Abstract
Background
Knowledge gaps remain in how gender‐related socioeconomic inequality affects sex disparities in cardiovascular diseases (CVD) prevention and outcome.
Methods and Results
Based on a nationwide population cohort, we enrolled 3 737 036 residents aged 35 to 75 years (2014–2021). Age‐standardized sex differences and the effect of gender‐related socioeconomic inequality (Gender Inequality Index) on sex disparities were explored in 9 CVD prevention indicators. Compared with men, women had seemingly better primary prevention (aspirin usage: relative risk [RR], 1.24 [95% CI, 1.18–1.31] and statin usage: RR, 1.48 [95% CI, 1.39–1.57]); however, women's status became insignificant or even worse when adjusted for metabolic factors. In secondary prevention, the sex disparities in usage of aspirin (RR, 0.65 [95% CI, 0.63–0.68]) and statin (RR, 0.63 [95% CI, 0.61–0.66]) were explicitly larger than disparities in usage of angiotensin‐converting enzyme inhibitors/angiotensin receptor blockers (RR, 0.88 [95% CI, 0.84–0.91]) or β blockers (RR, 0.67 [95% CI, 0.63–0.71]). Nevertheless, women had better hypertension awareness (RR, 1.09 [95% CI, 1.09–1.10]), similar hypertension control (RR, 1.01 [95% CI, 1.00–1.02]), and lower CVD mortality (hazard ratio, 0.46 [95% CI, 0.45–0.47]). Heterogeneities of sex disparities existed across all subgroups. Significant correlations existed between regional Gender Inequality Index values and sex disparities in usage of angiotensin‐converting enzyme inhibitors/angiotensin receptor blockers (Spearman correlation coefficient, r=−0.57, P=0.0013), hypertension control (r=−0.62, P=0.0007), and CVD mortality (r=0.45, P=0.014), which remained significant after adjusting for economic factors.
Conclusions
Notable sex disparities remain in CVD prevention and outcomes, with large subgroup heterogeneities. Gendered socioeconomic factors could reinforce such disparities. A sex‐specific perspective factoring in socioeconomic disadvantages could facilitate more targeted prevention policy making.
Keywords: cardiovascular disease, prevention, sex disparity, socioeconomic
Subject Categories: Cardiovascular Disease, Primary Prevention, Secondary Prevention, Women
Nonstandard Abbreviations and Acronyms
- GDP
Gross Domestic Product
- GII
Gender Inequality Index
Clinical Perspective.
What Is New?
Notable sex disparities remain in primary and secondary prevention of cardiovascular disease (CVD), which involves the access to prevention resources, performance of public health services, and outcome of clinical care; after adjusting for hypertension, dyslipidemia, and diabetes, women are in an unfavorable status in primary prevention of CVD.
Sex disparities vary across different subgroups of age, education, income, occupation, urbanity, and geographical region.
Societal gender inequality is a reinforcing factor in increasing the established sex disparities in CVD prevention; a higher regional Gender Inequality Index worsens the disadvantage in drug use for secondary CVD prevention and weakens the existing advantage in clinical service outcomes and CVD mortality in women. Such influence remains at work after adjusting for economic factors.
What Are the Clinical Implications?
Significant sex gaps in primary and secondary prevention of CVD call for attention, with more targeted efforts required for specific CVD prevention dimensions, such as aspirin and statin usage for secondary CVD prevention, as well as specific subpopulations, such as those from the rural and less developed social‐economic areas.
Societal gender inequalities substantially contribute to the sex gaps in CVD prevention, which could not be underestimated even with improved economic development.
Sex disparities have become a focal point in understanding the barriers to reducing global disease burden, particularly in the major chronic conditions like cardiovascular diseases (CVD). 1 , 2 The “understudied, underrecognized, underdiagnosed, and undertreated” status of CVD in women has kept being highlighted in recent studies. 1 , 3 Women's unfavorable statuses in CVD management could be an epitome of health inequity influenced by a myriad of factors from different levels of interest, not only individual but societal arenas. 1 , 3 Thus, a better understanding and more recognition of these factors could facilitate addressing inequity in the management of women's cardiovascular health, which is even more important in the developing regions with increasing prevalence of CVD. 2
Compared with the established research on sex disparities in biological and clinical tracks of CVD, 3 , 4 evidence in primary and secondary prevention of CVD is still rare. Among the studies involving discussion on the sex gaps in this regard, the research mostly explored the relations without comprehensively accounting for socioeconomic causes. 5 The PURE (Prospective Urban Rural Epidemiological) study is among the very few that provided knowledge on the variance of sex disparities in CVD prevention across countries and regions of different economic development levels. 6 However, it did not fully take into account the heterogeneities in local values and social disadvantages, which might be more relevant especially to discerning the disparity in health care access and use and further providing insights to facilitate more targeted policy making.
Based on the ChinaHEART (China Health Evaluation and Risk Reduction Through Nationwide Teamwork) study, formerly named China PEACE MPP (China Patient‐Centered Evaluative Assessment of Cardiac Events Million Persons Project), 7 a nationwide population‐based cohort that enrolled 3.5 million Chinese adults, our study aimed to examine the sex disparities in primary and secondary prevention of CVD across 31 provinces in the mainland China, with exploring the influence of gender‐related socioeconomic inequality. In this scenario, sex indicates a biological category of human being, as we recorded in the baseline interview, whereas gender represents a performative process shaped by the societal context and seen as a modifiable social determinant influencing CVD prevention via conception or behaviors.
METHODS
All data underlying this article cannot be shared publicly because the ChinaHEART Study is a national program; as government policy stipulates, it is not permissible for the researchers to make the raw data publicly available at this time.
Study Design and Participants
ChinaHEART is a government‐funded public health program that focuses on CVD risk and management in China. The design of this project has been detailed previously. 7 , 8 In short, from September 2014 to March 2021, 318 county‐level regions (193 rural counties, 125 urban districts) were selected 31 provinces in mainland China, which covered 71.0% of prefecture‐level cities throughout the country. A typical case sampling design was used in each province to provide diversity in geographic distribution, development levels, and population structure, as well as risk exposure and disease patterns. Population size, population stability, and local capacity to support the project were also considered in the selection of study sites (Data S1).
Residents who were 35 to 75 years old and had lived in the selected regions at least 6 months of the previous 12 months were invited to the program as potential eligible participants via extensive publicity campaigns on television and in newspapers. The overall response rate in communities was around 30%, which was higher than those in the similar studies conducted in China and Europe. 7 , 9 , 10 , 11 The central ethics committee at China National Center for Cardiovascular Diseases approved the project (No. 2014‐035). All enrolled participants provided written informed consent.
Data Collection and Definitions
Standardized in‐person interviews were conducted by trained personnel using electronic questionnaires with logical‐check function. Information on sociodemographic status (sex, age, marriage status, education, occupation, incomes), lifestyle, medical history, and medication use were collected. After a 5‐minute rest in a seated position, blood pressure was measured twice in the right upper arm using a standardized electronic blood pressure monitor (Omron HEM‐7430, Omron Corporation, Kyoto, Japan); if the difference between the 2 systolic blood pressure measurements was >10 mm Hg, a third measurement was obtained, and the mean value of the blood pressure would be calculated using the last 2 measurements. The use of medication, including name, dose, and frequency, was self‐reported if the participants had been taking drugs for blood pressure control, lipid lowering, glucose lowering, or antiplatelet in the past 2 weeks, and additionally confirmed by requiring the participants to bring their drug packaging (boxes) to the enrollment clinics (Data S1). Body weight, blood pressure, blood lipids, and glucose were measured using a standardized protocol and specialized devices.
Regional socioeconomic characteristics were collected at provincial level based on official reports and gray literature. We obtained the Gender Inequality Index (GII) as a gender‐related socioeconomic disadvantage indicator (Figure S1), which was established by the United Nations Development Programme for measuring gender inequality. 12 Its calculation is based on statistical data in 3 aspects: (1) reproductive health, measured by maternal mortality ratios and adolescent birth rates; (2) empowerment, measured by the proportion of parliamentary seats occupied by women and the proportions of women and men aged 25 years and older with at least some secondary education; and (3) economic status, expressed as labor market participation and measured by the labor force participation rates of women and men aged 15 years or older. 12 GII ranges from 0 to 1 (the greatest level of gender equality to the greatest level of gender inequality). We also collected the gross domestic product (GDP) per capita to represent the level of economic development.
Outcome Measures
We aimed to estimate the sex gaps in the indicators for primary and secondary prevention of CVD, performance of public health service and clinical care, as well as CVD mortality.
Based on the guideline recommendations on CVD prevention, the use of statin and aspirin was selected to assess the primary prevention in participants with high estimated CVD risk but no established CVD. High CVD risk was defined as a predicted 10‐year CVD risk ≥20% estimated according to the World Health Organization CVD risk model (Figure S2). 13 For the secondary prevention, the use of statins, aspirin, angiotensin‐converting enzyme inhibitors (ACEIs)/angiotensin receptor blockers (ARBs), and beta blockers in patients with established CVD (ie, self‐reported history of myocardial infarction, ischemic stroke, percutaneous coronary intervention, or coronary artery bypass grafting) was selected. 14 , 15 , 16
We also looked at the awareness of hypertension and blood pressure control (<140/90 mm Hg) in those aware of high blood pressure; the former reflected the performance of public health services, and the latter was for the outcome of clinical care. Hypertension was defined as systolic blood pressure ≥140 mm Hg, diastolic blood pressure ≥90 mm Hg, taking antihypertensive medication, or self‐report of a previous diagnosis of hypertension. 17 Awareness of hypertension was defined by self‐reporting of a previous diagnosis of hypertension among participants with hypertension, and hypertension control was defined as having the mean systolic blood pressure <140 mm Hg and the mean diastolic blood pressure <90 mm Hg among participants with hypertension. 17
CVD mortality was selected to assess the major outcome of CVD prevention. We ascertained the data of mortality through the National Mortality Surveillance System and Vital Registration of Chinese Center for Disease Control and Prevention. The death records in this system were reported by health care institutions in real time, then checked against the local residential records and health insurance records annually. The analyses were performed using the mortality data available up to December 31, 2021. All events were coded using International Classification of Diseases, Tenth Revision (ICD‐10). Person months were calculated from recruitment until the date of death or December 31, 2021, whichever occurred first.
Statistical Analysis
Categorical variables are shown as frequency and percentage and continuous variables as mean (SD). We calculated the rates of 9 outcome measures and their relative risk (RR) or hazard ratio (HR) of sex (female to male) at the national and provincial levels, which had been age standardized at provincial level according to the 2010 population census of China. Age‐standardized procedure was conducted to obtain the same age structure for both women and men at every 1‐year unit at the national and provincial levels. RRs or HRs were directly calculated by weighted female rates divided by weighted male rates for each indicator. As sex is an upstream and biological factor, we did not adjust for any other personal‐level factors except for age. Subgroup analysis and heterogeneities in sex disparities were also explored in age, education, income, occupation, urbanity, and geographical regions.
To demonstrate the association between gender‐related societal inequality and sex disparities in CVD prevention, we constructed scatterplots and fitting lines between the sex disparities (RRs or HRs) of outcome measures in each province and its corresponding GII as well as GDP per capita (at provincial level). The Spearman correlation coefficient and its P value were also reported in the scatterplots; provinces with fewer than 500 male or female eligible participants were excluded from the scatterplots to ensure a robust result.
We further developed multivariable mixed models with provinces as random effect using the PROC GLIMMIX process for binary outcomes and a proportional subdistribution hazards model for competing risks (proportional hazards assumptions had been checked by the method of Schoenfeld residuals) using Proc PHREG process for outcome of CVD death, with provincial level as a second layer level; the cross‐layer interactions of GII with sex, which could be interpreted as the association of GII and sex disparities, were added to the model. Statistically significant interactions indicated that men's and women's changes in their CVD prevention status were influenced differently by the changes in GII. For the interactive effect HR, if it is <1, this indicated that with the increase of societal gender inequality, the female population had a greater decrease in an indicator (eg, primary or secondary prevention) than the male population; if HR >1, it indicated that as societal gender inequality increased, the female population had a greater increase in an indicator (eg, CVD death). Besides sex, GII, and their interaction, we also included age groups, region (eastern/central/western), GDP per capita, and the interaction of GDP per capita with sex in the models to examine whether the correlations between GII and sex disparities were independent of regional economic status.
A 2‐sided P value <0.05 was considered statistically significant. The forest plots and scatterplots were generated using R software (version 3.4.1), and all other analyses were conducted using SAS software (version 9.4, SAS Institute, Cary, NC).
Role of Funding Source
The funders of the study have no role in study design, data collection, data analysis, data interpretation, or writing of the report. The corresponding authors have full access to all the data in the study with final responsibility for the decision to submit for publication.
RESULTS
From September 2014 to March 2021, a total of 3 737 036 participants were enrolled and 5790 (0.2%) were excluded due to missing data. For each province, at least 42 524 participants were included. Among the included individuals, the average age was 56.2 (SD 9.8) years and 59.4% (2 216 124) were women. There were 61.0% of participants living in rural areas, and 36.6%, 36.4%, and 33.1% living in the eastern, central, and western regions, respectively. Overall, 810 458 (21.7%) participants had received high school education or above, 1 775 074 (47.6%) were nonfarmers, and 619 160 (16.6%) reported an annual household income above 50 000 yuan (RMB). Among all the included participants, 472 184 had high estimated CVD risk but no established CVD, 123 009 had established CVD, and 1 771 262 had hypertension. The characteristics of the overall population, high CVD risk population, and population with established CVD are shown in Table 1.
Table 1.
Characteristics of Participants (Overall Population, High‐Risk Population Without Established CVD, and Population With CVD)
| Characteristics | Overall population | High‐risk population without established CVD (N=472 184) | Population with established CVD | |||
|---|---|---|---|---|---|---|
| (N=3 731 246) | ||||||
| Female sex (n=2 216 124) | Male sex (n=1 515 122) | Female sex (n=183 432) | Male sex (n=288 752) | Female sex (n=60 403) | Male sex (n=62 606) | |
| Age, y | ||||||
| 35–44 | 309 805 (13.98) | 204 510 (13.50) | 65 (0.04) | 971 (0.34) | 1799 (2.98) | 1736 (2.77) |
| 45–54 | 709 112 (32.00) | 435 323 (28.73) | 1735 (0.95) | 12 118 (4.20) | 9306 (15.41) | 10 036 (16.03) |
| 55–64 | 713 232 (32.18) | 484 097 (31.95) | 29 359 (16.01) | 73 138 (25.33) | 23 490 (38.89) | 24 126 (38.54) |
| 65–75 | 483 975 (21.84) | 391 192 (25.82) | 152 273 (83.00) | 202 525 (70.14) | 25 808 (42.72) | 26 708 (42.66) |
| Region | ||||||
| Eastern | 811 830 (36.63) | 552 896 (36.49) | 72 550 (39.56) | 111 882 (38.75) | 19 711 (32.63) | 22 059 (35.23) |
| Central | 678 485 (30.62) | 454 396 (29.99) | 56 743 (30.93) | 89 166 (30.88) | 24 044 (39.81) | 23 552 (37.62) |
| Western | 725 809 (32.75) | 507 830 (33.52) | 54 139 (29.51) | 87 704 (30.37) | 16 648 (27.56) | 16 995 (27.15) |
| Urbanity | ||||||
| Rural | 1 346 133 (60.74) | 931 539 (61.48) | 114 963 (62.67) | 185 333 (64.18) | 35 074 (58.07) | 35 326 (56.43) |
| Urban | 869 991 (39.26) | 583 583 (38.52) | 68 469 (37.33) | 103 419 (35.82) | 25 329 (41.93) | 27 280 (43.57) |
| Education | ||||||
| College and above | 132 937 (6.00) | 135 100 (8.92) | 2711 (1.48) | 12 661 (4.38) | 2500 (4.14) | 5192 (8.29) |
| High school | 291 608 (13.16) | 250 813 (16.55) | 12 236 (6.67) | 34 995 (12.12) | 7915 (13.10) | 11 375 (18.17) |
| Middle school | 662 504 (29.89) | 540 977 (35.71) | 34 342 (18.72) | 86 376 (29.91) | 16 024 (26.53) | 22 554 (36.03) |
| Primary school and below | 1 104 708 (49.85) | 572 475 (37.78) | 132 292 (72.12) | 152 002 (52.65) | 33 482 (55.43) | 23 031 (36.78) |
| Unknown | 24 367 (1.10) | 15 757 (1.04) | 1851 (1.01) | 2718 (0.94) | 482 (0.80) | 454 (0.73) |
| Occupation | ||||||
| Farmer | 1 123 073 (50.68) | 774 680 (51.13) | 100 795 (54.95) | 163 117 (56.49) | 29 036 (48.07) | 28 303 (45.21) |
| Not farmer | 1 059 084 (47.79) | 715 990 (47.26) | 80 129 (43.68) | 121 627 (42.12) | 30 675 (50.78) | 33 495 (53.50) |
| Unknown | 33 967 (1.53) | 24 452 (1.61) | 2508 (1.37) | 4008 (1.39) | 692 (1.15) | 808 (1.29) |
| Annual income, yuan/y | ||||||
| >50 000 | 344 924 (15.56) | 274 236 (18.10) | 20 535 (11.19) | 42 167 (14.60) | 7845 (12.99) | 10 703 (17.10) |
| 10 000–50 000 | 1 216 163 (54.89) | 839 616 (55.42) | 92 254 (50.30) | 153 922 (53.31) | 34 045 (56.36) | 35 323 (56.41) |
| <10 000 | 431 990 (19.49) | 264 491 (17.45) | 50 809 (27.70) | 65 697 (22.75) | 13 494 (22.34) | 11 811 (18.87) |
| Unknown | 223 047 (10.06) | 136 779 (9.03) | 19 834 (10.81) | 26 966 (9.34) | 5019 (8.31) | 4769 (7.62) |
| Married | 2 002 760 (90.37) | 1 423 244 (93.94) | 147 213 (80.25) | 264 860 (91.73) | 52 224 (86.46) | 58 799 (93.92) |
| Health insurance | 2 174 375 (98.12) | 1 487 437 (98.17) | 180 251 (98.27) | 284 216 (98.43) | 59 638 (98.73) | 61 871 (98.83) |
| Current smoker | 44 724 (2.02) | 698 149 (46.08) | 12 341 (6.73) | 167 857 (58.13) | 2645 (4.38) | 24 905 (39.78) |
| Current drinker | 38 121 (1.72) | 341 868 (22.56) | 4378 (2.39) | 84 048 (29.11) | 1082 (1.79) | 12 034 (19.22) |
| Body mass index category | ||||||
| Low weight | 48 091 (2.17) | 26 703 (1.76) | 3602 (1.96) | 6066 (2.10) | 917 (1.52) | 753 (1.20) |
| Normal weight | 952 492 (42.98) | 611 418 (40.35) | 60 817 (33.16) | 112 952 (39.12) | 19 877 (32.91) | 19 290 (30.81) |
| Overweight | 850 026 (38.36) | 634 878 (41.90) | 75 448 (41.13) | 120 492 (41.73) | 25 606 (42.39) | 28 748 (45.92) |
| Obesity | 365 515 (16.49) | 242 123 (15.98) | 43 565 (23.75) | 49 242 (17.05) | 14 003 (23.18) | 13 815 (22.07) |
| Hypertension | 1 020 696 (46.06) | 750 566 (49.54) | 170 194 (92.78) | 245 566 (85.04) | 45 078 (74.63) | 47 142 (75.3) |
Data were presented as frequency (n) and percentages (%). CVD indicates cardiovascular diseases.
Women had a better status in primary prevention than men, specifically in the use of statin (1.9% versus 1.3%, RR, 1.48 [95% CI, 1.39–1.57], P < 0.001) and aspirin (2.5% versus 2.0%, RR, 1.24 [95% CI, 1.18–1.31], P<0.001) in the high CVD risk group (Table 2). However, after adjusting for history of dyslipidemia, hypertension, and diabetes, the status of women changed to an insignificant or even worse one (aspirin, odds ratio [OR], 1.02 [95% CI, 0.96–1.09], P=0.493; statin, OR, 0.70 [95% CI, 0.64–0.77], P<0.001). By contrast, secondary prevention in female patients with CVD was significantly worse than in male patients. Women had lower rates of taking statin (8.7% versus 13.7%, RR, 0.63 [95% CI, 0.61–0.66], P<0.001), aspirin (11.9% versus 18.2%, RR, 0.65 [95% CI, 0.63–0.68], P<0.001), ACEI/ARB (10.1% versus 11.6%, RR, 0.88 [95% CI, 0.84–0.91], P<0.001), and β blocker (4.5% versus 6.7%, RR, 0.67 [95% CI, 0.63–0.71], P<0.001) than men (Table 2). Regarding blood pressure management, women had a better status in awareness of hypertension (48.3% versus 44.2%, RR, 1.09 [95% CI, 1.09–1.10], P<0.001), whereas no statistically significant difference existed in blood pressure control in those aware of high blood pressure (24.8% versus 24.6%, RR, 1.01 [95% CI, 1.00–1.02], P=0.124) (Table 2). Besides, women also had a significantly lower CVD mortality (405/100 000 people versus 881/100 000 people, HR, 0.46 [95% CI, 0.45–0.47], P<0.0001).
Table 2.
Nationwide Sex Disparities in Prevention of CVD
| Indicator | Female rates (events/total number of participants) | Male rates (events/total number of participants) | Effect (95% CI) | P value |
|---|---|---|---|---|
| Hypertension awareness | 48.3% (535 123/1 020 696) | 44.2% (370 670/750 566) | 1.09 (1.09–1.10) | <0.001 |
| Hypertension control in aware patients | 24.8% (131 820/535 123) | 24.6% (93 125/370 670) | 1.01 (1.00–1.02) | 0.124 |
| Aspirin usage in population with high CVD risk | 2.5% (4402/183 432) | 2.0% (5656/288 752) | 1.24 (1.18–1.31) | <0.001 |
| Statin usage in population with high CVD risk | 1.9% (3509/183 432) | 1.3% (3686/288 752) | 1.48 (1.39–1.57) | <0.001 |
| Aspirin usage in population with CVD | 11.9% (7649/60 403) | 18.2% (11 610/62 606) | 0.65 (0.63–0.68) | <0.001 |
| Statin usage in population with CVD | 8.7% (5739/60 403) | 13.7% (8732/62 606) | 0.63 (0.61–0.66) | <0.001 |
| Angiotensin‐converting enzyme inhibitor/angiotensin receptor blocker usage in population with CVD | 10.1% (6679/60 403) | 11.6% (7643/62 606) | 0.88 (0.84–0.91) | <0.001 |
| β Blocker usage in population with CVD | 4.5% (2930/60 403) | 6.7% (4229/62 606) | 0.67 (0.63–0.71) | <0.001 |
| CVD mortality in overall population | 405/100 000 people (13 267/2 216 124) | 881/100 000 people (20 293/1 515 122) | 0.46 (0.45–0.47) | <0.001 |
Effects are presented by relative risk (RR) and its 95% CI, except for the indicator‐CVD mortality in overall population, which is presented by hazard ratio (HR) and its 95% CI. Both female rates and male rates are at nationwide level and are age‐standardized according to the 2010 population census of China. CVD indicates cardiovascular diseases.
Sex disparities in prevention of CVD varied in the subgroups of age, education, income, occupation, regions, and urbanity. In particular, the statuses of women kept improving and became better than those of men with age, except blood pressure control and CVD mortality (Figure 1). For primary prevention, no significant difference was observed in the younger groups, but in those aged >55 years, women had a better status than men (Figure 1A and 1B). For secondary prevention, the gaps between women and men were gradually narrowed with age in the patients aged >55 years old (Figure 1C through 1F). In hypertension control, among those aware of their high blood pressure, there was a gradual change in women from a better status to a worse one with age compared with men (Figure 1).
Figure 1. Forest plots of sex gaps in subgroup populations.
A, Hypertension awareness. B, Hypertension control. C, Aspirin usage in population with high CVD risk. D, Statin usage in population with high CVD risk. E, Aspirin usage in population with CVD. F, Statin usage in population with CVD. G, ACEI/ARB usage in population with CVD. H, β Blocker usage in population with CVD. I, CVD mortality in overall population. ACEI indicates angiotensin‐converting enzyme inhibitor; ARB, angiotensin receptor blocker; CVD, cardiovascular diseases; and RR, relative risk. Both female rates and male rates are at the nationwide level and are age‐standardized according to the 2010 population census of China.
In general, the provincial GII in mainland China ranged from 0.035 to 0.342, with a median value of 0.130 (interquartile range: 0.083–0.183). The higher the GII level was in the province, the more disadvantaged the status would be found in women (Figure 2). Specifically, the sex disparities in the use of ACEI/ARB among the CVD population (Spearman r=−0.62, P=0.00069) and hypertension control in those aware of their high blood pressure (Spearman r=−0.57, P=0.0013) were negatively correlated with GII, whereas CVD mortality (Spearman r=0.46, P=0.011) in the overall population was positively correlated. In the meantime, regional GDP per capita played a facilitating role in improving the prevention and outcome of CVD in women (Figure S3). After controlling the confounding effect of economic development on the relation between GII and sex disparities in CVD prevention by including GDP per capita in the models, the adjusted results showed that the interaction between GII and sex disparities were still statistically significant in the use of ACEI/ARB (OR, 0.89 [95% CI, 0.81–0.98], P=0.021), as well as in hypertension control (OR, 0.92 [95% CI, 0.90–0.94], P < 0.0001) and CVD mortality in the overall population (OR, 1.09 [95% CI, 1.04–1.14], P=0.003) (Table 3).
Figure 2. Scatter plots of GII and sex gaps.
A, Hypertension awareness. B, Hypertension control. C, Aspirin usage in population with high CVD risk. D, Statin usage in population with high CVD risk. E, Aspirin usage in population with CVD. F, Statin usage in population with CVD. G, ACEI/ARB usage in population with CVD. H, β Blocker usage in population with CVD. I, CVD mortality in overall population. ACEI indicates angiotensin‐converting enzyme inhibitor; ARB, angiotensin receptor blocker; and CVD, cardiovascular diseases. The closed circles represent effect (relative risk or hazard ratios) of sex disparities at each provincial level, whiskers represent the 95% CI of the effect. Lines were fitted between the effects and its provincial Gender Inequality Index, the Spearman correlation coefficient and its P value were also reported.
Table 3.
Effect of GII and GDP Per Capita on Sex Disparities in Prevention of CVD
| Indicators | GII on sex disparities | GDP per capita on sex disparities | ||
|---|---|---|---|---|
| Effect (95% CI) | P value | Effect (95% CI) | P value | |
| Hypertension awareness | 0.99 (0.97–1.00) | 0.084 | 0.99 (0.99–1.00)* | <0.0001* |
| Hypertension control in aware patients | 0.92 (0.90–0.94)* | <0.0001* | 1.00 (1.00–1.01) | 0.82 |
| Aspirin usage in population with high CVD risk | 1.02 (0.92–1.12) | 0.75 | 1.01 (0.99–1.03) | 0.46 |
| Statin usage in population with high CVD risk | 1.05 (0.92–1.21) | 0.47 | 1.03 (1.01–1.06)* | 0.018* |
| Aspirin usage in population with CVD | 0.95 (0.87–1.03) | 0.19 | 1.00 (0.98–1.01) | 0.56 |
| Statin usage in population with CVD | 0.95 (0.86–1.05) | 0.30 | 1.00 (0.98–1.02) | 0.92 |
| Angiotensin‐converting enzyme inhibitor/angiotensin receptor blocker usage in population with CVD | 0.89 (0.81–0.98)* | 0.021* | 1.01 (0.99–1.03) | 0.24 |
| β Blocker usage in population with CVD | 0.99 (0.88–1.12) | 0.86 | 1.00 (0.98–1.03) | 0.82 |
| CVD mortality in overall population | 1.09 (1.04–1.15)* | 0.0002* | 1.00 (0.99–1.02) | 0.50 |
Effects are presented by OR (odds ratios) and its 95% CI, except for the indicator CVD mortality in overall population, which is presented by HR (hazard ratios) and its 95% CI. Variables in the models: age, gender, region, urbanity, GII, GDP per capita, GII*gender, and GDP per capita*gender. CVD indicates cardiovascular diseases; GDP, gross domestic product; and GII, Gender Inequality Index.
P<0.05.
DISCUSSION
To our knowledge, this is the first study looking at the sex disparities in CVD prevention and outcome with examining their associations with societal gender inequality, exploring the influence of gendered socioeconomic development on the former. The analysis demonstrated that notable sex disparities in different manners remained in primary and secondary prevention of CVD, with large heterogeneities across subgroups by age and other characteristics. Societal gender inequality serves as a reinforcing factor in enlarging the established sex disparities in CVD prevention, and such influence remains at work after adjusting for economic factors.
First, our analysis revealed a better status of women in primary prevention and CVD mortality; the former might be overrated because of the different CVD risk profiles between the sexes, whereas the latter could be mainly due to biological differences. Like in prior studies set in China and internationally, 5 , 6 , 18 higher crude rates of medication use for primary prevention in women were observed in this study. Furthermore, we found that among the dominating factors defining CVD risk (ie, blood pressure, plasma lipid, blood glucose, and smoking), 13 , 15 , 16 women had a far lower smoking rate than men of similarly high CVD risk 19 , 20 but higher levels in other three high risk factors, which were also observed in previous studies. 5 , 6 Metabolic risk factors typically arouse more attention in clinical practice than behavioral ones, hence women were more likely to use medications. The adjusted ORs between the sexes in our study, which indicated a worsening trend for women, further proved this explanation for the seemingly higher rates of primary prevention medication use in women. Regarding women's significantly lower cardiovascular mortality, this observation also aligned with the findings in previous research. In a study looking at sex differences in premature CVD mortality rates among World Health Organization member states, the mortality rate of men was one third higher than that of women. 21 The researchers argued that sex differences in CVD mortality should be as close to their inherent biological differences as possible. 21 One plausible explanation for women's lower mortality could be the protective effect of estrogen in women.
Second, the sex disparities in CVD prevention and outcome varied greatly across subgroups, especially with age. Younger women experienced much larger disadvantages than their older counterparts. Both biological mechanism and socioeconomic elements might be the major contributors. 5 , 6 In the younger women with CVD, the protective effects of estrogens could help attenuate the symptoms and also prevent or delay the development of hypertension and dyslipidemia. This may influence clinical decision‐making with this population less likely receiving appropriate treatment than their male counterparts of similar ages and conditions. 22 , 23 Additionally, adverse effects of medication among women have been identified as a key factor, 18 , 24 because insufficient participation of women in trials could influence physicians' decision and patients' adherence in using secondary prevention medications. 18 , 23 , 25 In younger women, pregnancy or considering pregnancy may even widen the sex gaps in drug use. And menopause might create a key environmental trigger leading to genetic effects that mediate hypertension and also affect the body reaction to treatment. 26
Third, gendered socioeconomic disadvantages could reinforce the existing sex disparities in CVD prevention independent of economic development. Economically less developed areas indeed often reported low gender equality. However, after adjusting for GDP per capita, a higher level of GII was still associated with a worsening status of women in hypertension control and CVD mortality, as well as in secondary prevention drug usage. These indicated that besides economic factors, the remaining sex gaps could keep weakening women's status in their access to and use of health care service, and further affecting their overall health outcomes. It is plausible that women's relative disadvantages in health literacy, internal motivation, and consumption ability could constrain their use of advanced medical service. This also explains why GII was particularly associated with the use of statin and ACEI/ARB: among the antihypertensives, these drugs have higher prices and lower availability in the primary health care settings. 27
As established studies pointed out, sex disparities in health are also one social dimension in which gender inequality is manifested in less developed societies. Therefore, the interventions to eradicate sex disparities in health should also engage in more efforts to improve women's disadvantages at the societal level. 27 , 28
The current findings have important policy implications. First, the risk profiles varied between the 2 sexes in the high‐risk CVD population. Therefore, sex‐specific interventions are required with different priorities set. For women particularly, more attention should be paid to improving the management of hypertension, dyslipidemia, or diabetes, as the estimated high CVD risk based on the established equation often comes later than the symptom onset in women compared with men. Second, the interventions to reduce sex disparities in health should engage in more initiatives to address the factors resulting in women's disadvantages at the societal level. 27 , 28 In research looking at the social status of Chinese women by comparing national surveys during the past 3 decades, it indicated that the gender stereotypes influenced by traditional gender roles still existed particularly in the division of labor, 29 despite that women in China have relatively high employment rates (reaching 43.2% in 2019 30 ). Economic growth and the increases in income may play only a limited role in overcoming such stereotypes, 29 as indicated by the remaining effect of regional GII after adjusting for GDP per capita in this study. China ranks among the top 40 countries in sex equality, 12 thus, in many other countries with similar or larger gender inequality, the implications are still highly relevant. 31
Our study has several limitations. First, a random sampling design was not available in this study. However, our analysis focused on diversity and associations, rather than the average prevalence. Moreover, we intended to maximize the geographic distribution and variability in our sampling design and calculated the sex RRs based on age standardization to mitigate the potential biases. Second, as in other large‐scale studies, the information on medication usage was collected based on self‐report. Thus, a recall bias may lead to underestimation and misclassification. Nevertheless, we made efforts, including asking the participants to bring their drug packaging to minimize the influence. Third, we did not exclude the participants with contraindications. In the scenario of both our study and those alike, information on some contraindications, like allergy to specific medications, was routinely not available. Nevertheless, besides the adverse effects of medication among women due to pregnancy, the current guidelines do not indicate different recommendations by the sexes in terms of contraindications. Thus, there would be few biases in the analysis. Fourth, the population in our study is made up of different generations, whereas the index of GII is only selected from a certain time point, which might affect analyzing the association between them. However, given that the GII at certain point also results from a long period of accumulation and the behaviors of different generations are a synergy of the past and the present, it is still reasonable to perform an analysis using GII that was calculated based on recent statistics.
Conclusions
In conclusion, distinct sex disparities were observed in CVD prevention across the nation. Societal gender inequality serves as a reinforcing factor in increasing the established sex disparities in CVD prevention and outcomes, in addition to economic factors. To reduce the burden of CVD in China and beyond, sex‐informed views factoring in the local socioeconomic gender inequality are required.
Sources of Funding
This work was supported by Chinese Academy of Medical Sciences Innovation Fund for Medical Science (2021‐1‐I2M‐011), and the National High Level Hospital Clinical Research Funding (2022‐GSP‐GG‐4).
Disclosures
None.
Supporting information
Data S1
Figures S1–S3
Acknowledgments
We acknowledge the contributions that have been made by the study teams at the Chinese National Center for Cardiovascular Diseases and the local sites in the collaborative network in the realms of study design and operations. Shengshou Hu and Xi Li conceived the ChinaHEART project and take responsibility for all aspects of it. Xi Li and Yunfeng Wang designed the study. Yunfeng Wang and Aoxi Tian wrote the first draft of the article, with further contributions from Chaoqun Wu, Jiapeng Lu, Xiaoyan Zhang, Xingyi Zhang, Yang Yang, Jianlan Cui, Wei Xu, Lijuan Song, Runsi Wang, Xi Li, Shengshou Hu. Aoxi Tian coordinated the literature search, and reviewed and commented on drafts of the article. Yunfeng Wang and Bowang Chen did the statistical analysis. All authors interpreted the data and approved the final version of the article. Yunfeng Wang, Chaoqun Wu, Jiapeng Lu, Bowang Chen, Xiaoyan Zhang, Xi Li, and Shengshou Hu had access to the raw data.
Y. Wang and A. Tian are co‐first authors.
Supplemental Material is available at https://www.ahajournals.org/doi/suppl/10.1161/JAHA.123.030203
For Sources of Funding and Disclosures, see page 10.
Contributor Information
Xi Li, Email: xi.li@nccd.org.cn.
Shengshou Hu, Email: huss@fuwaihospital.org.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data S1
Figures S1–S3