Causes and predictors of premature death in the Pars Cohort Study, Iran: a cohort study

Causes and predictors of premature death in the Pars Cohort Study, Iran: a cohort study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Causes and predictors of premature death in the Pars Cohort Study, Iran: a cohort study Fateme Ziamanesh, Sadaf G Sepanlou, Abdullah Gandomkar, Hossein Poustchi, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4328365/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Sep, 2024 Read the published version in BMC Public Health → Version 1 posted 3 You are reading this latest preprint version Abstract Background While death in old age is inevitable, premature death at younger ages is within our control. Premature mortality (death < 70 years) is a crucial indicator of health status and access to healthcare, with variations observed across regions. In North Africa and the Middle East, ischemic heart disease (IHD), road injuries, stroke, and chronic kidney disease are projected to be the main causes of premature mortality. Unfortunately, few studies have been conducted on premature mortality worldwide. This study aimed to analyze the causes of premature death and associated risk factors within the Pars Cohort Study. Methods The Pars cohort study is a prospective cohort study conducted in Fars Province, Iran, involving 9,264 individuals aged 40–75 years, 53.8% of whom were women. We assessed participants from baseline (2012–2014) to 2021. The data were gathered through interviews, biological samples, and physical examinations. The causes of premature mortality, hazard ratios (HRs), and population attributable fraction (PAF) with 95% confidence intervals (95% CIs) for the variables were calculated. Results Out of 388 deaths, 54% were premature. The most common causes of premature death included IHD (40%), stroke (11%), road traffic injuries (6%), lower respiratory infections (5%), and COVID-19 (3%). The predictive factors [adjusted HRs (95% CIs)] associated with premature mortality included age [year, 1.07 (1.04, 1.10)], tobacco [1.43 (0.96, 2.11)], opium [2.12 (1.39, 3.24)], hypertension [1.52 (1.10, 2.12)], waist circumference [centimeter, 1.03 (1.00, 1.05)], female sex [0.30 (0.19, 0.47)], education [> 8 years vs. no formal schooling, 0.46 (0.24, 0.88)], being married [0.60 (0.37, 0.97)], physical activity [3rd vs. 1st tertile, 0.38 (0.26, 0.57)], hip circumference [centimeter, 0.96 (0.92, 0.99)], estimated GFR [mL/min/1.73m², 0.99 (0.978, 0.999)], and wealth score [4th vs. 1st quartile, 0.54 (0.32, 0.90)]. The PAF (95% CI) for all modifiable predictors was 0.83 (0.62, 0.92). Conclusions The predominant causes of premature mortality were IHD and stroke. To mitigate premature deaths, paying simultaneous attention to both socioeconomic and behavioral factors is recommended. Premature mortality Pars cohort study Risk factor Protective factor Prevention Noncommunicable disease Cardiovascular disease Introduction Death in old age is inevitable, but death at younger ages is controllable ( 1 ). Premature death, defined as death under 70 years of age ( 2 ), is an important indicator of health status and access to health care ( 3 ). Premature death has significant negative psychological and socioeconomic impacts on affected families and societies. Studies have demonstrated that the loss of parents in childhood has many devastating consequences, including substance abuse, depression, violent behavior, education dropout, and a decrease in employment ( 4 ). Premature death prevalence and causes vary by region. For example, more than 85% of premature deaths due to noncommunicable diseases (NCDs) occur in low- and middle-income countries. Cardiovascular diseases (CVDs), cancers, chronic respiratory diseases, and diabetes account for more than 80% of all premature NCD deaths worldwide ( 5 , 6 ). A study has forecasted that by 2040, life expectancy will continue to vary worldwide, ranging from more than 85 years in four countries (Japan, Singapore, Spain, and Switzerland) to less than 65 years in certain African countries. In North Africa and the Middle East, ischemic heart disease, road injuries, stroke, and chronic kidney disease are projected to be the main causes of premature mortality ( 7 ). Therefore, in different regions, comprehensive studies are needed to evaluate the rates and predictors of premature deaths. This information will help healthcare decision-makers plan cost-effective interventions and allocate sufficient budgetary resources in each region ( 8 ). Unfortunately, few studies have been conducted on this topic worldwide, especially among low-to middle-income countries ( 9 – 11 ). The Pars Cohort Study (PCS) is a population-based prospective cohort study in Iran that, with its detailed medical, demographic, and socioeconomic data, provides a platform for investigating premature mortality ( 12 ). We aimed to analyze the causes of premature death and associated risk factors in the PCS participants. Methods Design, Setting, and Population The detailed methodological design of the PCS has been described previously ( 12 ).The PCS is an ongoing prospective cohort study conducted in the Valashahr region of Fars Province, Iran. All rural residents of the Valashahr district, within the age range of 40 to 75 years, were invited to participate in interviews, undergo physical examinations, and contribute biological samples as part of the study. Temporary residents of the district and unwillingness to participate were the only exclusion criteria of the PCS. A total of 9,264 individuals (with a 95% participation rate) were recruited at baseline (2012–2014) for this population-based study. The ethics committees of the Digestive Diseases Research Institute (DDRI) and Shiraz University of Medical Sciences (SUMS) approved the study protocol, and the ethics code of this study is IR.TUMS.SHARIATI.REC.1402.001. The completion and signing of the informed consent form were done in the presence of a third party, and their information was kept completely confidential. Data collection The data were collected through interviews, biological samples, and physical examinations. Trained interviewers applied detailed questionnaires to collect demographic data, medical history, and lifestyle information, including physical activity and wealth status. Physicians and nurses measured anthropometric indicators such as height, weight, waist and hip circumference, and blood pressure. In this study, our primary focus was on modifiable risk factors associated with an increased risk for premature mortality. Marital status was categorized as married and non-married. The alcohol, opium, and tobacco variables were defined as the use of the substance at least once a week for 6 months or more. Diabetes was characterized by self-report of a known case of diabetes mellitus or a fasting blood sugar ≥ 126 mg/dL. Physical activity was calculated based on the metabolic equivalent of task per minute per week according to the World Health Organization (WHO) guideline and categorized into tertiles ( 13 ). The education variable was divided into 4 groups based on the number of years of education: no formal schooling; 1–5 years; 6–8 years; and over 8 years. Brachial blood pressure was measured in a sitting position after 5 minutes of rest on each arm twice, 2 minutes apart. The second measurement on the side that had the highest value was considered the blood pressure variable ( 14 ). A systolic blood pressure of ≥ 140 mm Hg, a diastolic blood pressure of ≥ 90 mm Hg ( 15 ), or a self-reported history of hypertension confirmed by a physician was considered hypertension. Non-high-density lipoprotein cholesterol (non-HDL-C) is the result of subtracting the amount of HDL-C from the total cholesterol level, and is considered an indicator of dyslipidemia, which is classified into three groups: low (< 130), moderate (≥ 130 and < 160), and high (≥ 160 mg/dL) ( 16 , 17 ). The wealth score was calculated via multiple correspondence analysis, in which the required data were collected by asking detailed questions about the ownership of a house, a car, household appliances such as a TV, computer, and refrigerator, and the size of the house. The wealth score was divided into quartiles ( 18 ). The estimated glomerular filtration rate (eGFR) was calculated using the chronic kidney disease epidemiology (CKD-Epi) equation based on participants’ serum creatinine levels and categorized as low (< 60), moderate (60–90), and high (≥ 90 mL/min/1.73m 2 ) ( 19 , 20 ). Body-mass index (weight in kilograms divided by the square of height in meters, in kg/m2, BMI) and waist and hip circumferences (in centimeters) were measured using a standard protocol. The waist circumference was divided into two groups based on the appropriate cut-off (95 cm) for the Iranian population ( 21 , 22 ). Follow-up and mortality assessment The follow-up period was extended from the date of the first interview to the date of the last interview (up to December 31, 2021), age 70 years, or premature death (whichever occurred earlier). In this study, death before the age of 70 years was defined as premature death—the outcome variable. All study participants were followed up annually by telephone and with a follow-up form, which included 40 items, such as vital signs, hospitalizations, deaths, and the occurrence of new diseases. In the case of death, the follow-up team collected all medical records, including paraclinical reports related to the diagnosis and treatment, of the deceased individual. If the data were incomplete, the PCS team visited each participant’s home address, interviewed their relatives, and completed the verbal autopsy questionnaire. The verbal autopsy questionnaire showed both good validity and reliability in a large-scale follow-up study among the Iranian population ( 23 ). Two internists independently reviewed all documents of the deceased person and determined the exact cause of death based on the 10th revision of the International Classification of Diseases (ICD-10) ( 24 ). In case of discrepancies, an experienced internist reviewed all the documents together with the judgment of the other two experts and announced the final diagnosis of the cause of death. Statistical methods The study was conducted according to the methods and data sources of the WHO for causes of death at the country level from 2000 to 2019 ( 25 ). The garbage ICD codes used included C76, C80, I26.9, I46, I50, R00-94, R96-99, Y10-34 (Y12.9), C55, A40-41 (A41.9), J96, and N18. After the redistribution of garbage codes, the top 5 causes of death were identified in three categories: < 70 years, ≥ 70 years, and total. Because coronavirus disease 2019 (COVID-19) was common in recent years in this study, we considered COVID-19 as a separate code and did not combine it with other respiratory infections. To clarify the associations between predictor variables and premature death, Cox-proportional hazard regression models were used to determine hazard ratios (HRs) and 95% confidence intervals (CIs). For this analysis, we excluded participants aged ≥ 70 years and those with a history of chronic diseases, including ischemic heart disease (IHD), stroke, and cancer, at baseline. In this model, unadjusted, age- and sex-adjusted, and fully adjusted HRs were calculated for each variable. In the mutual model, HRs were adjusted for probable confounders that included continuous (age at baseline, waist and hip circumferences, eGFR, and non-HDL-C) and categorical (sex, ethnicity, marital status, education, wealth score, history of alcohol, tobacco, and opium use, physical activity, hypertension, and diabetes) variables. We used waist and hip circumferences in the models, as measures of obesity, because a large cohort of Iranian participants with the same age range showed that the significant impact of visceral adiposity becomes evident only when considering both waist circumference as a risk factor and hip circumference as a protective factor in the models ( 26 ). Consequently, hip-adjusted waist circumference was utilized as the adiposity variable in our analysis. The proportional hazard assumption was verified using the PH test. We used the "punafcc" command in Stata to calculate the PAF for modifiable factors based on mutual regression models. For this analysis, the reference distribution was set so that each participant was either not exposed to the risk factor or belonged to the category associated with the lowest risk of premature death (such as the highest level of education or eGFR ≥ 90). Stata statistical software (version 17, Stata Inc, College Station, Texas, USA) was used for data analysis. In addition to Stata, Excel (Microsoft Office Excel 2007) was used for data processing. We used complete case analysis, and P < 0.05 and 95% CIs not including one were considered statistically significant. Additionally, ChatGPT was used in some sections of the article for paraphrasing. Results During the follow-up, with a median (interquartile range boundaries, IQR) of 6.90 (6.27–7.37) years and loss to follow-up rate < 1%, a total of 388 deaths occurred, 213 (54%) of which were premature. Noncommunicable diseases (NCDs), communicable, maternal, perinatal, and nutritional conditions (CMPN), and injuries constituted 79%, 11%, and 8% of premature deaths, respectively. Within the NCD, CMPN, and injury groups, 63%, 50%, and 50%, respectively, occurred before the age of 70. The most common causes of death in both age categories (< 70 and ≥ 70 years) were IHD and stroke. Other causes of premature death included road traffic injuries (RTIs), lower respiratory infections, and COVID-19. The top 5 causes of death based on age categories are presented in Table 1 , and a detailed classification of death causes is provided in Supplementary Table 1, Additional File 1. Table 1 The top 5 causes of death in the Pars Cohort Study (2012–2021) < 70 years (n = 213) ≥ 70 years (n = 170) All (n = 388) a Ranks Causes Number (%) Causes Number (%) Causes Number (%) 1 Ischemic heart disease 85 (39.90) Ischemic heart disease 47 (27.65) Ischemic heart disease 135 (34.79) 2 Stroke 24 (11.26) Stroke 21 (12.35) Stroke 45 (11.60) 3 Road traffic injuries 12 (5.63) Road traffic injuries 12 (7.06) Road traffic injuries 24 (6.19) 4 Lower respiratory infections 10 (4.69) Stomach cancer 7 (4.12) Lower respiratory infections 17 (4.38) 5 COVID-19 b 7 (3.29) Prostate cancer 6 (3.53) Stomach cancer 11 (2.84) Numbers obtained based on WHO methods after redistribution of garbage ICD-10 codes, including 60 cardiovascular disease cases (I26.9, I46, I50), 5 cancer cases (C55, C76, C80), 1 external causes of morbidity and mortality(Y12.9), 21 infectious and parasitic cases (A41.9), 15 cases of symptoms, signs and ill-defined conditions (R00-R94, R96-R99), 3 cases of respiratory disease(J96), and 6 cases of genitourinary disease(N18). a Age of death in 5 participants was undetermined. b COVID-19 indicates coronavirus disease. Of the total participants (n = 9264), 46.16% were male. The mean (SD) age of all cohort participants at baseline was 52.64 ± 9.68 years. After excluding individuals aged ≥ 70 years at baseline and those with a history of IHD, stroke, or cancer (for determining HRs and PAF purposes), the remaining population was 7668, and 176 premature deaths occurred. The baseline characteristics of the participants are presented in Table 2 . The HRs (95% CIs) for the relationships between variables and premature death are shown in Table 3 . Based on the fully adjusted HRs (95% CIs), the risk of premature death was greater in older individuals [1.07(1.04, 1.10)], tobacco smokers [1.43(0.96, 2.11)], opium consumers [2.12(1.39, 3.24)], those with hypertension [1.52(1.10, 2.12)], and those with a greater waist circumference [1.03 (1.00, 1.05)]. The protective factors included female sex [0.30(0.19, 0.47)], higher education levels [> 8 years vs. no formal schooling, 0.46(0.24,0.88)], being married [0.60(0.37, 0.97)], higher physical activity [moderate and high vs. low, 0.50 (0.34,0.74) and 0.38(0.26,0.57), respectively], hip circumference [0.96(0.92, 0.99)], wealth score [4th vs 1st quartile, 0.54(0.32,0.90)], and eGFR [0.99(0.978,0.999)]. Table 2 Baseline characteristic of participants in the Pars Cohort Study a Variable Prevalence (total) (n = 7668) Prevalence (without premature death) (n = 7492) Prevalence (premature death) (n = 176) Age (year) b 50.52 (7.76) 50.40 (7.72) 55.69 (7.35) Sex (Men) 3538 (46.14) 3424 (45.70) 114 (64.77) Ethnicity Fars 4310 (56.21) 4220 (56.33) 90 (51.14) Turk 2987 (38.95) 2912 (38.87) 75 (42.61) Others 371 (4.84) 360 (4.81) 11 (6.25) Education No formal schooling 3419 (44.59) 3311(44.19) 108 (61.36) years of education ≤ 5 2438 (31.79) 2398 (32.01) 40 (22.73) 5 < years of education ≤ 8 889 (11.59) 875 (11.68) 14 (7.95) 8 < years of education 918 (11.97) 904 (12.07) 4 (7.95) Marital status (Married) 6895 (89.92) 6745 (90.03) 150 (85.23) Physical activity Low 2294 (29.92) 2219 (29.62) 75 (42.61) Moderate 2558 (33.36) 2510 (33.50) 48 (27.27) High 2816 (36.72) 2763 (36.88) 53 (30.11) Alcohol ever used 165 (2.15) 162 (2.16) 3 (1.70) Opium ever used 625 (8.15) 590 (7.88) 35 (19.89) Tobacco ever used 1581 (20.62) 1517 (20.25) 64 (36.36) Hypertension 1899 (24.77) 1832 (24.45) 67 (38.07) Diabetes 881 (11.49) 852 (11.37) 29 (16.48) BMI (kg/m 2 ) 25.85 (4.66) 25.86 (4.66) 25.25 (4.91) Waist circumference (cm) b 90.73 (12.10) 90.72 (12.07) 91.25 (13.04) ≥ 95cm 2914 (38.00) 2843 (37.95) 71 (40.34) Hip (cm) b 95.81 (7.95) 95.85 (7.95) 94.14 (7.84) Wealth score First quartile (reference) 1801 (23.49) 1746 (23.30) 55 (31.25) Second quartile 2169 (28.29) 2113 (28.20) 56 (31.82) Third quartile 1733 (22.60) 1697 (22.65) 36 (20.45) Fourth quartile 1964 (25.61) 1935 (25.83) 29 (16.48) Non-HDL-C (mg/dL) b 137.86 (39.10) 137.78 (39.03) 141.01 (41.96) < 130 3384 (44.13) 3316 (44.26) 68 (38.64) 130–159 2322 (30.28) 2263 (30.21) 59 (33.52) ≥ 160 1950 (25.43) 1902 (0.15) 48 (27.27) eGFR (mL/min/1.73m 2 ) b 74.90 (14.36) 75.03 (14.23) 69.26 (18.40) < 60 1107 (14.44) 1049 (14.00) 58 (32.95) 60–89 5393 (70.33) 5300 (70.74) 93 (52.84) ≥ 90 1168 (15.23) 1143 (15.26) 25 (14.20) Non-HDL-C indicates non-high-density lipoprotein cholesterol and eGFR, estimated glomerular filtration rate. a After excluding individuals aged ≥ 70 years at baseline and those with a history of IHD, stroke, or cancer. Missing data included 4 cases in education, 39 in waist, 1 in wealth, and 12 in non-HDL categories. Data are presented as number (percentage) or mean (SD). b These rows are demonstrated as mean (SD). Table 3 HRs (95% CIs) for relationships between variables and premature deaths in the Pars Cohort Study a Variable HR (95%CI) Crude p-value HR (95%CI) Age and sex-adjusted p-value HR (95%CI) Full adjusted b p-value Age (year) 1.11(1.09, 1.14) < 0.001 - - 1.07 (1.04, 1.10) < 0.001 Sex - - Men (reference) 1 - 1 Women 0.46(0.33, 0.62) < 0.001 - 0.30 (0.19, 0.47) < 0.001 Ethnicity Fars (reference) 1 1 1 Turk 1.19 (0.87, 1.62) 0.266 1.13 (0.83, 1.54) 0.440 1(0.73, 1.39) 0.982 Others 1.47 (0.78, 2.74) 0.232 1.51 (0.80, 2.82) 0.201 1.48 (0.77, 2.85) 0.236 Education No formal schooling (reference) 1 1 1 ≤5 years of education 0.50 (0.35, 0.72) < 0.001 0.70 (0.47, 1.03) 0.072 0.75 (0.50, 1.13) 0.171 5 < years of education ≤ 8 0.47 (0.27, 0.83) 0.009 0.52 (0.28, 0.94) 0.030 0.56 (0.30, 1.04) 0.068 8 < years of education 0.46 (0.26, 0.80) 0.006 0.46 (0.25, 0.85) 0.012 0.46 (0.24, 0.88) 0.020 Marital status Married 0.61 (0.40, 0.94) 0.024 0.50 (0.32, 0.80) 0.004 0.60 (0.37, 0.97) 0.036 Other (reference) 1 1 1 Physical activity Low (reference) 1 1 1 Moderate 0.55 (0.38, 0.79) 0.001 0.52 (0.36, 0.75) < 0.001 0.50 (0.34, 0.74) < 0.001 High 0.57 (0.40, 0.82) 0.002 0.44 (0.30, 0.63) < 0.001 0.38 (0.26, 0.57) < 0.001 Alcohol Never (reference) 1 1 1 Ever used 0.79 (0.25, 2.47) 0.685 0.82 (0.26, 2.57) 0.730 0.57 (0.18, 1.81) 0.337 Opium Never (reference) 1 1 1 Ever used 2.88 (1.99, 4.17) < 0.001 2.44 (1.64, 3.63) < 0.001 2.12 (1.39, 3.24) 0.001 Tobacco Never (reference) 1 1 1 Ever used 2.22 (1.63, 3.02) < 0.001 1.59 (1.11, 2.29) 0.012 1.43 (0.96, 2.11) 0.076 Hypertension No (reference) 1 1 1 Yes 1.93 (1.42, 2.63) < 0.001 1.55 (1.13, 2.11) 0.006 1.52 (1.10, 2.12) 0.012 Diabetes No (reference) 1 1 1 Yes 1.52 (1.01, 2.28) 0.043 1.30 (0.86, 1.95) 0.213 1.03 (0.67, 1.59) 0.895 Waist (cm) 1.05 (1.03, 1.07) < 0.001 1.03 (1.01, 1.1) 0.005 1.03 (1.00, 1.05) 0.019 Hip (cm) 0.91 (0.88, 0.94) < 0.001 0.95 (0.92, 0.98) 0.004 0.96 (0.92, 0.99) 0.018 Wealth score First quartile (reference) 1 1 1 Second quartile 0.83 (0.57, 1.20) 0.319 0.86 (0.59, 1.26) 0.445 0.89 (0.61, 1.31) 0.563 Third quartile 0.64(0.42, 0.98) 0.039 0.71 (0.46, 1.08) 0.110 0.77 (0.50, 1.21) 0.257 Fourth quartile 0.45 (0.28, 0.70) 0.001 0.53 (0.34, 0.85) 0.008 0.54 (0.32, 0.90) 0.017 Non-HDL (mg/dl) 1.00 (0.998, 1.010) 0.432 1.00 (0.997, 1.004) 0.549 1.00 (0.995, 1.003) 0.632 eGFR (ml/min/1.73 m 2 ) 0.97 (0.962, 0.982) < 0.001 0.99 (0.975, 0.997) 0.011 0.99 (0.978, 0.999) 0.037 Non-HDL-C indicates non-high-density lipoprotein cholesterol and eGFR, estimated glomerular filtration rate. a After excluding participants with age ≥ 70 years at baseline and a history of ischemic heart disease, stroke, or cancers. b Adjusted for all variables in the table. Table 2 and Table 3 should be cited here. Table 4 demonstrates the PAF for 11 modifiable risk factors, with the overall PAF (95% CI) for all variables being 0.83 (0.62,0.92). Socioeconomic risk factors (i.e., education and wealth score) accounted for 0.61 (0.33, 0.77), and behavioral variables (i.e., physical activity and tobacco and opium use) accounted for 0.48 (0.34,0.59) of the PAF. The highest PAFs were observed for education, physical activity, wealth score, and hypertension, respectively. Table 4 Population attributable fraction of modifiable variables for premature mortality in the Pars Cohort Study (2012–2021) Variable PAF Education 0.43 (0.04, 0.66) Physical activity 0.35 (0.21, 0.47) Alcohol use -0.01 (-0.05, 0.02) Opiate use 0.11 (0.07, 0.15) Tobacco use 0.11 (-0.002, 0.21) Hypertension 0.13 (0.05, 0.21) Diabetes 0.003 (-0.07, 0.07) Non-HDL-C 0.06 (-0.14, 0.22) Waist circumference 0.12 (-0.002, 0.23) Wealth score 0.33 (0.05, 0.52) eGFR -0.20 (-0.79, 0.19) Behavioral variables a 0.48 (0.34, 0.59) Metabolic variables b 0.14 (-0.34, 0.45) Socioeconomic variables c 0.61 (0.33, 0.77) All variables 0.83 (0.62, 0.92) Non-HDL-C indicates non-high-density lipoprotein cholesterol and eGFR, estimated glomerular filtration rate. a Behavioral variables include alcohol, opium, and tobacco use and physical activity. b Metabolic variables include non-HDL-C, hypertension, diabetes, eGFR, and waist circumference. c Socioeconomic variables include education and wealth score. Discussion Approximately 54% of the deaths in our study were considered premature. In each category—NCDs, CMPN conditions, and injuries—more than half of the deaths occurred prematurely. Among these premature deaths, 79% were attributed to NCDs, with vascular diseases such as IHD and stroke being the most common causes. The identified risk factors for premature death included older age, tobacco and opium consumption, hypertension, and increased waist circumference. Conversely, protective factors included being female, having higher education levels, being married, engaging in high physical activity, possessing a greater hip circumference, and having a greater wealth score. Socioeconomic variables, including education and wealth score, and behavioral variables, including alcohol, opium, and tobacco use and physical activity, exhibited the highest PAF, respectively. By effectively managing these modifiable risk factors, substantial reductions of 61% and 48% in premature deaths can be achieved, respectively. Globally, some studies have investigated the prevalence of premature death. For example, in the Golestan Cohort Study (GCS) conducted in northeastern Iran, the rate of premature death among participants aged 40–70 years was 63.3% ( 9 ). In our current investigation, a comparable 54% of deaths were deemed premature. In contrast, this rate was reported to be 4.5% in the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort study conducted among nine European countries with the same age range. According to the EPIC cohort study, 50% of premature deaths were attributed to cancer and 22% to CVDs ( 10 ). However, in the GCS, almost 50% of premature deaths are due to CVDs ( 9 ). Factors contributing to the elevated rates of premature deaths in this demographic may include both country-level and individual-level socioeconomic status; for example, rural residents with low levels of education (76% of participants had less than five years of education) and wealth score (52% falling within the first two quartiles of the wealth score) composed most of the participants in our study. Environmental factors, such as access to healthy food and venues for physical activity, and healthcare system factors are other important factors that affect premature mortality. The most common causes of premature death in PCS participants were IHD, stroke, RTIs, lower respiratory infections, and COVID-19. Our findings align with the Global Burden of Disease (GBD) study, underscoring IHD, stroke, and RTIs as the primary contributors to premature mortality in Iran, mirroring patterns observed in upper-middle-income countries. The GBD study demonstrated that the specific causes contributing to premature death varied globally ( 27 , 28 ). Premature deaths in the EPIC cohort study were primarily attributed to cancer (50%) and circulatory diseases (22%). In contrast, cohort studies conducted in Iran, including the PCS, GCS, and Tehran Lipid and Glucose Study, indicated that CVDs are the leading cause of death, with RTIs ranking among the top 5 causes of premature death. The leading causes of premature death in GCS participants are IHD, stroke, RTIs, stomach cancer, and esophageal cancer ( 9 ). The Tehran Lipid and Glucose Study cohort in Iran identified CVD, cancer, RTIs, sepsis, and pneumonia as the underlying causes of premature deaths ( 6 , 11 ). In this study, RTIs were the third leading cause of premature death. According to the Global Status Report on Road Safety 2018, 93% of the 1.35 million global road traffic deaths occurred in low- and middle-income countries in 2016 ( 29 ). RTIs remain a public health concern in Iran, despite evidence of a decline in RTIs across all countries ( 30 ). This study revealed that premature mortality due to the COVID-19 pandemic was one of the main causes of premature death, although only the first year of the pandemic was included in our analysis. The COVID-19 pandemic has had a significant impact on mortality worldwide. The Global Burden of Disease study revealed that while age-standardized mortality rates globally declined between 1950 and 2019 (a 62.8% decline), they increased considerably during the COVID-19 pandemic period (2020–21; by 5.1%) ( 31 ). Unprecedented reversals in adult mortality and life expectancy trends at the global and national levels during the pandemic highlighted the importance of paying comprehensive attention to both communicable and noncommunicable diseases simultaneously. Cohort studies play a pivotal role in uncovering modifiable risk factors for premature deaths. In the GCS, noteworthy protective factors included wealth score, physical activity, education, and fruit/vegetable consumption. Conversely, significant risk factors included opium use, tobacco consumption, diabetes, and hypertension. The cumulative impact of these factors accounted for 73% of the PAF. The factors associated with the highest PAF were wealth score, physical activity, and hypertension ( 9 ). In our study, modifiable factors played a pivotal role, contributing to 83% of premature deaths. The Tehran Lipid and Glucose Study identified hypertension, diabetes, and current smoking as significant risk factors for premature mortality. Controlling these risk factors, particularly diabetes, hypertension, and smoking, has the potential to reduce mortality by more than 40% ( 11 ). In the PCS, opium use, hypertension, and tobacco consumption were considered among the most important modifiable risk factors. Globally, some studies have sought to assess premature deaths, revealing regional variations in the significance of risk factors. These disparities arise from differences in the strength of associations and variations in the prevalence of these factors across diverse regions. The EPIC cohort study conducted on middle-aged individuals in Western Europe focused on evaluating modifiable causes of premature death ( 10 ). While smoking remains the primary contributor to premature mortality in Europe, other notable factors such as suboptimal dietary habits, overweight and obesity, hypertension, insufficient physical activity, and excessive alcohol consumption also play significant roles. The collective attributable fraction (AF) for these six risk factors was determined to be 57%. In the PCS, the predominant cause of death was vascular disease, which included IHD and stroke. CVD accounts for a substantial proportion of global deaths, accounting for 31% annually, a trend partially attributed to population growth and aging ( 32 ). Findings from the PURE cohort study indicated that a substantial 70% of CVD and associated deaths can be attributed to a limited set of modifiable factors ( 33 ). Certain factors, such as high blood pressure and education, exert a widespread influence globally, while others, such as household air pollution and unhealthy eating habits, exhibit variability based on a country's economic status. Metabolic elements emerged as the primary contributors to the risk of CVD, constituting 41.2% of the PAF, with hypertension representing the most significant portion at 22.3% of the PAF. The INTERSTROKE case-control study identified various factors associated with all strokes, including a history of hypertension or elevated blood pressure, regular physical activity, apolipoprotein (Apo)B/ApoA1 ratio, dietary habits, waist-to-hip ratio, psychosocial factors, current smoking status, cardiac causes, alcohol consumption, and diabetes mellitus. Together, these factors contributed to 90.7% of the Population attributable risk (PAR) for all strokes globally ( 34 ). A case-control study known as the INTERHEART study revealed that abnormal lipids, smoking, hypertension, diabetes, abdominal obesity, psychosocial factors, consumption of fruits, vegetables, alcohol, and regular physical activity are the primary contributors to the risk of myocardial infarction globally. Together, these nine risk factors were responsible for 90.4% of the total PAR ( 35 ). In our study, opium exhibited the highest HR among the variables. Opium consumption notably correlated with elevated risks of death from diverse causes, including circulatory diseases and cancer ( 36 ). In the GCS study, long-term opiate consumption was related to increased cardiovascular death, independent of other known risk factors ( 37 ). Opium consumption was also associated with an increased likelihood of developing various cancers ( 38 ). This study possesses notable strengths attributable to its prospective design, extensive and precise follow-up data, meticulous death ascertainment, and the incorporation of objective measurements alongside self-reported data. A comprehensive evaluation was conducted, including wealth score, eGFR, and non-HDL. Noteworthy strengths of this investigation included the availability of data for confounder adjustment and a less than 1% loss to follow-up rate. Several limitations characterize our study, including the limited sample size and the fact that the participants were solely from rural districts. The infrequent occurrence of premature death within our cohort may compromise the statistical power of our study, potentially impeding the detection of risk factors within specific subgroups. Furthermore, the absence of data on urban populations and individuals younger than 40 years can limit the generalizability of our findings. The impact of alcohol consumption could not be analyzed due to its low prevalence for religious reasons and absence from social habits. We recommend conducting further cohort studies on this subject to obtain comprehensive results and implement necessary interventions and policies based on the outcomes. To decrease premature death several measures can be taken. The PolyPars study which was conducted in the PCS to assess the effectiveness of polypill (two antihypertensive agents, a statin and aspirin) for primary and secondary prevention of CVD demonstrated that it can safely halve the risk of major CVDs ( 39 ). Also, paying simultaneous attention to socioeconomic and behavioral factors is recommended. Because opium and road traffic have impressive negative effects, new policies and public awareness on these topics are suggested. Conclusion Fifty-four percent of deaths were premature. NCDs constituted 79% of premature deaths. The most common causes of premature death were IHD, stroke, RTIs, lower respiratory infections, and COVID-19, respectively. The risk of premature death was greater in older individuals, tobacco and opium consumers, those with hypertension, and those with a greater waist circumference. The protective factors included female sex, higher education levels, being married, higher physical activity, hip circumference, and wealth score. Modifiable risk factors could reduce premature death by approximately 83%. Continued research, application of the findings from this study, and the formulation of policies in alignment with those findings will collectively play a crucial role in enhancing life expectancy and potentially mitigating the societal burden of avoidable outcomes. Abbreviations AF Attributable Fraction CIs Confidence intervals CKD-Epi Chronic Kidney Disease Epidemiology CMPN Communicable, maternal, perinatal, and nutritional conditions CVD Cardiovascular disease DDRI Digestive Diseases Research Institute eGFR Estimated glomerular filtration rate EPIC European Prospective Investigation into Cancer and Nutrition GBD Global Burden of Disease GCS Golestan Cohort Study HR hazard ratio ICD-10 10th revision of the International Classification of Diseases IHD Ischemic heart disease NCD Noncommunicable diseases non-HDL-C Non-high-density lipoprotein cholesterol PAR Population attributable risk PCS Pars Cohort Study RTI Road traffic injury SUMS Shiraz University of Medical Sciences WHO World Health Organization Declarations Ethics approval and consent to participate The ethics committees of the Digestive Diseases Research Institute (DDRI) and Shiraz University of Medical Sciences (SUMS) approved the study protocol, and the ethics code of this study is IR.TUMS.SHARIATI.REC.1402.001. The completion and signing of the informed consent form were done in the presence of a third party, and their information was kept completely confidential. Consent for publication Not applicable Availability of data and materials The datasets used and analyzed during the current study are available from the corresponding author upon reasonable request. Competing interests The authors declare that they have no competing interests. Funding This work was funded by Shiraz University of Medical Sciences, Grant Number [910210]. Authors' contributions RM, MN, FM, AG, SS, and HP conceptualized the study; FZ prepared the original draft; MN and RM contributed to the design of the study; and FZ, MN, and SS performed the analysis. FZ, MN, RM, and SS contributed to writing, reviewing, and editing the manuscript. MN and FZ contributed to the interpretation of the data. All authors reviewed and approved the final manuscript. Acknowledgements We thank the study participants for their cooperation. References Chen JT, Rehkopf DH, Waterman PD, Subramanian SV, Coull BA, Cohen B, et al. Mapping and measuring social disparities in premature mortality: the impact of census tract poverty within and across Boston neighborhoods, 1999–2001. J Urban Health. 2006;83(6):1063–84. Razeghian-Jahromi I, Ghasemi Mianrood Y, Dara M, Azami P. Premature Death, Underlying Reasons, and Preventive Experiences in Iran: A Narrative Review. Arch Iran Med. 2023;26(7):403–10. Peto R, Lopez AD, Norheim OF. Halving premature death. Science. 2014;345(6202):1272. Ellis J, Dowrick C, Lloyd-Williams M. The long-term impact of early parental death: lessons from a narrative study. J R Soc Med. 2013;106(2):57–67. Noncommunicable diseases World Health. Organisation2023 [ https://www.who.int/news-room/fact-sheets/detail/noncommunicable-diseases . Ziamanesh F, Mohajeri Tehrani MR, Hemmatabadi M, Sharghi S, Fallahi B, Haghpanah V, et al. Design and implementation of a national quality registry of thyroid cancer in Iran: study protocol. Journal of Diabetes & Metabolic Disorders; 2023. Foreman KJ, Marquez N, Dolgert A, Fukutaki K, Fullman N, McGaughey M, et al. Forecasting life expectancy, years of life lost, and all-cause and cause-specific mortality for 250 causes of death: reference and alternative scenarios for 2016-40 for 195 countries and territories. Lancet. 2018;392(10159):2052–90. Global regional. national incidence, prevalence, and years lived with disability for 328 diseases and injuries for 195 countries, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet. 2017;390(10100):1211–59. Nalini M, Oranuba E, Poustchi H, Sepanlou SG, Pourshams A, Khoshnia M, et al. Causes of premature death and their associated risk factors in the Golestan Cohort Study, Iran. BMJ Open. 2018;8(7):e021479. Muller DC, Murphy N, Johansson M, Ferrari P, Tsilidis KK, Boutron-Ruault M-C, et al. Modifiable causes of premature death in middle-age in Western Europe: results from the EPIC cohort study. BMC Med. 2016;14:1–11. Eslami A, Naghibi Irvani SS, Ramezankhani A, Fekri N, Asadi K, Azizi F, et al. Incidence and associated risk factors for premature death in the Tehran Lipid and Glucose Study cohort, Iran. BMC Public Health. 2019;19(1):1–7. Gandomkar A, Poustchi H, Moini M, Moghadami M, Imanieh H, Fattahi MR, et al. Pars cohort study of non-communicable diseases in Iran: protocol and preliminary results. Int J Public Health. 2017;62(3):397–406. Global Physical Activity Surveillance World Health Organisation2017 [updated 24. May 2017. http://www.who.int/chp/steps/GPAQ/en/ . Vinyoles E, Tafalla M, Robledo V, Marco M, Porta I, Muñoz M-A, et al. Interarm blood pressure measurement and the reference-arm assignment variability. Blood Press Monit. 2019;24(5):259–63. Hypertension: World Health Organization. 2023 [ https://www.who.int/news-room/fact-sheets/detail/hypertension#:~:text=Overview,get%20your%20blood%20pressure%20checked . Mach F, Baigent C, Catapano AL, Koskinas KC, Casula M, Badimon L, et al. 2019 ESC/EAS Guidelines for the management of dyslipidaemias: lipid modification to reduce cardiovascular risk. Eur Heart J. 2020;41(1):111–88. Pencina KM, Thanassoulis G, Wilkins JT, Vasan RS, Navar AM, Peterson ED, et al. Trajectories of Non-HDL Cholesterol Across Midlife: Implications for Cardiovascular Prevention. J Am Coll Cardiol. 2019;74(1):70–9. Islami F, Kamangar F, Nasrollahzadeh D, Aghcheli K, Sotoudeh M, Abedi-Ardekani B, et al. Socio-economic status and oesophageal cancer: results from a population-based case-control study in a high-risk area. Int J Epidemiol. 2009;38(4):978–88. Doganer YC, Rohrer JE, Aydogan U, Barcin C, Cayci T, Saglam K. Association of renal function, estimated by four equations, with coronary artery disease. Int Urol Nephrol. 2015;47(4):663–71. Levey AS, Stevens LA, Schmid CH, Zhang YL, Castro AF 3rd, Feldman HI, et al. A new equation to estimate glomerular filtration rate. Ann Intern Med. 2009;150(9):604–12. AZIZI F, Khalili D, Aghajani H, Esteghamati A, Hosseinpanah F, Delavari A, et al. Appropriate waist circumference cut-off points among Iranian adults: the first report of. the Iranian National Committee of Obesity; 2010. AZIZI F, Hadaegh F, KHALILI D, Esteghamati A, HOSSEIN PF, Delavari A, et al. Appropriate definition of metabolic syndrome among Iranian adults. report of the Iranian National Committee of Obesity; 2010. Khademi H, Etemadi A, Kamangar F, Nouraie M, Shakeri R, Abaie B, et al. Verbal autopsy: reliability and validity estimates for causes of death in the Golestan Cohort Study in Iran. PLoS ONE. 2010;5(6):e11183. International Statistical Classification of Diseases and Related Health Problems 10th Revision. World Health Organization [updated 25 My 2017. http://apps.who.int/classifications/icd10/browse/2016/en . WHO methods and. data sources for country-level causes of death 2000–2019 2020 [ https://cdn.who.int/media/docs/default-source/gho-documents/global-health-estimates/ghe2019_cod_methods.pdf . Nalini M, Sharafkhah M, Poustchi H, Sepanlou SG, Pourshams A, Radmard AR, et al. Comparing Anthropometric Indicators of Visceral and General Adiposity as Determinants of Overall and Cardiovascular Mortality. Arch Iran Med. 2019;22(6):301–9. Naghavi M, Abajobir AA, Abbafati C, Abbas KM, Abd-Allah F, Abera SF, et al. Global, regional, and national age-sex specific mortality for 264 causes of death, 1980–2016: a systematic analysis for the Global Burden of Disease Study 2016. lancet. 2017;390(10100):1151–210. Moradi-Lakeh M, Sepanlou SG, Karimi SM, Khalili N, Djalalinia S, Karimkhani C, et al. Trend of Socio-Demographic Index and Mortality Estimates in Iran and its Neighbors, 1990–2015; Findings of the Global Burden of Diseases 2015 Study. Arch Iran Med. 2017;20(7):419–28. Organization WH. Global status report on road safety 2015. World Health Organization; 2015. Bazargan-Hejazi S, Ahmadi A, Shirazi S, Ainy E, Djalalinia S, Fereshtehnejad S-M et al. The burden of road traffic injuries in Iran and 15 surrounding countries: 1990–2016. Arch Iran Med. 2018;21(12). Schumacher AE, Kyu HH, Aali A, Abbafati C, Abbas J, Abbasgholizadeh R et al. Global age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950–2021, and the impact of the COVID-19 pandemic: a comprehensive demographic analysis for the Global Burden of Disease Study 2021. The Lancet. 2024. WHO. Cardiovascular diseases fact sheet. WHO Geneva; 2017. Yusuf S, Joseph P, Rangarajan S, Islam S, Mente A, Hystad P, et al. Modifiable risk factors, cardiovascular disease, and mortality in 155 722 individuals from 21 high-income, middle-income, and low-income countries (PURE): a prospective cohort study. Lancet. 2020;395(10226):795–808. O'Donnell MJ, Chin SL, Rangarajan S, Xavier D, Liu L, Zhang H, et al. Global and regional effects of potentially modifiable risk factors associated with acute stroke in 32 countries (INTERSTROKE): a case-control study. lancet. 2016;388(10046):761–75. Yusuf S, Hawken S, Ôunpuu S, Dans T, Avezum A, Lanas F, et al. Effect of potentially modifiable risk factors associated with myocardial infarction in 52 countries (the INTERHEART study): case-control study. lancet. 2004;364(9438):937–52. Khademi H, Malekzadeh R, Pourshams A, Jafari E, Salahi R, Semnani S et al. Opium use and mortality in Golestan Cohort Study: prospective cohort study of 50 000 adults in Iran. BMJ. 2012;344. Nalini M, Shakeri R, Poustchi H, Pourshams A, Etemadi A, Islami F, et al. Long-term opiate use and risk of cardiovascular mortality: results from the Golestan Cohort Study. Eur J Prev Cardiol. 2021;28(1):98–106. Kamangar F, Shakeri R, Malekzadeh R, Islami F. Opium use: an emerging risk factor for cancer? Lancet Oncol. 2014;15(2):e69–77. Malekzadeh F, Gandomkar A, Poustchi H, Etemadi A, Roshandel G, Attar A et al. Effectiveness of polypill for primary and secondary prevention of cardiovascular disease: a pragmatic cluster-randomised controlled trial (PolyPars). Heart. 2024. Additional Declarations No competing interests reported. Supplementary Files SupplementaryTable1.docx Additional File 1: Supplementary Table 1. Detailed death causes according to ICD-10 codes .doc Cite Share Download PDF Status: Published Journal Publication published 27 Sep, 2024 Read the published version in BMC Public Health → Version 1 posted Editor assigned by journal 04 May, 2024 Submission checks completed at journal 04 May, 2024 First submitted to journal 26 Apr, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4328365","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":298588900,"identity":"de45c3fc-f1d7-448b-950c-ed8a3412c8ac","order_by":0,"name":"Fateme Ziamanesh","email":"","orcid":"","institution":"Digestive Disease Research Center, Digestive Diseases Research Institute, Tehran University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Fateme","middleName":"","lastName":"Ziamanesh","suffix":""},{"id":298588901,"identity":"fd359797-d023-40fd-a39b-3abfef166d7e","order_by":1,"name":"Sadaf G Sepanlou","email":"","orcid":"","institution":"Digestive Disease Research Center, Digestive Diseases Research Institute, Tehran University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Sadaf","middleName":"G","lastName":"Sepanlou","suffix":""},{"id":298588903,"identity":"6a011cf1-e536-4e4c-840f-443dacd45017","order_by":2,"name":"Abdullah Gandomkar","email":"","orcid":"","institution":"Non-Communicable Disease Research Center, Shiraz University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Abdullah","middleName":"","lastName":"Gandomkar","suffix":""},{"id":298588905,"identity":"2414409d-dfab-4ddc-985d-d48ee4f1fb80","order_by":3,"name":"Hossein Poustchi","email":"","orcid":"","institution":"Liver and Pancreatobiliary Diseases Research Center, Digestive Diseases Research Institute, Tehran University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Hossein","middleName":"","lastName":"Poustchi","suffix":""},{"id":298588907,"identity":"1518b2fb-9f93-4eec-8f14-d09d189ffd37","order_by":4,"name":"Fatemeh Malekzadeh","email":"","orcid":"","institution":"Digestive Disease Research Center, Digestive Diseases Research Institute, Tehran University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Fatemeh","middleName":"","lastName":"Malekzadeh","suffix":""},{"id":298588909,"identity":"a060e265-c40c-4281-81d0-7f1f3f10d0d6","order_by":5,"name":"Reza Malekzadeh","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtUlEQVRIiWNgGAWjYFAC5gbGhgo4T4IYLYxALWcMSNXS2GZAWB0c6LYfbHw4c96fxLX9Bxg//GCwyCeoxexMYrPhxm0GidtuJDBL9jBIWDYQ1HIgsU3yIVgLA4M00C+EnWh2/mH7z4dzgFrOH2D+TZyWG4ltjBsbgFoOJLARacuNh82SM44ZG28D6rXsMSDKYckHP/bUyMluO3/48I0fFXWkBDcwghgYSNIwCkbBKBgFowAnAACb+D7mrWz7EAAAAABJRU5ErkJggg==","orcid":"","institution":"Digestive Disease Research Center, Digestive Diseases Research Institute, Tehran University of Medical Sciences","correspondingAuthor":true,"prefix":"","firstName":"Reza","middleName":"","lastName":"Malekzadeh","suffix":""},{"id":298588910,"identity":"e365e710-324d-48f4-8a21-409d74c4d95e","order_by":6,"name":"Mahdi Nalini","email":"","orcid":"","institution":"Digestive Disease Research Center, Digestive Diseases Research Institute, Tehran University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Mahdi","middleName":"","lastName":"Nalini","suffix":""}],"badges":[],"createdAt":"2024-04-26 08:43:56","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4328365/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4328365/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12889-024-19583-7","type":"published","date":"2024-09-27T15:57:49+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":65628084,"identity":"3e5a2769-56f1-417a-a25a-bbe9d58694b1","added_by":"auto","created_at":"2024-09-30 16:17:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":876301,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4328365/v1/a7d71833-3912-4616-abb8-25495a36f8a6.pdf"},{"id":56144728,"identity":"dcee400b-7c02-4ef6-a4d7-dbcc3f64e51d","added_by":"auto","created_at":"2024-05-09 05:18:30","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":26249,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional File 1: Supplementary Table 1. Detailed death causes according to ICD-10 codes\u003c/p\u003e\n\u003cp\u003e.doc\u003c/p\u003e","description":"","filename":"SupplementaryTable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-4328365/v1/0d4d7dfed5aa4ab5a6964f32.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Causes and predictors of premature death in the Pars Cohort Study, Iran: a cohort study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDeath in old age is inevitable, but death at younger ages is controllable (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Premature death, defined as death under 70 years of age (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e), is an important indicator of health status and access to health care (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Premature death has significant negative psychological and socioeconomic impacts on affected families and societies. Studies have demonstrated that the loss of parents in childhood has many devastating consequences, including substance abuse, depression, violent behavior, education dropout, and a decrease in employment (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePremature death prevalence and causes vary by region. For example, more than 85% of premature deaths due to noncommunicable diseases (NCDs) occur in low- and middle-income countries. Cardiovascular diseases (CVDs), cancers, chronic respiratory diseases, and diabetes account for more than 80% of all premature NCD deaths worldwide (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). A study has forecasted that by 2040, life expectancy will continue to vary worldwide, ranging from more than 85 years in four countries (Japan, Singapore, Spain, and Switzerland) to less than 65 years in certain African countries. In North Africa and the Middle East, ischemic heart disease, road injuries, stroke, and chronic kidney disease are projected to be the main causes of premature mortality (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Therefore, in different regions, comprehensive studies are needed to evaluate the rates and predictors of premature deaths. This information will help healthcare decision-makers plan cost-effective interventions and allocate sufficient budgetary resources in each region (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Unfortunately, few studies have been conducted on this topic worldwide, especially among low-to middle-income countries (\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe Pars Cohort Study (PCS) is a population-based prospective cohort study in Iran that, with its detailed medical, demographic, and socioeconomic data, provides a platform for investigating premature mortality (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). We aimed to analyze the causes of premature death and associated risk factors in the PCS participants.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eDesign, Setting, and Population\u003c/h2\u003e \u003cp\u003eThe detailed methodological design of the PCS has been described previously (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e).The PCS is an ongoing prospective cohort study conducted in the Valashahr region of Fars Province, Iran. All rural residents of the Valashahr district, within the age range of 40 to 75 years, were invited to participate in interviews, undergo physical examinations, and contribute biological samples as part of the study. Temporary residents of the district and unwillingness to participate were the only exclusion criteria of the PCS. A total of 9,264 individuals (with a 95% participation rate) were recruited at baseline (2012\u0026ndash;2014) for this population-based study.\u003c/p\u003e \u003cp\u003e The ethics committees of the Digestive Diseases Research Institute (DDRI) and Shiraz University of Medical Sciences (SUMS) approved the study protocol, and the ethics code of this study is IR.TUMS.SHARIATI.REC.1402.001. The completion and signing of the informed consent form were done in the presence of a third party, and their information was kept completely confidential.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eData collection\u003c/h2\u003e \u003cp\u003eThe data were collected through interviews, biological samples, and physical examinations. Trained interviewers applied detailed questionnaires to collect demographic data, medical history, and lifestyle information, including physical activity and wealth status. Physicians and nurses measured anthropometric indicators such as height, weight, waist and hip circumference, and blood pressure. In this study, our primary focus was on modifiable risk factors associated with an increased risk for premature mortality.\u003c/p\u003e \u003cp\u003eMarital status was categorized as married and non-married. The alcohol, opium, and tobacco variables were defined as the use of the substance at least once a week for 6 months or more. Diabetes was characterized by self-report of a known case of diabetes mellitus or a fasting blood sugar\u0026thinsp;\u0026ge;\u0026thinsp;126 mg/dL. Physical activity was calculated based on the metabolic equivalent of task per minute per week according to the World Health Organization (WHO) guideline and categorized into tertiles (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). The education variable was divided into 4 groups based on the number of years of education: no formal schooling; 1\u0026ndash;5 years; 6\u0026ndash;8 years; and over 8 years. Brachial blood pressure was measured in a sitting position after 5 minutes of rest on each arm twice, 2 minutes apart. The second measurement on the side that had the highest value was considered the blood pressure variable (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). A systolic blood pressure of \u0026ge;\u0026thinsp;140 mm Hg, a diastolic blood pressure of \u0026ge;\u0026thinsp;90 mm Hg (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e), or a self-reported history of hypertension confirmed by a physician was considered hypertension. Non-high-density lipoprotein cholesterol (non-HDL-C) is the result of subtracting the amount of HDL-C from the total cholesterol level, and is considered an indicator of dyslipidemia, which is classified into three groups: low (\u0026lt;\u0026thinsp;130), moderate (\u0026ge;\u0026thinsp;130 and \u0026lt;\u0026thinsp;160), and high (\u0026ge;\u0026thinsp;160 mg/dL) (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). The wealth score was calculated via multiple correspondence analysis, in which the required data were collected by asking detailed questions about the ownership of a house, a car, household appliances such as a TV, computer, and refrigerator, and the size of the house. The wealth score was divided into quartiles (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). The estimated glomerular filtration rate (eGFR) was calculated using the chronic kidney disease epidemiology (CKD-Epi) equation based on participants\u0026rsquo; serum creatinine levels and categorized as low (\u0026lt;\u0026thinsp;60), moderate (60\u0026ndash;90), and high (\u0026ge;\u0026thinsp;90 mL/min/1.73m\u003csup\u003e2\u003c/sup\u003e) (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Body-mass index (weight in kilograms divided by the square of height in meters, in kg/m2, BMI) and waist and hip circumferences (in centimeters) were measured using a standard protocol. The waist circumference was divided into two groups based on the appropriate cut-off (95 cm) for the Iranian population (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eFollow-up and mortality assessment\u003c/h2\u003e \u003cp\u003eThe follow-up period was extended from the date of the first interview to the date of the last interview (up to December 31, 2021), age 70 years, or premature death (whichever occurred earlier). In this study, death before the age of 70 years was defined as premature death\u0026mdash;the outcome variable. All study participants were followed up annually by telephone and with a follow-up form, which included 40 items, such as vital signs, hospitalizations, deaths, and the occurrence of new diseases. In the case of death, the follow-up team collected all medical records, including paraclinical reports related to the diagnosis and treatment, of the deceased individual. If the data were incomplete, the PCS team visited each participant\u0026rsquo;s home address, interviewed their relatives, and completed the verbal autopsy questionnaire. The verbal autopsy questionnaire showed both good validity and reliability in a large-scale follow-up study among the Iranian population (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Two internists independently reviewed all documents of the deceased person and determined the exact cause of death based on the 10th revision of the International Classification of Diseases (ICD-10) (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). In case of discrepancies, an experienced internist reviewed all the documents together with the judgment of the other two experts and announced the final diagnosis of the cause of death.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical methods\u003c/h2\u003e \u003cp\u003eThe study was conducted according to the methods and data sources of the WHO for causes of death at the country level from 2000 to 2019 (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). The garbage ICD codes used included C76, C80, I26.9, I46, I50, R00-94, R96-99, Y10-34 (Y12.9), C55, A40-41 (A41.9), J96, and N18. After the redistribution of garbage codes, the top 5 causes of death were identified in three categories: \u0026lt; 70 years, \u0026ge;\u0026thinsp;70 years, and total. Because coronavirus disease 2019 (COVID-19) was common in recent years in this study, we considered COVID-19 as a separate code and did not combine it with other respiratory infections. To clarify the associations between predictor variables and premature death, Cox-proportional hazard regression models were used to determine hazard ratios (HRs) and 95% confidence intervals (CIs). For this analysis, we excluded participants aged\u0026thinsp;\u0026ge;\u0026thinsp;70 years and those with a history of chronic diseases, including ischemic heart disease (IHD), stroke, and cancer, at baseline. In this model, unadjusted, age- and sex-adjusted, and fully adjusted HRs were calculated for each variable. In the mutual model, HRs were adjusted for probable confounders that included continuous (age at baseline, waist and hip circumferences, eGFR, and non-HDL-C) and categorical (sex, ethnicity, marital status, education, wealth score, history of alcohol, tobacco, and opium use, physical activity, hypertension, and diabetes) variables. We used waist and hip circumferences in the models, as measures of obesity, because a large cohort of Iranian participants with the same age range showed that the significant impact of visceral adiposity becomes evident only when considering both waist circumference as a risk factor and hip circumference as a protective factor in the models (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Consequently, hip-adjusted waist circumference was utilized as the adiposity variable in our analysis. The proportional hazard assumption was verified using the PH test.\u003c/p\u003e \u003cp\u003eWe used the \"punafcc\" command in Stata to calculate the PAF for modifiable factors based on mutual regression models. For this analysis, the reference distribution was set so that each participant was either not exposed to the risk factor or belonged to the category associated with the lowest risk of premature death (such as the highest level of education or eGFR\u0026thinsp;\u0026ge;\u0026thinsp;90).\u003c/p\u003e \u003cp\u003eStata statistical software (version 17, Stata Inc, College Station, Texas, USA) was used for data analysis. In addition to Stata, Excel (Microsoft Office Excel 2007) was used for data processing. We used complete case analysis, and P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and 95% CIs not including one were considered statistically significant. Additionally, ChatGPT was used in some sections of the article for paraphrasing.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eDuring the follow-up, with a median (interquartile range boundaries, IQR) of 6.90 (6.27\u0026ndash;7.37) years and loss to follow-up rate\u0026thinsp;\u0026lt;\u0026thinsp;1%, a total of 388 deaths occurred, 213 (54%) of which were premature. Noncommunicable diseases (NCDs), communicable, maternal, perinatal, and nutritional conditions (CMPN), and injuries constituted 79%, 11%, and 8% of premature deaths, respectively. Within the NCD, CMPN, and injury groups, 63%, 50%, and 50%, respectively, occurred before the age of 70.\u003c/p\u003e \u003cp\u003eThe most common causes of death in both age categories (\u0026lt;\u0026thinsp;70 and \u0026ge;\u0026thinsp;70 years) were IHD and stroke. Other causes of premature death included road traffic injuries (RTIs), lower respiratory infections, and COVID-19. The top 5 causes of death based on age categories are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, and a detailed classification of death causes is provided in Supplementary Table\u0026nbsp;1, Additional File 1.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe top 5 causes of death in the Pars Cohort Study (2012\u0026ndash;2021)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;70 years\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;213)\u003c/p\u003e\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;70 years\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;170)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;388)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRanks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCauses\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCauses\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNumber\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCauses\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNumber\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIschemic heart disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85 (39.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIschemic heart disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e47 (27.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIschemic heart disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e135 (34.79)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStroke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (11.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStroke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21 (12.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eStroke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e45 (11.60)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRoad traffic injuries\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (5.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRoad traffic injuries\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12 (7.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRoad traffic injuries\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e24 (6.19)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLower respiratory infections\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (4.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStomach cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7 (4.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLower respiratory infections\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e17 (4.38)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCOVID-19\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (3.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProstate cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6 (3.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eStomach cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11 (2.84)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNumbers obtained based on WHO methods after redistribution of garbage ICD-10 codes, including 60 cardiovascular disease cases (I26.9, I46, I50), 5 cancer cases (C55, C76, C80), 1 external causes of morbidity and mortality(Y12.9), 21 infectious and parasitic cases (A41.9), 15 cases of symptoms, signs and ill-defined conditions (R00-R94, R96-R99), 3 cases of respiratory disease(J96), and 6 cases of genitourinary disease(N18). \u003csup\u003ea\u003c/sup\u003e Age of death in 5 participants was undetermined. \u003csup\u003eb\u003c/sup\u003eCOVID-19 indicates coronavirus disease.\u003c/p\u003e \u003cp\u003eOf the total participants (n\u0026thinsp;=\u0026thinsp;9264), 46.16% were male. The mean (SD) age of all cohort participants at baseline was 52.64\u0026thinsp;\u0026plusmn;\u0026thinsp;9.68 years. After excluding individuals aged\u0026thinsp;\u0026ge;\u0026thinsp;70 years at baseline and those with a history of IHD, stroke, or cancer (for determining HRs and PAF purposes), the remaining population was 7668, and 176 premature deaths occurred. The baseline characteristics of the participants are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The HRs (95% CIs) for the relationships between variables and premature death are shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Based on the fully adjusted HRs (95% CIs), the risk of premature death was greater in older individuals [1.07(1.04, 1.10)], tobacco smokers [1.43(0.96, 2.11)], opium consumers [2.12(1.39, 3.24)], those with hypertension [1.52(1.10, 2.12)], and those with a greater waist circumference [1.03 (1.00, 1.05)]. The protective factors included female sex [0.30(0.19, 0.47)], higher education levels [\u0026gt;\u0026thinsp;8 years vs. no formal schooling, 0.46(0.24,0.88)], being married [0.60(0.37, 0.97)], higher physical activity [moderate and high vs. low, 0.50 (0.34,0.74) and 0.38(0.26,0.57), respectively], hip circumference [0.96(0.92, 0.99)], wealth score [4th vs 1st quartile, 0.54(0.32,0.90)], and eGFR [0.99(0.978,0.999)].\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristic of participants in the Pars Cohort Study \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePrevalence (total)\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;7668)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePrevalence (without premature death)\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;7492)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePrevalence\u003c/p\u003e \u003cp\u003e(premature death)\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;176)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAge (year) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50.52 (7.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50.40 (7.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e55.69 (7.35)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSex (Men)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3538 (46.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3424 (45.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e114 (64.77)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eEthnicity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eFars\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4310 (56.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4220 (56.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e90 (51.14)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTurk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2987 (38.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2912 (38.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e75 (42.61)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e371 (4.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e360 (4.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11 (6.25)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eNo formal schooling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3419 (44.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3311(44.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e108 (61.36)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eyears of education\u0026thinsp;\u0026le;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2438 (31.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2398 (32.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40 (22.73)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e5\u0026thinsp;\u0026lt;\u0026thinsp;years of education\u0026thinsp;\u0026le;\u0026thinsp;8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e889 (11.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e875 (11.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14 (7.95)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e8\u0026thinsp;\u0026lt;\u0026thinsp;years of education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e918 (11.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e904 (12.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4 (7.95)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMarital status (Married)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6895 (89.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6745 (90.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e150 (85.23)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePhysical activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2294 (29.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2219 (29.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e75 (42.61)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2558 (33.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2510 (33.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e48 (27.27)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2816 (36.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2763 (36.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e53 (30.11)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAlcohol ever used\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e165 (2.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e162 (2.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (1.70)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eOpium ever used\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e625 (8.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e590 (7.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35 (19.89)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTobacco ever used\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1581 (20.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1517 (20.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e64 (36.36)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1899 (24.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1832 (24.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e67 (38.07)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e881 (11.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e852 (11.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29 (16.48)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.85 (4.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.86 (4.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.25 (4.91)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaist circumference (cm)\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90.73 (12.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90.72 (12.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e91.25 (13.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;95cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2914 (38.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2843 (37.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e71 (40.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eHip (cm)\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95.81 (7.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95.85 (7.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e94.14 (7.84)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eWealth score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eFirst quartile (reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1801 (23.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1746 (23.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e55 (31.25)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSecond quartile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2169 (28.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2113 (28.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e56 (31.82)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eThird quartile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1733 (22.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1697 (22.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36 (20.45)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eFourth quartile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1964 (25.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1935 (25.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29 (16.48)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eNon-HDL-C (mg/dL)\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e137.86 (39.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e137.78 (39.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e141.01 (41.96)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3384 (44.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3316 (44.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e68 (38.64)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e130\u0026ndash;159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2322 (30.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2263 (30.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e59 (33.52)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1950 (25.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1902 (0.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e48 (27.27)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eeGFR (mL/min/1.73m\u003csup\u003e2\u003c/sup\u003e)\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74.90 (14.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75.03 (14.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e69.26 (18.40)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1107 (14.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1049 (14.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e58 (32.95)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e60\u0026ndash;89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5393 (70.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5300 (70.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e93 (52.84)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1168 (15.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1143 (15.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25 (14.20)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNon-HDL-C indicates non-high-density lipoprotein cholesterol and eGFR, estimated glomerular filtration rate.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003ea\u003c/sup\u003e After excluding individuals aged\u0026thinsp;\u0026ge;\u0026thinsp;70 years at baseline and those with a history of IHD, stroke, or cancer. Missing data included 4 cases in education, 39 in waist, 1 in wealth, and 12 in non-HDL categories. Data are presented as number (percentage) or mean (SD).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003eb\u003c/sup\u003e These rows are demonstrated as mean (SD).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHRs (95% CIs) for relationships between variables and premature deaths in the Pars Cohort Study \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003cp\u003eCrude\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003cp\u003eAge and sex-adjusted\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003cp\u003eFull adjusted\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.11(1.09, 1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.07 (1.04, 1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMen (reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWomen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.46(0.33, 0.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.30 (0.19, 0.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEthnicity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFars (reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTurk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.19 (0.87, 1.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.13 (0.83, 1.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.440\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1(0.73, 1.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.982\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.47 (0.78, 2.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.51 (0.80, 2.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.48 (0.77, 2.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.236\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo formal schooling (reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;5 years of education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.50 (0.35, 0.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.70 (0.47, 1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.75 (0.50, 1.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.171\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u0026thinsp;\u0026lt;\u0026thinsp;years of education\u0026thinsp;\u0026le;\u0026thinsp;8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.47 (0.27, 0.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.52 (0.28, 0.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.56 (0.30, 1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u0026thinsp;\u0026lt;\u0026thinsp;years of education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.46 (0.26, 0.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.46 (0.25, 0.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.46 (0.24, 0.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.61 (0.40, 0.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.50 (0.32, 0.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.60 (0.37, 0.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther (reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow (reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.55 (0.38, 0.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.52 (0.36, 0.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.50 (0.34, 0.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.57 (0.40, 0.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.44 (0.30, 0.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.38 (0.26, 0.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever (reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEver used\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.79 (0.25, 2.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.685\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.82 (0.26, 2.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.730\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.57 (0.18, 1.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.337\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOpium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever (reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEver used\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.88 (1.99, 4.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.44 (1.64, 3.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.12 (1.39, 3.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTobacco\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever (reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEver used\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.22 (1.63, 3.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.59 (1.11, 2.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.43 (0.96, 2.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo (reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.93 (1.42, 2.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.55 (1.13, 2.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.52 (1.10, 2.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo (reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.52 (1.01, 2.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.30 (0.86, 1.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.03 (0.67, 1.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.895\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaist (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.05 (1.03, 1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.03 (1.01, 1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.03 (1.00, 1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHip (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.91 (0.88, 0.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.95 (0.92, 0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.96 (0.92, 0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWealth score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirst quartile (reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecond quartile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.83 (0.57, 1.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.319\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.86 (0.59, 1.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.445\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.89 (0.61, 1.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.563\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThird quartile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.64(0.42, 0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.71 (0.46, 1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.77 (0.50, 1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.257\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFourth quartile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.45 (0.28, 0.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.53 (0.34, 0.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.54 (0.32, 0.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-HDL (mg/dl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (0.998, 1.010)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.432\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00 (0.997, 1.004)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00 (0.995, 1.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.632\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR (ml/min/1.73 m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.97 (0.962, 0.982)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.99 (0.975, 0.997)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.99 (0.978, 0.999)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNon-HDL-C indicates non-high-density lipoprotein cholesterol and eGFR, estimated glomerular filtration rate.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003ea\u003c/sup\u003e After excluding participants with age\u0026thinsp;\u0026ge;\u0026thinsp;70 years at baseline and a history of ischemic heart disease, stroke, or cancers. \u003csup\u003eb\u003c/sup\u003e Adjusted for all variables in the table.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e should be cited here.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e demonstrates the PAF for 11 modifiable risk factors, with the overall PAF (95% CI) for all variables being 0.83 (0.62,0.92). Socioeconomic risk factors (i.e., education and wealth score) accounted for 0.61 (0.33, 0.77), and behavioral variables (i.e., physical activity and tobacco and opium use) accounted for 0.48 (0.34,0.59) of the PAF. The highest PAFs were observed for education, physical activity, wealth score, and hypertension, respectively.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePopulation attributable fraction of modifiable variables for premature mortality in the Pars Cohort Study (2012\u0026ndash;2021)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePAF\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.43 (0.04, 0.66)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.35 (0.21, 0.47)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.01 (-0.05, 0.02)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOpiate use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.11 (0.07, 0.15)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTobacco use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.11 (-0.002, 0.21)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.13 (0.05, 0.21)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.003 (-0.07, 0.07)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-HDL-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.06 (-0.14, 0.22)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaist circumference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.12 (-0.002, 0.23)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWealth score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.33 (0.05, 0.52)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.20 (-0.79, 0.19)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBehavioral variables \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.48 (0.34, 0.59)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetabolic variables \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.14 (-0.34, 0.45)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocioeconomic variables \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.61 (0.33, 0.77)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAll variables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.83 (0.62, 0.92)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNon-HDL-C indicates non-high-density lipoprotein cholesterol and eGFR, estimated glomerular filtration rate. \u003csup\u003ea\u003c/sup\u003e Behavioral variables include alcohol, opium, and tobacco use and physical activity. \u003csup\u003eb\u003c/sup\u003e Metabolic variables include non-HDL-C, hypertension, diabetes, eGFR, and waist circumference. \u003csup\u003ec\u003c/sup\u003e Socioeconomic variables include education and wealth score.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eApproximately 54% of the deaths in our study were considered premature. In each category\u0026mdash;NCDs, CMPN conditions, and injuries\u0026mdash;more than half of the deaths occurred prematurely. Among these premature deaths, 79% were attributed to NCDs, with vascular diseases such as IHD and stroke being the most common causes. The identified risk factors for premature death included older age, tobacco and opium consumption, hypertension, and increased waist circumference. Conversely, protective factors included being female, having higher education levels, being married, engaging in high physical activity, possessing a greater hip circumference, and having a greater wealth score. Socioeconomic variables, including education and wealth score, and behavioral variables, including alcohol, opium, and tobacco use and physical activity, exhibited the highest PAF, respectively. By effectively managing these modifiable risk factors, substantial reductions of 61% and 48% in premature deaths can be achieved, respectively.\u003c/p\u003e \u003cp\u003eGlobally, some studies have investigated the prevalence of premature death. For example, in the Golestan Cohort Study (GCS) conducted in northeastern Iran, the rate of premature death among participants aged 40\u0026ndash;70 years was 63.3% (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). In our current investigation, a comparable 54% of deaths were deemed premature. In contrast, this rate was reported to be 4.5% in the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort study conducted among nine European countries with the same age range. According to the EPIC cohort study, 50% of premature deaths were attributed to cancer and 22% to CVDs (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). However, in the GCS, almost 50% of premature deaths are due to CVDs (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Factors contributing to the elevated rates of premature deaths in this demographic may include both country-level and individual-level socioeconomic status; for example, rural residents with low levels of education (76% of participants had less than five years of education) and wealth score (52% falling within the first two quartiles of the wealth score) composed most of the participants in our study. Environmental factors, such as access to healthy food and venues for physical activity, and healthcare system factors are other important factors that affect premature mortality.\u003c/p\u003e \u003cp\u003eThe most common causes of premature death in PCS participants were IHD, stroke, RTIs, lower respiratory infections, and COVID-19. Our findings align with the Global Burden of Disease (GBD) study, underscoring IHD, stroke, and RTIs as the primary contributors to premature mortality in Iran, mirroring patterns observed in upper-middle-income countries. The GBD study demonstrated that the specific causes contributing to premature death varied globally (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Premature deaths in the EPIC cohort study were primarily attributed to cancer (50%) and circulatory diseases (22%). In contrast, cohort studies conducted in Iran, including the PCS, GCS, and Tehran Lipid and Glucose Study, indicated that CVDs are the leading cause of death, with RTIs ranking among the top 5 causes of premature death. The leading causes of premature death in GCS participants are IHD, stroke, RTIs, stomach cancer, and esophageal cancer (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). The Tehran Lipid and Glucose Study cohort in Iran identified CVD, cancer, RTIs, sepsis, and pneumonia as the underlying causes of premature deaths (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). In this study, RTIs were the third leading cause of premature death. According to the Global Status Report on Road Safety 2018, 93% of the 1.35\u0026nbsp;million global road traffic deaths occurred in low- and middle-income countries in 2016 (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). RTIs remain a public health concern in Iran, despite evidence of a decline in RTIs across all countries (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study revealed that premature mortality due to the COVID-19 pandemic was one of the main causes of premature death, although only the first year of the pandemic was included in our analysis. The COVID-19 pandemic has had a significant impact on mortality worldwide. The Global Burden of Disease study revealed that while age-standardized mortality rates globally declined between 1950 and 2019 (a 62.8% decline), they increased considerably during the COVID-19 pandemic period (2020\u0026ndash;21; by 5.1%) (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Unprecedented reversals in adult mortality and life expectancy trends at the global and national levels during the pandemic highlighted the importance of paying comprehensive attention to both communicable and noncommunicable diseases simultaneously.\u003c/p\u003e \u003cp\u003eCohort studies play a pivotal role in uncovering modifiable risk factors for premature deaths. In the GCS, noteworthy protective factors included wealth score, physical activity, education, and fruit/vegetable consumption. Conversely, significant risk factors included opium use, tobacco consumption, diabetes, and hypertension. The cumulative impact of these factors accounted for 73% of the PAF. The factors associated with the highest PAF were wealth score, physical activity, and hypertension (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). In our study, modifiable factors played a pivotal role, contributing to 83% of premature deaths. The Tehran Lipid and Glucose Study identified hypertension, diabetes, and current smoking as significant risk factors for premature mortality. Controlling these risk factors, particularly diabetes, hypertension, and smoking, has the potential to reduce mortality by more than 40% (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). In the PCS, opium use, hypertension, and tobacco consumption were considered among the most important modifiable risk factors.\u003c/p\u003e \u003cp\u003eGlobally, some studies have sought to assess premature deaths, revealing regional variations in the significance of risk factors. These disparities arise from differences in the strength of associations and variations in the prevalence of these factors across diverse regions. The EPIC cohort study conducted on middle-aged individuals in Western Europe focused on evaluating modifiable causes of premature death (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). While smoking remains the primary contributor to premature mortality in Europe, other notable factors such as suboptimal dietary habits, overweight and obesity, hypertension, insufficient physical activity, and excessive alcohol consumption also play significant roles. The collective attributable fraction (AF) for these six risk factors was determined to be 57%.\u003c/p\u003e \u003cp\u003eIn the PCS, the predominant cause of death was vascular disease, which included IHD and stroke. CVD accounts for a substantial proportion of global deaths, accounting for 31% annually, a trend partially attributed to population growth and aging (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). Findings from the PURE cohort study indicated that a substantial 70% of CVD and associated deaths can be attributed to a limited set of modifiable factors (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Certain factors, such as high blood pressure and education, exert a widespread influence globally, while others, such as household air pollution and unhealthy eating habits, exhibit variability based on a country's economic status. Metabolic elements emerged as the primary contributors to the risk of CVD, constituting 41.2% of the PAF, with hypertension representing the most significant portion at 22.3% of the PAF. The INTERSTROKE case-control study identified various factors associated with all strokes, including a history of hypertension or elevated blood pressure, regular physical activity, apolipoprotein (Apo)B/ApoA1 ratio, dietary habits, waist-to-hip ratio, psychosocial factors, current smoking status, cardiac causes, alcohol consumption, and diabetes mellitus. Together, these factors contributed to 90.7% of the Population attributable risk (PAR) for all strokes globally (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). A case-control study known as the INTERHEART study revealed that abnormal lipids, smoking, hypertension, diabetes, abdominal obesity, psychosocial factors, consumption of fruits, vegetables, alcohol, and regular physical activity are the primary contributors to the risk of myocardial infarction globally. Together, these nine risk factors were responsible for 90.4% of the total PAR (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn our study, opium exhibited the highest HR among the variables. Opium consumption notably correlated with elevated risks of death from diverse causes, including circulatory diseases and cancer (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). In the GCS study, long-term opiate consumption was related to increased cardiovascular death, independent of other known risk factors (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). Opium consumption was also associated with an increased likelihood of developing various cancers (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study possesses notable strengths attributable to its prospective design, extensive and precise follow-up data, meticulous death ascertainment, and the incorporation of objective measurements alongside self-reported data. A comprehensive evaluation was conducted, including wealth score, eGFR, and non-HDL. Noteworthy strengths of this investigation included the availability of data for confounder adjustment and a less than 1% loss to follow-up rate. Several limitations characterize our study, including the limited sample size and the fact that the participants were solely from rural districts. The infrequent occurrence of premature death within our cohort may compromise the statistical power of our study, potentially impeding the detection of risk factors within specific subgroups. Furthermore, the absence of data on urban populations and individuals younger than 40 years can limit the generalizability of our findings. The impact of alcohol consumption could not be analyzed due to its low prevalence for religious reasons and absence from social habits. We recommend conducting further cohort studies on this subject to obtain comprehensive results and implement necessary interventions and policies based on the outcomes.\u003c/p\u003e \u003cp\u003eTo decrease premature death several measures can be taken. The PolyPars study which was conducted in the PCS to assess the effectiveness of polypill (two antihypertensive agents, a statin and aspirin) for primary and secondary prevention of CVD demonstrated that it can safely halve the risk of major CVDs (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). Also, paying simultaneous attention to socioeconomic and behavioral factors is recommended. Because opium and road traffic have impressive negative effects, new policies and public awareness on these topics are suggested.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eFifty-four percent of deaths were premature. NCDs constituted 79% of premature deaths. The most common causes of premature death were IHD, stroke, RTIs, lower respiratory infections, and COVID-19, respectively. The risk of premature death was greater in older individuals, tobacco and opium consumers, those with hypertension, and those with a greater waist circumference. The protective factors included female sex, higher education levels, being married, higher physical activity, hip circumference, and wealth score. Modifiable risk factors could reduce premature death by approximately 83%. Continued research, application of the findings from this study, and the formulation of policies in alignment with those findings will collectively play a crucial role in enhancing life expectancy and potentially mitigating the societal burden of avoidable outcomes.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAF Attributable Fraction\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCIs Confidence intervals\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCKD-Epi Chronic Kidney Disease Epidemiology\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCMPN Communicable, maternal, perinatal, and nutritional conditions\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCVD Cardiovascular disease\u003c/p\u003e\n\u003cp\u003eDDRI Digestive Diseases Research Institute\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eeGFR Estimated glomerular filtration rate\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEPIC European Prospective Investigation into Cancer and Nutrition\u003c/p\u003e\n\u003cp\u003eGBD Global Burden of Disease\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGCS Golestan Cohort Study\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHR hazard ratio\u003c/p\u003e\n\u003cp\u003eICD-10 10th revision of the International Classification of Diseases\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIHD Ischemic heart disease\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNCD Noncommunicable diseases\u0026nbsp;\u003c/p\u003e\n\u003cp\u003enon-HDL-C Non-high-density lipoprotein cholesterol\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePAR Population attributable risk\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePCS Pars Cohort Study\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRTI Road traffic injury\u003c/p\u003e\n\u003cp\u003eSUMS Shiraz University of Medical Sciences\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWHO World Health Organization\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe ethics committees of the Digestive Diseases Research Institute (DDRI) and Shiraz University of Medical Sciences (SUMS) approved the study protocol, and the ethics code of this study is IR.TUMS.SHARIATI.REC.1402.001. The completion and signing of the informed consent form were done in the presence of a third party, and their information was kept completely confidential.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was funded by Shiraz University of Medical Sciences, Grant Number [910210].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRM, MN, FM, AG, SS, and HP conceptualized the study; FZ prepared the original draft; MN and RM contributed to the design of the study; and FZ, MN, and SS performed the analysis. FZ, MN, RM, and SS contributed to writing, reviewing, and editing the manuscript. MN and FZ contributed to the interpretation of the data. All authors reviewed and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the study participants for their cooperation.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eChen JT, Rehkopf DH, Waterman PD, Subramanian SV, Coull BA, Cohen B, et al. Mapping and measuring social disparities in premature mortality: the impact of census tract poverty within and across Boston neighborhoods, 1999\u0026ndash;2001. J Urban Health. 2006;83(6):1063\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRazeghian-Jahromi I, Ghasemi Mianrood Y, Dara M, Azami P. Premature Death, Underlying Reasons, and Preventive Experiences in Iran: A Narrative Review. Arch Iran Med. 2023;26(7):403\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeto R, Lopez AD, Norheim OF. Halving premature death. Science. 2014;345(6202):1272.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEllis J, Dowrick C, Lloyd-Williams M. The long-term impact of early parental death: lessons from a narrative study. J R Soc Med. 2013;106(2):57\u0026ndash;67.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNoncommunicable diseases World Health. Organisation2023 [ \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/news-room/fact-sheets/detail/noncommunicable-diseases\u003c/span\u003e\u003cspan address=\"https://www.who.int/news-room/fact-sheets/detail/noncommunicable-diseases\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZiamanesh F, Mohajeri Tehrani MR, Hemmatabadi M, Sharghi S, Fallahi B, Haghpanah V, et al. Design and implementation of a national quality registry of thyroid cancer in Iran: study protocol. Journal of Diabetes \u0026amp; Metabolic Disorders; 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eForeman KJ, Marquez N, Dolgert A, Fukutaki K, Fullman N, McGaughey M, et al. Forecasting life expectancy, years of life lost, and all-cause and cause-specific mortality for 250 causes of death: reference and alternative scenarios for 2016-40 for 195 countries and territories. Lancet. 2018;392(10159):2052\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGlobal regional. national incidence, prevalence, and years lived with disability for 328 diseases and injuries for 195 countries, 1990\u0026ndash;2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet. 2017;390(10100):1211\u0026ndash;59.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNalini M, Oranuba E, Poustchi H, Sepanlou SG, Pourshams A, Khoshnia M, et al. Causes of premature death and their associated risk factors in the Golestan Cohort Study, Iran. BMJ Open. 2018;8(7):e021479.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMuller DC, Murphy N, Johansson M, Ferrari P, Tsilidis KK, Boutron-Ruault M-C, et al. Modifiable causes of premature death in middle-age in Western Europe: results from the EPIC cohort study. BMC Med. 2016;14:1\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEslami A, Naghibi Irvani SS, Ramezankhani A, Fekri N, Asadi K, Azizi F, et al. Incidence and associated risk factors for premature death in the Tehran Lipid and Glucose Study cohort, Iran. BMC Public Health. 2019;19(1):1\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGandomkar A, Poustchi H, Moini M, Moghadami M, Imanieh H, Fattahi MR, et al. Pars cohort study of non-communicable diseases in Iran: protocol and preliminary results. Int J Public Health. 2017;62(3):397\u0026ndash;406.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGlobal Physical Activity Surveillance World Health Organisation2017 [updated 24. May 2017. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.who.int/chp/steps/GPAQ/en/\u003c/span\u003e\u003cspan address=\"http://www.who.int/chp/steps/GPAQ/en/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVinyoles E, Tafalla M, Robledo V, Marco M, Porta I, Mu\u0026ntilde;oz M-A, et al. Interarm blood pressure measurement and the reference-arm assignment variability. Blood Press Monit. 2019;24(5):259\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHypertension: World Health Organization. 2023 [ \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/news-room/fact-sheets/detail/hypertension#:~:text=Overview,get%20your%20blood%20pressure%20checked\u003c/span\u003e\u003cspan address=\"https://www.who.int/news-room/fact-sheets/detail/hypertension#:~:text=Overview,get%20your%20blood%20pressure%20checked\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMach F, Baigent C, Catapano AL, Koskinas KC, Casula M, Badimon L, et al. 2019 ESC/EAS Guidelines for the management of dyslipidaemias: lipid modification to reduce cardiovascular risk. Eur Heart J. 2020;41(1):111\u0026ndash;88.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePencina KM, Thanassoulis G, Wilkins JT, Vasan RS, Navar AM, Peterson ED, et al. Trajectories of Non-HDL Cholesterol Across Midlife: Implications for Cardiovascular Prevention. J Am Coll Cardiol. 2019;74(1):70\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIslami F, Kamangar F, Nasrollahzadeh D, Aghcheli K, Sotoudeh M, Abedi-Ardekani B, et al. Socio-economic status and oesophageal cancer: results from a population-based case-control study in a high-risk area. Int J Epidemiol. 2009;38(4):978\u0026ndash;88.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDoganer YC, Rohrer JE, Aydogan U, Barcin C, Cayci T, Saglam K. Association of renal function, estimated by four equations, with coronary artery disease. Int Urol Nephrol. 2015;47(4):663\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLevey AS, Stevens LA, Schmid CH, Zhang YL, Castro AF 3rd, Feldman HI, et al. A new equation to estimate glomerular filtration rate. Ann Intern Med. 2009;150(9):604\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAZIZI F, Khalili D, Aghajani H, Esteghamati A, Hosseinpanah F, Delavari A, et al. Appropriate waist circumference cut-off points among Iranian adults: the first report of. the Iranian National Committee of Obesity; 2010.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAZIZI F, Hadaegh F, KHALILI D, Esteghamati A, HOSSEIN PF, Delavari A, et al. Appropriate definition of metabolic syndrome among Iranian adults. report of the Iranian National Committee of Obesity; 2010.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhademi H, Etemadi A, Kamangar F, Nouraie M, Shakeri R, Abaie B, et al. Verbal autopsy: reliability and validity estimates for causes of death in the Golestan Cohort Study in Iran. PLoS ONE. 2010;5(6):e11183.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eInternational Statistical Classification of Diseases and Related Health Problems 10th Revision. World Health Organization [updated 25 My 2017. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://apps.who.int/classifications/icd10/browse/2016/en\u003c/span\u003e\u003cspan address=\"http://apps.who.int/classifications/icd10/browse/2016/en\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWHO methods and. data sources for country-level causes of death 2000\u0026ndash;2019 2020 [ \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cdn.who.int/media/docs/default-source/gho-documents/global-health-estimates/ghe2019_cod_methods.pdf\u003c/span\u003e\u003cspan address=\"https://cdn.who.int/media/docs/default-source/gho-documents/global-health-estimates/ghe2019_cod_methods.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNalini M, Sharafkhah M, Poustchi H, Sepanlou SG, Pourshams A, Radmard AR, et al. Comparing Anthropometric Indicators of Visceral and General Adiposity as Determinants of Overall and Cardiovascular Mortality. Arch Iran Med. 2019;22(6):301\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNaghavi M, Abajobir AA, Abbafati C, Abbas KM, Abd-Allah F, Abera SF, et al. Global, regional, and national age-sex specific mortality for 264 causes of death, 1980\u0026ndash;2016: a systematic analysis for the Global Burden of Disease Study 2016. lancet. 2017;390(10100):1151\u0026ndash;210.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoradi-Lakeh M, Sepanlou SG, Karimi SM, Khalili N, Djalalinia S, Karimkhani C, et al. Trend of Socio-Demographic Index and Mortality Estimates in Iran and its Neighbors, 1990\u0026ndash;2015; Findings of the Global Burden of Diseases 2015 Study. Arch Iran Med. 2017;20(7):419\u0026ndash;28.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOrganization WH. Global status report on road safety 2015. World Health Organization; 2015.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBazargan-Hejazi S, Ahmadi A, Shirazi S, Ainy E, Djalalinia S, Fereshtehnejad S-M et al. The burden of road traffic injuries in Iran and 15 surrounding countries: 1990\u0026ndash;2016. Arch Iran Med. 2018;21(12).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchumacher AE, Kyu HH, Aali A, Abbafati C, Abbas J, Abbasgholizadeh R et al. Global age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950\u0026ndash;2021, and the impact of the COVID-19 pandemic: a comprehensive demographic analysis for the Global Burden of Disease Study 2021. The Lancet. 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWHO. Cardiovascular diseases fact sheet. WHO Geneva; 2017.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYusuf S, Joseph P, Rangarajan S, Islam S, Mente A, Hystad P, et al. Modifiable risk factors, cardiovascular disease, and mortality in 155 722 individuals from 21 high-income, middle-income, and low-income countries (PURE): a prospective cohort study. Lancet. 2020;395(10226):795\u0026ndash;808.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eO'Donnell MJ, Chin SL, Rangarajan S, Xavier D, Liu L, Zhang H, et al. Global and regional effects of potentially modifiable risk factors associated with acute stroke in 32 countries (INTERSTROKE): a case-control study. lancet. 2016;388(10046):761\u0026ndash;75.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYusuf S, Hawken S, \u0026Ocirc;unpuu S, Dans T, Avezum A, Lanas F, et al. Effect of potentially modifiable risk factors associated with myocardial infarction in 52 countries (the INTERHEART study): case-control study. lancet. 2004;364(9438):937\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhademi H, Malekzadeh R, Pourshams A, Jafari E, Salahi R, Semnani S et al. Opium use and mortality in Golestan Cohort Study: prospective cohort study of 50 000 adults in Iran. BMJ. 2012;344.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNalini M, Shakeri R, Poustchi H, Pourshams A, Etemadi A, Islami F, et al. Long-term opiate use and risk of cardiovascular mortality: results from the Golestan Cohort Study. Eur J Prev Cardiol. 2021;28(1):98\u0026ndash;106.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKamangar F, Shakeri R, Malekzadeh R, Islami F. Opium use: an emerging risk factor for cancer? Lancet Oncol. 2014;15(2):e69\u0026ndash;77.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMalekzadeh F, Gandomkar A, Poustchi H, Etemadi A, Roshandel G, Attar A et al. Effectiveness of polypill for primary and secondary prevention of cardiovascular disease: a pragmatic cluster-randomised controlled trial (PolyPars). Heart. 2024.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Premature mortality, Pars cohort study, Risk factor, Protective factor, Prevention, Noncommunicable disease, Cardiovascular disease","lastPublishedDoi":"10.21203/rs.3.rs-4328365/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4328365/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eWhile death in old age is inevitable, premature death at younger ages is within our control. Premature mortality (death\u0026thinsp;\u0026lt;\u0026thinsp;70 years) is a crucial indicator of health status and access to healthcare, with variations observed across regions. In North Africa and the Middle East, ischemic heart disease (IHD), road injuries, stroke, and chronic kidney disease are projected to be the main causes of premature mortality. Unfortunately, few studies have been conducted on premature mortality worldwide. This study aimed to analyze the causes of premature death and associated risk factors within the Pars Cohort Study.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThe Pars cohort study is a prospective cohort study conducted in Fars Province, Iran, involving 9,264 individuals aged 40\u0026ndash;75 years, 53.8% of whom were women. We assessed participants from baseline (2012\u0026ndash;2014) to 2021. The data were gathered through interviews, biological samples, and physical examinations. The causes of premature mortality, hazard ratios (HRs), and population attributable fraction (PAF) with 95% confidence intervals (95% CIs) for the variables were calculated.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOut of 388 deaths, 54% were premature. The most common causes of premature death included IHD (40%), stroke (11%), road traffic injuries (6%), lower respiratory infections (5%), and COVID-19 (3%). The predictive factors [adjusted HRs (95% CIs)] associated with premature mortality included age [year, 1.07 (1.04, 1.10)], tobacco [1.43 (0.96, 2.11)], opium [2.12 (1.39, 3.24)], hypertension [1.52 (1.10, 2.12)], waist circumference [centimeter, 1.03 (1.00, 1.05)], female sex [0.30 (0.19, 0.47)], education [\u0026gt;\u0026thinsp;8 years vs. no formal schooling, 0.46 (0.24, 0.88)], being married [0.60 (0.37, 0.97)], physical activity [3rd vs. 1st tertile, 0.38 (0.26, 0.57)], hip circumference [centimeter, 0.96 (0.92, 0.99)], estimated GFR [mL/min/1.73m\u0026sup2;, 0.99 (0.978, 0.999)], and wealth score [4th vs. 1st quartile, 0.54 (0.32, 0.90)]. The PAF (95% CI) for all modifiable predictors was 0.83 (0.62, 0.92).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe predominant causes of premature mortality were IHD and stroke. To mitigate premature deaths, paying simultaneous attention to both socioeconomic and behavioral factors is recommended.\u003c/p\u003e","manuscriptTitle":"Causes and predictors of premature death in the Pars Cohort Study, Iran: a cohort study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-09 05:14:54","doi":"10.21203/rs.3.rs-4328365/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorAssigned","content":"","date":"2024-05-04T04:22:53+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-05-04T04:22:52+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2024-04-26T08:41:29+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cd3d3e4a-a55f-48f5-bcd7-b6ca43d11ab1","owner":[],"postedDate":"May 9th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-09-30T16:10:58+00:00","versionOfRecord":{"articleIdentity":"rs-4328365","link":"https://doi.org/10.1186/s12889-024-19583-7","journal":{"identity":"bmc-public-health","isVorOnly":false,"title":"BMC Public Health"},"publishedOn":"2024-09-27 15:57:49","publishedOnDateReadable":"September 27th, 2024"},"versionCreatedAt":"2024-05-09 05:14:54","video":"","vorDoi":"10.1186/s12889-024-19583-7","vorDoiUrl":"https://doi.org/10.1186/s12889-024-19583-7","workflowStages":[]},"version":"v1","identity":"rs-4328365","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4328365","identity":"rs-4328365","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}