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Overview
Pathophysiology is a field that is ever-changing due to technical advances and new research. The writing assignments that you will complete review some of the newest research on the diseases and disorders. This writing assignment should include all proper APA (7th ed.) rules for a scientific paper, which include a title page, text, and sources page.
Instructions
1. Using a news website, such as
Google News
Links to an external site.
, search for an article related to any of the topics in this module. It is recommended that you also utilize the LIRN Library when conducting research for this activity. You can access the LIRN Library in your Canvas navigation bar. The article you select should include information about topics such as research on the chosen topic, new treatments, new technology, and/or diagnostic tools. It should be acurrentarticle (i.e., published within the last two years).
2. Complete a 300-word summary of the article.
3. Please refer to the APA 7th edition style guide and
template provided for this assignment
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4. Use at least one source/article; however, you are welcome to add additional sources to include introductory information, if needed.
5. Donotuse quotes from the article unless completely necessary. Instead, paraphrase because quotes will not be included in your word count.
Due
Sunday, by 11:59 p.m.
Submission and Assessment Guidelines
Submit an essay (300 words) response via Turnitin.
The activity is graded based on the Written Assignment Rubric. It is expected that you use correct grammar and that your responses are in complete sentences.
This activity is worth 25 points. RESEARCH ARTICLE
Risk of cardiovascular disease in patients with
alcohol use disorder: A population-based
retrospective cohort study
Chieh SungID
1,2, Chi-Hsiang ChungID
3,4, Fu-Huang Lin3*, Wu-Chien ChienID
3,5,6*, Chien-
An Sun7,8, Chang-Huei Tsao9,10, Chih-Erh Weng2
1 Graduate Institute of Medical Sciences, National Defense Medical Center, Taipei, Taiwan, 2 Department of
Nursing, Keelung Chang Gung Memorial Hospital, Keelung, Taiwan, 3 School of Public Health, National
Defense Medical Center, Taipei, Taiwan, 4 Taiwanese Injury Prevention and Safety Promotion Association,
Taipei, Taiwan, 5 Graduate Institute of Life Sciences, National Defense Medical Center, Taipei, Taiwan,
6 Department of Medical Research, Tri-Service General Hospital, National Defense Medical Center, Taipei,
Taiwan, 7 Department of Public Health, College of Medicine, Fu-Jen Catholic University, New Taipei City,
Taiwan, 8 Big Data Research Center, College of Medicine, Fu-Jen Catholic University, New Taipei City,
Taiwan, 9 Department of Medical Research, Tri-Service General Hospital, Taipei City, Taiwan,
10 Department of Microbiology & Immunology, National Defense Medical Center, Taipei City, Taiwan
* [emailprotected] (F-HL); [emailprotected] (W-CC)
Abstract
The complex effects of alcohol consumption on the cardiovascular system vary with mean
daily consumption and duration of intake. This population-based retrospective cohort study
aimed to explore the risk of cardiovascular disease (CVD) in patients with alcohol use disor-
der (AUD). Data was collected from the Taiwan National Health Insurance Research Data-
base from 2000 to 2013. A total of 7,420 patients with AUD were included in our study
group, and 29,680 age- and sex-matched controls without AUD in the control group. Cox
proportional hazard regression analysis was used to investigate the effects of AUD on the
risk of CVD. Most patients were men aged 2544 years. At the end of the follow-up period,
the AUD group had a significantly higher incidence of CVD (27.39% vs. 19.97%, P<0.001) and more comorbidities than the control group. The AUD group also exhibited a significantly higher incidence of CVD than the control group based on the Cox regression analysis and Fine and Grays competing risk model (adjusted hazard ratio [AHR] = 1.447, 95% confi- dence interval [CI] = 1.3721.52 5, P<0.001). Furthermore, male sex, diabetes mellitus, hypertension, hyperlipidemia, chronic kidney disease, chronic obstructive pulmonary dis- ease, anxiety, depression, and a high Charlson Comorbidity Index were also associated with an increased risk of CVD. Patients with AUD in different CVD subgroups, such as those with CVD, ischemic heart disease (IHD), and stroke, were at a significantly higher risk of dis- ease than those without AUD; CVD (AHR = 1.447, 95% CI = 1.3721.525, P<0.001), IHD (AHR = 1.304, 95% CI = 1.2141.401, P<0.001), and stroke (AHR = 1.640, 95% CI = 1.5191.770, P<0.001). The risk also significantly differed among patients in the different CVD subgroups. We observed an association between AUD and development of CVD even after adjusting for several comorbidities and medications in our nationwide population cohort. PLOS ONE PLOS ONE | https://doi.org/10.1371/journal.pone.0276690 October 25, 2022 1 / 17 a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Sung C, Chung C-H, Lin F-H, Chien W-C, Sun C-A, Tsao C-H, et al. (2022) Risk of cardiovascular disease in patients with alcohol use disorder: A population-based retrospective cohort study. PLoS ONE 17(10): e0276690. https://doi. org/10.1371/journal.pone.0276690 Editor: Kuang-Hsi Chang, Tungs Taichung MetroHarbor Hospital, TAIWAN Received: March 30, 2022 Accepted: October 11, 2022 Published: October 25, 2022 Copyright: 2022 Sung et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: This study uses third party data. Taiwan launched a single-payer National Health Insurance program on March 1, 1995. The database of this program contains registration files and original claim data for reimbursement. Large computerized databases derived from this system by the National Health Insurance Administration. Investigators interested may submit a formal proposal to NHIRD(http://nhird.nhri.org.tw). We have provided S1 Table in terms of outcome measures and comorbidities, abbreviations and ICD-9-CM, which describes the relevant dataset https://orcid.org/0000-0002-5759-137X https://orcid.org/0000-0002-4576-9900 https://orcid.org/0000-0002-3286-0780 https://doi.org/10.1371/journal.pone.0276690 http://crossmark.crossref.org/dialog/?doi=10.1371/journal.pone.0276690&domain=pdf&date_stamp=2022-10-25 http://crossmark.crossref.org/dialog/?doi=10.1371/journal.pone.0276690&domain=pdf&date_stamp=2022-10-25 http://crossmark.crossref.org/dialog/?doi=10.1371/journal.pone.0276690&domain=pdf&date_stamp=2022-10-25 http://crossmark.crossref.org/dialog/?doi=10.1371/journal.pone.0276690&domain=pdf&date_stamp=2022-10-25 http://crossmark.crossref.org/dialog/?doi=10.1371/journal.pone.0276690&domain=pdf&date_stamp=2022-10-25 http://crossmark.crossref.org/dialog/?doi=10.1371/journal.pone.0276690&domain=pdf&date_stamp=2022-10-25 https://doi.org/10.1371/journal.pone.0276690 https://doi.org/10.1371/journal.pone.0276690 http://creativecommons.org/licenses/by/4.0/ http://nhird.nhri.org.tw Introduction Alcohol use disorder (AUD) is characterized by compulsive alcohol seeking, loss of control with regard to limiting intake, and persistent alcohol use despite awareness of the harmful con- sequences such as alcoholic liver disease, cancer, cardiovascular disease (CVD), cirrhosis, and neuropsychiatric disorders [13]. Heterogeneous associations exist between the level of alcohol consumption and the initial presentation of CVD. Previous studies indicate that low-to-moderate levels of alcohol con- sumption could reduce the risk of most CVDs. Thus, the relationship between alcohol con- sumption and CVDs is complex and controversial [4]. Hence, to enhance the understanding of the risk of CVDs associated with AUD, we conducted a large, nationwide, population-based nested cohort study using Taiwans National Health Insurance Research Database (NHIRD). Methods Data source The National Health Insurance Program (NHI) was launched in Taiwan in 1995 and covers more than 99% of the Taiwanese population (more than 23 million beneficiaries). The NHIRD contains the following encrypted data: patient identification number; date of birth; sex; dates of admission and discharge; worldwide class of sicknesses, 9th Revision, medical modification (ICD-nine-CM) diagnostic and system codes (as many as 5 each); and outcomes. The longitu- dinal medical health insurance Database 2005 (LHID 2005), which we used, is a subset of the NHIRD. The LHID 2005 carries approximately 1 million randomly selected records of benefi- ciaries, representing approximately 5% of the population in Taiwan in 2005, for scientific utili- zation. Statistics from 20002013 were extracted from the NHIRD. Analysis of data from 2000 to 2013 using the Universal Health Coverage database, Tandem Inpatient expenditures by admissions (DD),Registry for contracted medical facilities (HOSB),Registry for beneficiaries(ID) ,Registry for catastrophic illness patients (HV), variables include diagnosis, surgery, disposition, hospitalization and discharge dates, length of stay and medical costs;Registry for contracted medical facilities (HOSB)the variables include hospital location and hospital level. According to the law, medical institutions are required to report outpatient (including emergency) and inpatient expenses to the Health Insurance Bureau every month. Therefore, Therefore, health insurance data is a representative empirical data in the field of medical and health-related research, and analytical results thereof can be used as a reference for medical and health policies, providing an important research resource. The NHI Administration peri- odically reviews medical records in a random manner to verify the accuracy of diagnoses. This review was conducted in accordance with the World Medical Association Code of Ethics (Hel- sinki Declaration). This study was approved by the Institutional Review Board of Tri-Service General Hospital at the National Defense Medical Center in Taipei, Taiwan, and the require- ment of individual consent was waived because all identifying data were encrypted (TSGH IRB No. B-111-10). The NHIRD is a publicly available database that contains depersonalized patient information to ensure patient anonymity. Study sample The study comprised a cohort of patients aged above 18 years from the LHID 2005 database who were newly diagnosed with alcohol use disorder, namely alcoholic psychosis (ICD-9-CM 291), alcohol abuse (ICD-9-CM 303.0, 305.0), and alcohol dependency syndrome (ICD-9-CM 303.9). We utilized the LHID to estimate the incidence of alcohol-related illnesses as previously PLOS ONE Cardiovascular risk and alcohol use disorder PLOS ONE | https://doi.org/10.1371/journal.pone.0276690 October 25, 2022 2 / 17 names, variables, descriptions to be requested. We have also added a minimal data set from the National Health Insurance Research Database (NHIRD) in our methods section. The authors confirm they did not have any special access privileges. Funding: This study was funded by the TSGH-B- 111018 Special plan. Competing interests: The authors have declared that no competing interests exist. https://doi.org/10.1371/journal.pone.0276690 specified in the Centers for Disease Control and Preventions "Chronic Causes" of "Alcohol- Related ICD Codes"(https://nccd.cdc.gov/DPH/ARDI/Info/ICDCodes.aspx) and as previously documented in the literature [5,6]. CVD was identified using the codes for ischemic heart dis- ease (IHD) (ICD-9-CM 410414) and stroke (ICD-9-CM 430438). We excluded patients with a history of AUD, aged<18 years of age, whose sex was unknown, who had CVD before tracking, with incomplete tracking data, and who were diagnosed with AUD before the index date, the inclusion and exclusion criteria are shown in Fig 1. Those who comprised the control group were also selected from the LHID 2005. The study and control cohorts were selected with 1:4 matching according to sex, age, and index date. The date of the diagnosis of an alco- hol-related disease was used as the index date. Outcome measurement and comorbidities Patients with baseline comorbidities, IHD (ICD-9-CM 410414), stroke (ICD-9-CM 430 438), diabetes mellitus (DM) (ICD-9-CM 250), hyperlipidemia (ICD-9-CM 272.0272.4), hypertension (HTN) (ICD-9-CM 401405), obesity (ICD-9-CM 278), depression (ICD- 9-CM296.2e296.3, 300.4), anxiety (ICD-9-CM 300.02), chronic kidney disease (CKD) (ICD- 9-CM 585), chronic obstructive pulmonary disease (COPD) (ICD-9-CM 490e496), liver cir- rhosis (ICD-9-CM571), tobacco use disorder (ICD-9-CM350.1), and drug use disorder (ICD- 9-CM304, 305.2305.9), are listed in S1 Table. All patients were followed up from the index date until the first diagnosis of CVD, death, withdrawal from the NHI program, or 31 December 2013. The covariates included sex, age Fig 1. The flowchart of study sample selection from the National Health Insurance Research Database in Taiwan. Abbreviations: AUD, alochol use disorder; CVD, cardiovascular disease. https://doi.org/10.1371/journal.pone.0276690.g001 PLOS ONE Cardiovascular risk and alcohol use disorder PLOS ONE | https://doi.org/10.1371/journal.pone.0276690 October 25, 2022 3 / 17 https://nccd.cdc.gov/DPH/ARDI/Info/ICDCodes.aspx https://doi.org/10.1371/journal.pone.0276690.g001 https://doi.org/10.1371/journal.pone.0276690 group (1824, 2544, 4564,65), geographical area of residence (), urbanization level of resi- dence (levels 14), low-income, catastrophic illness, Charlson comorbidity indexrevised (CCI_R), season at diagnosis of CVD (spring, summer, autumn, winter), level of care (hospital center, regional hospital, local hospital). The urbanization level of residence was defined according to the population and various indicators of development: level 1 was defined as a population >1,250,000, with a specific designation of political, economic, cultural, and metro-
politan development; level 2 was defined as a population of 500,0001,249,999, with an impor-
tant role in politics, economy, and culture; levels 3 and 4 were defined as populations of
149,999499,999 and<149,999, respectively. Statistical analysis The clinical characteristics of patients enrolled in the study are expressed in numerical form. We compared the distribution of categorical characteristics and baseline comorbidities between the case and control groups using Fishers exact test and the chi-squared test. Contin- uous variables are presented as means and standard deviations and were compared using t- tests. As the primary goal of the study was to determine whether the clinical characteristics of the patients were associated with the development of CVD, Fine and Grays survival analysis and regression analysis were used to determine the risk of CVD (competing with mortality), and the results are presented as hazard ratios (HRs) with the associated 95% confidence inter- vals (CIs). Associations between time-to-event outcomes and clinical characteristics were examined using the KaplanMeier method and multivariate Cox regression analysis with step- wise selection. The results are presented as adjusted HRs with the corresponding 95% CIs. All statistical analyses were performed using IBM SPSS Statistics for Windows version 22.0. (released 2013, IBM Corp., Armonk, NY, USA). A two-tailed P-value of<0.05 was considered statistically significant. Results Among the 987,403 patients in the LHID 20002013, 12,601 were diagnosed with AUD; 7,420 patients were assigned to the study cohort and 29,680 age-, sex-, and comorbidity-matched patients were assigned to the comparison (control) cohort (Fig 1). The baseline data of the patient and control groups are shown in Table 1. The patients were predominantly men (92.84%), with an average age of 43.12 11.85 years. Our findings revealed that low-income, DM, liver cirrhosis, CKD, drug use disorder, anxiety, depression, location, Urbanization level, and level of care significantly differed between the study and con- trol groups. In most patients with alcohol-related diseases, the diseases were diagnosed and treated in northern Taiwan, and middle Taiwan, with a combination of urbanization level 1 and 2 cities, and these patients were predominantly treated in regional hospital, or a local hos- pital. There were no significant differences in sex, age, CCI, and season between the groups. Fig 2 shows the KaplanMeier survival curve of patients with CVD stratified by AUD using the log-rank test; patients with AUD had a significantly higher cumulative risk of developing CVD 14 years after the index date (log-rank test, P<0.001). As indicated in Table 2, at the end of the 14-year follow-up period, patients with AUD had significantly higher incidences of CVD (27.39% vs 19.97%, P<0.001) and several comorbidities than did controls without AUD. Patients with AUD also exhibited a significantly higher incidence of CVD than did controls without AUD, according to the Cox regression analysis (adjusted HR [AHR] = 1.447, 95% CI = 1.3721.525, P<0.001). In addition, male sex (AHR = 1.206, 95% CI = 1.0961.327, P<0.001), DM (AHR = 1.363, 95% CI = 1.2931.437, P<0.001), HTN (AHR = 1.699, 95% PLOS ONE Cardiovascular risk and alcohol use disorder PLOS ONE | https://doi.org/10.1371/journal.pone.0276690 October 25, 2022 4 / 17 https://doi.org/10.1371/journal.pone.0276690 Table 1. Characteristics of the patient and control groups at baseline. AUD Total With Without P Variables n % n % n % Total 37,100 7,420 20.00 29,680 80.00 Sex 0.999 Male 34,445 92.84 6,889 92.84 27,556 92.84 Female 2,655 7.16 531 7.16 2,124 7.16 Age (mean SD, y) 43.29 13.19 43.12 11.85 43.32 13.50 0.223 Age groups (y) 0.999 1824 1,115 3.01 223 3.01 892 3.01 2544 22,075 59.50 4,415 59.50 17,660 59.50 4564 11,695 31.52 2,339 31.52 9,356 31.52 65 2,215 5.97 443 5.97 1,772 5.97 Low-income <0.001 Without 36,675 98.85 7,300 98.38 29,375 98.97 With 425 1.15 120 1.62 305 1.03 Catastrophic illness 0.638 Without 34,040 91.75 6,798 91.62 27,242 91.79 With 3,060 8.25 622 8.38 2,438 8.21 DM <0.001 Without 34,586 93.22 6,781 91.39 27,805 93.68 With 2,514 6.78 639 8.61 1,875 6.32 HTN 0.219 Without 35,078 94.55 6,994 94.26 28,084 94.62 With 2,022 5.45 426 5.74 1,596 5.38 Hyperlipidemia <0.001 Without 36,107 97.32 7,066 95.23 29,041 97.85 With 993 2.68 354 4.77 639 2.15 Obesity 0.901 Without 37,084 99.96 7,417 99.96 29,667 99.96 With 16 0.04 3 0.04 13 0.04 Liver cirrhosis <0.001 Without 32,111 86.55 4,467 60.20 27,644 93.14 With 4,989 13.45 2,953 39.80 2,036 6.86 CKD <0.001 Without 36,224 97.64 7,294 98.30 28,930 97.47 With 876 2.36 126 1.70 750 2.53 COPD 0.756 Without 36,055 97.18 7,207 97.13 28,848 97.20 With 1,045 2.82 213 2.87 832 2.80 Tobacco use disorder 0.617 Without 37,099 100.00 7,420 100.00 29,679 100.00 With 1 0.00 0 0.00 1 0.00 Drug use disorder <0.001 Without 37,021 99.79 7,358 99.16 29,663 99.94 With 79 0.21 62 0.84 17 0.06 Anxiety <0.001 Without 36,961 99.63 7,359 99.18 29,602 99.74 With 139 0.37 61 0.82 78 0.26 (Continued) PLOS ONE Cardiovascular risk and alcohol use disorder PLOS ONE | https://doi.org/10.1371/journal.pone.0276690 October 25, 2022 5 / 17 https://doi.org/10.1371/journal.pone.0276690 CI = 1.6151.787, P<0.001), hyperlipidemia (AHR = 1.869, 95% CI = 1.7352.012, P<0.001), CKD (AHR = 1.395, 95% CI = 1.2731.529, P<0.001), COPD (AHR = 0.883, 95% CI = 0.810 0.963, P<0.001), anxiety (AHR = 2.044, 95% CI = 1.5972.616, P<0.001), depression (AHR = 1.642, 95% CI = 1.5101.807, P<0.001), and CCI (AHR = 1.262, 95% CI = 1.215 1.312, P<0.001) were associated with an increased risk of CVD development (Table 3). Table 4 presents the results of analyses, stratified by demographic factors and comorbidities and Fine and Grays competing risk model. The incidence of CVD was higher in the case cohort than in the control cohort (3,801.64 vs. 2,884.75 per 105 person-years), and the overall incidence of CVD was 1.447-fold higher in the case cohort than in the control cohort. The risk of CVD is higher for low-income AUD patients than for those without low-income, compared with those without low-income households (AHR = 3.383; 95% CI = 3.2093.566; with com- peting risk model AHR = 2.293, 95% CI = 2.0342.564, P<0.001). In addition, the risk of CVD was 2.806 times higher in obese patients with AUD, and 2.089 times higher by the competing risk model (AHR = 2.806; 95% CI = 2.6622.958; With competing risk model AHR = 2.089, 95% CI = 1.8532.336, P<0.001). We categorized the CVD cohort into CVD subgroups according to the ICD-9-CM codes. Table 5 shows that patients with AUD in different CVD subgroups, such as CVD, IHD, and stroke, were at a significantly higher risk than those without AUD: CVD (AHR = 1.447, 95% Table 1. (Continued) AUD Total With Without P Variables n % n % n % Depression <0.001 Without 36,590 98.63 6,980 94.07 29,610 99.76 With 510 1.37 440 5.93 70 0.24 CCI_R 0.40 1.44 0.44 1.09 0.39 1.52 0.935 Season 0.999 Spring (MarMay) 9,155 24.68 1,831 24.68 7,324 24.68 Summer (JunAug) 8,745 23.57 1,749 23.57 6,996 23.57 Autumn (SepNov) 9,320 25.12 1,864 25.12 7,456 25.12 Winter (DecFeb) 9,880 26.63 1,976 26.63 7,904 26.63 Location <0.001 Northern Taiwan 14,715 39.66 2,823 38.05 11,892 40.07 Middle Taiwan 10,284 27.72 2,149 28.96 8,135 27.41 Southern Taiwan 9,422 25.40 1,683 22.68 7,739 26.07 Eastern Taiwan 2,501 6.74 715 9.64 1,786 6.02 Outlets islands 178 0.48 50 0.67 128 0.43 Urbanization level <0.001 1 (highest) 12,782 34.45 2,416 32.56 10,366 34.93 2 14,876 40.10 2,933 39.53 11,943 40.24 3 3,290 8.87 668 9.00 2,622 8.83 4 (lowest) 6,152 16.58 1,403 18.91 4,749 16.00 Level of care <0.001 Hospital center 10,701 28.84 1,742 23.48 8,959 30.19 Regional hospital 12,466 33.60 3,228 43.50 9,238 31.13 Local hospital 13,933 37.56 2,450 33.02 11,483 38.69 P: Chi-squared/Fishers exact test for categorical variables and t-test for continuous variables. AUD = Alcohol use disorder, DM = diabetes mellitus, HTN = hypertension, COPD = chronic obstructive pulmonary disease, CKD = chronic kidney disease, CCI = Charlson comorbidity index, SD = standard deviation. https://doi.org/10.1371/journal.pone.0276690.t001 PLOS ONE Cardiovascular risk and alcohol use disorder PLOS ONE | https://doi.org/10.1371/journal.pone.0276690 October 25, 2022 6 / 17 https://doi.org/10.1371/journal.pone.0276690.t001 https://doi.org/10.1371/journal.pone.0276690 CI = 1.3721.525, P<0.001), IHD (AHR = 1.304, 95% CI = 1.2141.401, P<0.001), and stroke (AHR = 1.640, 95% CI = 1.5191.770, P<0.001). Moreover, our findings revealed significant differences in the risks of CVD, IHD, and stroke among subgroups with and without AUD. Of note, in the AUD-stratified analysis, the effects of alcohol abuse on the risk of CVD, IHD, and stroke were not significantly different, similar to the results of the competing risk model: CVD (AHR = 1.500, 95% CI = 1.3301.677, P<0.001), IHD (AHR = 1.424, 95% CI = 1.2511.607, P<0.001), and stroke (AHR = 1.596, 95% CI = 1.4001.806, P<0.001). These results show the importance of abstinence from alcohol. Discussion Alcohol has a strong effect on the human body and mind, even at low doses; its neurotoxic, hepatotoxic, and carcinogenic properties make it a potent risk factor for disease burden [7]. To the best of our knowledge, this is the first national cohort study to establish a substantial correlation between AUD and CVD. The results indicate that patients with AUD have an increased risk of CVD. In addition, the risk of developing IHD and stroke was significantly higher in patients with AUD than in those without AUD. Several epidemiological studies published in the previous three decades have reported a car- dio protective effect of low-to-moderate alcohol intake; however, the number of published studies alone is not an indicator of the strength of the evidence on this effect, let alone a causal effect. Many drinkers cite health benefits, mainly for cardio-protection, as a reason for Fig 2. KaplanMeier curve of the CVD due to alcohol-related diseases. Abbreviations: CVD, cardiovascular disease; AUD, alchol use disorder. https://doi.org/10.1371/journal.pone.0276690.g002 PLOS ONE Cardiovascular risk and alcohol use disorder PLOS ONE | https://doi.org/10.1371/journal.pone.0276690 October 25, 2022 7 / 17 https://doi.org/10.1371/journal.pone.0276690.g002 https://doi.org/10.1371/journal.pone.0276690 Table 2. Characteristics of the patient and control groups at the study endpoint. AUD Total With Without P Variables n % n % n % Total 37,100 7,420 20.00 29,680 80.00 CVD <0.001 Without 29,140 78.54 5,388 72.61 23,752 80.03 With 7,960 21.46 2,032 27.39 5,928 19.97 Sex 0.999 Male 34,445 92.84 6,889 92.84 27,556 92.84 Female 2,655 7.16 531 7.16 2,124 7.16 Age (y) 49.18 14.48 49.33 12.51 49.14 14.94 0.329 Age groups (y) <0.001 1824 524 1.41 52 0.70 472 1.59 2544 15,783 42.54 2,963 39.93 12,820 43.19 4564 14,981 40.38 3,496 47.12 11,485 38.70 65 5,812 15.67 909 12.25 4,903 16.52 Low-income <0.001 Without 36,092 97.28 7,048 94.99 29,044 97.86 With 1,008 2.72 372 5.01 636 2.14 Catastrophic illness <0.001 Without 29,827 80.40 5,417 73.01 24,410 82.24 With 7,273 19.60 2,003 26.99 5,270 17.76 DM <0.001 Without 32,560 87.76 6,298 84.88 26,262 88.48 With 4,540 12.24 1,122 15.12 3,418 11.52 HTN <0.001 Without 32,226 86.86 6,607 89.04 25,619 86.32 With 4,874 13.14 813 10.96 4,061 13.68 Hyperlipidemia <0.001 Without 35,790 96.47 7,209 97.16 28,581 96.30 With 1,310 3.53 211 2.84 1,099 3.70 Obesity 0.030 Without 37,072 99.92 7,419 99.99 29,653 99.91 With 28 0.08 1 0.01 27 0.09 Liver cirrhosis <0.001 Without 32,626 87.94 4,999 67.37 27,627 93.08 With 4,474 12.06 2,421 32.63 2,053 6.92 CKD <0.001 Without 35,413 95.45 7,008 94.45 28,405 95.70 With 1,687 4.55 412 5.55 1,275 4.30 COPD <0.001 Without 34,693 93.51 6,736 90.78 27,957 94.19 With 2,407 6.49 684 9.22 1,723 5.81 Tobacco use disorder 0.002 Without 37,089 99.97 7,413 99.91 29,676 99.99 With 11 0.03 7 0.09 4 0.01 Drug use disorder <0.001 Without 37,066 99.91 7,404 99.78 29,662 99.94 With 34 0.09 16 0.22 18 0.06 (Continued) PLOS ONE Cardiovascular risk and alcohol use disorder PLOS ONE | https://doi.org/10.1371/journal.pone.0276690 October 25, 2022 8 / 17 https://doi.org/10.1371/journal.pone.0276690 drinking alcohol, despite often-raised concerns in the scientific literature regarding the causal- ity of a cardio protective effect. The effect of alcohol on the risk of IHD also makes this an intriguing and sometimes con- troversial topic in terms of disease epidemiology and public policy. The quality of epidemio- logical studies has substantially improved in the previous three decades. However, several studies have used recent abstainers as the reference group, and this can lead to systematic bias and erroneous conclusions; hence, high-quality epidemiological evidence is needed to provide a clear picture of the topic. When examining average alcohol consumption in comparison to lifetime abstinence, the relationship between alcohol consumption and IHD risk follows a J- curve. The curve shows a more detrimental association with lower average alcohol levels for women than for men [8]. This is consistent with our literature, which indicates that patients Table 2. (Continued) AUD Total With Without P Variables n % n % n % Anxiety 0.075 Without 36,936 99.56 7,378 99.43 29,558 99.59 With 164 0.44 42 0.57 122 0.41 Depression <0.001 Without 36,677 98.86 7,173 96.67 29,504 99.41 With 423 1.14 247 3.33 176 0.59 CCI_R 0.20 0.52 0.37 0.60 0.16 0.48 <0.001 Season 0.124 Spring (MarMay) 9,046 24.38 1,808 24.37 7,238 24.39 Summer (JunAug) 9,458 25.49 1,816 24.47 7,642 25.75 Autumn (SepNov) 9,600 25.88 1,959 26.40 7,641 25.74 Winter (DecFeb) 8,996 24.25 1,837 24.76 7,159 24.12 Location <0.001 Northern Taiwan 14,573 39.28 2,759 37.18 11,814 39.80 Middle Taiwan 10,450 28.17 2,180 29.38 8,270 27.86 Southern Taiwan 9,354 25.21 1,706 22.99 7,648 25.77 Eastern Taiwan 2,564 6.91 730 9.84 1,834 6.18 Outlets islands 159 0.43 45 0.61 114 0.38 Urbanization level <0.001 1 (highest) 12,217 32.93 2,152 29.00 10,065 33.91 2 15,618 42.10 3,152 42.48 12,466 42.00 3 3,140 8.46 661 8.91 2,479 8.35 4 (lowest) 6,125 16.51 1,455 19.61 4,670 15.73 Level of care <0.001 Hospital center 12,053 32.49 2,046 27.57 10,007 33.72 Regional hospital 14,788 39.86 3,244 43.72 11,544 38.89 Local hospital 10,259 27.65 2,130 28.71 8,129 27.39 Mortality <0.001 Without 34,361 92.62 6,376 85.93 27,985 94.29 With 2,739 7.38 1,044 14.07 1,695 5.71 P: Chi-squared/Fishers exact test for categorical variables and t-test for continuous variables. AUD = Alcohol use disorder, CVD = cardiovascular disease, DM = diabetes mellitus, HTN = hypertension, COPD = chronic obstructive pulmonary disease, CKD = chronic kidney disease, CCI = Charlson comorbidity index. https://doi.org/10.1371/journal.pone.0276690.t002 PLOS ONE Cardiovascular risk and alcohol use disorder PLOS ONE | https://doi.org/10.1371/journal.pone.0276690 October 25, 2022 9 / 17 https://doi.org/10.1371/journal.pone.0276690.t002 https://doi.org/10.1371/journal.pone.0276690 Table 3. Risk factors for cardiovascular disease according to Cox regression analysis. Variables Crude HR 95% CI 95% CI P Adjusted HR 95% CI 95% CI P AUD Without Reference Reference With 1.334 1.268 1.403 <0.001 1.447 1.372 1.525 <0.001 Sex Male 1.266 1.151 1.392 <0.001 1.206 1.096 1.327 <0.001 Female Reference Reference Age groups (y) 1824 Reference Reference 2544 1.106 0.626 1.952 0.728 1.032 0.528 1.647 0.809 4564 1.478 0.838 2.605 0.177 1.095 0.621 1.933 0.753 65 2.160 1.225 3.811 0.008 1.511 0.856 2.668 0.155 Low-income Without Reference Reference With 0.942 0.840 1.056 0.304 1.001 0.891 1.124 0.986 Catastrophic illness Without Reference Reference With 1.018 0.967 1.073 0.487 1.001 0.949 1.056 0.963 DM Without Reference Reference With 1.730 1.645 1.819 <0.001 1.363 1.293 1.437 <0.001 HTN Without Reference Reference With 1.997 1.906 2.092 <0.001 1.699 1.615 1.787 <0.001 Hyperlipidemia Without Reference Reference With 2.282 2.124 2.451 <0.001 1.869 1.735 2.012 <0.001 Obesity Without Reference Reference With 1.103 0.500 1.631 0.735 1.136 0.407 1.333 0.312 Liver cirrhosis Without Reference Reference With 1.111 1.035 1.192 0.004 1.006 0.854 1.204 0.061 CKD Without Reference Reference With 1.423 1.300 1.558 <0.001 1.395 1.273 1.529 <0.001 COPD Without Reference Reference With 0.943 0.866 1.027 0.178 0.883 0.810 0.963 0.005 Tobacco use disorder Without Reference Reference With 1.005 0.021 1.045 0.055 1.025 0.032 1.597 0.136 Drug use disorder Without Reference Reference With 1.007 0.109 1.047 0.060 1.064 0.149 1.445 0.185 Anxiety Without Reference Reference With 1.771 1.385 2.265 <0.001 2.044 1.597 2.616 <0.001 (Continued) PLOS ONE Cardiovascular risk and alcohol use disorder PLOS ONE | https://doi.org/10.1371/journal.pone.0276690 October 25, 2022 10 / 17 https://doi.org/10.1371/journal.pone.0276690 with AUD have a higher mortality rate than those without AUD, and the gender component is also consistent with the literature. Average alcohol consumption alone is not sufficient to describe the alcohol-IHD relation- ship. Drinking patterns play an important role, and both episodic and chronic heavy drinking may negate any beneficial effect of alcohol consumption on IHD risk and even elevate the risk substantially. Nevertheless, in several epidemiological and short-term experimental studies, relative to lifetime abstinence, having one to two drinks per drinking day without episodic heavy drinking showed a beneficial association with the risk of IHD [8]. The alcohol-IHD rela- tionship fulfills the criteria for a causal association as proposed by Hill [9]. Whether one detects an inverse, U-shaped, or J-shaped relationship depends on the distribution of drinking patterns in a given population. The prevalence of heavy drinking patterns has been on the rise in several countries, such as Canada, the US, the UK, and several East SHOW MORE... Question Prior to beginning work on this journal, visit theCareer ServicesLinks to an external site.webpage to learn about University of Arizona Global Campus Career and Alumni Services. Review theDigital Footprint: Build Your BrandLinks to an external site.and theSearch for JobsLinks to an external site.videos from the LinkedIn courseLearning LinkedIn for Students. Additionally, watch theHow to Build Your Personal Brand on LinkedIn?Links to an external site.video from LinkedIn, which will help you learn more about the importance of your personal brand as a marketing or business professional.To complete this journal, you need a job title. If you are already employed, you are welcome to use your current work title. If not employed, or if you do not wish to use your title, you can select a global marketing job title that interests you by visiting websites likeCareerBuilderLinks to an external site.,IndeedLinks to an external site.or LinkedInJobsLinks to an external site..The Personal Branding Activity is comprised of two parts that will be submitted in two different weeks. Please note that Part 1 of the Personal Branding Activity is made up of two sections listed below. Part 2 of the Personal Branding Activity will be submitted for theWeek 5 - Journal.Section A: Your LinkedIn ProfileFor Section A,Create a profile onLinkedInLinks to an external site., or edit your current LinkedIn profile.Refer to theCreate an Effective LinkedIn ProfileLinks to an external site.checklist from University of Arizona Global Campus Career Services as a guide for important items you may not have considered.Enhance your profile to look more professional and noticeable by applying the tips found in the videoHow to Build Your Personal Brand on LinkedIn?Links to an external site.and the webpage articleHow to Create a Killer LinkedIn Profile That Will Get You NoticedLinks to an external site.on LinkedIn.Section B: Personal BrandingFor Section B,Design a business card withCanvaLinks to an external site.using the global marketing job title you selected or your current job title.Please be aware that there is a Marketing & Communications templates section you can use before you begin to design your card.Include a QR (quick response) code to your LinkedIn profile page on the business card.Use aQR Code GeneratorLinks to an external site., and include the image on your business card. Put the link to the LinkedIn page on your card.Download your design as a PDF file.Upload a Word or PDF document containing your business card design that includes the QR code to your LinkedIn page. Include the link to your LinkedIn profile link in the comments section when submitting to Waypoint if the URL link is not included in your business card.Carefully review theGrading RubricLinks to an external site.for the criteria that will be used to evaluate your assignment.