−0.5822, 0.2351] | −132.51 |
| Gender (Women vs. Men) | 0.02702 | 0.714 | [−0.1176, 0.1716] | 20.63 |
| Marital Status (Married) | 0.06461 | 0.016 | [0.0122, 0.1170] | 49.34 |
| Marital Status (Previously Married) | −0.00542 | 0.757 | [−0.0398, 0.0290] | −4.14 |
| Marital Status (Cohabiting) | −0.01469 | 0.515 | [−0.0589, 0.0295] | −11.22 |
| Alcohol Consumption (Yes vs. No) | 0.06538 | 0.189 | [−0.0322, 0.1629] | 49.93 |
| Smoking Status (Yes vs. No) | 0.01303 | 0.316 | [−0.0124, 0.0385] | 9.95 |
| Fruit Consumption (< 4 servings) | −0.08503 | 0.388 | [−0.2779, 0.1078] | −64.94 |
| Vegetable Consumption (< 4 servings) | 0.05917 | 0.250 | [−0.0416, 0.1599] | 45.19 |
| Obesity Status (Yes vs. No) | 0.00252 | 0.967 | [−0.1171, 0.1222] | 1.92 |
| Constant | 0.18204 | 0.553 | [−0.4200, 0.7840] | 139.02 |
Discussion
We aimed to examine the prevalence of hypertension medication use among adults in Cape Verde and assess residential inequalities in adherence to medication use. The observed prevalence of medication use (40.9%) in Cape Verde is comparable to the global average prevalence of medication use (42%) [3]. However, the overall prevalence of medication use is slightly lower than what was earlier documented in the 2019 May Measurement Month (51.3%) [6]. Given that the data used for the present study was collected around the COVID-19 period, the pandemic may have disrupted the individual’s access to medication; hence, the difference between our estimated prevalence and that of Azevedo et al. [6].
The study revealed that urban residents had a higher medication use prevalence than rural residents. Furthermore, urban residents had 88% higher likelihood of using medications to manage their blood pressure than rural residents. This aligns with Li et al. [18] study that found similar patterns. Similarly, it is consistent with a related study from Indonesia, where there was a higher medication adherence in urban areas (38.2%) than in rural areas (23.6%) [23]. The result is also supported by the inequality analysis that found a moderate level of residential inequality in medication use, which favored urban residents. A plausible explanation for lower medication adherence in rural settings includes limited healthcare infrastructure, lower availability of pharmacies, and economic constraints [15, 20]. Rural areas may experience “pharmacy deserts,” where limited outlets struggle to maintain consistent supplies, especially of newer or specialized drugs. Transportation challenges exacerbate this issue [14]; rural patients often lack reliable means to refill prescriptions, leading to skipped doses or discontinuation.
Interestingly, our study found that residential inequalities explained only a small portion (8.78%) of the gap in medication use, with age being the most significant contributor. The result is similar to a study conducted in China, where residential inequalities explained only 10.01% of the disparity in medication use [18]. This suggests that while location matters, other factors (e.g., healthcare access, affordability, etc.) may play a larger role in determining adherence. Consistent with extant literature [24, 25], we found the women were 70% more likely than men to use an antihypertensive medication. Women are widely reported to be more health-conscious and more in tune with health-seeking behaviors [19, 26]. Culturally, traditional masculinity norms that equate health-seeking with weakness may contribute to the lower medication use among men [27].
The study also affirms that living with comorbidities such as diabetes and overweight/obesity was associated with higher odds of using antihypertensive medication. This is inconsistent with Pan et al. [28], who found no significant association between physical comorbidities and antihypertensive medication use. It is also in contrast to another study that found no significant association between physical comorbidities and medication use, but a significant association for mental health morbidities [24]. However, the findings resonate with Tam et al.’s study [20] that found a significant positive association between physical comorbidities and medication use among persons living with hypertension. Persons living with both hypertension and diabetes are at a higher risk of renal failure and other serious complications [29]. And so, it is likely that such adults may have a high perceived seriousness which serves as a motivation for them to take medications to manage their blood pressure.
Strengths and limitations
This study contributes to narrowing the existing knowledge gap in relation to inequalities in medication use among persons living with hypertension in Cape Verde. The decomposition analysis was another strength of this study, as it allowed us to understand the extent to which rural-urban inequalities contribute to disparities in antihypertensive medication use. Despite these strengths, there are some limitations that must be considered. First, the study is based on cross-sectional data; hence, causality cannot be established [30]. Like Ma et al. [31], our decomposition analysis revealed that the unexplained component absorbed more than half of the total residential disparity in medication adherence. However, the data used did not include some key confounders such as cost of antihypertensive medication, availability of medication, nearness to health facilities or pharmacies, healthcare provider density, or cultural beliefs. These confounders must be controlled for in future studies. Also, given the self-reported nature of hypertension medication use, there is the likelihood of recall bias and social desirability bias. Given the absence of data on the specific antihypertension medications used, we were unable to determine whether the observed inequalities vary for different antihypertension medications. Moving forward, future studies can adopt qualitative research designs to gain deeper insights into the residential inequalities established in this study. Future studies must include more healthcare system factors to provide a more nuanced understanding of the rural-urban disparities in hypertension management.
Conclusion
In conclusion, there is a moderate level of residential inequality in medication use among persons living with hypertension. This inequality favors urban residents. Any interventions to improve antihypertensive medication uptake must prioritize rural residents. Practically, this can be achieved by offsetting the financial and logistical barriers, such as medication costs and transportation challenges in rural areas. Furthermore, the findings highlight the need for the public health sector in Cape Verde to implement awareness campaigns emphasizing the importance of medication adherence, particularly among individuals without comorbidities and younger adults, who exhibited lower medication use. These campaigns should leverage community health workers to educate and encourage adherence to prescribed treatments especially in rural areas of Cape Verde.
Acknowledgements
We acknowledge the WHO for granting us free access to the dataset used in this study.
Abbreviations
- AOR
Adjusted odds ratio
- BMI
Body mass index
- CI
Confidence interval
- LMIC
Low-and-middle-income Country
- WHA
World health assembly
- WHO
World health organization
Author contributions
JO and CA conceptualized and designed the study. JO curated the data and performed the formal analyses. JO and CA drafted the initial manuscript. KSD reviewed the initial manuscript for its accuracy. All authors reviewed the final manuscript and approved its submission. JO had the final responsibility of submitting the manuscript.
Funding
None.
Data availability
The datasets are publicly available in the WHO NCD Microdata Repository: [https://extranet.who.int/ncdsmicrodata/index.php/catalog/935](https://extranet.who.int/ncdsmicrodata/index.php/catalog/935). Accession number/ID: CPV\_2020\_STEPS\_v01.
Declarations
Ethics approval and consent to participate
We did not seek ethical approval as this has already been done for all the STEPS surveys of NCD risk factors. However, ethical approval for the 2020 WHO STEPS survey was granted by the National Ethics Committee for Health Research and the Data Protection Commission (CNEPS). We formally requested the data from the WHO NCD Microdata Repository: https://extranet.who.int/ncdsmicrodata/index.php/home.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
References
- 1.Wang C, Yuan Y, Zheng M, Pan AN, Wang M, Zhao M, Li Y, Yao S, Chen S, Wu S, Xue H. Association of age of onset of hypertension with cardiovascular diseases and mortality. J Am Coll Cardiol. 2020;75(23):2921–30. [DOI] [PubMed] [Google Scholar]
- 2.Fang J. Prevalence of self-reported hypertension and antihypertensive medication use among adults aged≥ 18 years—United States, 2011–2015. MMWR. Morbidity and mortality weekly report. 2018;67. 10.15585/mmwr.mm6707a4 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.World Health Organization. Global report on hypertension: the race against a silent killer. World Health Organization; 2023. [Google Scholar]
- 4.Banatvala N, Cowan M, Rani M, Riley L. Accountability for NCD prevention and control. InNoncommunicable Diseases 2023 Jan 30 (pp. 253–260). Routledge.
- 5.Azevedo V, Dias L, Soares I, Garcia G, Beaney T, Xia X, Poulter NR. May measurement month 2017: results from Cabo Verde—Sub-Saharan Africa. Eur Heart J Supplements. 2019;21(SupplementD):D28–30. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Azevedo V, Dias L, Garcia G, Soares I, Silva M, Delgado I, Lima F, Sanches N, Beaney T, Partington G, Poulter NR. May measurement month 2019: an analysis of blood pressure screening results from cape Verde. Eur Heart J Supplements. 2021;23(SupplementB):B37–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Burnier M, Egan BM. Adherence in hypertension: a review of prevalence, risk factors, impact, and management. Circul Res. 2019;124(7):1124–40. [DOI] [PubMed] [Google Scholar]
- 8.Oparil S, Acelajado MC, Bakris GL, Berlowitz DR, Cífková R, Dominiczak AF, Grassi G, Jordan J, Poulter NR, Rodgers A, Whelton PK. Hypertension. Nat Reviews Disease Primers. 2018;4:18014. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Thew HZ, Ching SM, Lim HM, Mos MH, Tze LC, Low KF, Shaari N, Lin JY, Lee KW, Ramachandran V. Prevalence and determinants of medications non-adherence among patients with uncontrolled hypertension in primary care setting in Sarawak, malaysia: A cross-sectional study. Malaysian Family Physician: Official J Acad Family Physicians Malaysia. 2022;17(3):128. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Zhou D, Xi B, Zhao M, Wang L, Veeranki SP. Uncontrolled hypertension increases risk of all-cause and cardiovascular disease mortality in US adults: the NHANES III linked mortality study. Sci Rep. 2018;8(1):9418. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Oh SW. Medication adherence and effective management of hypertension. Korean J Family Med. 2021;42(2):89. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Almaghamsi SA, Alzahrani KM. Exploring the influence of perceived social support on medication adherence among patients with chronic hypertension in 2024: a cross-sectional study from Jeddah, Kingdom of Saudi Arabia. Cureus. 2024;16(9 ), e69522. 10.7759/cureus.69522. [DOI] [PMC free article] [PubMed]
- 13.Commodore-Mensah Y, Turkson-Ocran RA, Foti K, Cooper LA, Himmelfarb CD. Associations between social determinants and hypertension, stage 2 hypertension, and controlled blood pressure among men and women in the United States. Am J Hypertens. 2021;34(7):707–17. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Gupta S, Dhamija JP, Mohan I, Gupta R. Qualitative study of barriers to adherence to antihypertensive medication among rural women in India. Int J Hypertens. 2019;2019(1):5749648. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15. Sekkarie A. Prevalence of self-reported hypertension and antihypertensive medication use among adults—United States, 2017–2021. MMWR. Morbidity and Mortality Weekly Report. 2024;73. 10.15585/mmwr.mm7309a1 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Su M, Zhang Q, Bai X, Wu C, Li Y, Mossialos E, et al. Availability, cost, and prescription patterns of antihypertensive medications in primary health care in China: a nationwide cross-sectional survey. Lancet. 2017;390(10112):2559–68. [DOI] [PubMed] [Google Scholar]
- 17.Passi-Solar A, Scholes S, Mindell JS, Ruiz M. P33 socioeconomic inequalities in the prevalence and management of hypertension: A multilevel analysis of 13,605 men and women in Chile.European Heart Journal, 2019; 40(Supplement_1). 10.1093/eurheartj/ehz748.0695
- 18.Li J, Shi L, Li S, Xu L, Qin W, Wang H. Urban-rural disparities in hypertension prevalence, detection, and medication use among Chinese adults from 1993 to 2011. Int J Equity Health. 2017;16:1–0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Samanic CM. Prevalence of self-reported hypertension and antihypertensive medication use by county and rural-urban classification—United States, 2017. MMWR. Morbidity and Mortality Weekly Report. 2020;69. 10.15585/mmwr.mm6918a1 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Tam HL, Chung SF, Wang Q. Urban-rural disparities in hypertension management among middle-aged and older patients: results of a 2018 Chinese National study. Chronic Illn. 2023;19(3):581–90. [DOI] [PubMed] [Google Scholar]
- 21.Okyere J, Ayebeng C, Dickson KS. Prevalence of diabetes and its associated factors in Cape Verde: an analysis of the 2020 WHO STEPS survey on non-communicable diseases risk factors. BMC Endocr Disord. 2024;24(1):264. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Christian AK, Osei-Appaw AA, Sawyerr RT, Wiredu Agyekum M. Hypertension, diabetes, and cardiovascular disease nexus: investigating the role of urbanization and lifestyle in Cabo Verde. Glob Health Action. 2024;17(1):2414524. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Rikmasari Y, Andayani TM, Kristina SA, Endarti D. Urban-Rural Difference in Adherence Treatment of Hypertensive Patients In South Sumatra Indonesia. InBIO Web of Conferences 2023 (Vol. 75, p. 05021). EDP Sciences.
- 24.Calderón-Larrañaga A, Diaz E, Poblador-Plou B, Gimeno-Feliu LA, Abad-Díez JM, Prados-Torres A. Non-adherence to antihypertensive medication: the role of mental and physical comorbidity. Int J Cardiol. 2016;207:310–6. [DOI] [PubMed] [Google Scholar]
- 25.Nishimura S, Kumamaru H, Shoji S, Sawano M, Kohsaka S, Miyata H. Adherence to antihypertensive medication and its predictors among non-elderly adults in Japan. Hypertens Res. 2020;43(7):705–14. [DOI] [PubMed] [Google Scholar]
- 26.Kapoor D, Munjal A. Functional foods: the new secret of the health conscious Indian women!! Glob Bus Rev. 2017;18(3):750–65. [Google Scholar]
- 27.Leone JE, Rovito MJ, Mullin EM, Mohammed SD, Lee CS. Development and testing of a conceptual model regarding men’s access to health care. Am J Mens Health. 2017;11(2):262–74. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Pan J, Wu L, Wang H, Lei T, Hu B, Xue X, Li Q. Determinants of hypertension treatment adherence among a Chinese population using the therapeutic adherence scale for hypertensive patients. Medicine. 2019;98(27):e16116. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Roy S, Schweiker-Kahn O, Jafry B, Masel-Miller R, Raju R S, O’Neill L M, et al. Risk factors and comorbidities associated with diabetic kidney disease. J Prim Care Community Health. 2021;12:21501327211048556. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Huang AA, Huang SY. Shapely additive values can effectively visualize pertinent covariates in machine learning when predicting hypertension. J Clin Hypertens. 2023;25(12):1135–44. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Ma X, Zhang Y, Zhang M, Li X, Yin H, Li K, Jing M. Decomposing the effect of drug benefit program on antihypertensive medication adherence among the elderly in urban China. Patient Preference and Adherence. 2019 Jul 11:1111–23. [DOI] [PMC free article] [PubMed]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The datasets are publicly available in the WHO NCD Microdata Repository: [https://extranet.who.int/ncdsmicrodata/index.php/catalog/935](https://extranet.who.int/ncdsmicrodata/index.php/catalog/935). Accession number/ID: CPV\_2020\_STEPS\_v01.