Factors affecting the travel mode choice of the urban elderly in healthcare activity: comparison between core area and suburban area

Sustainable Cities and Society 52 (2020) 101868

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Factors affecting the travel mode choice of the urban elderly in healthcare activity: comparison between core area and suburban area

T

Mingyang Dua, Lin Chenga, , Xuefeng Lia, Jingzong Yangb ⁎

a b

School of Transportation, Southeast University, Nanjing, Jiangsu 211189, China School of Information, Baoshan University, Baoshan, Yunnan 678000, China

ARTICLE INFO

ABSTRACT

Keywords: Urban elderly Healthcare travel characteristic Core area Suburb Mode choice Influential factors

This study investigated the travel characteristics and influential factors of travel mode choice for healthcare activity by the elderly in core area and suburb. The results of descriptive analysis and multinomial logistic models show that: (1) Bus and walking are main modes for the elderly to seek medical treatment. The elderly in suburb travel longer distance and are more dependent on bus than those in core area. (2) The service efficiency improvements of hospitals could promote the elderly in core area to choose green modes, while those in suburb are more likely to utilize cars to obtain high-quality medical resources. (3) For long-distance travel, the influence of family economic backgrounds on the mode choice is more significant in suburb, and the difference of family economic will further aggravate the internal differentiation in long-distance travel among the elderly in suburb. (4) Interestingly, the elderly in core area tend to utilize cars to seek for healthcare when they live with their offspring, while those in suburb tend to use cars and taxis when they have serious illness and require companion. Finally, relevant policies and suggestions were proposed to improve the accessibility and fairness of healthcare travel for the elderly.

1. Introduction With the acceleration of aging process, as of 2018, there were more than 249 million elderly people above 60 years old in China, accounting for 17.9% of the total population (National Bureau of Statistics, 2019). It is estimated that this number will reach 480 million by 2050 (Feng & Yang, 2015; Wen, Albert, & Von Haaren, 2018). Due to the change of physiological structure, the travel demands of the elderly are gradually transformed from the demands associated with the livelihood to those associated with personal and family life, spirit and psychology. Among these, the most important one is the increasing travel demand for medical activity due to physical function decline. At the same time, with the rapid spread of urbanization in China, the dualistic phenomenon between city center and suburb is obvious, and the activity space for the elderly to seek for medical care expands continuously (Gao, Han, Wang, & Zhang, 2017). Driven by the dual backgrounds of aging and urbanization, the travel of the elderly in healthcare activity faces following problems: 1) Medical facilities with good quality are mostly concentrated in core area and attract numerous patients, it invisibly leads to the relatively long travel distance for the elderly in suburb, which will affect the mode choice in different locations and may even result in social exclusion of vulnerable groups (McGrail & Humphreys, ⁎

2009; Tao & Shen, 2018). 2) The physical ability decline will inhibit the elderly to use travel modes which require high physical strength (Feng & Yang, 2015; Schwanen, Dijst, & Dieleman, 2001), which results in the poor accessibility of healthcare travel for the elderly in some areas, and the difficulty in seeking for medical treatment still exists (Cheng & Lian, 2018). 3) Limited by their activity abilities and the condition of illness, some elderly people need to be accompanied and escorted by their family members in the process of seeking medical treatment, which directly affects the distribution of family cars and the activities of other members (Schwanen et al., 2001). Under this background, the healthcare travel behavior of the elderly is not just about the individual behavior but the behavior outcome interacting with the household, transportation, geographical location and medical facilities (Feng, 2016). Therefore, the deep discussion about the travel behavior of the elderly and the travel decision-making mechanism for healthcare activities in different urban locations is particularly significant for this vulnerable population. It can not only help determine what measures can meet their activity demands, but also improve the accessibility of healthcare travel for the elderly and promote the healthy development of the city (Schmocker, Quddus, Noland, & Bell, 2008; Syed, Gerber, & Sharp, 2013). The objective of this study is to explore the travel behavior and

Corresponding author at: School of Transportation, Southeast University, Nanjing, Jiangsu 211189, China. E-mail addresses: [email protected] (M. Du), [email protected] (L. Cheng), [email protected] (X. Li), [email protected] (J. Yang).

https://doi.org/10.1016/j.scs.2019.101868 Received 19 March 2019; Received in revised form 27 September 2019; Accepted 28 September 2019 Available online 30 September 2019 2210-6707/ © 2019 Elsevier Ltd. All rights reserved.

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influential factors of travel mode choice of the elderly for healthcare activity in urban core area and suburb. The contributions are mainly reflected in three aspects. Firstly, the travel characteristics of the elderly for healthcare activity in these two areas are compared and analyzed, which could effectively reflect the demands and obstacles of healthcare travel. Secondly, multinomial logistic (MNL) models are established to investigate the similarities and differences of the factors affecting the healthcare travel mode choice of the elderly in core area and suburb, with considering some special variables, such as activity ability, the severity of illness, accompanied by family members and living with their offspring, so as to make the analysis of medical behavior choice more comprehensive. Finally, through the case study of the elderly in Fuzhou, relevant intervention strategies and suggestions are proposed for improving healthcare accessibility and promoting the fairness of healthcare travel.

elderly in northwest area of the city had a poor accessibility to highlevel hospitals. The result was consistent with the conclusion of Tao and Shen (2018)’s study in Shanghai. Besides, Jr and Hoenig (2010) found that the disabled elderly who had difficulty in walking used fewer office visits and more home health visits compared with the counterpart. Awoke et al. (2017) examined the factors related with the utilization of public and private healthcare service. The results showed that older age, higher education level and higher wealth were associated with the utilization of private outpatient services, while the possession of health insurance positively influenced the use of public health facilities. 2.2. Equity in healthcare facilities utilization among the elderly Equitable access to and utilization of healthcare facilities is a core objective for healthcare systems in China, and it is also regarded as one of the important symbols for the urban life quality (Cheng et al., 2016). In the background of population aging, it is particularly important to promote the healthy development of the population by improving the equity of healthcare service utilization (Fu et al., 2018; Zhang, Cao, Liu, & Huang, 2016). For example, Crespo-Cebada and Urbanos-Garrido (2012) examined the factors affecting the inequalities in general practitioner (GP) services utilization by the elderly in Spanish. The results revealed that the presence of pro-poor inequality in both the access and the frequency of use for GP services, which was mainly due to the unequal distribution of need factors, such as ill-health indicators. The contribution of non-need factors to income related inequality was larger for the number of GP visits than for the probability of positive use. Based on the survey of China health and retirement longitudinal study, Fu et al. (2018) adopted concentration index and horizontal inequity to measure the inequity of health services utilization. The results concluded that the disparity of household consumption expenditure was the greatest inequality factor favoring the better-off. Urban Employee Basic Medical Insurance made a pro-wealth contribution to inequality in the use frequency of healthcare services. While New Rural Cooperative Medical Scheme made limited contributions on reducing unfairness in inpatient use. In addition, Noronha and Andrade (2005) studied the inequality in health for outpatient services and hospital admissions. The results suggested that more schooling was related to a higher number of outpatient medical visits in Santiago and Mexico city, individuals with more schooling were more inclined to be hospitalized in São Paulo. The cities in the countries with high income inequality and low human development index tended to have the greatest inequalities in healthcare services utilization. The conclusion is consistent with the results of Channon, Andrade, Noronha, Leone, and Dilip (2012)). Besides, Terraneo (2015) investigated the magnitude of educational inequities in healthcare services usage by the elderly in 12 European countries. The results identified a clear pro-educated gradient is found for specialists and dentist visits, whereas no educational disparity was found for GP use. Individuals with higher educational level have more relational, communicative and cognitive resources that allow them to take more effective actions for their health, however, the institutional context might change this relationship.

2. Literature review According to the existing retrieved literatures, few attempts have been done on the influential factors of healthcare travel behavior for the elderly, therefore the review mainly focuses on the impact of medical facilities on travel decisions, the influential factors of the utilization of medical service, the equity in healthcare facilities utilization among the elderly and relevant methods, which could provide the basis for this study. 2.1. Influential factors of the elderly’s medical activity As the foundation of people’s life safety and health, hospitals can provide essential resources and services for people’s survivals and developments (Cheng et al., 2016). Their spatial distribution and service quality will have a certain impact on the healthcare travel decisions and the utilization of medical service for the elderly. In terms of the impact of medical facilities on travel decisions, Adams and Wright (1991) reported that rural elderly persons were more inclined to travel longer distances to urban hospitals when their nearest rural hospital is small. Similarly, Tai, Porell, and Adams (2004) demonstrated that patients reporting a lack of a medical home tended to travel further distances to seek for healthcare service. In addition, based on the temporal geography, Li, Zhang, and Du (2018) found that with the increase of the travel duration for elderly’s medical activity, they tended to use taxi and car rather than walking and public transportation. For weak constraint medical activity, the shorter the healthcare duration for consultation, the more likely the elderly selected walking. Besides, based on the national household travel survey across US, Probst, Laditka, Wang, and Johnson (2006) identified that nearly all residents sought for healthcare by a personal vehicle, only 5.46% of travelers used public transportation and walking. The rural residents spent more time in travel for medical care than those in urban communities. Besides, compared with whites, African American was more likely to experience a high travel time burden and the use of public transportation was independently associated with it, which suggested that transportation might be a contributor to health disparities. In the case of influential factors of medical services utilization, increased travel distance between residents and providers is thought to decrease the medical facilities utilization (Bronstein & Morrisey, 1990). And this effect is larger for the elderly with reduced access to transportation, and those living in sparsely populated areas (Cheng et al., 2016). Then, Nemet and Bailey (2000) further examined the relationship between travel distance and the healthcare facilities utilization, and discovered that the variation in utilization rates seemed more closely related with experiencing activity places than the distance. In addition, based on the study of bus trip impedance in Xi’an, China, Zhang, Li, and Shi (2016) found that the traffic accessibility of highlevel hospitals significantly decreased from city center to suburb, the

2.3. Research methods review In terms of the research on the equity in healthcare facilities utilization among the elderly, scholars mainly applied mathematical statistics methods to carry out the study based on survey data. For example, Crespo-Cebada and Urbanos-Garrido (2012); Noronha and Andrade (2005) and Channon et al. (2012) established zero-truncated negative binomial models and negative binomial hurdle models to analyze the inequalities healthcare services use. Fu et al. (2018) applied the method of concentration index decomposition to explore influential factors of the unfairness for health services utilization. In addition, Terraneo (2015) utilized a fixed effects approach to present the magnitude of educational inequities in healthcare services use. 2

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For the research regarding the influential factors of the elderly’s medical activity, the methods were more diverse. For example, Zhang, Li et al. (2016) evaluated the traffic accessibility of urban high-level hospitals for the elderly in Xi’an, China, based on the improved potential model of bus impedance. Awoke et al. (2017) used multinomial logistic regression to investigate the variables associated with public and private healthcare service use. Health system responsiveness was compared using chi-square tests. In addition, based on the temporal geography, the concept of healthcare constraint degree was proposed by Li et al. (2018), and it was utilized to classify the medical activities of the elderly. Besides, Schuurman, Fiedler, Grzybowski, and Grund (2006)) applied a vector-based GIS network analysis to model catchments that better represented the access to healthcare facilities in rural areas based on travel-time. As is evident from the above review, recent researches regarding the healthcare activity for the elderly mainly focus on the influential factors of medical facilities utilization, the researches regarding the travel decisions of medical activities for the elderly mainly consider the impacts of the number or the level of medical facilities, and the distance from medical facilities (Adams & Wright, 1991; Li et al., 2018; Tai et al., 2004). These studies may be more reasonable if they had considered the elderly’s special individual properties such as activity ability, the severity of illness, household attributes such as accompanied by family members and living with their offspring, and the attributes of medical institutions such as short time for healthcare registration and diagnosis, good medical environment and other factors, which have a certain impact on the elderly’s mode choice for the medical travel. In addition, affected by the layout of medical facilities, good-quality medical resources are mostly concentrated in core area, which results in the difference in the medical travel of the elderly in urban core and suburban areas (Zhang, Li et al., 2016; Probst et al., 2006). In this case, exploring the influential factors of this difference in these two areas can not only make up for the lack of existing research, but also is significant for promoting the fairness of healthcare travel for the elderly.

During the survey, the investigators would explain relevant questions and terms to the respondents to help them better understand the questions and options, and then record the answers of the elderly. Because some elderly people were limited by their own physical functions, such as poor eyesight and hearing, some of the surveys were completed by their family members according to the actual situation of their healthcare travel. In particular, in order to study the influence of geographical location on the travel modes choice, this study divided municipal districts in Fuzhou into two categories: core area (the city center and the most urbanized area) and suburban area (the peripheral suburb). Additionally, compared with other travel purposes, there are some differences in medical travel for the elderly. For example, the condition of illness will reduce their activity abilities and some elderly people need to be accompanied by their family members in the process of seeking medical treatment; due to the need of regular prescription, examination or medical treatment, medical travel of the elderly has a cyclical characteristic (Schwanen et al., 2001). Besides, medical institutions attributes also have a certain impact on medical travel (Adams & Wright, 1991; Tai et al., 2004). These particular factors were considered in the survey. More specifically, in the process of the survey, the elderly answered four parts contents as for the current or recent medical travel experiences: (1) Individual attributes include gender, age, education level, income, occupation before retired, healthcare frequency, activity ability and the severity of illness. Among them, as for activity ability, the elderly answered questions from four aspects: the maximum tolerable walking time without load, the maximum waiting time for the bus, the difficulty of getting on and off the bus, standing on the bus, the difficulty of getting on and off taxi and car. The walking and waiting time were continuous variables, and the rest were evaluated from five levels: very laborious, a little laborious, not laborious basically, quite relaxed, and very relaxed. Then, the factor value of each mode normalized, particularly, each factor of bus normalized firstly, summed together and normalized again into one value. Finally, the four values of travel modes were weighted sum according to the proportion of travel modes in suburban or core area in the survey, and the values were normalized again to determine the classification. For the other variables, each question presented specific options for respondents to choose. (2) Household attributes include income, ownership of cars, bicycles or electric bicycles, and whether or not living with offspring. (3) Healthcare travel information includes travel distance, mode, purpose, origin, travel time, whether or not accompanied by family members, the convenience of bus and walking. Among them, travel duration and travel distance were continuous variables in the original survey. Travel modes include walk, bicycle, bus, subway, car, taxi and others. The other question presented specific options for respondents to select. (4) Medical institutions attributes include short time for healthcare registration and diagnosis, high popularity, cheap medical cost, good service attitude and medical environment. And five levels (strongly agree, relatively agree, not sure, relatively disagree, strongly disagree) were offered for respondents to choose.

3. Data 3.1. City context Fuzhou, located in Fujian province, is the political, cultural and transportation center in the southeast of China. As one of the central cities of the economic zone on west side of strait, as of 2017, the permanent resident population was 7.66 million and the GDP reached 708.55 billion. There were 1.02 million private cars, 90 bus lines and 1 subway line (Fuzhou Statistics Bureau, 2018). According to the population development planning of Fujian Province, by 2030, the proportion of the elderly population aged 65 and over will reach 16% (Fujian Provincial People’s Government, 2018), the number of the elderly aged over 80 will continue to increase, and the ageing is getting more and more serious. In addition, the total number of medical facilities and health institutions in the city was 3959, with 2.75 doctors per thousand people. However, the distribution of medical resources is not balanced and most of the high-quality medical resources are mainly concentrated in core area (Fuzhou Municipal People’s Government, 2018). These factors are of importance to understand the healthcare travel behavior of the elderly in different urban locations. Therefore, Fuzhou was selected as a case city for analysis.

A total of 1358 samples were sent out, while a small number of respondents did not provide privacy information such as age, income and severity of illness. After excluding these missing information samples and doubtful samples, 1179 valid surveys were recovered and the recovery rate was 86.82%. Finally, a Chi-square test was further utilized to determine whether there was a difference in the percentage distribution of the survey samples and the population (the percentage distribution of the population is shown in Table 1). The result reveals an insignificant difference

3.2. Data source The face-to-face survey was conducted in Fuzhou, China, as shown in Fig. 1, by undergraduate students of Fuzhou University. The period of survey lasted from October 16th to 30th, 2017 and the locations of investigation were selected in residential area, hospitals, university for the aged, parks and so on. In order to ensure the survey quality, relevant trainings for the investigators were conducted before the survey. 3

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Fig. 1. Survey area.

account for more than half. In addition, the proportion of the elderly with undergraduate degree or above (includes junior college degree) in core area is 14.3%, which is higher than that in suburb. Similarly, income, number of cars and the proportion of living with their offspring in core area were also higher than those in counterpart. Then, the chi-square test of each variable in core and suburban areas was carried out. The results show that there are significant differences in education level, income, car ownership and household structure between these two areas (P < 0.05). If the healthcare travel of the urban elderly is related to individual and family attributes, it might lead to the differences of healthcare behavior in different locations.

Table 1 Basic attributes. Items Description Gender

Male Female

Age

> 80 71∼80 60∼70

Education level Personal monthly income (CNY) Household monthly income (CNY) Ownership of cars Living with their offspring 1

≥Undergraduate High school ≤Middle school > 4000 2000∼4000 ≤2000 > 8000 ≤8000 Yes No Yes No

Core area (%)

Suburban area (%)

P-value

47.9 (48.32)1 52.1 (51.68) 14.1 (12.76) 34.7 (31.84) 51.2 (55.4) 14.3 41.5 44.2 12.4 54.2 33.4 22.0 78.0 38.8 61.2 41.3 58.7

48.9

.624

51.1 15.8

.532

33.6 50.6 9.0 35.2 55.8 8.1 51.9 40.0 10.2 89.8 29.7 70.3 30.0 70.0

4. Characteristics analysis

.000

This section mainly compares the healthcare travel characteristics of urban elderly in core and suburban area, as shown in Tables 2 and 3. As for the distribution of distance, the healthcare travel within 3 km in core area accounts for 66.2%, which is 14% higher than that in suburb. In contrast, the travel over 5 km in suburb accounts for 29.6%, which is nearly twice as much as that in counterpart. This indicates that healthcare travel distance of the elderly in core area is relatively shorter, and the distribution of medical facilities in this area is more concentrated. In addition, the distance above 10 km still account for

.000 .000 .000 .000

Real data of Fuzhou from National Statistics Bureau in brackets.

Table 2 Mode choice in different distances in core area.

(for gender, p = 0.929; for age, p = 0.111). This demonstrates that the samples reasonably well represent the population. 3.3. Basic attributes The information of basic attributes is shown in Table 1. As shown in the table, the sample in core area and suburb were 482 (40.88%) and 697 (59.12%), respectively. In terms of gender and age, the proportions of men and women are relatively balanced, and the group of 60∼70 4

Distance (km)

Walk

Bicycle

Bus

Taxi

Car

Total

[0,1) [1,2) [2,3) [3,5) [5,10) > = 10 Total

71.0 50.4 33.1 10.7 2.9 0.0 34.2

7.2 3.9 0.0 0.0 0.0 0.0 2.1

17.4 37.2 48.8 60.7 67.1 77.8 46.5

1.4 3.9 7.4 11.9 8.6 0.0 6.4

2.9 4.7 10.7 16.7 21.4 22.2 10.8

14.3 26.8 25.1 17.4 14.5 1.9 100.0

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The model has following advantages: low technical threshold, easy implementation, simplicity, robustness and generality, in addition, the model is characterized by low sample requirements, mature technology and low error rate. The use of MNL can make the problems more detailed and the exploration of multiple explanatory variables and factor levels are more reasonable (Wang, Wang, Zhu, & Song, 2015). Based on the above reasons, MNL model was utilized in this study to uncover the influential factors affecting the modes choice for healthcare activity by the elderly. MNL model is a discrete choice model based on random utility theory. The utility function includes a fixed and a random term, the utility of a senior person, n, choosing travel mode, i, is given as:

Table 3 Mode choice in different distances in suburban area. Distance (km)

Walk

Bicycle

Bus

Taxi

Car

Total

[0,1) [1,2) [2,3) [3,5) [5,10) > = 10 Total

58.3 42.3 24.7 7.3 0.0 0.0 19.8

3.3 0.0 2.2 0.0 0.0 0.0 0.9

35.0 46.8 55.9 67.2 72.1 74.3 59.8

0.0 3.6 4.3 7.3 4.7 1.4 4.2

3.3 7.2 12.9 18.2 23.3 24.3 15.4

8.7 16.2 27.1 19.9 18.8 10.8 100.0

10.8% in suburb, compared to only 1.9% in core area. This implies that a certain proportion of the elderly in suburb will travel a longer distance, possibly in order to get better medical resources, which are generally distributed in core area. In terms of travel modes choice, bus and walking are main modes for the elderly to seek medical treatment. The main reason is that Chinese government has preferential policies for the elderly to take bus, and it also provides them with convenient facilities, such as priority seats, which increases the trust in bus for this population. This is quite different from the elderly people in most of developed countries where the senior people mainly rely on private cars. Perhaps the era of largescale motorization in China began in 2008, most of the elderly did not use cars before they retired, consequently, they still retained their previous travel habits (Feng & Yang, 2015). By comparison, bicycles and electric bicycles are rarely utilized by the elderly due to high operational skills, physical demands and low safety factors (Schwanen et al., 2001). More specifically, when the travel distance is less than 2 km, walking is the primary mode and the proportion in core area is generally higher than that in suburb. The main reason is that walking and medical facilities in core area are more friendly and perfect compared with those in counterpart, which improves the healthcare travel accessibility. When the distance is more than 2 km, the proportion of walking reduces, and bus gradually becomes the preferred mode. And, the elderly in suburb (59.8%) are more dependent on bus than those in core area (46.5%). It suggests that under the circumstance that public transport facilities in suburb are relatively limited in China recently (Tao & Shen, 2018), it is possible to increase the differentiation of healthcare travel among the elderly in different urban locations. Interestingly, the proportion of taxis in core area is higher than that in suburb, perhaps taxi resources are more concentrated in core area. Finally, when the distance is greater than 5 km, the proportion of car is gradually increasing although bus still dominates, the possible explanation is that the high mobility of cars can weaken the space-time constraints of healthcare travel. If the use of car is related to personal income and family car ownership, it is probably that some low-income families in suburb may be excluded from better medical facilities due to limited transportation resources. Through above analysis, we can conclude that the travel distance and travel mode of healthcare activities by the elderly are different in urban and suburban areas. The reasons for this will be explored in the following MNL models.

Uin = Vin + Vin =

1 1 Xin

(1)

in

+

2 2 Xin

+

+ k Xink

(2)

where in is random term, its introduction is due to that unobserved taste variations, unobserved attributes, measurement errors are not deterministic. Vin is related to individual and household attributes, travel information and medical facilities attributes. It is assumed that Xink is a linear function of above variables, and k is the parameter estimated by the maximum likelihood method. Vin is independent of in , and in obey the assumption of a Gumel distribution, the probability of a senior person, n, selecting travel mode, i, is given as (Emenike et al., 2017):

Pin = eVin

eVjn j Cn

(3)

where Cn is the set of travel modes including walk, taxi, car and bus. For specific solution process, firstly, the healthcare travel mode choice of the urban elderly has different influential factors (independent variables). These variables need to be calibrated before establishing MNL models, the result of calibration and notes are shown in Table 4. Then, in MNL models, walk, taxi, car and bus are dependent variables, and the choice of bus is set as the reference category. As for all independent variables in Table 4, the items with a calibration value 0 are set as the reference items. All the independent variables are selected with an introducing probability of 0.05 and rejecting probability of 0.1. Finally, the accuracy of established MNL models can be determined by Cox and Snell, Nagelkerke, McFadden and Chi-square value (McBain & Caulfield, 2018). The calculation process of the model is shown in Fig. 2. 5.2. Results and discussion The results of significance tests for MNL models are shown in Table 5. Cox and Snell are 0.491 and 0.339 (more than 0.3), Nagelkerke are 0.553 and 0.397 (more than 0.3), and McFadden are 0.309 and 0.269 (more than 0.2), these values are all within acceptable ranges. Chi-square (−2*Log-likelihood values for the equal probability model minus the corresponding value for final MNL model) are 62.107 and 93.470, the improvement in the data fit illustrates the superiority of the established MNL models. And the significances of the final models are less than 0.05, which indicates that selected variables have significant influences on modes choice under the confidence of 95%. The model results of core area and suburb are shown in Table 5. As shown in the table, with the increase of travel time for medical treatment, the elderly in core and suburban areas will both reduce walking and adopt more bus and car. This is consistent with the analysis result in section 4. In terms of the inconvenience of bus, the coefficient of walking in core area is significantly positive, and that of car is also positive but not significant. By comparison, the utility of car in suburb is significantly positive. This suggests that buses are more likely to be replaced by cars in suburb when they are inconvenient. In contrast, the elderly in core area tend to replace bus by walking even though cars are also utilized. At the same time, when the hospital is within walking

5. Model and results 5.1. Multinomial logistic model MNL model is a type of modeling process which applies multiple equations to regress k categories of a dependent variable to multiple independent variables, estimating k-1 logistic equations (McBain & Caulfield, 2018). It can deal with the categories and continuous variables at the same time and it is usually utilized in researches of mode choices with regard to the travel behavior (Du & Cheng, 2018; Li & Zhao, 2015; Li, Zhang, Du, & Yang, 2019; Liu, Ji, Liu, He, & Ma, 2017). 5

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Table 4 Variable definitions and calibration. Variable

Definitions and notes

Travel attribute Travel distance (km) Travel mode Travel purpose Travel origin Travel time (min) Accompanied by family members Convenience of bus Convenience of walking Attributes of Medical Institutions Short time for healthcare registration and diagnosis High popularity Cheap cost Good medical service Good medical environment Individual attributes Gender Age Education level Personal income (CNY/month) Occupation before retired Healthcare frequency (times/month) Activity ability Severity of illness Household attributes Household income(CNY/month) Ownership of cars Ownership of bicycles Ownership of electric bicycles Living with their offspring

>5=1≤5=0 Walk = 1 Taxi = 2 Car = 3 Bus = 0, because the proportion of the subway travel is very small, therefore it was merged with bus group. Treatment = 1 Prescribe medicine in regular = 2 Physical examination = 0 Suburban area = 1 Core area = 0 Continuous variable. Yes = 1 No = 0 Strongly disagree = 1 Relatively disagree = 2 Not sure = 3 Relatively agree = 4 Strongly agree = 0 Strongly agree = 1 Relatively agree = 2 Not sure = 3 Relatively disagree = 4 Strongly disagree = 0 Strongly Strongly Strongly Strongly Strongly

agree = 1 agree = 1 agree = 1 agree = 1 agree = 1

Relatively Relatively Relatively Relatively Relatively

agree = 2 agree = 2 agree = 2 agree = 2 agree = 2

Not Not Not Not Not

sure = 3 sure = 3 sure = 3 sure = 3 sure = 3

Relatively Relatively Relatively Relatively Relatively

disagree = 4 disagree = 4 disagree = 4 disagree = 4 disagree = 4

Strongly Strongly Strongly Strongly Strongly

disagree = 0 disagree = 0 disagree = 0 disagree = 0 disagree = 0

Male = 1 Female = 0 > 80 = 1 71∼80 = 2 60∼70 = 0 ≥Undergraduate = 1 High school = 2 ≤ Middle school = 3 > 4000 = 1 2000∼4000 = 2 ≤ 2000 = 0 Worker = 1 Employee = 2 Personnel of public institutions = 3 Officer = 4 Self-employed = 0 > 2 = 1 1∼2 = 2 ≤ 1 = 0 Strong(> 0.6) = 1 Medium (0.3∼0.6) = 2 Weak(≤0.3) = 0 Serious = 1 Not serious = 0 > 8000 = 1 ≤ 8000 = 0 Yes = 1 No = 0 Yes = 1 No = 0 Yes = 1 No = 0 Yes = 1 No = 0

distance, most elderly people in core area will walk instead of using cars. One explanation is that: in Fuzhou, the distribution of medical service facilities in suburb is more dispersed, in order to reduce spatial exclusion, the elderly persons tend to use cars to decrease poor accessibility. Another explanation may be found in previous research that in suburb, the infrastructure, such as walking facilities, lag behind than that in core area (Tao & Shen, 2018). Consequently, when planning and building a new hospital in suburb, the site selection and the accessibility of surrounding transport should be comprehensively considered. In addition, when the purpose of healthcare travel is to prescribe medicine in regular, the elderly in suburb are more likely to use cars. For the medical institutions attributes, the elderly in core area pay more attention to the efficiency of medical service, while those in suburb focus on the popularity of hospitals. In particularly, in core area, the shorter time for healthcare registration and diagnosis tends to significantly increase the likelihood of the elderly selecting green travel modes, such as walking and bus, and reduce the use of cars. However, in suburb, the elderly usually use cars rather than walking to highquality hospitals. It implies that the elderly in suburb need more mobile and flexible cars to seek better medical resources. The reason may be the result of the following two aspects: in Fuzhou, high-quality medical resources, especially high level hospitals, are mainly concentrated in core area (Fuzhou Municipal People’s Government, 2018). The accessibility of medical facilities is gradually decreasing from city center to urban fringe (Tao & Shen, 2018). In addition, in order to explore the influence of different household incomes on travel mode choice under long-distance healthcare travel, this study interacts household monthly income and travel distance, and the results are significant in suburb but not in core area. This may be due to the fact that the proportion of long-distance travel in suburb is significantly higher than that in core area, as discussed in section 4. Then, for the long-distance travel in suburb, the elderly with high family-income tend to choose cars while those with low income tend to choose buses. This indicates that in suburb, the high-income families

Fig. 2. Solution process of MNL model.

6

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Table 5 Model results in core area and suburb. Variable

Core area Walk

Suburb Taxi

Car

Travel attribute Travel time (min) −1.556*** −0.418 −0.021 Convenience of bus = Strongly disagree 1.609*** .275** 0.659 Convenience of bus = Relatively disagree .875** 0.162 0.264 Convenience of walking = Strongly agree 2.784** −5.052 −2.658*** Convenience of walking = Relatively agree 1.659* −7.629 −1.492** Travel purpose = Prescribe medicine Attributes of Medical Institutions Short time for healthcare = Strongly agree 3.609** −3.851 −1.403** Short time for healthcare = Relatively agree 1.795* −2.815 −.995** High popularity = Strongly agree High popularity = Relatively agree Household attributes HI (> 8000 CNY)* distance (> 5 km) HI (≤8000 CNY)* distance (> 5 km) HI (> 8000 CNY)* distance (≤5 km) HI (≤8000 CNY)* distance (≤5 km) Ownership of cars = Yes −0.089 −1.326 .875*** Living with their offspring = Yes 0.288 1.386 1.792** Individual attributes Age (> 80) Age (70∼80) Education level (≥Undergraduate) Education level (High school) Personal income (> 4000 CNY) −8.461 3.787 6.044*** Personal income (2000∼4000 CNY) −6.152 1.712 3.360*** Activity ability = Strong .093** −1.991 −1.771 Activity ability = Medium .006** −1.056 −1.463 Illness (Serious) * Companion (Yes) −0.799 0.32 6.106 Illness (Serious) * Companion (No) −1.204 −0.493 −0.522 Illness (Not serious) * Companion (Yes) 0.816 0.512 4.224 Illness (Not serious) * Companion (No) – – – Constant 3.305 14.9 −13.319 Core area: Cox and Snell = 0.491, Nagelkerke = 0.553, McFadden = 0.309, Chi-square = 62.107. Suburban area: Cox and Snell = 0.339, Nagelkerke = 0.397, McFadden = 0.269, Chi-square = 93.470.

Walk

Taxi

Car

−2.173*** 0.626 0.487

−.516** .315** 0.076

−0.006 1.771*** 1.156***

−2.303***

−8.704

3.566***

−3.827** −2.725**

−0.424 −1.424

7.869*** 6.801***

−7.479 −4.79 −1.412* – −0.296 −1.079

−0.206 −0.644 .134* – −2.707** −0.217

.399** −.181** 1.359 – 1.051*** 0.291

−2.858*** −1.827*** .455*** .126*

−0.031 −0.174 −0.885 −0.198

.843* 0.493 −.921* −1.269*

−.781*** −0.197 −4.22 −7.263 0.453 – 26.535

−0.178 −0.268 2.094*** −1.857 1.783 – 12.81

−0.321 −0.523 3.268*** −1.708** 2.272** – −18.309

HI represents household income, * significance of 10%, ** significance of 5%, ***significance of 1%.

have higher requirements on the quality of medical care and service than those with low-income, and the difference of family economy background also cause the internal differentiation of long-distance healthcare travel for the elderly in suburb. Besides, regardless of the core and suburban areas, the elderly who own cars in their families tend to choose cars rather than other travel modes (Bocker, Van Amen, & Helbich, 2017). For the variable of living with their offspring, the coefficient of car in core area is significantly positive, and that is weak positive in suburb. This shows that the family structure affects the travel behavior of the elderly (Paez, Scott, Potoglou, Kanaroglou, & Newbold, 2006), when the elderly live with their offspring, the children take more responsibility to escort the elderly in healthcare activity, and it “bundle” them to a certain extent. In addition, the elderly in core area prefer cars than those in suburb. This is mainly because the medical facilities in core area are relatively centralized and the activity space of the offspring is relatively fixed, they can take the elderly to seek medical care by the way in the daily commuting process. However, in suburb, the relatively scattered medical facilities, coupled with the long daily commuting distance of residents, limit the possibility of taking the elderly to seek medical treatment along the way. For individual attributes, in core area, the impact of personal monthly income is significant. The higher the income, the more inclined to use cars. This is similar to the results of Kim and Ulfarsson (2004). By comparison, in suburb, age and education level have a significant impact, and with the increase of age, the proportion of walking decreases while the use of car increases significantly. Perhaps as they grow older, their ability constraints make them more likely to be

accompanied by their families by cars (74% of 81∼90 elderly people are accompanied by family members, while the proportion is 61% in the group of 60∼70) (Schwanen et al., 2001). And the higher the education level, the more using of walking and bus and the less using of cars. This is consistent with Schwanen’s findings, the higher the education level, the stronger the awareness of environmental protection and the willingness to green travel (Schwanen et al., 2001). As for individual activity ability variable, there are different phenomena in core area and suburb. In core area, the senior people are more likely to walk when their activity ability are strong. By comparison, the elderly in suburb utilize bus more frequently. It is understandable that walking facilities in core area are relatively more perfect than those in suburb. Besides, the elderly with weaker activity ability tend to use cars and taxis rather than buses, the main reason is that these elderly cannot stand for a long time waiting for the bus and getting on and off the bus is inconvenient, while cars and taxis could provide door-to-door services and reduce the physical consumption of the elderly people in healthcare travel. Finally, because the elderly often need to be accompanied when they are seriously ill, therefore, the severity of illness and whether accompanied by family members variables are interacted in this study. The results show that the interactive variable is not significant in core area, while in suburb, the elderly are more likely to use cars and taxis when they are seriously ill and need companion. It is possible that when the elderly in suburb are seriously ill, they usually need to travel a relatively long distance to seek for better medical services. Cars and taxis can just compensate for the inadequate accessibility of the healthcare travel (Szeto, Yang, Wong, Li, & Wong, 2017). 7

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5.3. Policy implications and suggestions

the accessibility evaluation and site selection of the medical facilities also need to consider the accessibility and reliability of public transit facilities and the convenience of walking facilities around existing medical resources.

In order to improve the accessibility and satisfaction of healthcare travel for the elderly, relevant policies and suggestions are proposed from perspectives of the hospital and government. From a hospital perspective, three aspects including site selection, appointment system, and service efficiency and quality can be considered. (1) At present, in China, medical facility planning still regards “service radius” and “indicator of per thousand persons” as main criterions (Sun, Lv, & Zhao, 2015). The results in this study show that the elderly in suburb is more dependent on bus; for the long-distance healthcare travel in suburb, the elderly with high family-income tend to choose car while those with low family-income tend to use bus. Consequently, in order to improve the accessibility and fairness of healthcare travel for the elderly in suburb, especially for those who have poor economic background and have to travel long distances in order to obtain better medical resources. The newly planned or constructed suburban hospitals should be built in area with high accessibility to public transport, not only considering the space fairness, but also considering the coordination of the hospital location and public transport facilities, so as to increase the convenience of healthcare travel in suburb. (2) Then, as shown by the model, the elderly who live with their offspring are more inclined to use cars and this phenomenon is more obvious in core area. Therefore, the hospital can attempt to open medical appointment service, so that the time to make medical appointment for the elderly could try to overlap the time to arrange the activities for family members, which could minimize the conflict of family activities, and realize car sharing. (3) As shown in the model, the improvement of service efficiency of hospitals in core area is conducive to increasing the probability of green travel for the elderly to seek healthcare service. Additionally, since the core area in Fuzhou has more high-quality medical resources, a certain percentage of the elderly in suburb often seek healthcare service in core area with the help of cars. Consequently, as for other cities with similar contexts, the hospitals in core area and suburb can focus on different aspects: the hospitals in suburb should pay attention to the improvement of service quality, and those in core area should improve their service efficiency. With respect to the government, (1) the elderly in suburb are more dependent on bus for healthcare travel, while the elderly with weak activity ability use public transport less. Thus, the suburban areas should not only focus on increasing the construction of new bus lines, but also pay more attention to improving the bus travel environment and services, such as improving the waiting room environment (increasing courtesy seats for the elderly to waiting for bus), barrier-free facilities for up and down buses, which may increase the use of public transport for the elderly with weak activity ability. (2) Then, as the model results show that in suburb, when the elderly is seriously illness and need to be accompanied, they tend to use cars for healthcare travel. For this reason, the government and hospitals can open a “companion” service car with low costs in suburb. For the elderly with low-income who cannot afford to buy a private car and need to be accompanied, drivers could go to the elderly’s home and escort them to the hospital, and then send them back after healthcare treatment. This could not only alleviate the difficulty of healthcare travel of the elderly in suburb, but also could weaken the influence of different family-income background on the difference of healthcare travel of the elderly to a certain extent. (3) Besides, in terms of the accessibility evaluation and site selection of the medical facilities, the factors such as distance from arterial streets, travel time area to access existing medical resources, land cost, contamination, and population density are important (Schuurman et al., 2006; Vahidnia, Alesheikh, & Alimohammadi, 2009). The results of this study show that bus and walking are the main modes for the elderly to seek medical treatment. In view of the increasingly serious aging phenomenon in Chinese cities and the physical decline of the elderly, their dependences on medical facilities and services will be higher (Zhang, Li et al., 2016), in this particular context,

6. Conclusions This study explored the influential factors of travel modes choice of healthcare activities by urban elderly in different locations. Firstly, based on 1179 healthcare travel samples of the elderly in Fuzhou, China, the differences of healthcare travel characteristics between core area and suburb were compared and analyzed. The statistical results show that: the elderly in suburb travel longer distance and are more depend on buses than those in core area. Bus and walking are primary modes for the elderly to seek for medical service and bicycles and electric bicycles are utilized less because of the physical effort limitation. Then, MNL models were utilized to investigate the factors affecting the travel modes choice for healthcare activity, and the results show that: (1) the inconvenience of bus promotes the elderly to utilize cars in suburb, while those in core area tend to replace bus by walking even though the car is also utilized. (2) In core area, the increase of medical service efficiency tends to significantly increase the likelihood of the elderly choosing green travel modes, such as walking and bus. While in suburb, the elderly tend to use cars to seek better medical resources. (3) For long-distance (> 5 km) healthcare travel, the influence of household income on the travel mode choice is significant in suburb. And the difference of family economy background may also cause the internal differentiation of long-distance healthcare travel for the elderly in suburb. (4) In core area, the senior people prefer walking when their activity abilities are strong, compared to those in suburb utilize bus more. And the elderly with weaker activity ability show a tendency to use cars and taxis. (5) The elderly in core area are more inclined to utilize cars when they live with their offspring, by comparison, the elderly in suburb tend to use cars and taxis when they are seriously ill and need to be accompanied by family members. Finally, several policies and suggestions were proposed from perspectives of the hospital and government to improve the accessibility and fairness of healthcare travel for the elderly. Finally, further studies are still necessary. First of all, different types of diseases may also affect the mode choice of healthcare travel for the elderly. It may be considered to add disease types to the influential factors for further discussion. Then, this research is based on multinomial logistic regression model, how to improve and optimize this model could be further explored. Finally, our work discusses the differences of the healthcare travel for the elderly between urban core area and suburb, and the discussion on the differences between urban and rural area is also an interesting and meaningful topic. Declaration of Competing Interest The authors declare no conflict of interest. Acknowledgments This work was supported by National Natural Science Foundation of China (No. 51578150). We are grateful for valuable improvement suggestions from the editor and anonymous reviewers. References Adams, E. K., & Wright, G. E. (1991). Hospital choice of Medicare beneficiaries in a rural market: why not the closest? The Journal of Rural Health, 7(2), 134–152. https://doi. org/10.1080/16549716.2017.1301723. Awoke, M. A., Negin, J., Moller, J., Farell, P., Yawson, A. E., Biritwum, R. B., ... Kowal, P. (2017). Predictors of public and private healthcare utilization and associated health system responsiveness among older adults in Ghana. Global Health Action, 10, 1301723. https://doi.org/10.1080/16549716.2017.1301723.

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