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Clinical
Factors affecting unmet healthcare needs of older people in Korea Y.H. Ahn1 RN, PhD, HHC-APN, N.H. Kim2 O.K. Ham4 RN, PhD, MCHES
RDH, PhD, C.B.
Kim3
MD, PhD
&
1 Professor, Department of Nursing, Institute of Lifestyle Medicine, 2 Associate Professor, Department of Dental Hygiene, 3 Professor, Department of Preventive Medicine, Yonsei University Institute for Poverty Alleviation and International Development, Wonju College of Medicine Yonsei University, Wonju, 4 Associate Professor, Department of Nursing, College of Medicine, Inha University, Incheon, Republic of Korea
AHN Y.H., KIM N.H., KIM C.B. & HAM O.K. (2013) Factors affecting unmet healthcare needs of older people in Korea. International Nursing Review 60, 510–519 Background: Despite the fact that the National Health Insurance programmes have ensured universal coverage for Koreans, disparities in access to health care and unmet healthcare needs still exist in Korea. Aim: The purpose of this study was to analyse factors affecting unmet healthcare needs of older people in Korea. Methods: This study had a cross-sectional, descriptive design using secondary data taken from the Korean National Health and Nutrition Survey conducted in 2007–2009. A complex sampling design was used, and the participants included a nationally representative sample of 3943 people older than 64 years. Socio-demographic variables, subjective health, existence of chronic diseases, quality of life and unmet healthcare needs were included in the study instruments. Logistic regression analyses were performed in order to examine the relationship between unmet healthcare needs and independent variables. Results: According to the results, 29.4% of older women and 14.0% of older men had not visited clinics or hospitals when they needed to obtain healthcare services (unmet healthcare needs) during the past 12 months. Older women [odds ratio (OR) = 1.831, 95% confidence interval (CI) = 1.428–2.347] and those with poor subjective health (OR = 1.708, 95% CI = 1.371–2.126) and arthritis (OR = 1.278, 95% CI = 1.029–1.586) were more likely to have unmet healthcare needs than their counterparts. Conclusions: Efforts to decrease unmet healthcare needs, targeting high-risk groups (especially for older women), are needed in order to prevent disability, decrease mortality and promote the quality of life of older people. Keywords: Assessment of Healthcare Needs, Chronic Diseases, Older People
Introduction Unmet healthcare needs are defined as healthcare services perceived by patients or determined by healthcare providers Correspondence address: Dr Ok Kyung Ham, 100 Inha-ro, Nam-gu, Incheon 402-751, Republic of Korea; Tel: +82-32-860-8211 (office), +82-10-3427-5974 (cell); Fax: +82-32-874-5880; E-mail: [email protected]; [email protected].
© 2013 International Council of Nurses
that are not received or are delayed (Bennett et al. 2012; Yang 2010). Prevalence of unmet healthcare needs differs significantly by country, from 4% in the UK to 39% in the USA (OECD 2011), while 12.2% of adults with chronic conditions in Canada (Ronksley et al. 2012) and 10–25% of older people with perceived health problems in Spain had unmet healthcare needs in the preceding 12-month period (Alonso et al. 1997).
510
Unmet healthcare needs
In Korea, although National Health Insurance (NHI) programmes have ensured universal coverage since 1989, disparities in access to health care and unmet healthcare needs still exist (Shin & Shon 2009). Higher co-payment rates, between 20% and 55%, may foster unmet healthcare needs among vulnerable groups, such as low-income, people with disabilities, infants and older people (Ham & Lee 2007; Song 2010). Yang (2010) reported that the prevalence of unmet healthcare needs has decreased since 2005 among adults and the older people. However, previous studies estimated that 16.6–24.2% of adults had unmet healthcare needs in Korea. They reported an association of age, physical health, economic status, health beliefs and subjective health with unmet healthcare needs among Koreans (Huh & Lee 2011; Song 2010). Kim (2008) also reported an association of gender, type of health insurance, occupation and education levels with unmet healthcare needs in Korea. Existence of unmet healthcare needs may lead to development of complications, aggravation of disease severity and increased mortality, especially among older people (Song 2010), and understanding of the factors affecting unmet healthcare needs may provide information that can be used in developing programmes to promote appropriate use of healthcare services among those with high risk of unmet healthcare needs. Aday & Andersen (1974) reported that healthcare service utilization is determined by three factors: predisposing, enabling and need. Predisposing factors include age, gender, race, religion and health beliefs, while enabling factors include income, insurance coverage, family support and rural/urban residence; and need factors are actual and perceived needs for healthcare services along with illness levels. They argued that the combined effects of these three factors determine healthcare service utilization. Ham & Lee (2007) reported a significant association of predisposing, enabling and need factors with healthcare service utilization among hypertensive patients in Korea. Thus, the purpose of this study was to identify the proportions of unmet healthcare needs perceived by older people and to analyse factors affecting unmet healthcare needs of older people in Korea.
Methods Sample
This study had a cross-sectional, descriptive design using secondary data taken from the Korean National Health and Nutrition Survey (KNHANES IV) conducted in 2007–2009. The KNHANES is a cross-sectional and nationally representative survey conducted periodically in South Korea by the Korea Centers for Disease Control and Prevention (KCDC). The KNHANES uses a stratified, clustered and systematic sampling
© 2013 International Council of Nurses
511
design for selection of household units. The sampling frame for the survey used in this study was based on the 2005 population and housing census in Korea. South Korea was divided into 246 198 areas (primary sampling units), and 200 survey areas (sampling frames) were randomly selected according to geographic area (16 administrative districts), place of residence (urban/rural) and residential type (apartment/non-apartment); 23 households from each sampling frame were sampled. Data for the KNHANES are collected annually using a rolling survey sampling method. The sample was weighted to adjust for oversampling, non-responders and post-stratification (Kim et al. 2013). The KNHANES IV used a complex sampling design and combined data collected in 2007, 2008 and 2009, considering cluster variable (geographical areas), stratification variable (urban/rural and apartment/non-apartment houses) and weighted value (annual number of samples). Among the components of the KNHANES, data from the health interview survey, which were collected targeting adults older than 19 years, were utilized in the current study. The response rate was 74.5% for the KNHANES IV. In the current study, 3943 participants older than 64 years were included in the analysis. Permission to use the KNHANES IV data was obtained from the KCDC based on the study objectives. Measurements
Data from the KNHANES health interview survey were collected using standardized home interviews. Information on socio-demographics, subjective health, diagnosis of chronic diseases, quality of life and unmet healthcare needs was included. Subjective health was measured using a 5-point Likert scale (1 = very poor, 5 = very good). For classification of chronic disease categories, elders were asked for each chronic disease whether they had been diagnosed by physicians [stroke, arthritis, osteoporosis, hypertension and diabetes mellitus (DM)]. Quality of life was measured using the EuroQol health status descriptive system (EQ-5D), which assesses the level of selfreported problems in five dimensions (mobility, self-care, usual activities, pain/discomfort and anxiety/depression) (EuroQol Group 2013). EQ-5D was measured with five questions, with three response levels (no problem, some problems and extreme problems). A single health index score was calculated using a combination of the five items based on the valuation set developed by the KCDC; scores range from −0.17 to 1.00, with higher scores indicating better quality of life (Nam et al. 2007). The existence of unmet healthcare needs was measured using the question ‘during the past 12 months, have you ever not visited clinics or hospitals when you needed to obtain healthcare services?’ and scored yes (1) or no (0). Annual and monthly household income was obtained, which incorporated
512
Y. H. Ahn et al.
salary, pension, government subsidies, income from interests and property, and allowance from children and relatives. Monthly household income (equivalent income) was determined considering the number of family members (monthly household income/✓the number of family members), and determination of quartile income was based on Korean national household income quartile criteria (Statistics Korea 2009). Based on the framework of Aday & Andersen (1974), age, gender, education level, employment status and subjective health were included as predisposing factors; quartile income, place of residence, types of health insurance and purchase of private health insurance were incorporated as enabling factors; and quality of life and chronic disease status were included as need factors. Data analysis
Data analysis was performed using the PASW 18.0 (SPSS Inc., Chicago, IL, USA) complex samples analysis module. Descriptive statistics, such as frequencies, percentages, means, standard deviations and standard errors were, used to describe sociodemographic and disease characteristics. Chi-square tests were performed for comparison of differences in unmet healthcare needs according to socio-demographic and disease characteristics. Bivariate (model 1) and multiple (models 2, 3, and 4) logistic regression analyses were performed in order to examine the relationship between unmet healthcare needs and independent variables. Statistical significance was determined at the 0.05 probability level.
Results Socio-demographic characteristics and unmet healthcare needs
Sixty percent of the participants were women and 35.5% of all participants were age 75 or older. Seventy-three percent had attained education less than elementary school graduation and 8.4% were beneficiaries of the medical-aid programme. Among socio-demographic characteristics, place of residence (P = 0.009), gender, education, types of health insurance, quartile income and subjective health showed a significant association with unmet healthcare needs (P < 0.001). Those residing in rural areas, women, medical aid beneficiaries, and those with less education, fourth quartile income and poor subjective health were more likely to have unmet healthcare needs than their counterparts (Table 1). Disease characteristics and unmet healthcare needs
Forty-one percent of participants had arthritis, 18.6% had osteoporosis, 47.7% had hypertension and 17.2% had DM, while the estimated quality of life (EQ-5D) was 0.82
© 2013 International Council of Nurses
(SE = 0.004). Unmet healthcare needs showed a significant association with quality of life and diagnosis of arthritis and osteoporosis (P < 0.001). Those diagnosed as arthritis and osteoporosis, and those with perceived lower quality of life were more likely to have unmet healthcare needs than their counterparts (Table 2). Factors affecting unmet healthcare needs
Model 1 (bivariate logistic regression) indicated that rural residency [odds ratio (OR) = 1.313, 95% confidence interval (CI) = 1.069–1.613], women (OR = 2.562, 95% CI = 2.123– 3.092), elementary level education (OR = 3.074, 95% CI = 1.706–5.539), medical aid beneficiaries (OR = 1.894, 95% CI = 1.380–2.599), fourth quartile income level (OR = 2.064, 95% CI = 1.412–3.018), poor subjective health (OR = 2.902, 95% CI = 2.429–3.467), diagnosis of arthritis (OR = 2.046, 95% CI = 1.709–2.449) and osteoporosis (OR = 1.898, 95% CI = 1.550–2.324), and quality of life (OR = 0.024, 95% CI = 0.014– 0.043) showed a significant association with unmet healthcare needs. Model 2, which included socio-demographic variables, showed that women (OR = 2.144, 95% CI = 1.718–2.675), fourth quartile income (OR = 1.492, 95% CI = 1.006–2.211) and poor subjective health (OR = 2.469, 95% CI = 2.030– 3.004) showed an association with unmet healthcare needs. In model 3, which included disease categories and quality of life, the existence of arthritis (OR = 1.470, 95% CI = 1.209– 1.787), osteoporosis (OR = 1.367, 95% CI = 1.104–1.692) and DM (OR = 0.641, 95% CI = 0.495–0.830), and quality of life (OR = 0.026, 95% CI = 0.015–0.047) showed a significant association with unmet healthcare needs among the study participants. In model 4, combined effects of socio-demographic and disease characteristics were analysd simultaneously; according to the results, women (OR = 1.831, 95% CI = 1.428−2.347), those with poor subjective health (OR = 1.708, 95% CI = 1.371– 2.126) and existence of arthritis (OR = 1.278, 95% CI = 1.029– 1.586) were significant, indicating that elders with these characteristics were more likely to have unmet healthcare needs than their counterparts. The results also showed that unemployment (OR = 0.718, 95% CI = 0.557–0.927), existence of stroke (OR = 0.572, 95% CI = 0.385–0.848) and DM (OR = 0.599, 95% CI = 0.456–0.785), and quality of life (OR = 0.042, 95% CI = 0.022–0.078) were also significant, indicating that unemployed elders, and those with stroke, DM and higher quality of life were less likely to have unmet healthcare needs than their counterparts. The Nagelkerke R2 value was 0.213 (model 4), suggesting that the model has predictive ability in quantifying unmet healthcare needs (Bewick et al. 2005) (Table 3).
Unmet healthcare needs
513
Table 1 Unmet healthcare needs according to socio-demographic characteristics Total
Place of residence Urban Rural Gender Male Female Age 65–74 ≥75 Level of education Elementary Middle school High school College or over Health insurance type NHI (local) NHI (employee’s) Medical-aid Household income 4th quartile 3rd quartile 2nd quartile 1st quartile Employment status Unemployed Employed Private health insurance No Yes Subjective healtha Good Poor
χ2
Unmet needs
P
n
Weighted n
%
n
Weighted n
% (SE)
2178 1765
3 441 875 1 603 691
68.2 31.8
468 462
705 134 455 469
21.5 (.012) 26 (.015)
12.06
0.009
1590 2353
2 045 714 2 999 853
40.5 59.5
230 700
284 523 876 080
14.0 (.011) 29.4 (.012)
125.32
Clinical
Factors affecting unmet healthcare needs of older people in Korea Y.H. Ahn1 RN, PhD, HHC-APN, N.H. Kim2 O.K. Ham4 RN, PhD, MCHES
RDH, PhD, C.B.
Kim3
MD, PhD
&
1 Professor, Department of Nursing, Institute of Lifestyle Medicine, 2 Associate Professor, Department of Dental Hygiene, 3 Professor, Department of Preventive Medicine, Yonsei University Institute for Poverty Alleviation and International Development, Wonju College of Medicine Yonsei University, Wonju, 4 Associate Professor, Department of Nursing, College of Medicine, Inha University, Incheon, Republic of Korea
AHN Y.H., KIM N.H., KIM C.B. & HAM O.K. (2013) Factors affecting unmet healthcare needs of older people in Korea. International Nursing Review 60, 510–519 Background: Despite the fact that the National Health Insurance programmes have ensured universal coverage for Koreans, disparities in access to health care and unmet healthcare needs still exist in Korea. Aim: The purpose of this study was to analyse factors affecting unmet healthcare needs of older people in Korea. Methods: This study had a cross-sectional, descriptive design using secondary data taken from the Korean National Health and Nutrition Survey conducted in 2007–2009. A complex sampling design was used, and the participants included a nationally representative sample of 3943 people older than 64 years. Socio-demographic variables, subjective health, existence of chronic diseases, quality of life and unmet healthcare needs were included in the study instruments. Logistic regression analyses were performed in order to examine the relationship between unmet healthcare needs and independent variables. Results: According to the results, 29.4% of older women and 14.0% of older men had not visited clinics or hospitals when they needed to obtain healthcare services (unmet healthcare needs) during the past 12 months. Older women [odds ratio (OR) = 1.831, 95% confidence interval (CI) = 1.428–2.347] and those with poor subjective health (OR = 1.708, 95% CI = 1.371–2.126) and arthritis (OR = 1.278, 95% CI = 1.029–1.586) were more likely to have unmet healthcare needs than their counterparts. Conclusions: Efforts to decrease unmet healthcare needs, targeting high-risk groups (especially for older women), are needed in order to prevent disability, decrease mortality and promote the quality of life of older people. Keywords: Assessment of Healthcare Needs, Chronic Diseases, Older People
Introduction Unmet healthcare needs are defined as healthcare services perceived by patients or determined by healthcare providers Correspondence address: Dr Ok Kyung Ham, 100 Inha-ro, Nam-gu, Incheon 402-751, Republic of Korea; Tel: +82-32-860-8211 (office), +82-10-3427-5974 (cell); Fax: +82-32-874-5880; E-mail: [email protected]; [email protected].
© 2013 International Council of Nurses
that are not received or are delayed (Bennett et al. 2012; Yang 2010). Prevalence of unmet healthcare needs differs significantly by country, from 4% in the UK to 39% in the USA (OECD 2011), while 12.2% of adults with chronic conditions in Canada (Ronksley et al. 2012) and 10–25% of older people with perceived health problems in Spain had unmet healthcare needs in the preceding 12-month period (Alonso et al. 1997).
510
Unmet healthcare needs
In Korea, although National Health Insurance (NHI) programmes have ensured universal coverage since 1989, disparities in access to health care and unmet healthcare needs still exist (Shin & Shon 2009). Higher co-payment rates, between 20% and 55%, may foster unmet healthcare needs among vulnerable groups, such as low-income, people with disabilities, infants and older people (Ham & Lee 2007; Song 2010). Yang (2010) reported that the prevalence of unmet healthcare needs has decreased since 2005 among adults and the older people. However, previous studies estimated that 16.6–24.2% of adults had unmet healthcare needs in Korea. They reported an association of age, physical health, economic status, health beliefs and subjective health with unmet healthcare needs among Koreans (Huh & Lee 2011; Song 2010). Kim (2008) also reported an association of gender, type of health insurance, occupation and education levels with unmet healthcare needs in Korea. Existence of unmet healthcare needs may lead to development of complications, aggravation of disease severity and increased mortality, especially among older people (Song 2010), and understanding of the factors affecting unmet healthcare needs may provide information that can be used in developing programmes to promote appropriate use of healthcare services among those with high risk of unmet healthcare needs. Aday & Andersen (1974) reported that healthcare service utilization is determined by three factors: predisposing, enabling and need. Predisposing factors include age, gender, race, religion and health beliefs, while enabling factors include income, insurance coverage, family support and rural/urban residence; and need factors are actual and perceived needs for healthcare services along with illness levels. They argued that the combined effects of these three factors determine healthcare service utilization. Ham & Lee (2007) reported a significant association of predisposing, enabling and need factors with healthcare service utilization among hypertensive patients in Korea. Thus, the purpose of this study was to identify the proportions of unmet healthcare needs perceived by older people and to analyse factors affecting unmet healthcare needs of older people in Korea.
Methods Sample
This study had a cross-sectional, descriptive design using secondary data taken from the Korean National Health and Nutrition Survey (KNHANES IV) conducted in 2007–2009. The KNHANES is a cross-sectional and nationally representative survey conducted periodically in South Korea by the Korea Centers for Disease Control and Prevention (KCDC). The KNHANES uses a stratified, clustered and systematic sampling
© 2013 International Council of Nurses
511
design for selection of household units. The sampling frame for the survey used in this study was based on the 2005 population and housing census in Korea. South Korea was divided into 246 198 areas (primary sampling units), and 200 survey areas (sampling frames) were randomly selected according to geographic area (16 administrative districts), place of residence (urban/rural) and residential type (apartment/non-apartment); 23 households from each sampling frame were sampled. Data for the KNHANES are collected annually using a rolling survey sampling method. The sample was weighted to adjust for oversampling, non-responders and post-stratification (Kim et al. 2013). The KNHANES IV used a complex sampling design and combined data collected in 2007, 2008 and 2009, considering cluster variable (geographical areas), stratification variable (urban/rural and apartment/non-apartment houses) and weighted value (annual number of samples). Among the components of the KNHANES, data from the health interview survey, which were collected targeting adults older than 19 years, were utilized in the current study. The response rate was 74.5% for the KNHANES IV. In the current study, 3943 participants older than 64 years were included in the analysis. Permission to use the KNHANES IV data was obtained from the KCDC based on the study objectives. Measurements
Data from the KNHANES health interview survey were collected using standardized home interviews. Information on socio-demographics, subjective health, diagnosis of chronic diseases, quality of life and unmet healthcare needs was included. Subjective health was measured using a 5-point Likert scale (1 = very poor, 5 = very good). For classification of chronic disease categories, elders were asked for each chronic disease whether they had been diagnosed by physicians [stroke, arthritis, osteoporosis, hypertension and diabetes mellitus (DM)]. Quality of life was measured using the EuroQol health status descriptive system (EQ-5D), which assesses the level of selfreported problems in five dimensions (mobility, self-care, usual activities, pain/discomfort and anxiety/depression) (EuroQol Group 2013). EQ-5D was measured with five questions, with three response levels (no problem, some problems and extreme problems). A single health index score was calculated using a combination of the five items based on the valuation set developed by the KCDC; scores range from −0.17 to 1.00, with higher scores indicating better quality of life (Nam et al. 2007). The existence of unmet healthcare needs was measured using the question ‘during the past 12 months, have you ever not visited clinics or hospitals when you needed to obtain healthcare services?’ and scored yes (1) or no (0). Annual and monthly household income was obtained, which incorporated
512
Y. H. Ahn et al.
salary, pension, government subsidies, income from interests and property, and allowance from children and relatives. Monthly household income (equivalent income) was determined considering the number of family members (monthly household income/✓the number of family members), and determination of quartile income was based on Korean national household income quartile criteria (Statistics Korea 2009). Based on the framework of Aday & Andersen (1974), age, gender, education level, employment status and subjective health were included as predisposing factors; quartile income, place of residence, types of health insurance and purchase of private health insurance were incorporated as enabling factors; and quality of life and chronic disease status were included as need factors. Data analysis
Data analysis was performed using the PASW 18.0 (SPSS Inc., Chicago, IL, USA) complex samples analysis module. Descriptive statistics, such as frequencies, percentages, means, standard deviations and standard errors were, used to describe sociodemographic and disease characteristics. Chi-square tests were performed for comparison of differences in unmet healthcare needs according to socio-demographic and disease characteristics. Bivariate (model 1) and multiple (models 2, 3, and 4) logistic regression analyses were performed in order to examine the relationship between unmet healthcare needs and independent variables. Statistical significance was determined at the 0.05 probability level.
Results Socio-demographic characteristics and unmet healthcare needs
Sixty percent of the participants were women and 35.5% of all participants were age 75 or older. Seventy-three percent had attained education less than elementary school graduation and 8.4% were beneficiaries of the medical-aid programme. Among socio-demographic characteristics, place of residence (P = 0.009), gender, education, types of health insurance, quartile income and subjective health showed a significant association with unmet healthcare needs (P < 0.001). Those residing in rural areas, women, medical aid beneficiaries, and those with less education, fourth quartile income and poor subjective health were more likely to have unmet healthcare needs than their counterparts (Table 1). Disease characteristics and unmet healthcare needs
Forty-one percent of participants had arthritis, 18.6% had osteoporosis, 47.7% had hypertension and 17.2% had DM, while the estimated quality of life (EQ-5D) was 0.82
© 2013 International Council of Nurses
(SE = 0.004). Unmet healthcare needs showed a significant association with quality of life and diagnosis of arthritis and osteoporosis (P < 0.001). Those diagnosed as arthritis and osteoporosis, and those with perceived lower quality of life were more likely to have unmet healthcare needs than their counterparts (Table 2). Factors affecting unmet healthcare needs
Model 1 (bivariate logistic regression) indicated that rural residency [odds ratio (OR) = 1.313, 95% confidence interval (CI) = 1.069–1.613], women (OR = 2.562, 95% CI = 2.123– 3.092), elementary level education (OR = 3.074, 95% CI = 1.706–5.539), medical aid beneficiaries (OR = 1.894, 95% CI = 1.380–2.599), fourth quartile income level (OR = 2.064, 95% CI = 1.412–3.018), poor subjective health (OR = 2.902, 95% CI = 2.429–3.467), diagnosis of arthritis (OR = 2.046, 95% CI = 1.709–2.449) and osteoporosis (OR = 1.898, 95% CI = 1.550–2.324), and quality of life (OR = 0.024, 95% CI = 0.014– 0.043) showed a significant association with unmet healthcare needs. Model 2, which included socio-demographic variables, showed that women (OR = 2.144, 95% CI = 1.718–2.675), fourth quartile income (OR = 1.492, 95% CI = 1.006–2.211) and poor subjective health (OR = 2.469, 95% CI = 2.030– 3.004) showed an association with unmet healthcare needs. In model 3, which included disease categories and quality of life, the existence of arthritis (OR = 1.470, 95% CI = 1.209– 1.787), osteoporosis (OR = 1.367, 95% CI = 1.104–1.692) and DM (OR = 0.641, 95% CI = 0.495–0.830), and quality of life (OR = 0.026, 95% CI = 0.015–0.047) showed a significant association with unmet healthcare needs among the study participants. In model 4, combined effects of socio-demographic and disease characteristics were analysd simultaneously; according to the results, women (OR = 1.831, 95% CI = 1.428−2.347), those with poor subjective health (OR = 1.708, 95% CI = 1.371– 2.126) and existence of arthritis (OR = 1.278, 95% CI = 1.029– 1.586) were significant, indicating that elders with these characteristics were more likely to have unmet healthcare needs than their counterparts. The results also showed that unemployment (OR = 0.718, 95% CI = 0.557–0.927), existence of stroke (OR = 0.572, 95% CI = 0.385–0.848) and DM (OR = 0.599, 95% CI = 0.456–0.785), and quality of life (OR = 0.042, 95% CI = 0.022–0.078) were also significant, indicating that unemployed elders, and those with stroke, DM and higher quality of life were less likely to have unmet healthcare needs than their counterparts. The Nagelkerke R2 value was 0.213 (model 4), suggesting that the model has predictive ability in quantifying unmet healthcare needs (Bewick et al. 2005) (Table 3).
Unmet healthcare needs
513
Table 1 Unmet healthcare needs according to socio-demographic characteristics Total
Place of residence Urban Rural Gender Male Female Age 65–74 ≥75 Level of education Elementary Middle school High school College or over Health insurance type NHI (local) NHI (employee’s) Medical-aid Household income 4th quartile 3rd quartile 2nd quartile 1st quartile Employment status Unemployed Employed Private health insurance No Yes Subjective healtha Good Poor
χ2
Unmet needs
P
n
Weighted n
%
n
Weighted n
% (SE)
2178 1765
3 441 875 1 603 691
68.2 31.8
468 462
705 134 455 469
21.5 (.012) 26 (.015)
12.06
0.009
1590 2353
2 045 714 2 999 853
40.5 59.5
230 700
284 523 876 080
14.0 (.011) 29.4 (.012)
125.32
