Ambient Fine Particulate Matter and Mortality Risk among People with Disability in Korea Based on the National Health Insurance Database: A Retrospective Cohort Study

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This study examined the association between long-term exposure to ambient fine particulate matter (PM 2.5 ) and mortality risk in PWD considering disability type and severity. Methods Data from the Korean National Health Insurance Service and Statistics Korea were analyzed in this retrospective cohort study, including 2,880,265 individuals (41,501,709 person-years), of which 176,410 were PWD (2,011,231 person-years). PM 2.5 exposure was estimated using simulated data from 2006 to 2019. Causes of death included all causes, non-accidental causes, respiratory disease, lung cancer, and cardiovascular disease. Cox proportional hazard models were used to estimate hazard ratios (HRs) for mortality associated with PM 2.5 stratified by disability type and severity. Results PWD, particularly those with severe disabilities or specific impairments such as kidney problems or brain lesions, showed significantly high mortality risks from all causes, non-accidental causes, and cardiovascular diseases due to PM 2.5 exposure. For individuals with kidney impairment, the HR (95% confidence interval) for mortality on increasing PM 2.5 by 10 µg/m 3 was 1.79 (1.27–2.52) from all causes, while for those with brain lesions, it was 1.10 (1.00–1.22) from cardiovascular disease. PWD were not susceptible to mortality from respiratory causes. Conclusions This study highlights the increased vulnerability of PWD, especially those with severe disabilities or specific impairments, to the adverse effects of PM 2.5 exposure. Targeted interventions tailored to disability type and severity, along with stricter air quality standards and specialized healthcare approaches, are needed. PM2.5 mortality people with disabilities disability type disability severity Figures Figure 1 Figure 2 Figure 3 Figure 4 Background In 2019, ambient air pollution led to an estimated 4.2 million premature deaths worldwide [1]. Fine particulate matter (PM 2.5 ) can be derived from primary sources, such as fuel combustion in power generation facilities, industries, or vehicles, and secondary sources, such as chemical reactions between gases [2]. Adverse health effects of exposure to ambient PM 2.5 , such as cardiovascular diseases, respiratory diseases, neurological diseases, and cancer, have been reported. Furthermore, PM 2.5 is associated with a high risk of mortality from all causes, non-accidental causes, and cardiovascular diseases [2]. PM 2.5 poses a significant health risk to vulnerable populations, including minority populations, people with chronic diseases, people with disabilities (PWD), and children. Minority populations, such as non-whites, are often considered vulnerable to traffic-related exposure to air pollutants [3]. One study found that people with kidney failure requiring dialysis represented a subgroup particularly susceptible to PM 2.5 -associated health effects [4]. Previous studies have highlighted that children may also be vulnerable to exposure to PM 2.5 , in terms of behavioral problems and effects on the respiratory system [5,6]. The health effects of PM 2.5 can vary across regions owing to differences in population characteristics and components of PM 2.5 [7]. These population characteristics include various vulnerability factors such as social, economic, and health conditions. Therefore, the impact of PM 2.5 exposure on PWD in vulnerable groups may vary depending on these factors. PWD encounter various challenges related to their pre-existing conditions, limited mobility, medical accessibility, and potential socioeconomic disadvantages, making them particularly susceptible to environmental risks [8]. This vulnerability could be further heightened by air pollution, as PWD may experience more severe health consequences and additional challenges in accessing healthcare services. Approximately 1.3 billion people, 16% of the world's population, experience significant disabilities [9]. In Korea, the registered disability system has been a crucial component of the social and health infrastructure, covering approximately 5% of the population since 2009 [10]. Although this percentage is lower than that in Western developed countries, it is expected to increase owing to the aging population and the growing recognition of disabilities. Previous studies have examined the association between exposure to particulate matter (PM) and health outcomes in the PWD. Previous studies have reported that short-term PM exposure increases the risk of hospital admission for cardiovascular problems in PWD [11, 12]. One study reported that short-term exposure to PM with diameter ≤ 10 µm (PM 10 ) increased the mortality risk in PWD [13]. Most studies have examined particulate matter and health risks only in the general population. While previous studies have examined the effects of PM on the health of PWD, they have only focused on short-term exposure. This leaves a significant gap in our understanding of how ambient PM 2.5 long-term exposure affects the mortality risk among PWD. In addition, traditional epidemiological studies may not consider the various ways in which disability intersects with factors such as age and disability type. The absence of targeted research further obscures the specific vulnerabilities and needs of PWD facing air pollution, leading to a gap in policy and public health interventions designed to protect this group. Given the heightened vulnerability of this demographic to environmental pollutants, coupled with their unique health and social challenges, investigating the impact of ambient PM 2.5 on the mortality risk among PWD is essential. Therefore, this study aimed to fill this knowledge gap by examining the long-term association between ambient PM 2.5 and mortality risk among PWD using the National Health Insurance Database (DB). We also used the Statistics Korea Database to determine specific causes of mortality. Methods Database This retrospective cohort study used a 5% sample cohort (n = 2,897,075) from the Korean National Health Insurance System (KNHIS) DB, documenting every claim made by the Korean population since 2002. It includes individual-level personal data, offering information on residential history [14] in four main parts: an annual qualification DB, health insurance claims DB, DB for mortality records, and health check-up DB. The qualification DB encompasses details such as personal identification number (PIN), geographical region, sex, age, and insurance fees as a proxy of economic status, along with the type and severity of disability. The death DB records the PIN and date of death, while the health check-up DB includes data from clinical examinations and history taking. The following were excluded from among 2,897,075 persons: death before 2006 (n = 12,229), change from PWD to non-disabled (ND) (n = 3,491), no information on air pollutant concentration (n = 1,088), and those born before 1900 who were still alive (i.e., aged > 120 years; n = 2). Finally, 2,880,265 individuals (41,501,709 person-years [PY]), including 176,410 PWD (2,011,231 PY) and 2,703,855 ND (39,490,478 PY), were included in this study cohort (Fig. 1 ). Type and severity of disability Disability information was obtained from the KNHIS qualification DB. In Korea, the Enforcement Decree of the Act on Welfare of PWD has defined the following 15 disability types in four fields since 2003: impairment in external bodily functions including hearing, visual, physical, speech, and facial functions or brain lesions; impairment in internal organs including epilepsy or kidney, hepatic, or cardiac dysfunction, intestinal/urinary fistula, or respiratory dysfunction; developmental disability including intellectual or autistic disorder; and mental disorders. After the abolition of the grading system for PWD on July 1, 2019, disabilities are now classified as either “severely disabled” or “mild disabled” [15]. Ambient PM 2.5 Simulated data incorporate factors such as weather conditions, human-made and natural emissions, and the transport of chemicals [16–19]. The analysis utilized the Community Multiscale Air Quality (CMAQ) system (ver. 4.7.1; https://www.epa.gov/cmaq ), equipped with the aerosol module, ver. 5, and Statewide Air Pollution Research Centre model, ver. 99. Weather simulations were conducted using the Weather Research and Forecasting model (ver. 3.3.1). Initial field data were sourced from the National Center for Environmental Protection. CMAQ-ready meteorological data were generated using the Meteorology Interface Processor (ver. 3.6). The Korean National Emissions Inventory was processed using the Sparse Matrix Operator Kernel Emissions system (ver. 3.1). Estimates of biogenic emissions were generated using the Model of Emissions of Gases and Aerosols from Nature. The boundary conditions for the 9-km domain were derived from simulations of the 27-km domain. The CMAQ system provided hourly PM 2.5 concentration across Korean cities within the 9-km domain. Daily average PM 2.5 concentrations for each city were calculated from January 1, 2006, to December 31, 2019. As information on PM 2.5 concentrations was only available from 2006 onwards, the entry point was defined as the first appearance in the cohort after 2006 for the ND. For PWD, the entry point was 2006 for those who acquired their disability before 2006 or the time of acquisition for those who acquired their disability after 2006. We assigned a monthly concentration for each municipality based on their monthly address information and then averaged over the year for each individual. For each individual, the average annual concentration was applied to their year of entry. Mortality We excluded deaths up to 2006 from our analysis because we had information on PM 2.5 concentrations only from 2006 onwards. The causes of death were obtained by referring to the death cause DB of Statistics Korea. We classified the causes of death into five types as follows: all causes; non-accidental causes, all causes excluding external causes (International Classification of Disease, 10th version [ICD-10] codes: V01–Y98); respiratory disease (ICD-10: J00–J99); lung cancer (ICD-10: C33–C34); and cardiovascular disease (ICD-10: I00–I99). Covariates Information on age and sex was sourced from the qualification DB of the KNHIS. Insurance fees as proxy of economic status were classified into two types: company and local fees. The monthly insurance fee was adjusted by dividing it by the square root of the family members' count. This adjusted fee was then organized into quartiles for each insurance type. Medical aid groups were classified separately. Information on smoking history and history of chronic diseases was obtained from the Health Check-Up DB. The Korean contextual deprivation index was used as a regional variable at the city district level to reflect the contextual effect of the residential area. This indicator can evaluate regional socioeconomic levels in a multidimensional manner [20]. Using the 2015 National Statistical Office Population and Housing Census, six indicators at the individual level and four at the household level were calculated for each city district. The six indicators at the individual level were as follows: proportion of adults aged 35–64 years with an education level of below a high school diploma, percentage of female household heads, percentage of divorced and widowed people aged ≥ 15 years, low social class proportion based on the adult head of household aged 15–64 years, unemployment rate for men aged 15–64 years, and proportion of the older population aged ≥ 65 years. At the household level, four indicators were identified: the proportion of single-person households, proportion of households without homes, proportion of households without cars, and proportion of households residing in housing options other than apartments. The aforementioned 10 indicators were converted to z-scores using a standard normal distribution, and the values were added to calculate the total score. A higher score indicated a lower socioeconomic level in the region [20]. Statistical analysis Differences in the general characteristics between PWD and ND or severe and mild groups were evaluated using the t-test or chi-square test. To assess the differences in general characteristics among ND individuals, those with mild disabilities, and those with severe disabilities, analysis of variance or the chi-square test was employed. Hazard ratios (HRs) and 95% confidence intervals (CIs) for mortality on increasing PM 2.5 by 10 µg/m 3 were calculated using the Cox proportional hazard model adjusted for age, sex, insurance fee, smoking history, chronic disease status, and contextual deprivation index. The model was stratified by the presence, type, and severity of the disability. Results Table 1 shows the distribution of the general characteristics according to the disability and its severity. PWD accounted for 6.1% of the study sample. The annual proportion of PWD was similar to that reported in formal Korean statistics (Additional file 1). The PWD cohort included men, older patients, patients in medical aid group, participants of health check-ups, patients with smoking history, and patients with chronic diseases. The proportions of men and history of stroke were higher in the severely disabled subgroup than in the mildly disabled subgroup. Table 1 General characteristics by disability and severity in 5% sample cohort using KNHIS database, 2006–2019 All ND PWD All Mild Severe N % N % N % N % N % p-value a p-value b p-value c All 2880265 100 2703855 93.9 176410 6.1 108028 3.8 68382 2.4 Follow-up time (years), Mean (SD) 14.4 3.5 14.6 3.3 11.4 5.0 11.6 4.9 11.1 5.2 < 0.0001 < 0.0001 < 0.0001 Follow-up time (years), Median (min–max) 16.0 (1–16) 16.0 (1–16) 13.0 (1–16) 13.0 (1–16) 13.0 (1–16) Person-years 41501709 39490478 2011231 1251398 759833 Gender < 0.0001 < 0.0001 < 0.0001 Male 1456397 50.6 1353183 50.0 103214 58.5 62027 57.4 41187 60.2 Female 1423868 49.4 1350672 50.0 73196 41.5 46001 42.6 27195 39.8 Age at entry year (years), Mean (SD) 32.2 21.8 30.7 21.1 55.4 18.9 58.6 16.4 50.4 21.5 < 0.0001 < 0.0001 < 0.0001 Age at entry year (years) < 0.0001 < 0.0001 < 0.0001 00–09 559552 19.4 554748 20.5 4804 2.7 907 0.8 3897 5.7 10–19 342219 11.9 336873 12.5 5346 3.0 995 0.9 4351 6.4 20–29 424578 14.7 416806 15.4 7772 4.4 3583 3.3 4189 6.1 30–39 470472 16.3 455641 16.9 14831 8.4 8204 7.6 6627 9.7 40–49 442340 15.4 415076 15.4 27264 15.5 16407 15.2 10857 15.9 50–59 295326 10.3 261489 9.7 33837 19.2 21891 20.3 11946 17.5 60–69 192089 6.7 153798 5.7 38291 21.7 25939 24.0 12352 18.1 70–79 111350 3.9 79773 3.0 31577 17.9 21281 19.7 10296 15.1 80+ 42339 1.5 29651 1.1 12688 7.2 8821 8.2 3867 5.7 Entry year < 0.0001 < 0.0001 < 0.0001 2006 2395503 83.2 2297823 85.0 97680 55.4 54999 50.9 42681 62.4 2007 44811 1.6 34330 1.3 10481 5.9 7020 6.5 3461 5.1 2008 43988 1.5 33525 1.2 10463 5.9 7122 6.6 3341 4.9 2009 42015 1.5 31023 1.1 10992 6.2 7683 7.1 3309 4.8 2010 39334 1.4 32684 1.2 6650 3.8 4841 4.5 1809 2.6 2011 38357 1.3 34255 1.3 4102 2.3 2569 2.4 1533 2.2 2012 37660 1.3 34093 1.3 3567 2.0 2092 1.9 1475 2.2 2013 34745 1.2 31393 1.2 3352 1.9 1884 1.7 1468 2.1 2014 35225 1.2 31838 1.2 3387 1.9 1878 1.7 1509 2.2 2015 35503 1.2 31496 1.2 4007 2.3 2200 2.0 1807 2.6 2016 34646 1.2 29842 1.1 4804 2.7 3053 2.8 1751 2.6 2017 31795 1.1 26424 1.0 5371 3.0 3571 3.3 1800 2.6 2018 31748 1.1 25791 1.0 5957 3.4 4205 3.9 1752 2.6 2019 34935 1.2 29338 1.1 5597 3.2 4911 4.5 686 1.0 Type and grade of Insurance fee d < 0.0001 < 0.0001 < 0.0001 Medical aid 90144 3.1 63743 2.4 26401 15.0 9872 9.1 16529 24.2 Local Q1 282913 9.8 264588 9.8 18325 10.4 11773 10.9 6552 9.6 Local Q2 284576 9.9 271987 10.1 12589 7.1 7702 7.1 4887 7.1 Local Q3 275211 9.6 264094 9.8 11117 6.3 6871 6.4 4246 6.2 Local Q4 280327 9.7 261336 9.7 18991 10.8 12615 11.7 6376 9.3 Company Q1 456358 15.8 433530 16.0 22828 12.9 14684 13.6 8144 11.9 Company Q2 402069 14.0 382419 14.1 19650 11.1 12756 11.8 6894 10.1 Company Q3 403007 14.0 382892 14.2 20115 11.4 13335 12.3 6780 9.9 Company Q4 405660 14.1 379266 14.0 26394 15.0 18420 17.1 7974 11.7 Contextual deprivation index < 0.0001 < 0.0001 < 0.0001 T1 966298 33.5 894063 33.1 72235 40.9 44400 41.1 27835 40.7 T2 971775 33.7 914698 33.8 57077 32.4 34923 32.3 22154 32.4 T3 942192 32.7 895094 33.1 47098 26.7 28705 26.6 18393 26.9 Contextual deprivation index, Mean (SD) 155.3 61.2 156.2 60.9 143.0 64.4 142.7 64.6 143.6 64.2 < 0.0001 < 0.0001 < 0.0001 Health check-up < 0.0001 < 0.0001 < 0.0001 No 1147095 39.8 1095770 40.5 51325 29.1 20623 19.1 30702 44.9 Yes 1733170 60.2 1608085 59.5 125085 70.9 87405 80.9 37680 55.1 Health behaviors among health check-up Smoking history < 0.0001 < 0.0001 < 0.0001 No 974760 56.2 909136 56.5 65624 52.5 44526 50.9 21098 56.0 Yes 754912 43.6 696051 43.3 58861 47.1 42575 48.7 16286 43.2 Unknown 3498 0.2 2898 0.2 600 0.5 304 0.3 296 0.8 History of alcohol intake < 0.0001 < 0.0001 < 0.0001 No 709787 41.0 640097 39.8 69690 55.7 45381 51.9 24309 64.5 Yes 1020010 58.9 965200 60.0 54810 43.8 41726 47.7 13084 34.7 Unknown 3373 0.2 2788 0.2 585 0.5 298 0.3 287 0.8 Physical activity < 0.0001 < 0.0001 < 0.0001 No 338794 19.5 302971 18.8 35823 28.6 21672 24.8 14151 37.6 Yes 1390877 80.3 1302226 81.0 88651 70.9 65415 74.8 23236 61.7 Unknown 3499 0.2 2888 0.2 611 0.5 318 0.4 293 0.8 History of chronic disease < 0.0001 < 0.0001 < 0.0001 No 998511 57.6 957825 59.6 40686 32.5 28399 32.5 12287 32.6 Yes 526895 30.4 455138 28.3 71757 57.4 51288 58.7 20469 54.3 Unknown 207764 12.0 195122 12.1 12642 10.1 7718 8.8 4924 13.1 History of hypertension (yes) 428089 24.7 369313 23.0 58776 47.0 42620 48.8 16156 42.9 < 0.0001 < 0.0001 < 0.0001 History of heart disease (yes) 77417 4.5 64192 4.0 13225 10.6 9642 11.0 3583 9.5 < 0.0001 < 0.0001 < 0.0001 History of stroke (yes) 40301 2.3 28342 1.8 11959 9.6 6871 7.9 5088 13.5 < 0.0001 < 0.0001 < 0.0001 History of diabetes (yes) 181913 10.5 153974 9.6 27939 22.3 19595 22.4 8344 22.1 < 0.0001 < 0.0001 < 0.0001 Type of disability < 0.0001 Physical – – – – 80698 45.7 63425 58.7 17273 25.3 Brain lesion – – – – 21941 12.4 7058 6.5 14883 21.8 Visual – – – – 17134 9.7 14003 13.0 3131 4.6 Hearing – – – – 24103 13.7 18565 17.2 5538 8.1 Speech – – – – 1565 0.9 779 0.7 786 1.1 Intellectual – – – – 11277 6.4 194 0.2 11083 16.2 Autistic – – – – 1281 0.7 45 0.0 1236 1.8 Mental – – – – 5584 3.2 82 0.1 5502 8.0 Kidney – – – – 6907 3.9 925 0.9 5982 8.7 Cardiac – – – – 732 0.4 47 0.0 685 1.0 Respiratory – – – – 1490 0.8 43 0.0 1447 2.1 Hepatic – – – – 1147 0.7 670 0.6 477 0.7 Facial – – – – 185 0.1 116 0.1 69 0.1 Intestinal/Urinary – – – – 1794 1.0 1702 1.6 92 0.1 Epilepsy – – – – 572 0.3 374 0.3 198 0.3 p-value estimated using t-test, analysis of variance, or chi-square test a difference between non-disabled and disabled b difference among non-disabled, mild, and severe disabled c difference between mild and severe disabled among disabled d insurance fee per month/sqrt (number of family member) ND: Non-Disabled, PWD: Person with Disability; SD: standard deviation Figure 2 illustrates the distribution of PM 2.5 , by disability, entry year, and type of disability. The concentration was higher in the ND group than in the PWD group until 2010. Figure 3 A illustrates the HR and 95% CI of PM 2.5 (per increment of 10 µg/m 3 ) for mortality by disability and severity. In severely disabled subgroup, the HRs were significantly high for all causes (1.133, 95% CI: 1.028–1.248), non-accidental (1.148, 95% CI: 1.038–1.269), and cardiovascular diseases (1.109, 95% CI: 1.032–1.191), whereas those for respiratory mortality were significantly low. The detailed results are presented in Additional files 2 and 3. Figure 3 B illustrates the HR and 95% CI of PM 2.5 (per increment of 10 µg/m 3 ) for mortality by disability type. The hazard ratios were statistically high for mortality from all causes (1.786, 95% CI: 1.265–2.520) and non-accidental cause (1.847, 95% CI: 1.301–2.624) in PWD with kidney impairment. The HR for cardiovascular disease was significantly high among PWD with brain lesions (1.104, 95% CI: 1.003–1.216). However, the hazard ratio for non-accidental mortality in PWD with visual impairment and respiratory mortality in PWD with brain lesions indicated inverse associations. The detailed results are presented in online Additional file 2 and 4. Among PWD with external impairment, hazard ratio of PM 2.5 (per increment of 10 µg/m 3 ) was statistically high for cardiovascular disease (1.089, 95% CI: 1.028–1.155), while it was statistically low for respiratory disease (0.861, 95% CI: 0.785–0.945) (Fig. 3 C). The detailed results are presented in online Additional file 2 and 5. Figure 4 illustrates the HR and 95% CI of PM 2.5 (per increment of 10 µg/m 3 ) for mortality by disability and severity stratified by age group. The HR for non-accidental mortality was statistically high in severely disabled PWD aged < 65 years (1.070, 95% CI: 1.004–1.140) and that for cardiovascular disease mortality was statistically high in severely disabled PWD aged ≥ 65 years (1.128, 95% CI: 1.035–1.229). However, respiratory disease mortality was significantly low in the PWD of all ages. Detailed results are presented in online Additional file 2 and 6. Discussion This study showed that exposure to ambient PM 2.5 was linked to increased mortality from all causes, non-accidental causes, and cardiovascular diseases, especially among individuals with severe disabilities. Furthermore, individuals with kidney impairment and brain lesions with disabilities had a high mortality risk. However, exposure to ambient PM 2.5 had inverse associations with respiratory mortality in individuals with disabilities. In this study, PWD were found to have a high risk of mortality associated with PM 2.5 than those without disabilities. While some previous studies focused on the association between PM and hospital admission, very few have delved into the specific association between PM 2.5 and mortality in PWD. Our results are similar to those of previous studies in this field. One study found that Medicaid enrollees with low socioeconomic status and disabilities had a high risk of hospital admissions due to cardiovascular diseases from short-term exposure to PM 2.5 than non-Medicaid-eligible Medicare enrollees [11]. Another study found that PWD had a high risk of hospital admission for cardiovascular issues due to short-term exposure to PM 10 than those without disabilities [12]. Our research further indicates that individuals with severe disabilities have a high mortality risk than those with mild disabilities. One study showed that an increase in residential greenness, which mediates the reduction in air pollution, significantly decreased the total mortality impact. This protective effect was high for people with mild disabilities than for those with severe disabilities, supporting our findings [21]. Furthermore, another study reported that people with severe disabilities had a higher risk of hospital admission for cardiovascular issues due to short-term exposure to PM 10 than those with mild disabilities [12]. This study found that individuals with disabilities involving kidney impairment and brain lesions had a high risk of mortality associated with increasing PM 2.5 . A previous Korean study showed that individuals with brain lesions had a high risk of hospital admission for cardiovascular issues due to PM exposure than those with other types of disabilities [12], although the risk for those with kidney impairment was not evaluated separately. They employed a cohort of one million samples from the KNHIS and utilized a case–crossover design to assess the short-term effect of PM 10 exposure on the hospital admission rate, which is different from the method used in our study. The study did not evaluate the effects of disability type in detail; individuals with specific types of disabilities may be more vulnerable to PM 2.5 and further research is needed to determine the underlying reasons. This study also observed an inverse association between PM 2.5 and respiratory disease mortality. Previous studies have reported controversial results; some studies reported a high risk of respiratory mortality associated with PM 2.5 , whereas others did not. One study analyzing the Canadian Census Health and Environment Cohort reported a significantly negative association between PM 2.5 and respiratory mortality, with negative and null findings for chronic obstructive pulmonary disease (COPD) mortality [22]. Another study reported an unexpected inverse association between PM 2.5 and mortality from respiratory disease and COPD [23]. Other studies have reported a negative and null association between PM 2.5 and respiratory disease or COPD mortality [24–26]. Studies conducted in Korea have reported a significant negative association between PM 10 and respiratory mortality [27, 28]. We suggest several possible reasons for the inconsistent data on the association between PM exposure and respiratory diseases. First, a complex interplay exists among socioeconomic status, PM, and health. High socioeconomic status is often correlated with living in urbanized areas with high PM exposure. This correlation can confound the association between PM exposure and health outcomes, particularly in countries like Korea, where urban concentrations are high. A study reported that the association between PM 2.5 , respiratory disease, and COPD mortality was null in an unadjusted model but became significant after the model was adjusted for socioeconomic and behavioral covariates [29]. To solve this problem, we adjusted our model for individual socioeconomic status and the area deprivation index; however, residual confounding may still exist. Second, patients with worsened COPD progression due to PM are likely to die from cardiovascular diseases, presenting a competing risk that could mislead the association between PM and respiratory mortality (Pope et al. 2004). Third, oxidant gases, such as ozone or nitrogen dioxide could alter the relationship between PM 2.5 and respiratory mortality, as increases in the levels of these gases enhance lung epithelium permeability, potentially intensifying the harmful effects of PM 2.5 . Threshold levels of oxidant gas concentrations may weaken the association between PM 2.5 and respiratory mortality, necessitating further research in this regard [30]. Fourth, factors such as inaccuracies in exposure measurements, chemical composition differences in PM 2.5 , the complex association between ambient and indoor air pollution and bioaerosol pollutants, individual health and underlying conditions, genetic predispositions, the effects of medications, protective behaviors, and adaptive responses of individuals continuously exposed to high levels of air pollution could blur the association, leading to inconsistent findings [31, 32]. Further research using in-depth information on these factors is needed to clarify the complex association between PM and respiratory health effects. Our findings highlight that the effect of PM 2.5 on mortality among people with severe disabilities was statistically significant for non-accidental causes in individuals aged 65 years. Owing to the scarcity of research on PWD, the observed age-based differences are challenging to explain; however, several reasons could contribute to this. Younger individuals generally have a lower baseline mortality risk but may be exposed to PM to a greater extent because of their tendency to breathe through their mouths and engage in outdoor activities [33]; their immature immune systems [34], combined with various predisposing factors, lower socioeconomic status, and reduced access to healthcare in PWD, may worsen their susceptibility. Further research is required to understand the susceptibility factors and identify specific diseases to which young individuals with disabilities are susceptible. Exposure to PM 2.5 is linked to several cardiovascular risk factors including hypertension, type 2 diabetes, obesity, dyslipidemia, intimal–medial thickness, atherosclerosis, and coronary artery calcification. Older individuals who are likely to have chronic conditions may be vulnerable to mortality triggered by these cardiovascular disease risk factors [35–38]. PM 2.5 exposure can increase the health risks in PWD via several potential mechanisms. Some disabilities may limit a person's mobility or confine them to environments with poor air quality, such as poorly ventilated homes or facilities. Reduced mobility can lead to prolonged exposure to PM 2.5 , increasing the risk of adverse health effects. Individuals with disabilities are more likely to experience poverty and social exclusion, which leads to poor health outcomes. Additionally, chronic stress and psychological conditions, which may be more prevalent among populations with disabilities, can worsen the health impacts of PM 2.5 . The evidence presented in previous studies have suggested that psychological stress activates inflammatory responses [39, 40]. Additionally, PM 2.5 can lead to systemic inflammation and trigger sympathetic activation within the cardiovascular system [41]. Stress can increase an individual's susceptibility to the harmful effects of PM 2.5 due to its potential to worsen stress-induced inflammatory processes. This study's strength lies in its adjustment for socioeconomic levels in the statistical model at both individual and regional levels, using insurance fees and the regional deprivation index, respectively. As Korea has higher PM 2.5 concentrations in areas with better socioeconomic levels, it can act as a serious confounding variable. Therefore, the model was designed to account for this. Furthermore, this study benefits from the ability to adjust for various individual health behaviors and medical histories by utilizing health checkup data from the KNHIS. To the best of our knowledge, this study is the first in Korea to analyze the mortality effects of long-term exposure to PM 2.5 while considering disability types in detail. The results provide valuable data for the development of healthcare policies for individuals with disabilities. This study had some limitations. As we used regional PM 2.5 , there may be errors in the actual PM 2.5 exposure of individuals. However, we attempted to minimize this error by assigning PM 2.5 , considering personal addresses on a monthly basis. Another limitation of this study is that the results are not fully generalizable because we used 2006 as the year of initial entry and analyzed PM 2.5 exposure in the year of entry. Further research is required to determine the health effects of long-term PM 2.5 exposure in individuals with disabilities that occurs over multiple periods. Future research should address these limitations by studying the changes in the definition of long-term exposure in PWD and by advancing the Cox proportional hazard model with time-varying exposure. Conclusion The findings of this study reveal that PWD, especially those with severe disabilities or specific impairments such as kidney problems or brain lesions, exhibit high mortality risks from all-cause, non-accidental causes, and cardiovascular diseases due to PM 2.5 . These results highlight the increased vulnerability of PWD to air pollution and emphasize the need for tailored interventions that consider disability type and severity. This study underscores the importance of strengthening air quality standards and developing targeted healthcare approaches to protect vulnerable populations. Abbreviations CI confidence intervals CMAQ Community Multiscale Air Quality COPD chronic obstructive pulmonary disease DB database HR hazard ratios ICD-10 International Classification of Disease,10th version KNHIS Korean National Health Insurance System ND non-disabled PIN personal identification number PM particulate matter PM2.5 fine particulate matter PWD people with disabilities PY person-years. Declarations Ethical approval and Consent to participate The study protocol was approved by the Institutional Review Board (IRB) of Dankook University (IRB NO. DKU 2023–04‑004-004). We confirmed that all methods were carried out according to relevant guidelines and regulations. This study was conducted using secondary data; the need for informed consent was waived by the IRB of Dankook University (IRB NO. DKU 2023–04‑004-004). Consent to publish Not applicable. Availability of Data and Materials The mortality data for NDs and PWD can be obtained from the KNHIS DB at [https://nhiss.nhis.or.kr/bd/ab/bdaba021eng.do] and MicroData Integrated Service (MDIS) of Statistics Korea at [https://mdis.kostat.go.kr/eng/index.do;jsessionid=xE2MQqswZ8GURLn21zZjuCvWiiepxG2tEHA4BF6JyulkCzBQzqz5 DnvgFcuOQ2pq.mdexwas2_servlet_engine2]. For using the KNHIS DB, there is a need for reasonable requests, IRB admission, and permission from KNHIS and it is protected by strict confidentiality. For using the MDIS data, there is a need for reasonable requests, IRB admission, and permission from Statistics Korea and it is protected by strict confidentiality. The PM 2.5 modeling data is not available to the public due to the results of an ongoing project. Competing interests The authors declare that they have no competing interests. Funding This work was supported by the National Research Foundation of Korea grant funded by the Korean government (No. 2023R1A2C1002801). The funding body had or has no involvement in study design; collection, management, analysis, and interpretation of data; or the decision to submit for publication. The funding body will be informed of any planned publications, and documentation provided. Author contributions JC - data curation, software, visualization, writing (original draft), writing (review and editing). HL - data curation, data interpretation, writing (review and editing). HJK - data collection, data interpretation, writing (review and editing). MH - data interpretation, writing (review and editing). SK - data collection, writing (review and editing). KHC - conceptualization, study design, funding acquisition, methodology, formal analysis, software, supervision, project administration, literature review, data interpretation, writing (original draft), writing (review and editing). All authors had final responsibility for the decision to submit for publication. 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Am J Epidemiol. 2007;167(6):667 − 75. doi: 10.1093/aje/kwm359 Liu Y-Z, Wang Y-X, Jiang C-L. Inflammation: The Common Pathway of Stress-Related Diseases. Front Hum Neurosci. 2017;11:316 doi: 10.3389/fnhum.2017.00316 Maydych V. The Interplay Between Stress, Inflammation, and Emotional Attention: Relevance for Depression. Front Neurosci. 2019;13 doi: 10.3389/fnins.2019.00384 Thangavel P, Park D, Lee Y-C. Recent Insights into Particulate Matter (PM2.5)-Mediated Toxicity in Humans: An Overview. Int J Environ Res Public Health. 2022;19(12):7511. doi: 10.3390/ijerph19127511 Additional Declarations No competing interests reported. Supplementary Files AdditionalFile1.docx Additional file 1.docx Comparison between participants and total Korean population by disability Please provide the description for Supplementary Table 1. AdditionalFile2.docx Additional file 2.docx Number of deaths by cause of death and disability type Please provide the description for Supplementary Table 2. AdditionalFile3.docx Additional file 3.docx Hazard ratio (HR) and 95% confidence intervals (CI) of fine particulate matter (per 10 ㎍/㎥) for mortality by disability and severity among 5% sample cohort in Korea using National Health Insurance Database, 2006-2019 – Figure 3 Please provide the description for Supplementary Table 3. AdditionalFile4.docx Additional file 4.docx Hazard ratio (HR) and 95% confidence intervals (CI) of fine particulate matter (per 10㎍/㎥) for mortality by disability type among disabled from the 5% sample cohort in Korea using National Health Insurance Database, 2006-2019 – Figure 4 Please provide the description for Supplementary Table 4. AdditionalFile5.docx Additional file 5.docx Hazard ratio (HR) and 95% confidence intervals (CI) of fine particulate matter (per 10 ㎍/㎥) for mortality by disability type and severity among disabled from the 5% sample cohort in Korea using National Health Insurance Database, 2006-2019 – Figure 5 Please provide the description for Supplementary Table 5. AdditionalFile6.docx Additional file 6.docx Hazard ratio (HR) and 95% confidence intervals (CI) of fine particulate matter (per 10 ㎍/㎥) for mortality by disability type and severity among disabled stratified with age group from the 5% sample cohort in Korea using National Health Insurance Database, 2006-2019 – Figure 6 Please provide the description for Supplementary Table 6. Cite Share Download PDF Status: Published Journal Publication published 05 May, 2025 Read the published version in BMC Public Health → Version 1 posted Editorial decision: Revision requested 13 Aug, 2024 Editor assigned by journal 12 Aug, 2024 Submission checks completed at journal 12 Aug, 2024 First submitted to journal 09 Aug, 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-4884473","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":339541147,"identity":"be1d83a1-77a0-4a82-8ffb-211585c26a26","order_by":0,"name":"Jonghyuk Choi","email":"","orcid":"","institution":"Dankook University College of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jonghyuk","middleName":"","lastName":"Choi","suffix":""},{"id":339541148,"identity":"b7d151d8-82e9-47f7-9c80-ea825dd0228a","order_by":1,"name":"Hyungryul Lim","email":"","orcid":"","institution":"Ajou University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Hyungryul","middleName":"","lastName":"Lim","suffix":""},{"id":339541149,"identity":"0d7dd943-f453-4ff7-8ef0-ae5b41605b1d","order_by":2,"name":"Ho-Jang Kwon","email":"","orcid":"","institution":"Dankook University College of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Ho-Jang","middleName":"","lastName":"Kwon","suffix":""},{"id":339541150,"identity":"9ff834c8-dffa-4a27-a541-6f3805d4c0e2","order_by":3,"name":"Mina Ha","email":"","orcid":"","institution":"Dankook University College of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Mina","middleName":"","lastName":"Ha","suffix":""},{"id":339541151,"identity":"308fef2e-62ee-4a1a-bee8-7ced69fa2fc3","order_by":4,"name":"Soontae Kim","email":"","orcid":"","institution":"Ajou University","correspondingAuthor":false,"prefix":"","firstName":"Soontae","middleName":"","lastName":"Kim","suffix":""},{"id":339541152,"identity":"8ba65ccc-2dd0-4f6d-a3ac-aa4a46d2caa0","order_by":5,"name":"Kyung-Hwa Choi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6UlEQVRIie3NMQuCQBTA8XcItgiudxR9houDi8APYwi6OIdbTedStNq3MIJoaDAcWu4DNHYETg5NgTSU0BhpbQ33h8eDBz8egE73r6HZo2+DW28A+jq0E4OR2a9knGbfkmEcq2u1M9Hm6BUqiRwGnfyMVvvPpCclIwtpGVwWw0EqfQ6WT1FafCYYh9BFApv85HKiRO4AhIDOWRMJLnckqMWS4PYidtlGXF5/cTHFISdrkXOo/6K0iViSjxYio1iWE5JIn5m4oIekiXTiy6kS2XQZB1syj5zB0vaUmjeQ98x6fgI6nU6ne+8JxzNSzlFUllAAAAAASUVORK5CYII=","orcid":"","institution":"Dankook University College of Medicine","correspondingAuthor":true,"prefix":"","firstName":"Kyung-Hwa","middleName":"","lastName":"Choi","suffix":""}],"badges":[],"createdAt":"2024-08-09 05:25:00","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4884473/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4884473/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12889-025-22923-w","type":"published","date":"2025-05-05T15:57:26+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":64181816,"identity":"853a929d-fec7-49b2-a532-ece1f1e9dca0","added_by":"auto","created_at":"2024-09-09 15:03:22","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":193345,"visible":true,"origin":"","legend":"\u003cp\u003eParticipant selection process for 5% sample cohort from the Korean National Health Insurance Database\u003c/p\u003e","description":"","filename":"Onlinefig1.png","url":"https://assets-eu.researchsquare.com/files/rs-4884473/v1/e74f1c77837b9b357a8587b4.png"},{"id":64181815,"identity":"0f79e8bc-1a95-4263-b77e-9a5e26e24038","added_by":"auto","created_at":"2024-09-09 15:03:22","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":93512,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of ambient fine particulate matter (PM\u003csub\u003e2.5\u003c/sub\u003e) concentration by disability in the 5% sample cohort in Korea.\u003c/p\u003e\n\u003cp\u003e[A]: severity; [B]: entry year; [C]: type of disability\u003c/p\u003e\n\u003cp\u003eD1: physical; D2: brain lesion; D3: visual; D4: hearing; D5: speech; D6: intellectual; D7: autistic; D8: mental; D9: kidney; D10: cardiac; D11: respiratory; D12: hepatic; D13: facial; D14: intestinal or urinary fistula; D15: epilepsy\u003c/p\u003e","description":"","filename":"Onlinefig2.png","url":"https://assets-eu.researchsquare.com/files/rs-4884473/v1/c54302bdf339b97fea4639c3.png"},{"id":64181424,"identity":"7cbcf2f1-115c-430d-b28f-7fb0468dd807","added_by":"auto","created_at":"2024-09-09 14:55:24","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":838706,"visible":true,"origin":"","legend":"\u003cp\u003eHRs and 95% CI of fine particulate matter for mortality by disability, disability type, and disability severity\u003c/p\u003e\n\u003cp\u003e[A]: All causes; [B]: non-accidental cause; [C]: respiratory disease (J00-J99); [D]: lung cancer (C33-C34); [E]: cardiovascular disease (I00-I99)\u003c/p\u003e\n\u003cp\u003eCox proportional hazard model adjusted for sex, age, level of insurance fee, smoking history, having chronic disease, and contextual deprivation index. ND, non-disabled; PWD, people with disability.\u003c/p\u003e\n\u003cp\u003eThe disability type was classified by Korean law: D1, physical; D2, brain lesion; D3, visual; D4, hearing; D5, speech; D6, intellectual; D7, autistic; D8, mental; D9, kidney; D10, cardiac; D11, respiratory; D12, hepatic; D13, facial; D14, intestinal or urinary fistula; D15, epilepsy. External: impairment in external bodily functions includes hearing, visual, physical, speech, facial, or brain lesion. Internal: Disability in internal organs includes epilepsy, kidney, hepatic, or cardiac dysfunction, intestinal or urinary fistula, or respiratory dysfunction. Developmental: Developmental disability includes intellectual or autistic disorder.\u003c/p\u003e","description":"","filename":"figure31.png","url":"https://assets-eu.researchsquare.com/files/rs-4884473/v1/3c4d7e6607b2da7e3c801f0b.png"},{"id":64181420,"identity":"578c1e09-782e-43b2-b290-abf3dba45611","added_by":"auto","created_at":"2024-09-09 14:55:22","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":152108,"visible":true,"origin":"","legend":"\u003cp\u003eHRs and 95% CI of fine particulate matter for mortality by disability type and severity in disabled persons, stratified by age\u003c/p\u003e\n\u003cp\u003e[A]: All causes; [B]: non-accidental cause; [C]: respiratory disease (J00-J99); [D]: lung cancer (C33-C34); [E]: cardiovascular disease (I00-I99)\u003c/p\u003e\n\u003cp\u003eCox proportional hazard model was adjusted for sex, age, level of insurance fee, smoking history, having chronic disease, and contextual deprivation index.\u003c/p\u003e","description":"","filename":"figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4884473/v1/69d810969726c50f130668f7.png"},{"id":82537521,"identity":"85b407e8-6913-4cef-a595-e6900874ea87","added_by":"auto","created_at":"2025-05-12 16:07:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4020197,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4884473/v1/40237923-f7b4-4d54-9370-45bbb5fa66a9.pdf"},{"id":64181414,"identity":"842ded50-5462-45a4-883b-3396830ed1a3","added_by":"auto","created_at":"2024-09-09 14:55:21","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":20177,"visible":true,"origin":"","legend":"\u003cp\u003e1. \u003cstrong\u003eAdditional file 1.docx\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eComparison between participants and total Korean population by disability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePlease provide the description for Supplementary Table 1.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"AdditionalFile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-4884473/v1/206cab87d53990074c1132b8.docx"},{"id":64181415,"identity":"1881ec5b-a15f-485b-8ff0-8d3eaa4792dd","added_by":"auto","created_at":"2024-09-09 14:55:21","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":22647,"visible":true,"origin":"","legend":"\u003cp\u003e2. \u003cstrong\u003eAdditional file 2.docx\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNumber of deaths by cause of death and disability type\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePlease provide the description for Supplementary Table 2.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"AdditionalFile2.docx","url":"https://assets-eu.researchsquare.com/files/rs-4884473/v1/8c344b47484f9ada7fe8063d.docx"},{"id":64181423,"identity":"facc7612-4ef6-4a37-83a9-71cb9cf21bde","added_by":"auto","created_at":"2024-09-09 14:55:24","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":19177,"visible":true,"origin":"","legend":"\u003cp\u003e3. \u003cstrong\u003eAdditional file 3.docx\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHazard ratio (HR) and 95% confidence intervals (CI) of fine particulate matter (per 10 ㎍/㎥) for mortality by disability and severity among 5% sample cohort in Korea using National Health Insurance Database, 2006-2019 – Figure 3\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePlease provide the description for Supplementary Table 3.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"AdditionalFile3.docx","url":"https://assets-eu.researchsquare.com/files/rs-4884473/v1/01871329ac08abd86ebf313a.docx"},{"id":64181418,"identity":"4b549bec-5fbb-444a-93bb-842d55d707f6","added_by":"auto","created_at":"2024-09-09 14:55:22","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":20172,"visible":true,"origin":"","legend":"\u003cp\u003e4. \u003cstrong\u003eAdditional file 4.docx\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHazard ratio (HR) and 95% confidence intervals (CI) of fine particulate matter (per 10㎍/㎥) for mortality by disability type among disabled from the 5% sample cohort in Korea using National Health Insurance Database, 2006-2019 – Figure 4\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePlease provide the description for Supplementary Table 4.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"AdditionalFile4.docx","url":"https://assets-eu.researchsquare.com/files/rs-4884473/v1/b17440f76e292182578c0bbc.docx"},{"id":64181421,"identity":"e07a1731-66bd-4840-9c94-9fca073db2a7","added_by":"auto","created_at":"2024-09-09 14:55:22","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":20979,"visible":true,"origin":"","legend":"\u003cp\u003e5. \u003cstrong\u003eAdditional file 5.docx\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHazard ratio (HR) and 95% confidence intervals (CI) of fine particulate matter (per 10 ㎍/㎥) for mortality by disability type and severity among disabled from the 5% sample cohort in Korea using National Health Insurance Database, 2006-2019 – Figure 5\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePlease provide the description for Supplementary Table 5.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"AdditionalFile5.docx","url":"https://assets-eu.researchsquare.com/files/rs-4884473/v1/f3db7d58d7a7071cf8252286.docx"},{"id":64181817,"identity":"2f33c762-5943-49dd-9a8d-31dde1f931f3","added_by":"auto","created_at":"2024-09-09 15:03:22","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":18765,"visible":true,"origin":"","legend":"\u003cp\u003e6. \u003cstrong\u003eAdditional file 6.docx\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHazard ratio (HR) and 95% confidence intervals (CI) of fine particulate matter (per 10 ㎍/㎥) for mortality by disability type and severity among disabled stratified with age group from the 5% sample cohort in Korea using National Health Insurance Database, 2006-2019 – Figure 6\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePlease provide the description for Supplementary Table 6.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"AdditionalFile6.docx","url":"https://assets-eu.researchsquare.com/files/rs-4884473/v1/390172109a98cca955b2d310.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Ambient Fine Particulate Matter and Mortality Risk among People with Disability in Korea Based on the National Health Insurance Database: A Retrospective Cohort Study","fulltext":[{"header":"Background","content":"\u003cp\u003eIn 2019, ambient air pollution led to an estimated 4.2\u0026nbsp;million premature deaths worldwide [1]. Fine particulate matter (PM\u003csub\u003e2.5\u003c/sub\u003e) can be derived from primary sources, such as fuel combustion in power generation facilities, industries, or vehicles, and secondary sources, such as chemical reactions between gases [2]. Adverse health effects of exposure to ambient PM\u003csub\u003e2.5\u003c/sub\u003e, such as cardiovascular diseases, respiratory diseases, neurological diseases, and cancer, have been reported. Furthermore, PM\u003csub\u003e2.5\u003c/sub\u003e is associated with a high risk of mortality from all causes, non-accidental causes, and cardiovascular diseases [2].\u003c/p\u003e \u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e poses a significant health risk to vulnerable populations, including minority populations, people with chronic diseases, people with disabilities (PWD), and children. Minority populations, such as non-whites, are often considered vulnerable to traffic-related exposure to air pollutants [3]. One study found that people with kidney failure requiring dialysis represented a subgroup particularly susceptible to PM\u003csub\u003e2.5\u003c/sub\u003e-associated health effects [4]. Previous studies have highlighted that children may also be vulnerable to exposure to PM\u003csub\u003e2.5\u003c/sub\u003e, in terms of behavioral problems and effects on the respiratory system [5,6]. The health effects of PM\u003csub\u003e2.5\u003c/sub\u003e can vary across regions owing to differences in population characteristics and components of PM\u003csub\u003e2.5\u003c/sub\u003e [7]. These population characteristics include various vulnerability factors such as social, economic, and health conditions. Therefore, the impact of PM\u003csub\u003e2.5\u003c/sub\u003e exposure on PWD in vulnerable groups may vary depending on these factors. PWD encounter various challenges related to their pre-existing conditions, limited mobility, medical accessibility, and potential socioeconomic disadvantages, making them particularly susceptible to environmental risks [8]. This vulnerability could be further heightened by air pollution, as PWD may experience more severe health consequences and additional challenges in accessing healthcare services.\u003c/p\u003e \u003cp\u003eApproximately 1.3\u0026nbsp;billion people, 16% of the world's population, experience significant disabilities [9]. In Korea, the registered disability system has been a crucial component of the social and health infrastructure, covering approximately 5% of the population since 2009 [10]. Although this percentage is lower than that in Western developed countries, it is expected to increase owing to the aging population and the growing recognition of disabilities.\u003c/p\u003e \u003cp\u003ePrevious studies have examined the association between exposure to particulate matter (PM) and health outcomes in the PWD. Previous studies have reported that short-term PM exposure increases the risk of hospital admission for cardiovascular problems in PWD [11, 12]. One study reported that short-term exposure to PM with diameter\u0026thinsp;\u0026le;\u0026thinsp;10 \u0026micro;m (PM\u003csub\u003e10\u003c/sub\u003e) increased the mortality risk in PWD [13]. Most studies have examined particulate matter and health risks only in the general population. While previous studies have examined the effects of PM on the health of PWD, they have only focused on short-term exposure. This leaves a significant gap in our understanding of how ambient PM\u003csub\u003e2.5\u003c/sub\u003e long-term exposure affects the mortality risk among PWD. In addition, traditional epidemiological studies may not consider the various ways in which disability intersects with factors such as age and disability type. The absence of targeted research further obscures the specific vulnerabilities and needs of PWD facing air pollution, leading to a gap in policy and public health interventions designed to protect this group.\u003c/p\u003e \u003cp\u003eGiven the heightened vulnerability of this demographic to environmental pollutants, coupled with their unique health and social challenges, investigating the impact of ambient PM\u003csub\u003e2.5\u003c/sub\u003e on the mortality risk among PWD is essential. Therefore, this study aimed to fill this knowledge gap by examining the long-term association between ambient PM\u003csub\u003e2.5\u003c/sub\u003e and mortality risk among PWD using the National Health Insurance Database (DB). We also used the Statistics Korea Database to determine specific causes of mortality.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eDatabase\u003c/h2\u003e \u003cp\u003eThis retrospective cohort study used a 5% sample cohort (n\u0026thinsp;=\u0026thinsp;2,897,075) from the Korean National Health Insurance System (KNHIS) DB, documenting every claim made by the Korean population since 2002. It includes individual-level personal data, offering information on residential history [14] in four main parts: an annual qualification DB, health insurance claims DB, DB for mortality records, and health check-up DB. The qualification DB encompasses details such as personal identification number (PIN), geographical region, sex, age, and insurance fees as a proxy of economic status, along with the type and severity of disability. The death DB records the PIN and date of death, while the health check-up DB includes data from clinical examinations and history taking.\u003c/p\u003e \u003cp\u003eThe following were excluded from among 2,897,075 persons: death before 2006 (n\u0026thinsp;=\u0026thinsp;12,229), change from PWD to non-disabled (ND) (n\u0026thinsp;=\u0026thinsp;3,491), no information on air pollutant concentration (n\u0026thinsp;=\u0026thinsp;1,088), and those born before 1900 who were still alive (i.e., aged\u0026thinsp;\u0026gt;\u0026thinsp;120 years; n\u0026thinsp;=\u0026thinsp;2). Finally, 2,880,265 individuals (41,501,709 person-years [PY]), including 176,410 PWD (2,011,231 PY) and 2,703,855 ND (39,490,478 PY), were included in this study cohort (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eType and severity of disability\u003c/h2\u003e \u003cp\u003eDisability information was obtained from the KNHIS qualification DB. In Korea, the Enforcement Decree of the Act on Welfare of PWD has defined the following 15 disability types in four fields since 2003: impairment in external bodily functions including hearing, visual, physical, speech, and facial functions or brain lesions; impairment in internal organs including epilepsy or kidney, hepatic, or cardiac dysfunction, intestinal/urinary fistula, or respiratory dysfunction; developmental disability including intellectual or autistic disorder; and mental disorders. After the abolition of the grading system for PWD on July 1, 2019, disabilities are now classified as either \u0026ldquo;severely disabled\u0026rdquo; or \u0026ldquo;mild disabled\u0026rdquo; [15].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eAmbient PM\u003csub\u003e2.5\u003c/sub\u003e\u003c/h2\u003e \u003cp\u003eSimulated data incorporate factors such as weather conditions, human-made and natural emissions, and the transport of chemicals [16\u0026ndash;19]. The analysis utilized the Community Multiscale Air Quality (CMAQ) system (ver. 4.7.1; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.epa.gov/cmaq\u003c/span\u003e\u003cspan address=\"https://www.epa.gov/cmaq\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), equipped with the aerosol module, ver. 5, and Statewide Air Pollution Research Centre model, ver. 99. Weather simulations were conducted using the Weather Research and Forecasting model (ver. 3.3.1). Initial field data were sourced from the National Center for Environmental Protection. CMAQ-ready meteorological data were generated using the Meteorology Interface Processor (ver. 3.6). The Korean National Emissions Inventory was processed using the Sparse Matrix Operator Kernel Emissions system (ver. 3.1). Estimates of biogenic emissions were generated using the Model of Emissions of Gases and Aerosols from Nature. The boundary conditions for the 9-km domain were derived from simulations of the 27-km domain. The CMAQ system provided hourly PM\u003csub\u003e2.5\u003c/sub\u003e concentration across Korean cities within the 9-km domain. Daily average PM\u003csub\u003e2.5\u003c/sub\u003e concentrations for each city were calculated from January 1, 2006, to December 31, 2019.\u003c/p\u003e \u003cp\u003eAs information on PM\u003csub\u003e2.5\u003c/sub\u003e concentrations was only available from 2006 onwards, the entry point was defined as the first appearance in the cohort after 2006 for the ND. For PWD, the entry point was 2006 for those who acquired their disability before 2006 or the time of acquisition for those who acquired their disability after 2006. We assigned a monthly concentration for each municipality based on their monthly address information and then averaged over the year for each individual. For each individual, the average annual concentration was applied to their year of entry.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eMortality\u003c/h2\u003e \u003cp\u003eWe excluded deaths up to 2006 from our analysis because we had information on PM\u003csub\u003e2.5\u003c/sub\u003e concentrations only from 2006 onwards. The causes of death were obtained by referring to the death cause DB of Statistics Korea. We classified the causes of death into five types as follows: all causes; non-accidental causes, all causes excluding external causes (International Classification of Disease, 10th version [ICD-10] codes: V01\u0026ndash;Y98); respiratory disease (ICD-10: J00\u0026ndash;J99); lung cancer (ICD-10: C33\u0026ndash;C34); and cardiovascular disease (ICD-10: I00\u0026ndash;I99).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eCovariates\u003c/h2\u003e \u003cp\u003eInformation on age and sex was sourced from the qualification DB of the KNHIS. Insurance fees as proxy of economic status were classified into two types: company and local fees. The monthly insurance fee was adjusted by dividing it by the square root of the family members' count. This adjusted fee was then organized into quartiles for each insurance type. Medical aid groups were classified separately. Information on smoking history and history of chronic diseases was obtained from the Health Check-Up DB. The Korean contextual deprivation index was used as a regional variable at the city district level to reflect the contextual effect of the residential area. This indicator can evaluate regional socioeconomic levels in a multidimensional manner [20]. Using the 2015 National Statistical Office Population and Housing Census, six indicators at the individual level and four at the household level were calculated for each city district. The six indicators at the individual level were as follows: proportion of adults aged 35\u0026ndash;64 years with an education level of below a high school diploma, percentage of female household heads, percentage of divorced and widowed people aged\u0026thinsp;\u0026ge;\u0026thinsp;15 years, low social class proportion based on the adult head of household aged 15\u0026ndash;64 years, unemployment rate for men aged 15\u0026ndash;64 years, and proportion of the older population aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years. At the household level, four indicators were identified: the proportion of single-person households, proportion of households without homes, proportion of households without cars, and proportion of households residing in housing options other than apartments. The aforementioned 10 indicators were converted to z-scores using a standard normal distribution, and the values were added to calculate the total score. A higher score indicated a lower socioeconomic level in the region [20].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eDifferences in the general characteristics between PWD and ND or severe and mild groups were evaluated using the t-test or chi-square test. To assess the differences in general characteristics among ND individuals, those with mild disabilities, and those with severe disabilities, analysis of variance or the chi-square test was employed. Hazard ratios (HRs) and 95% confidence intervals (CIs) for mortality on increasing PM\u003csub\u003e2.5\u003c/sub\u003e by 10 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e were calculated using the Cox proportional hazard model adjusted for age, sex, insurance fee, smoking history, chronic disease status, and contextual deprivation index. The model was stratified by the presence, type, and severity of the disability.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the distribution of the general characteristics according to the disability and its severity. PWD accounted for 6.1% of the study sample. The annual proportion of PWD was similar to that reported in formal Korean statistics (Additional file 1). The PWD cohort included men, older patients, patients in medical aid group, participants of health check-ups, patients with smoking history, and patients with chronic diseases. The proportions of men and history of stroke were higher in the severely disabled subgroup than in the mildly disabled subgroup.\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\u003eGeneral characteristics by disability and severity in 5% sample cohort using KNHIS database, 2006\u0026ndash;2019\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"14\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c3\" namest=\"c2\" rowspan=\"2\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c5\" namest=\"c4\" rowspan=\"2\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c11\" namest=\"c6\"\u003e \u003cp\u003ePWD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eMild\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003eSevere\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\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\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003ep-value\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003ep-value\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003ep-value\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2880265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2703855\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e93.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e176410\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e108028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e68382\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFollow-up time (years), Mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e11.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e11.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e5.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFollow-up time (years), Median (min\u0026ndash;max)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1\u0026ndash;16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(1\u0026ndash;16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(1\u0026ndash;16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e13.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(1\u0026ndash;16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e13.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(1\u0026ndash;16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerson-years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41501709\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39490478\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2011231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1251398\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e759833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\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 \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1456397\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1353183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e103214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e58.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e62027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e57.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e41187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e60.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1423868\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1350672\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e73196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e41.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e46001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e42.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e27195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e39.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge at entry year (years), Mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e55.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e18.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e58.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e16.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e50.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e21.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge at entry year (years)\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 \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e00\u0026ndash;09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e559552\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e554748\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4804\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e907\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3897\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e5.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u0026ndash;19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e342219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e336873\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5346\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4351\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e6.4\u003c/p\u003e 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colname=\"c5\"\u003e \u003cp\u003e85.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e97680\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e55.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e54999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e50.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e42681\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e62.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44811\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10481\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3461\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e5.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd 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colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31496\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1807\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34646\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29842\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4804\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1751\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31795\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26424\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3571\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31748\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25791\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5957\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1752\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34935\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29338\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5597\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4911\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e686\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eType and grade of Insurance fee\u003csup\u003ed\u003c/sup\u003e\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedical aid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63743\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26401\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9872\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e9.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e16529\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e24.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocal Q1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e282913\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e264588\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e11773\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e10.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6552\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e9.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocal Q2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e284576\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e271987\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12589\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7702\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4887\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e7.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocal Q3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e275211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e264094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e6.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocal Q4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e280327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e261336\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e12615\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e11.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6376\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e9.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCompany Q1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e456358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e433530\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22828\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e14684\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e13.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e8144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e11.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCompany Q2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e402069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e382419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e12756\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e11.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6894\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e10.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCompany Q3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e403007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e382892\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e13335\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e12.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6780\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e9.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCompany Q4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e405660\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e379266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26394\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e18420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e17.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e7974\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e11.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContextual deprivation index\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 \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e966298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e894063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e72235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e40.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e44400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e41.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e27835\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e40.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e971775\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e914698\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e57077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e32.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e34923\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e32.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e22154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e32.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e942192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e895094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e47098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e26.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e28705\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e26.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e18393\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e26.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContextual deprivation index, Mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e155.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e156.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e60.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e143.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e64.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e142.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e64.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e143.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e64.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth check-up\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 \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1147095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1095770\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e51325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e29.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e20623\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e19.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e30702\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e44.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\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\u003e1733170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1608085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e59.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e125085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e70.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e87405\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e80.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e37680\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e55.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eHealth behaviors among health check-up\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking history\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 \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e974760\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e909136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e56.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e65624\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e52.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e44526\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e50.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e21098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e56.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\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\u003e754912\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e696051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e58861\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e47.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e42575\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e48.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e16286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e43.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3498\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e296\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of alcohol intake\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 \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e709787\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e640097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e69690\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e55.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e45381\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e51.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e24309\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e64.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\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\u003e1020010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e965200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e60.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e54810\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e43.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e41726\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e47.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e13084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e34.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3373\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2788\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e585\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e287\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\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 \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e338794\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e302971\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e35823\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e28.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e21672\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e24.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e14151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e37.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\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\u003e1390877\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1302226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e81.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e88651\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e70.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e65415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e74.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e23236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e61.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2888\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e611\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of chronic disease\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 \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e998511\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e957825\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e59.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e40686\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e32.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e28399\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e32.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e12287\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e32.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\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\u003e526895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e455138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e71757\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e57.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e51288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e58.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e20469\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e54.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e207764\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e195122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7718\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4924\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e13.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of hypertension (yes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e428089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e369313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e58776\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e47.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e42620\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e48.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e16156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e42.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of heart disease (yes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e77417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e64192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e11.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3583\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e9.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of stroke (yes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28342\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11959\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e13.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of diabetes (yes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e181913\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e153974\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e27939\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e22.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e19595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e22.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e8344\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e22.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType of disability\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 \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e80698\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e45.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e63425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e58.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e17273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e25.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrain lesion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e14883\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e21.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVisual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e14003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e13.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e4.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHearing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e18565\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e17.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5538\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e8.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpeech\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1565\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e779\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e786\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntellectual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e11083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e16.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAutistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMental\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5584\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e8.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKidney\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6907\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e925\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5982\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e8.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiac\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e732\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e685\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRespiratory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1490\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1447\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHepatic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e670\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e477\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFacial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntestinal/Urinary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1794\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1702\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEpilepsy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e572\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e374\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"14\"\u003ep-value estimated using t-test, analysis of variance, or chi-square test\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"14\"\u003e\u003csup\u003ea\u003c/sup\u003edifference between non-disabled and disabled\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"14\"\u003e\u003csup\u003eb\u003c/sup\u003edifference among non-disabled, mild, and severe disabled\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"14\"\u003e\u003csup\u003ec\u003c/sup\u003edifference between mild and severe disabled among disabled\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"14\"\u003e\u003csup\u003ed\u003c/sup\u003einsurance fee per month/sqrt (number of family member)\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"14\"\u003eND: Non-Disabled, PWD: Person with Disability; SD: standard deviation\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates the distribution of PM\u003csub\u003e2.5\u003c/sub\u003e, by disability, entry year, and type of disability. The concentration was higher in the ND group than in the PWD group until 2010. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA illustrates the HR and 95% CI of PM\u003csub\u003e2.5\u003c/sub\u003e (per increment of 10 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e) for mortality by disability and severity. In severely disabled subgroup, the HRs were significantly high for all causes (1.133, 95% CI: 1.028\u0026ndash;1.248), non-accidental (1.148, 95% CI: 1.038\u0026ndash;1.269), and cardiovascular diseases (1.109, 95% CI: 1.032\u0026ndash;1.191), whereas those for respiratory mortality were significantly low. The detailed results are presented in Additional files 2 and 3.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB illustrates the HR and 95% CI of PM\u003csub\u003e2.5\u003c/sub\u003e (per increment of 10 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e) for mortality by disability type. The hazard ratios were statistically high for mortality from all causes (1.786, 95% CI: 1.265\u0026ndash;2.520) and non-accidental cause (1.847, 95% CI: 1.301\u0026ndash;2.624) in PWD with kidney impairment. The HR for cardiovascular disease was significantly high among PWD with brain lesions (1.104, 95% CI: 1.003\u0026ndash;1.216). However, the hazard ratio for non-accidental mortality in PWD with visual impairment and respiratory mortality in PWD with brain lesions indicated inverse associations. The detailed results are presented in online Additional file 2 and 4.\u003c/p\u003e \u003cp\u003eAmong PWD with external impairment, hazard ratio of PM\u003csub\u003e2.5\u003c/sub\u003e (per increment of 10 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e) was statistically high for cardiovascular disease (1.089, 95% CI: 1.028\u0026ndash;1.155), while it was statistically low for respiratory disease (0.861, 95% CI: 0.785\u0026ndash;0.945) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). The detailed results are presented in online Additional file 2 and 5.\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e illustrates the HR and 95% CI of PM\u003csub\u003e2.5\u003c/sub\u003e (per increment of 10 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e) for mortality by disability and severity stratified by age group. The HR for non-accidental mortality was statistically high in severely disabled PWD aged\u0026thinsp;\u0026lt;\u0026thinsp;65 years (1.070, 95% CI: 1.004\u0026ndash;1.140) and that for cardiovascular disease mortality was statistically high in severely disabled PWD aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years (1.128, 95% CI: 1.035\u0026ndash;1.229). However, respiratory disease mortality was significantly low in the PWD of all ages. Detailed results are presented in online Additional file 2 and 6.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study showed that exposure to ambient PM\u003csub\u003e2.5\u003c/sub\u003e was linked to increased mortality from all causes, non-accidental causes, and cardiovascular diseases, especially among individuals with severe disabilities. Furthermore, individuals with kidney impairment and brain lesions with disabilities had a high mortality risk. However, exposure to ambient PM\u003csub\u003e2.5\u003c/sub\u003e had inverse associations with respiratory mortality in individuals with disabilities.\u003c/p\u003e \u003cp\u003eIn this study, PWD were found to have a high risk of mortality associated with PM\u003csub\u003e2.5\u003c/sub\u003e than those without disabilities. While some previous studies focused on the association between PM and hospital admission, very few have delved into the specific association between PM\u003csub\u003e2.5\u003c/sub\u003e and mortality in PWD. Our results are similar to those of previous studies in this field. One study found that Medicaid enrollees with low socioeconomic status and disabilities had a high risk of hospital admissions due to cardiovascular diseases from short-term exposure to PM\u003csub\u003e2.5\u003c/sub\u003e than non-Medicaid-eligible Medicare enrollees [11]. Another study found that PWD had a high risk of hospital admission for cardiovascular issues due to short-term exposure to PM\u003csub\u003e10\u003c/sub\u003e than those without disabilities [12].\u003c/p\u003e \u003cp\u003eOur research further indicates that individuals with severe disabilities have a high mortality risk than those with mild disabilities. One study showed that an increase in residential greenness, which mediates the reduction in air pollution, significantly decreased the total mortality impact. This protective effect was high for people with mild disabilities than for those with severe disabilities, supporting our findings [21]. Furthermore, another study reported that people with severe disabilities had a higher risk of hospital admission for cardiovascular issues due to short-term exposure to PM\u003csub\u003e10\u003c/sub\u003e than those with mild disabilities [12].\u003c/p\u003e \u003cp\u003eThis study found that individuals with disabilities involving kidney impairment and brain lesions had a high risk of mortality associated with increasing PM\u003csub\u003e2.5\u003c/sub\u003e. A previous Korean study showed that individuals with brain lesions had a high risk of hospital admission for cardiovascular issues due to PM exposure than those with other types of disabilities [12], although the risk for those with kidney impairment was not evaluated separately. They employed a cohort of one million samples from the KNHIS and utilized a case\u0026ndash;crossover design to assess the short-term effect of PM\u003csub\u003e10\u003c/sub\u003e exposure on the hospital admission rate, which is different from the method used in our study. The study did not evaluate the effects of disability type in detail; individuals with specific types of disabilities may be more vulnerable to PM\u003csub\u003e2.5\u003c/sub\u003e and further research is needed to determine the underlying reasons.\u003c/p\u003e \u003cp\u003eThis study also observed an inverse association between PM\u003csub\u003e2.5\u003c/sub\u003e and respiratory disease mortality. Previous studies have reported controversial results; some studies reported a high risk of respiratory mortality associated with PM\u003csub\u003e2.5\u003c/sub\u003e, whereas others did not. One study analyzing the Canadian Census Health and Environment Cohort reported a significantly negative association between PM\u003csub\u003e2.5\u003c/sub\u003e and respiratory mortality, with negative and null findings for chronic obstructive pulmonary disease (COPD) mortality [22]. Another study reported an unexpected inverse association between PM\u003csub\u003e2.5\u003c/sub\u003e and mortality from respiratory disease and COPD [23]. Other studies have reported a negative and null association between PM\u003csub\u003e2.5\u003c/sub\u003e and respiratory disease or COPD mortality [24\u0026ndash;26]. Studies conducted in Korea have reported a significant negative association between PM\u003csub\u003e10\u003c/sub\u003e and respiratory mortality [27, 28]. We suggest several possible reasons for the inconsistent data on the association between PM exposure and respiratory diseases. First, a complex interplay exists among socioeconomic status, PM, and health. High socioeconomic status is often correlated with living in urbanized areas with high PM exposure. This correlation can confound the association between PM exposure and health outcomes, particularly in countries like Korea, where urban concentrations are high. A study reported that the association between PM\u003csub\u003e2.5\u003c/sub\u003e, respiratory disease, and COPD mortality was null in an unadjusted model but became significant after the model was adjusted for socioeconomic and behavioral covariates [29]. To solve this problem, we adjusted our model for individual socioeconomic status and the area deprivation index; however, residual confounding may still exist. Second, patients with worsened COPD progression due to PM are likely to die from cardiovascular diseases, presenting a competing risk that could mislead the association between PM and respiratory mortality (Pope et al. 2004). Third, oxidant gases, such as ozone or nitrogen dioxide could alter the relationship between PM\u003csub\u003e2.5\u003c/sub\u003e and respiratory mortality, as increases in the levels of these gases enhance lung epithelium permeability, potentially intensifying the harmful effects of PM\u003csub\u003e2.5\u003c/sub\u003e. Threshold levels of oxidant gas concentrations may weaken the association between PM\u003csub\u003e2.5\u003c/sub\u003e and respiratory mortality, necessitating further research in this regard [30]. Fourth, factors such as inaccuracies in exposure measurements, chemical composition differences in PM\u003csub\u003e2.5\u003c/sub\u003e, the complex association between ambient and indoor air pollution and bioaerosol pollutants, individual health and underlying conditions, genetic predispositions, the effects of medications, protective behaviors, and adaptive responses of individuals continuously exposed to high levels of air pollution could blur the association, leading to inconsistent findings [31, 32]. Further research using in-depth information on these factors is needed to clarify the complex association between PM and respiratory health effects.\u003c/p\u003e \u003cp\u003eOur findings highlight that the effect of PM\u003csub\u003e2.5\u003c/sub\u003e on mortality among people with severe disabilities was statistically significant for non-accidental causes in individuals aged\u0026thinsp;\u0026lt;\u0026thinsp;65 years and for cardiovascular disease mortality in those aged\u0026thinsp;\u0026gt;\u0026thinsp;65 years. Owing to the scarcity of research on PWD, the observed age-based differences are challenging to explain; however, several reasons could contribute to this. Younger individuals generally have a lower baseline mortality risk but may be exposed to PM to a greater extent because of their tendency to breathe through their mouths and engage in outdoor activities [33]; their immature immune systems [34], combined with various predisposing factors, lower socioeconomic status, and reduced access to healthcare in PWD, may worsen their susceptibility. Further research is required to understand the susceptibility factors and identify specific diseases to which young individuals with disabilities are susceptible. Exposure to PM\u003csub\u003e2.5\u003c/sub\u003e is linked to several cardiovascular risk factors including hypertension, type 2 diabetes, obesity, dyslipidemia, intimal\u0026ndash;medial thickness, atherosclerosis, and coronary artery calcification. Older individuals who are likely to have chronic conditions may be vulnerable to mortality triggered by these cardiovascular disease risk factors [35\u0026ndash;38].\u003c/p\u003e \u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e exposure can increase the health risks in PWD via several potential mechanisms. Some disabilities may limit a person's mobility or confine them to environments with poor air quality, such as poorly ventilated homes or facilities. Reduced mobility can lead to prolonged exposure to PM\u003csub\u003e2.5\u003c/sub\u003e, increasing the risk of adverse health effects. Individuals with disabilities are more likely to experience poverty and social exclusion, which leads to poor health outcomes. Additionally, chronic stress and psychological conditions, which may be more prevalent among populations with disabilities, can worsen the health impacts of PM\u003csub\u003e2.5\u003c/sub\u003e. The evidence presented in previous studies have suggested that psychological stress activates inflammatory responses [39, 40]. Additionally, PM\u003csub\u003e2.5\u003c/sub\u003e can lead to systemic inflammation and trigger sympathetic activation within the cardiovascular system [41]. Stress can increase an individual's susceptibility to the harmful effects of PM\u003csub\u003e2.5\u003c/sub\u003e due to its potential to worsen stress-induced inflammatory processes.\u003c/p\u003e \u003cp\u003e This study's strength lies in its adjustment for socioeconomic levels in the statistical model at both individual and regional levels, using insurance fees and the regional deprivation index, respectively. As Korea has higher PM\u003csub\u003e2.5\u003c/sub\u003e concentrations in areas with better socioeconomic levels, it can act as a serious confounding variable. Therefore, the model was designed to account for this. Furthermore, this study benefits from the ability to adjust for various individual health behaviors and medical histories by utilizing health checkup data from the KNHIS. To the best of our knowledge, this study is the first in Korea to analyze the mortality effects of long-term exposure to PM\u003csub\u003e2.5\u003c/sub\u003e while considering disability types in detail. The results provide valuable data for the development of healthcare policies for individuals with disabilities.\u003c/p\u003e \u003cp\u003eThis study had some limitations. As we used regional PM\u003csub\u003e2.5\u003c/sub\u003e, there may be errors in the actual PM\u003csub\u003e2.5\u003c/sub\u003e exposure of individuals. However, we attempted to minimize this error by assigning PM\u003csub\u003e2.5\u003c/sub\u003e, considering personal addresses on a monthly basis. Another limitation of this study is that the results are not fully generalizable because we used 2006 as the year of initial entry and analyzed PM\u003csub\u003e2.5\u003c/sub\u003e exposure in the year of entry. Further research is required to determine the health effects of long-term PM\u003csub\u003e2.5\u003c/sub\u003e exposure in individuals with disabilities that occurs over multiple periods. Future research should address these limitations by studying the changes in the definition of long-term exposure in PWD and by advancing the Cox proportional hazard model with time-varying exposure.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe findings of this study reveal that PWD, especially those with severe disabilities or specific impairments such as kidney problems or brain lesions, exhibit high mortality risks from all-cause, non-accidental causes, and cardiovascular diseases due to PM\u003csub\u003e2.5\u003c/sub\u003e. These results highlight the increased vulnerability of PWD to air pollution and emphasize the need for tailored interventions that consider disability type and severity. This study underscores the importance of strengthening air quality standards and developing targeted healthcare approaches to protect vulnerable populations.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003econfidence intervals\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCMAQ\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCommunity Multiscale Air Quality\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCOPD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003echronic obstructive pulmonary disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003edatabase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehazard ratios\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eICD-10\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInternational Classification of Disease,10th version\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eKNHIS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eKorean National Health Insurance System\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eND\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003enon-disabled\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePIN\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003epersonal identification number\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eparticulate matter\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePM2.5\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003efine particulate matter\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePWD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003epeople with disabilities\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePY\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eperson-years.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical approval and Consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study protocol was approved by the Institutional Review Board (IRB) of Dankook University (IRB NO. DKU 2023\u0026ndash;04‑004-004). We confirmed that all methods were carried out according to relevant guidelines and regulations. This study was conducted using secondary data; the need for informed consent was waived by the IRB of Dankook University (IRB NO. DKU 2023\u0026ndash;04‑004-004).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability\u0026nbsp;of Data and Materials\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe mortality data for NDs and PWD can be obtained from the KNHIS DB at [https://nhiss.nhis.or.kr/bd/ab/bdaba021eng.do] and MicroData Integrated Service (MDIS) of Statistics Korea at [https://mdis.kostat.go.kr/eng/index.do;jsessionid=xE2MQqswZ8GURLn21zZjuCvWiiepxG2tEHA4BF6JyulkCzBQzqz5 DnvgFcuOQ2pq.mdexwas2_servlet_engine2]. For using the KNHIS DB, there is a need for reasonable requests, IRB admission, and permission from KNHIS and it is protected by strict confidentiality. For using the MDIS data, there is a need for reasonable requests, IRB admission, and permission from Statistics Korea and it is protected by strict confidentiality. The PM\u003csub\u003e2.5\u003c/sub\u003e modeling data is not available to the public due to the results of an ongoing project.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Research Foundation of Korea grant funded by the Korean government (No. 2023R1A2C1002801).\u003cstrong\u003e\u0026nbsp;The funding body had or has no involvement in study design; collection, management, analysis, and interpretation of data; or the decision to submit for publication. The funding body will be informed of any planned publications, and documentation provided.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJC -\u0026nbsp;\u003c/strong\u003edata curation, software, visualization, writing (original draft), writing (review and editing).\u003cstrong\u003e\u0026nbsp;HL -\u0026nbsp;\u003c/strong\u003edata curation, data interpretation, writing (review and editing). \u003cstrong\u003eHJK -\u0026nbsp;\u003c/strong\u003edata collection, data interpretation, writing (review and editing). \u003cstrong\u003eMH -\u0026nbsp;\u003c/strong\u003edata interpretation, writing (review and editing).\u003cstrong\u003e\u0026nbsp;SK -\u0026nbsp;\u003c/strong\u003edata collection, writing (review and editing).\u003cstrong\u003e\u0026nbsp;KHC -\u0026nbsp;\u003c/strong\u003econceptualization, study design, funding acquisition, methodology, formal analysis, software, supervision, project administration, literature review, data interpretation, writing (original draft), writing (review and editing). All authors had final responsibility for the decision to submit for publication. \u003cstrong\u003eKHC\u003c/strong\u003e had final responsibility for submission and is the guarantor.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003e\u003cspan\u003eWorld Health Organization. Ambient (outdoor) air pollution: World Health Organization; 2022 [Available from: https://www.who.int/news-room/fact-sheets/detail/ambient-(outdoor)-air-quality-and-health accessed Jan. 23 2024.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eWorld Health Organization. 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J Am Coll Cardiol. 2002;39(6):935\u0026thinsp;\u0026minus;\u0026thinsp;42. doi: 10.1016/s0735-1097(02)01715-1\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eKaufman JD, Adar SD, Barr RG, Budoff M, Burke GL, Curl CL, et al. Association between air pollution and coronary artery calcification within six metropolitan areas in the USA (the Multi-Ethnic Study of Atherosclerosis and Air Pollution): a longitudinal cohort study. Lancet. 2016;388(10045):696\u0026ndash;704. doi: 10.1016/s0140-6736(16)00378-0\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eDiez Roux AV, Auchincloss AH, Franklin TG, Budoff M, Burke GL, Curl CL, et al. Long-term Exposure to Ambient Particulate Matter and Prevalence of Subclinical Atherosclerosis in the Multi-Ethnic Study of Atherosclerosis. Am J Epidemiol. 2007;167(6):667\u0026thinsp;\u0026minus;\u0026thinsp;75. doi: 10.1093/aje/kwm359\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eLiu Y-Z, Wang Y-X, Jiang C-L. Inflammation: The Common Pathway of Stress-Related Diseases. Front Hum Neurosci. 2017;11:316 doi: 10.3389/fnhum.2017.00316\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eMaydych V. The Interplay Between Stress, Inflammation, and Emotional Attention: Relevance for Depression. Front Neurosci. 2019;13 doi: 10.3389/fnins.2019.00384\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eThangavel P, Park D, Lee Y-C. Recent Insights into Particulate Matter (PM2.5)-Mediated Toxicity in Humans: An Overview. Int J Environ Res Public Health. 2022;19(12):7511. doi: 10.3390/ijerph19127511\u003c/span\u003e\u003c/li\u003e\n\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":"PM2.5, mortality, people with disabilities, disability type, disability severity","lastPublishedDoi":"10.21203/rs.3.rs-4884473/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4884473/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003ePeople with disabilities (PWD) may be more vulnerable to the adverse health effects of air pollution than the general population. This study examined the association between long-term exposure to ambient fine particulate matter (PM\u003csub\u003e2.5\u003c/sub\u003e) and mortality risk in PWD considering disability type and severity.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eData from the Korean National Health Insurance Service and Statistics Korea were analyzed in this retrospective cohort study, including 2,880,265 individuals (41,501,709 person-years), of which 176,410 were PWD (2,011,231 person-years). PM\u003csub\u003e2.5\u003c/sub\u003e exposure was estimated using simulated data from 2006 to 2019. Causes of death included all causes, non-accidental causes, respiratory disease, lung cancer, and cardiovascular disease. Cox proportional hazard models were used to estimate hazard ratios (HRs) for mortality associated with PM\u003csub\u003e2.5\u003c/sub\u003e stratified by disability type and severity.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003ePWD, particularly those with severe disabilities or specific impairments such as kidney problems or brain lesions, showed significantly high mortality risks from all causes, non-accidental causes, and cardiovascular diseases due to PM\u003csub\u003e2.5\u003c/sub\u003e exposure. For individuals with kidney impairment, the HR (95% confidence interval) for mortality on increasing PM\u003csub\u003e2.5\u003c/sub\u003e by 10 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e was 1.79 (1.27\u0026ndash;2.52) from all causes, while for those with brain lesions, it was 1.10 (1.00\u0026ndash;1.22) from cardiovascular disease. PWD were not susceptible to mortality from respiratory causes.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThis study highlights the increased vulnerability of PWD, especially those with severe disabilities or specific impairments, to the adverse effects of PM\u003csub\u003e2.5\u003c/sub\u003e exposure. Targeted interventions tailored to disability type and severity, along with stricter air quality standards and specialized healthcare approaches, are needed.\u003c/p\u003e","manuscriptTitle":"Ambient Fine Particulate Matter and Mortality Risk among People with Disability in Korea Based on the National Health Insurance Database: A Retrospective Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-09 14:55:17","doi":"10.21203/rs.3.rs-4884473/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-08-13T06:52:20+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-08-13T00:55:10+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-08-13T00:55:06+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2024-08-09T05:23:02+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":"6d6f378a-1baa-4fb8-8345-a6ff5824eef6","owner":[],"postedDate":"September 9th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-05-12T16:01:17+00:00","versionOfRecord":{"articleIdentity":"rs-4884473","link":"https://doi.org/10.1186/s12889-025-22923-w","journal":{"identity":"bmc-public-health","isVorOnly":false,"title":"BMC Public Health"},"publishedOn":"2025-05-05 15:57:26","publishedOnDateReadable":"May 5th, 2025"},"versionCreatedAt":"2024-09-09 14:55:17","video":"","vorDoi":"10.1186/s12889-025-22923-w","vorDoiUrl":"https://doi.org/10.1186/s12889-025-22923-w","workflowStages":[]},"version":"v1","identity":"rs-4884473","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4884473","identity":"rs-4884473","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}