Receiving or not Deemed Necessary Healthcare Services

doi:10.21203/rs.3.rs-1873885/v1

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Research Article

Receiving or not Deemed Necessary Healthcare Services

https://doi.org/10.21203/rs.3.rs-1873885/v1

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Journal Publication

published 31 Jan, 2023

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Background

Avoiding deemed necessary healthcare needs may worsen prognosis and treatment options, and damage people’s ability to perform their roles in society. Our study investigates why people avoid healthcare services in an upper-middle-income country, Turkey.

Methods

We apply TurkStat’s 2012 Health Survey Data that includes a comprehensive health and social-demographic information of 28,055 survey participants who were 15 + aged. We prefer to use bivariate probit model to analyze the avoiding behaviour in inpatient level in accordance with outpatient level because of the observed significant correlation between people’s avoiding behaviour under tertiary and lower level of health care.

Results

According to our descriptive analysis, we see that 2.6% of 15 + aged population were avoiding deemed necessary hospital services. Furthermore, it is found that high cost (31%), organizational factors (21%) and fear (12%) are prominent reasons of avoiding tertiary care. Thereafter, in our bivariate probit model findings, we figure out that being covered by social security schemes decreases the probability of avoiding both outpatient and inpatient health services by 6.9%. Moreover, being female, living in rural area, having lower income increase the chance of being avoider in both stages of healthcare.

Conclusions

We conclude that social inequalities were main underlying determinants of the avoiding behavior. As health and effective provision of health care are vital for the smooth functioning of society, we suggest that improve health care protection of people from disadvantaged social groups and develop better organizational factors to prevent difficulty of having treatment at policlinics.

Healthcare Avoidance

Tertiary Care

Characteristic and Reasons of Avoiders

Bivariate Probit Model

Turkey

Medical care avoidance is such a paradox that actually people could save their lives or ameliorate suffering. People all over the world avoid health care services for some reasons and this may lead to worsen prognosis and treatment options. Diagnosis the diseases lately require more radical treatment and can lead to irreversible conclusion or even death. In addition to negative effects on health and treatment process, it has financial burden on health system and household budget. When treatments are carried out at late stage of the illness, expenses for the healing becomes higher to compared to the earlier detection due to requirements to more radical treatments, longer operation and hospital times.

While timely intervention is so important, why do people avoid medical care? Though this phenomenon was discussed under psychology of decision making under risky situations [1], it has socio-economic determinants [2, 3] and social consequences as bad human health related to worse functioning society: “Society wants its members to be healthy, and the unattainability of this desire shows a discrepancy between social value and reality. Such a discrepancy represents a social problem.” [4].

Kannan and Veazie [5] explored the reason of avoidance for the USA by using the Health Information National Trends Survey 2007. They found that approximately one third of adults were avoider even they deemed necessary and the main reason elicited by them are preference for self-care or alternative care, dislike or distrust of doctors, fear or dislike of medical treatments, time, and money; respondents also endorsed discomfort with body examinations, fear of having a serious illness, and thoughts of dying. Taber, Leyva, and Persoskie [6] used the data collected as part of Health Information National Trends Survey 2008 and they reached three main categories of reasons with their research. The first reason was negative evaluations of seeking medical care, such as factors related doctors, healthcare facilities, and emotional concerns. The second reason was low perceived need to seek medical care, as they often expect their illness or symptoms to improve over time and third reason was traditional barriers to medical care, such as high cost, no health insurance, and time constraints. Araujo, Silva, Galvao and Pereira [2] analyzed adults in the Manaus metropolitan region in Brazil and found that people belonging to disadvantaged socioeconomic groups were more likely to avoid visiting dentist than visiting doctor and so that additional policies are need to improve equality in health care services. In addition, there are numerous studies in literature investigated avoiding of seeking medical care behavior and identified factors as to gender [7, 8, 9], living area [10, 11] or age [12]. There are also some studies searched avoiding factors for chronic diseases [13] such as heart disease [8, 14–18], cancer [19], stroke [17], breast cancer [20–22], HIV [23, 24], tuberculosis [25, 26], Parkinson [27], mental health [28–30], osteoarthritis [31].

Avoidance in seeking medical care may happen in different stages of health care provision [32]. Our study explores the determinants of avoiding deemed necessary healthcare services in hospitalization stage examining an upper middle income country case. In Turkey, there have been health system reforms called “Health Transformation Programme” (HTP) since 2003. Overall, these reforms improve the accessibility and mitigate inequalities as universal health insurance was introduced and social security intuition’s (SSI) coverage of services have been expanded remarkably [33]. Indeed, since the noticeable progress in the health outcomes and the health system, the HTP was pointed out as a successful good practice example by WHO [34]. Though access to medical care services is necessary but not sufficient, people should not avoid or delay to access these services to save their health. Thus, our study aims to contribute our understanding of peoples’ avoiding behavior considering the Turkey case. As similar to other developing countries experiences [35, 36] the referral system does not work efficiently in Turkey, and indeed we observe significant correlation between the avoiding behaviour in accessing outpatient level and hospitalization level services, thus we prefer to apply bivariate probit model to analyze the avoiding behaviour in inpatient level in accordance with outpatient level usage of services.

Our study is organized as follows: Firstly, we introduced our data, which is the Turkish Statistical Institute (TurkStat)’s health survey data 2012, and compared the avoiders versus non-avoiders examining the characteristics of participants who received or not health care service even though treatment recommended by doctor, either as an inpatient or a day patient (less than 24 hours) in the last 12 months. Next, the reasons of avoiding behavior in tertiary care are briefly introduced. Thereafter the findings from the bivariate probit regression models are presented. In line with the findings, assessment and policy recommendations are stated in the discussion section.

Data Source

In this study Health Survey, conducted by TurkStat for the first time in 2012, has been used as a data source. The 2012 Health Survey was conducted with 14,400 households with 84.44% response rate. The rural-urban stratification was done considering 3,744 households from rural and 10,656 households from urban (with 83.78% and 86.32% response rates respectively). Totally 37,979 individuals’ information have been collected, 28,055 of them were aged at least 15 years old, 15+. Health Survey 2012’s study design is given in detail in TurkStat [37].

In our study, first we compare the characteristics of avoiders versus non-avoiders of healthcare at hospital utilization stage. Thus, the answers for the question coded S074000000, in Part 10 of Part B of the survey, considering hospital services, asking participants whether she/he has not received health care service even though treatment recommended by doctor, either as an inpatient or a day patient in the last 12 months have been analyzed.

Descriptive statistics

Table 1 presents a comparison of the characteristics of avoiders and non-avoiders such as demographic features, social status and health conditions. In addition to characteristics of avoiders and non-avoiders, ${\chi }^{2}$ test was applied for categorical variables and t-test was applied for continuous variables. First of all, 730 individuals constituting 2.6% of 15 + aged population were avoider and 27244 individuals constituting 97.11% of 15 + aged population were non-avoider.[1] It is seen that avoiders more likely to be female, elderly, living rural area, married, low educated, have green card - public scheme for poor- and have lower income. Furthermore, working ones and retired ones are less likely to avoid hospitalization stage, but avoiders are more likely to be occupied with house duties. Moreover, avoiders health conditions were worse compared to non-avoiders.

Table 1

Avoiders versus non avoiders: Characteristics
	Avoider (n = 730)	Non-Avoider (n = 27244)	${{\chi }}^{2}$value, p value
	n (%)	n (%)
Gender			${\chi }^{2}$(1) = 52.61, p < 0.001
Male	240(32.88%)	12651 (46.44%)
Female	490 (67.12%)	14593 (53.56%)
Age			${\chi }^{2}$ (6) = 63.67, p < 0.001
15–24	71 (9.73%)	5036 (18.48%)	${\chi }^{2}$(1) = 36.548, p < 0.001
25–34	119 (16.3%)	5471 (20.08%)	${\chi }^{2}$ (1) = 6.353, p = 0.012
35–44	158 (21.64%)	5377 (19.74%)	${\chi }^{2}$ (1) = 1.629, p = 0.202
45–54	153 (20.96%)	4760 (17.47%)	${\chi }^{2}$ (1) = 5.971, p = 0.015
55–64	98 (13.42%)	3353 (12.31%)	${\chi }^{2}$ (1) = 0.821, p = 0.365
65–74	79 (10.82%)	2029 (7.45%)	${\chi }^{2}$ (1) = 11.618,p = 0.001
75+	52 (7.12%)	1218 (4.47%)	${\chi }^{2}$ (1) = 11.543,p = 0.001
Location			${\chi }^{2}$ (1) = 31.127,p < 0.001
Urban	472 (64.66%)	20127 (73.88%)
Rural	258 (35.34%)	7117 (26.12%)
Marital status			${\chi }^{2}$ (3) = 83.298,p < 0.001
Single	81 (11.1%)	6307 (23.15%)	${\chi }^{2}$ (1) = 58.625,p < 0.001
Married	540 (73.97%)	18624 (68.36%)	${\chi }^{2}$ (1) = 10.38, p = 0.001
Widow	84 (11.51%)	1718 (6.31%)	${\chi }^{2}$ (1) = 31.909,p < 0.001
Divorced	25 (3.42%)	595 (2.18%)	${\chi }^{2}$ (1) = 5.05, p = 0.025
Education			${\chi }^{2}$ (4) = 77.278,p < 0.001
Illiterate	135 (18.49%)	2750 (10.09%)	${\chi }^{2}$ (1) = 54.224,p < 0.001
Primary school	416 (56.99%)	14699 (53.95%)	${\chi }^{2}$ (1) = 2.633, p = 0.105
Elementary school	37 (5.07%)	1643 (6.03%)	${\chi }^{2}$ (1) = 1.166, p = 0.280
High school	86 (11.78%)	4841 (17.77%)	${\chi }^{2}$ (1) = 17.569,p < 0.001
University and higher degree	56 (7.67%)	3311 (12.15%)	${\chi }^{2}$ (1) = 16.015 p < 0.001
Estimated household monthly income			${\chi }^{2}$ (3) = 74.162,p < 0.001
Less or equal to 750 TLǂ	242 (33.8%)	5679 (21.08%)	${\chi }^{2}$ (1) = 64.521,p < 0.001
751 TL to 1300TL	218 (30.45%)	8383 (31.12%)	${\chi }^{2}$1) = 0.275, p = 0.600
1301 TL to 2300TL	161 (22.49%)	7933 (29.45%)	${\chi }^{2}$ (1) = 17.251,p < 0.001
Greater than 2300TL	95 (13.27%)	4939 (18.34%)	${\chi }^{2}$ (1) = 12.605,p < 0.001
Working statue			${\chi }^{2}$ (5) = 96.88, p < 0.001
Works	235 (32.19%)	10188 (37.40%)	${\chi }^{2}$ (1) = 8.235, p = 0.004
Looking for a job or seasonal worker	33 (4.52%)	1028 (3.77%)	${\chi }^{2}$ (1) = 1.088, p = 0.297
Student	28 (3.84%)	2797 (10.27%)	${\chi }^{2}$ (1) = 32.385,p < 0.001
Occupied with house duties	288 (39.45%)	8682 (31.87%)	${\chi }^{2}$ (1) = 18.774,p < 0.001
Retired	61 (8.36%)	3011 (11.05%)	${\chi }^{2}$ (1) = 5.285, p = 0.022
Other	85 (11.64%)	1538 (5.65%)	${\chi }^{2}$ (1) = 46.809,p < 0.001
Heath insurance statue			${\chi }^{2}$ (3) = 79.408,p < 0.001
SSI^*	533 (73.01%)	22802 (83.7%)	${\chi }^{2}$ (1) = 58.641,p < 0.001
Green card^**	122 (16.71%)	2385 (8.75%)	${\chi }^{2}$ (1) = 55.187,p < 0.001
Private insurance	20 (2.74%)	916 (3.36%)	${\chi }^{2}$ (1) = 0.852, p = 0.356
Out of pocket	55 (7.53%)	1141 (4.19%)	${\chi }^{2}$ (1) = 19.451,p < 0.001
Perceived health			${\chi }^{2}$ (4) = 617.25,p < 0.001
Very good	24 (3.29%)	3550 (13.03%)	${\chi }^{2}$ (1) = 60.557,p < 0.001
Good	209 (28.63%)	15301 (56.17%)	${\chi }^{2}$ (1) = 218.16,p < 0.001
Fair	303 (41.51%)	6377 (23.41%)	${\chi }^{2}$ (1) = 128.13,p < 0.001
Bad	156 (21.37%)	1786 (6.56%)	${\chi }^{2}$ (1) = 241.52,p < 0.001
Very bad	38 (5.21%)	255 (0.83%)	${\chi }^{2}$ (1) = 146.42,p < 0.001
Have a health problem more than 6 months			${\chi }^{2}$ (1) = 289.08,p < 0.001
Yes	481 (65.98%)	9619 (35.33%)
No	248 (34.02%)	17608 (64.67%)
Health problem restrict daily life			${\chi }^{2}$ (2) = 624.89,p < 0.001
Seriously	257 (35.25%)	2673 (9.84%)	${\chi }^{2}$ (1) = 446.24,p < 0.001
Not seriously	237 (32.51%)	5664 (20.85%)	${\chi }^{2}$ (1) = 58.23, p < 0.001
Not restricted at all	235 (32.24%)	18.827 (69.31%)	${\chi }^{2}$(1) = 448.93,p < 0.001
Feel pain or discomfort in last four weeks			${\chi }^{2}$ (1) = 535.76,p < 0.001
Feel	402 (55.14%)	5412 (19.88%)
Not Feel	327 (44.86%)	21806 (80.12%)
Feel happy in last four weeks			${\chi }^{2}$ (4) = 277.70,p < 0.001
Always	74 (10.21%)	3718 (13.76%)	${\chi }^{2}$ (1) = 7.475, p = 0.006
Most of the times	198 (27.31%)	12146 (44.94%)	${\chi }^{2}$ (1) = 87.90, p < 0.001
Sometimes	295 (40.69%)	9214 (34.09%)	${\chi }^{2}$ (1) = 13.763,p < 0.001
Rarely	116 (16.00%)	1578 (5.84%)	${\chi }^{2}$ (1) = 127.44,p < 0.001
Never	42 (5.79%)	369 (1.37%)	${\chi }^{2}$ (1) = 95.036,p < 0.001
Number of persons to trust			${\chi }^{2}$ (1) = 20.955,p < 0.001
Less or equal to 1	197 (26.99%)	5472 (20.09%)
Greater or equal to 2	533 (73.01%)	21772 (79.91%)
Obese			${\chi }^{2}$ (1) = 21.268,p < 0.001
Yes	180(24.66%)	4901(17.99%)
No	550(75.34%)	22343(82.01%)
BMI^***	28.617 (43.92)	27.718 (34.34)	t-test = 0.693,p = 0.4886
Non-prescription medications in last two weeks			${\chi }^{2}$ (1) = 88.257,p < 0.001
Yes	158 (21.64%)	2901 (10.65%)
No	572 (78.36%)	24343 (89.35%)
Have any-chronic diseases			${\chi }^{2}$(1) = 268.61, p < 0.001
Yes	568 (80.27%)	13497(50.46%)
No	144(19.73%)	13747 (50.54%)
Hypertension			${\chi }^{2}$ (1) = 50.092,p < 0.001
Yes	182 (24.93%)	4171 (15.31%)
No	548 (75.07%)	23073 (84.69%)
Back musculoskeletal system disorders			${\chi }^{2}$ (1) = 360.38,p < 0.001
Yes	272 (37.26%)	3515 (12.9%)
No	458 (62.74%)	23729 (87.1%)
Rheumatismal joint disease			${\chi }^{2}$ (1) = 243.95,p < 0.001
Yes	197 (26.99%)	2777 (9.93%)
No	533 (73.01%)	25197 (90.07%)
Gastric ulcer			${\chi }^{2}$ (1) = 203.78,p < 0.001
Yes	165 (22.6%)	2144 (7.87%)
No	565 (77.4%)	25100 (92.13%)
Diabetes			${\chi }^{2}$ (1) = 16.74,p < 0.001
Yes	87 (11.92%)	2120 (7.78%)
No	643 (88.08%)	25124 (92.22%)
Cancer			${\chi }^{2}$1) = 6.173, p = 0.013
Yes	11 (1.51%)	194 (0.71%)
No	719 (98.5%)	27050 (99.29%)
Have mental health problems			${\chi }^{2}$ (1) = 164.75,p < 0.001
Yes	102 (13.97%)	1123(4.12%)
No	628 (86.03%)	26121 (95.882%)
ǂTL: Turkish Lira, SSI : Social Security Institute, Green Card a security scheme aim to cover the poor individuals to receive health services, ^**Body Mass Index. If BMI < 18.5 underweight, 18.5 ≤ BMI ≤ 24.9 normal,25 ≤ BMI ≤ 29.9 overweight and BMI ≥ 30 obesity.

Reasons of Avoiding

The 2012 health survey of TurkStat asks the avoiders to select one of the eight pre-identified reasons. As seen from Fig. 1, the main reason of avoiding hospitalization in Turkey is high cost. Almost one third of the participants refrained from hospitalization since they cannot afford health services because of expensive prices or nonpayment by insurance. Organizational factors follow the high costs. 21% of the participants pointed out that because of difficulties in achieving treatment at polyclinics and other reasons related to health organization they avoid hospitalization. Fear of medical treatment or surgery appears as the third important reason as 12% of respondents indicated it as the major cause of avoiding. Consequently, all these eight reasons together explain 85% of the avoiding behavior; the remaining participants (15%) marked other reasons option.

Cross Distribution of Avoiding Behaviour under Outpatient and Inpatient Care

Since the avoidance may occur at different stages of healthcare provision, we would like to see in which stage of utilization people are more likely to avoid. Our study finds that 2.6% of the 15 + aged participants were avoider in the hospitalization stage, while Sulku and Tokatlioglu [38] study using the same survey indicated that 12.6% of the 15 + aged participants were avoider for the primary healthcare services and/or outpatient services. We may say that since individuals use hospital services for more serious illness, 10%-point decrease in avoidance in hospitalization stage is not surprising.

Furthermore, we investigated the cross distribution of avoiders and non-avoiders under primary and/or outpatient levels versus hospitalization level services. Behavior of avoiders for the primary healthcare services and/or outpatient services (in short be called as first services) and hospitalization is investigated more detailed in Table 2. Among all participants, 480 individuals were avoiders under both level of services and 24126 individuals were non-avoiders under both level of services. Thus, we see that in total 88.23% (=(480 + 24126)/27889) of participants did not change their attitude of avoiding or not avoiding under different stages of health utilization.

Table 2

Crosstab of primary/outpatient services and hospitalization
		Primary healthcare services and/or outpatient services
		Avoiders	Non-Avoiders	Total
Hospitalization	Avoiders	480	249	729
Hospitalization	Non-Avoiders	3034	24126	27160
	Total	3514	24375	27889

[1] Since missing observations have very small share (0.29%), we simply ignore them.

Because of the observed relationship between the avoiding/non-avoiding behaviour in accessing outpatient level and hospitalization level services, the bivariate probit model is employed to examine the avoiding behaviour in inpatient level in accordance with outpatient level usage of services. Now, we define people’s decision of avoiding or not outpatient care and inpatient care as the two dependent variables in the bivariate probit model and present them respectively as ${y}_{1}$ and ${y}_{2}$ as given below:

$${y}_{1}=\left\{\begin{array}{c}1,\hspace{0.25em}\hspace{0.25em}\hspace{0.25em}\hspace{0.25em}\text{ }\text{a}\text{v}\text{o}\text{i}\text{d}\text{,}\text{ }\text{o}\text{u}\text{t}\text{p}\text{a}\text{t}\text{i}\text{e}\text{n}\text{t}\text{ }\\ 0,\hspace{0.25em}\hspace{0.25em}\hspace{0.25em}\hspace{0.25em}not avoid, outpatient \end{array}{y}_{2}=\left\{\begin{array}{c}1,\hspace{0.25em}\hspace{0.25em}\hspace{0.25em}\hspace{0.25em}\text{ }\text{a}\text{v}\text{o}\text{i}\text{d}\text{,}\text{ }\text{i}\text{n}\text{p}\text{a}\text{t}\text{i}\text{e}\text{n}\text{t}\text{ }\\ 0,\hspace{0.25em}\hspace{0.25em}\hspace{0.25em}\hspace{0.25em}\text{ }\text{n}\text{o}\text{t}\text{ }\text{a}\text{v}\text{o}\text{i}\text{d}\text{,}\text{ }\text{i}\text{n}\text{p}\text{a}\text{t}\text{i}\text{e}\text{n}\text{t}\text{ }\end{array}\right.\right.$$

here the first dependent variable, ${y}_{1}$, takes 1 if the individual avoids medical care deemed necessary in outpatient level, and the second dependent variable, 𝑦₂, takes 1 if the individual avoids necessary medical care in inpatient level.

A natural extension of the probit model, bivariate probit model would be to allow more than one equation considering the correlation between error terms, as the seemingly unrelated regressions model. The general structure of a two-equation bivariate probit model is as follows [39]:

$$\begin{array}{c}{y}_{1}^{*}={x}_{1}^{{\prime }}{\beta }_{1}+{\epsilon }_{1}, {y}_{1}=1\text{ }\text{ }\text{ }\text{ }\text{i}\text{f}\text{ }{y}_{1}^{*}>0, 0\text{ }\text{o}\text{t}\text{h}\text{e}\text{r}\text{w}\text{i}\text{s}\text{e}\text{ }\\ {y}_{2}^{*}={x}_{2}^{{\prime }}{\beta }_{2}+{\epsilon }_{2}, {y}_{2}=1\text{ }\text{ }\text{ }\text{ }\text{i}\text{f}\text{ }{y}_{2}^{*}>0 , 0\text{ }\text{o}\text{t}\text{h}\text{e}\text{r}\text{w}\text{i}\text{s}\text{e}\text{ }\end{array}$$

here ${y}_{1}^{\text{*}}$ and ${y}_{2}^{\text{*}}$ are latent variables if they are positive then we can observe respectively 𝑦₁ & 𝑦₂, and ${x}_{1},{x}_{2}$ are explanatory variables, and ${\epsilon }_{1}$&${\epsilon }_{2}$ are error terms such that: $E\left[{\epsilon }_{1}\mid {x}_{1},{x}_{2}\right]=E\left[{\epsilon }_{2}\mid {x}_{1},{x}_{2}\right]=0,$ $\text{V}\text{a}\text{r}\left[{\epsilon }_{1}\mid {x}_{1},{x}_{2}\right]=\text{V}\text{a}\text{r}\left[{\epsilon }_{2}\mid {x}_{1},{x}_{2}\right]=1,$ $\text{Corr}\text{ }\left[{\epsilon }_{1},{\epsilon }_{2}\mid {x}_{1},{x}_{2}\right]=\rho$. The bivariate probit model (1) introduces the correlation between error terms (rho:$\rho$) and concludes that the two avoidance behavior shown by the two dependent variables are also correlative.

It should be noted that the bivariate probit model has been applied on a sample of 12,032 respondents aged 15 years old or older which was randomly taken from 2012 Health Survey. As it may be seen in Table 3 the correlation between the avoidance outpatient services and inpatient services is high, being rho = 0.5661 and significant. It means that people who avoid/non-avoid visiting doctor for outpatient are more likely to avoid/non-avoid inpatient care services. Especially since Health Transformation Programme in 2003–2012, in order to implement the referral system in Turkey some legislations have been carried out. However, due to the health system and social factors, they were not implemented [40]. Considering both the high correlation coefficient and status of the referral system in Turkey, it is convenient to use the bivariate probit model to estimate behavior of avoidances.

First, we have analyzed determinants of avoiding by using bivariate probit model. Then, marginal effects are estimated across the four joint probabilities: avoiding neither in outpatient level nor inpatient level $P\left({y}_{1}=0 \text{a}\text{n}\text{d} {y}_{2}=0\right),$ avoiding in outpatient level but not inpatient level $P\left({y}_{1}=1 \text{a}\text{n}\text{d} {y}_{2}=0\right),$ not avoiding in outpatient level but inpatient level $P\left({y}_{1}=0 \text{a}\text{n}\text{d} {y}_{2}=1\right),$ avoiding in both level$P({y}_{1}=1 \text{a}\text{n}\text{d} {y}_{2}=1)$.

Table 3 shows the coefficient of bivariate probit regression model in which the dependent variables avoiding attitudes ${y}_{1}$ and ${y}_{2}$ and explanatory variables, that have been chosen by literature [5, 6, 10, 26, 32] are considered socio-economics factors. Here, age takes one if the individual's age is 45 and over and zero otherwise; education level is takes one if individual's education level is high school and over and zero otherwise. Household monthly variable is expressed in four categories: =0 if less or equal to 750 TL, = 1 if 751 TL to 1300TL, = 2 if 1301 TL to 2300TL and = 3 if greater than 2300TL; SSI takes one if the individual has social insurance or green card and zero otherwise, and persons to trust takes one if the number of people to trust greater or equal to two and zero if the number of people to trust is less or equal to one. Furthermore, we generated two indexes, index 1 and index 2, by considering the following three questions on well-being in 2012 Health Survey Data: “feel pain or discomfort in last four weeks”, “feel happy in last four weeks” and “perceived health”. The multiple-choice answers for the each of the question were ‘all of the time’, ‘most of the time’, ‘some of the time’, ‘a little of the time’ and ‘none of the time’. The binary variable “Index 1” represents the best perceived state of the individuals which takes one if the respondent answers each of the three questions as ‘all of the time’ and zero otherwise. On the contrary, “Index 2” represents the worst perceived case of the individuals that takes one if the respondent answers each of the three questions as ‘none of the time’ and zero otherwise. BMI is the numerical value of the individual's body mass index.

Table 3

Bivariate Probit Regression of Avoiding Outpatient and Inpatient Healthcare Services
Avoid, Outpatient					Avoid, Inpatient
Variables	Coefficient	p	95% Confidence			Coefficient	p	95% Confidence
Female	0.183	p < 0.001*	0.115	0.250	Female	0.165	0.004*	0.051	0.279
Age ≥ 45	-0.038	0.257	-0.105	0.028	Age ≥ 45	0.028	0.608	-0.080	0.136
Urban	-0.012	0.732	-0.083	0.058	Urban	-0.015	0.783	-0.126	0.095
Married	0.054	0.110	-0.012	0.119	Married	0.067	0.229	-0.042	0.176
Education level	-0.044	0.266	-0.121	0.033	Education level	-0.083	0.249	-0.225	0.058
Income (reference: Less or equal to 750 TL)					Income (reference: Less or equal to 750 TL)
751 TL to 1300 TL	-0.189	p < 0.001*	-0.267	-0.110	751 TL to 1300 TL	-0.143	0.023*	-0.266	-0.020
1301 TL to 2300 TL	-0.243	p < 0.001*	-0.329	-0.157	1301 TL to 2300 TL	-0.211	0.004*	-0.352	-0.069
Greater than 2300 TL	-0.265	p < 0.001*	-0.370	-0.160	Greater than 2300 TL	-0.236	0.013*	-0.423	-0.049
Employed	0.206	p < 0.001*	0.134	0.278	Employed	0.086	0.172	-0.037	0.208
SSI	-0.350	p < 0.001*	-0.458	-0.243	SSI	-0.058	0.540	-0.244	0.128
Persons to trust ≥ 2	-0.166	p < 0.001*	-0.235	-0.096	Persons to trust ≥ 2	-0.109	0.049*	-0.217	0.000
Index 1	-0.463	p < 0.001*	-0.711	-0.215	Index 1	0.001	0.994	-0.360	0.363
Index 2	0.595	0.142	-0.199	1.390	Index 2	-4.594	p < 0.001*	-4.821	-4.367
BMI	-0.047	0.008*	-0.082	-0.012	BMI	-0.063	0.011*	-0.112	-0.015
Using non-prescription medications	0.445	p < 0.001*	0.364	0.525	Using non-prescription medications	0.291	p < 0.001*	0.166	0.416
Any-chronic	0.412	p < 0.001*	0.345	0.478	Any-chronic	0.562	p < 0.001*	0.443	0.680
Diabetes	-0.062	0.257	-0.170	0.045	Diabetes	-0.062	0.440	-0.218	0.095
Cancer	-0.178	0.297	-0.512	0.157	Cancer	0.086	0.712	-0.371	0.543
Constant	-0.816	p < 0.001*	-0.991	-0.641	Constant	-2.025	p < 0.001*	-2.300	-1.750
Note: Significant at 5%*
athrho = 0.6418926 (p = 0.0000), 95% Confidence: [0.5708817–0.7129034]
rho = 0.5661868, 95% Confidence: [0.5160065–0.6124943]
n = 11,824, Log pseudolikelihood =-5652.9269, Wald chi² = 2472.15, Prob > chi² = 0.0000

Table 4

Marginal Effects of Bivariate Probit Regression
Not Avoid Outpatient and Not Avoid Inpatient					Avoid Outpatient and Not Avoid Inpatient
Variables	Coefficient	p	95% Confidence		Variables	Coefficient	p	95% Confidence
Female	-0.040	p < 0.001*	-0.054	-0.026	Female	0.031	p < 0.001*	0.019	0.044
Age ≥ 45					Age ≥ 45
Urban					Urban
Married	-0.012	0.081*	-0.026	0.001	Married
Education level					Education level
Income (reference: Less or equal to 750 TL)					Income (reference: Less or equal to 750 TL)
751 TL to 1300 TL	0.044	p < 0.001*	0.026	0.062	751 TL to 1300 TL	-0.035	p < 0.001*	-0.051	-0.019
1301 TL to 2300 TL	0.055	p < 0.001*	0.036	0.074	1301 TL to 2300 TL	-0.044	p < 0.001*	-0.061	-0.026
Greater than 2300 TL	0.060	p < 0.001*	0.038	0.082	Greater than 2300 TL	-0.047	p < 0.001*	-0.067	-0.027
Employed	-0.042	p < 0.001*	-0.057	-0.027	Employed	0.038	p < 0.001*	0.024	0.051
SSI	0.069	p < 0.001*	0.047	0.091	SSI	-0.066	p < 0.001*	-0.086	-0.046
Persons to trust ≥ 2	0.035	p < 0.001*	0.020	0.049	Persons to trust ≥ 2	-0.029	p < 0.001*	-0.042	-0.016
Index 1	0.089	0.001*	0.037	0.142	Index 1	-0.089	p < 0.001*	-0.134	-0.045
Index 2					Index 2	0.230	0.004*	0.075	0.384
BMI	0.011	0.003*	0.004	0.018	BMI	-0.008	0.024*	-0.014	-0.001
Using non-prescription medications	-0.093	p < 0.001*	-0.110	-0.077	Using non-prescription medications	0.079	p < 0.001*	0.064	0.094
Any-chronic	-0.094	p < 0.001*	-0.107	-0.080	Any-chronic	0.065	p < 0.001*	0.053	0.078
Diabetes					Diabetes
Cancer					Cancer
Note: Significant at 5%*

It can be seen from Table 3 that people of married and female are more likely to avoid heath care in both inpatient and outpatient health services, while people of having higher education level, living urban area and higher income level are less likely to avoid. Being + 45 age, best state of the individuals (index 1), worst state of the individuals (index 2) and having cancer have different effects on the avoidance of inpatient and outpatient health service. Individuals in these groups are less likely to avoid outpatient level and more likely to avoid inpatient level. In worst state of the individuals (index 2), the situation is the opposite. The higher number of people that individuals can trust and the higher BMI decrease the possibility of avoiding health services. But having any-chronic disease and using non-prescription medications participants are more likely to delay or avoid inpatient and outpatient health services.

Table 4 shows the marginal effects of two joint probabilities[2]: First not avoidance of outpatient and inpatient services; secondly avoidance of outpatient services but not avoidance of inpatient services. In parallel with the expectations, the signs of joint probabilities are the opposite of each other. As seen in Table 4, if the individual is female, the probability of avoiding both outpatient and inpatient health services decreases by 4%. When the individual having any-chronic disease or using non-prescription medications, this probability increases by more than 9%. If the individual is female, the probability of avoiding both outpatient and inpatient health services decreases by 4%. Participants who are covered by social security institution -SSI- and green card- a public scheme for poor, the probability of avoiding both outpatient and inpatient health services decreases by 6.9%.

[2] The reason for choosing these two joint possibilities (P(y₁ = 0 and y₂ = 0), P(y₁ = 1 and y₂ = 0) ) is that their coefficients are approximately 10 times higher than other two joint possibilities (P(y₁ = 0 and y₂ = 0), P(y₁ = 1 and y₂ = 1)). The other two joint probabilities are available upon request.

Our study aims to determine why people avoid tertiary care considering an upper income country’s experience. We examine the factors affecting the avoidance of inpatient services in accordance with outpatient services in Turkey, by using the 2012 Health Survey via bivariate probit model. Empirical literature generally focused on developed country cases [5, 6, 8, 9, 11, 13, 14, 15, 17, 22, 27, 28, 30, 31, 41–46], so we contribute to the knowledge considering a developing country case.

According to our descriptive statistics analysis 2.6% of the participants had not received deemed necessary inpatient services, and avoiders were more likely to be female, married, middle aged or older, low-educated, living in rural area and have lower income, chronic disease and use non-prescription medications. Moreover, it is found that high cost (31%), organizational factors (21%) and fear (12%) are main reasons of avoiding. Similar to our findings, “money restriction” was the first avoiding reason in Safer et al. [32]; but Ye et al. [30], Kannan and Veazie [5] and Morowatisharifabad et al. [9] indicate “fear” as one of the most important reasons.

Mostly, our bivariate probit model findings were in line with the literature. Parallel to literature [6, 17, 42, 47, 48] we found that disadvantaged groups tend to avoiding necessary healthcare services. Similar to Sweeny [1] and Taber, Leyva, and Persoskie [6], an important finding from our analysis was that lower income level are more likely to avoid. Although Kannan and Veazie [5], Spleen et al. [10] and Tong et al. [7] found evidence that being male was related to health care avoidance, we found this relation for being female. As oppose to Kannan and Veazie’s [5] evidence on that BMI associated with greater avoidance; we observed the higher BMI decreasing the possibility of avoiding health services. Similar to Spleen et al. [10], we found a negative relationship between living urban area and health care avoidance. Furthermore our study finds out that perceived health affects differently people’s avoiding attitude at different levels of healthcare. Eventhough people with the worst perceived health intend to avoid healthcare in outpatient level, they behave less likely to avoid in inpatient level. Even this attitude underlines that the avoided healthcare needs may require more serious and costly treatment.

People all over the world may have unmet health care need for some reasons and that situations may lead more radical treatment and financial burden both on health system and individuals. Our study has explored to determine why people avoid necessary healthcare services in hospitalization stage examining a developing country case. Firstly, it was found that high cost, organizational factors and fear were the main reasons of avoiding inpatient health services. Thereafter, as we figured out a relationship between the households’ avoiding behaviour in accessing outpatient level and hospitalization level services, we applied bivariate probit model and found that, in both stages of healthcare, avoiders were more likely to be female, married, middle aged or older, low-educated, living in rural area and had lower income, chronic disease and used non-prescription medications.

Avoiding of health care services is an important issue because of negative effects on health and financial burdens on health system. In the light of our findings, we recommend to policy makers to improve health care protection of the disadvantaged groups and develop better organizational factors to prevent difficulty of having treatment at policlinics. Finally, as there is a limited number of studies especially on developing countries, we should indicate that further studies are required to distinguish people’s avoiding attitude at different stages of health care utilization. Indeed, the influence of COVID19 pandemic on avoiding behavior appears as crucial topic to search.

Ethics approval and consent to participate

The authors used Turkish Statistical Institute (TurkStat)’s 2012 Health Survey Data with official permission. We used this data with administrative permission given to us via agreement between our faculty -Ankara Haci Bayram Veli University Faculty of Economics and Administrative Science- and TurkStat. The micro data was prepared by TurkStat in accordance with the Regulation named "Regulation of Procedure and Principles of Data Confidentiality and Confident Data Security in Official Statistics" which was issued by the Article 13 of Statistics Law of Turkey No. 5429 and entered into force with the date of June 20, 2006, following that of its publication in the Official Journal No: 26204. The data used in this study was anonymised by TurkStat before its use.

Consent for publication

Not applicable.

Availability of data and materials

TurkStat prohibits the sharing of the data with the third parts. The TurkStat 2012 Health Survey Data can not be copied or released to any other person or organization, as stated in the Statistics Law of Turkey No. 5429 and Article 14. If someone wants to request the data from this study, she/he should contact to the TurkStat.

Authors' contributions

All authors contributed equally, read, and approved the final version of the manuscript.

Funding

The authors have no support or funding to report.

Conflict of interest

There is no conflict of interest.

Acknowledgements

The first draft of the study has been presented at 7th International Health Sciences and Management Congress Uskudar University 17 June 2022, Istanbul/Turkey. We would to thank to all participants for their valuable comments.

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Receiving or not Deemed Necessary Healthcare Services

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Background

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Reasons of Avoiding

Cross Distribution of Avoiding Behaviour under Outpatient and Inpatient Care

Results

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