Effectiveness of primary care-based chronic disease management program on glycated hemoglobin levels

Effectiveness of primary care-based chronic disease management program on glycated hemoglobin levels | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Effectiveness of primary care-based chronic disease management program on glycated hemoglobin levels Juan Kim, Il Yun, Eun-Cheol Park, Min Jin Ha This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4209917/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background : Diabetes is one of the most common causes of cardiovascular disease and has led to death over time. For better management of glycated hemoglobin levels, which is a well-known index in diagnosing diabetes, many countries have been launching chronic disease management programs. Therefore, this study aimed to evaluate the effectiveness of the primary care-based chronic disease management integrated pilot program (PCDMP) in controlling glycated hemoglobin (HbA1c) levels in Korea. Nation-wide data from the 2019–2021 Korea National Health and Nutrition Examination Survey were used. Methods : We analyzed nationwide health examination data combined with various regional data sources. Using appropriate inclusion criteria for this study, a total of 13,901 individuals were suitable for the analysis. A generalized linear mixed model was applied to consider the clustered structure of the regional level data, where individual-level data, containing demographic characteristics and health-related information, were nested. Results : Individuals living in areas with a low PCDMP participation (< 30.37%) had odds (95% CI, 1.08–1.82) of exhibiting glycated hemoglobin levels ≥ 6.5% greater than did those residing in areas with a high PCDMP participation (≥ 30.37%). According to the stratified analysis of HbA1c levels, people residing in areas with low PCDMP participation had significantly greater odds of having an HbA1c > 6.5%, which may indicate diabetes mellitus. Conclusion : The lower the number of internal medicine clinics in an area participating in PCDMP was, the greater the possibility of an HbA1c > 6.5%. Our findings advocate the need for the government to pay attention to chronic disease management programs, which may lead individuals to have normal HbA1c levels (< 6.5%). diabetes glycated hemoglobin HbA1c primary care chronic disease management Figures Figure 1 Background Chronic diseases are significant global health challenges that contribute to morbidity and mortality worldwide. Among chronic conditions, diabetes mellitus is a leading cause of cardiovascular disease and death. Various factors affect the development of diabetes, such as obesity, unhealthy diets, and aging [ 1 , 2 ]. Approximately 463 million adults worldwide were living with diabetes in 2019, and this number is expected to increase to 700 million by 2045, according to the International Diabetes Federation (IDF) [ 3 ]. Hemoglobin A1c (HbA1c), a glycated form of hemoglobin, is a critical marker for monitoring long-term glucose control in individuals with diabetes [ 4 ]. Elevated HbA1c levels are indicators of poor glycemic control and an increased risk of diabetes-related complications [ 5 ]. Thus, maintaining appropriate HbA1c levels is a fundamental goal in diabetes treatment. The management of chronic diseases such as diabetes requires various approaches, including lifestyle modifications, pharmacological interventions, and patient education. Therefore, chronic disease management programs have emerged as a promising strategy for improving the care and outcomes of individuals living with diabetes [ 6 ]. In South Korea, the ‘primary care-based chronic disease management integrated pilot program (PCDMP)’ was initiated in 2019 to integrate the ‘community-based primary care pilot project’ started in 2014 and the ‘chronic disease management charges pilot project’ started in 2016 to improve primary care-based and patient-centered medical systems [ 7 ]. Recent research has shown a growing interest in evaluating the effectiveness of chronic disease management programs in lowering HbA1c levels among individuals with diabetes. Numerous studies have explored the association between participation in such programs and glycemic control, with varying results. Some studies have reported significant reductions in HbA1c levels among program participants [ 8 , 9 , 10 ], while others have found more modest or inconclusive effects [ 11 ]. However, no nationwide study has examined the impact of participation in a pilot program at a clinic in a residential area on HbA1c levels. Therefore, this study aimed to assess the effects of participation in a pilot program on hemoglobin A1c levels in each residential area using a multilevel model. Materials and Methods Data and study population This study integrated three nation-wide big data sources. The first was the 2019–2021 Korea National Health and Nutrition Examination Survey (KNHANES), a cross-sectional and national survey in which individual-level data were gathered annually to evaluate the health status, health behavior, and nutritional status of the South Korean population to provide basic data for developing nationwide health policies [ 12 ]. Our study needed both individual-level and region-level information to describe the relationship between regional PCDMP participation rates and individual HbA1c levels, so we integrated the first dataset with two datasets from two big data sources. The second dataset, for region-level data, was the 2021 medical use statistics by region from the National Health Insurance Service (NHIS) [ 13 ]. The last dataset includes the participation rate of internal medicine clinics in the PCDMP manually calculated using data from the PCDMP website by the Korea Health Promotion Institute (KHEPI) and the Health Insurance Review & Assessment Service (HIRA) bigdata open portal [ 14 ]. Among 22,559 individuals who completed KNHANES, we excluded ones who had been diagnosed with diabetes by doctors. Then, subjects with age < 19 are omitted as well as individuals whose HbA1c levels missing. The STROBE (Strengthening The Reporting of Observational Studies in Epidemiology) diagram in Fig. 1 depicts the inclusion and exclusion criteria for the study population used in our study. At all, a total of 13,901 individuals were included in the study. There was no need for additional ethical approval because all participants provided informed consent in advance, and the data were available to the public. Measures The outcome variable was the HbA1c percentage, the best benchmark for tracking glycemic control and a potential diabetes indicator. Then, we generated a binary variable indicating an HbA1c level > 6.5% as high HbA1c and an HbA1c level \(\le\) 6.5 as low HbA1c [ 15 ]. On the other hand, for stratified analysis based on HbA1c level, we generated another categorical variable with 3 levels as low, middle, and high HbA1c when the HbA1c level of an individual is \(\le\) 5.7%, > 5.7% and 6.5%, respectively [ 16 ]. The variable of interest was the regional participation rate of the PCDMP, observed by the KHEPI. The participation rate ranged from 0–62.07% and was determined by dividing the number of internal medicine clinics participating in the PCDMP by the total number of internal medicine clinics for each of the 17 regions in Korea. A binary variable was formed based on the criterion that residential areas with a participation rate greater than the median (30.37%) indicate a high participation and areas with a participation rate less than the median are considered to indicate a low participation. For individual-level covariates, socioeconomic variables (sex, age, household income, occupation), health behavior patterns (drinking and smoking), and health condition factors (body mass index [BMI] and chronic disease status) were included. The health behavior patterns are categorized by the severity of drinking and smoking. Then, BMI is divided into obesity, normal, and low weight, and chronic disease status has 4 subgroups: hypertension, hyperlipidemia, both, and none. Moreover, the regional-level covariates were residential area, number of hospital beds per 1000 people, number of clinics per 1000 people, and number of doctors per 1000 people. They are binary variables indicating whether a residential area’s value is greater than or less than the median of each variable. Statistical analysis Descriptive statistics were expressed as frequencies (n) and percentages (%), and we investigated the association between HbA1c and socioeconomic, health behavior, and health condition characteristics using Pearson’s chi-square tests. We performed a multilevel analysis to examine the association between PCDMP participation and HbA1c. We conducted the multilevel logistic regression model that allows a random intercept for region and fixed effects for all individual-level covariates as well as region-level covariates. For sensitivity analysis, we conducted the multilevel multinomial logistic regression analysis to investiate the association between PCDMP participation and HbA1c stratified by the HbA1c levels. The main findings are displayed as adjusted odds ratios (aORs) and 95% confidence intervals (CIs), and the statitistical significance is decided by controlling type 1 error rate at 5%. For all the statistical analyses, SAS version 9.4 (SAS Institute, Inc.; Cary, NC, USA) was used, and p-value < 0.05 is set as a significant level at a 95% confidence level. Results Figure 1 presents the STROBE flow diagram describing the study population. We initially acquired 22,559 individuals from KNHANES. From the population, 3,868 people were excluded due to age criteria. Then, another 2,793 individuals who had been diagnosed with diabetes by doctors were excluded. From the remaining 15,898 population, missing values in HbA1c levels are excluded (n = 381). Finally, after calculating the regional PCDMP participation rate, we only included 13,901 individuals who live in regions where the PCDMP participation rate is not 0%. The general features of the study population are displayed in Table 1. A total of 617 people (4.44%) out of the 13,901 people eligible for the analysis had high HbA1c, as indicated by an HbA1c level of 6.5% or higher. However, only individuals who were not diagnosed with diabetes by doctors were included. According to previous reports, obese men older than 60 years with high incomes, a blue-collar job, smoking, not drinking, both hypertension and hyperlipidemia and poor subjective health status were more likely to have high HbA1c. Furthermore, a proportion of individuals living in rural cities with low PCDMP participation, low number of doctors, high number of hospital beds, and high number of clinics per 1000 people had high HbA1c. Table 1. General characteristics of the study population Characteristics HbA1c TOTAL High Low P value N % N % N % 13,901 100 617 4.44 13,284 95.56 Region Level PCDMP participation 0.002 High (> 30.37%) 7,485 53.85 298 3.98 7,187 96.02 Low (≤ 30.37%) 6,416 46.15 319 4.97 6,097 95.03 Region 0.007 Metropolitan city 6,346 45.65 249 3.92 6,097 96.08 Rural 7,555 54.35 368 4.87 7,187 95.13 Hospital beds per 1000 people 0.020 High (> 33.23) 4,757 34.22 238 5.00 4,519 95.00 Low (≤ 33.23) 9,144 65.78 379 4.14 8,765 95.86 Clinics per 1000 people 0.09) 3,775 27.16 204 5.40 3,571 94.60 Low (≤ 0.09) 10,126 72.84 413 4.08 9,713 95.92 Doctors per 1000 people 0.005 High (> 2.58) 7,485 53.85 298 3.98 7,187 96.02 Low (≤ 2.58) 6,416 46.15 319 4.97 6,097 95.03 Individual Level Sex <.001 Male 6,083 43.76 328 5.39 5,755 94.61 Female 7,818 56.24 289 3.70 7,529 96.30 Age <.001 19~39 4,091 29.43 45 1.10 4,046 98.90 40~49 2,633 18.94 92 3.49 2,541 96.51 50~59 2,621 18.85 145 5.53 2,476 94.47 over 60 4,556 32.77 335 7.35 4,221 92.65 Income High 2,290 16.47 161 7.03 2,129 92.97 Middle 7,208 51.85 318 4.41 6,890 95.59 Low 4,403 31.67 138 3.13 4,265 96.87 Occupation <.001 White-collar 3,637 26.16 110 3.02 3,527 96.98 Pink-collar 1,882 13.54 67 3.56 1,815 96.44 Blue-collar 3,087 22.21 189 6.12 2,898 93.88 Housewife or Inoccupation 5,295 38.09 251 4.74 5,044 95.26 BMI <.001 Obesity 4,784 34.41 383 8.01 4,401 91.99 Normal 8,392 60.37 222 2.65 8,170 97.35 Low weight 725 5.22 12 1.66 713 98.34 Drinking 0.034 Frequently 5,936 42.70 249 4.19 5,687 95.81 Occasionally 4,115 29.60 169 4.11 3,946 95.89 None 3,850 27.70 199 5.17 3,651 94.83 Smoking <.001 Current smoker 2,322 16.70 122 5.25 2,200 94.75 Ex-smoker 3,143 22.61 173 5.50 2,970 94.50 None 8,436 60.69 322 3.82 8,114 96.18 Chronic Disease Status <.001 Only hypertension 1648 11.86 136 8.25 1,512 91.75 Only hyperlipidemia 1103 7.93 57 5.17 1,046 94.83 Both 1312 9.44 120 9.15 1,192 90.85 None 9839 70.78 304 3.09 9,535 96.91 Table 2 presents the results of the multilevel analysis for the association between regional participation in the PCDMP and individual HbA1c. According to the full model, adjusting individual and region-level covariates, the regional participation of the PCDMP had a significant effect on HbA1c. In detail, the odds of an individual exhibiting high HbA1c were 1.4 times greater for individuals residing in areas with a low PCDMP participation than those living in areas with a high PCDMP participation. (95% CI, 1.08–1.82) Table 2. Adjusted odds ratios of HbA1c by characteristics of individual- and region-level (multilevel model) Variables HbA1c Full model aOR 95% CI Region Level PCDMP participation High (> 30.37%) 1.00 Low (≤ 30.37%) 1.40 (1.08 1.82) Region Metropolitan city 1.00 Rural 0.86 (0.66 1.13) Hospital bed per 1000 people High (> 33.23) 1.00 Low (≤ 33.23) 0.90 (0.75 1.09) Clinic per 1000 people High (> 0.09) 1.00 Low (≤ 0.09) 1.03 (0.83 1.27) Individual Level Sex Male 1.00 Female 0.75 (0.59 0.96) Age 19~39 1.00 40~49 3.02 (2.10 4.36) 50~59 4.84 (3.41 6.88) over 60 5.63 (3.96 8.00) Income High 1.00 Middle 1.50 (1.15 1.97) Low 1.22 (0.99 1.52) Occupation White-collar 1.00 Pink-collar 0.92 (0.67 1.27) Blue-collar 1.05 (0.81 1.37) Housewife or Inoccupation 1.01 (0.77 1.32) BMI Obesity 2.99 (2.51 3.55) Normal 1.00 Low weight 0.75 (0.41 1.35) Drinking Frequently 0.97 (0.78 1.20) Occasionally 1.13 (0.90 1.41) None 1.00 Smoking Current smoker 1.38 (1.05 1.82) Ex-smoker 1.06 (0.82 1.37) None 1.00 Chronic Disease Status Only hypertension 1.38 (1.09 1.73) Only hyperlipidemia 1.06 (0.79 1.44) Both 1.42 (1.11 1.81) None 1.00 aOR, adjusted odds ratio, CI, confidence interval, SE, standard error *Doctor variable are a linear combination of other variables as shown. *Referece: HbA1c = abnormal The results of the sensitivity analysis by HbA1c level are shown in Table 3. People living in areas with low participation of PCDMP were 1.08 times (95% CI, 0.96-1.21) and 1.40 times (95% CI, 1.08-1.82) more likely to develop middle HbA1c and high HbA1c, respectively. In other words, there is a greater probability of developing middle or high HbA1c than low HbA1c in residential areas where few clinics participate in the PCDMP. Table 3. Results of subgroup analysis stratified by dependent variables Variables HbA1c Low (HbA1c ≤ 5.7%) Middle (5.7% 6.5%) OR OR 95% CI OR 95% CI PCDMP participation High (> 30.37%) 1 Low (≤ 30.37%) 1.08 (0.96 - 1.21) 1.4 (1.08 - 1.82) aOR, adjusted odds ratio, CI, confidence interval *p<.0001 Discussion This study investigated how PCDMP affects individuals' health outcomes, specifically, their HbA1c levels. As described earlier, the PCDMP aims to increase patient acceptance and establish a chronic disease management system in local clinics [ 11 ]. Additionally, we utilized HbA1c levels > 6.5% as a high HbA1c indicator [ 17 ]. To evaluate the PCDMP as a well-established program and progress to become an official program of the nation, we should investigate whether participating in the PCDMP is helpful for individuals living in such residential areas in terms of HbA1c. For instance, many studies have explored the effect of a PCDMP on the risk of complications and managing hypertension in hypertension patients in South Korea [ 18 , 19 ] and concluded that the program had positive effects on the health of hypertension patients. This study, on the other hand, examined the effects of participating in the PCDMP on HbA1c using mixed-effects logistic regression. The main findings of this study are as follows: First, people living in areas with low PCDMP participation have odds of exhibiting high HbA1c, which is 1.40 times greater than that of residents in areas with high PCDMP participation. Second, the likelihood of having high HbA1c was significantly greater in those living in areas with low PCDMP participation. In other words, living in areas with a low PCDMP increases the risk of exhibiting high HbA1c. Our findings are similar to those of previous studies investigating the effects of participating in PCDMP on the health behaviors of hypertension patients [ 17 , 18 ]. One of the early studies on the effect of the PCDMP on the risk of complications in patients with hypertension in Korea found that the hazard ratio was significantly lower for patients participating in the PCDMP than for patients not participating in the program for all 4 complications—hypertension, myocardial infarction, stroke, chronic kidney disease, and heart failure [ 17 ]. Globally, several studies have explored the positive impact of chronic disease management programs in primary care on diabetes management [ 19 , 20 ]. However, to the best of our knowledge, this is the first study to examine the association between regional PCDMP participation and individual HbA1c levels in nondiabetic individuals utilizing data from local clinics’ PCDMP participation and the KNHANES containing individual health information. Our study is distinguished from early findings in that the regional covariates were adjusted with a multilevel approach using mixed-effects considering the clustering effects of regions in the datasets. Furthermore, external validity would be high when a large sample is included. Our study's significant conclusions included the possibility that PCDMP may help individuals who are not diagnosed with diabetes by doctors not to get diabetes, which may lead to a decreased possibility of reduced healthcare expenses [ 21 ]. Consequently, the PCDMP might be an affordable diabetes management approach. On the other hand, research has shown that the PCDMP's diabetes education program and required testing in local healthcare facilities have no appreciable impact on diabetic patients' ability to maintain a healthy blood sugar level, an alternative diabetes index [ 22 ]. Nonetheless, this study has a few limitations. First, the KNHANES data used in the study were secondary, and it was not feasible to perform a time-series analysis to monitor changes in individual health status as the survey items differed annually. A second constraint was that residential areas were divided into only 17 regions in Korea when we calculated the participation of the PCDMP. In addition, the current PCDMP is a pilot program run by the Korean government; thus, if it becomes an official program, the procedures and details of the program are subject to change. Third, individuals’ HbA1c levels are divided into two categories, \(\ge\) 6.5% and < 6.5%, where information may be lost about individuals with HbA1c levels of nearly 6.5%. Additionally, diagnosing diabetes is not solely based on HbA1c levels; rather, other tests, including screening tests and glycemia tests, may be used; moreover, there are no perfect guidelines for diabetes diagnosis [ 23 – 25 ]. Finally, in statistical analysis, few regional covariates are linear combinations of other variables, which leads to unreliable and unstable estimates of regression coefficients [ 26 ]. Conclusion At the national level, efforts are still being made to effectively manage chronic illnesses through primary care. The Korean government aims to run integrated national primary care-based chronic disease management programs and regulations that emphasize the positive aspects of the current pilot programs while addressing their shortcomings. This study is significant since it provides a comprehensive evaluation of the efficacy of the PCDMP in Korea, increasing the importance of primary care. By enhancing continuity of care and averting complications, increasing the role of primary diabetes care can greatly enhance the health outcomes of individuals. These findings offer essential information to help improve primary care, which is increasingly important globally. Abbreviations PCDMP: primary care-based chronic disease management integrated pilot program; HbA1c: glycated hemoglobin; KNHANES: Korea National Health and Nutrition Examination Survey; NHIS: National Health Insurance Service; CI: Confidence interval; IDF: International diabetes federation; KHEPI: Korea Health Promotion Institute; HIRA: Health Insurance Review & Assessment Service; BMI: Body mass index; GLMM: Generalized linear mixed model; aOR: Adjusted odds ratio Declarations Acknowledgments We would like to thank the members of the Institute of Health Services Research at Yonsei University for their advice on the further development of this study. Authors’ contributions Juan Kim and Il Yun made substantial contributions to the concept or design of the work; Juan Kim and Il Yun contributed to the acquisition, analysis, or interpretation of the data; Juan Kim drafted the article; and Eun-Cheol Park and Min Jin Ha critically revised the article for important intellectual content. All the authors approved the version to be published. Funding Min Jin Ha was supported by National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) [No. 2022R1A2C1091488]. Data availability statement The datasets analyzed in the present study are publicly available. First, the KNHANES data are available online: https://knhanes.kdca.go.kr. Second, the NHIS data are available online: https://www.nhis.or.kr/nhis/together/wbhaec06900m01.do. Third, the HIRA big data portal data are available online: https://opendata.hira.or.kr/op/opc/olapYadmStatInfoTab4.do. Ethics approval and consent to participate As the KNHANES complies with the Declaration of Helsinki and provides publicly accessible data, further ethical approval for the use of these data was not required. Consent for publication Not applicable Competing interests The authors declare no competing interests to declare. References Bae JH, Han KD, Ko SH, Yang YS, Choi JH, Choi KM, et al (2011). Diabetes Fact Sheet in Korea 2021. Diabetes Metab J;46:417-26. Ramachandran A, Wan Ma RC, Snehalatha C (2021). Diabetes in Asia. The Lancet 2010;375:408-18. IDF. IDF Diabetes Atlas 2021–10th edition. Brussels, Belgium: International Diabetes Federation (IDF). David B Sacks, David E Bruns, David E Goldstein, Noel K Maclaren, Jay M McDonald, Marian Parrott (2002), Guidelines and Recommendations for Laboratory Analysis in the Diagnosis and Management of Diabetes Mellitus, Clinical Chemistry , Volume 48, Issue 3, Pages 436–472, https://doi.org/10.1093/clinchem/48.3.436 American Diabetes Association (2006); Standards of Medical Care in Diabetes–2006. Diabetes Care ; 29 (suppl_1): S4–S42. https://doi.org/10.2337/diacare.29.s1.06.s4 American Diabetes Association Professional Practice Committee (2022); 7. Diabetes Technology: Standards of Medical Care in Diabetes—2022 . Diabetes Care ; 45 (Supplement_1): S97–S112. https://doi.org/10.2337/dc22-S007 Cho B (2021). Review and assessment to support chronic noncommunicable diseases management in the primary care in Korea. Health Insurance Review & Assessment Service Research;1:31-5. Lorig, K. R., Ritter, P., Stewart, A. L., Sobel, D. S., Brown Jr, B. W., Bandura, A., ... & Holman, H. R. (2001). Chronic disease self-management program: 2-year health status and health care utilization outcomes. Medical care, 39(11), 1217-1223. Hyun, M. K., Lee, J. W., & Ko, S.-H. (2023). Chronic disease management program applied to type 2 diabetes patients and prevention of diabetic complications: A retrospective cohort study using nationwide data. BMC Public Health, 23(1). https://doi.org/10.1186/s12889-023-15763-z Ahn, S., Basu, R., Smith, M. L., Jiang, L., Lorig, K., Whitelaw, N., & Ory, M. G. (2013). The impact of chronic disease self-management programs: Healthcare Savings through a community-based intervention. BMC Public Health, 13(1). https://doi.org/10.1186/1471-2458-13-1141 Rothman, R. L., Malone, R., Bryant, B., Wolfe, C., Padgett, P., DeWalt, D. A., ... & Schillinger, D. (2005). The Spoken Knowledge in Low Literacy in Diabetes scale: a diabetes knowledge scale for vulnerable patients. Diabetes Educator, 31(2), 215-224. doi:10.1177/0145721705275002 Kim, H. et al (2013) . Factors affecting the validity of self-reported data on health services from the community health survey in Korea. Yonsei medical journal 54 , 1040-1048. Seong SC, Kim YY, Khang YH, Park JH, Kang HJ, Lee H, et al (2017). Data resource profile: the national health information database of the National Health Insurance Service in South Korea. Int J Epidemiol. 46:799–800. Service HIRaA (2022). The results for diabetes quality assessment 2020. Wonju: HIRA. Abbas, Y. Glycosylated Hemoglobin (2011). The importance in management of type 2 diabetes. J. Stress Physiol. Biochem.7, 122–129. Sherwani, S. I., Khan, H. A., Ekhzaimy, A., Masood, A., & Sakharkar, M. K. (2016). Significance of HbA1c Test in Diagnosis and Prognosis of Diabetic Patients. Biomarker insights, 11, 95–104. https://doi.org/10.4137/BMI.S38440 Sherwani SI, Khan HA, Ekhzaimy A, Masood A, Sakharkar MK (2016). Significance of HbA1c Test in Diagnosis and Prognosis of Diabetic Patients. Biomarker Insights. 11. doi:10.4137/BMI.S38440 Lee SA, Park H, Kim W, Song SO, Lim H, Chun SY (2016). The Effect of Chronic Disease Management Program on the Risk of Complications in Patients With Hypertension in Korea. J Korean Med Sci. 37(31):e243. https://doi.org/10.3346/jkms.2022.37.e243 Lee E‒W, Kim H-S, Yoo B-N, Lee E-J, Park J-H (2022). Effect of a Primary Care-Based Chronic Disease Management Program for Hypertension Patients in South Korea. Iran J Public Health. 51(3):624-633. Forjuoh SN, Ory MG, Jiang L, Vuong AM, Bolin JN (2014). Impact of chronic disease self-management programs on type 2 diabetes management in primary care. World J Diabetes. 5(3):407-14. doi: 10.4239/wjd.v5.i3.407 Clément Pimouguet, Mélanie Le Goff, Rodolphe Thiébaut, Jean François Dartigues and Catherine Helmer (2011). Effectiveness of disease-management programs for improving diabetes care: a meta-analysis CMAJ. 183 (2) E115-E127; DOI: https://doi.org/10.1503/cmaj.091786 Jeon SY, Lee mSA, Jang JH, Song SO, Kim HK, Yim HS, et al (2020). Cost-effectiveness analysis of clinic-level chronic disease management system focusing on hypertension. National health insurance service Ilsan hospital. Cheong W, Yim J, Oh D-K, Im J-S, Ko KP, Kim YM (2013). Effects of chronic disease management based on clinics for blood pressure or glycemic control in patients with hypertension or type 2 diabetes mellitus. Journal of agricultural medicine and community health 38:108-15. Inzucchi, S. E. (2012). Diagnosis of diabetes. New England Journal of Medicine , 367 (6), 542–550. https://doi.org/10.1056/nejmcp1103643 Barr, R. G., Nathan, D. M., Meigs, J. B., & Singer, D. E. (2002). Tests of glycemia for the diagnosis of type 2 diabetes mellitus. Annals of Internal Medicine , 137 (4), 263. https://doi.org/10.7326/0003-4819-137-4-200208200-00011 Goldstein, D. E., Little, R. R., Lorenz, R. A., Malone, J. I., Nathan, D., Peterson, C. M., & Sacks, D. B. (2004). Tests of glycemia in diabetes. Diabetes care , 27 (7), 1761-1773. Midi, H., Sarkar, S. K., & Rana, S. (2013). Collinearity diagnostics of binary logistic regression model. Journal of Interdisciplinary Mathematics , 13 (3), 253–267. https://doi.org/10.1080/09720502.2010.10700699 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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-4209917","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":288093533,"identity":"00bee6a5-4b7a-487a-9547-0b5e0c4baef8","order_by":0,"name":"Juan Kim","email":"","orcid":"","institution":"Yonsei University College of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Juan","middleName":"","lastName":"Kim","suffix":""},{"id":288093534,"identity":"f646580f-eed7-4ff5-81bc-ef1fbb7f81fd","order_by":1,"name":"Il Yun","email":"","orcid":"","institution":"Yonsei University","correspondingAuthor":false,"prefix":"","firstName":"Il","middleName":"","lastName":"Yun","suffix":""},{"id":288093535,"identity":"1158d491-5195-4837-862a-11d5947e1cd6","order_by":2,"name":"Eun-Cheol Park","email":"","orcid":"","institution":"Yonsei University College of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Eun-Cheol","middleName":"","lastName":"Park","suffix":""},{"id":288093539,"identity":"c746264a-b341-4a6b-b2ae-7c7461d7af80","order_by":3,"name":"Min Jin Ha","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAo0lEQVRIiWNgGAWjYFCCMwwMHxgYEkjTwjiDRC08DMw8JGnRbTx78LNt2+E8+Qbmhx+I0mJ24FyydG7b4WKDA2zGEkRqOWMA0pK4AcgmzmFALca/LYFa5jewfyNai5k0I1BLwwEe4m0xs+w5l5644TBPMZF+uXHG+MaPMuvE+e3tG4kLMQaJAwwMjGxABjNx6oGAvwFI/CFa+SgYBaNgFIxEAACRBjPfTmfXlwAAAABJRU5ErkJggg==","orcid":"","institution":"Yonsei University","correspondingAuthor":true,"prefix":"","firstName":"Min","middleName":"Jin","lastName":"Ha","suffix":""}],"badges":[],"createdAt":"2024-04-03 04:44:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4209917/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4209917/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54394278,"identity":"0f5decfa-d120-4f31-90f1-5cd605e433ee","added_by":"auto","created_at":"2024-04-09 21:11:14","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":24236,"visible":true,"origin":"","legend":"\u003cp\u003eSTROBE diagram\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4209917/v1/5447034c1f59693ce1ce2efc.jpeg"},{"id":90970916,"identity":"abb99268-6630-4af6-b79c-2f86de19963a","added_by":"auto","created_at":"2025-09-10 07:39:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1239321,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4209917/v1/e12a70dd-4304-4355-a6b7-e6cf48551536.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Effectiveness of primary care-based chronic disease management program on glycated hemoglobin levels","fulltext":[{"header":"Background","content":"\u003cp\u003eChronic diseases are significant global health challenges that contribute to morbidity and mortality worldwide. Among chronic conditions, diabetes mellitus is a leading cause of cardiovascular disease and death. Various factors affect the development of diabetes, such as obesity, unhealthy diets, and aging [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Approximately 463\u0026nbsp;million adults worldwide were living with diabetes in 2019, and this number is expected to increase to 700\u0026nbsp;million by 2045, according to the International Diabetes Federation (IDF) [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHemoglobin A1c (HbA1c), a glycated form of hemoglobin, is a critical marker for monitoring long-term glucose control in individuals with diabetes [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Elevated HbA1c levels are indicators of poor glycemic control and an increased risk of diabetes-related complications [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Thus, maintaining appropriate HbA1c levels is a fundamental goal in diabetes treatment. The management of chronic diseases such as diabetes requires various approaches, including lifestyle modifications, pharmacological interventions, and patient education. Therefore, chronic disease management programs have emerged as a promising strategy for improving the care and outcomes of individuals living with diabetes [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn South Korea, the \u0026lsquo;primary care-based chronic disease management integrated pilot program (PCDMP)\u0026rsquo; was initiated in 2019 to integrate the \u0026lsquo;community-based primary care pilot project\u0026rsquo; started in 2014 and the \u0026lsquo;chronic disease management charges pilot project\u0026rsquo; started in 2016 to improve primary care-based and patient-centered medical systems [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRecent research has shown a growing interest in evaluating the effectiveness of chronic disease management programs in lowering HbA1c levels among individuals with diabetes. Numerous studies have explored the association between participation in such programs and glycemic control, with varying results. Some studies have reported significant reductions in HbA1c levels among program participants [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], while others have found more modest or inconclusive effects [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. However, no nationwide study has examined the impact of participation in a pilot program at a clinic in a residential area on HbA1c levels. Therefore, this study aimed to assess the effects of participation in a pilot program on hemoglobin A1c levels in each residential area using a multilevel model.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData and study population\u003c/h2\u003e \u003cp\u003eThis study integrated three nation-wide big data sources. The first was the 2019\u0026ndash;2021 Korea National Health and Nutrition Examination Survey (KNHANES), a cross-sectional and national survey in which individual-level data were gathered annually to evaluate the health status, health behavior, and nutritional status of the South Korean population to provide basic data for developing nationwide health policies [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Our study needed both individual-level and region-level information to describe the relationship between regional PCDMP participation rates and individual HbA1c levels, so we integrated the first dataset with two datasets from two big data sources. The second dataset, for region-level data, was the 2021 medical use statistics by region from the National Health Insurance Service (NHIS) [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The last dataset includes the participation rate of internal medicine clinics in the PCDMP manually calculated using data from the PCDMP website by the Korea Health Promotion Institute (KHEPI) and the Health Insurance Review \u0026amp; Assessment Service (HIRA) bigdata open portal [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAmong 22,559 individuals who completed KNHANES, we excluded ones who had been diagnosed with diabetes by doctors. Then, subjects with age\u0026thinsp;\u0026lt;\u0026thinsp;19 are omitted as well as individuals whose HbA1c levels missing. The STROBE (Strengthening The Reporting of Observational Studies in Epidemiology) diagram in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e depicts the inclusion and exclusion criteria for the study population used in our study. At all, a total of 13,901 individuals were included in the study. There was no need for additional ethical approval because all participants provided informed consent in advance, and the data were available to the public.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eMeasures\u003c/h2\u003e \u003cp\u003eThe outcome variable was the HbA1c percentage, the best benchmark for tracking glycemic control and a potential diabetes indicator. Then, we generated a binary variable indicating an HbA1c level\u0026thinsp;\u0026gt;\u0026thinsp;6.5% as high HbA1c and an HbA1c level \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\le\\)\u003c/span\u003e\u003c/span\u003e 6.5 as low HbA1c [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. On the other hand, for stratified analysis based on HbA1c level, we generated another categorical variable with 3 levels as low, middle, and high HbA1c when the HbA1c level of an individual is \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\le\\)\u003c/span\u003e\u003c/span\u003e 5.7%, \u0026gt; 5.7% and \u0026lt; 6.5%, and \u0026gt; 6.5%, respectively [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The variable of interest was the regional participation rate of the PCDMP, observed by the KHEPI. The participation rate ranged from 0\u0026ndash;62.07% and was determined by dividing the number of internal medicine clinics participating in the PCDMP by the total number of internal medicine clinics for each of the 17 regions in Korea. A binary variable was formed based on the criterion that residential areas with a participation rate greater than the median (30.37%) indicate a high participation and areas with a participation rate less than the median are considered to indicate a low participation.\u003c/p\u003e \u003cp\u003eFor individual-level covariates, socioeconomic variables (sex, age, household income, occupation), health behavior patterns (drinking and smoking), and health condition factors (body mass index [BMI] and chronic disease status) were included. The health behavior patterns are categorized by the severity of drinking and smoking. Then, BMI is divided into obesity, normal, and low weight, and chronic disease status has 4 subgroups: hypertension, hyperlipidemia, both, and none. Moreover, the regional-level covariates were residential area, number of hospital beds per 1000 people, number of clinics per 1000 people, and number of doctors per 1000 people. They are binary variables indicating whether a residential area\u0026rsquo;s value is greater than or less than the median of each variable.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eDescriptive statistics were expressed as frequencies (n) and percentages (%), and we investigated the association between HbA1c and socioeconomic, health behavior, and health condition characteristics using Pearson\u0026rsquo;s chi-square tests. We performed a multilevel analysis to examine the association between PCDMP participation and HbA1c. We conducted the multilevel logistic regression model that allows a random intercept for region and fixed effects for all individual-level covariates as well as region-level covariates. For sensitivity analysis, we conducted the multilevel multinomial logistic regression analysis to investiate the association between PCDMP participation and HbA1c stratified by the HbA1c levels. The main findings are displayed as adjusted odds ratios (aORs) and 95% confidence intervals (CIs), and the statitistical significance is decided by controlling type 1 error rate at 5%. For all the statistical analyses, SAS version 9.4 (SAS Institute, Inc.; Cary, NC, USA) was used, and p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 is set as a significant level at a 95% confidence level.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eFigure 1 presents the STROBE flow diagram describing the study population. We initially acquired 22,559 individuals from KNHANES. From the population, 3,868 people were excluded due to age criteria. Then, another 2,793 individuals who had been diagnosed with diabetes by doctors were excluded. From the remaining 15,898 population, missing values in HbA1c levels are excluded (n = 381). Finally, after calculating the regional PCDMP participation rate, we only included 13,901 individuals who live in regions where the PCDMP participation rate is not 0%.\u003c/p\u003e\n\u003cp\u003eThe general features of the study population are displayed in Table 1. A total of 617 people (4.44%) out of the 13,901 people eligible for the analysis had high HbA1c, as indicated by an HbA1c level of 6.5% or higher. However, only individuals who were not diagnosed with diabetes by doctors were included. According to previous reports, obese men older than 60 years with high incomes, a blue-collar job, smoking, not drinking, both hypertension and hyperlipidemia and poor subjective health status were more likely to have high HbA1c. Furthermore, a proportion of individuals living in rural cities with low PCDMP participation, low number of doctors, high number of hospital beds, and high number of clinics per 1000 people had high HbA1c.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"632\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"75.79113924050633%\" colspan=\"9\" style=\"width: 99.6835%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 1. General characteristics of the study population\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.24683544303797%\" colspan=\"2\" rowspan=\"4\" style=\"width: 36.8671%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"57.75316455696203%\" colspan=\"7\" style=\"width: 62.9746%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHbA1c\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.397260273972602%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eTOTAL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.684931506849313%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eHigh\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.671232876712327%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eLow\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.246575342465754%\" rowspan=\"3\" style=\"width: 6.0126%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP value\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.252396166134186%\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.696485623003195%\"\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.460063897763579%\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.322683706070286%\"\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.293929712460063%\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.97444089456869%\"\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.252396166134186%\"\u003e\n \u003cp\u003e13,901\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.696485623003195%\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.460063897763579%\"\u003e\n \u003cp\u003e617\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.322683706070286%\"\u003e\n \u003cp\u003e4.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.293929712460063%\"\u003e\n \u003cp\u003e13,284\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.97444089456869%\"\u003e\n \u003cp\u003e95.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.031645569620252%\" valign=\"bottom\" style=\"width: 14.0823%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eRegion Level\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.21518987341772%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.544303797468354%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.2784810126582276%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.170886075949367%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.550632911392405%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.069620253164556%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.9113924050632916%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.227848101265822%\" valign=\"bottom\" style=\"width: 6.0126%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.24683544303797%\" colspan=\"2\" valign=\"bottom\" style=\"width: 36.8671%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePCDMP participation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.544303797468354%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.2784810126582276%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.170886075949367%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.550632911392405%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.069620253164556%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.9113924050632916%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.227848101265822%\" style=\"width: 6.0126%;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.031645569620252%\" valign=\"bottom\" style=\"width: 14.0823%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.21518987341772%\" valign=\"bottom\"\u003e\n \u003cp\u003eHigh (\u0026gt; 30.37%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.544303797468354%\"\u003e\n \u003cp\u003e7,485\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.2784810126582276%\"\u003e\n \u003cp\u003e53.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.170886075949367%\"\u003e\n \u003cp\u003e298\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.550632911392405%\"\u003e\n \u003cp\u003e3.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.069620253164556%\"\u003e\n \u003cp\u003e7,187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.9113924050632916%\"\u003e\n \u003cp\u003e96.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.227848101265822%\" style=\"width: 6.0126%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.031645569620252%\" valign=\"bottom\" style=\"width: 14.0823%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.21518987341772%\" valign=\"bottom\"\u003e\n \u003cp\u003eLow (\u0026le; 30.37%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.544303797468354%\"\u003e\n \u003cp\u003e6,416\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.2784810126582276%\"\u003e\n \u003cp\u003e46.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.170886075949367%\"\u003e\n \u003cp\u003e319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.550632911392405%\"\u003e\n \u003cp\u003e4.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.069620253164556%\"\u003e\n \u003cp\u003e6,097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.9113924050632916%\"\u003e\n \u003cp\u003e95.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.227848101265822%\" style=\"width: 6.0126%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.031645569620252%\" valign=\"bottom\" style=\"width: 14.0823%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegion\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.21518987341772%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.544303797468354%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.2784810126582276%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.170886075949367%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.550632911392405%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.069620253164556%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.9113924050632916%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.227848101265822%\" style=\"width: 6.0126%;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.031645569620252%\" valign=\"bottom\" style=\"width: 14.0823%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.21518987341772%\" valign=\"bottom\"\u003e\n \u003cp\u003eMetropolitan city\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.544303797468354%\"\u003e\n \u003cp\u003e6,346\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.2784810126582276%\"\u003e\n \u003cp\u003e45.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.170886075949367%\"\u003e\n \u003cp\u003e249\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.550632911392405%\"\u003e\n \u003cp\u003e3.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.069620253164556%\"\u003e\n \u003cp\u003e6,097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.9113924050632916%\"\u003e\n \u003cp\u003e96.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.227848101265822%\" style=\"width: 6.0126%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.031645569620252%\" valign=\"bottom\" style=\"width: 14.0823%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.21518987341772%\" valign=\"bottom\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.544303797468354%\"\u003e\n \u003cp\u003e7,555\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.2784810126582276%\"\u003e\n \u003cp\u003e54.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.170886075949367%\"\u003e\n \u003cp\u003e368\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.550632911392405%\"\u003e\n \u003cp\u003e4.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.069620253164556%\"\u003e\n \u003cp\u003e7,187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.9113924050632916%\"\u003e\n \u003cp\u003e95.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.227848101265822%\" style=\"width: 6.0126%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50.79113924050633%\" colspan=\"3\" valign=\"bottom\" style=\"width: 50.1583%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHospital beds per 1000 people\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.2784810126582276%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.170886075949367%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.550632911392405%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.069620253164556%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.9113924050632916%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.227848101265822%\" style=\"width: 6.0126%;\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.031645569620252%\" valign=\"bottom\" style=\"width: 14.0823%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.21518987341772%\" valign=\"bottom\"\u003e\n \u003cp\u003eHigh (\u0026gt; 33.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.544303797468354%\"\u003e\n \u003cp\u003e4,757\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.2784810126582276%\"\u003e\n \u003cp\u003e34.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.170886075949367%\"\u003e\n \u003cp\u003e238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.550632911392405%\"\u003e\n \u003cp\u003e5.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.069620253164556%\"\u003e\n \u003cp\u003e4,519\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.9113924050632916%\"\u003e\n \u003cp\u003e95.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.227848101265822%\" style=\"width: 6.0126%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.031645569620252%\" valign=\"bottom\" style=\"width: 14.0823%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.21518987341772%\" valign=\"bottom\"\u003e\n \u003cp\u003eLow (\u0026le; 33.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.544303797468354%\"\u003e\n \u003cp\u003e9,144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.2784810126582276%\"\u003e\n \u003cp\u003e65.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.170886075949367%\"\u003e\n \u003cp\u003e379\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.550632911392405%\"\u003e\n \u003cp\u003e4.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.069620253164556%\"\u003e\n \u003cp\u003e8,765\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.9113924050632916%\"\u003e\n \u003cp\u003e95.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.227848101265822%\" style=\"width: 6.0126%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.24683544303797%\" colspan=\"2\" valign=\"bottom\" style=\"width: 36.8671%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eClinics per 1000 people\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.544303797468354%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.2784810126582276%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.170886075949367%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.550632911392405%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.069620253164556%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.9113924050632916%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.227848101265822%\" style=\"width: 6.0126%;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.031645569620252%\" valign=\"bottom\" style=\"width: 14.0823%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.21518987341772%\" valign=\"bottom\"\u003e\n \u003cp\u003eHigh (\u0026gt; 0.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.544303797468354%\"\u003e\n \u003cp\u003e3,775\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.2784810126582276%\"\u003e\n \u003cp\u003e27.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.170886075949367%\"\u003e\n \u003cp\u003e204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.550632911392405%\"\u003e\n \u003cp\u003e5.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.069620253164556%\"\u003e\n \u003cp\u003e3,571\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.9113924050632916%\"\u003e\n \u003cp\u003e94.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.227848101265822%\" style=\"width: 6.0126%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.031645569620252%\" valign=\"bottom\" style=\"width: 14.0823%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.21518987341772%\" valign=\"bottom\"\u003e\n \u003cp\u003eLow (\u0026le; 0.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.544303797468354%\"\u003e\n \u003cp\u003e10,126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.2784810126582276%\"\u003e\n \u003cp\u003e72.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.170886075949367%\"\u003e\n \u003cp\u003e413\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.550632911392405%\"\u003e\n \u003cp\u003e4.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.069620253164556%\"\u003e\n \u003cp\u003e9,713\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.9113924050632916%\"\u003e\n \u003cp\u003e95.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.227848101265822%\" valign=\"bottom\" style=\"width: 6.0126%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50.79113924050633%\" colspan=\"3\" valign=\"bottom\" style=\"width: 50.1583%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDoctors per 1000 people\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.2784810126582276%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.170886075949367%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.550632911392405%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.069620253164556%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.9113924050632916%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.227848101265822%\" valign=\"bottom\" style=\"width: 6.0126%;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.031645569620252%\" valign=\"bottom\" style=\"width: 14.0823%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.21518987341772%\" valign=\"bottom\"\u003e\n \u003cp\u003eHigh (\u0026gt; 2.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.544303797468354%\"\u003e\n \u003cp\u003e7,485\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.2784810126582276%\"\u003e\n \u003cp\u003e53.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.170886075949367%\"\u003e\n \u003cp\u003e298\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.550632911392405%\"\u003e\n \u003cp\u003e3.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.069620253164556%\"\u003e\n \u003cp\u003e7,187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.9113924050632916%\"\u003e\n \u003cp\u003e96.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.227848101265822%\" valign=\"bottom\" style=\"width: 6.0126%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.031645569620252%\" valign=\"bottom\" style=\"width: 14.0823%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.21518987341772%\" valign=\"bottom\"\u003e\n \u003cp\u003eLow (\u0026le; 2.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.544303797468354%\"\u003e\n \u003cp\u003e6,416\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.2784810126582276%\"\u003e\n \u003cp\u003e46.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.170886075949367%\"\u003e\n \u003cp\u003e319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.550632911392405%\"\u003e\n \u003cp\u003e4.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.069620253164556%\"\u003e\n \u003cp\u003e6,097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.9113924050632916%\"\u003e\n \u003cp\u003e95.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.227848101265822%\" valign=\"bottom\" style=\"width: 6.0126%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.24683544303797%\" colspan=\"2\" valign=\"bottom\" style=\"width: 36.8671%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eIndividual Level\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.544303797468354%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.2784810126582276%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.170886075949367%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.550632911392405%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.069620253164556%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.9113924050632916%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.227848101265822%\" valign=\"bottom\" style=\"width: 6.0126%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.031645569620252%\" valign=\"bottom\" style=\"width: 14.0823%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.21518987341772%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.544303797468354%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.2784810126582276%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.170886075949367%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.550632911392405%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.069620253164556%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.9113924050632916%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.227848101265822%\" style=\"width: 6.0126%;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.031645569620252%\" valign=\"bottom\" style=\"width: 14.0823%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.21518987341772%\" valign=\"bottom\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.544303797468354%\"\u003e\n \u003cp\u003e6,083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.2784810126582276%\"\u003e\n \u003cp\u003e43.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.170886075949367%\"\u003e\n \u003cp\u003e328\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.550632911392405%\"\u003e\n \u003cp\u003e5.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.069620253164556%\"\u003e\n \u003cp\u003e5,755\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.9113924050632916%\"\u003e\n \u003cp\u003e94.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.227848101265822%\" style=\"width: 6.0126%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.031645569620252%\" valign=\"bottom\" style=\"width: 14.0823%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.21518987341772%\" valign=\"bottom\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.544303797468354%\"\u003e\n \u003cp\u003e7,818\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.2784810126582276%\"\u003e\n \u003cp\u003e56.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.170886075949367%\"\u003e\n \u003cp\u003e289\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.550632911392405%\"\u003e\n \u003cp\u003e3.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.069620253164556%\"\u003e\n \u003cp\u003e7,529\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.9113924050632916%\"\u003e\n \u003cp\u003e96.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.227848101265822%\" style=\"width: 6.0126%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.031645569620252%\" valign=\"bottom\" style=\"width: 14.0823%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.21518987341772%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.544303797468354%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.2784810126582276%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.170886075949367%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.550632911392405%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.069620253164556%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.9113924050632916%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.227848101265822%\" style=\"width: 6.0126%;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.031645569620252%\" valign=\"bottom\" style=\"width: 14.0823%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.21518987341772%\" valign=\"bottom\"\u003e\n \u003cp\u003e19~39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.544303797468354%\"\u003e\n \u003cp\u003e4,091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.2784810126582276%\"\u003e\n \u003cp\u003e29.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.170886075949367%\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.550632911392405%\"\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.069620253164556%\"\u003e\n \u003cp\u003e4,046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.9113924050632916%\"\u003e\n \u003cp\u003e98.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.227848101265822%\" valign=\"bottom\" style=\"width: 6.0126%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.031645569620252%\" valign=\"bottom\" style=\"width: 14.0823%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.21518987341772%\" valign=\"bottom\"\u003e\n \u003cp\u003e40~49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.544303797468354%\"\u003e\n \u003cp\u003e2,633\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.2784810126582276%\"\u003e\n \u003cp\u003e18.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.170886075949367%\"\u003e\n \u003cp\u003e92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.550632911392405%\"\u003e\n \u003cp\u003e3.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.069620253164556%\"\u003e\n \u003cp\u003e2,541\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.9113924050632916%\"\u003e\n \u003cp\u003e96.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.227848101265822%\" style=\"width: 6.0126%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.031645569620252%\" valign=\"bottom\" style=\"width: 14.0823%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.21518987341772%\" valign=\"bottom\"\u003e\n \u003cp\u003e50~59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.544303797468354%\"\u003e\n \u003cp\u003e2,621\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.2784810126582276%\"\u003e\n \u003cp\u003e18.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.170886075949367%\"\u003e\n \u003cp\u003e145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.550632911392405%\"\u003e\n \u003cp\u003e5.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.069620253164556%\"\u003e\n \u003cp\u003e2,476\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.9113924050632916%\"\u003e\n \u003cp\u003e94.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.227848101265822%\" style=\"width: 6.0126%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.031645569620252%\" valign=\"bottom\" style=\"width: 14.0823%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.21518987341772%\" valign=\"bottom\"\u003e\n \u003cp\u003eover 60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.544303797468354%\"\u003e\n \u003cp\u003e4,556\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.2784810126582276%\"\u003e\n \u003cp\u003e32.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.170886075949367%\"\u003e\n \u003cp\u003e335\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.550632911392405%\"\u003e\n \u003cp\u003e7.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.069620253164556%\"\u003e\n \u003cp\u003e4,221\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.9113924050632916%\"\u003e\n \u003cp\u003e92.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.227848101265822%\" style=\"width: 6.0126%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.031645569620252%\" valign=\"bottom\" style=\"width: 14.0823%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIncome\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.21518987341772%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.544303797468354%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.2784810126582276%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.170886075949367%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.550632911392405%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.069620253164556%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.9113924050632916%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.227848101265822%\" valign=\"bottom\" style=\"width: 6.0126%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.031645569620252%\" valign=\"bottom\" style=\"width: 14.0823%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.21518987341772%\" valign=\"bottom\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.544303797468354%\"\u003e\n \u003cp\u003e2,290\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.2784810126582276%\"\u003e\n \u003cp\u003e16.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.170886075949367%\"\u003e\n \u003cp\u003e161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.550632911392405%\"\u003e\n \u003cp\u003e7.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.069620253164556%\"\u003e\n \u003cp\u003e2,129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.9113924050632916%\"\u003e\n \u003cp\u003e92.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.227848101265822%\" valign=\"bottom\" style=\"width: 6.0126%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.031645569620252%\" valign=\"bottom\" style=\"width: 14.0823%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.21518987341772%\" valign=\"bottom\"\u003e\n \u003cp\u003eMiddle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.544303797468354%\"\u003e\n \u003cp\u003e7,208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.2784810126582276%\"\u003e\n \u003cp\u003e51.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.170886075949367%\"\u003e\n \u003cp\u003e318\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.550632911392405%\"\u003e\n \u003cp\u003e4.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.069620253164556%\"\u003e\n \u003cp\u003e6,890\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.9113924050632916%\"\u003e\n \u003cp\u003e95.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.227848101265822%\" style=\"width: 6.0126%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.031645569620252%\" valign=\"bottom\" style=\"width: 14.0823%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.21518987341772%\" valign=\"bottom\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.544303797468354%\"\u003e\n \u003cp\u003e4,403\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.2784810126582276%\"\u003e\n \u003cp\u003e31.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.170886075949367%\"\u003e\n \u003cp\u003e138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.550632911392405%\"\u003e\n \u003cp\u003e3.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.069620253164556%\"\u003e\n \u003cp\u003e4,265\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.9113924050632916%\"\u003e\n \u003cp\u003e96.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.227848101265822%\" style=\"width: 6.0126%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.031645569620252%\" valign=\"bottom\" style=\"width: 14.0823%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOccupation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.21518987341772%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.544303797468354%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.2784810126582276%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.170886075949367%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.550632911392405%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.069620253164556%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.9113924050632916%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.227848101265822%\" style=\"width: 6.0126%;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.031645569620252%\" valign=\"bottom\" style=\"width: 14.0823%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.21518987341772%\" valign=\"bottom\"\u003e\n \u003cp\u003eWhite-collar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.544303797468354%\"\u003e\n \u003cp\u003e3,637\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.2784810126582276%\"\u003e\n \u003cp\u003e26.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.170886075949367%\"\u003e\n \u003cp\u003e110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.550632911392405%\"\u003e\n \u003cp\u003e3.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.069620253164556%\"\u003e\n \u003cp\u003e3,527\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.9113924050632916%\"\u003e\n \u003cp\u003e96.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.227848101265822%\" style=\"width: 6.0126%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.031645569620252%\" valign=\"bottom\" style=\"width: 14.0823%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.21518987341772%\" valign=\"bottom\"\u003e\n \u003cp\u003ePink-collar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.544303797468354%\"\u003e\n \u003cp\u003e1,882\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.2784810126582276%\"\u003e\n \u003cp\u003e13.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.170886075949367%\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.550632911392405%\"\u003e\n \u003cp\u003e3.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.069620253164556%\"\u003e\n \u003cp\u003e1,815\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.9113924050632916%\"\u003e\n \u003cp\u003e96.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.227848101265822%\" style=\"width: 6.0126%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.031645569620252%\" valign=\"bottom\" style=\"width: 14.0823%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.21518987341772%\" valign=\"bottom\"\u003e\n \u003cp\u003eBlue-collar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.544303797468354%\"\u003e\n \u003cp\u003e3,087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.2784810126582276%\"\u003e\n \u003cp\u003e22.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.170886075949367%\"\u003e\n \u003cp\u003e189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.550632911392405%\"\u003e\n \u003cp\u003e6.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.069620253164556%\"\u003e\n \u003cp\u003e2,898\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.9113924050632916%\"\u003e\n \u003cp\u003e93.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.227848101265822%\" style=\"width: 6.0126%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.031645569620252%\" valign=\"bottom\" style=\"width: 14.0823%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.21518987341772%\" valign=\"bottom\"\u003e\n \u003cp\u003eHousewife or Inoccupation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.544303797468354%\"\u003e\n \u003cp\u003e5,295\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.2784810126582276%\"\u003e\n \u003cp\u003e38.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.170886075949367%\"\u003e\n \u003cp\u003e251\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.550632911392405%\"\u003e\n \u003cp\u003e4.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.069620253164556%\"\u003e\n \u003cp\u003e5,044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.9113924050632916%\"\u003e\n \u003cp\u003e95.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.227848101265822%\" style=\"width: 6.0126%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.031645569620252%\" valign=\"bottom\" style=\"width: 14.0823%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.21518987341772%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.544303797468354%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.2784810126582276%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.170886075949367%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.550632911392405%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.069620253164556%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.9113924050632916%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.227848101265822%\" style=\"width: 6.0126%;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.031645569620252%\" valign=\"bottom\" style=\"width: 14.0823%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.21518987341772%\" valign=\"bottom\"\u003e\n \u003cp\u003eObesity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.544303797468354%\"\u003e\n \u003cp\u003e4,784\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.2784810126582276%\"\u003e\n \u003cp\u003e34.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.170886075949367%\"\u003e\n \u003cp\u003e383\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.550632911392405%\"\u003e\n \u003cp\u003e8.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.069620253164556%\"\u003e\n \u003cp\u003e4,401\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.9113924050632916%\"\u003e\n \u003cp\u003e91.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.227848101265822%\" style=\"width: 6.0126%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.031645569620252%\" valign=\"bottom\" style=\"width: 14.0823%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.21518987341772%\" valign=\"bottom\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.544303797468354%\"\u003e\n \u003cp\u003e8,392\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.2784810126582276%\"\u003e\n \u003cp\u003e60.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.170886075949367%\"\u003e\n \u003cp\u003e222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.550632911392405%\"\u003e\n \u003cp\u003e2.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.069620253164556%\"\u003e\n \u003cp\u003e8,170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.9113924050632916%\"\u003e\n \u003cp\u003e97.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.227848101265822%\" style=\"width: 6.0126%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.031645569620252%\" valign=\"bottom\" style=\"width: 14.0823%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.21518987341772%\" valign=\"bottom\"\u003e\n \u003cp\u003eLow weight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.544303797468354%\"\u003e\n \u003cp\u003e725\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.2784810126582276%\"\u003e\n \u003cp\u003e5.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.170886075949367%\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.550632911392405%\"\u003e\n \u003cp\u003e1.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.069620253164556%\"\u003e\n \u003cp\u003e713\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.9113924050632916%\"\u003e\n \u003cp\u003e98.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.227848101265822%\" style=\"width: 6.0126%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.031645569620252%\" valign=\"bottom\" style=\"width: 14.0823%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDrinking\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.21518987341772%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.544303797468354%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.2784810126582276%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.170886075949367%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.550632911392405%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.069620253164556%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.9113924050632916%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.227848101265822%\" style=\"width: 6.0126%;\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.031645569620252%\" valign=\"bottom\" style=\"width: 14.0823%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.21518987341772%\" valign=\"bottom\"\u003e\n \u003cp\u003eFrequently\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.544303797468354%\"\u003e\n \u003cp\u003e5,936\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.2784810126582276%\"\u003e\n \u003cp\u003e42.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.170886075949367%\"\u003e\n \u003cp\u003e249\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.550632911392405%\"\u003e\n \u003cp\u003e4.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.069620253164556%\"\u003e\n \u003cp\u003e5,687\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.9113924050632916%\"\u003e\n \u003cp\u003e95.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.227848101265822%\" style=\"width: 6.0126%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.031645569620252%\" valign=\"bottom\" style=\"width: 14.0823%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.21518987341772%\" valign=\"bottom\"\u003e\n \u003cp\u003eOccasionally\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.544303797468354%\"\u003e\n \u003cp\u003e4,115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.2784810126582276%\"\u003e\n \u003cp\u003e29.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.170886075949367%\"\u003e\n \u003cp\u003e169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.550632911392405%\"\u003e\n \u003cp\u003e4.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.069620253164556%\"\u003e\n \u003cp\u003e3,946\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.9113924050632916%\"\u003e\n \u003cp\u003e95.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.227848101265822%\" style=\"width: 6.0126%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.031645569620252%\" valign=\"bottom\" style=\"width: 14.0823%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.21518987341772%\" valign=\"bottom\"\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.544303797468354%\"\u003e\n \u003cp\u003e3,850\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.2784810126582276%\"\u003e\n \u003cp\u003e27.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.170886075949367%\"\u003e\n \u003cp\u003e199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.550632911392405%\"\u003e\n \u003cp\u003e5.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.069620253164556%\"\u003e\n \u003cp\u003e3,651\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.9113924050632916%\"\u003e\n \u003cp\u003e94.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.227848101265822%\" style=\"width: 6.0126%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.031645569620252%\" valign=\"bottom\" style=\"width: 14.0823%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmoking\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.21518987341772%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.544303797468354%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.2784810126582276%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.170886075949367%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.550632911392405%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.069620253164556%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.9113924050632916%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.227848101265822%\" style=\"width: 6.0126%;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.031645569620252%\" valign=\"bottom\" style=\"width: 14.0823%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.21518987341772%\" valign=\"bottom\"\u003e\n \u003cp\u003eCurrent smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.544303797468354%\"\u003e\n \u003cp\u003e2,322\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.2784810126582276%\"\u003e\n \u003cp\u003e16.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.170886075949367%\"\u003e\n \u003cp\u003e122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.550632911392405%\"\u003e\n \u003cp\u003e5.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.069620253164556%\"\u003e\n \u003cp\u003e2,200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.9113924050632916%\"\u003e\n \u003cp\u003e94.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.227848101265822%\" style=\"width: 6.0126%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.031645569620252%\" valign=\"bottom\" style=\"width: 14.0823%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.21518987341772%\" valign=\"bottom\"\u003e\n \u003cp\u003eEx-smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.544303797468354%\"\u003e\n \u003cp\u003e3,143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.2784810126582276%\"\u003e\n \u003cp\u003e22.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.170886075949367%\"\u003e\n \u003cp\u003e173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.550632911392405%\"\u003e\n \u003cp\u003e5.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.069620253164556%\"\u003e\n \u003cp\u003e2,970\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.9113924050632916%\"\u003e\n \u003cp\u003e94.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.227848101265822%\" style=\"width: 6.0126%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.031645569620252%\" valign=\"bottom\" style=\"width: 14.0823%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.21518987341772%\" valign=\"bottom\"\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.544303797468354%\"\u003e\n \u003cp\u003e8,436\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.2784810126582276%\"\u003e\n \u003cp\u003e60.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.170886075949367%\"\u003e\n \u003cp\u003e322\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.550632911392405%\"\u003e\n \u003cp\u003e3.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.069620253164556%\"\u003e\n \u003cp\u003e8,114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.9113924050632916%\"\u003e\n \u003cp\u003e96.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.227848101265822%\" style=\"width: 6.0126%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.24683544303797%\" colspan=\"2\" valign=\"bottom\" style=\"width: 36.8671%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChronic Disease Status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.544303797468354%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.2784810126582276%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.170886075949367%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.550632911392405%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.069620253164556%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.9113924050632916%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.227848101265822%\" style=\"width: 6.0126%;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.031645569620252%\" valign=\"bottom\" style=\"width: 14.0823%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.21518987341772%\" valign=\"bottom\"\u003e\n \u003cp\u003eOnly hypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.544303797468354%\"\u003e\n \u003cp\u003e1648\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.2784810126582276%\"\u003e\n \u003cp\u003e11.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.170886075949367%\"\u003e\n \u003cp\u003e136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.550632911392405%\"\u003e\n \u003cp\u003e8.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.069620253164556%\"\u003e\n \u003cp\u003e1,512\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.9113924050632916%\"\u003e\n \u003cp\u003e91.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.227848101265822%\" style=\"width: 6.0126%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.031645569620252%\" valign=\"bottom\" style=\"width: 14.0823%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.21518987341772%\" valign=\"bottom\"\u003e\n \u003cp\u003eOnly hyperlipidemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.544303797468354%\"\u003e\n \u003cp\u003e1103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.2784810126582276%\"\u003e\n \u003cp\u003e7.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.170886075949367%\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.550632911392405%\"\u003e\n \u003cp\u003e5.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.069620253164556%\"\u003e\n \u003cp\u003e1,046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.9113924050632916%\"\u003e\n \u003cp\u003e94.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.227848101265822%\" valign=\"bottom\" style=\"width: 6.0126%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.031645569620252%\" valign=\"bottom\" style=\"width: 14.0823%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.21518987341772%\" valign=\"bottom\"\u003e\n \u003cp\u003eBoth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.544303797468354%\"\u003e\n \u003cp\u003e1312\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.2784810126582276%\"\u003e\n \u003cp\u003e9.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.170886075949367%\"\u003e\n \u003cp\u003e120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.550632911392405%\"\u003e\n \u003cp\u003e9.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.069620253164556%\"\u003e\n \u003cp\u003e1,192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.9113924050632916%\"\u003e\n \u003cp\u003e90.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.227848101265822%\" valign=\"bottom\" style=\"width: 6.0126%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.031645569620252%\" valign=\"bottom\" style=\"width: 14.0823%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.21518987341772%\" valign=\"bottom\"\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.544303797468354%\"\u003e\n \u003cp\u003e9839\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.2784810126582276%\"\u003e\n \u003cp\u003e70.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.170886075949367%\"\u003e\n \u003cp\u003e304\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.550632911392405%\"\u003e\n \u003cp\u003e3.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.069620253164556%\"\u003e\n \u003cp\u003e9,535\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.9113924050632916%\"\u003e\n \u003cp\u003e96.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.227848101265822%\" style=\"width: 6.0126%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2 presents the results of the multilevel analysis for the association between regional participation in the PCDMP and individual HbA1c. According to the full model, adjusting individual and region-level covariates, the regional participation of the PCDMP had a significant effect on HbA1c. In detail, the odds of an individual exhibiting high HbA1c were 1.4 times greater for individuals residing in areas with a low PCDMP participation than those living in areas with a high PCDMP participation. (95% CI, 1.08\u0026ndash;1.82)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"574\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\" valign=\"bottom\" style=\"width: 70.4263%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 2. Adjusted odds ratios of HbA1c by characteristics of individual- and region-level (multilevel model)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"76.13240418118467%\" colspan=\"2\" rowspan=\"3\" style=\"width: 50.4915%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.86759581881533%\" colspan=\"3\" valign=\"bottom\" style=\"width: 19.9349%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHbA1c\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"3\" valign=\"bottom\" style=\"width: 19.9349%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFull model\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.73722627737226%\" valign=\"bottom\" style=\"width: 15.2003%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eaOR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"72.26277372262774%\" colspan=\"2\" valign=\"bottom\" style=\"width: 4.7346%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"53.31010452961672%\" valign=\"bottom\" style=\"width: 34.1016%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eRegion Level\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.822299651567945%\" valign=\"bottom\" style=\"width: 16.3899%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.620209059233449%\" valign=\"bottom\" style=\"width: 15.2003%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"bottom\" style=\"width: 4.6262%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.968641114982578%\" valign=\"bottom\" style=\"width: 4.6262%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"76.13240418118467%\" colspan=\"2\" valign=\"bottom\" style=\"width: 50.4915%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePCDMP participation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.620209059233449%\" style=\"width: 15.2003%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" style=\"width: 4.6262%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.968641114982578%\" valign=\"bottom\" style=\"width: 4.6262%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"53.31010452961672%\" valign=\"bottom\" style=\"width: 34.1016%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.822299651567945%\" valign=\"bottom\" style=\"width: 16.3899%;\"\u003e\n \u003cp\u003eHigh (\u0026gt; 30.37%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.620209059233449%\" style=\"width: 15.2003%;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"bottom\" style=\"width: 4.6262%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.968641114982578%\" valign=\"bottom\" style=\"width: 4.6262%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"53.31010452961672%\" valign=\"bottom\" style=\"width: 34.1016%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.822299651567945%\" valign=\"bottom\" style=\"width: 16.3899%;\"\u003e\n \u003cp\u003eLow (\u0026le; 30.37%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.620209059233449%\" style=\"width: 15.2003%;\"\u003e\n \u003cp\u003e1.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"bottom\" style=\"width: 4.6262%;\"\u003e\n \u003cp\u003e(1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.968641114982578%\" valign=\"bottom\" style=\"width: 4.6262%;\"\u003e\n \u003cp\u003e1.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"53.31010452961672%\" valign=\"bottom\" style=\"width: 34.1016%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegion\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.822299651567945%\" valign=\"bottom\" style=\"width: 16.3899%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.620209059233449%\" style=\"width: 15.2003%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" style=\"width: 4.6262%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.968641114982578%\" valign=\"bottom\" style=\"width: 4.6262%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"53.31010452961672%\" valign=\"bottom\" style=\"width: 34.1016%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.822299651567945%\" valign=\"bottom\" style=\"width: 16.3899%;\"\u003e\n \u003cp\u003eMetropolitan city\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.620209059233449%\" style=\"width: 15.2003%;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" style=\"width: 4.6262%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.968641114982578%\" valign=\"bottom\" style=\"width: 4.6262%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"53.31010452961672%\" valign=\"bottom\" style=\"width: 34.1016%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.822299651567945%\" valign=\"bottom\" style=\"width: 16.3899%;\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.620209059233449%\" style=\"width: 15.2003%;\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" style=\"width: 4.6262%;\"\u003e\n \u003cp\u003e(0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.968641114982578%\" valign=\"bottom\" style=\"width: 4.6262%;\"\u003e\n \u003cp\u003e1.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"53.31010452961672%\" valign=\"bottom\" style=\"width: 34.1016%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHospital bed per 1000 people\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.822299651567945%\" valign=\"bottom\" style=\"width: 16.3899%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.620209059233449%\" style=\"width: 15.2003%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" style=\"width: 4.6262%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.968641114982578%\" valign=\"bottom\" style=\"width: 4.6262%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"53.31010452961672%\" valign=\"bottom\" style=\"width: 34.1016%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.822299651567945%\" valign=\"bottom\" style=\"width: 16.3899%;\"\u003e\n \u003cp\u003eHigh (\u0026gt; 33.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.620209059233449%\" style=\"width: 15.2003%;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" style=\"width: 4.6262%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.968641114982578%\" valign=\"bottom\" style=\"width: 4.6262%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"53.31010452961672%\" valign=\"bottom\" style=\"width: 34.1016%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.822299651567945%\" valign=\"bottom\" style=\"width: 16.3899%;\"\u003e\n \u003cp\u003eLow (\u0026le; 33.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.620209059233449%\" style=\"width: 15.2003%;\"\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" style=\"width: 4.6262%;\"\u003e\n \u003cp\u003e(0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.968641114982578%\" valign=\"bottom\" style=\"width: 4.6262%;\"\u003e\n \u003cp\u003e1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"76.13240418118467%\" colspan=\"2\" valign=\"bottom\" style=\"width: 50.4915%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eClinic per 1000 people\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.620209059233449%\" style=\"width: 15.2003%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" style=\"width: 4.6262%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.968641114982578%\" valign=\"bottom\" style=\"width: 4.6262%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"53.31010452961672%\" valign=\"bottom\" style=\"width: 34.1016%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.822299651567945%\" valign=\"bottom\" style=\"width: 16.3899%;\"\u003e\n \u003cp\u003eHigh (\u0026gt; 0.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.620209059233449%\" style=\"width: 15.2003%;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" style=\"width: 4.6262%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.968641114982578%\" valign=\"bottom\" style=\"width: 4.6262%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"53.31010452961672%\" valign=\"bottom\" style=\"width: 34.1016%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.822299651567945%\" valign=\"bottom\" style=\"width: 16.3899%;\"\u003e\n \u003cp\u003eLow (\u0026le; 0.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.620209059233449%\" style=\"width: 15.2003%;\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" style=\"width: 4.6262%;\"\u003e\n \u003cp\u003e(0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.968641114982578%\" valign=\"bottom\" style=\"width: 4.6262%;\"\u003e\n \u003cp\u003e1.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"76.13240418118467%\" colspan=\"2\" valign=\"bottom\" style=\"width: 50.4915%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eIndividual Level\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.620209059233449%\" valign=\"bottom\" style=\"width: 15.2003%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"bottom\" style=\"width: 4.6262%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.968641114982578%\" valign=\"bottom\" style=\"width: 4.6262%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"53.31010452961672%\" valign=\"bottom\" style=\"width: 34.1016%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.822299651567945%\" valign=\"bottom\" style=\"width: 16.3899%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.620209059233449%\" valign=\"top\" style=\"width: 15.2003%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\" style=\"width: 4.6262%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.968641114982578%\" valign=\"bottom\" style=\"width: 4.6262%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"53.31010452961672%\" valign=\"bottom\" style=\"width: 34.1016%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.822299651567945%\" valign=\"bottom\" style=\"width: 16.3899%;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.620209059233449%\" valign=\"top\" style=\"width: 15.2003%;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\" style=\"width: 4.6262%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.968641114982578%\" valign=\"bottom\" style=\"width: 4.6262%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"53.31010452961672%\" valign=\"bottom\" style=\"width: 34.1016%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.822299651567945%\" valign=\"bottom\" style=\"width: 16.3899%;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.620209059233449%\" valign=\"top\" style=\"width: 15.2003%;\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\" style=\"width: 4.6262%;\"\u003e\n \u003cp\u003e(0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.968641114982578%\" valign=\"bottom\" style=\"width: 4.6262%;\"\u003e\n \u003cp\u003e0.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"53.31010452961672%\" valign=\"bottom\" style=\"width: 34.1016%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.822299651567945%\" valign=\"bottom\" style=\"width: 16.3899%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.620209059233449%\" valign=\"top\" style=\"width: 15.2003%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\" style=\"width: 4.6262%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.968641114982578%\" valign=\"bottom\" style=\"width: 4.6262%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"53.31010452961672%\" valign=\"bottom\" style=\"width: 34.1016%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.822299651567945%\" valign=\"bottom\" style=\"width: 16.3899%;\"\u003e\n \u003cp\u003e19~39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.620209059233449%\" valign=\"top\" style=\"width: 15.2003%;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"bottom\" style=\"width: 4.6262%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.968641114982578%\" valign=\"bottom\" style=\"width: 4.6262%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"53.31010452961672%\" valign=\"bottom\" style=\"width: 34.1016%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.822299651567945%\" valign=\"bottom\" style=\"width: 16.3899%;\"\u003e\n \u003cp\u003e40~49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.620209059233449%\" valign=\"top\" style=\"width: 15.2003%;\"\u003e\n \u003cp\u003e3.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\" style=\"width: 4.6262%;\"\u003e\n \u003cp\u003e(2.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.968641114982578%\" valign=\"bottom\" style=\"width: 4.6262%;\"\u003e\n \u003cp\u003e4.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"53.31010452961672%\" valign=\"bottom\" style=\"width: 34.1016%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.822299651567945%\" valign=\"bottom\" style=\"width: 16.3899%;\"\u003e\n \u003cp\u003e50~59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.620209059233449%\" valign=\"top\" style=\"width: 15.2003%;\"\u003e\n \u003cp\u003e4.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\" style=\"width: 4.6262%;\"\u003e\n \u003cp\u003e(3.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.968641114982578%\" valign=\"bottom\" style=\"width: 4.6262%;\"\u003e\n \u003cp\u003e6.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"53.31010452961672%\" valign=\"bottom\" style=\"width: 34.1016%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.822299651567945%\" valign=\"bottom\" style=\"width: 16.3899%;\"\u003e\n \u003cp\u003eover 60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.620209059233449%\" valign=\"top\" style=\"width: 15.2003%;\"\u003e\n \u003cp\u003e5.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\" style=\"width: 4.6262%;\"\u003e\n \u003cp\u003e(3.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.968641114982578%\" valign=\"bottom\" style=\"width: 4.6262%;\"\u003e\n \u003cp\u003e8.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"53.31010452961672%\" valign=\"bottom\" style=\"width: 34.1016%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIncome\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.822299651567945%\" valign=\"bottom\" style=\"width: 16.3899%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.620209059233449%\" valign=\"top\" style=\"width: 15.2003%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\" style=\"width: 4.6262%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.968641114982578%\" valign=\"bottom\" style=\"width: 4.6262%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"53.31010452961672%\" valign=\"bottom\" style=\"width: 34.1016%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.822299651567945%\" valign=\"bottom\" style=\"width: 16.3899%;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.620209059233449%\" valign=\"top\" style=\"width: 15.2003%;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\" style=\"width: 4.6262%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.968641114982578%\" valign=\"bottom\" style=\"width: 4.6262%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"53.31010452961672%\" valign=\"bottom\" style=\"width: 34.1016%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.822299651567945%\" valign=\"bottom\" style=\"width: 16.3899%;\"\u003e\n \u003cp\u003eMiddle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.620209059233449%\" valign=\"top\" style=\"width: 15.2003%;\"\u003e\n \u003cp\u003e1.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\" style=\"width: 4.6262%;\"\u003e\n \u003cp\u003e(1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.968641114982578%\" valign=\"bottom\" style=\"width: 4.6262%;\"\u003e\n \u003cp\u003e1.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"53.31010452961672%\" valign=\"bottom\" style=\"width: 34.1016%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.822299651567945%\" valign=\"bottom\" style=\"width: 16.3899%;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.620209059233449%\" valign=\"top\" style=\"width: 15.2003%;\"\u003e\n \u003cp\u003e1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\" style=\"width: 4.6262%;\"\u003e\n \u003cp\u003e(0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.968641114982578%\" valign=\"bottom\" style=\"width: 4.6262%;\"\u003e\n \u003cp\u003e1.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"53.31010452961672%\" valign=\"bottom\" style=\"width: 34.1016%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOccupation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.822299651567945%\" valign=\"bottom\" style=\"width: 16.3899%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.620209059233449%\" valign=\"top\" style=\"width: 15.2003%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\" style=\"width: 4.6262%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.968641114982578%\" valign=\"bottom\" style=\"width: 4.6262%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"53.31010452961672%\" valign=\"bottom\" style=\"width: 34.1016%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.822299651567945%\" valign=\"bottom\" style=\"width: 16.3899%;\"\u003e\n \u003cp\u003eWhite-collar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.620209059233449%\" valign=\"top\" style=\"width: 15.2003%;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\" style=\"width: 4.6262%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.968641114982578%\" valign=\"bottom\" style=\"width: 4.6262%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"53.31010452961672%\" valign=\"bottom\" style=\"width: 34.1016%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.822299651567945%\" valign=\"bottom\" style=\"width: 16.3899%;\"\u003e\n \u003cp\u003ePink-collar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.620209059233449%\" valign=\"top\" style=\"width: 15.2003%;\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\" style=\"width: 4.6262%;\"\u003e\n \u003cp\u003e(0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.968641114982578%\" valign=\"bottom\" style=\"width: 4.6262%;\"\u003e\n \u003cp\u003e1.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"53.31010452961672%\" valign=\"bottom\" style=\"width: 34.1016%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.822299651567945%\" valign=\"bottom\" style=\"width: 16.3899%;\"\u003e\n \u003cp\u003eBlue-collar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.620209059233449%\" valign=\"top\" style=\"width: 15.2003%;\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\" style=\"width: 4.6262%;\"\u003e\n \u003cp\u003e(0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.968641114982578%\" valign=\"bottom\" style=\"width: 4.6262%;\"\u003e\n \u003cp\u003e1.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"53.31010452961672%\" valign=\"bottom\" style=\"width: 34.1016%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.822299651567945%\" valign=\"bottom\" style=\"width: 16.3899%;\"\u003e\n \u003cp\u003eHousewife or Inoccupation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.620209059233449%\" valign=\"top\" style=\"width: 15.2003%;\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\" style=\"width: 4.6262%;\"\u003e\n \u003cp\u003e(0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.968641114982578%\" valign=\"bottom\" style=\"width: 4.6262%;\"\u003e\n \u003cp\u003e1.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"53.31010452961672%\" valign=\"bottom\" style=\"width: 34.1016%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.822299651567945%\" valign=\"bottom\" style=\"width: 16.3899%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.620209059233449%\" valign=\"top\" style=\"width: 15.2003%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\" style=\"width: 4.6262%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.968641114982578%\" valign=\"bottom\" style=\"width: 4.6262%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"53.31010452961672%\" valign=\"bottom\" style=\"width: 34.1016%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.822299651567945%\" valign=\"bottom\" style=\"width: 16.3899%;\"\u003e\n \u003cp\u003eObesity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.620209059233449%\" valign=\"top\" style=\"width: 15.2003%;\"\u003e\n \u003cp\u003e2.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\" style=\"width: 4.6262%;\"\u003e\n \u003cp\u003e(2.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.968641114982578%\" valign=\"bottom\" style=\"width: 4.6262%;\"\u003e\n \u003cp\u003e3.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"53.31010452961672%\" valign=\"bottom\" style=\"width: 34.1016%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.822299651567945%\" valign=\"bottom\" style=\"width: 16.3899%;\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.620209059233449%\" valign=\"top\" style=\"width: 15.2003%;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\" style=\"width: 4.6262%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.968641114982578%\" valign=\"bottom\" style=\"width: 4.6262%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"53.31010452961672%\" valign=\"bottom\" style=\"width: 34.1016%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.822299651567945%\" valign=\"bottom\" style=\"width: 16.3899%;\"\u003e\n \u003cp\u003eLow weight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.620209059233449%\" valign=\"top\" style=\"width: 15.2003%;\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\" style=\"width: 4.6262%;\"\u003e\n \u003cp\u003e(0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.968641114982578%\" valign=\"bottom\" style=\"width: 4.6262%;\"\u003e\n \u003cp\u003e1.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"53.31010452961672%\" valign=\"bottom\" style=\"width: 34.1016%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDrinking\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.822299651567945%\" valign=\"bottom\" style=\"width: 16.3899%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.620209059233449%\" valign=\"top\" style=\"width: 15.2003%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\" style=\"width: 4.6262%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.968641114982578%\" valign=\"bottom\" style=\"width: 4.6262%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"53.31010452961672%\" valign=\"bottom\" style=\"width: 34.1016%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.822299651567945%\" valign=\"bottom\" style=\"width: 16.3899%;\"\u003e\n \u003cp\u003eFrequently\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.620209059233449%\" valign=\"top\" style=\"width: 15.2003%;\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\" style=\"width: 4.6262%;\"\u003e\n \u003cp\u003e(0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.968641114982578%\" valign=\"bottom\" style=\"width: 4.6262%;\"\u003e\n \u003cp\u003e1.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"53.31010452961672%\" valign=\"bottom\" style=\"width: 34.1016%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.822299651567945%\" valign=\"bottom\" style=\"width: 16.3899%;\"\u003e\n \u003cp\u003eOccasionally\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.620209059233449%\" valign=\"top\" style=\"width: 15.2003%;\"\u003e\n \u003cp\u003e1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\" style=\"width: 4.6262%;\"\u003e\n \u003cp\u003e(0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.968641114982578%\" valign=\"bottom\" style=\"width: 4.6262%;\"\u003e\n \u003cp\u003e1.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"53.31010452961672%\" valign=\"bottom\" style=\"width: 34.1016%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.822299651567945%\" valign=\"bottom\" style=\"width: 16.3899%;\"\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.620209059233449%\" valign=\"top\" style=\"width: 15.2003%;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\" style=\"width: 4.6262%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.968641114982578%\" valign=\"bottom\" style=\"width: 4.6262%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"53.31010452961672%\" valign=\"bottom\" style=\"width: 34.1016%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmoking\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.822299651567945%\" valign=\"bottom\" style=\"width: 16.3899%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.620209059233449%\" valign=\"top\" style=\"width: 15.2003%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\" style=\"width: 4.6262%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.968641114982578%\" valign=\"bottom\" style=\"width: 4.6262%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"53.31010452961672%\" valign=\"bottom\" style=\"width: 34.1016%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.822299651567945%\" valign=\"bottom\" style=\"width: 16.3899%;\"\u003e\n \u003cp\u003eCurrent smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.620209059233449%\" valign=\"top\" style=\"width: 15.2003%;\"\u003e\n \u003cp\u003e1.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\" style=\"width: 4.6262%;\"\u003e\n \u003cp\u003e(1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.968641114982578%\" valign=\"bottom\" style=\"width: 4.6262%;\"\u003e\n \u003cp\u003e1.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"53.31010452961672%\" valign=\"bottom\" style=\"width: 34.1016%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.822299651567945%\" valign=\"bottom\" style=\"width: 16.3899%;\"\u003e\n \u003cp\u003eEx-smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.620209059233449%\" valign=\"bottom\" style=\"width: 15.2003%;\"\u003e\n \u003cp\u003e1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"bottom\" style=\"width: 4.6262%;\"\u003e\n \u003cp\u003e(0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.968641114982578%\" valign=\"bottom\" style=\"width: 4.6262%;\"\u003e\n \u003cp\u003e1.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"53.31010452961672%\" valign=\"bottom\" style=\"width: 34.1016%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.822299651567945%\" valign=\"bottom\" style=\"width: 16.3899%;\"\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.620209059233449%\" valign=\"top\" style=\"width: 15.2003%;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\" style=\"width: 4.6262%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.968641114982578%\" valign=\"bottom\" style=\"width: 4.6262%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"76.13240418118467%\" colspan=\"2\" valign=\"bottom\" style=\"width: 50.4915%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChronic Disease\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eStatus\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.620209059233449%\" valign=\"top\" style=\"width: 15.2003%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\" style=\"width: 4.6262%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.968641114982578%\" valign=\"bottom\" style=\"width: 4.6262%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"53.31010452961672%\" valign=\"bottom\" style=\"width: 34.1016%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.822299651567945%\" valign=\"bottom\" style=\"width: 16.3899%;\"\u003e\n \u003cp\u003eOnly hypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.620209059233449%\" valign=\"top\" style=\"width: 15.2003%;\"\u003e\n \u003cp\u003e1.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\" style=\"width: 4.6262%;\"\u003e\n \u003cp\u003e(1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.968641114982578%\" valign=\"bottom\" style=\"width: 4.6262%;\"\u003e\n \u003cp\u003e1.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"53.31010452961672%\" valign=\"bottom\" style=\"width: 34.1016%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.822299651567945%\" valign=\"bottom\" style=\"width: 16.3899%;\"\u003e\n \u003cp\u003eOnly hyperlipidemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.620209059233449%\" valign=\"top\" style=\"width: 15.2003%;\"\u003e\n \u003cp\u003e1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\" style=\"width: 4.6262%;\"\u003e\n \u003cp\u003e(0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.968641114982578%\" valign=\"bottom\" style=\"width: 4.6262%;\"\u003e\n \u003cp\u003e1.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"53.31010452961672%\" valign=\"bottom\" style=\"width: 34.1016%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.822299651567945%\" valign=\"bottom\" style=\"width: 16.3899%;\"\u003e\n \u003cp\u003eBoth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.620209059233449%\" valign=\"top\" style=\"width: 15.2003%;\"\u003e\n \u003cp\u003e1.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\" style=\"width: 4.6262%;\"\u003e\n \u003cp\u003e(1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.968641114982578%\" valign=\"bottom\" style=\"width: 4.6262%;\"\u003e\n \u003cp\u003e1.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"53.31010452961672%\" valign=\"bottom\" style=\"width: 34.1016%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.822299651567945%\" valign=\"bottom\" style=\"width: 16.3899%;\"\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.620209059233449%\" valign=\"top\" style=\"width: 15.2003%;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\" style=\"width: 4.6262%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.968641114982578%\" valign=\"bottom\" style=\"width: 4.6262%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"53.31010452961672%\" valign=\"bottom\" style=\"width: 34.1016%;\"\u003e\n \u003cp\u003eaOR, adjusted odds ratio, CI, confidence interval, SE, standard error\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.822299651567945%\" valign=\"bottom\" style=\"width: 16.3899%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.620209059233449%\" valign=\"bottom\" style=\"width: 15.2003%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"bottom\" style=\"width: 4.6262%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.968641114982578%\" valign=\"bottom\" style=\"width: 4.6262%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"93.03135888501743%\" colspan=\"5\" valign=\"bottom\" style=\"width: 75.7372%;\"\u003e\n \u003cp\u003e*Doctor variable are a linear combination of other variables as shown.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"76.13240418118467%\" colspan=\"5\" valign=\"bottom\" style=\"width: 75.7372%;\"\u003e\n \u003cp\u003e*Referece: HbA1c = abnormal\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe results of the sensitivity analysis by HbA1c level are shown in Table 3. People living in areas with low participation of PCDMP were 1.08 times (95% CI, 0.96-1.21) and 1.40 times (95% CI, 1.08-1.82) more likely to develop middle HbA1c and high HbA1c, respectively. In other words, there is a greater probability of developing middle or high HbA1c than low HbA1c in residential areas where few clinics participate in the PCDMP.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"629\" style=\"margin-right: calc(42%); width: 58%;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.879173290938%\" colspan=\"7\" valign=\"bottom\" style=\"width: 99.684%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 3. Results of subgroup analysis stratified by dependent variables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.879173290938%\" colspan=\"2\" rowspan=\"3\" style=\"width: 19.0546%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.99682034976153%\" colspan=\"2\" valign=\"bottom\" style=\"width: 14.7194%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHbA1c\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.99682034976153%\" valign=\"bottom\" style=\"width: 7.6621%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"37.99682034976153%\" valign=\"bottom\" style=\"width: 31.4376%;\" colspan=\"2\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.096317280453256%\" valign=\"bottom\" style=\"width: 8.9728%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLow\u003cbr\u003e\u0026nbsp; (HbA1c \u0026le; 5.7%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.994334277620396%\" colspan=\"2\" valign=\"bottom\" style=\"width: 13.4088%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMiddle\u0026nbsp;\u003cbr\u003e\u0026nbsp; (5.7% \u0026lt; HbA1c \u0026le; 6.5%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.81019830028329%\" valign=\"bottom\" style=\"width: 33.1754%;\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eHigh\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(HbA1c \u0026gt; 6.5%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.096317280453256%\" valign=\"bottom\" style=\"width: 8.9728%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.73087818696884%\" valign=\"bottom\" style=\"width: 5.8474%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.26345609065156%\" valign=\"bottom\" style=\"width: 7.6621%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.614730878186968%\" valign=\"bottom\" style=\"width: 6.8556%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.195467422096318%\" valign=\"bottom\" style=\"width: 13.5096%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; 95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.41176470588235%\" valign=\"bottom\" style=\"width: 12.0981%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePCDMP participation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.467408585055644%\" valign=\"bottom\" style=\"width: 6.9564%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.400635930047695%\" valign=\"bottom\" style=\"width: 8.9728%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.267090620031796%\" valign=\"bottom\" style=\"width: 5.8474%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.81081081081081%\" valign=\"bottom\" style=\"width: 7.6621%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.518282988871224%\" valign=\"bottom\" style=\"width: 6.8556%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.262321144674086%\" valign=\"bottom\" style=\"width: 13.5096%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.41176470588235%\" valign=\"bottom\" style=\"width: 12.0981%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.467408585055644%\" valign=\"bottom\" style=\"width: 6.9564%;\"\u003e\n \u003cp\u003eHigh (\u0026gt; 30.37%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.400635930047695%\" valign=\"bottom\" style=\"width: 8.9728%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.267090620031796%\" valign=\"bottom\" style=\"width: 5.8474%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.81081081081081%\" valign=\"bottom\" style=\"width: 7.6621%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.518282988871224%\" valign=\"bottom\" style=\"width: 6.8556%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.262321144674086%\" valign=\"bottom\" style=\"width: 13.5096%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.41176470588235%\" valign=\"bottom\" style=\"width: 12.0981%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.86804451510334%\" colspan=\"2\" valign=\"bottom\" style=\"width: 15.9292%;\"\u003e\n \u003cp\u003eLow (\u0026le; 30.37%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.267090620031796%\" valign=\"bottom\" style=\"width: 5.8474%;\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.81081081081081%\" valign=\"bottom\" style=\"width: 7.6621%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;(0.96 - 1.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.518282988871224%\" valign=\"bottom\" style=\"width: 6.8556%;\"\u003e\n \u003cp\u003e1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.262321144674086%\" valign=\"bottom\" style=\"width: 13.5096%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; (1.08 - 1.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"56.27980922098569%\" colspan=\"7\" style=\"width: 99.684%;\"\u003e\n \u003cp\u003eaOR, adjusted odds ratio, CI, confidence interval\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.41176470588235%\" style=\"width: 99.684%;\" colspan=\"7\"\u003e\n \u003cp\u003e*p\u0026lt;.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study investigated how PCDMP affects individuals' health outcomes, specifically, their HbA1c levels. As described earlier, the PCDMP aims to increase patient acceptance and establish a chronic disease management system in local clinics [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Additionally, we utilized HbA1c levels\u0026thinsp;\u0026gt;\u0026thinsp;6.5% as a high HbA1c indicator [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. To evaluate the PCDMP as a well-established program and progress to become an official program of the nation, we should investigate whether participating in the PCDMP is helpful for individuals living in such residential areas in terms of HbA1c. For instance, many studies have explored the effect of a PCDMP on the risk of complications and managing hypertension in hypertension patients in South Korea [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] and concluded that the program had positive effects on the health of hypertension patients.\u003c/p\u003e \u003cp\u003eThis study, on the other hand, examined the effects of participating in the PCDMP on HbA1c using mixed-effects logistic regression. The main findings of this study are as follows: First, people living in areas with low PCDMP participation have odds of exhibiting high HbA1c, which is 1.40 times greater than that of residents in areas with high PCDMP participation. Second, the likelihood of having high HbA1c was significantly greater in those living in areas with low PCDMP participation. In other words, living in areas with a low PCDMP increases the risk of exhibiting high HbA1c. Our findings are similar to those of previous studies investigating the effects of participating in PCDMP on the health behaviors of hypertension patients [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. One of the early studies on the effect of the PCDMP on the risk of complications in patients with hypertension in Korea found that the hazard ratio was significantly lower for patients participating in the PCDMP than for patients not participating in the program for all 4 complications\u0026mdash;hypertension, myocardial infarction, stroke, chronic kidney disease, and heart failure [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGlobally, several studies have explored the positive impact of chronic disease management programs in primary care on diabetes management [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. However, to the best of our knowledge, this is the first study to examine the association between regional PCDMP participation and individual HbA1c levels in nondiabetic individuals utilizing data from local clinics\u0026rsquo; PCDMP participation and the KNHANES containing individual health information. Our study is distinguished from early findings in that the regional covariates were adjusted with a multilevel approach using mixed-effects considering the clustering effects of regions in the datasets. Furthermore, external validity would be high when a large sample is included. Our study's significant conclusions included the possibility that PCDMP may help individuals who are not diagnosed with diabetes by doctors not to get diabetes, which may lead to a decreased possibility of reduced healthcare expenses [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Consequently, the PCDMP might be an affordable diabetes management approach. On the other hand, research has shown that the PCDMP's diabetes education program and required testing in local healthcare facilities have no appreciable impact on diabetic patients' ability to maintain a healthy blood sugar level, an alternative diabetes index [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNonetheless, this study has a few limitations. First, the KNHANES data used in the study were secondary, and it was not feasible to perform a time-series analysis to monitor changes in individual health status as the survey items differed annually. A second constraint was that residential areas were divided into only 17 regions in Korea when we calculated the participation of the PCDMP. In addition, the current PCDMP is a pilot program run by the Korean government; thus, if it becomes an official program, the procedures and details of the program are subject to change. Third, individuals\u0026rsquo; HbA1c levels are divided into two categories, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\ge\\)\u003c/span\u003e\u003c/span\u003e 6.5% and \u0026lt;\u0026thinsp;6.5%, where information may be lost about individuals with HbA1c levels of nearly 6.5%. Additionally, diagnosing diabetes is not solely based on HbA1c levels; rather, other tests, including screening tests and glycemia tests, may be used; moreover, there are no perfect guidelines for diabetes diagnosis [\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Finally, in statistical analysis, few regional covariates are linear combinations of other variables, which leads to unreliable and unstable estimates of regression coefficients [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eAt the national level, efforts are still being made to effectively manage chronic illnesses through primary care. The Korean government aims to run integrated national primary care-based chronic disease management programs and regulations that emphasize the positive aspects of the current pilot programs while addressing their shortcomings. This study is significant since it provides a comprehensive evaluation of the efficacy of the PCDMP in Korea, increasing the importance of primary care. By enhancing continuity of care and averting complications, increasing the role of primary diabetes care can greatly enhance the health outcomes of individuals. These findings offer essential information to help improve primary care, which is increasingly important globally.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003ePCDMP: primary care-based chronic disease management integrated pilot program; HbA1c: glycated hemoglobin; KNHANES: Korea National Health and Nutrition Examination Survey; NHIS: National Health Insurance Service; CI: Confidence interval; IDF: International diabetes federation; KHEPI: Korea Health Promotion Institute; HIRA: Health Insurance Review \u0026amp; Assessment Service; BMI: Body mass index; GLMM: Generalized linear mixed model; aOR: Adjusted odds ratio\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank the members of the Institute of Health Services Research at Yonsei University for their advice on the further development of this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJuan Kim and Il Yun made substantial contributions to the concept or design of the work; Juan Kim and Il Yun contributed to the acquisition, analysis, or interpretation of the data; Juan Kim drafted the article; and Eun-Cheol Park and Min Jin Ha critically revised the article for important intellectual content. All the authors approved the version to be published.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMin Jin Ha was supported by National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) [No. 2022R1A2C1091488].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analyzed in the present study are publicly available. First, the KNHANES data are available online: https://knhanes.kdca.go.kr. Second, the NHIS data are available online: https://www.nhis.or.kr/nhis/together/wbhaec06900m01.do. Third, the HIRA big data portal data are available online: https://opendata.hira.or.kr/op/opc/olapYadmStatInfoTab4.do.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs the KNHANES complies with the Declaration of Helsinki and provides publicly accessible data, further ethical approval for the use of these data was not required.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests to declare.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBae JH, Han KD, Ko SH, Yang YS, Choi JH, Choi KM, et al (2011). Diabetes Fact Sheet in Korea 2021. Diabetes Metab J;46:417-26.\u003c/li\u003e\n\u003cli\u003eRamachandran A, Wan Ma RC, Snehalatha C (2021). Diabetes in Asia. The Lancet 2010;375:408-18.\u003c/li\u003e\n\u003cli\u003eIDF. IDF Diabetes Atlas 2021\u0026ndash;10th edition. Brussels, Belgium: International Diabetes Federation (IDF).\u003c/li\u003e\n\u003cli\u003eDavid B Sacks, David E Bruns, David E Goldstein, Noel K Maclaren, Jay M McDonald, Marian Parrott (2002), Guidelines and Recommendations for Laboratory Analysis in the Diagnosis and Management of Diabetes Mellitus, \u003cem\u003eClinical Chemistry\u003c/em\u003e, Volume 48, Issue 3, Pages 436\u0026ndash;472, https://doi.org/10.1093/clinchem/48.3.436\u003c/li\u003e\n\u003cli\u003eAmerican Diabetes Association (2006); Standards of Medical Care in Diabetes\u0026ndash;2006. \u003cem\u003eDiabetes Care\u003c/em\u003e; 29 (suppl_1): S4\u0026ndash;S42. https://doi.org/10.2337/diacare.29.s1.06.s4\u003c/li\u003e\n\u003cli\u003eAmerican Diabetes Association Professional Practice Committee (2022); 7. Diabetes Technology: \u003cem\u003eStandards of Medical Care in Diabetes\u0026mdash;2022\u003c/em\u003e. \u003cem\u003eDiabetes Care\u003c/em\u003e; 45 (Supplement_1): S97\u0026ndash;S112. https://doi.org/10.2337/dc22-S007\u003c/li\u003e\n\u003cli\u003eCho B (2021). Review and assessment to support chronic noncommunicable diseases management in the primary care in Korea. Health Insurance Review \u0026amp; Assessment Service Research;1:31-5.\u003c/li\u003e\n\u003cli\u003eLorig, K. R., Ritter, P., Stewart, A. L., Sobel, D. S., Brown Jr, B. W., Bandura, A., ... \u0026amp; Holman, H. R. (2001). Chronic disease self-management program: 2-year health status and health care utilization outcomes. Medical care, 39(11), 1217-1223.\u003c/li\u003e\n\u003cli\u003eHyun, M. K., Lee, J. W., \u0026amp; Ko, S.-H. (2023). Chronic disease management program applied to type 2 diabetes patients and prevention of diabetic complications: A retrospective cohort study using nationwide data. BMC Public Health, 23(1). https://doi.org/10.1186/s12889-023-15763-z \u003c/li\u003e\n\u003cli\u003eAhn, S., Basu, R., Smith, M. L., Jiang, L., Lorig, K., Whitelaw, N., \u0026amp; Ory, M. G. (2013). The impact of chronic disease self-management programs: Healthcare Savings through a community-based intervention. BMC Public Health, 13(1). https://doi.org/10.1186/1471-2458-13-1141 \u003c/li\u003e\n\u003cli\u003eRothman, R. L., Malone, R., Bryant, B., Wolfe, C., Padgett, P., DeWalt, D. A., ... \u0026amp; Schillinger, D. (2005). The Spoken Knowledge in Low Literacy in Diabetes scale: a diabetes knowledge scale for vulnerable patients. Diabetes Educator, 31(2), 215-224. doi:10.1177/0145721705275002\u003c/li\u003e\n\u003cli\u003eKim, H.\u003cem\u003e \u003c/em\u003eet al (2013)\u003cem\u003e.\u003c/em\u003e Factors affecting the validity of self-reported data on health services from the community health survey in Korea. \u003cem\u003eYonsei medical journal\u003c/em\u003e\u003cstrong\u003e54\u003c/strong\u003e, 1040-1048.\u003c/li\u003e\n\u003cli\u003eSeong SC, Kim YY, Khang YH, Park JH, Kang HJ, Lee H, et al (2017). Data resource profile: the national health information database of the National Health Insurance Service in South Korea. \u003cem\u003eInt J Epidemiol.\u003c/em\u003e46:799\u0026ndash;800.\u003c/li\u003e\n\u003cli\u003eService HIRaA (2022). The results for diabetes quality assessment 2020. Wonju: HIRA.\u003c/li\u003e\n\u003cli\u003eAbbas, Y. Glycosylated Hemoglobin (2011). The importance in management of type 2 diabetes. J. Stress Physiol. Biochem.7, 122\u0026ndash;129.\u003c/li\u003e\n\u003cli\u003eSherwani, S. I., Khan, H. A., Ekhzaimy, A., Masood, A., \u0026amp; Sakharkar, M. K. (2016). Significance of HbA1c Test in Diagnosis and Prognosis of Diabetic Patients. Biomarker insights, 11, 95\u0026ndash;104. https://doi.org/10.4137/BMI.S38440\u003c/li\u003e\n\u003cli\u003eSherwani SI, Khan HA, Ekhzaimy A, Masood A, Sakharkar MK (2016). Significance of HbA1c Test in Diagnosis and Prognosis of Diabetic Patients. Biomarker Insights. 11. doi:10.4137/BMI.S38440\u003c/li\u003e\n\u003cli\u003eLee SA, Park H, Kim W, Song SO, Lim H, Chun SY (2016). The Effect of Chronic Disease Management Program on the Risk of Complications in Patients With Hypertension in Korea. J Korean Med Sci. 37(31):e243. https://doi.org/10.3346/jkms.2022.37.e243\u003c/li\u003e\n\u003cli\u003eLee E‒W, Kim H-S, Yoo B-N, Lee E-J, Park J-H (2022). Effect of a Primary Care-Based Chronic Disease Management Program for Hypertension Patients in South Korea. Iran J Public Health. 51(3):624-633.\u003c/li\u003e\n\u003cli\u003eForjuoh SN, Ory MG, Jiang L, Vuong AM, Bolin JN (2014). Impact of chronic disease self-management programs on type 2 diabetes management in primary care. World J Diabetes. 5(3):407-14. doi: 10.4239/wjd.v5.i3.407\u003c/li\u003e\n\u003cli\u003eCl\u0026eacute;ment Pimouguet, M\u0026eacute;lanie Le Goff, Rodolphe Thi\u0026eacute;baut, Jean Fran\u0026ccedil;ois Dartigues and Catherine Helmer (2011). Effectiveness of disease-management programs for improving diabetes care: a meta-analysis CMAJ. 183 (2) E115-E127; DOI: https://doi.org/10.1503/cmaj.091786\u003c/li\u003e\n\u003cli\u003eJeon SY, Lee mSA, Jang JH, Song SO, Kim HK, Yim HS, et al (2020). Cost-effectiveness analysis of clinic-level chronic disease management system focusing on hypertension. National health insurance service Ilsan hospital.\u003c/li\u003e\n\u003cli\u003eCheong W, Yim J, Oh D-K, Im J-S, Ko KP, Kim YM (2013). Effects of chronic disease management based on clinics for blood pressure or glycemic control in patients with hypertension or type 2 diabetes mellitus. Journal of agricultural medicine and community health 38:108-15.\u003c/li\u003e\n\u003cli\u003eInzucchi, S. E. (2012). Diagnosis of diabetes. \u003cem\u003eNew England Journal of Medicine\u003c/em\u003e, \u003cem\u003e367\u003c/em\u003e(6), 542\u0026ndash;550. https://doi.org/10.1056/nejmcp1103643\u003c/li\u003e\n\u003cli\u003eBarr, R. G., Nathan, D. M., Meigs, J. B., \u0026amp; Singer, D. E. (2002). Tests of glycemia for the diagnosis of type 2 diabetes mellitus. \u003cem\u003eAnnals of Internal Medicine\u003c/em\u003e, \u003cem\u003e137\u003c/em\u003e(4), 263. https://doi.org/10.7326/0003-4819-137-4-200208200-00011\u003c/li\u003e\n\u003cli\u003eGoldstein, D. E., Little, R. R., Lorenz, R. A., Malone, J. I., Nathan, D., Peterson, C. M., \u0026amp; Sacks, D. B. (2004). Tests of glycemia in diabetes. \u003cem\u003eDiabetes care\u003c/em\u003e, \u003cem\u003e27\u003c/em\u003e(7), 1761-1773.\u003c/li\u003e\n\u003cli\u003eMidi, H., Sarkar, S. K., \u0026amp; Rana, S. (2013). Collinearity diagnostics of binary logistic regression model. \u003cem\u003eJournal of Interdisciplinary Mathematics\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e(3), 253\u0026ndash;267. https://doi.org/10.1080/09720502.2010.10700699\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"diabetes, glycated hemoglobin, HbA1c, primary care, chronic disease management","lastPublishedDoi":"10.21203/rs.3.rs-4209917/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4209917/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e:\u003cstrong\u003e \u003c/strong\u003eDiabetes is one of the most common causes of cardiovascular disease and has led to death over time. For better management of glycated hemoglobin levels, which is a well-known index in diagnosing diabetes, many countries have been launching chronic disease management programs. Therefore, this study aimed to evaluate the effectiveness of the primary care-based chronic disease management integrated pilot program (PCDMP) in controlling glycated hemoglobin (HbA1c) levels in Korea. Nation-wide data from the 2019–2021 Korea National Health and Nutrition Examination Survey were used.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: We analyzed\u003cstrong\u003e \u003c/strong\u003enationwide health examination data combined with various regional data sources. Using appropriate inclusion criteria for this study, a total of 13,901 individuals were suitable for the analysis. A generalized linear mixed model was applied to consider the clustered structure of the regional level data, where individual-level data, containing demographic characteristics and health-related information, were nested.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e:\u003cstrong\u003e \u003c/strong\u003eIndividuals living in areas with a low PCDMP participation (\u0026lt; 30.37%) had odds (95% CI, 1.08–1.82) of exhibiting glycated hemoglobin levels ≥ 6.5% greater than did those residing in areas with a high PCDMP participation (≥ 30.37%). According to the stratified analysis of HbA1c levels, people residing in areas with low PCDMP participation had significantly greater odds of having an HbA1c \u0026gt; 6.5%, which may indicate diabetes mellitus.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: The lower the number of internal medicine clinics in an area participating in PCDMP was, the greater the possibility of an HbA1c \u0026gt; 6.5%. Our findings advocate the need for the government to pay attention to chronic disease management programs, which may lead individuals to have normal HbA1c levels (\u0026lt; 6.5%).\u003c/p\u003e","manuscriptTitle":"Effectiveness of primary care-based chronic disease management program on glycated hemoglobin levels","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-09 21:10:34","doi":"10.21203/rs.3.rs-4209917/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"20639ee8-dd05-4838-8988-21b04e2c9aab","owner":[],"postedDate":"April 9th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-10T07:38:50+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-09 21:10:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4209917","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4209917","identity":"rs-4209917","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}