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The Study of Health Related Quality of Life and Its Effect on Health Service Utilization in Chinese Population

Author: WangZuo
Tutor: HuangJianShi;RaoKeQin
School: Beijing Union Medical College
Course: Epidemiology and Health Statistics
Keywords: Health related quality of life HRQOL Health service utilization EQ-5D Need management Predictive model Tobit model Two-part model Quality-adjusted life years
CLC: R197.1
Type: PhD thesis
Year: 2010
Downloads: 691
Quote: 1
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Abstract


BackgroundThe World Health Organization defined health as "a state of complete physical, mental, and social well-being and not merely the absence of disease or infirmity" in 1948. Eleven years later (1959), the American scholar Dunn proposed the concept of holistic health as a perfect state of physical, social, mental, emotional, and spiritual health. As the medical model changed from the experience based medical model to the bio-medical model, and to the bio-psycho-social-environment model, the definition and measurement of health are constantly changing.With the rapid development of economic status and the improvement of living standards, people are now not only concerned about the length of survival, but also concerned about the quality of life. Therefore, the concept of health related quality of life has been emerged. Compared with existing health status indicators, health related quality of life not only reflects physical health, but also reflects the psychological, social and emotional aspects of health.In 1974, the Government of Canada released the Lalonde report. This report pointed out that the determinants of the health including the biological (genetic factors), environmental (natural and social environment), lifestyle and behavior, and health service system factors. Thereafter, health service system as one of the determinants of health was paid more attention to gradually. Western countries have studied the health related quality of life for more than 2 decades. They used the health related quality of life as a tool to measure health status and to access the impacts about the population health service utilization status. However, there are few studies on health related quality of life in China. After literature review, we found only three related studies. The methodologies, subjects, and contents of these three studies are subject to improvement.Health management as a new industry and a new disciple has been emerged in China since 2000. However, due to lack of theoretical research, the development of health management in China is slowly. Therefore, the study of health related quality of life and its effect on health service utilization have practical meanings regarding to health management in China.In this study, we used 2008 Forth National Health Service Survey data to describe the health related quality of life in Chinese population and to identify the main factors influencing health related quality of life in urban and rural residents. In addition, we studied the relationship between health related quality of life and outpatient and inpatient service utilization in Chinese population. Based on above findings, we established models to predict the probability and cost of medical services in hypertensive patients in urban and rural areas. We also predicted the losses of quality-adjusted life years due to nine classes of common chronic diseases in the next 40 years in China.ObjectivesThere are two main objectives of this study.1. To analyze the status of health related quality of life measured by EQ-5D scale and to identify the main influence factors in general population in China; and2. To explore the impact of the index of health related quality of life on the outpatient and inpatient service utilization in China.There are also three sub-objectives under the main objectives.1. To analyze the impact of nine classes of common chronic diseases in China in relation to health related quality of life and quality-adjusted life years. 2. To estimate the losses of quality-adjusted life years due to nine classes of common chronic diseases in next 40 years in China.3. To establish models to predict the probability and cost of medical services in hypertensive patients in urban and rural areas in China.MethodsThe discriptions of methodology are as follows.1. Statistic description and inference:describing the distribution of the socio-economic and demography factors, life style factors, chronic diseases status, and indexes of health related quality of life (including mobility, self-care, usual activities, pain/discomfort, anxiety/depression, Visual Analogue Scale (VAS), and index) in different groups of population.2. Tobit model:taking into account data censored due to ceiling effect of EQ-5D, we used Tobit Model to analyze the impact of five dimensions of EQ-5D on VAS in different groups of population and to analysis the impact of nine classes of chronic diseases on index of EQ-5D in different age groups.3. Multivariate non-conditional logistic regression model:analyzing the influence factors of whether there are health problems in five dimensions of EQ-5D and the impact of five dimensions and VAS on inpatient and outpatient service utilization in urban and rural residents.4. Two-part model:including logistic regression model to predict probability and multivariate linear regression to predict cost of health service utilization.The software uesd in our study included:Epi DATA 3.0, SAS 9.12, and ArcGis 8.3.ResultsThe dataset of the study contains 120,738 residents from 94 countries,31 provinces in China.1. The result of status of health related quality of life in Chinese population.The five dimensions of EQ-5D scale generates total of 243 answers. Our study finds the study subjects produced 167 of 243 answers. The largest proportion of the study subjects’ answer to health status is no problems in all five dimensions, accounting for 87.01% of total study population (105,057/120,738); followed by moderate problems in "pain/discomfort "dimension, accounting for 3.14% of total study population (3,789/120,738). Only 0.08% of total study population self-reported severe health problems in five dimentsions (91/120,738).Of all the study subjects, the most frequently reported problems are moderate or severe problems in pain/discomfort, accounting for 9.25% of the study population; followed by anxious/depression and mobility, accounting for 6.43% and 5.14% of the study population respectively. The least frequently reported problems are moderate or severe problems in self-care, accounting for 3.26% of the study population.The average VAS of the study population is 80.14 (SD:14.88). Of them, the average score for the urban population was 79.33 and 80.44 for the rural population. Among the study population,11,378 subjects gave the full mark "100" for VAS, accounting for 9.42% of total study population. On the other side,24 subjects marked their VAS "0", accounting for 0.02% of total study population.The Japanese and British index systems were used to calculate the health index of Chinese population in this study. The average health index was 0.956 (SD:0.124) using Japanese system, with the average index of 0.960 (SD:0.120) in male, and 0.952 (SD: 0.127) in female population. The average health index was 0.955 (SD:0.144) using British system. In male population, the average index is 0.960 (SD:0.140), and in female population,the index is 0.951 (SD:0.148).The results of the Tobit model showed the largest marginal effect is "pain/discomfort" (7.89), followed by "anxious/depression" (7.74). Urban population has the largest value of marginal effect in "anxious/depression" (8.58); rural population has the largest value of marginal effect in "pain/discomfort" (8.15). Male population has higher value of marginal effect in "pain/discomfort" (8.29) than female (7.61). Female has higher value of marginal effect in "anxious/depression"(7.85) than male (7.55). Population in 15-35 years old age group has larger marginal effect in "anxious/depression" and "pain/discomfort"dimensions. Population in over 35years old age group has largest marginal effect in "pain/discomfort" dimension.The results of the influence factors of five dimensions and VAS showed that the possibility of health problems in old population is higher than in young population. VAS is lower in old population than in young population. Besides age, the factors such as educational levels, employment status, income levels, and medical insurance have impact on health related quality of life as well. People with healthier life style have better status of health related quality of life. People who have more chronic diseases have higher possibility of health problems in five dimensions, and lower VAS scores.2. The result of the impact of health related quality of life on health service utilization.The study found that health related quality of life associated with the use of both outpatient and inpatient services.Of five dimensions, "pain/discomfort" has the largest impact on outpatient service utilization, the OR value in "pain/discomfort" is 1.792 in urban population, and is 2.098 in rural population. The presence of health problems in "self care" dimension could reduce the probability of outpatient visits in urban population (OR:0.647). The presence of health problems in "mobility" could reduce the probability of outpatient visits in rural population (OR:0.813). The presence of problems in "anxiety/depression" dimension could increase the probability of outpatient service (OR:1.144). The study also found that both in urban and rural areas, VAS has protective effects in outpatient service utilization (OR<1).In urban population, the probility of hospitalization is 1.42 times in residents who have problems in "mobility" dimension than those who have not. The probility of hospitalization is 1.66 times in residents who have problems in "usual activity" dimension than those who have not.In rural population, the probility of hospitalization is 1.29 times in residents who have problems in "mobility" dimension than those who have not. The probility of hospitalization is 1.46 times in residents who have problems in "usual activity" dimension than who have not.In addition, "anxiety / depression" could increase the possibility of hospitalization in rural population (OR 1.155). The higher VAS score has protective effects in inpatient service utilization in both urban and rural areas (OR<1).3.The establishment of predictive models for hypertensive patients.1.The predictive model of probility of urban hypertensive patient outpatient service utilization:Y = -5.4588 + insur * 0.6639 + in3 * 0.5979 - c2 * 0.4530 - h10* 0.1676 + i202 * 0.9616 + i203 * 0.7497 + chronic3 * 1.7797 + chronic4 * 1.4647 -sport5* 0.2058 Insur=insurance; in3=low income group; c2=middle size city; h10=the distance between home and the nearest hospital; i202=sererity of disease: moderate; i203=severity of disease-severe; chronic3=having 2chronic diseases at the same time; chronic4=having 3 chronic diseases at the same time; sport5=having more than 6 times exercise weekly.2.The predictive model of urban hypertensive patients’ outpatient service cost: utilization:X = -0.0834 - active * 0.1339 + selfcare * 0.1288 + insur * 0.1334 + in3 * 0.0897 - c3 * 0.0845 + chronic3 * 0.1085 - sport4 * 0.0814 - sport5 * 0.0787 + loca2 * 1.7450 + loca3 * 1.7755 + loca4 * 1.9510 + treat3 * 0.5536 active=mobility dimension; selfcare=self care dimension; Insur=insurance; in3= low income group; c3=small size city; chronic3=having 2 chronic diseases at the same time; sport4=having 3-5 times exercise weekly; sport5=having more than 6 times exercise weekly; loca2=hospital in country /city /district; loca3=hospital in city; loca4=hospital in province; reat3=intramuscular. 3.The predictive model of probility of rural hypertensive patient outpatient service utilization:Y = -4.6181 - active * 0.2878 - hun2 * 0.3087 + r2 * 0.9207 + r3 * 0.6253 + r4 * 0.5294 + i202 * 1.6341 + i203 * 1.7269 + chronic3 * 0.5691 + smoke3* 0.2205 active=mobility dimension; hun2=married; r2=rural in class 2; r3=rural in class 3; r4=rural in class 4; i202=sererity of disease: moderate; i203=severityofdisease:severe; chronic 3=having 2 chronic diseases at the same time; smoke3=smoking.4.The predictive model of rural hypertensive patients’ outpatient service cost:X = -0.1397 - usual * 0.1123 + hun4 * 0.0671 + in3 * 0.0933 + r3 * 0.0388 + i202 * 0.3805 + i203 * 0.2450 + smoke3 * 0.0436 + loca2 * 1.2268 + loca3 * 0.4564 + loca4 * 1.3907 + treat2 * 1.1978 + treat3 * 0.2610 usual-usual activity dimension; hun4=widowed; in3=low income group; r3=rural in class 3; i202=sererity of disease: moderate; i203=severity of disease-severe; smoke3=smoking; loca2=hospital incountry/city/district; loca3=hospital in city; loca4=hospital in province; treat2=oral medication; treat3=intramuscular.5.The predictive model of probility of urban hypertensive patient inpatient service utilization:Y = -3.7530 + ageg5 * 0.3812 + active * 0.4597 - m57 * 0.0114 + chronic3 *0.5000 ageg5=over 60years old age group; active=mobility dimension; m57=VAS score; chronic3=having 2 chronic diseases at the same time. 6.The predictive model of urban hypertensive patients’ inpatient service cost:X = 0.1422-depress*0.1019 - m57 * 0.0024 - emp * 0.1192 + in3 * 0.1732 + chronic3 * 0.1048 + opera * 1.6410 + hos11 * 0.0330 depress=anxious/depression dimension; m.57=VAS score; emp=employment status; in3=low income group; chronic3=having 2 chronic diseases at the same time; opera=having operation during hospitalization hosll =number of days during hospitalization.7.The predictive model of probility of rural hypertensive patient inpatient service utilization:Y = -5.7638 + hun4 * 0.3604 + r2 * 1.2567 + r3 * 0.8694 + r4 * 1.0097 + chronic3 * 0.4827 + acl3 * 0.8675 hun4=widowed; r2=rural in class 2; r3=rural in class 3; r4=rural in class 4; 2chronic3=having 2 chronic diseases at the same time; cl3= drinking alcohol at least 3 times weekly.8.The predictive model of rural hypertensive patients’ inpatient service cost:X = -0.1847 - selfcare * 0.0757 - m57 * 0.0024 + hun4 * 0.0909 - insur * 0.1078 - in2 * 0.0508 + r2 * 0.2146 + r3 * 0.1131 + r4 * 0.1220 + chronic3 * 0.0703 + opera * 1.8640 + hosll * 0.0597 + acl3 * 0.1017 - sport2 * 0.3529 - sport3 * 0.1497 - sport5 * 0.1127 selfcare=self care dimension; m57=VAS score; hun4=widowed; insur=imsurance; in2=middle income group; r2=rural in class 2; r3=rural in class 3; r4=rural in class 4; chronic3=having 2 chronic diseases at the same time; opera=having operation during hospitalization; hosll =number of days during hospitalization; acl3= drinking alcohol at least 3 times weekly; sport2=having no more than 1 time exercise weekly; sport3=having 1-2 times exercise weekly; sport5=having more than 6 times exercise weekly.4. Estimation of the losses of quality-adjusted life years due to nine classes of common chronic diseasesAll nine classes of chronic diseases could decrease the health index significantly overall and regardless of age.Permanent disability has the largest marginal effect on health index in total population, with the value of 0.1175; followed by carcinoma, with the value of 0.0842. Cardiovascular diseases have the smallest marginal effect on health index, with the value of 0.0323; followed by respiratory diseases, with the value of 0.0352. The marginal effects of nine classes of chronic diseases increase with age.The loss of quality-adjusted life years due to cardiovascular diseases is 238.7 years per 100,000 people per year overall. In 15-50 years age group, musculoskeletal diseases cause the largest loss of quality-adjusted life year (145.1 years per 100,000 people per year). In over 50 years old age group, cardiovascular diseases cause the heaviest disease burden. The number of the loss of quality-adjusted life year increases with age. The losses of quality-adjusted life year due to respiratory and cardiovascular diseases are 1,000-2,000 times higher in over 60 years age group than in 15-30 year age group.The proportion of people in over 60 years age group in total population will be gradually increasing from 12.56% in 2010 to 28.78% in 2050. The number of people in over 60 years age group is increasing by 153% from 2010 to 2050. The loss of quality-adjusted life years due to 9 classes of chronic diseases is predicted 7,960,596 years overall in 2010. It will increase to 9,982,808 years in 2020,12,927,305 years in 2030,14,213,016 years in 2040, and 14,892,228 years in 2050.Conclusions1. Evaluation of health related quality of life in Chinese residents using the EQ-5D scale showed that two most frequent health problems are "pain/discomfort" and "anxiety/depression".2. Health related quality of life has different distribution pattern in different population groups. Overall, male population’s health related quality of life is better than that in female population. Health related quality of life decreases with age. Educational levels, employment status, income levels, and medical insurance also have impact on health related quality of life.3. People with healthier life style such as not smoking, drinking less, and physical exercise regularly have better status of health related quality of life.4. The impact of chronic diseases on health related quality of life is significant in Chinese population. People who have more chronic diseases have worse status of health related quality of life.5. The EQ-5D scale has been proved to be a valid measurement of health related quality of life in Chinese population, with a significant ceiling effect.6. Health related quality of life measured by the EQ-5D scale is an independent factors influencing outpatient and inpatient service utilization.7. Health related quality of life can be used in predicting probability and cost of health service utilization.8. The nine classes of chronic diseases could decrease the status of health related quality of life. Permanent disability and carcinoma have the largest effect on the status of health related quality of life.9. With aging population, the loss of quality-adjusted life years due to nine classes of common chronic diseases will increase by 87.07% in next 40 years.

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