Abstract
Background
Cardiovascular disease (CVD) is the leading cause of death in the United States, and rates of CVD incidence vary widely by race and ethnicity. Cigarette smoking is associated with increased risk of CVD. The purpose of the study was: 1) to examine smoking prevalence over time across Asian and Pacific Islander (API) and multi-race API subgroups; 2) to determine whether the CVD risk associated with smoking differed among these subgroups.
Methods
We identified patients belonging to 7 single race/ethnicity groups, 4 multi-race/ethnicity groups, and a non-Hispanic White (NHW) comparison group at two large health systems in Hawaii and California. We estimated annual smoking prevalence from 2011 through 2018 by group and gender. We examined incidence of CVD events by smoking status and race/ethnicity, and computed hazard ratios for CVD events by age, gender, race/ethnicity, census block median household income, census block college degree, and study site using Cox regression.
Results
Of the 12 groups studied, the Asian Indian and Chinese American groups had the lowest smoking prevalence, and the Asian + Pacific Islander multiracial group had the highest smoking prevalence. The prevalence of smoking decreased from 2011 to 2018 for all groups. Multi-race/ethnicity groups had higher risk of CVD than the NHW group. There was no significant interaction between race/ethnicity and smoking in models predicting CVD, but the association between race/ethnicity and CVD incidence was attenuated after adjusting for smoking status.
Conclusions
There is considerable heterogeneity in smoking prevalence and the risk of CVD among API subgroups.
Keywords: Cardiovascular disease, Race/ethnic groups, Multi-race groups, Health disparities, Prevalence, Incidence rates, Survival analysis
1. Introduction
Cardiovascular disease (CVD), which includes coronary heart disease (CHD), peripheral vascular disease (PVD), and stroke, is the leading cause of death in the United States (U.S.) [1], but the rates vary widely by race and ethnic group. Recent reports note that CVD mortality is significantly lower among Asian people compared to Non-Hispanic White people [2,3]. However, there is considerable heterogeneity among Asian population subgroups [2,4]; for example Asian Indian and Filipino populations have higher proportional mortality from ischemic heart disease and stroke than Non-Hispanic White populations [5]. Additionally, the prevalence of obesity, diabetes, and hypertension, all contributing conditions to CVD, are higher among Pacific Islanders than among other Asian American groups [6,7].
Cardiovascular health among Asian and Pacific Islander (API) individuals is important, as they represent the fastest growing racial/ethnic group in the U.S. Individuals identifying as a single API group currently comprise 5.9 % of the population (19.3 million); including individuals identifying as multiracial (API as well as another racial/ethnic group), that percentage increases to 7.2 %. (23.8 million) [8]. Although API individuals represent over forty diverse cultures, most research studies aggregate them into one category due to limited sample sizes of many Asian subgroups, including Korean, Vietnamese, and Native Hawaiian and other Pacific Islander (NHPI) subgroups [[9], [10], [11], [12]]. Available literature indicates higher levels of CVD risk factors among NHPI individuals compared to the general US population including low levels of physical activity, dietary risk factors, high tobacco use, and high rates of overweight and obesity—as well as consistently high incidence of diabetes and heart disease, and subsequently shorter life expectancy [13]. Although Asian Americans as an aggregated group (not including NHPI individuals) have lower risk for CVD than White populations, some Asian subgroups, such as South Asian populations, are at heightened risk for CVD, facing premature and more severe coronary artery disease, and decreased insulin sensitivity compared to White individuals [14,15]. Moreover, very little is known about CVD in the multi-racial API population. Of the 24 million people who identified as Asian in the 2020 U.S. census, 4.1 million people (17 %), identified as multi-racial [16]. The proportion of multiracial people is even larger among Pacific Islanders. Of the 1.59 million people who identified as Pacific Islander in the 2020 census, 900,000 (57 %) identified as multi-racial. However, very few studies clearly define the multiple racial/ethnic admixtures of individuals included in study populations beyond classifying them as “mixed” or “other.”[17]
Current smoking is associated with increased risk of CVD and is a cause in approximately one of every four deaths from CVD [18]. Since smoking is a modifiable cardiovascular risk factor, disparities in cardiovascular outcomes could be affected by changes in smoking rates. However, available smoking prevalence in API populations is data out-of-date [[19], [20], [21]], and trends over time are rarely reported.
In addition to a paucity of studies reporting results among disaggregated Asian subpopulations, there have been few studies that have examined the impact of smoking on CVD among multi-racial API individuals. [22,23] Only a handful of previous studies have examined the contribution of smoking to ethnic differences in the CVD risks, and these studies yielded inconsistent findings [22,23].
To fill these fundamental gaps in our knowledge of CVD risks among API subpopulations, the purpose of the study was to (1) examine trends in smoking prevalence over time across eleven single- and multi-racial API subgroups, as well as a White, non-Hispanic control group; and (2) determine whether the CVD risk associated with smoking was similar among API and multiracial API subgroups and the non-Hispanic white population. Understanding the impacts of smoking as a CVD risk factor on fast-growing API populations is critical for eliminating racial and ethnic disparities in health and improving population-level cardiovascular care. For researchers and clinicians undertaking such improvements in care, this study may help to focus efforts on high-risk groups in which disparities in smoking prevalence and smoking-related CVD risk are greatest or most urgent.
2. Methods
2.1. Study population
California and Hawaii have the largest API populations in the US [24]. This study included the API and NHW populations served by two large healthcare systems in Hawaii and northern California. Our study population included adults ages 18 years and older with one or more primary care visits at internal medicine, family medicine, or gynecology/obstetrics over any 2-year period between 2011 and 2018. The completeness, sensitivity, and positive predictive values of smoking history documentation in the Epic® electronic health record (EHR) have increased considerably in recent years, indicating that EHRs are now a valid source for identifying persons with a history of smoking for research and clinical purposes [25]. Moreover, evidence shows that smoking prevalence derived from the primary care EHR is aligned with data from the General Household Survey (GHS), a nationally representative survey, suggesting that the EHR is an alternative way of monitoring smoking prevalence in the population [26]. Finally, studies on disaggregated API groups as well as multi-racial groups are often limited by small sample sizes. Utilizing EHR data allows us to examine smaller populations within the larger API category to provide more stable estimates that are difficult to obtain with general survey data [27,28].
The second part of the analysis uses a retrospective (or historical) cohort design. Individuals who otherwise met the criteria above were entered into the cohort if there was no evidence in the EHR of CVD for the previous five years. Thus, all individuals included in the cohort in 2011 had no history of any CVD condition of interest documented in the EHR from at least 2006–2010. Individuals could enter the cohort after 2011 at the time that this criterion was met. Cohort members were followed until 2018 for new CVD events using encounter diagnoses or problem list (whichever occurred first).
Censoring occurred on June 30th of the year when the patient last appears in the EHR. This cohort was divided into three mutually exclusive smoking groups based on the last available instance in the first year of the five-year clearance period using self-reported smoking status in the EHR data: current smoking, former smoking, and never smoking.
2.2. Measurement and study design
Both study sites use instances of the EHR. Both sites maintain research data warehouses that conform with the standards of the Virtual Data Warehouse (VDW) created and maintained by the Health Care Systems Research Network [29]. The VDW is a standardized method for sharing EHR information using standard variables, variable definitions, and formats. The VDW enables rapid and efficient use of EHR data for research purposes across sites while maintaining privacy, and has supported large research networks in the areas of cancer, cardiovascular disease, mental health, and diabetes [30,31].
Participants self-reported race/ethnicity upon joining each health system. For this study, we selected patients who identified as Non-Hispanic White, Asian, Native Hawaiian/Other Pacific Islander, or multiple race categories. This study population was then classified into 8 single race/ethnicity groups (Asian Indian, Japanese, Chinese, Filipino, Korean, Native Hawaiian, Other Pacific Islander, and non-Hispanic White [NHW]) and 4 multi-race groups: Asian + Pacific Islander (A+PI), Asian + White (A+NHW), Pacific Islander + White (PI+NHW), and Pacific Islander + Asian + White (PI+A+NHW). These 12 groups are mutually exclusive.
We identified each person's self-reported smoking status (current/former/non-smoker) for each year between 2011 and 2018 using the last available status in the calendar year: Annual smoking prevalence was defined as the number of current smokers divided by the total number of eligible patients in that year. Eligible patients were included in the denominator for each year in which they had at least one primary care visit in the previous two years. Approximately 21.8 % of all eligible patients had missing data on smoking status and were excluded from the analysis. To check and understand the missing data, we compared the mean age, gender, race/ethnicity composition, and sites. We found these factors to be quite similar between patients with and without documented smoking status. Although the differences are statistically significant, this reflects the large sample size. Missingness does not seem to threaten the comparisons in our analyses. The average annual percent change was calculated by the difference between smoking prevalence in 2011 and 2018 divided by eight.
CVD conditions included coronary heart disease (CHD), stroke (hemorrhagic and ischemic), and peripheral vascular disease (arterial and venous, PVD); we also included a summary category of any CVD. These conditions were defined by ICD-9 or ICD-10 diagnosis codes associated with an ambulatory (outpatient), inpatient, or emergency department encounters, or present in the problem list.
We also extracted baseline demographic information including age, gender and study site, as well as median household income level, and the percentage of population with a college degree in their census ZIP Code Tabulation Areas [32], based on residential address.
2.3. Data analysis
We calculated crude (unadjusted) and age-adjusted annual smoking prevalence estimates for each of the 12 race/ethnicity groups by gender using EHR data. To examine whether smoking increases the risk of CVD and explains observed racial/ethnic disparities in CVD, we examined CVD incidence rates (per 100,000 person-years) for CVD events and the Kaplan-Meier survival curves showing CVD events by smoking status and race/ethnicity from 2011 to 2018. We computed hazard ratios (HR) of CVD events accounting for age, gender, race/ethnicity, census block median household income, proportion of census block with a college degree, and study site using Cox regression (Model 1). Adding smoking status to Model 1, we estimated HRs adjusted for smoking status (Model 2). To examine if there were racial and ethnic differences in the relationship between smoking and incident CVD, multiplicative interaction terms between smoking and race/ethnicity were tested. Statistical significance for all comparisons including interactions was defined as p < 0.05. SAS® Version 9.4 (Cary, NC) was used for all analyses. This work was reviewed and approved by the Institutional Review Boards at of all study sites and granted a waiver of Health Insurance Portability and Accountability Act Authorization and a waiver of informed consent as a data-only study.
3. Results
3.1. Trends in smoking prevalence
Overall, the Asian Indian and Chinese American groups had the lowest smoking prevalence and A+PI group had the highest of all 12 groups (Table 1). All API and multi-race subgroups except for Asian Indian and Chinese American groups had higher smoking prevalence than NHW regardless of gender. Indeed, in 2018, the highest smoking prevalence (13.1 %), among A+PI and PI+NHW women, was 2.35 times higher than among NHW women (3.9 %) and 25.2 times higher than the lowest smoking rate (0.5 %), among Asian Indian American women. Similarly, in 2018, the highest smoking rate (19.2 %), among other Pacific Islander men, was two times higher than for NHW men (6.4 %) and 4.19 times higher than the lowest smoking rate (3.7 %), among Chinese American men. Of the Asian single race groups, Filipino American men had the highest smoking prevalence (12.4 %) followed by Korean (11.7 %) and Japanese American men (9.2 %), while Korean American women had the highest prevalence (5.2 %) followed by Japanese (4.7 %) and Filipino American women (4.3 %). Of the Native Hawaiian/Other Pacific Islander single race groups, Other Pacific Islander men had higher smoking prevalence (19.2 %) than Native Hawaiian men (13.1 %), while Native Hawaiian women had higher smoking prevalence (12.9 %) than Other Pacific Islander women (9.8 %). Of all the multi-race groups, A+PI men had the highest smoking prevalence (17.5 %) followed by PI+A+NHW (16.1 %), PI+NHW (15.1 %) and A+NHW men (12 %), while both A+PI and PI+NHW women had the highest smoking prevalence (13.1 %) followed by PI+A+NHW women (11.1 %) and A+NHW (5.8 %). The Appendix contains information on the baseline characteristics of the overall sample at the first year they entered the cohort.
Table 1.
Prevalence of current smoking (unadjusted) among adult patients who used primary care services at two large healthcare systems in northern California and Hawaii, 2011–2018.
| Year | NHW | Asian Indian | Chinese | Filipino | Japanese | Korean | Native Hawaiian | Other PI | PI+A+NHW | A+NHW | A+PI | PI+NHW | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Female | 2011 | 5810 (6.0 %) | 88 (0.6 %) | 202 (1.2 %) | 883 (6.3 %) | 673 (6.9 %) | 226 (8.5 %) | 138 (20.8 %) | 209 (14.2 %) | 498 (16.8 %) | 335 (10.2 %) | 585 (16.5 %) | 415 (16.9 %) |
| 2012 | 5829 (5.6 %) | 88 (0.5 %) | 196 (1.1 %) | 878 (6.0 %) | 616 (6.2 %) | 205 (7.4 %) | 107 (17.0 %) | 196 (13.1 %) | 460 (15.2 %) | 325 (9.7 %) | 565 (15.9 %) | 402 (16.2 %) | |
| 2013 | 5802 (5.1 %) | 97 (0.5 %) | 185 (0.9 %) | 842 (5.3 %) | 551 (5.5 %) | 181 (6.3 %) | 97 (15.6 %) | 190 (11.9 %) | 435 (14.1 %) | 307 (8.6 %) | 504 (14.0 %) | 350 (14.2 %) | |
| 2014 | 5693 (4.8 %) | 106 (0.5 %) | 181 (0.9 %) | 831 (5.0 %) | 550 (5.3 %) | 158 (5.1 %) | 89 (14.5 %) | 176 (10.8 %) | 432 (13.7 %) | 282 (7.7 %) | 519 (14.3 %) | 345 (13.6 %) | |
| 2015 | 5732 (4.6 %) | 113 (0.5 %) | 180 (0.8 %) | 927 (5.0 %) | 579 (5.3 %) | 201 (6.0 %) | 101 (15.3 %) | 183 (9.1 %) | 472 (13.2 %) | 292 (7.3 %) | 576 (14.4 %) | 381 (13.6 %) | |
| 2016 | 5317 (4.3 %) | 116 (0.5 %) | 187 (0.8 %) | 829 (4.6 %) | 512 (4.8 %) | 191 (5.7 %) | 80 (13.2 %) | 178 (9.2 %) | 414 (12.2 %) | 265 (6.8 %) | 536 (13.8 %) | 346 (13.3 %) | |
| 2017 | 4830 (4.1 %) | 107 (0.5 %) | 182 (0.8 %) | 840 (4.6 %) | 491 (4.6 %) | 179 (5.3 %) | 75 (12.8 %) | 176 (9.4 %) | 404 (11.7 %) | 270 (6.9 %) | 537 (13.9 %) | 349 (13.3 %) | |
| 2018 | 4599 (3.9 %) | 111 (0.5 %) | 166 (0.7 %) | 797 (4.3 %) | 490 (4.7 %) | 174 (5.2 %) | 75 (12.9 %) | 185 (9.8 %) | 387 (11.1 %) | 232 (5.8 %) | 511 (13.1 %) | 337 (13.1 %) | |
| Male | 2011 | 6458 (8.5 %) | 843 (5.8 %) | 579 (4.8 %) | 1500 (16.0 %) | 756 (11.6 %) | 177 (12.9 %) | 115 (18.9 %) | 302 (22.6 %) | 377 (20.7 %) | 357 (15.8 %) | 587 (21.0 %) | 320 (18.2 %) |
| 2012 | 6596 (8.2 %) | 905 (5.9 %) | 548 (4.5 %) | 1514 (15.5 %) | 737 (11.2 %) | 187 (13.3 %) | 112 (17.7 %) | 271 (20.7 %) | 362 (19.3 %) | 349 (15.1 %) | 546 (19.4 %) | 309 (17.6 %) | |
| 2013 | 6691 (7.7 %) | 922 (5.4 %) | 581 (4.3 %) | 1471 (14.2 %) | 705 (10.4 %) | 186 (12.4 %) | 113 (18.6 %) | 315 (22.5 %) | 319 (16.4 %) | 325 (13.7 %) | 518 (18.1 %) | 278 (15.5 %) | |
| 2014 | 6721 (7.3 %) | 1047 (5.5 %) | 630 (4.4 %) | 1518 (13.9 %) | 673 (9.9 %) | 184 (11.8 %) | 110 (17.3 %) | 287 (20.3 %) | 343 (16.8 %) | 319 (13.4 %) | 533 (18.3 %) | 289 (16.1 %) | |
| 2015 | 6960 (7.3 %) | 1076 (5.3 %) | 609 (4.0 %) | 1563 (13.6 %) | 676 (9.7 %) | 191 (11.4 %) | 86 (14.3 %) | 312 (20.5 %) | 366 (17.1 %) | 316 (12.6 %) | 545 (18.0 %) | 311 (16.5 %) | |
| 2016 | 6709 (7.0 %) | 1002 (5.0 %) | 612 (3.9 %) | 1648 (13.8 %) | 703 (10.0 %) | 195 (11.3 %) | 84 (14.4 %) | 335 (20.8 %) | 376 (17.0 %) | 344 (13.2 %) | 559 (17.9 %) | 291 (15.3 %) | |
| 2017 | 6198 (6.7 %) | 940 (4.9 %) | 612 (3.9 %) | 1548 (13.0 %) | 671 (9.6 %) | 186 (10.7 %) | 71 (12.9 %) | 296 (18.9 %) | 357 (16.2 %) | 315 (12.2 %) | 552 (17.8 %) | 300 (15.7 %) | |
| 2018 | 5918 (6.4 %) | 958 (4.8 %) | 590 (3.7 %) | 1488 (12.4 %) | 625 (9.2 %) | 212 (11.7 %) | 69 (13.1 %) | 305 (19.2 %) | 352 (16.1 %) | 327 (12.0 %) | 542 (17.5 %) | 283 (15.1 %) | |
| All | 2011 | 12,268 (7.1 %) | 931 (3.1 %) | 781 (2.7 %) | 2383 (10.2 %) | 1429 (8.8 %) | 403 (10.0 %) | 253 (19.9 %) | 511 (18.2 %) | 875 (18.3 %) | 692 (12.5 %) | 1172 (18.5 %) | 735 (17.5 %) |
| 2012 | 12,425 (6.8 %) | 993 (3.1 %) | 744 (2.5 %) | 2392 (9.8 %) | 1353 (8.2 %) | 392 (9.4 %) | 219 (17.4 %) | 467 (16.6 %) | 822 (16.7 %) | 674 (11.9 %) | 1111 (17.5 %) | 711 (16.8 %) | |
| 2013 | 12,493 (6.2 %) | 1019 (2.9 %) | 766 (2.3 %) | 2313 (8.8 %) | 1256 (7.4 %) | 367 (8.4 %) | 210 (17.1 %) | 505 (16.9 %) | 754 (15.0 %) | 632 (10.6 %) | 1022 (15.8 %) | 628 (14.7 %) | |
| 2014 | 12,414 (5.9 %) | 1153 (2.9 %) | 811 (2.3 %) | 2349 (8.6 %) | 1223 (7.1 %) | 342 (7.4 %) | 199 (15.9 %) | 463 (15.3 %) | 775 (14.9 %) | 601 (10.0 %) | 1052 (16.1 %) | 634 (14.6 %) | |
| 2015 | 12,692 (5.8 %) | 1189 (2.8 %) | 789 (2.1 %) | 2490 (8.3 %) | 1255 (7.0 %) | 392 (7.8 %) | 187 (14.8 %) | 495 (14.1 %) | 838 (14.7 %) | 608 (9.3 %) | 1121 (16.0 %) | 692 (14.8 %) | |
| 2016 | 12,026 (5.5 %) | 1118 (2.6 %) | 799 (2.1 %) | 2477 (8.2 %) | 1215 (6.9 %) | 386 (7.6 %) | 164 (13.8 %) | 513 (14.5 %) | 790 (14.1 %) | 609 (9.3 %) | 1095 (15.7 %) | 637 (14.1 %) | |
| 2017 | 11,028 (5.2 %) | 1047 (2.6 %) | 794 (2.1 %) | 2388 (7.9 %) | 1162 (6.6 %) | 365 (7.1 %) | 146 (12.9 %) | 472 (13.7 %) | 761 (13.5 %) | 585 (9.0 %) | 1089 (15.6 %) | 649 (14.3 %) | |
| 2018 | 10,517 (5.0 %) | 1069 (2.5 %) | 756 (1.9 %) | 2285 (7.5 %) | 1115 (6.4 %) | 386 (7.4 %) | 144 (13.0 %) | 490 (14.1 %) | 739 (13.1 %) | 559 (8.3 %) | 1053 (15.1 %) | 620 (14.0 %) |
The estimated prevalence of smoking decreased from 2011 to 2018 for both genders in all race/ethnicity groups, based on both unadjusted (Table 1) and age-adjusted prevalence (Fig. 1). The trends were relatively stable, ranging from declines of 0.01 % per year among Asian Indians to 0.34 % per year among Filipinos. The smoking prevalence decrease was especially notable among Native Hawaiian women and men compared to other groups, with annual decreases of 0.99 % and 0.73 %, respectively. However, the unadjusted smoking prevalence among Native Hawaiian women and men were still 2.31 and 1.04 times higher, respectively, than that of NHW in 2018. Among multi-racial groups, the decline in smoking prevalence was largest in the PI+A+NHW group, with an annual decrease of 0.65 %, and smallest in the A+PI group, with an annual decrease of 0.43 %.
Fig. 1.
Age-adjusted smoking prevalence by gender and race/ethnicity at two large healthcare systems in northern California and Hawaii, 2011–2018.
3.2. Survival time to incident CVD and risk factors
At baseline, 313,085 patients were alive and eligible for the survival analysis. Among these patients, 18,282 (5.8 %) were current smokers, 74,096 (23.7 %) were former smokers, and 220,707 (70.5 %) were non-smokers (Table 2). The majority were female (57.3 %). Mean age was 48.4 years (SD=15.7), and the non-Hispanic White group made up 57.7 % of the sample.
Table 2.
Population demographics for the analysis of incident cardiovascular disease at two large healthcare systems in northern California and Hawaii, 2011–2018.
| Smoking status |
||||
|---|---|---|---|---|
| All (313,085) |
Current (18,282) |
Former (74,096) |
Never (220,707) |
|
| Mean (SD) | Mean (SD) | Mean (SD) | Mean (SD) | |
| Age | 48.4 (15.7) | 44.6 (14.2) | 54.0 (15.9) | 46.8 (15.3) |
| Proportion of census block with college degree | 0.47 (0.23) | 0.36 (0.21) | 0.44 (0.23) | 0.50 (0.23) |
| Census block median household income | $97,104.65 ($43,814.71) | $81,106.27 ($35,552.55) | $90,982.36 ($42,152.63) | $10,0475.2 ($44,458.16) |
| N (col%) | N (row%) | N (row%) | N (row%) | |
| Female | 179,291 (57.3) | 8263(4.6) | 36,568(20.4) | 134,460(75.0) |
| Male | 133,794 (42.7) | 10,019(7.5) | 37,528(28.0) | 86,247(64.5) |
| Race/Ethnicity | ||||
| Non-Hispanic White | 180,577 (57.7) | 9792(5.4) | 49,652(27.5) | 121,133(67.1) |
| Asian Indian | 31,830 (10.2) | 776(2.4) | 2937(9.2) | 28,117(88.3) |
| Chinese | 30,315 (9.7) | 612(2.0) | 2849(9.4) | 26,854(88.6) |
| Filipino | 24,438 (7.8) | 2013(8.2) | 5645(23.1) | 16,780(68.7) |
| Japanese | 15,697 (5.0) | 1079(6.9) | 4453(28.4) | 10,165(64.8) |
| Korean | 3998 (1.3) | 288(7.2) | 895(22.4) | 2815(70.4) |
| Native Hawaiian | 1443 (0.5) | 258(17.9) | 437(30.3) | 748(51.8) |
| Other Pacific Islander | 3165 (1.0) | 480(15.2) | 856(27.0) | 1829(57.8) |
| PI+A+NHW | 5094 (1.6) | 726(14.3) | 1571(30.8) | 2797(54.9) |
| A+NHW | 5735 (1.8) | 569(9.9) | 1366(23.8) | 3800(66.3) |
| A+PI | 6527 (2.1) | 1057(16.2) | 2071(31.7) | 3399(52.1) |
| PI+NHW | 4266 (1.4) | 632(14.8) | 1364(32.0) | 2270(53.2) |
| Location | ||||
| Hawaii | 88,237 (28.2) | 9250(10.5) | 25,713(29.1) | 53,274(60.4) |
| California | 224,848 (71.8) | 9032(4.0) | 48,383(21.5) | 167,433(74.5) |
| Incident CVD | 20,677 (6.6) | 1129 (6.2 %) | 8129 (11.0 %) | 11,419 (5.2 %) |
Age, Proportion census block college degree, and Census block median household income are shown as mean (SD); all other values shown as N (%).
Abbreviations: CVD: cardiovascular disease, NHW: non-Hispanic White, PI+A+NHW: Pacific Islander + Asian + White, A+NHW: Asian + White, A+PI: Asian + Pacific Islander, PI+NHW: Pacific Islander + White.
Over a median follow-up of 5 years, there were 20,677 incident CVD events (incidence rate, 14.2/1000 person-years). The median survival time free of CVD was associated with both race/ethnicity and smoking status. Specifically, the survival time to incident CVD of API multi-race groups was shorter than that of the NHW group, whose survival time was shorter than that of API single race groups (Fig. 2). The survival time of current smokers was shorter than that of former smokers, which was shorter than that of non-smokers.
Fig. 2.
Kaplan-Meier survival curves by smoking status and race/ethnicity at two large healthcare systems in northern California and Hawaii, 2011–2018.
The Cox models yield similar results to the Kaplan-Meier curves (Table 3). Asian single race groups had lower incidence of CVD than the NHW group (lowest risk group: Korean, HR=0.59; 95 % CI, 0.49–0.70) while multi-racial groups, except for the A+NHW group, had higher incidence of CVD than the NHW group, with the highest risk group being A+PI (HR=1.34; 95 % CI, 1.22–1.47). The Native Hawaiian and other Pacific Islander groups were not significantly different from the NHW group (Model 1). After adjustment for smoking status (Model 2), the association between race/ethnicity and CVD was attenuated, but not significantly. Both former and current smokers had a higher incidence of CVD than never smokers after adjustment for age, gender, race/ethnicity, census block group median household income, proportion of census block group with a college degree, and study site (current smokers: HR= 1.64, 95 % CI, 1.55 −1.74; former smokers: HR= 1.23, 95 % CI, 1.19 −1.27) (Model 2). We did not find significant interaction between race/ethnicity and smoking status, so the final COX model only includes main effects of risk factors.
Table 3.
Hazard ratios of incident CVD among API and NHW patients at two large healthcare systems in northern California and Hawaii, 2011–2018.
| Model 1 HR (95 % CI) |
Model 2 HR (95 % CI) |
|
|---|---|---|
| Race/ethnicity | ||
| Non-Hispanic White | 1 (reference) | 1 (reference) |
| Asian Indian | 0.76 (0.71,0.83)⁎⁎ | 0.80 (0.74,0.87)⁎⁎ |
| Chinese | 0.61 (0.57,0.64)⁎⁎ | 0.64 (0.60,0.68)⁎⁎ |
| Filipino | 0.85 (0.80,0.90)⁎⁎ | 0.86 (0.81,0.92)⁎⁎ |
| Japanese | 0.65 (0.61,0.69)⁎⁎ | 0.66 (0.62,0.71)⁎⁎ |
| Korean | 0.59 (0.49,0.70)⁎⁎ | 0.59 (0.49,0.70)⁎⁎ |
| Native Hawaiian | 1.00 (0.80,1.25) | 0.96 (0.76,1.20) |
| Other Pacific Islander | 0.97 (0.81,1.16) | 0.95 (0.80,1.14) |
| PI+A+NHW | 1.40 (1.25,1.56)⁎⁎ | 1.36 (1.22,1.51)⁎⁎ |
| A+NHW | 1.12 (0.98,1.28) | 1.11 (0.97,1.26) |
| A+PI | 1.34 (1.22,1.47)⁎⁎ | 1.32 (1.20,1.45)⁎⁎ |
| PI+NHW | 1.32 (1.19,1.47)⁎⁎ | 1.29 (1.17,1.44)⁎⁎ |
| Female (reference=male) | 0.69 (0.67,0.71)⁎⁎ | 0.72 (0.70,0.74)⁎⁎ |
| Age, per year | 1.06 (1.06,1.06)⁎⁎ | 1.06 (1.06,1.06)⁎⁎ |
| California (reference=Hawaii | 1.04 (1.00,1.08)* | 1.06 (1.02,1.10)⁎⁎ |
| Census block median household income, per 10,000 | 1.00 (1.00,1.01) | 1.00 (1.00,1.01) |
| Proportion census block college degree, per 10 % | 0.96 (0.95,0.97)⁎⁎ | 0.96 (0.95,0.97)⁎⁎ |
| Smoking status | ||
| Current | – | 1.64 (1.55,1.74)⁎⁎ |
| Former | – | 1.23 (1.19,1.27)⁎⁎ |
| Never | – | 1 (reference) |
P < 0.05.
P < 0.01.
4. Discussion
This paper adds to the growing literature on disaggregated API health outcomes and studies on multiracial Asians and Pacific Islanders by describing smoking prevalence among single-race and multiple-race Asian and Pacific Islanders and examining its association with incident CVD. Compared to other major racial/ethnic groups, API individuals have been understudied in smoking-related health disparities research [20], despite the fact that the U.S. API population is diverse and fast-growing[33] and smoking prevalence in Asian countries is generally much higher than in the U.S [34].. Internationally, smoking prevalence has decreased between 2006 and 2012 among individuals from Japan (27.0 %−23.3 %), India (15.5 %−13.3 %), South Korea (25.6 %−23.9 %), Vietnam (21.4 %−20.6 %), the Philippines (24.3 %−23.7 %), and Samoa (25.1 %−23.6 %), while remaining largely the same in China (23.9 %−24.2 %), North Korea (23.4 %−23.4 %), and Fuji (14.3 %−14.1 %) [35]. In the U.S., smoking rates have declined between 2002 and 2016, with the Asian population generally having lower smoking prevalence (8.0 %), and Pacific Islanders having higher smoking prevalence (18.5 %) than the Non-Hispanic white population (13.3 %) [36,37]. When disaggregated by Asian ethnic group, Filipino peoples had higher smoking rates (12.4 %) than Asian Indian (5.1 %) and Chinese people (5.9 %), but there remains limited data for other Asian and Pacific Islander groups [38].
Consistent with previous studies [36,38], we found that the smoking prevalence among all 12 race/ethnicity groups studied declined in the past decade for both men and women. Smoking prevalence varied greatly across API subgroups, with multi-race groups such as the A+PI group having the highest burden. Previous studies have shown that among U.S. populations, Native Hawaiian and other Pacific Islander populations report the highest smoking prevalence among API subgroups, while Asian Indian, Chinese, and Japanese populations report the lowest, with prevalence among NHW populations at intermediate levels [20,21]. The 2005 California Health Interview Survey data indicated that smoking prevalence was significantly higher among Asian American men than among NHW men, but lower among Asian American women than among NHW women [19]. We also found prominent gender difference in smoking prevalence among API subgroups. Additionally, smoking prevalence was particularly high among some Asian subgroups [[39], [40], [41], [42]]. When comparing our data to California Health Interview Survey estimates from 2017 to 2022 [43], we see similar trends. In the CHIS, Chinese (4.1 %) and Asian Indian (3.2 %) individuals had the lowest prevalence of smoking relative to other Asian groups. In CHIS, smoking prevalence was highest among Korean individuals (9.1 %). Similarly, smoking prevalence was high among Korean individuals in CASPER (7.4 %) and was second to Filipino (7.5 %). However, in all cases, with the exception of Filipino (7.5 % in CASPER, 5.1 % in CHIS) and Japanese (6.4 % in CASPER, 5.6 % in CHIS), the prevalence of smoking was generally lower in CASPER when compared to CHIS.Prior data suggests that nationally, smoking prevalence rates are highest among Filipino men (16.8 %) and women (9.0 %) compared to Asian Indian and Chinese populations [38]. According to the smoking prevalence data from the California Health Interview Survey in 2021, the overall smoking rate is 5.9 % (5.3–6.6 %) in Central and Northern California [44]. In California, the highest smoking prevalence rates have been reported by Vietnamese-, Korean-, and Filipino- American men and Japanese- and Korean- American women, and the lowest rates reported by Chinese- and Japanese-American men and Chinese- and Filipino-American women [19]. These data are consistent with our findings that among Asian subgroups, Korean and Filipino men and Japanese and Korean women had the highest smoking prevalence, while we found that Chinese and Asian Indian men and women had the lowest smoking prevalence rates. One of the reasons for the difference is due to different Asian subgroups (i.e., Vietnamese vs. Asian Indian) and geographic areas (i.e., California vs. California and Hawaii combined) being included in the two studies. Following an increase in smoking prevalence among men in most low- and middle-income countries in Asia in the 1980s, the prevalence showed a modest decrease in the mid-1990s in many of these countries [45]. However, smoking prevalence in men did not decrease in several countries in Asia (including Indonesia, which has a male adult daily smoking prevalence of 57.1 %, the highest in the world). Higher-income populations in Asia, including Japan, Hong Kong (China), and Singapore, have had some of the highest decreases in male smoking prevalence in Asia from 1980 to 2012 [46]. For Asian women, the prevalence of smoking remains very low, but smoking behaviors vary by countries and regions [47].
The combination of toxicologic evidence and evidence on cardiovascular effects show that smoking results in inflammatory responses and alterations in lipid composition [48,49], which can lead to many cardiovascular conditions [50,51]. In our sample, both current and former smokers had higher risk of CVD compared to non-smokers, consistent with previous studies examining Asian populations [48,[52], [53], [54], [55]]. Multi-race groups had higher risk of CVD compared to NHW. There is growing evidence that API individuals may have unique social determinants of health such as acculturation, socioeconomic status, social context, and health literacy that contribute to their risk for CVD [[56], [57], [58]], and these factors may interact with smoking to further increase their risk. In our study, differences in smoking rates seemed to exacerbate racial/ethnic disparities in CVD incidence, as there was some (non-significant) attenuation in the effect of race/ethnicity on CVD incidence after adjusting for smoking status.
Taken together, these findings suggest that promoting education and awareness about the heterogeneity of CVD and smoking prevalence in the API and multi-race groups is important. Discouraging cigarette smoking and better understanding the heterogeneity of CVD and smoking prevalence can promote better cardiovascular health among API and multi-racial groups [59,60]. Cultural diversity exists within the API population, so smoking cessation interventions that are tailored to the specific needs and preferences of API subgroups may be particularly important in reducing their risk for CVD. For example, a media-led campaign among Vietnamese-American men, including but not limited to Vietnamese-language media, health education materials, and activities targeting physicians, youth, and businesses, had a significant effect on decreasing smoking rates and increasing smoking abstinence [61]. Kaholokula et al. also recognized that religion/spirituality is an integral part of Native Hawaiian culture, and found that church-based support of smoking cessation may help Native Hawaiians for whom religion/spirituality is an important source of inspiration and guidance [62]. Such interventions may need to take into account many unique social and cultural factors that contribute to their smoking behaviors. Therefore, a multicomponent strategy may be beneficial for addressing the social, psychological, and cultural factors related to smoking among API and multi-race groups.
We acknowledge several limitations of the current study. The study included patients who were currently receiving care from one of two different health care systems, so the results may not be generalizable to other US API populations or patients not in regular care. Moreover, using data from two large health systems with a generally insured patient population may limit the generalizability of our findings, although a high proportion of the US population is insured, including Medicare and Medicaid coverage (these two groups are included in the KP membership). Also, a recent study found that Kaiser Permanente members were comparable to those living in the communities from which the membership was drawn, and the membership was sufficiently large and diverse to support research activities [63]. Second, the measurement of some smoking behaviors, such as duration of smoking, age at which patient first smoked, type of tobacco smoked, level of smoking, and passive smoking, are limited by the available data and not reported in this study [64,65]. Third, validating smoking status can be challenging in clinical settings and may lead to misclassification of smoking status and information bias. We did check smoking status at all encounters during the year of enrollment. More comprehensive assessments of smoking behaviors, and biological confirmation of smoking, are required to better understand potential biases in self-reported smoking data that could impact results of studies such as this one. Finally, CVD was defined by ICD codes, and the true rates may have been higher; however, there is little reason to think this under-reporting would differ by API subgroup.
5. Conclusions
Overall, we found considerable heterogeneity in both smoking prevalence and the risk of CVD among disaggregated Asian and Pacific Islander groups in addition to multi-racial API groups. Although some groups, such as those of Chinese ethnicity, had lower smoking prevalence and CVD risk when compared to the NHW population, other groups, including the Native Hawaiian and Pacific Islander groups, had greater burden of smoking and CVD than the NHW group. Rates of smoking and CVD were particularly high for multiracial Asian and Pacific Islander groups, who represent a growing population in the U.S. The disaggregation of data on API individuals and on mixed racial categories is crucial to understanding and addressing racial and ethnic disparities in smoking prevalence and CVD. Our findings may provide policymakers with an entry point for better understanding this heterogeneous group of peoples and developing more relevant and impactful approaches to address population-level disparities in smoking prevalence and CVD.
Sources of financial support
This study was funded by grant HL 126,172 from the National Heart, Lung, and Blood Institute, National Institutes of Health to Drs. Waitzfelder, Palaniappan, and Fortmann (as Multiple P.I.s). Additional support for this manuscript was provided by the National Center for Advancing Translational Sciences, National Institutes of Health, Award Number 1UL1TR003142. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.
Availability of data and materials
The datasets generated and/or analyzed during the current study are not available for replication because they contain patient health information that could compromise the privacy of research participants but are available from the corresponding author on reasonable request.
CRediT authorship contribution statement
Jiang Li: Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Project administration, Methodology, Investigation, Conceptualization. Yihe G. Daida: Writing – review & editing, Conceptualization. Adrian Matias Bacong: Writing – review & editing, Validation. Ana Gabriela Rosales: Writing – review & editing, Validation, Methodology, Formal analysis, Data curation. Timothy B. Frankland: Writing – review & editing, Validation, Data curation. Alexandra Varga: Writing – review & editing, Data curation. Sukyung Chung: Writing – review & editing, Validation. Stephen P. Fortmann: Writing – review & editing, Funding acquisition, Conceptualization. Beth Waitzfelder: Writing – review & editing, Funding acquisition, Conceptualization. Latha Palaniappan: Writing – review & editing, Funding acquisition, Conceptualization.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgments
The authors would like to thank the following individuals. From the Center for Health Research: Andreea Rawlings for data analysis, Neon Brooks for editorial work, and Stefan Massimino for project management; from the Palo Alto Medical Foundation Research Institute, Sutter Health: Kevin J. Hays for data extraction work and Meghan C. Martinez for project management; from the Center for Integrated Health Care Research: Jonathan Lai for project management, Connor Howick, Michelle Choi, and Olena Pishchalenko for recruitment, and Valentyna Pishchalenko for data extraction and assistance in creating the data dictionary.
Appendix. Population demographics for the analysis of smoking prevalence at two large healthcare systems in northern California and Hawaii, 2011–2018
| Race/Ethnicity | Age in years mean (SD) | Female, N (%) | Income median (25th, 75th) | Education% college median (25th, 75th) | Baseline Smoking Status, N (%) |
||
|---|---|---|---|---|---|---|---|
| Current | Former | Never | |||||
| All | 45.0 (17.1) | 298,367 (56.0) | 88,143 (65,625, 115,000) | 0.46 (0.29, 0.64) | 42,952 (8.1) | 106,895 (20.1) | 383,062 (71.9) |
| Non-Hispanic White | 50.1 (17.3) | 170,761 (55.4) | 87,978 (64,764, 117,000) | 0.48 (0.32, 0.65) | 23,703 (7.7) | 74,656 (24.2) | 209,611 (68.1) |
| Asian Indian | 38.2 (10.7) | 29,419 (52.6) | 103,259 (82,580, 127,000) | 0.63 (0.48, 0.76) | 1974 (3.5) | 4195 (7.5) | 49,793 (89.0) |
| Chinese | 45.9 (16.0) | 29,627 (60.2) | 102,118 (76,587, 133,000) | 0.58 (0.39, 0.72) | 1432 (2.9) | 4103 (8.3) | 43,663 (88.7) |
| Filipino | 43.9 (16.3) | 25,740 (57.7) | 78,674 (61,136, 95,053) | 0.25 (0.15, 0.38) | 5005 (11.2) | 7896 (17.7) | 31,686 (71.1) |
| Japanese | 53.5 (18.9) | 14,209 (58.9) | 81,357 (63,000, 102,000) | 0.36 (0.25, 0.50) | 2292 (9.5) | 5459 (22.6) | 16,358 (67.9) |
| Korean | 44.0 (14.5) | 5001 (65.9) | 88,636 (62,000, 117,000) | 0.48 (0.31, 0.66) | 786 (10.4) | 1397 (18.4) | 5410 (71.2) |
| Native Hawaiian | 44.9 (17.4) | 885 (49.8) | 74,219 (56,096, 90,703) | 0.24 (0.13, 0.36) | 347 (19.5) | 416 (23.4) | 1015 (57.1) |
| Other Pacific Islander | 40.7 (14.4) | 3151 (51.6) | 66,595 (47,156, 85,144) | 0.23 (0.12, 0.36) | 1140 (18.7) | 1180 (19.3) | 3789 (62.0) |
| PI+A+NHW | 38.6 (16.3) | 4875 (56.7) | 73,958 (57,986, 89,038) | 0.25 (0.16, 0.36) | 1574 (18.3) | 1830 (21.3) | 5195 (60.4) |
| A+NHW | 37.2 (14.7) | 5636 (57.6) | 80,393 (62,000, 101,000) | 0.35 (0.23, 0.52) | 1274 (13.0) | 1768 (18.1) | 6737 (68.9) |
| A+PI | 42.2 (17.2) | 5370 (51.4) | 73,998 (57,250,89,401) | 0.24 (0.15, 0.35) | 2122 (20.3) | 2429 (23.2) | 5900 (56.5) |
| PI+NHW | 43.9 (17.8) | 3693 (54.5) | 73,438 (56,739, 88,143) | 0.25 (0.15, 0.36) | 1303 (19.2) | 1566 (23.1) | 3905 (57.6) |
References
- 1.Muncan B. Cardiovascular disease in racial/ethnic minority populations: illness burden and overview of community-based interventions. Public Health Rev. 2018;39:32. doi: 10.1186/s40985-018-0109-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Shah N.S., Xi K., Kapphahn K.I., et al. Cardiovascular and cerebrovascular disease mortality in Asian American Subgroups. Circ Cardiovasc Qual Outcomes. 2022;15(5) doi: 10.1161/CIRCOUTCOMES.121.008651. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Shah N.S., Shimbo D., Muntner P., et al. Hypertension-related cardiovascular mortality in Asian American subgroups. Am J Prev Med. February 7, 2023 doi: 10.1016/j.amepre.2023.01.003. Published onlineS0749-3797(23)00003-X. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Volgman A.S., Palaniappan L.S., Aggarwal N.T., et al. Atherosclerotic cardiovascular disease in South Asians in the United States: epidemiology, risk factors, and treatments: a scientific statement from the american heart association. Circulation. 2018;138(1):e1–e34. doi: 10.1161/CIR.0000000000000580. [DOI] [PubMed] [Google Scholar]
- 5.Jose P.O., Frank A.T.H., Kapphahn K.I., et al. Cardiovascular disease mortality in Asian Americans. J Am Coll Cardiol. 2014;64(23):2486–2494. doi: 10.1016/j.jacc.2014.08.048. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Đoàn L.N., Takata Y., Hooker K., Mendez-Luck C., Irvin V.L. Trends in cardiovascular disease by Asian American, native hawaiian, and pacific islander ethnicity, medicare health outcomes survey 2011-2015. J Gerontol A Biol Sci Med Sci. 2022;77(2):299–309. doi: 10.1093/gerona/glab262. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Bacong A.M., Gibbs S.L., Rosales A.G., et al. Obesity disparities among adult single-race and multiracial asian and pacific islander populations. JAMA Netw Open. 2024;7(3) doi: 10.1001/jamanetworkopen.2024.0734. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.United States Census Bureau. American Community Survey Demographic and Housing Estimates: 2019 ACS 1-Year Estimates Data Profiles 2019. Accessed October 2, 2020. https://data.census.gov/cedsci/table?q=united%20states&g=0100000US&tid=ACSDP1Y2019.DP05&hidePreview=false.
- 9.Lew R., Tanjasiri S.P. Slowing the epidemic of tobacco use among Asian Americans and Pacific Islanders. Am J Public Health. 2003;93(5):764–768. doi: 10.2105/ajph.93.5.764. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Srinivasan S., Guillermo T. Toward improved health: disaggregating Asian American and native Hawaiian/Pacific Islander data. Am J Public Health. 2000;90(11):1731–1734. doi: 10.2105/ajph.90.11.1731. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Holland A.T., Palaniappan L.P. Problems with the collection and interpretation of Asian-American health data: omission, aggregation, and extrapolation. Ann Epidemiol. 2012;22(6):397–405. doi: 10.1016/j.annepidem.2012.04.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Nguyen A.B., Chawla N., Noone A.M., Srinivasan S. Disaggregated data and beyond: future queries in cancer control research. Cancer Epidemiol Biomark Prev Publ Am Assoc Cancer Res Cosponsored Am Soc Prev Oncol. 2014;23(11):2266–2272. doi: 10.1158/1055-9965.EPI-14-0387. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Moy K.L., Sallis J.F., David K.J. Health Indicators of Native Hawaiian and Pacific Islanders in the United States. J Community Health. 2010;35(1):81–92. doi: 10.1007/s10900-009-9194-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Volgman A.S., Palaniappan L.S., Aggarwal N.T., et al. Atherosclerotic cardiovascular disease in South Asians in the United States: epidemiology, risk factors, and treatments: a scientific statement from the american heart association. Circulation. 2018;138(1):1–34. doi: 10.1161/CIR.0000000000000580. [DOI] [PubMed] [Google Scholar]
- 15.Palaniappan L., Garg A., Enas E., et al. South asian cardiovascular disease & cancer risk: genetics & pathophysiology. J Community Health. 2018;43(6):1100–1114. doi: 10.1007/s10900-018-0527-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Bureau U.C. 20.6 Million People in the U.S. Identify as Asian, Native Hawaiian or Pacific Islander. Census.gov 2022. Accessed April 28, 2023. https://www.census.gov/library/stories/2022/05/aanhpi-population-diverse-geographically-dispersed.html.
- 17.Mays V.M., Ponce N.A., Washington D.L., Cochran S.D. Classification of race and ethnicity: implications for public health. Annu Rev Public Health. 2003;24(1):83–110. doi: 10.1146/annurev.publhealth.24.100901.140927. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.National Center for Chronic Disease Prevention and Health Promotion (US) Office on Smoking and Health . Centers for Disease Control and Prevention (US); 2014. The Health Consequences of Smoking—50 Years of Progress: A Report of the Surgeon General.http://www.ncbi.nlm.nih.gov/books/NBK179276/ Accessed June 12, 2019. [PubMed] [Google Scholar]
- 19.Maxwell A.E., Crespi C.M., Alano R.E., Sudan M., Bastani R. Health risk behaviors among five Asian American subgroups in California: identifying intervention priorities. J Immigr Minor Health Cent Minor Public Health. 2012;14(5):890–894. doi: 10.1007/s10903-011-9552-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Herzog T.A., Pokhrel P. Ethnic differences in smoking rate, nicotine dependence, and cessation-related variables among adult smokers in Hawaii. J Community Health. 2012;37(6):1226–1233. doi: 10.1007/s10900-012-9558-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Nguyen A.B. Disaggregating Asian American and Native Hawaiian and Other Pacific Islander (AANHOPI) adult tobacco use: findings from wave 1 of the population assessment of tobacco and health (PATH) Study, 2013-2014. J Racial Ethn Health Disparities. 2019;6(2):356–363. doi: 10.1007/s40615-018-00532-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Sluyter J.D., Hughes A.D., Thom S.A.M., et al. Arterial waveform parameters in a large, population-based sample of adults: relationships with ethnicity and lifestyle factors. J Hum Hypertens. 2016 doi: 10.1038/jhh.2016.78. Published online December 22. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.de Munter JS l, Agyemang C., Stronks K., van Valkengoed I.G.M. Association of physical activity, smoking, and alcohol intake with CVD-related hospital discharge in people of European, South Asian, or African descent. Eur J Prev Cardiol. 2013;20(1):80–88. doi: 10.1177/2047487311434232. [DOI] [PubMed] [Google Scholar]
- 24.Bureau UC. Race and Ethnicity in the United States: 2010 Census and 2020 Census. Census.gov 2021. Accessed May 5, 2022. https://www.census.gov/library/visualizations/interactive/race-and-ethnicity-in-the-united-state-2010-and-2020-census.html.
- 25.Chen L.H., Quinn V., Xu L., et al. The accuracy and trends of smoking history documentation in electronic medical records in a large managed care organization. Subst Use Misuse. 2013;48(9):731–742. doi: 10.3109/10826084.2013.787095. [DOI] [PubMed] [Google Scholar]
- 26.Szatkowski L., Lewis S., McNeill A., Huang Y., Coleman T. Can data from primary care medical records be used to monitor national smoking prevalence? J Epidemiol Community Health. 2012;66(9):791–795. doi: 10.1136/jech.2010.120154. [DOI] [PubMed] [Google Scholar]
- 27.Jamal A., Srinivasan M., Kim G., Huang R.J., Palaniappan L. Why are we going backward? Barriers to disaggregated racial information in federal data sets. Am J Public Health. 2023;113(8):852–855. doi: 10.2105/AJPH.2023.307339. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Adia A.C., Nguyen K.H., Ponce N.A. EHR Data and inclusion of multiracial asian american, native hawaiian, and pacific islander people—opportunities for advancing data-centered equity in health research. JAMA Netw Open. 2024;7(3) doi: 10.1001/jamanetworkopen.2024.0719. [DOI] [PubMed] [Google Scholar]
- 29.Ross T.R., Ng D., Brown J.S., et al. The HMO research network virtual data warehouse: a public data model to support collaboration. EGEMS. 2014;2(1):1049. doi: 10.13063/2327-9214.1049. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Go A.S., Magid D.J., Wells B., et al. The Cardiovascular Research Network: a new paradigm for cardiovascular quality and outcomes research. Circ Cardiovasc Qual Outcomes. 2008;1(2):138–147. doi: 10.1161/CIRCOUTCOMES.108.801654. [DOI] [PubMed] [Google Scholar]
- 31.Nichols G.A., Desai J., Elston Lafata J., et al. Construction of a multisite DataLink using electronic health records for the identification, surveillance, prevention, and management of diabetes mellitus: the SUPREME-DM project. Prev Chronic Dis. 2012;9:E110. doi: 10.5888/pcd9.110311. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Azar K.M.J., Lockhart S.H., Shen Z., et al. Persistence of disparities among Racially/Ethnically marginalized groups in the coronavirus disease 2019 pandemic regardless of statewide shelter-in-place policies: an analysis from northern california. Am J Epidemiol. 2021;190(11):2300–2313. doi: 10.1093/aje/kwab191. [DOI] [PubMed] [Google Scholar]
- 33.Budiman A., Ruiz N.G. Asian Americans are the fastest-growing racial or ethnic group in the U.S. Pew Research Center. Accessed May 5, 2022. https://www.pewresearch.org/fact-tank/2021/04/09/asian-americans-are-the-fastest-growing-racial-or-ethnic-group-in-the-u-s/.
- 34.Ra C.K., Pehlivan N., Kim H., Sussman S., Unger J.B., Businelle M.S. Smoking prevalence among Asian Americans: associations with education, acculturation, and gender. Prev Med Rep. 2022;30 doi: 10.1016/j.pmedr.2022.102035. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Ng M., Freeman M.K., Fleming T.D., et al. Smoking prevalence and cigarette consumption in 187 countries, 1980-2012. JAMA. 2014;311(2):183–192. doi: 10.1001/jama.2013.284692. [DOI] [PubMed] [Google Scholar]
- 36.Cornelius M.E. Tobacco product use among adults — United States, 2020. MMWR Morb Mortal Wkly Rep. 2022:71. doi: 10.15585/mmwr.mm7111a1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Agaku I.T., Odani S., Okuyemi K.S., Armour B. Disparities in current cigarette smoking among US adults, 2002–2016. Tob Control. 2020;29(3):269–276. doi: 10.1136/tobaccocontrol-2019-054948. [DOI] [PubMed] [Google Scholar]
- 38.Rao M., Bar L., Yu Y., et al. Disaggregating Asian American cigarette and alternative tobacco product use: results from the national health interview survey (NHIS) 2006–2018. J Racial Ethn Health Disparities. 2022;9(3):856–864. doi: 10.1007/s40615-021-01024-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Fang J., Zhang Z., Ayala C., Thompson-Paul A.M., Loustalot F. Cardiovascular health among non-hispanic Asian Americans: NHANES, 2011-2016. J Am Heart Assoc. 2019;8(13) doi: 10.1161/JAHA.118.011324. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Perini W, Snijder MB, Peters RJG, Stronks K, Kunst AE. Increased cardiovascular disease risk in international migrants is independent of residence duration or cultural orientation: the HELIUS study. J Epidemiol Community Health. 2018 Sep;72(9):825-831. 10.1136/jech-2018-210595. Epub 2018 May 5. PMID: 29730606. [DOI] [PubMed]
- 41.Ghai N.R., Jacobsen S.J., Van Den Eeden S.K., et al. A comparison of lifestyle and behavioral cardiovascular disease risk factors between Asian Indian and White non-Hispanic men. Ethn Dis. 2012;22(2):168–174. [PubMed] [Google Scholar]
- 42.Ye J, Rust G, Baltrus P, Daniels E. Cardiovascular risk factors among Asian Americans: results from a National Health Survey. Ann Epidemiol. 2009;19(10):718–723. doi: 10.1016/j.annepidem.2009.03.022. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43.UCLA Center for Health Policy Research. AskCHIS 2017-2022. Current Smoking Status – Adults Compared by Asian Ethnicity Groups (7 Level). Accessed March 22, 2024. Available at htpp://ask.chis.ucla.edu.
- 44.2021 California Health Interview Survey. AskCHIS. Accessed June 14, 2023. https://ask.chis.ucla.edu/AskCHIS/tools/_layouts/AskChisTool/home.aspx#/results.
- 45.Islami F., Torre L.A., Jemal A. Global trends of lung cancer mortality and smoking prevalence. Transl Lung Cancer Res. 2015;4(4):327–338. doi: 10.3978/j.issn.2218-6751.2015.08.04. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 46.Mackay J., Ritthiphakdee B., Reddy K.S. Tobacco control in Asia. Lancet North Am Ed. 2013;381(9877):1581–1587. doi: 10.1016/S0140-6736(13)60854-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 47.Yang J.J., Yu D., Wen W., et al. Tobacco smoking and mortality in asia: a pooled meta-analysis. JAMA Netw Open. 2019;2(3) doi: 10.1001/jamanetworkopen.2019.1474. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 48.Tang R., Yang S., Liu W., et al. Smoking is a risk factor of coronary heart disease through HDL-C in Chinese T2DM patients: a mediation analysis. J Healthc Eng. 2020;2020 doi: 10.1155/2020/8876812. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 49.Song W., Guan J., He P., Fan S., Zhi H., Wang L. Mediating effects of lipids on the association between smoking and coronary artery disease risk among Chinese. Lipids Health Dis. 2020;19(1):149. doi: 10.1186/s12944-020-01325-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 50.Aldaham S., Foote J.A., Chow H.H.S., Hakim I.A. Smoking status effect on inflammatory markers in a randomized trial of current and former heavy smokers. Int J Inflamm. 2015 doi: 10.1155/2015/439396. 2015. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 51.Hertle E., van Greevenbroek M.M., Arts I.C., et al. Distinct associations of complement C3a and its precursor C3 with atherosclerosis and cardiovascular disease. The CODAM study. Thromb Haemost. 2014;111(6):1102–1111. doi: 10.1160/TH13-10-0831. [DOI] [PubMed] [Google Scholar]
- 52.Yang J.J., Yu D., Shu X.O., et al. Reduction in total and major cause-specific mortality from tobacco smoking cessation: a pooled analysis of 16 population-based cohort studies in Asia. Int J Epidemiol. 2022;50(6):2070–2081. doi: 10.1093/ije/dyab087. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 53.Lee P.N., Forey B.A., Thornton A.J., Coombs K.J. The relationship of cigarette smoking in Japan to lung cancer, COPD, ischemic heart disease and stroke: a systematic review. F1000Res. 2018;7:204. doi: 10.12688/f1000research.14002.1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 54.Baba S., Iso H., Mannami T., et al. Cigarette smoking and risk of coronary heart disease incidence among middle-aged Japanese men and women: the JPHC Study Cohort I. Eur J Cardiovasc Prev Rehabil. 2006;13(2):207–213. doi: 10.1097/01.hjr.0000194417.16638.3d. [DOI] [PubMed] [Google Scholar]
- 55.Honjo K., Iso H., Tsugane S., et al. The effects of smoking and smoking cessation on mortality from cardiovascular disease among Japanese: pooled analysis of three large-scale cohort studies in Japan. Tob Control. 2010;19(1):50–57. doi: 10.1136/tc.2009.029751. [DOI] [PubMed] [Google Scholar]
- 56.Palaniappan L.P., Araneta M.R.G., Assimes T.L., et al. Call to action: cardiovascular disease in Asian Americans a science advisory from the american heart association. Circulation. 2010;122(12):1242–1252. doi: 10.1161/CIR.0b013e3181f22af4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 57.Sun H., Lin M., Russell E.M., et al. The impact of global and local Polynesian genetic ancestry on complex traits in Native Hawaiians. PLoS Genet. 2021;17(2) doi: 10.1371/journal.pgen.1009273. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 58.Min L.Y., Islam R.B., Gandrakota N., Shah M.K. The social determinants of health associated with cardiometabolic diseases among Asian American subgroups: a systematic review. BMC Health Serv Res. 2022;22(1):257. doi: 10.1186/s12913-022-07646-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 59.Gallucci G., Tartarone A., Lerose R., Lalinga A.V., Capobianco A.M. Cardiovascular risk of smoking and benefits of smoking cessation. J Thorac Dis. 2020;12(7):3866–3876. doi: 10.21037/jtd.2020.02.47. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 60.Stuart-Shor E.M., Berra K., Kamau M.W., Kumanyika S. Behavioral strategies for cardiovascular risk reduction in diverse and underserved Racial/Ethnic groups. Circulation. 2012;125(1):171–184. doi: 10.1161/CIRCULATIONAHA.110.968495. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 61.Jenkins C.N., McPhee S.J., Le A., Pham G.Q., Ha N.T., Stewart S. The effectiveness of a media-led intervention to reduce smoking among Vietnamese-American men. Am J Public Health. 1997;87(6):1031–1034. doi: 10.2105/ajph.87.6.1031. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 62.Kaholokula J. Culturally informed smoking cessation strategies for native hawaiians. Nicotine Tob Res Off J Soc Res Nicotine Tob. 2008;10(4):671–681. doi: 10.1080/14622200801978763. [DOI] [PubMed] [Google Scholar]
- 63.Davis A.C., Voelkel J.L., Remmers C.L., Adams J.L., McGlynn E.A. Comparing kaiser permanente members to the general population: implications for generalizability of research. Perm J. 2023;27(2):87–98. doi: 10.7812/TPP/22.172. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 64.Mahabee-Gittens E.M., Merianos A.L., Gordon J.S., Stone L., Semenova O., Matt G.E. Electronic health record classification of tobacco smoke exposure and cotinine levels in hospitalized pediatric patients. Hosp Pediatr. 2019 doi: 10.1542/hpeds.2018-0247. Published online. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 65.Middleton C., Bruns D. Improving screening and education for secondhand smoke exposure in primary care settings. AJN Am J Nurs. 2019;119(8):51–58. doi: 10.1097/01.NAJ.0000577456.92598.a5. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The datasets generated and/or analyzed during the current study are not available for replication because they contain patient health information that could compromise the privacy of research participants but are available from the corresponding author on reasonable request.