Beyond Healthcare Spending: Inequalities in the Use of Preventive Health Services among Europeans aged over 50

Beyond Healthcare Spending: Inequalities in the Use of Preventive Health Services among Europeans aged over 50 | 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 Beyond Healthcare Spending: Inequalities in the Use of Preventive Health Services among Europeans aged over 50 Pablo Moya-Martínez, Isabel Pardo-García, Roberto Martinez-Lacoba, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6628048/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 This study examines the factors influencing the use of preventive health services (PHS) among individuals over 50 living in their own homes, in light of rising healthcare expenditures. Drawing on Andersen’s Behavioural Model (ABM), the analysis incorporates individual-level variables (such as personality traits, Internet use, trust, and praying) and contextual country-level data. The study uses data from the SHARE survey and international sources (World Bank, Eurostat, OECD), covering 29,201 individuals across 15 European countries. Initial analyses employed multiple logistic regression models by country, followed by multilevel logistic regression models with individual factors at level one and country-level factors at level two. Results show that women are more likely than men to use preventive services such as eye exams and dental visits. Higher income, better satisfaction with health coverage, supplementary insurance, higher education, and Internet use increase the likelihood of using preventive services (except for general practitioner visits). Being married is also positively associated with service use, while living in smaller towns is linked to greater preventive use of specialists, flu vaccinations, and mammograms. Among personality traits, conscientiousness is the most strongly associated with increased use of mammograms, eye exams, and dental visits. The models explain between 10.5% and 35.8% of the variation in service use. Although the World Health Organization prioritizes access to PHS, the findings suggest limited progress. Strengthening prevention strategies not only improves quality of life but also helps reduce healthcare costs over time. health prevention Andersen’s Behavioural Model aging Figures Figure 1 Figure 2 Introduction Decision making in life depends on whether the benefit is immediate or delayed or whether such benefit will ever exist. These are intertemporal choices (Chapman et al., 2001; Loewenstein & Thaler, 1989). When such decisions are taken with regard to preventive health behaviours (PHBs), a dual payoff may arise, whereby, on the one hand, the likelihood of developing diseases is reduced and/or, if they do occur, the probability of treatment being more effective and less costly increases (AbdulRaheem, 2023). However, not only do PHBs depend on individual aspects, such as socioeconomic, health or personal characteristics (AbdulRaheem, 2023; Obino & Werle, 2011), there are also factors related to the society, country or region in which an individual lives. The latter include the distance to the preventive health service (PHS), their existence, availability or how the service is funded, in short, the design of the healthcare system. Several models have been developed to analyse the factors affecting health service use, although the most widely used is Andersen’s Behavioural Model (ABM) or one of its subsequent versions (Ricketts & Goldsmith, 2005a). This model was developed on the basis of the National Health Surveys first administered by the United States Public Health Services in the 1930s. A review of the model has been published (Andersen, 2008). ABM classifies the factors that influence health service utilisation into predisposing, enabling and need factors. These are replicated at two levels, contextual characteristics, which refer to those that are external to the individual and particular to the environment in which they live; and individual characteristics, which are intrinsic to the person and their particular situation (Andersen & Davidson, 2007). Within the economic framework, the contextual level involves supply-side characteristics, while the individual level entails demand-side ones. A diagram of ABM is included in Fig. 1 , while the Annex (table A1) contains all the variables that are typically included in the levels and factors (Lederle et al., 2021). Considering ABM, our study focuses on the use of PHS among the European population aged over 50. The Survey of Health, Ageing and Retirement (SHARE) in Europe (Börsch-Supan et al., 2013) allows for the analysis of a variety of factors included in ABM and for the testing of additional ones (personality traits, internet, use, trust and praying). In our ABM-based models, we adjust for long-term illness, limitations on activities of daily living and health status, thus allowing us to find the effects of service use as preventive measures for the remaining variables. Additionally, the survey enables us to analyse a broad set of services, such as visits to the general practitioner (GP) or specialist, flu vaccination, eye exams, mammography screening, colorectal cancer screening and dental visits. To the best of our knowledge, few studies have explored PHS in older adults in Europe, and those that do so are more than ten years old (Carrieri & Wuebker, 2013; Jusot et al., 2012; Schmitz & Wübker, 2011). Many of these works use the Survey on Health Ageing and Retirement in Europe (SHARE). Carrieri & Wuebker (2013) use concentration indices to reveal the inequalities in breast cancer screening and blood testing. The study by Jusot et al. (2012) does not use ABM and includes only a small set of individual-level variables (sex, age, income, education, self-assessed health, limitations in activities of daily living and reported chronic conditions). Although the study adjusts for country, it does not do so by contextual variables in its first model. Subsequently, it only presents models with one contextual variable, meaning that they might reflect effects of other variables not included. Notwithstanding, the results suggest the existence of health system factors that impact the propensity towards service use across countries. The work by Schmitz &Wübker (2011) assesses physician quality in terms of flu vaccination services. A study carried out in Italy (Carrieri et al., 2009) analyses pap smear and mammography utilisation, finding underuse of preventive care, primarily determined by demand. Another European study examines the regular use of blood tests, eye exams, gynaecological visits and mammograms, reporting social inequalities and differences between countries in terms of preventive service use (Sirven & Or, 2010). Consequently, the present work updates and advances the previous literature, as it delves into individual and contextual factors, as well as encompassing a large number of European nations. Therefore, the main aim of this study is to analyse the factors that contribute to differences in the use of healthcare services, primarily PHS, among individuals aged over 50 and living at home. Material and methods Sample The data used for the analysis were extracted from four sources: the ninth wave of the Survey on Health Ageing and Retirement in Europe, administered between October 2021 and September 2022 in 27 European countries (Bergmann, 2024), and statistical data provided by the World Bank Group, EuroStat Statistics and OECD Statistics. Our sample consisted of 69,447 individuals that participated in Wave 9 of SHARE across 27 European countries. In addition to the individuals selected, the data includes other members of the same households who were also interviewed, regardless of their age. However, we decided to exclude these additional household members (retaining only the primary individual) since including various persons from the same household would duplicate the household-level variables, thus distorting the analysis. After this initial selection, we obtained a sample of 43,542 individuals residing in different households (NA’s = 19 were also discarded). Additionally, we excluded individuals under the age of 50 (169; 0.39%) and those that were permanent residents of care homes (19; 0.04%), yielding a final sample of 43,354 participants. The contextual variables (country-level variables) were not available for all the countries included in the sample of individual variables and thus we excluded the individuals from Israel, Luxembourg, Estonia, Cyprus, Finland, Latvia, Malta, Portugal, Switzerland, Slovakia and Belgium. There then remained a total of 30,567 over-50-year-olds. In order to properly detect the effects, we discarded countries with fewer than 900 subjects, since, for each factor, about 20 subjects are needed to assess approximately 45 factors at a time. The countries dropped were Bulgaria and Romania, resulting in a final sample of 29,201 individuals from 15 countries (Austria, Croatia, Czech Republic, Denmark, France, Germany, Greece, Hungary, Italy, Lithuania, Netherlands, Poland, Spain and Sweden). Individual characteristics variables ABM subdivides both the individual and contextual variables into predisposing factors, enabling factors and need factors. We followed this criterion, with the data presented below (see Fig. 1 and Table A1). Our predisposing factors were sex, age, marital status, household size, educational level, employment status and born in the country (as proxy of ethnicity). Those related to beliefs were trust, praying and satisfaction with basic health coverage. Table A1 shows the variables in the model that could not be assessed due to their not being available in the survey, those closely correlated with other variables or with a high number of missing values. The age categories were "50–59", "60–69", "70–79", "80+", while marital status was categorised as married (which included those reporting a registered partnership), divorced/separated, never married and widowed. The household size variable was classified into 1 member, 2 members, 43 members and 4 or more members. As regards educational level, with the aim of harmonising the denominations across the different countries studios, the survey administrators establish the ISCED-97 system of classification with codes from 0 (pre-primary education, none) to 6 (second stage of tertiary education). We recoded into 3 levels: <= primary education, secondary education (levels 2, 3, 4) and tertiary education (levels 5 and 6). For the employment status variable, we grouped the survey categories as follows: “not actively working” included “retired”, “unemployed”, “permanently sick or disabled” and “homemaker”; actively working, meanwhile, corresponded to “employed or self-employed (including working for family business)”. The few individuals that selected the category of “other” were recorded as missing values. In addition, we incorporated a number of variables (Internet use, trust, praying and personality traits) that had not previously been included in ABM, and about whose influence on service utilisation we hypothesised. To ascertain the participant’s level of adaptation to technology, we formulated a dichotomous question on Internet use. The questionnaire thus asked participants if they had used the Internet, for e-mailing, searching for information, making purchases or for any other purpose at least once in the last seven days. The participants scored their trust on a scale from 0 to 10. The praying variable shows the frequency with which individuals engage in this activity, being categorised into never and sometimes (from more than once a day to less than a week), providing a proxy for whether the participant is a believer or not. As regards personality traits, since Wave 7, the SHARE has included the 10-item Big-Five inventory (BFI-10). This established personality inventory measures the “Big Five” personality dimensions with two items each. The variables scored from 1 to 5 are openness, conscientiousness, extraversion, agreeableness and neuroticism (Rammstedt & John, 2007). A more detailed description of the questions involved can be found in the Annex. The last of the predisposing individual factors was satisfaction with basic health coverage. Two levels of satisfaction were considered: dissatisfied/very dissatisfied and satisfied/very satisfied. The individual enabling factors examined were household income (economic capacity) supplementary health insurance (SHI), social support and its proximity, and geographic location. As a measure of household economic capacity, we calculated the equivalised income . Various scales exist, although the most current and simple to use is that proposed by the OECD (OECD, 2013). The Square Root Scale divides household income by the square root of household size (e.g., this implies that a household of four persons has needs twice as large as one composed of a single person). The survey provides the two variables required. The first question asks about the number of people living in the household, while the second asks about overall income, after taxes and contributions, that the entire household had in an average month in the last year. Given the differences in purchasing power between countries, we calculated the Square Root Scale, categorising into tertiles by country (T1, T2, T3). Supplementary Health Insurance (SHI) is considered as a binary variable indicating whether the individual has coverage (1) or not (0). Similarly, the social support indicates whether the respondent has support or not. Additionally, the proximity variable considers whether the individual has access to a social support network less than 5km from their place of residence. The last variable in this block is geographic location, which indicates whether the respondent lives in a big city, a large town, rural area or village or a small town. The individual need factors were divided into perceived and evaluated variables. The perceived factor was self-perceived health status, while the evaluated factors included whether the individual had limitations in their activities of daily living or had a long-term illness. The effects captured by these latter variables help assess the use of healthcare services, particularly preventive services, as they account for a significant part of utilisation driven by necessity. Self-perceived health status was rated over five categories (Excellent, Very Good, Good, Fair and Poor), although we decided to combine the first two into just one. In terms of the limitations on activities of daily living, the survey asked to what extent, for the past 6 months, the respondent had been limited because of a health problem in activities that people usually do. The response options were Severely Limited, Limited but not Severely, and Not Limited. Finally, the long-term illness variable had a dichotomous response. Contextual characteristic variables For the predisposing factors, it is important to consider those that capture the effects of each country’s and/or region’s particular characteristics, such as culture, traditions, beliefs, ethnic diversity, etc. There are no databases that include these characteristics, and so they are accounted for in our analysis as unobservable factors captured by the country variable. Regarding the enabling factors related to funding, for per capita community income and wealth, we used gross domestic product (GDP) per inhabitant in purchasing power standard (PPS), which allows for cross-country comparisons considering differences in the standard of living in each country. This variable was obtained from the World Bank To obtain information on per capita expenditure on health services, it is necessary to consider not only the total amount but also the composition of the expenditure, which can originate from different sources in varying percentages, from government schemes and compulsory contributory health care financing schemes, voluntary health care payment schemes, and household out-of-pocket payments. Therefore, our preliminary analysis includes both the total amount (per capita expenditure on health services in all schemes) as well as the percentage corresponding to each category. These variables were taken from Eurostat. To analyse health insurance coverage, the data were obtained from the OECD, which provides information on the percentage of coverage (percentage of the total government or compulsory insurance expenditure for a specific service) for therapeutic appliances, pharmaceuticals, dental care, outpatient primary and specialist care and inpatient care. The last published figures dated back to 2020. Data for dental care for France, Italy and Portugal were missing, and we thus drew on articles in the scientific literature for these values (Bindi et al., 2017; Simões et al., 2018; Winkelmann et al., 2022). Under the organisation category in our enabling variables, we included amount and distribution of health service facilities and personnel. Eurostat statistics provide data on the number of professionals per 100,000 inhabitants for the following categories: physicians, midwives (EU-recognised qualification), nurses (EU-recognised qualification), dentists, and pharmacists. Other variables in this section include structure and quality of services provided, distance to the nearest health facility, medicine availability and infrastructure (see Table A1). As a proxy for these factors, we used inhabitants per hospital bed and inhabitants per device for computed tomography (CT) scanners and magnetic resonance imaging (MRI). Regarding contextual need variables, we included three variables from Eurostat on self-reported unmet needs for health care . These variables reflect the percentage of people unable to receive care for financial reasons, distance or transportation to the health facility and waiting lists. Dependent variables The dependent variables for our models inform on GP contacts, specialist contacts, flu vaccination, eye exams, mammogram screening, cancer screening and dentist visits. All except GP and specialist visits were binary variables; GP and specialist visits were reported as a number of times between zero and a maximum. As the aim of this study was to observe differences and similarities between the individuals that use health services for prevention and those that do not (being why adjustments were made for health status, long-term illness and limitations), the variables were transformed into binary ones. This approach also resolves the common problem of skewness in the data. For all the variables, respondents were asked about service utilisation over the last 12 months, expect for the cases of eye exam, mammogram and cancer screening, for which they were asked about the last 2 years. A more detailed description can be found under Detailed Description of Dependent Variables in the Annex. Missing values Having selected the variables according to their availability and ABM, we found that several had missing values. Table A2 shows the number and percentage of missing values for each variable. Consequently, the first step was to conduct Little's (1988) test statistic to assess whether the data were Missing Completely At Random (MCAR) (Little, 1988). A non-significant result means that the cases of missing values can be eliminated as their being missing is random. If the result is significant, as occurred here, it is necessary to check whether the data are Missing At Random (MAR). In this case, the probability of a value being missing depends only on the observed values and not those unobserved, which means that, after controlling for all the available data, any missing value is completely random (Graham, 2009; Schafer & Graham, 2002). Several procedures exist to evaluate this condition, but hypothesis tests are a simple method to check whether the absence of data is related to other variables, using chi-squared tests for categorical variables or t-tests for continuous variables (Harrison & Pius, 2020). Since all the variables in our study were categorical, we conducted this test for each variable with more than 300 missing values against the remaining variables. In all cases, each of the variables with missing values was significant in the test with respect to more than variable. This finding shows the data are MAR. An appropriate method to work with such data is the Multiple Imputation by Chained Equations (MICE), which is based on the Fully Conditional Specification, where each incomplete variable is imputed using a separate model. It can impute mixtures of continuous, binary and categorical variables. A more detailed explanation can be found in (Azur et al., 2011). Finally, and in order to test the robustness of our results, we implemented the models for the analysis based on different samples: one imputing missing values using the abovementioned technique and another without imputation. Analyses Descriptive statistics were calculated using the distribution of frequencies and percentages for the categorical variables and means and standard deviations for the continuous ones. We examined the association between the predictors and the outcome by means of Pearson’s chi-squared test and Student’s test, depending on the type of variable. The predictors that had a significant effect (p < 0.05) on the outcome variables in the bivariate analysis were included in the Multilevel Logistic Regression Models to determine the factors that best predicted the use of the services evaluated. Multiple Logistic Regression models were used as a first approach to estimate the individual parameters specified in ABM on health service use for each country. Meanwhile, the correlation between the contextual variables was analysed in order to avoid problems of collinearity in the models. Those that obtained values greater than 0.7 were discarded. The following step involved a hierarchical approach using Multilevel Logistic Regression Models in which the first level corresponded to the individual variables of ABM (those referring to the subject) and the second level was that of its contextual factors (those for the country). The adequacy of the model fit was verified using the likelihood ratio test for goodness of fit, while multicollinearity between predictors was also checked for using Variance Inflation Factor values (VIF < 2.0). We ran the models with all the contextual variables. Among those showing a correlation higher than 0.7, we selected the one we considered less problematic and more representative. When running the models, we also verified the existence of collinearity between all the variables (contextual and individual); some were dropped due to their very high VIF values. A correlation analysis was performed to see which variables might be attempting to explain the same part of the variability in the dependent variables. The correlation table can be found in the Annex (see Table A3). Of the two with a very high correlation, we chose only one, taking into account the highest degree of affinity with the model. Adjusted odds ratios were calculated to estimate the level of association with service use. The association was considered significant at p < 0.05. Additionally, we calculated the (conditional \(\:{R}_{conditional}^{2}\) ) and marginal ( \(\:{R}_{marginal}^{2}\) ) coefficients of determination. The former represents the variance explained of the complete model (effects at subject and country level) and the latter only represents the country-level effects (random) (Johnson, 2014; Nakagawa et al., 2017; Nakagawa & Schielzeth, 2013). Because the units of measurement for the contextual variables were different, they were normalised by dividing by their mean. This allowed them to have a mean of 1 and facilitated better comparison and interpretation of the results. For example, if a country obtained a value of 1.05 in an indicator, it would be interpreted as the probability of using the PHS being 5% above the average (0.95 would indicate the opposite). As we found no major differences between the results, the data shown in the tables reflect the imputation of missing values. The analyses were performed with the R statistical software package version 4.4.2, using R-Studio (version 2024.09.1) and the lme4 for the Multilevel Logistic Regression Models. Results Table 1 displays the individual and dependent variables of the final sample. There are 24.06% more women than men. A total of 65.13% are aged between 60 and 79, 55.58% are married and 24.84% are widowed. More than half pray, and 83.06% are satisfied or very satisfied with their health coverage and 37.32% have supplementary health insurance. Meanwhile, 62.19% report their health is between good and excellent, 50.65% report having no limitations on activities of daily living, while 55.08% have a long-term illness. As for the continuous variables (transformed into a score from 0 to 10), the mean score for trust is 6.75 and those for extraversion, agreeableness, conscientiousness, neuroticism and openness (big five inventory) are 7.54, 7.84, 7.18, 5.18 and 6.48, respectively. Regarding utilisation of services, the dependent variables, a large majority have visited their GP once (93.1%), with visits to specialists being somewhat lower (61.61%); 50.15% have visited the dentist and below this percentage are the other services, the lowest being cancer screening followed by mammogram (see Table 1 ). Table 1 Descriptive statistics for individual variables Independent variables n % Female 18114 62.03 Age 50–59 5239 17.94 60–69 9880 33.83 70–79 9140 31.30 80+ 4942 16.92 Marital status Divorced/Separated 3602 12.34 Married 16229 55.58 Never married 2107 7.22 Widowed 7263 24.87 Education Primary or less 4354 14.91 Secondary 17905 61.32 Tertiary 6942 23.77 Employment status Working 5405 18.51 Born in country Yes 27284 93.44 Trust (Range 0–10) a 6.75 2.43 Praying Never 10734 36.76 Sometimes or more 18467 63.24 Health coverage satisfaction Dissatisfied/Very Dissatisfied 4947 16.94 Satisfied/Very Satisfied 24254 83.06 Home income tertiles T1 - poorest 9722 33.29 T2 - middle 9731 33.32 T3 - richest 9748 33.38 Supplementary health insurance Yes 10897 37.32 Social support Yes 7332 25.11 Social network in 5km Yes 25268 86.53 Residence situation A big city 6944 23.78 A large town 5167 17.69 A rural area or village 9955 34.09 A small town 7135 24.43 Subjective health state Excellent\Very good 6493 22.24 Good 11667 39.95 Fair 8059 27.60 Poor 2982 10.21 ADL limitations Severely limited 4943 16.93 Limited, but not severely 9469 32.43 No limited 14789 50.65 Long-term illness Yes 16084 55.08 Internet use Yes 17747 60.78 Big five inventory (Range 0–5) a Extraversion 3.51 0.90 Agreeableness 3.77 0.84 Conscientiousness 3.92 0.95 Neuroticism 2.59 0.99 Openness 3.24 0.94 Dependents variables Contact with GP 27186 93.10 Contact with Specialist 17991 61.61 Flu vaccination 12287 42.08 Cancer Screening 7284 24.94 Mammogram 9342 31.99 Eye exam 13350 45.72 Dentist visit 14644 50.15 GP: General Practitioner; ADL: Activities of Daily Living; a :Mean(SD) Table 2 shows, for each dependent variable on service use in the last year, the percentage of respondents that used services for the different categories of the independent variables and, additionally, whether there are significant differences. Broadly speaking, there are significant differences in practically all the variables, which suggests they are candidates for explaining part of the variability in service utilisation. Table 2 Percent of persons who use the service GP Specialist Flu vaccination Cancer screening Mammogram Eye exam Dentist visit Sex Male 92.57** 60*** 42.98* 26.45*** 0*** 42.87*** 48.87*** Female 93.42 62.6 41.53 24.02 41.59 47.46 50.93 Age 50–59 92.29** 58.27*** 21.4*** 29.11*** 46.76*** 43.52*** 57.11*** 60–69 92.9 60.36 36.35 30.51 44.34 43.04 53.43 70–79 93.21 65.59 51.85 23.6 22.9 49.4 49 80+ 94.15 60.28 57.39 11.9 8.46 46.6 38.32 Marital status Divorced/Separated 92.31*** 62.02*** 34.84*** 27.85*** 37.76*** 50.28*** 57*** Married 92.96 62.06 43.02 27.43 34.85 45.38 53.11 Never married 91.17 56.38 37.07 24.11 30.52 40.48 48.79 Widowed 94.37 61.93 45.02 18.2 23.17 45.72 40.53 Education Primary or less 95.22*** 55.54*** 55.4*** 14.24*** 18.21*** 35.3*** 26.99*** Secondary 93.25 61.68 36.02 24.97 32.67 44.49 48.55 Tertiary 91.39 65.25 49.35 31.6 38.88 55.42 68.8 Employment status Not actively working 93.39*** 62.95*** 45.7*** 23.62*** 28.78*** 45.87 47.37*** Working 91.84 55.73 26.12 30.79 46.12 45.05 62.41 Born in country No 93.48 63.64 38.97** 24.93 30.88 47.84 54.04*** Yes 93.07 61.47 42.30 24.95 32.07 45.57 49.88 Trust (Range 0–10) a 6.75 6.73*** 6.71** 6.88*** 6.99*** 6.89*** 6.85*** Praying Never 92.13*** 61.51 44.63*** 30.24*** 34.21*** 50.38*** 60.28*** Sometimes 93.66 61.67 40.59 21.87 30.7 43.01 44.26 Health coverage Dissatisfied/Very Dissatisfied 93.19 63.82*** 35.54*** 20.11*** 29.23*** 41.06*** 38.1*** Satisfied/Very Satisfied 93.08 61.16 43.41 25.93 32.56 46.67 52.61 Income T1 - poorest 93.29* 58.87*** 38.84*** 22.77*** 29.38*** 43.78*** 46.67*** T2 - middle 93.52 62.7 43.4 25.16 31.81 45.62 48.47 T3 - richest 92.49 63.26 43.99 26.9 34.79 47.75 55.29 Supplementary health insurance No 92.61*** 61.94 42.37 20.01*** 29.39*** 44.06*** 46.77*** Yes 93.92 61.05 41.59 33.24 36.37 48.5 55.82 Social support No 92.98 58.89*** 40.22*** 24.83 33.04*** 43.53*** 49.76* Yes 93.45 69.74 47.61 25.29 28.87 52.24 51.31 Social network in 5km No 93.77 59.34** 40.63 21.79*** 27.84*** 46.02 47.93** Yes 93 61.96 42.3 25.44 32.64 45.67 50.49 Residence situation A big city 92.07*** 66.09*** 50.66*** 25.82** 33.57** 49.06*** 53.95*** A large town 92.3 60.75 42.58 23.59 30.48 46.7 50.59 A rural area or village 93.9 59.62 34.35 24.2 31.96 43.83 47.13 A small town 93.57 60.66 44.13 26.11 31.59 44.39 50.34 Subjective health state Excellent\Very good 89.8*** 51.15*** 39.63*** 28.17*** 39.29*** 45.57*** 62.59*** Good 93.38 59.31 39.72 24.69 33.57 45.48 51.32 Fair 94.48 69.38 46.67 23.55 27.25 47.2 44.81 Poor 95.44 72.4 44.23 22.67 22.74 42.96 32.93 ADL limitations Severely limited 94.44*** 72.67*** 44.59*** 23.93 26.04*** 46.23*** 42.26*** Limited, but not severely 94.06 69.25 44.98 25.57 30.59 49.33 49.78 No limited 92.03 53.03 39.38 24.88 34.88 43.23 53.02 Long term illness No 92.39*** 51.12*** 37.78*** 23.44*** 32.97** 40.63*** 50.72 Yes 93.68 70.17 45.58 26.18 31.19 49.87 49.68 Internet use No 94.59*** 58.14*** 41.44 16.14*** 18.66*** 37.06*** 29.8*** Yes 92.14 63.85 42.49 30.62 40.6 51.3 63.28 Big five inventory (Range 0–5) a Extraversion 3.51 3.51 3.51 3.53* 3.6*** 3.59*** 3.56*** Agreeableness 3.77 3.77** 3.74*** 3.87*** 3.85*** 3.78 3.78 Conscientiousness 3.93*** 3.92 3.91 3.85*** 3.93 4.01*** 3.99*** Neuroticism 2.60*** 2.60*** 2.64*** 2.56*** 2.51*** 2.60* 2.57** Openness 3.25*** 3.24** 3.29*** 3.24 3.30*** 3.35*** 3.33*** GP: General Practitioner; ADL: Activities of Daily Living; a :Mean(SD); For significant differences, we used Pearson Chi-square Test or T-Student Test depending on de nature of variable. ***: p < 0.001; ** p < 0.01; * p < 0.05 It is worth noting that the less pronounced differences are in the use of GPs and specialists. The individuals with only primary education or below visit specialists slightly less and are notably less likely to have flu vaccination, cancer screening, mammograms, eye exams or dentist visits. Those still working (recall that we analysed > 50 years) are less likely to go for flu vaccinations and cancer screening, but more likely to have a mammogram or visit a dentist. It is also worth underlining that those who never pray are more likely to use all the services with the exception of GP and specialist services (which are used in similar percentages). The poorest also make less use of all the preventive services, except the GP, which is used similarly to the rest. Those with supplementary health insurance made greater use of cancer screening, mammograms, eye exams and dentist visits. Those who report excellent/ very good subjective health make less use of GP and specialist services and more use of the others. Finally, the individuals that do not use the Internet also make less use of cancer screening, mammograms, eye exams and dentist visits. It should be noted that these are unadjusted results. The contextual variables, meanwhile, showed strong correlations, with the models exhibiting high VIF values. Following the analyses and various tests, five contextual variables best fitted the models: health expenditure in all financing schemes, percentage of health expenditure in voluntary schemes, percentage of health expenditure on out-of-pocket payments, dental care health insurance coverage (% of total expenditure dedicated to dental care coverage) and nurses (number of nursing staff per 100,000 inhabitants). As described in the methodology section, all the variables were centred, allowing for easier comparison. Figure 2 shows the differences relative to the mean (value 1) between the countries in the study. Significant differences can be seen that corroborate the need to include contextual variables in the analysis. For example, Austria, Germany, and the Netherlands present the greatest difference in total health expenditure compared to the mean, but for other variables, such as dental health expenditure or voluntary health care payment schemes, there are greater differences. The comparison for the variables not used in the final model can be found in the supplementary material. Table 3 shows the final logistic regression models on use of preventive services. The variables on health status (health status, activities of daily living limitations, long-term illness) are not mentioned as they are considered to be adjustment variables; they are necessary, however, to understand the rest as preventive use. As ABM predicts, most variables are significant with the exception of preventive use of GP service. The results suggest that women are more likely to make preventive use of eye exams and dentist visits. The variables of income (higher), health coverage satisfaction, having supplementary health insurance, a higher educational level and Internet use increase the likelihood of preventive service use for all the models (except for GP use). Being married is also associated with increased likelihood in most of the models and place of residence (living in a small town, for example) is found to be a protective factor for preventive visits to specialist, flu vaccination and mammograms. The most prominent of the big five inventory variables was conscientiousness as related to the increased likelihood of use of mammogram, eye exam and dentist services. Our models explain between 10.5% and 35.8% of the variability in the use of these services. Table 3 Multilevel Logistic Regression Models. Individual characteristics GP Specialist visit Flu vaccination Cancer Screening Mammogram Eye exam Dentist visits Sex Female (ref. male) 1.079 1.038 1.031 0.924* 1.259*** 1.405*** Age 60–69 (ref. 50–59) 1.061 0.973 1.733*** 1.094* 1.027 1.034 1.014 70–79 1.081 1.120* 3.114*** 0.750*** 0.333*** 1.381*** 0.908* 80+ 1.092 0.859* 3.929*** 0.363*** 0.121*** 1.400*** 0.777*** Marital status Married (ref. Divorced/separated) 1.016 1.248*** 1.568*** 1.121* 1.184** 1.092* 1.253*** Never married 0.804* 0.931 1.148* 0.924 0.928 0.814** 0.924 Widowed 1.105 1.100* 1.374*** 0.944 0.932 1.046 0.982 Education Secondary (ref. Primary or less) 0.861* 1.122** 0.978 1.163** 1.207** 1.222*** 1.432*** Tertiary 0.773** 1.382*** 1.310*** 1.268*** 1.398*** 1.477*** 2.074*** Employment status Working (ref. not actively working) 1.012 0.843*** 0.639*** 0.968 1.169** 0.937 1.077 Born in country No (ref. yes) 1.037 0.997 0.888* 0.879* 0.791** 0.944 0.968 Trust (0–10) 0.987 1.016** 1.005 1.017* 1.007 0.996 1.010* Praying Sometimes (ref. never) 0.999 1.100** 0.954 1.034 1.019 1.024 1.012 Health coverage Satisfied/Very Satisfied (ref. dissatisfied/very dissatisfied) 1.148* 0.918* 1.318*** 0.932 1.022 0.886** 1.008 Income T2 - middle (ref. T1 - poorest) 1.043 1.171*** 1.166*** 1.122** 1.237*** 1.048 1.073* T3 - richest 1.001 1.260*** 1.367*** 1.161*** 1.242*** 1.105** 1.196*** Supplementary health insurance Yes (ref. no) 1.058 1.168*** 1.194*** 1.272*** 1.244*** 1.332*** 1.477*** Social support Yes (ref. no) 0.982 1.290*** 1.038 1.054 0.905* 1.157*** 1.013 Social network in 5km Yes (ref. no) 0.927 1.121** 1.149** 1.138** 1.116* 1.078* 1.091* Residence situation A large town (ref. big city) 1.015 0.801*** 0.807*** 0.930 0.826*** 0.986 1.026 A rural area or village 1.193** 0.730*** 0.717*** 0.940 0.889* 0.825*** 0.931* A small town 1.055 0.795*** 0.818*** 0.945 0.807*** 0.812*** 0.924* Subjective health state Good (ref. Excellent\Very good) 1.415*** 1.152*** 1.115** 1.055 1.034 1.094* 0.935* Fair 1.502*** 1.604*** 1.315*** 1.157** 1.017 1.186*** 0.882** Poor 1.657*** 1.729*** 1.270*** 1.358*** 0.987 1.105 0.702*** ADL limitations Limited, but not severely (ref. severely limited) 0.987 0.970 1.100* 1.061 0.982 1.185*** 1.207*** No limited 0.825* 0.689*** 1.100* 1.047 1.016 1.151** 1.254*** Long-term illness Yes (ref. no) 0.969 1.745*** 1.473*** 1.255*** 1.229*** 1.384*** 1.150*** Internet use Yes (ref. no) 0.963 1.570*** 1.143*** 1.386*** 1.607*** 1.535*** 2.031*** Big five inventory Extraversion; (0–5) 1.051* 1.058*** 1.010 1.040* 1.053* 1.032* 1.019 Agreeableness; (0–5) 0.995 0.991 0.984 0.980 0.969 0.996 0.955* Conscientiousness; (0–5) 0.997 1.022 0.980 1.050** 1.115*** 1.062*** 1.084*** Neuroticism; (0–5) 1.065* 1.081*** 1.018 1.031* 1.005 1.039** 1.021 Openness; (0–5) 0.995 1.018 1.047** 1.012 1.011 1.043** 1.023 Contextual characteristics Health expenditure in all Financing Schemes 0.924 1.058 1.236** 1.269*** 1.161*** 1.131*** 1.416*** Percent of Health Expenditure in Voluntary Schemes 1.005 0.977 0.977 0.981 0.963* 0.973* 0.939*** Percent of Health Expenditure in Out-of-pocket 1.007 1.057 0.909 0.881* 0.959 1.041 0.921* Dental Care Health insurance coverage (% of total expenditure dedicate to dental care coverage) 1.009 1.065** 0.897** 0.971 1.026 1.044** 0.984 Nurses (number of personnel per 100,000 inhabitants) 1.048 0.891** 1.026 0.916* 0.898*** 0.967 0.939* R 2 conditional 0.0517 0.1550 0.2404 0.1587 0.2958 0.1438 0.3352 R 2 marginal 0.1058 0.1952 0.3284 0.2061 0.3202 0.1651 0.3585 Log-likelihood -7017.97 -17379.52 -15778.17 -14603.62 -10111.10 -18275.63 -15852.26 Observations 29201 18114 29201 Coefficients represent Odds Ratio (OR). For contextual characteristics ORs were transformed in OR 0.1 = (OR unit ) 0.1 this gives us the probability of change for an increment of 10% respect the mean value; GP: General Practitioner; ADL: Activities of Daily Living; ***: p < 0.001; ** p < 0.01; * p < 0.05 Finally, Table 4 analyses the contextual variables from the previous models. When interpreting the results, it is important to take into account that a contextual variable not being statistically significant does not indicate its importance in a greater or lesser likelihood of using the preventive services under study. Rather, it shows whether there are differences between the value for each country and the mean value of that variable across all the countries analysed leading to an increase or decrease in service utilisation (since the values are mean-centred). We observe that countries having above-the-mean GDP increase the likelihood of their citizens engaging in cancer screening, eye exams, and dentist visits. Additionally, citizens of countries with a higher-than-the-mean percentage of healthcare coverage in outpatient primary and specialist care are more likely to use eye exam and dentist visit services. Meanwhile, in countries where the percentage of individuals reporting financial reasons for unmet healthcare needs is above the mean, the likelihood of specialist visits decreases. Table 4 Multilevel logistic regression model with one contextual variable Dependent variables Contextual variable GP Specialist visit Flu vaccination Cancer Screening Mammogram Eye exam Dentist visits Model 1 Gross Domestic Product 0.917 0.99 1.203 1.247** 1.084 1.194*** 1.492*** Health Care Expenditure : Model 2 Government schemes 0.985 0.974* 1.059** 1.014 1.008 1.022 1.053* Model 3 Social health insurance schemes 1.01 1.048** 0.958 1.018 1.019 1.031 1.018 Model 4 Voluntary health care payment schemes 0.986 1.012 0.994 1.036 1.011 1.005 1.008 Model 5 Household out-of-pocket payment 0.968 1.011 1.119 1 0.997 1.054 1.096 Health Care Coverage : Model 6 Pharmaceuticals 1.049 1.088 1.154 1.089 1.111 1.071 1.106 Model 7 Dental care 1.007 1.031 0.944 1.008 1.025 1.042* 1.026 Model 8 Outpatient primary and specialist care 0.867 1.006 0.988 1.404** 1.225* 1.321*** 1.755*** Model 9 Inpatient care 0.963 0.993 0.869 1.218 1.074 1.35* 1.516 Health Graduates : Model 10 Physicians 1.081 0.881 1.031 1.119 0.96 0.981 1.033 Model 11 Midwives 0.985 0.978 0.968 0.935* 0.974 0.991 0.975 Model 12 Nurses 1.02 0.955 1.054 0.991 0.978 1.053 1.089 Model 13 Dentists 1.005 0.971 0.893 0.943 0.952 0.985 0.93 Model 14 Pharmacists 1.031 0.972 1.03 0.988 1.001 0.995 0.939 Infrastructures : Model 15 Beds 0.961 0.919** 1.17*** 1.039 1.009 1.005 1.097 Model 16 Computed tomography scanners 1.004 1.019 0.924 0.952 0.973 0.955 0.914 Model 17 Magnetic resonance imaging units 1.02 0.979 0.949 0.962 0.953* 0.971 0.941 Self-report unmet need for health care : Model 18 Financial reasons 1.038 0.876*** 1.047 0.965 0.947 1.008 1.007 Model 19 Distance or transportation to facility 1.064* 1.009 1.008 0.997 1.013 0.992 0.975 Model 20 Waiting list 1.027 0.923 0.955 1.057 0.988 1.063 1.089 The rest of individual variables were included in all models; Coefficients represent Odds Ratio (OR) transformed in OR 0.1 = (OR unit ) 0.1 this gives us the probability of change for an increment of 10% respect the mean value; GP: General Practitioner; ***: p < 0.001; ** p < 0.01; * p < 0.05; Discussion This study draws on one of the most widely used and accepted theoretical models of health service utilisation, Andersen’s Behavioural Model (ABM), in order to analyse over-50-year-olds’ use of primary preventive health services (PHS) in Europe (Babitsch et al., 2012; Ricketts & Goldsmith, 2005b). We use the most recent data from the Survey on Health Ageing and Retirement (SHARE), including subjects from 15 countries, considering information on both individual variables and country-level (contextual) variables obtained from various sources (Bank Group, EuroStat Statistics and OECD Statistics). Moreover, we incorporate several novel variables (personality traits, internet use, trust and praying) that have not hitherto been evaluated under ABM. With regard to the effect of the individual variables of the utilisation of PHS, the main results of our work suggest that the use of technology – specifically, Internet use as a proxy for the level of an individual’s technological knowledge – is associated with increased use of preventive services. Two studies have examined this association with similar findings. One uses data from an earlier wave of the SHARE related to preventive services in cancer screenings, mammograms, cholesterol tests and flu vaccination (Nam et al., 2019a). The other study focuses only on cancer screening (Xavier et al., 2013). The present study extends knowledge of the field by including preventive use of consultations with specialists, eye exams and dentist visits. In addition, the study by Nam et al., (2019b) also evidences that having a partner also fosters the PHS use, particularly as regards the influence of women on men. Our study finds that married individuals are more likely to use PHS, which helps determine the population groups that should be the focus of actions for prevention (individuals that are divorced/separated, widow and never married). Nonetheless, widowed persons also seem more likely to be vaccinated against flu. Our study reveals the existence of a problem in usage of preventive health services (PHS) as the size of an individual’s place of residence (from big cities to rural areas or villages). A study conducted in the United States more than 20 years ago already evidenced the disadvantaged situation of rural areas in terms of preventive service use (Casey et al., 2001). A more recent study in the same country also reports shortcomings in rural areas regarding preventive check-ups and dental treatment utilisation, which, in the long term, lead to higher treatment costs (Lee et al., 2021). Although the literature on this topic is not extensive, other research confirms that this issue remains unresolved in countries such as Australia, Spain, and China (Colman, 2020; De La Cruz-Sánchez & Aguirre-Gómez, 2014; Liu et al., 2016; Xu et al., 2019). In contrast, a study conducted in Japan suggests that the higher the population density, the lower is the correlation with stomach cancer screening, with such correlations being non-significant for colorectal cancer screening and flu vaccination (Hatano et al., 2013). A possible explanation for these findings is that the coefficients may be biased in compensation for the omission of important variables. The study justifies the association found by suggesting that social support (social capital) is stronger in smaller communities, which may positively influence health prevention behaviours. Despite the limited number, some successful interventions have sought to mitigate this issue (Nagykaldi et al., 2020) by creating the role of a so-called Community-Based Wellness Coordinator , tasked with monitoring patients by integrating information from all the actors involved and from electronic medical records. Having supplementary health insurance (SHI) is found to be a robust predictor of PHS utilisation. Practically all European countries finance health coverage and function satisfactorily, so SHI plays a limited role that is highly dependent on public and compulsory health spending in each country (Purcel et al., 2023), although it does cover gaps such as faster access to treatment or greater choice of medical care (Sagan & Thomson, 2016). Given that other variables (e.g. income, health status) are adjusted for, it is possible that people in Europe with SHI are the most healthcare conscious. Additionally, preventive services can be offered by SHI, such that it is an out-of-pocket expense and thus not using it (having it simply as insurance) may make individuals feel they are wasting their money. (Ricketts & Goldsmith, 2005a). A study supports the tendency to make greater use of healthcare services in individuals with a health insurance contract (Anderson et al., 2012). Finally, the individual variables of social support and having a social network within 5km have a great impact on PHS utilisation. These variables, and particularly social support, have been a subject of study since the mid-1970s in pursuit of guiding preventive interventions and treatment (Lakey & Lutz, 1996). We find that greater social support drives more preventive health behaviours. Research in this field is now focused on more specific problems, such as youth suicide and spinal cord injury (Le Fort et al., 2021; Standley & Foster-Fishman, 2021). With respect to the contextual (country-level) variables, our findings suggest that, despite cross-country differences, their impact on PHS use is not great. In pursuit of greater equality between EU Member States, those with lower mean scores on these variables might focus on increasing their per capita GDP and their health care coverage for primary outpatient and specialist care, as well as looking at reducing levels of self-reported unmet needs for health care. Conclusions The present study makes an analysis of PHS using Andersen’s Behavioural Model, including a large number of both individual-level and contextual variables, succeeding in explaining as much as 35.8% of the variability in service usage. Future research should strive to enhance these results by seeking other individual or contextual characteristics that might affect the use of these services, as well as improving the quantity and quality of the country-level variables. Our findings advance the understanding of the barriers to and facilitators of engagement in prevention, enabling the design of strategies that promote equity in access. It would also be interesting to consider preventive intervention on digital literacy, given that the link between internet use and preventive health behaviours is important because such behaviours are associated not only with the health of the older population but also with substantial reductions in health care expenditures. Equal access to PHS has long been regarded as a priority by the World Health Organisation. However, our data evidence that little progress has been made toward this goal among older adults in Europe. Bolstering prevention strategies leads to higher quality of life and helps reduce healthcare costs in the medium and long term. Also of great importance is integrating and homogenising services related to prevention (primary care, specialised care, social services, etc.), as well as encouraging community participation in developing a preventive system that is currently either non-existent or fragmented. We must acknowledge that while autonomy in management allows for a closer approach to the problem and potentially better solutions, a high degree of fragmentation without coordination makes efficient resource management impossible to manage resources efficiently. It is crucial to harmonise the databases for healthcare resource and population management such that the information required for decision making can flow from municipalities to regions and, from there, to countries and finally to the EU as the ultimate guarantor of social well-being. Limitations One of the main limitations of the present work is that not all the EU countries included in the SHARE survey could be evaluated together due to the lack of data on many necessary contextual variables. Here, it is worth highlighting the difficulty of obtaining contextual variables for a large set of European countries, given the differences between healthcare systems and the lack of a unified and standardised European database where such characteristics can be found, including, for example, aspects of healthcare centralisation or decentralisation, payment mechanisms, patient pathways, health care quality measurements and types of preventive programmes (opportunistic or organised, screening, coverages etc.). There are also variables in some categories in ABM that we were unable to test, such as structure and quality of services provided, distances to closest health facility, medicine availability and infrastructure. A future challenge for European institutions is to build a database with standardised information on the above-mentioned aspects, as well as other potentially key factors (at the country or regional level), such as cultural and traditional characteristics, ethnic diversity, and religion. Declarations Ethics approval and consent to participate: Not applicable. The data used comes from the anonymised and public accessible SHARE (https://share-eric.eu/) database as well as other public sources in the case of macro variables. Competing interests: The authors declare that they have no conflict of interest Author’s contributions: PM, RM conceived the idea for the study and wrote the initial draft of the paper, EM and IP collected the data and performed the analyses. All authors contributed to the debate, critical writing and final revision of the manuscript. All authors approve the final version of the paper. Transparency statement: The corresponding author on behalf of the rest of the authors guarantees precision, transparency and honesty of the data and the information which is contained in the study; that non-relevant information has been omitted; and that all discrepancies between authors have been adequately solved and described. Competing interest: Nothing to declare. Consent to Participate declaration : This study uses secondary data from the SHARE project. All participants in the SHARE survey provided informed consent at the time of data collection by the original researchers. Ethics declaration and norm or standard: The SHARE project was conducted in accordance with the Declaration of Helsinki and received ethics approvals in each participating country. For more details, see http://www.share-project.org. Name of the Approval Committee / IRB: The original data collection for SHARE received ethics approval from the Ethics Council of the Max Planck Society and other relevant national ethics committees. Funding declaration: This work was supported by the project SBPLY/21/180501/000066 of the Regional Government of Castilla-La Mancha and ERDF and Project 2023- GRIN-34431-Funding of Research Group “Economía, Alimentación y Sociedad” of Universidad de Castilla-La Mancha. Human Ethics and Consent to Participate declarations: Human Ethics and Consent to Participate declarations: not applicable. This study relies exclusively on anonymized secondary data provided by the SHARE project. References AbdulRaheem, Y. (2023). Unveiling the Significance and Challenges of Integrating Prevention Levels in Healthcare Practice. In Journal of Primary Care and Community Health (Vol. 14). SAGE Publications Inc. https://doi.org/10.1177/21501319231186500 Andersen, R. M. (2008). 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Changes in inequality in utilization of preventive care services: Evidence on China’s 2009 and 2015 health system reform. International Journal for Equity in Health , 18 (1). https://doi.org/10.1186/s12939-019-1078-z Zhang, S., Chen, Q., & Zhang, B. (2019). Understanding healthcare utilization in China through the andersen behavioral model: Review of evidence from the China health and nutrition survey. Risk Management and Healthcare Policy , 12 , 209–224. https://doi.org/10.2147/RMHP.S218661 Additional Declarations No competing interests reported. Supplementary Files annex.docx 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. 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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-6628048","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":509538219,"identity":"12e751d9-3df1-4b70-b3c3-48f33d7ce5e3","order_by":0,"name":"Pablo Moya-Martínez","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0ElEQVRIiWNgGAWjYFACHsYDEAYzkC4gTgsDVAtbAgODAWlaeAyI02LOfvbAgQ8M2+QMjp/5JvHDYBsDP/8B/Fose/ISDs5guG1scCZ3m2SPwW0GyRkJ+LUYHMgxOMzDcDtxw4HcbdIMQC0GNwg4zOD8G6iW82+egbXYnyfgMIMbMFtu5LBBbGEg4DDLGe+AfjG4bSx545mxJdAvPBI3CGgx5889+OBDxW05vvPJD2/8ADL4+wk5DEYqQBXy4FfPgBR38g0E1Y6CUTAKRsFIBQD9bEjvJSs5xAAAAABJRU5ErkJggg==","orcid":"","institution":"University of Castilla-La Mancha","correspondingAuthor":true,"prefix":"","firstName":"Pablo","middleName":"","lastName":"Moya-Martínez","suffix":""},{"id":509538221,"identity":"52dce741-c85b-401b-b9bb-a8578eb666fc","order_by":1,"name":"Isabel Pardo-García","email":"","orcid":"","institution":"University of Castilla-La Mancha","correspondingAuthor":false,"prefix":"","firstName":"Isabel","middleName":"","lastName":"Pardo-García","suffix":""},{"id":509538222,"identity":"557d3f37-fa75-4a62-8d11-9e924beeaa50","order_by":2,"name":"Roberto Martinez-Lacoba","email":"","orcid":"","institution":"University of Castilla-La Mancha","correspondingAuthor":false,"prefix":"","firstName":"Roberto","middleName":"","lastName":"Martinez-Lacoba","suffix":""},{"id":509538224,"identity":"acae3bf0-5c04-4d2f-941c-4cf5f9b0a5c7","order_by":3,"name":"Elisa Amo-Saus","email":"","orcid":"","institution":"University of Castilla-La Mancha","correspondingAuthor":false,"prefix":"","firstName":"Elisa","middleName":"","lastName":"Amo-Saus","suffix":""}],"badges":[],"createdAt":"2025-05-09 11:23:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6628048/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6628048/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90616749,"identity":"b2076b74-0476-480d-8426-fa759c647d3f","added_by":"auto","created_at":"2025-09-04 18:47:28","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":26418,"visible":true,"origin":"","legend":"\u003cp\u003eResearch model\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6628048/v1/c2a4fd2a98a2f49e5ed758b8.png"},{"id":90616486,"identity":"6b7134ee-f2e4-493d-b8de-3e765c74309f","added_by":"auto","created_at":"2025-09-04 18:39:28","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":291797,"visible":true,"origin":"","legend":"\u003cp\u003eDifferences from mean in final contextual variables.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6628048/v1/c43d6d6edd180e4ef354c439.png"},{"id":91136825,"identity":"7f129449-68c9-48a1-9a5f-85ef86a5ec2c","added_by":"auto","created_at":"2025-09-12 03:46:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1731024,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6628048/v1/ccd684ba-7f92-4bfe-8c44-1b7574c663b9.pdf"},{"id":90616751,"identity":"dbc76a29-30a9-4482-9092-e1c47dcb7c0d","added_by":"auto","created_at":"2025-09-04 18:47:29","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":223301,"visible":true,"origin":"","legend":"","description":"","filename":"annex.docx","url":"https://assets-eu.researchsquare.com/files/rs-6628048/v1/cd98ab85b8198e2ef8fdbfd7.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Beyond Healthcare Spending: Inequalities in the Use of Preventive Health Services among Europeans aged over 50","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDecision making in life depends on whether the benefit is immediate or delayed or whether such benefit will ever exist. These are intertemporal choices (Chapman et al., 2001; Loewenstein \u0026amp; Thaler, 1989). When such decisions are taken with regard to preventive health behaviours (PHBs), a dual payoff may arise, whereby, on the one hand, the likelihood of developing diseases is reduced and/or, if they do occur, the probability of treatment being more effective and less costly increases (AbdulRaheem, 2023). However, not only do PHBs depend on individual aspects, such as socioeconomic, health or personal characteristics (AbdulRaheem, 2023; Obino \u0026amp; Werle, 2011), there are also factors related to the society, country or region in which an individual lives. The latter include the distance to the preventive health service (PHS), their existence, availability or how the service is funded, in short, the design of the healthcare system.\u003c/p\u003e\u003cp\u003eSeveral models have been developed to analyse the factors affecting health service use, although the most widely used is Andersen\u0026rsquo;s Behavioural Model (ABM) or one of its subsequent versions (Ricketts \u0026amp; Goldsmith, 2005a). This model was developed on the basis of the National Health Surveys first administered by the United States Public Health Services in the 1930s. A review of the model has been published (Andersen, 2008).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eABM classifies the factors that influence health service utilisation into predisposing, enabling and need factors. These are replicated at two levels, contextual characteristics, which refer to those that are external to the individual and particular to the environment in which they live; and individual characteristics, which are intrinsic to the person and their particular situation (Andersen \u0026amp; Davidson, 2007). Within the economic framework, the contextual level involves supply-side characteristics, while the individual level entails demand-side ones. A diagram of ABM is included in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, while the Annex (table A1) contains all the variables that are typically included in the levels and factors (Lederle et al., 2021).\u003c/p\u003e\u003cp\u003eConsidering ABM, our study focuses on the use of PHS among the European population aged over 50. The Survey of Health, Ageing and Retirement (SHARE) in Europe (B\u0026ouml;rsch-Supan et al., 2013) allows for the analysis of a variety of factors included in ABM and for the testing of additional ones (personality traits, internet, use, trust and praying). In our ABM-based models, we adjust for long-term illness, limitations on activities of daily living and health status, thus allowing us to find the effects of service use as preventive measures for the remaining variables. Additionally, the survey enables us to analyse a broad set of services, such as visits to the general practitioner (GP) or specialist, flu vaccination, eye exams, mammography screening, colorectal cancer screening and dental visits.\u003c/p\u003e\u003cp\u003eTo the best of our knowledge, few studies have explored PHS in older adults in Europe, and those that do so are more than ten years old (Carrieri \u0026amp; Wuebker, 2013; Jusot et al., 2012; Schmitz \u0026amp; W\u0026uuml;bker, 2011). Many of these works use the Survey on Health Ageing and Retirement in Europe (SHARE). Carrieri \u0026amp; Wuebker (2013) use concentration indices to reveal the inequalities in breast cancer screening and blood testing. The study by Jusot et al. (2012) does not use ABM and includes only a small set of individual-level variables (sex, age, income, education, self-assessed health, limitations in activities of daily living and reported chronic conditions). Although the study adjusts for country, it does not do so by contextual variables in its first model. Subsequently, it only presents models with one contextual variable, meaning that they might reflect effects of other variables not included. Notwithstanding, the results suggest the existence of health system factors that impact the propensity towards service use across countries. The work by Schmitz \u0026amp;W\u0026uuml;bker (2011) assesses physician quality in terms of flu vaccination services. A study carried out in Italy (Carrieri et al., 2009) analyses pap smear and mammography utilisation, finding underuse of preventive care, primarily determined by demand. Another European study examines the regular use of blood tests, eye exams, gynaecological visits and mammograms, reporting social inequalities and differences between countries in terms of preventive service use (Sirven \u0026amp; Or, 2010). Consequently, the present work updates and advances the previous literature, as it delves into individual and contextual factors, as well as encompassing a large number of European nations.\u003c/p\u003e\u003cp\u003eTherefore, the main aim of this study is to analyse the factors that contribute to differences in the use of healthcare services, primarily PHS, among individuals aged over 50 and living at home.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eSample\u003c/h2\u003e\u003cp\u003eThe data used for the analysis were extracted from four sources: the ninth wave of the Survey on Health Ageing and Retirement in Europe, administered between October 2021 and September 2022 in 27 European countries (Bergmann, 2024), and statistical data provided by the World Bank Group, EuroStat Statistics and OECD Statistics.\u003c/p\u003e\u003cp\u003eOur sample consisted of 69,447 individuals that participated in Wave 9 of SHARE across 27 European countries. In addition to the individuals selected, the data includes other members of the same households who were also interviewed, regardless of their age. However, we decided to exclude these additional household members (retaining only the primary individual) since including various persons from the same household would duplicate the household-level variables, thus distorting the analysis. After this initial selection, we obtained a sample of 43,542 individuals residing in different households (NA\u0026rsquo;s\u0026thinsp;=\u0026thinsp;19 were also discarded). Additionally, we excluded individuals under the age of 50 (169; 0.39%) and those that were permanent residents of care homes (19; 0.04%), yielding a final sample of 43,354 participants.\u003c/p\u003e\u003cp\u003eThe contextual variables (country-level variables) were not available for all the countries included in the sample of individual variables and thus we excluded the individuals from Israel, Luxembourg, Estonia, Cyprus, Finland, Latvia, Malta, Portugal, Switzerland, Slovakia and Belgium. There then remained a total of 30,567 over-50-year-olds.\u003c/p\u003e\u003cp\u003eIn order to properly detect the effects, we discarded countries with fewer than 900 subjects, since, for each factor, about 20 subjects are needed to assess approximately 45 factors at a time. The countries dropped were Bulgaria and Romania, resulting in a final sample of 29,201 individuals from 15 countries (Austria, Croatia, Czech Republic, Denmark, France, Germany, Greece, Hungary, Italy, Lithuania, Netherlands, Poland, Spain and Sweden).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eIndividual characteristics variables\u003c/h3\u003e\n\u003cp\u003eABM subdivides both the individual and contextual variables into predisposing factors, enabling factors and need factors. We followed this criterion, with the data presented below (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Table A1).\u003c/p\u003e\u003cp\u003eOur \u003cem\u003epredisposing factors\u003c/em\u003e were sex, age, marital status, household size, educational level, employment status and born in the country (as proxy of ethnicity). Those related to beliefs were trust, praying and satisfaction with basic health coverage. Table A1 shows the variables in the model that could not be assessed due to their not being available in the survey, those closely correlated with other variables or with a high number of missing values.\u003c/p\u003e\u003cp\u003eThe age categories were \"50\u0026ndash;59\", \"60\u0026ndash;69\", \"70\u0026ndash;79\", \"80+\", while marital status was categorised as married (which included those reporting a registered partnership), divorced/separated, never married and widowed. The household size variable was classified into 1 member, 2 members, 43 members and 4 or more members.\u003c/p\u003e\u003cp\u003eAs regards educational level, with the aim of harmonising the denominations across the different countries studios, the survey administrators establish the ISCED-97 system of classification with codes from 0 (pre-primary education, none) to 6 (second stage of tertiary education). We recoded into 3 levels: \u0026lt;= primary education, secondary education (levels 2, 3, 4) and tertiary education (levels 5 and 6).\u003c/p\u003e\u003cp\u003eFor the employment status variable, we grouped the survey categories as follows: \u0026ldquo;not actively working\u0026rdquo; included \u0026ldquo;retired\u0026rdquo;, \u0026ldquo;unemployed\u0026rdquo;, \u0026ldquo;permanently sick or disabled\u0026rdquo; and \u0026ldquo;homemaker\u0026rdquo;; actively working, meanwhile, corresponded to \u0026ldquo;employed or self-employed (including working for family business)\u0026rdquo;. The few individuals that selected the category of \u0026ldquo;other\u0026rdquo; were recorded as missing values.\u003c/p\u003e\u003cp\u003eIn addition, we incorporated a number of variables (Internet use, trust, praying and personality traits) that had not previously been included in ABM, and about whose influence on service utilisation we hypothesised. To ascertain the participant\u0026rsquo;s level of adaptation to technology, we formulated a dichotomous question on Internet use. The questionnaire thus asked participants if they had used the Internet, for e-mailing, searching for information, making purchases or for any other purpose at least once in the last seven days.\u003c/p\u003e\u003cp\u003eThe participants scored their trust on a scale from 0 to 10. The praying variable shows the frequency with which individuals engage in this activity, being categorised into never and sometimes (from more than once a day to less than a week), providing a proxy for whether the participant is a believer or not.\u003c/p\u003e\u003cp\u003eAs regards personality traits, since Wave 7, the SHARE has included the 10-item Big-Five inventory (BFI-10). This established personality inventory measures the \u0026ldquo;Big Five\u0026rdquo; personality dimensions with two items each. The variables scored from 1 to 5 are openness, conscientiousness, extraversion, agreeableness and neuroticism (Rammstedt \u0026amp; John, 2007). A more detailed description of the questions involved can be found in the Annex.\u003c/p\u003e\u003cp\u003eThe last of the predisposing individual factors was satisfaction with basic health coverage. Two levels of satisfaction were considered: dissatisfied/very dissatisfied and satisfied/very satisfied.\u003c/p\u003e\u003cp\u003eThe \u003cem\u003eindividual enabling factors\u003c/em\u003e examined were household income (economic capacity) supplementary health insurance (SHI), social support and its proximity, and geographic location.\u003c/p\u003e\u003cp\u003eAs a measure of household economic capacity, we calculated the \u003cem\u003eequivalised income\u003c/em\u003e. Various scales exist, although the most current and simple to use is that proposed by the OECD (OECD, 2013). The Square Root Scale divides household income by the square root of household size (e.g., this implies that a household of four persons has needs twice as large as one composed of a single person). The survey provides the two variables required. The first question asks about the number of people living in the household, while the second asks about \u003cem\u003eoverall income, after taxes and contributions, that the entire household had in an average month in the last year.\u003c/em\u003e Given the differences in purchasing power between countries, we calculated the Square Root Scale, categorising into tertiles by country (T1, T2, T3).\u003c/p\u003e\u003cp\u003eSupplementary Health Insurance (SHI) is considered as a binary variable indicating whether the individual has coverage (1) or not (0). Similarly, the social support indicates whether the respondent has support or not. Additionally, the proximity variable considers whether the individual has access to a social support network less than 5km from their place of residence. The last variable in this block is geographic location, which indicates whether the respondent lives in a big city, a large town, rural area or village or a small town.\u003c/p\u003e\u003cp\u003eThe \u003cem\u003eindividual need factors\u003c/em\u003e were divided into perceived and evaluated variables. The perceived factor was self-perceived health status, while the evaluated factors included whether the individual had limitations in their activities of daily living or had a long-term illness. The effects captured by these latter variables help assess the use of healthcare services, particularly preventive services, as they account for a significant part of utilisation driven by necessity.\u003c/p\u003e\u003cp\u003eSelf-perceived health status was rated over five categories (Excellent, Very Good, Good, Fair and Poor), although we decided to combine the first two into just one.\u003c/p\u003e\u003cp\u003eIn terms of the limitations on activities of daily living, the survey asked to what extent, for the past 6 months, the respondent had been limited because of a health problem in activities that people usually do. The response options were Severely Limited, Limited but not Severely, and Not Limited. Finally, the long-term illness variable had a dichotomous response.\u003c/p\u003e\n\u003ch3\u003eContextual characteristic variables\u003c/h3\u003e\n\u003cp\u003eFor the predisposing factors, it is important to consider those that capture the effects of each country\u0026rsquo;s and/or region\u0026rsquo;s particular characteristics, such as culture, traditions, beliefs, ethnic diversity, etc. There are no databases that include these characteristics, and so they are accounted for in our analysis as unobservable factors captured by the country variable.\u003c/p\u003e\u003cp\u003eRegarding the enabling factors related to funding, for per capita community income and wealth, we used gross domestic product (GDP) per inhabitant in purchasing power standard (PPS), which allows for cross-country comparisons considering differences in the standard of living in each country. This variable was obtained from the World Bank\u003c/p\u003e\u003cp\u003eTo obtain information on per capita expenditure on health services, it is necessary to consider not only the total amount but also the composition of the expenditure, which can originate from different sources in varying percentages, from government schemes and compulsory contributory health care financing schemes, voluntary health care payment schemes, and household out-of-pocket payments. Therefore, our preliminary analysis includes both the total amount (per capita expenditure on health services in all schemes) as well as the percentage corresponding to each category. These variables were taken from Eurostat.\u003c/p\u003e\u003cp\u003eTo analyse health insurance coverage, the data were obtained from the OECD, which provides information on the percentage of coverage (percentage of the total government or compulsory insurance expenditure for a specific service) for therapeutic appliances, pharmaceuticals, dental care, outpatient primary and specialist care and inpatient care. The last published figures dated back to 2020. Data for dental care for France, Italy and Portugal were missing, and we thus drew on articles in the scientific literature for these values (Bindi et al., 2017; Sim\u0026otilde;es et al., 2018; Winkelmann et al., 2022).\u003c/p\u003e\u003cp\u003eUnder the organisation category in our enabling variables, we included amount and distribution of health service facilities and personnel. Eurostat statistics provide data on the number of professionals per 100,000 inhabitants for the following categories: physicians, midwives (EU-recognised qualification), nurses (EU-recognised qualification), dentists, and pharmacists. Other variables in this section include structure and quality of services provided, distance to the nearest health facility, medicine availability and infrastructure (see Table A1). As a proxy for these factors, we used inhabitants per hospital bed and inhabitants per device for computed tomography (CT) scanners and magnetic resonance imaging (MRI).\u003c/p\u003e\u003cp\u003eRegarding contextual \u003cem\u003eneed\u003c/em\u003e variables, we included three variables from Eurostat on \u003cem\u003eself-reported unmet needs for health care\u003c/em\u003e. These variables reflect the percentage of people unable to receive care for financial reasons, distance or transportation to the health facility and waiting lists.\u003c/p\u003e\n\u003ch3\u003eDependent variables\u003c/h3\u003e\n\u003cp\u003eThe dependent variables for our models inform on GP contacts, specialist contacts, flu vaccination, eye exams, mammogram screening, cancer screening and dentist visits. All except GP and specialist visits were binary variables; GP and specialist visits were reported as a number of times between zero and a maximum. As the aim of this study was to observe differences and similarities between the individuals that use health services for prevention and those that do not (being why adjustments were made for health status, long-term illness and limitations), the variables were transformed into binary ones. This approach also resolves the common problem of skewness in the data.\u003c/p\u003e\u003cp\u003eFor all the variables, respondents were asked about service utilisation over the last 12 months, expect for the cases of eye exam, mammogram and cancer screening, for which they were asked about the last 2 years. A more detailed description can be found under Detailed Description of Dependent Variables in the Annex.\u003c/p\u003e\n\u003ch3\u003eMissing values\u003c/h3\u003e\n\u003cp\u003eHaving selected the variables according to their availability and ABM, we found that several had missing values. Table A2 shows the number and percentage of missing values for each variable.\u003c/p\u003e\u003cp\u003eConsequently, the first step was to conduct Little's (1988) test statistic to assess whether the data were Missing Completely At Random (MCAR) (Little, 1988). A non-significant result means that the cases of missing values can be eliminated as their being missing is random. If the result is significant, as occurred here, it is necessary to check whether the data are Missing At Random (MAR). In this case, the probability of a value being missing depends only on the observed values and not those unobserved, which means that, after controlling for all the available data, any missing value is completely random (Graham, 2009; Schafer \u0026amp; Graham, 2002). Several procedures exist to evaluate this condition, but hypothesis tests are a simple method to check whether the absence of data is related to other variables, using chi-squared tests for categorical variables or t-tests for continuous variables (Harrison \u0026amp; Pius, 2020). Since all the variables in our study were categorical, we conducted this test for each variable with more than 300 missing values against the remaining variables. In all cases, each of the variables with missing values was significant in the test with respect to more than variable. This finding shows the data are MAR.\u003c/p\u003e\u003cp\u003eAn appropriate method to work with such data is the Multiple Imputation by Chained Equations (MICE), which is based on the Fully Conditional Specification, where each incomplete variable is imputed using a separate model. It can impute mixtures of continuous, binary and categorical variables. A more detailed explanation can be found in (Azur et al., 2011).\u003c/p\u003e\u003cp\u003eFinally, and in order to test the robustness of our results, we implemented the models for the analysis based on different samples: one imputing missing values using the abovementioned technique and another without imputation.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eAnalyses\u003c/h2\u003e\u003cp\u003eDescriptive statistics were calculated using the distribution of frequencies and percentages for the categorical variables and means and standard deviations for the continuous ones. We examined the association between the predictors and the outcome by means of Pearson\u0026rsquo;s chi-squared test and Student\u0026rsquo;s test, depending on the type of variable. The predictors that had a significant effect (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) on the outcome variables in the bivariate analysis were included in the Multilevel Logistic Regression Models to determine the factors that best predicted the use of the services evaluated.\u003c/p\u003e\u003cp\u003eMultiple Logistic Regression models were used as a first approach to estimate the individual parameters specified in ABM on health service use for each country. Meanwhile, the correlation between the contextual variables was analysed in order to avoid problems of collinearity in the models. Those that obtained values greater than 0.7 were discarded.\u003c/p\u003e\u003cp\u003eThe following step involved a hierarchical approach using Multilevel Logistic Regression Models in which the first level corresponded to the individual variables of ABM (those referring to the subject) and the second level was that of its contextual factors (those for the country). The adequacy of the model fit was verified using the likelihood ratio test for goodness of fit, while multicollinearity between predictors was also checked for using Variance Inflation Factor values (VIF\u0026thinsp;\u0026lt;\u0026thinsp;2.0).\u003c/p\u003e\u003cp\u003eWe ran the models with all the contextual variables. Among those showing a correlation higher than 0.7, we selected the one we considered less problematic and more representative. When running the models, we also verified the existence of collinearity between all the variables (contextual and individual); some were dropped due to their very high VIF values. A correlation analysis was performed to see which variables might be attempting to explain the same part of the variability in the dependent variables. The correlation table can be found in the Annex (see Table A3). Of the two with a very high correlation, we chose only one, taking into account the highest degree of affinity with the model.\u003c/p\u003e\u003cp\u003eAdjusted odds ratios were calculated to estimate the level of association with service use. The association was considered significant at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Additionally, we calculated the (conditional \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{R}_{conditional}^{2}\\)\u003c/span\u003e\u003c/span\u003e) and marginal (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{R}_{marginal}^{2}\\)\u003c/span\u003e\u003c/span\u003e) coefficients of determination. The former represents the variance explained of the complete model (effects at subject and country level) and the latter only represents the country-level effects (random) (Johnson, 2014; Nakagawa et al., 2017; Nakagawa \u0026amp; Schielzeth, 2013).\u003c/p\u003e\u003cp\u003eBecause the units of measurement for the contextual variables were different, they were normalised by dividing by their mean. This allowed them to have a mean of 1 and facilitated better comparison and interpretation of the results. For example, if a country obtained a value of 1.05 in an indicator, it would be interpreted as the probability of using the PHS being 5% above the average (0.95 would indicate the opposite).\u003c/p\u003e\u003cp\u003eAs we found no major differences between the results, the data shown in the tables reflect the imputation of missing values.\u003c/p\u003e\u003cp\u003eThe analyses were performed with the R statistical software package version 4.4.2, using R-Studio (version 2024.09.1) and the lme4 for the Multilevel Logistic Regression Models.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e displays the individual and dependent variables of the final sample. There are 24.06% more women than men. A total of 65.13% are aged between 60 and 79, 55.58% are married and 24.84% are widowed. More than half pray, and 83.06% are satisfied or very satisfied with their health coverage and 37.32% have supplementary health insurance. Meanwhile, 62.19% report their health is between good and excellent, 50.65% report having no limitations on activities of daily living, while 55.08% have a long-term illness. As for the continuous variables (transformed into a score from 0 to 10), the mean score for trust is 6.75 and those for extraversion, agreeableness, conscientiousness, neuroticism and openness (big five inventory) are 7.54, 7.84, 7.18, 5.18 and 6.48, respectively. Regarding utilisation of services, the dependent variables, a large majority have visited their GP once (93.1%), with visits to specialists being somewhat lower (61.61%); 50.15% have visited the dentist and below this percentage are the other services, the lowest being cancer screening followed by mammogram (see Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eDescriptive statistics for individual variables\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eIndependent variables\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003en\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e%\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFemale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18114\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e62.03\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAge\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e50\u0026ndash;59\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5239\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e17.94\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e60\u0026ndash;69\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9880\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e33.83\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e70\u0026ndash;79\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9140\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e31.30\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e80+\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4942\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e16.92\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMarital status\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDivorced/Separated\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3602\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e12.34\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMarried\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16229\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e55.58\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNever married\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2107\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e7.22\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWidowed\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7263\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e24.87\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEducation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePrimary or less\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4354\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e14.91\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSecondary\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17905\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e61.32\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTertiary\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6942\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e23.77\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEmployment status\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWorking\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5405\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e18.51\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBorn in country\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e27284\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e93.44\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTrust (Range 0\u0026ndash;10)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.75\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.43\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePraying\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNever\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10734\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e36.76\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSometimes or more\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18467\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e63.24\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHealth coverage satisfaction\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDissatisfied/Very Dissatisfied\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4947\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e16.94\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSatisfied/Very Satisfied\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24254\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e83.06\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHome income tertiles\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eT1 - poorest\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9722\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e33.29\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eT2 - middle\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9731\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e33.32\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eT3 - richest\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9748\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e33.38\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSupplementary health insurance\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10897\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e37.32\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSocial support\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7332\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e25.11\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSocial network in 5km\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25268\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e86.53\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eResidence situation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eA big city\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6944\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e23.78\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eA large town\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5167\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e17.69\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eA rural area or village\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9955\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e34.09\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eA small town\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7135\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e24.43\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSubjective health state\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eExcellent\\Very good\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6493\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e22.24\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGood\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11667\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e39.95\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFair\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8059\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e27.60\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePoor\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2982\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e10.21\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eADL limitations\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSeverely limited\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4943\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e16.93\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLimited, but not severely\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9469\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e32.43\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo limited\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14789\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e50.65\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLong-term illness\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16084\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e55.08\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eInternet use\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17747\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e60.78\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBig five inventory (Range 0\u0026ndash;5)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eExtraversion\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.51\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.90\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAgreeableness\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.77\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.84\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eConscientiousness\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.92\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.95\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNeuroticism\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.59\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.99\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOpenness\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.94\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eDependents variables\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eContact with GP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e27186\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e93.10\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eContact with Specialist\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17991\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e61.61\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFlu vaccination\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12287\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e42.08\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCancer Screening\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7284\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e24.94\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMammogram\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9342\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e31.99\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEye exam\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13350\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e45.72\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDentist visit\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14644\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e50.15\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"4\"\u003eGP: General Practitioner; ADL: Activities of Daily Living; \u003csup\u003ea\u003c/sup\u003e :Mean(SD)\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e shows, for each dependent variable on service use in the last year, the percentage of respondents that used services for the different categories of the independent variables and, additionally, whether there are significant differences. Broadly speaking, there are significant differences in practically all the variables, which suggests they are candidates for explaining part of the variability in service utilisation.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab2\" style=\"width: 1003px;\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003ePercent of persons who use the service\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth style=\"width: 166px;\" align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth style=\"width: 164px;\" align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth style=\"width: 62px;\" align=\"left\"\u003e\n\u003cp\u003eGP\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"width: 70px;\" align=\"left\"\u003e\n\u003cp\u003eSpecialist\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"width: 97.4884px;\" align=\"left\"\u003e\n\u003cp\u003eFlu vaccination\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"width: 103px;\" align=\"left\"\u003e\n\u003cp\u003eCancer screening\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"width: 95px;\" align=\"left\"\u003e\n\u003cp\u003eMammogram\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"width: 67px;\" align=\"left\"\u003e\n\u003cp\u003eEye exam\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"width: 77px;\" align=\"left\"\u003e\n\u003cp\u003eDentist visit\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 166px;\" align=\"left\"\u003e\n\u003cp\u003eSex\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 164px;\" align=\"left\"\u003e\n\u003cp\u003eMale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\n\u003cp\u003e92.57**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 70px;\" align=\"left\"\u003e\n\u003cp\u003e60***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 97.4884px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e42.98*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 103px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e26.45***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 95px;\" align=\"left\"\u003e\n\u003cp\u003e0***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 67px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e42.87***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 77px;\" align=\"left\"\u003e\n\u003cp\u003e48.87***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 166px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 164px;\" align=\"left\"\u003e\n\u003cp\u003eFemale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\n\u003cp\u003e93.42\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 70px;\" align=\"left\"\u003e\n\u003cp\u003e62.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 97.4884px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e41.53\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 103px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e24.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 95px;\" align=\"left\"\u003e\n\u003cp\u003e41.59\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 67px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e47.46\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 77px;\" align=\"left\"\u003e\n\u003cp\u003e50.93\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 166px;\" align=\"left\"\u003e\n\u003cp\u003eAge\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 164px;\" align=\"left\"\u003e\n\u003cp\u003e50\u0026ndash;59\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\n\u003cp\u003e92.29**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 70px;\" align=\"left\"\u003e\n\u003cp\u003e58.27***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 97.4884px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e21.4***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 103px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e29.11***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 95px;\" align=\"left\"\u003e\n\u003cp\u003e46.76***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 67px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e43.52***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 77px;\" align=\"left\"\u003e\n\u003cp\u003e57.11***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 166px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 164px;\" align=\"left\"\u003e\n\u003cp\u003e60\u0026ndash;69\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\n\u003cp\u003e92.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 70px;\" align=\"left\"\u003e\n\u003cp\u003e60.36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 97.4884px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e36.35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 103px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e30.51\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 95px;\" align=\"left\"\u003e\n\u003cp\u003e44.34\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 67px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e43.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 77px;\" align=\"left\"\u003e\n\u003cp\u003e53.43\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 166px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 164px;\" align=\"left\"\u003e\n\u003cp\u003e70\u0026ndash;79\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\n\u003cp\u003e93.21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 70px;\" align=\"left\"\u003e\n\u003cp\u003e65.59\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 97.4884px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e51.85\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 103px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e23.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 95px;\" align=\"left\"\u003e\n\u003cp\u003e22.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 67px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e49.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 77px;\" align=\"left\"\u003e\n\u003cp\u003e49\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 166px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 164px;\" align=\"left\"\u003e\n\u003cp\u003e80+\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\n\u003cp\u003e94.15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 70px;\" align=\"left\"\u003e\n\u003cp\u003e60.28\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 97.4884px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e57.39\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 103px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e11.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 95px;\" align=\"left\"\u003e\n\u003cp\u003e8.46\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 67px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e46.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 77px;\" align=\"left\"\u003e\n\u003cp\u003e38.32\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 166px;\" align=\"left\"\u003e\n\u003cp\u003eMarital status\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 164px;\" align=\"left\"\u003e\n\u003cp\u003eDivorced/Separated\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\n\u003cp\u003e92.31***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 70px;\" align=\"left\"\u003e\n\u003cp\u003e62.02***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 97.4884px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e34.84***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 103px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e27.85***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 95px;\" align=\"left\"\u003e\n\u003cp\u003e37.76***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 67px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e50.28***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 77px;\" align=\"left\"\u003e\n\u003cp\u003e57***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 166px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 164px;\" align=\"left\"\u003e\n\u003cp\u003eMarried\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\n\u003cp\u003e92.96\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 70px;\" align=\"left\"\u003e\n\u003cp\u003e62.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 97.4884px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e43.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 103px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e27.43\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 95px;\" align=\"left\"\u003e\n\u003cp\u003e34.85\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 67px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e45.38\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 77px;\" align=\"left\"\u003e\n\u003cp\u003e53.11\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 166px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 164px;\" align=\"left\"\u003e\n\u003cp\u003eNever married\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\n\u003cp\u003e91.17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 70px;\" align=\"left\"\u003e\n\u003cp\u003e56.38\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 97.4884px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e37.07\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 103px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e24.11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 95px;\" align=\"left\"\u003e\n\u003cp\u003e30.52\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 67px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e40.48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 77px;\" align=\"left\"\u003e\n\u003cp\u003e48.79\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 166px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 164px;\" align=\"left\"\u003e\n\u003cp\u003eWidowed\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\n\u003cp\u003e94.37\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 70px;\" align=\"left\"\u003e\n\u003cp\u003e61.93\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 97.4884px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e45.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 103px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e18.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 95px;\" align=\"left\"\u003e\n\u003cp\u003e23.17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 67px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e45.72\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 77px;\" align=\"left\"\u003e\n\u003cp\u003e40.53\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 166px;\" align=\"left\"\u003e\n\u003cp\u003eEducation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 164px;\" align=\"left\"\u003e\n\u003cp\u003ePrimary or less\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\n\u003cp\u003e95.22***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 70px;\" align=\"left\"\u003e\n\u003cp\u003e55.54***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 97.4884px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e55.4***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 103px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e14.24***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 95px;\" align=\"left\"\u003e\n\u003cp\u003e18.21***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 67px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e35.3***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 77px;\" align=\"left\"\u003e\n\u003cp\u003e26.99***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 166px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 164px;\" align=\"left\"\u003e\n\u003cp\u003eSecondary\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\n\u003cp\u003e93.25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 70px;\" align=\"left\"\u003e\n\u003cp\u003e61.68\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 97.4884px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e36.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 103px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e24.97\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 95px;\" align=\"left\"\u003e\n\u003cp\u003e32.67\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 67px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e44.49\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 77px;\" align=\"left\"\u003e\n\u003cp\u003e48.55\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 166px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 164px;\" align=\"left\"\u003e\n\u003cp\u003eTertiary\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\n\u003cp\u003e91.39\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 70px;\" align=\"left\"\u003e\n\u003cp\u003e65.25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 97.4884px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e49.35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 103px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e31.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 95px;\" align=\"left\"\u003e\n\u003cp\u003e38.88\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 67px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e55.42\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 77px;\" align=\"left\"\u003e\n\u003cp\u003e68.8\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 166px;\" align=\"left\"\u003e\n\u003cp\u003eEmployment status\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 164px;\" align=\"left\"\u003e\n\u003cp\u003eNot actively working\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\n\u003cp\u003e93.39***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 70px;\" align=\"left\"\u003e\n\u003cp\u003e62.95***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 97.4884px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e45.7***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 103px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e23.62***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 95px;\" align=\"left\"\u003e\n\u003cp\u003e28.78***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 67px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e45.87\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 77px;\" align=\"left\"\u003e\n\u003cp\u003e47.37***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 166px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 164px;\" align=\"left\"\u003e\n\u003cp\u003eWorking\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\n\u003cp\u003e91.84\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 70px;\" align=\"left\"\u003e\n\u003cp\u003e55.73\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 97.4884px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e26.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 103px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e30.79\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 95px;\" align=\"left\"\u003e\n\u003cp\u003e46.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 67px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e45.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 77px;\" align=\"left\"\u003e\n\u003cp\u003e62.41\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 166px;\" align=\"left\"\u003e\n\u003cp\u003eBorn in country\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 164px;\" align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\n\u003cp\u003e93.48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 70px;\" align=\"left\"\u003e\n\u003cp\u003e63.64\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 97.4884px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e38.97**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 103px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e24.93\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 95px;\" align=\"left\"\u003e\n\u003cp\u003e30.88\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 67px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e47.84\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 77px;\" align=\"left\"\u003e\n\u003cp\u003e54.04***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 166px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 164px;\" align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\n\u003cp\u003e93.07\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 70px;\" align=\"left\"\u003e\n\u003cp\u003e61.47\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 97.4884px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e42.30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 103px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e24.95\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 95px;\" align=\"left\"\u003e\n\u003cp\u003e32.07\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 67px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e45.57\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 77px;\" align=\"left\"\u003e\n\u003cp\u003e49.88\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 166px;\" align=\"left\"\u003e\n\u003cp\u003eTrust (Range 0\u0026ndash;10)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 164px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\n\u003cp\u003e6.75\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 70px;\" align=\"left\"\u003e\n\u003cp\u003e6.73***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 97.4884px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e6.71**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 103px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e6.88***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 95px;\" align=\"left\"\u003e\n\u003cp\u003e6.99***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 67px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e6.89***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 77px;\" align=\"left\"\u003e\n\u003cp\u003e6.85***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 166px;\" align=\"left\"\u003e\n\u003cp\u003ePraying\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 164px;\" align=\"left\"\u003e\n\u003cp\u003eNever\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\n\u003cp\u003e92.13***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 70px;\" align=\"left\"\u003e\n\u003cp\u003e61.51\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 97.4884px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e44.63***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 103px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e30.24***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 95px;\" align=\"left\"\u003e\n\u003cp\u003e34.21***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 67px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e50.38***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 77px;\" align=\"left\"\u003e\n\u003cp\u003e60.28***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 166px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 164px;\" align=\"left\"\u003e\n\u003cp\u003eSometimes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\n\u003cp\u003e93.66\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 70px;\" align=\"left\"\u003e\n\u003cp\u003e61.67\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 97.4884px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e40.59\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 103px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e21.87\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 95px;\" align=\"left\"\u003e\n\u003cp\u003e30.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 67px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e43.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 77px;\" align=\"left\"\u003e\n\u003cp\u003e44.26\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 166px;\" align=\"left\"\u003e\n\u003cp\u003eHealth coverage\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 164px;\" align=\"left\"\u003e\n\u003cp\u003eDissatisfied/Very Dissatisfied\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\n\u003cp\u003e93.19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 70px;\" align=\"left\"\u003e\n\u003cp\u003e63.82***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 97.4884px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e35.54***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 103px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e20.11***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 95px;\" align=\"left\"\u003e\n\u003cp\u003e29.23***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 67px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e41.06***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 77px;\" align=\"left\"\u003e\n\u003cp\u003e38.1***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 166px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 164px;\" align=\"left\"\u003e\n\u003cp\u003eSatisfied/Very Satisfied\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\n\u003cp\u003e93.08\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 70px;\" align=\"left\"\u003e\n\u003cp\u003e61.16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 97.4884px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e43.41\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 103px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e25.93\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 95px;\" align=\"left\"\u003e\n\u003cp\u003e32.56\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 67px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e46.67\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 77px;\" align=\"left\"\u003e\n\u003cp\u003e52.61\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 166px;\" align=\"left\"\u003e\n\u003cp\u003eIncome\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 164px;\" align=\"left\"\u003e\n\u003cp\u003eT1 - poorest\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\n\u003cp\u003e93.29*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 70px;\" align=\"left\"\u003e\n\u003cp\u003e58.87***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 97.4884px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e38.84***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 103px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e22.77***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 95px;\" align=\"left\"\u003e\n\u003cp\u003e29.38***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 67px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e43.78***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 77px;\" align=\"left\"\u003e\n\u003cp\u003e46.67***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 166px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 164px;\" align=\"left\"\u003e\n\u003cp\u003eT2 - middle\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\n\u003cp\u003e93.52\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 70px;\" align=\"left\"\u003e\n\u003cp\u003e62.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 97.4884px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e43.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 103px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e25.16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 95px;\" align=\"left\"\u003e\n\u003cp\u003e31.81\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 67px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e45.62\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 77px;\" align=\"left\"\u003e\n\u003cp\u003e48.47\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 166px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 164px;\" align=\"left\"\u003e\n\u003cp\u003eT3 - richest\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\n\u003cp\u003e92.49\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 70px;\" align=\"left\"\u003e\n\u003cp\u003e63.26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 97.4884px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e43.99\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 103px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e26.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 95px;\" align=\"left\"\u003e\n\u003cp\u003e34.79\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 67px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e47.75\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 77px;\" align=\"left\"\u003e\n\u003cp\u003e55.29\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 166px;\" align=\"left\"\u003e\n\u003cp\u003eSupplementary health insurance\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 164px;\" align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\n\u003cp\u003e92.61***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 70px;\" align=\"left\"\u003e\n\u003cp\u003e61.94\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 97.4884px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e42.37\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 103px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e20.01***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 95px;\" align=\"left\"\u003e\n\u003cp\u003e29.39***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 67px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e44.06***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 77px;\" align=\"left\"\u003e\n\u003cp\u003e46.77***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 166px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 164px;\" align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\n\u003cp\u003e93.92\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 70px;\" align=\"left\"\u003e\n\u003cp\u003e61.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 97.4884px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e41.59\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 103px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e33.24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 95px;\" align=\"left\"\u003e\n\u003cp\u003e36.37\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 67px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e48.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 77px;\" align=\"left\"\u003e\n\u003cp\u003e55.82\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 166px;\" align=\"left\"\u003e\n\u003cp\u003eSocial support\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 164px;\" align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\n\u003cp\u003e92.98\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 70px;\" align=\"left\"\u003e\n\u003cp\u003e58.89***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 97.4884px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e40.22***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 103px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e24.83\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 95px;\" align=\"left\"\u003e\n\u003cp\u003e33.04***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 67px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e43.53***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 77px;\" align=\"left\"\u003e\n\u003cp\u003e49.76*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 166px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 164px;\" align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\n\u003cp\u003e93.45\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 70px;\" align=\"left\"\u003e\n\u003cp\u003e69.74\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 97.4884px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e47.61\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 103px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e25.29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 95px;\" align=\"left\"\u003e\n\u003cp\u003e28.87\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 67px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e52.24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 77px;\" align=\"left\"\u003e\n\u003cp\u003e51.31\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 166px;\" align=\"left\"\u003e\n\u003cp\u003eSocial network in 5km\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 164px;\" align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\n\u003cp\u003e93.77\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 70px;\" align=\"left\"\u003e\n\u003cp\u003e59.34**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 97.4884px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e40.63\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 103px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e21.79***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 95px;\" align=\"left\"\u003e\n\u003cp\u003e27.84***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 67px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e46.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 77px;\" align=\"left\"\u003e\n\u003cp\u003e47.93**\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 166px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 164px;\" align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\n\u003cp\u003e93\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 70px;\" align=\"left\"\u003e\n\u003cp\u003e61.96\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 97.4884px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e42.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 103px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e25.44\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 95px;\" align=\"left\"\u003e\n\u003cp\u003e32.64\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 67px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e45.67\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 77px;\" align=\"left\"\u003e\n\u003cp\u003e50.49\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 166px;\" align=\"left\"\u003e\n\u003cp\u003eResidence situation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 164px;\" align=\"left\"\u003e\n\u003cp\u003eA big city\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\n\u003cp\u003e92.07***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 70px;\" align=\"left\"\u003e\n\u003cp\u003e66.09***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 97.4884px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e50.66***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 103px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e25.82**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 95px;\" align=\"left\"\u003e\n\u003cp\u003e33.57**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 67px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e49.06***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 77px;\" align=\"left\"\u003e\n\u003cp\u003e53.95***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 166px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 164px;\" align=\"left\"\u003e\n\u003cp\u003eA large town\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\n\u003cp\u003e92.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 70px;\" align=\"left\"\u003e\n\u003cp\u003e60.75\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 97.4884px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e42.58\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 103px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e23.59\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 95px;\" align=\"left\"\u003e\n\u003cp\u003e30.48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 67px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e46.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 77px;\" align=\"left\"\u003e\n\u003cp\u003e50.59\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 166px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 164px;\" align=\"left\"\u003e\n\u003cp\u003eA rural area or village\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\n\u003cp\u003e93.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 70px;\" align=\"left\"\u003e\n\u003cp\u003e59.62\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 97.4884px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e34.35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 103px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e24.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 95px;\" align=\"left\"\u003e\n\u003cp\u003e31.96\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 67px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e43.83\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 77px;\" align=\"left\"\u003e\n\u003cp\u003e47.13\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 166px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 164px;\" align=\"left\"\u003e\n\u003cp\u003eA small town\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\n\u003cp\u003e93.57\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 70px;\" align=\"left\"\u003e\n\u003cp\u003e60.66\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 97.4884px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e44.13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 103px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e26.11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 95px;\" align=\"left\"\u003e\n\u003cp\u003e31.59\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 67px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e44.39\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 77px;\" align=\"left\"\u003e\n\u003cp\u003e50.34\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 166px;\" align=\"left\"\u003e\n\u003cp\u003eSubjective health state\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 164px;\" align=\"left\"\u003e\n\u003cp\u003eExcellent\\Very good\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\n\u003cp\u003e89.8***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 70px;\" align=\"left\"\u003e\n\u003cp\u003e51.15***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 97.4884px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e39.63***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 103px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e28.17***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 95px;\" align=\"left\"\u003e\n\u003cp\u003e39.29***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 67px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e45.57***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 77px;\" align=\"left\"\u003e\n\u003cp\u003e62.59***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 166px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 164px;\" align=\"left\"\u003e\n\u003cp\u003eGood\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\n\u003cp\u003e93.38\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 70px;\" align=\"left\"\u003e\n\u003cp\u003e59.31\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 97.4884px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e39.72\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 103px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e24.69\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 95px;\" align=\"left\"\u003e\n\u003cp\u003e33.57\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 67px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e45.48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 77px;\" align=\"left\"\u003e\n\u003cp\u003e51.32\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 166px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 164px;\" align=\"left\"\u003e\n\u003cp\u003eFair\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\n\u003cp\u003e94.48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 70px;\" align=\"left\"\u003e\n\u003cp\u003e69.38\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 97.4884px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e46.67\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 103px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e23.55\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 95px;\" align=\"left\"\u003e\n\u003cp\u003e27.25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 67px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e47.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 77px;\" align=\"left\"\u003e\n\u003cp\u003e44.81\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 166px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 164px;\" align=\"left\"\u003e\n\u003cp\u003ePoor\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\n\u003cp\u003e95.44\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 70px;\" align=\"left\"\u003e\n\u003cp\u003e72.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 97.4884px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e44.23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 103px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e22.67\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 95px;\" align=\"left\"\u003e\n\u003cp\u003e22.74\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 67px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e42.96\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 77px;\" align=\"left\"\u003e\n\u003cp\u003e32.93\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 166px;\" align=\"left\"\u003e\n\u003cp\u003eADL limitations\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 164px;\" align=\"left\"\u003e\n\u003cp\u003eSeverely limited\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\n\u003cp\u003e94.44***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 70px;\" align=\"left\"\u003e\n\u003cp\u003e72.67***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 97.4884px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e44.59***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 103px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e23.93\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 95px;\" align=\"left\"\u003e\n\u003cp\u003e26.04***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 67px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e46.23***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 77px;\" align=\"left\"\u003e\n\u003cp\u003e42.26***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 166px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 164px;\" align=\"left\"\u003e\n\u003cp\u003eLimited, but not severely\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\n\u003cp\u003e94.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 70px;\" align=\"left\"\u003e\n\u003cp\u003e69.25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 97.4884px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e44.98\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 103px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e25.57\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 95px;\" align=\"left\"\u003e\n\u003cp\u003e30.59\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 67px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e49.33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 77px;\" align=\"left\"\u003e\n\u003cp\u003e49.78\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 166px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 164px;\" align=\"left\"\u003e\n\u003cp\u003eNo limited\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\n\u003cp\u003e92.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 70px;\" align=\"left\"\u003e\n\u003cp\u003e53.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 97.4884px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e39.38\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 103px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e24.88\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 95px;\" align=\"left\"\u003e\n\u003cp\u003e34.88\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 67px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e43.23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 77px;\" align=\"left\"\u003e\n\u003cp\u003e53.02\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 166px;\" align=\"left\"\u003e\n\u003cp\u003eLong term illness\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 164px;\" align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\n\u003cp\u003e92.39***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 70px;\" align=\"left\"\u003e\n\u003cp\u003e51.12***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 97.4884px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e37.78***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 103px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e23.44***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 95px;\" align=\"left\"\u003e\n\u003cp\u003e32.97**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 67px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e40.63***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 77px;\" align=\"left\"\u003e\n\u003cp\u003e50.72\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 166px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 164px;\" align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\n\u003cp\u003e93.68\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 70px;\" align=\"left\"\u003e\n\u003cp\u003e70.17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 97.4884px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e45.58\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 103px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e26.18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 95px;\" align=\"left\"\u003e\n\u003cp\u003e31.19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 67px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e49.87\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 77px;\" align=\"left\"\u003e\n\u003cp\u003e49.68\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 166px;\" align=\"left\"\u003e\n\u003cp\u003eInternet use\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 164px;\" align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\n\u003cp\u003e94.59***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 70px;\" align=\"left\"\u003e\n\u003cp\u003e58.14***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 97.4884px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e41.44\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 103px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e16.14***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 95px;\" align=\"left\"\u003e\n\u003cp\u003e18.66***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 67px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e37.06***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 77px;\" align=\"left\"\u003e\n\u003cp\u003e29.8***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 166px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 164px;\" align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\n\u003cp\u003e92.14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 70px;\" align=\"left\"\u003e\n\u003cp\u003e63.85\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 97.4884px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e42.49\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 103px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e30.62\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 95px;\" align=\"left\"\u003e\n\u003cp\u003e40.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 67px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e51.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 77px;\" align=\"left\"\u003e\n\u003cp\u003e63.28\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 166px;\" align=\"left\"\u003e\n\u003cp\u003eBig five inventory\u003c/p\u003e\n\u003cp\u003e(Range 0\u0026ndash;5)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 164px;\" align=\"left\"\u003e\n\u003cp\u003eExtraversion\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\n\u003cp\u003e3.51\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 70px;\" align=\"left\"\u003e\n\u003cp\u003e3.51\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 97.4884px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.51\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 103px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.53*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 95px;\" align=\"left\"\u003e\n\u003cp\u003e3.6***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 67px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.59***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 77px;\" align=\"left\"\u003e\n\u003cp\u003e3.56***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 166px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 164px;\" align=\"left\"\u003e\n\u003cp\u003eAgreeableness\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\n\u003cp\u003e3.77\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 70px;\" align=\"left\"\u003e\n\u003cp\u003e3.77**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 97.4884px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.74***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 103px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.87***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 95px;\" align=\"left\"\u003e\n\u003cp\u003e3.85***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 67px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.78\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 77px;\" align=\"left\"\u003e\n\u003cp\u003e3.78\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 166px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 164px;\" align=\"left\"\u003e\n\u003cp\u003eConscientiousness\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\n\u003cp\u003e3.93***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 70px;\" align=\"left\"\u003e\n\u003cp\u003e3.92\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 97.4884px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.91\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 103px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.85***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 95px;\" align=\"left\"\u003e\n\u003cp\u003e3.93\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 67px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e4.01***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 77px;\" align=\"left\"\u003e\n\u003cp\u003e3.99***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 166px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 164px;\" align=\"left\"\u003e\n\u003cp\u003eNeuroticism\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\n\u003cp\u003e2.60***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 70px;\" align=\"left\"\u003e\n\u003cp\u003e2.60***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 97.4884px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.64***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 103px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.56***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 95px;\" align=\"left\"\u003e\n\u003cp\u003e2.51***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 67px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.60*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 77px;\" align=\"left\"\u003e\n\u003cp\u003e2.57**\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 166px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 164px;\" align=\"left\"\u003e\n\u003cp\u003eOpenness\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 62px;\" align=\"left\"\u003e\n\u003cp\u003e3.25***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 70px;\" align=\"left\"\u003e\n\u003cp\u003e3.24**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 97.4884px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.29***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 103px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 95px;\" align=\"left\"\u003e\n\u003cp\u003e3.30***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 67px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.35***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 77px;\" align=\"left\"\u003e\n\u003cp\u003e3.33***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 927px;\" colspan=\"9\"\u003eGP: General Practitioner; ADL: Activities of Daily Living; \u003csup\u003ea\u003c/sup\u003e :Mean(SD); For significant differences, we used Pearson Chi-square Test or T-Student Test depending on de nature of variable. ***: p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; * p\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eIt is worth noting that the less pronounced differences are in the use of GPs and specialists. The individuals with only primary education or below visit specialists slightly less and are notably less likely to have flu vaccination, cancer screening, mammograms, eye exams or dentist visits. Those still working (recall that we analysed\u0026thinsp;\u0026gt;\u0026thinsp;50 years) are less likely to go for flu vaccinations and cancer screening, but more likely to have a mammogram or visit a dentist. It is also worth underlining that those who never pray are more likely to use all the services with the exception of GP and specialist services (which are used in similar percentages). The poorest also make less use of all the preventive services, except the GP, which is used similarly to the rest. Those with supplementary health insurance made greater use of cancer screening, mammograms, eye exams and dentist visits. Those who report excellent/ very good subjective health make less use of GP and specialist services and more use of the others. Finally, the individuals that do not use the Internet also make less use of cancer screening, mammograms, eye exams and dentist visits. It should be noted that these are unadjusted results.\u003c/p\u003e\n\u003cp\u003eThe contextual variables, meanwhile, showed strong correlations, with the models exhibiting high VIF values. Following the analyses and various tests, five contextual variables best fitted the models: health expenditure in all financing schemes, percentage of health expenditure in voluntary schemes, percentage of health expenditure on out-of-pocket payments, dental care health insurance coverage (% of total expenditure dedicated to dental care coverage) and nurses (number of nursing staff per 100,000 inhabitants).\u003c/p\u003e\n\u003cp\u003eAs described in the methodology section, all the variables were centred, allowing for easier comparison. Figure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e shows the differences relative to the mean (value 1) between the countries in the study. Significant differences can be seen that corroborate the need to include contextual variables in the analysis. For example, Austria, Germany, and the Netherlands present the greatest difference in total health expenditure compared to the mean, but for other variables, such as dental health expenditure or voluntary health care payment schemes, there are greater differences. The comparison for the variables not used in the final model can be found in the supplementary material.\u003c/p\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3 shows the final logistic regression models on use of preventive services. The variables on health status (health status, activities of daily living limitations, long-term illness) are not mentioned as they are considered to be adjustment variables; they are necessary, however, to understand the rest as preventive use. As ABM predicts, most variables are significant with the exception of preventive use of GP service. The results suggest that women are more likely to make preventive use of eye exams and dentist visits. The variables of income (higher), health coverage satisfaction, having supplementary health insurance, a higher educational level and Internet use increase the likelihood of preventive service use for all the models (except for GP use). Being married is also associated with increased likelihood in most of the models and place of residence (living in a small town, for example) is found to be a protective factor for preventive visits to specialist, flu vaccination and mammograms. The most prominent of the big five inventory variables was conscientiousness as related to the increased likelihood of use of mammogram, eye exam and dentist services. Our models explain between 10.5% and 35.8% of the variability in the use of these services.\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eMultilevel Logistic Regression Models.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eIndividual characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSpecialist visit\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFlu vaccination\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCancer Screening\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMammogram\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEye exam\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDentist visits\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSex\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFemale (ref. male)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.079\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.038\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.031\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.924*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.259***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.405***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAge\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e60\u0026ndash;69 (ref. 50\u0026ndash;59)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.061\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.973\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.733***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.094*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.027\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.034\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.014\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e70\u0026ndash;79\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.081\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.120*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.114***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.750***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.333***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.381***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.908*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e80+\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.092\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.859*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.929***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.363***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.121***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.400***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.777***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMarital status\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMarried (ref. Divorced/separated)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.016\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.248***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.568***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.121*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.184**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.092*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.253***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNever married\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.804*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.931\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.148*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.924\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.928\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.814**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.924\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWidowed\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.105\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.100*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.374***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.944\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.932\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.046\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.982\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEducation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSecondary (ref. Primary or less)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.861*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.122**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.978\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.163**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.207**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.222***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.432***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTertiary\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.773**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.382***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.310***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.268***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.398***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.477***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.074***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEmployment status\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWorking (ref. not actively working)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.012\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.843***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.639***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.968\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.169**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.937\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.077\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBorn in country\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo (ref. yes)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.037\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.997\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.888*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.879*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.791**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.944\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.968\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTrust\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(0\u0026ndash;10)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.987\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.016**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.005\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.017*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.007\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.996\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.010*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePraying\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSometimes (ref. never)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.999\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.100**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.954\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.034\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.024\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.012\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHealth coverage\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSatisfied/Very Satisfied (ref. dissatisfied/very dissatisfied)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.148*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.918*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.318***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.932\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.022\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.886**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.008\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIncome\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eT2 - middle (ref. T1 - poorest)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.043\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.171***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.166***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.122**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.237***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.048\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.073*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eT3 - richest\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.260***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.367***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.161***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.242***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.105**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.196***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSupplementary health insurance\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes (ref. no)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.058\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.168***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.194***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.272***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.244***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.332***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.477***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSocial support\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes (ref. no)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.982\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.290***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.038\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.054\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.905*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.157***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.013\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSocial network in 5km\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes (ref. no)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.927\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.121**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.149**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.138**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.116*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.078*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.091*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eResidence situation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eA large town (ref. big city)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.015\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.801***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.807***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.930\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.826***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.986\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.026\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eA rural area or village\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.193**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.730***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.717***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.940\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.889*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.825***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.931*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eA small town\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.055\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.795***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.818***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.945\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.807***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.812***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.924*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSubjective health state\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGood (ref. Excellent\\Very good)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.415***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.152***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.115**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.055\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.034\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.094*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.935*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFair\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.502***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.604***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.315***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.157**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.017\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.186***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.882**\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePoor\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.657***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.729***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.270***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.358***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.987\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.105\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.702***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eADL limitations\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLimited, but not severely (ref. severely limited)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.987\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.970\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.100*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.061\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.982\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.185***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.207***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo limited\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.825*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.689***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.100*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.047\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.016\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.151**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.254***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLong-term illness\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes (ref. no)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.969\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.745***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.473***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.255***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.229***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.384***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.150***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eInternet use\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes (ref. no)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.963\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.570***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.143***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.386***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.607***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.535***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.031***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBig five inventory\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eExtraversion; (0\u0026ndash;5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.051*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.058***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.010\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.040*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.053*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.032*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.019\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAgreeableness; (0\u0026ndash;5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.995\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.991\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.984\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.980\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.969\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.996\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.955*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eConscientiousness; (0\u0026ndash;5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.997\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.022\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.980\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.050**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.115***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.062***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.084***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNeuroticism; (0\u0026ndash;5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.065*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.081***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.018\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.031*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.005\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.039**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.021\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOpenness; (0\u0026ndash;5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.995\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.018\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.047**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.012\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.011\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.043**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.023\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eContextual characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eHealth expenditure in all Financing Schemes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.924\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.058\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.236**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.269***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.161***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.131***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.416***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003ePercent of Health Expenditure in Voluntary Schemes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.005\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.977\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.977\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.981\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.963*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.973*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.939***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003ePercent of Health Expenditure in Out-of-pocket\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.007\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.057\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.909\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.881*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.959\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.041\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.921*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eDental Care Health insurance coverage (% of total expenditure dedicate to dental care coverage)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.009\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.065**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.897**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.971\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.026\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.044**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.984\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eNurses (number of personnel per 100,000 inhabitants)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.048\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.891**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.026\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.916*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.898***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.967\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.939*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e conditional\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0517\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.1550\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.2404\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.1587\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.2958\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.1438\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.3352\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e marginal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.1058\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.1952\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.3284\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.2061\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.3202\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.1651\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.3585\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLog-likelihood\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-7017.97\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-17379.52\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-15778.17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-14603.62\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-10111.10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-18275.63\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-15852.26\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eObservations\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e29201\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18114\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e29201\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"9\"\u003eCoefficients represent Odds Ratio (OR). For contextual characteristics ORs were transformed in OR\u003csub\u003e0.1\u003c/sub\u003e = (OR\u003csub\u003eunit\u003c/sub\u003e)\u003csup\u003e0.1\u003c/sup\u003e this gives us the probability of change for an increment of 10% respect the mean value; GP: General Practitioner; ADL: Activities of Daily Living; ***: p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; * p\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eFinally, Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e analyses the contextual variables from the previous models. When interpreting the results, it is important to take into account that a contextual variable not being statistically significant does not indicate its importance in a greater or lesser likelihood of using the preventive services under study. Rather, it shows whether there are differences between the value for each country and the mean value of that variable across all the countries analysed leading to an increase or decrease in service utilisation (since the values are mean-centred). We observe that countries having above-the-mean GDP increase the likelihood of their citizens engaging in cancer screening, eye exams, and dentist visits. Additionally, citizens of countries with a higher-than-the-mean percentage of healthcare coverage in outpatient primary and specialist care are more likely to use eye exam and dentist visit services. Meanwhile, in countries where the percentage of individuals reporting financial reasons for unmet healthcare needs is above the mean, the likelihood of specialist visits decreases.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab4\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eMultilevel logistic regression model with one contextual variable\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth colspan=\"7\" align=\"left\"\u003e\n\u003cp\u003eDependent variables\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eContextual variable\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSpecialist visit\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFlu vaccination\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCancer Screening\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMammogram\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEye exam\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDentist visits\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModel 1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGross Domestic Product\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.917\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.99\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.203\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.247**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.084\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.194***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.492***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eHealth Care Expenditure\u003c/strong\u003e:\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModel 2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGovernment schemes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.985\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.974*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.059**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.014\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.008\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.022\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.053*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModel 3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSocial health insurance schemes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.048**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.958\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.018\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.031\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.018\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModel 4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eVoluntary health care payment schemes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.986\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.012\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.994\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.036\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.011\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.005\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.008\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModel 5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHousehold out-of-pocket payment\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.968\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.011\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.119\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.997\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.054\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.096\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eHealth Care Coverage\u003c/strong\u003e:\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModel 6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePharmaceuticals\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.049\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.088\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.154\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.089\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.111\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.071\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.106\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModel 7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDental care\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.007\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.031\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.944\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.008\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.025\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.042*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.026\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModel 8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOutpatient primary and specialist care\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.867\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.006\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.988\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.404**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.225*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.321***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.755***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModel 9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eInpatient care\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.963\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.993\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.869\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.218\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.074\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.35*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.516\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eHealth Graduates\u003c/strong\u003e:\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModel 10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePhysicians\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.081\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.881\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.031\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.119\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.96\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.981\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.033\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModel 11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMidwives\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.985\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.978\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.968\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.935*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.974\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.991\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.975\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModel 12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNurses\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.955\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.054\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.991\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.978\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.053\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.089\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModel 13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDentists\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.005\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.971\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.893\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.943\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.952\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.985\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.93\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModel 14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePharmacists\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.031\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.972\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.988\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.995\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.939\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eInfrastructures\u003c/strong\u003e:\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModel 15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBeds\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.961\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.919**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.17***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.039\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.009\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.005\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.097\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModel 16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eComputed tomography scanners\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.004\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.924\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.952\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.973\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.955\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.914\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModel 17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMagnetic resonance imaging units\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.979\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.949\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.962\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.953*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.971\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.941\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eSelf-report unmet need for health care\u003c/strong\u003e:\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModel 18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFinancial reasons\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.038\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.876***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.047\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.965\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.947\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.008\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.007\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModel 19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDistance or transportation to facility\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.064*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.009\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.008\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.997\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.013\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.992\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.975\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModel 20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWaiting list\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.027\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.923\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.955\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.057\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.988\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.063\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.089\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"9\"\u003eThe rest of individual variables were included in all models; Coefficients represent Odds Ratio (OR) transformed in OR\u003csub\u003e0.1\u003c/sub\u003e = (OR\u003csub\u003eunit\u003c/sub\u003e)\u003csup\u003e0.1\u003c/sup\u003e this gives us the probability of change for an increment of 10% respect the mean value; GP: General Practitioner; ***: p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; * p\u0026thinsp;\u0026lt;\u0026thinsp;0.05;\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study draws on one of the most widely used and accepted theoretical models of health service utilisation, Andersen\u0026rsquo;s Behavioural Model (ABM), in order to analyse over-50-year-olds\u0026rsquo; use of primary preventive health services (PHS) in Europe (Babitsch et al., 2012; Ricketts \u0026amp; Goldsmith, 2005b). We use the most recent data from the Survey on Health Ageing and Retirement (SHARE), including subjects from 15 countries, considering information on both individual variables and country-level (contextual) variables obtained from various sources (Bank Group, EuroStat Statistics and OECD Statistics). Moreover, we incorporate several novel variables (personality traits, internet use, trust and praying) that have not hitherto been evaluated under ABM.\u003c/p\u003e\u003cp\u003eWith regard to the effect of the individual variables of the utilisation of PHS, the main results of our work suggest that the use of technology \u0026ndash; specifically, Internet use as a proxy for the level of an individual\u0026rsquo;s technological knowledge \u0026ndash; is associated with increased use of preventive services. Two studies have examined this association with similar findings. One uses data from an earlier wave of the SHARE related to preventive services in cancer screenings, mammograms, cholesterol tests and flu vaccination (Nam et al., 2019a). The other study focuses only on cancer screening (Xavier et al., 2013). The present study extends knowledge of the field by including preventive use of consultations with specialists, eye exams and dentist visits.\u003c/p\u003e\u003cp\u003eIn addition, the study by Nam et al., (2019b) also evidences that having a partner also fosters the PHS use, particularly as regards the influence of women on men. Our study finds that married individuals are more likely to use PHS, which helps determine the population groups that should be the focus of actions for prevention (individuals that are divorced/separated, widow and never married). Nonetheless, widowed persons also seem more likely to be vaccinated against flu.\u003c/p\u003e\u003cp\u003eOur study reveals the existence of a problem in usage of preventive health services (PHS) as the size of an individual\u0026rsquo;s place of residence (from big cities to rural areas or villages). A study conducted in the United States more than 20 years ago already evidenced the disadvantaged situation of rural areas in terms of preventive service use (Casey et al., 2001). A more recent study in the same country also reports shortcomings in rural areas regarding preventive check-ups and dental treatment utilisation, which, in the long term, lead to higher treatment costs (Lee et al., 2021). Although the literature on this topic is not extensive, other research confirms that this issue remains unresolved in countries such as Australia, Spain, and China (Colman, 2020; De La Cruz-S\u0026aacute;nchez \u0026amp; Aguirre-G\u0026oacute;mez, 2014; Liu et al., 2016; Xu et al., 2019).\u003c/p\u003e\u003cp\u003eIn contrast, a study conducted in Japan suggests that the higher the population density, the lower is the correlation with stomach cancer screening, with such correlations being non-significant for colorectal cancer screening and flu vaccination (Hatano et al., 2013). A possible explanation for these findings is that the coefficients may be biased in compensation for the omission of important variables. The study justifies the association found by suggesting that social support (social capital) is stronger in smaller communities, which may positively influence health prevention behaviours. Despite the limited number, some successful interventions have sought to mitigate this issue (Nagykaldi et al., 2020) by creating the role of a so-called \u003cem\u003eCommunity-Based Wellness Coordinator\u003c/em\u003e, tasked with monitoring patients by integrating information from all the actors involved and from electronic medical records.\u003c/p\u003e\u003cp\u003eHaving supplementary health insurance (SHI) is found to be a robust predictor of PHS utilisation. Practically all European countries finance health coverage and function satisfactorily, so SHI plays a limited role that is highly dependent on public and compulsory health spending in each country (Purcel et al., 2023), although it does cover gaps such as faster access to treatment or greater choice of medical care (Sagan \u0026amp; Thomson, 2016). Given that other variables (e.g. income, health status) are adjusted for, it is possible that people in Europe with SHI are the most healthcare conscious. Additionally, preventive services can be offered by SHI, such that it is an out-of-pocket expense and thus not using it (having it simply as insurance) may make individuals feel they are wasting their money. (Ricketts \u0026amp; Goldsmith, 2005a). A study supports the tendency to make greater use of healthcare services in individuals with a health insurance contract (Anderson et al., 2012).\u003c/p\u003e\u003cp\u003eFinally, the individual variables of social support and having a social network within 5km have a great impact on PHS utilisation. These variables, and particularly social support, have been a subject of study since the mid-1970s in pursuit of guiding preventive interventions and treatment (Lakey \u0026amp; Lutz, 1996). We find that greater social support drives more preventive health behaviours. Research in this field is now focused on more specific problems, such as youth suicide and spinal cord injury (Le Fort et al., 2021; Standley \u0026amp; Foster-Fishman, 2021).\u003c/p\u003e\u003cp\u003eWith respect to the contextual (country-level) variables, our findings suggest that, despite cross-country differences, their impact on PHS use is not great. In pursuit of greater equality between EU Member States, those with lower mean scores on these variables might focus on increasing their per capita GDP and their health care coverage for primary outpatient and specialist care, as well as looking at reducing levels of self-reported unmet needs for health care.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe present study makes an analysis of PHS using Andersen\u0026rsquo;s Behavioural Model, including a large number of both individual-level and contextual variables, succeeding in explaining as much as 35.8% of the variability in service usage. Future research should strive to enhance these results by seeking other individual or contextual characteristics that might affect the use of these services, as well as improving the quantity and quality of the country-level variables.\u003c/p\u003e\u003cp\u003eOur findings advance the understanding of the barriers to and facilitators of engagement in prevention, enabling the design of strategies that promote equity in access. It would also be interesting to consider preventive intervention on digital literacy, given that the link between internet use and preventive health behaviours is important because such behaviours are associated not only with the health of the older population but also with substantial reductions in health care expenditures.\u003c/p\u003e\u003cp\u003eEqual access to PHS has long been regarded as a priority by the World Health Organisation. However, our data evidence that little progress has been made toward this goal among older adults in Europe. Bolstering prevention strategies leads to higher quality of life and helps reduce healthcare costs in the medium and long term.\u003c/p\u003e\u003cp\u003eAlso of great importance is integrating and homogenising services related to prevention (primary care, specialised care, social services, etc.), as well as encouraging community participation in developing a preventive system that is currently either non-existent or fragmented. We must acknowledge that while autonomy in management allows for a closer approach to the problem and potentially better solutions, a high degree of fragmentation without coordination makes efficient resource management impossible to manage resources efficiently. It is crucial to harmonise the databases for healthcare resource and population management such that the information required for decision making can flow from municipalities to regions and, from there, to countries and finally to the EU as the ultimate guarantor of social well-being.\u003c/p\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eLimitations\u003c/h2\u003e\u003cp\u003eOne of the main limitations of the present work is that not all the EU countries included in the SHARE survey could be evaluated together due to the lack of data on many necessary contextual variables. Here, it is worth highlighting the difficulty of obtaining contextual variables for a large set of European countries, given the differences between healthcare systems and the lack of a unified and standardised European database where such characteristics can be found, including, for example, aspects of healthcare centralisation or decentralisation, payment mechanisms, patient pathways, health care quality measurements and types of preventive programmes (opportunistic or organised, screening, coverages etc.). There are also variables in some categories in ABM that we were unable to test, such as structure and quality of services provided, distances to closest health facility, medicine availability and infrastructure. A future challenge for European institutions is to build a database with standardised information on the above-mentioned aspects, as well as other potentially key factors (at the country or regional level), such as cultural and traditional characteristics, ethnic diversity, and religion.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003eEthics approval and consent to participate:\u003c/em\u003e Not applicable. The data used comes from the anonymised and public accessible SHARE (https://share-eric.eu/) database as well as other public sources in the case of macro variables.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCompeting interests:\u003c/em\u003e The authors declare that they have no conflict of interest\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAuthor\u0026rsquo;s contributions:\u003c/em\u003e PM, RM conceived the idea for the study and wrote the initial draft of the paper, EM and IP collected the data and performed the analyses. All authors contributed to the debate, critical writing and final revision of the manuscript. All authors approve the final version of the paper.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTransparency statement:\u003c/em\u003e The corresponding author on behalf of the rest of the authors guarantees precision, transparency and honesty of the data and the information which is contained in the study; that non-relevant information has been omitted; and that all discrepancies between authors have been adequately solved and described. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCompeting interest:\u0026nbsp;\u003c/em\u003eNothing to declare.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConsent to Participate declaration\u003c/em\u003e: This study uses secondary data from the SHARE project. All participants in the SHARE survey provided informed consent at the time of data collection by the original researchers.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eEthics declaration and norm or standard:\u0026nbsp;\u003c/em\u003eThe SHARE project was conducted in accordance with the Declaration of Helsinki and received ethics approvals in each participating country. For more details, see http://www.share-project.org.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eName of the Approval Committee / IRB:\u0026nbsp;\u003c/em\u003eThe original data collection for SHARE received ethics approval from the Ethics Council of the Max Planck Society and other relevant national ethics committees.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFunding declaration:\u0026nbsp;\u003c/em\u003eThis work was supported by the project SBPLY/21/180501/000066 of the Regional Government of Castilla-La Mancha and ERDF and Project 2023- GRIN-34431-Funding of Research Group \u0026ldquo;Econom\u0026iacute;a, Alimentaci\u0026oacute;n y Sociedad\u0026rdquo; of Universidad de Castilla-La Mancha.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eHuman Ethics and Consent to Participate declarations:\u0026nbsp;\u003c/em\u003eHuman Ethics and Consent to Participate declarations: not applicable. This study relies exclusively on anonymized secondary data provided by the SHARE project.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAbdulRaheem, Y. (2023). Unveiling the Significance and Challenges of Integrating Prevention Levels in Healthcare Practice. In \u003cem\u003eJournal of Primary Care and Community Health\u003c/em\u003e (Vol. 14). SAGE Publications Inc. https://doi.org/10.1177/21501319231186500\u003c/li\u003e\n \u003cli\u003eAndersen, R. M. (2008). \u003cem\u003eNational Health Surveys and the Behavioral Model of Health Services Use\u003c/em\u003e.\u003c/li\u003e\n \u003cli\u003eAndersen, R. M., \u0026amp; Davidson, P. L. (2007). \u003cem\u003eIndividual and Contextual Indicators Improving Access. In: Andersen RM, Rice TH, Kominski GF, eds. Changing the US Health Care System, 3rd edn. 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J., d\u0026rsquo;Orsi, E., Wardle, J., Demakakos, P., Smith, S. G., \u0026amp; Von Wagner, C. (2013). Internet use and cancer-preventive behaviors in older adults: Findings from a longitudinal cohort study. \u003cem\u003eCancer Epidemiology Biomarkers and Prevention\u003c/em\u003e, \u003cem\u003e22\u003c/em\u003e(11), 2066\u0026ndash;2074. https://doi.org/10.1158/1055-9965.EPI-13-0542\u003c/li\u003e\n \u003cli\u003eXu, Y., Zhang, T., \u0026amp; Wang, D. (2019). Changes in inequality in utilization of preventive care services: Evidence on China\u0026rsquo;s 2009 and 2015 health system reform. \u003cem\u003eInternational Journal for Equity in Health\u003c/em\u003e, \u003cem\u003e18\u003c/em\u003e(1). https://doi.org/10.1186/s12939-019-1078-z\u003c/li\u003e\n \u003cli\u003eZhang, S., Chen, Q., \u0026amp; Zhang, B. (2019). Understanding healthcare utilization in China through the andersen behavioral model: Review of evidence from the China health and nutrition survey. \u003cem\u003eRisk Management and Healthcare Policy\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e, 209\u0026ndash;224. https://doi.org/10.2147/RMHP.S218661\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":"health prevention, Andersen’s Behavioural Model, aging","lastPublishedDoi":"10.21203/rs.3.rs-6628048/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6628048/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study examines the factors influencing the use of preventive health services (PHS) among individuals over 50 living in their own homes, in light of rising healthcare expenditures. Drawing on Andersen\u0026rsquo;s Behavioural Model (ABM), the analysis incorporates individual-level variables (such as personality traits, Internet use, trust, and praying) and contextual country-level data. The study uses data from the SHARE survey and international sources (World Bank, Eurostat, OECD), covering 29,201 individuals across 15 European countries.\u003c/p\u003e\u003cp\u003eInitial analyses employed multiple logistic regression models by country, followed by multilevel logistic regression models with individual factors at level one and country-level factors at level two. Results show that women are more likely than men to use preventive services such as eye exams and dental visits. Higher income, better satisfaction with health coverage, supplementary insurance, higher education, and Internet use increase the likelihood of using preventive services (except for general practitioner visits). Being married is also positively associated with service use, while living in smaller towns is linked to greater preventive use of specialists, flu vaccinations, and mammograms.\u003c/p\u003e\u003cp\u003eAmong personality traits, conscientiousness is the most strongly associated with increased use of mammograms, eye exams, and dental visits. The models explain between 10.5% and 35.8% of the variation in service use.\u003c/p\u003e\u003cp\u003eAlthough the World Health Organization prioritizes access to PHS, the findings suggest limited progress. Strengthening prevention strategies not only improves quality of life but also helps reduce healthcare costs over time.\u003c/p\u003e","manuscriptTitle":"Beyond Healthcare Spending: Inequalities in the Use of Preventive Health Services among Europeans aged over 50","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-04 18:39:24","doi":"10.21203/rs.3.rs-6628048/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":"04f8ea30-c7f6-4e6c-a405-87f9827e4c98","owner":[],"postedDate":"September 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-12T03:38:33+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-04 18:39:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6628048","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6628048","identity":"rs-6628048","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}