Why do People Avoid Visiting Specialist Doctors? Answers from a Developing Country: Turkey Case

Abstract

Avoid seeking medical care deemed necessary has severe negative effects on health. Indeed, delayed presentation of patients puts a significant burden on healthcare costs. There is limited literature examining the avoidance behaviour of people. Our study explores the reasons for avoiding considering the Turkey case. TurkStat’s health research survey 2012 data is employed to examine why people avoid visiting the specialist doctor when needed to consult a specialist for healthcare service. Descriptive statistics analysis is conducted. It is seen that among 28,056 respondents 12.6% were avoiders. Compared to non-avoiders, avoiders were more likely to be female, to live in rural area, to make out-of-pocket health payments, to be illiterate and to have low income. Indeed, the avoiders were more likely to have worse perceived health, negative emotions, mental disorders, serious health problems and chronic diseases. It is found that for approximately one-third of avoiders’ the main reason for avoiding was high costs. 22% of the avoiders reported that they could not take time. The other avoiding reasons were organisational factors, fear of medical treatment/surgery, distance/transportation, late appointment dates, having nobody to accompany and non-supportive family/relatives. In Turkey, interventions combating healthcare avoidance behaviours in at-risk populations should be developed.

Keywords

Patient delay healthcare utilisation medical care avoidance characteristic of avoiders Turkey

“Every illness is also the vehicle for a plea for love and attention. One of the commonest conflicts of man is caused by the discrepancy between his need for affection and the amount and quality of the affection which his environment is able and willing to grant him.”

Michael Balint (1974)

Introduction

Delayed medical care has effects on a negative health and financial burden on the health system (Taber et al., 2015; Weller et al., 2012). First of all, it may cause a late recognition of the disease, worsen response to treatment, reduce the chance of overcoming the illness that developed to the later stages that’s why to reduce survival, cause possibly avoidable human suffering, limit the success of medical treatment and make the treatment costlier in terms of time, finance and also burdens much stress emotionally and socially for the patients and health providers (Byrne, 2008; Kannan & Veazie, 2014; Moser et al., 2006; Reynolds et al., 2013; Richards et al., 1999; Rogers et al., 2011). Even though the delay may occur within the health system, the majority of delays are caused by the time taken for the patient to present themselves (Macdonald et al., 2006; Rogers et al., 2011). Late presentation of patients puts a significant burden on health care costs by increasing the implementation of more radical treatments, increasing the length of hospitalisation and operation times and distressing limited hospital resources (Prasanna et al., 2013; Rogers et al., 2011; Taber et al., 2015).

Avoidance in seeking medical care can be classified into three stages: Appraisal delay, illness delay and utilisation delay (Safer et al., 1979). Appraisal delay is the time spent to appraise a symptom as a sign of disease. Illness delay occurs after noticing the illness up to deciding to seek professional health care. Utilisation delay is the time spent between considering medical care as necessary and going to the clinic and using its services. So, why do people avoid medical care when they suspect it is necessary? There is no sole factor but multiple factors of avoidance behaviour, such as not knowledge of symptoms, behavioural reasons, techniques for coping with illness, emotional reactions, the negative vision of the danger of illness, socio-demographic factors and barriers (e.g., time, money) which limits to access health centres (Carrillo et al., 2011; Moore et al., 2004; Safer et al., 1979; Sweeny, 2008; Taber et al., 2015). In literature, there are a few and limited numbers of studies examining the behaviour of avoidance of people and dominantly most of them scrutinize the case of the USA or the subpopulations in the USA (Byrne, 2008; Kannan & Veazie, 2014; Larkey et al., 2001; Love, 1991; Moser et al., 2006; Smith et al., 2018; Taber et al., 2015; Vanderpool & Huang, 2010). There are very few examples considering other populations (Araujo et al., 2017; Demissie et al., 2002; Iskandarsyah et al., 2013; Persson et al., 2013; Tiruneh et al., 2018; Zhao et al., 2014). Most of these studies are focused on certain diseases, but there are very few studies (Kannan & Veazie, 2014; Leyva et al., 2020; Smith et al., 2018; Taber et al., 2015) examining the factors underlying avoiding behaviour regardless of the disease. There is a certain need for more studies in this area to determine predictors and reasons for avoidance behaviour in order to identify patients at risk considering different populations and to build the basis for health-promoting interventions (Byrne, 2008).

Our study aims to answer this question by considering the case of Turkey. Within this context, Turkey’s 2012 Health Research survey data is employed that is published by Turkish Statistical Institute (TurkStat) to construct European Union comparable public health statistics. In the survey, considering primary healthcare services and/or outpatient services the individuals were questioned whether they avoid visiting the specialist doctor even though needed to consult a specialist for healthcare service. In this study, the characteristics of the avoiders compared to non-avoiders are determined and the reasons for avoiding behaviour are explored. Furthermore, the relationship between gender and avoiding reasons are examined thoroughly.

Our research exploring the healthcare avoidance behaviour of the Turkish population contributes significantly to the literature considering a developing country case. Indeed, the Turkish case is so interesting and exciting as Turkey has been reforming its health system since 2003 via the “Health Transformation Programme” (HTP). Social Security Institution (SSI) has been initiated to integrate the security schemes under one umbrella and eliminate the fragmentation in the health financing system, and the Universal Health Insurance system has been introduced in October 2008. The 2003–2012 reform period was successful in rich its aim of enhancement of equity by improving health insurance coverage and access to healthcare services for all citizens, providing an accessible, efficient and financially sustainable health system to prevent inequalities in health outcomes and provide protection against catastrophic health expenditures (Atun et al., 2013). During the reform period, the health indicators have been improved significantly, such as life expectancy at birth (in years) rose to 78 in 2016 from 72.5 in 2002; the infant mortality rate per 1000 live births declined to 9.7 in 2016 from 22.6 in 2005 and this current rate is almost equal to the WHO European region average (9.8 per 1000 live births in 2015). Indeed, in Turkey, the maternal mortality per 100,000 live births decreased from 64.0 in 2002 to 14.7 in 2016 which is slightly less than WHO European Region’s average (16 per 100,000 live births in 2015) (MoH, 2017). Moreover, the population’s satisfaction with health services reached 75.9% in 2011 from 39.5% in 2003 (Atun et al., 2013). Indeed, throughout the HTP the out-of-pocket health expenditure share of total health expenditures decreased to 17.8% in 2014 from 18.5% in 2003 (MoH, 2017). Because of the remarkable improvement in the health outcomes and the health system, the Turkish health system reforming experience has been considered a successful good practice example for both developing and developed countries by World Health Organisation (WHO, 2012).

The rest of the study was organised as follows: First, TurkStat 2012 Health Research data is introduced. Then the measures are set in the first part of the findings section. Characteristics of avoiders with respect to non-avoiders are examined in the second part of the findings section. Finally, reasons for avoiding and the gender and the avoiding tendency are examined in the findings section. Assessment and policy recommendations regarding our findings and possible future studies are stated in the discussion section.

Data

The data is obtained from TurkStat’s Health survey 2012. TurkStat conducts health surveys since 2012 to gather public health statistics which are European Union (EU) comparable. Since every EU candidate country should provide EU-comparable public health statistics TurkStat conducts this survey including all questions in the modules that are requested by European Community Statistical Office and also some additional questions for national needs. 2012 Health survey is the first study collecting EU-comparable comprehensive data which also fulfils national requirements.

Health survey provides health data for 0–6, 7–14 and 15+ age groups. For 0–14 years old children the data on the diseases and accidents that occurred in the past 6 months and types of health services taken are collected. For the persons 15 years old or older the data collected on the general health status, health care services usage and indicators describing health. The survey provides wide-ranging information, such as for chronic diseases, functional abilities in carrying out daily activities, personal care, use of medicines, vaccinations and height and weight values, alcohol consumption and smoking habits and so on. Indeed, the data on socio-economic indicators, such as working status, income level and insurance status were also collected.

The 2012 Health Survey has been conducted with 14,400 households with an 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 persons’ information has been collected, 28,055 of them were aged at least 15 years old, (15+), which constitutes 73.9% (= 28,055/37,979*100). The sample correctly represents the whole population.¹ Health Survey 2012’s study design is given in detail in TurkStat (2012).

Findings

Measures

In Part B of the 2012 health survey the question considering primary healthcare services and/or outpatient services, asks participants ‘whether during the past 12 months they avoid visiting the specialist doctor even-though they needed to consult a specialist for healthcare service’. In the question, healthcare service means outpatient care services and emergency services obtained from specialist doctors at work or school, hospitals (except inpatient care services), health centres or special polyclinics etc. It was seen that among 28,056 respondents 3,524 (12.6%) were avoiders, 24,429 (87.1%) were non-avoiders, 102 (0.4%) did not know and 1 (0.0%) did not want to answer.

Characteristics of Avoiders versus Non-avoiders

First of all, characteristics including socio-economics factors and health status of participants who avoid visiting a specialist doctor for outpatient or emergency services versus whom do not avoid have been compared in Table 1. We applied the χ² test for categorical variables and the t-test for continuous variables.

First of all, when we consider gender, we observe that 39% of the avoiders were male and 60.2% were female whereas among non-avoiders 47% were male and 53% were female. So avoiders are more likely to be female (indeed the difference is statistically significant (χ²(1) = 64.63, p < 0.001).

In the 2012 health survey data ‘age’ was provided as a categorical variable, from 15+ to 75+ there are seven categories: [15–24], [25–34], [35,44], [45,54], [55,64], [65,74] and 75+. The age distribution of avoiders versus that of non-avoiders is statistically significantly different (χ²(6) = 163.69, p < 0.001) and the avoiders are more likely to be older. When we consider each age group separately, first we see that 11.2% of avoiders and 19.2% of non-avoiders were aged [15–24] and the difference was statistically significant (χ²(1) = 134.70, p < 0.001). Thus, the non-avoiders are more likely to be younger. However, middle-aged participants, aged from 35 to 54, are more likely to be avoided. Furthermore, there was no statistically significant difference among the distribution of older aged, that is aged ≥ 55.

Compared to non-avoiders, avoiders are more likely to be married or widowed and more likely to live in a rural area, to be illiterate and less likely to have a university or higher degree of education. Additionally, the avoiders are more likely to be working or occupied with house duties, and less likely to be a student or retired.

Moreover, avoiders are more likely to have an estimated monthly household income less or equal to 750TL as 29.7% of the avoiders and 20.2% of the non-avoiders belong to this lowest income group (χ²(1) = 160.784, p < 0.001). Non-avoiders are significantly more likely to belong to higher income groups. Indeed, considering the health insurance statute it was observed that 7.7% of avoiders and 3.8% of non-avoiders make out-of-pocket payments, and the shares are statistically significantly different (χ²(1) = 117.74, p < 0.001). Besides, the avoiders are more likely to hold Green Card, a health scheme for the poor.

When we consider the perceived health it is seen that compared to avoiders, non-avoiders are more likely to feel very good (5.8%–13.8% respectively, p < 0.001) or good health (43.5%–57.2% respectively, p < 0.001) and to feel happy in last 4 weeks always (9.8%–14.2% respectively, p < 0.001) or most of the times (32.5%–46.2% respectively, p < 0.001). Indeed, non-avoiders are significantly more likely to have at least 2 persons to trust.

Interestingly, the avoiders are more likely to have a health problem for more than 6 months and to have a problem restricting their daily life either seriously or non-seriously. Besides, 65.2% of avoiders have been feeling pain (or discomfort) in the last 4 weeks while only 38.8% of non-avoiders were feeling it. Furthermore, the avoiders are more likely to use the non-prescription drug² compared to non-avoiders (20.7% vs. 9.6%).

Even though there is no statistically significant difference between the average body mass indices (BMI) between avoiders and non-avoiders (respectively 28.53 vs. 27.60, t-test = 1.275, p = 0.203), the avoiders are slightly more likely to be obese.

Moreover, it was observed that 69.2% of avoiders and 47.7% of non-avoiders have any chronic diseases, thus avoiders have significantly more likely to have any chronic diseases (χ²(1) = 573.77, p < 0.001). Analyzing the 2012 health data it was seen that the top observed five chronic diseases in Turkey were hypertension, back musculoskeletal system disorders (like lumbago, back hernia), rheumatismal joint disease (romatoid artrit), gastric ulcer and diabetes.³ According to our findings, compared to non-avoiders the avoiders are more likely to have each of these chronic illnesses. However, there was no statistically significant difference in the percentage of having cancer between avoiders and non-avoiders (respectively 0.6% and 0.8%, χ² (1) = 1.047, p = 0.306).

Finally, it was observed that the avoiders are rather more likely to have mental health problems (respectively 12.9% and 8.0%, χ²(1) = 93.42, p < 0.001) that exist at least for 6 months and require taking medicine.

Table 1.

Avoiders versus Non-Avoiders: Characteristics.

	Avoider (n = 3,524)	Non-Avoider (n = 24,429)	χ² Value, p-Value
	n (%)	n (%)
Gender			χ²(1) = 64.63, p < 0.001
Male	1,401 (39.8%)	11,476 (47%)
Female	2,123 (60.2%)	12,953 (53.0%)
Age			χ² (6) = 163.69, p < 0.001
15–24	394 (11.2%)	4,704 (19.3%)	χ² (1) = 134.70, p < 0.001
25–34	684 (19.4%)	4,903 (20.1%)	χ² (1) = 0.843, p = 0.359
35–44	834 (23.7%)	4,697 (19.2%)	χ² (1) = 38.22, p < 0.001
45–54	725 (20.6%)	4,185 (17.1%)	χ² (1) = 25.19, p < 0.001
55–64	457 (13.0%)	2,992 (12.2%)	χ² (1) = 1.48, p = 0.224
65–74	271 (7.7%)	1,832 (7.5%)	χ² (1) = 0.161, p = 0.689
75+	159 (4.5%)	1,115 (4.6%)	χ² (1) = 0.02, p = 0.889
Location			χ² (1) = 31.14, p < 0.001
Urban	2,460 (69.8%)	18,135 (74.2%)
Rural	1,064 (30.2%)	6,294 (25.8%)
Marital status			χ² (3) = 165.96, p < 0.001
Single	533 (15.1%)	5,843 (23.9%)	χ² (1) = 135.28, p < 0.001
Married	2,570 (72.9%)	16,584 (67.9%)	χ² (1) = 36.259, p < 0.001
Widow	313 (8.9%)	1,490 (6.1 %)	χ² (1) = 39.511, p < 0.001
Divorced	108 (3.1%)	511 (2.1%)	χ² (1) = 13.46, p < 0.001
Education			χ² (4) = 118.44, p < 0.001
Illiterate	534 (15.3%)	2,355 (9.7%)	χ² (1) = 100.99, p < 0.001
Primary school	1,884 (54.0%)	13,209 (54.5%)	χ² (1) = 0.463, p = 0.496
Elementary school	214 (6.1%)	1,467 (6.1%)	χ² (1) = 0.025, p = 0.875
High school	520 (14.9%)	4,403 (18.2%)	χ² (1) = 22.673, p < 0.001
University and higher degree	339 (9.7%)	2,789 (11.5%)	χ² (1) = 10.12, p < 0.003
Estimated household monthly income			χ² (3) = 174.30, p < 0.001
Less or equal to 750 TL†	1,033 (29.7%)	4,881 (20.2%)	χ² (1) = 160.784, p < 0.001
751 TL to 1,300 TL	1,052 (30.2%)	7,544 (31.2%)	χ²1) = 1.535, p = 0.215
1,301 TL to 2,300 TL	853 (24.5%)	7,229 (29.9%)	χ² (1) = 43.493, p < 0.001
Greater than 2,300 TL	542 (15.6%)	4,491 (18.6%)	χ² (1) = 18.829, p < 0.001
Working statue			χ² (5) = 252.59, p < 0.001
Works	1,388 (39.4%)	9,030 (37.0%)	χ² (1) = 7.723, p = 0.005
Looking for a job or seasonal worker	144 (4.1%)	913 (3.7%)	χ² (1) = 1.029, p = 0.310
Student	177 (5.0%)	2,643 (10.8%)	χ² (1) = 114.09, p < 0.001
Occupied with house duties	1,244 (35.3%)	7,719 (31.6%)	χ² (1) = 19.374, p < 0.001
Retired	251 (7.1%)	2,826 (11.6%)	χ² (1) = 62.145, p < 0.001
Other‡	320 (9.1%)	1,297 (5.3%)	χ² (1) = 80.358, p < 0.001
Heath insurance statue			χ² (3) = 285.98, p < 0.001
SSI	2,612 (74.1%)	20,711 (84.8%)	χ² (1) = 253.436, p < 0.001
Green card	502 (14.2%)	1,993 (8.2%)	χ² (1) = 140.34, p < 0.001
Private insurance	137 (3.9%)	799 (3.3%)	χ² (1) = 3.620, p = 0.057
Out of pocket	273 (7.7%)	925 (3.8%)	χ² (1) = 117.74, p < 0.001
Perceived health			χ² (4) = 831.00, p < 0.001
Very good	206 (5.8%)	3,365 (13.8%)	χ² (1) = 173.78, p < 0.001
Good	1,532 (43.5%)	13,960 (57.2%)	χ² (1) = 232.94, p < 0.001
Fair	1,214 (34.5%)	5,467 (22.4%)	χ² (1) = 246.65, p < 0.001
Bad	475 (13.5%)	1,471 (6.0%)	χ² (1) = 264.408, p < 0.001
Very bad	96 (2.7%)	162 (0.7%)	χ² (1) = 143.05, p < 0.001
Have a health problem for more than 6 months			χ² (1) = 356.27, p < 0.001
Yes	1,776 (50.4%)	8,323 (34.1%)
No	1,745 (49.6%)	16,090 (65.9%)
Health problems restrict daily life			χ² (2) = 665.70, p < 0.001
Seriously	704 (20.0%)	2,226 (9.1%)	χ² (1) = 387.428, p < 0.001
Not seriously	1,038 (29.5%)	4,859 (20.0%)	χ² (1) = 169.232, p < 0.001
Not restricted at all	1,776 (50.5%)	17,267 (70.9%)	χ² (1) = 583.512, p < 0.001
Feel pain or discomfort in the last 4 weeks			χ² (1) = 885.13, p < 0.001
Feel	2,299 (65.2%)	9,471 (38.8%)
Not feel	1,225 (34.8%)	14,958 (61.2%)
Feel happy in the last 4 weeks			χ² (4) = 656.37, p < 0.001
Always	344 (9.8%)	3,445 (14.2%)	χ² (1) = 49.528, p < 0.001
Most of the time	1,137 (32.5%)	11,198 (46.2%)	χ² (1) = 230.098, p < 0.001
Sometimes	1,453 (41.5%)	8,047 (33.2%)	χ² (1) = 93.33, p < 0.001
Rarely	421 (12.0%)	1,278 (5.3%)	χ² (1) = 243.248, p < 0.001
Never	146 (4.2%)	266 (1.1 %)	χ² (1) = 197.808, p < 0.001
Number of persons to trust			χ² (1) = 80.23, p < 0.001
Less or equal to 1	914 (25.9%)	4,751 (19.4%)
Greater or equal to 2	2,610 (74.1%)	19,678 (80.6%)
Obese			χ² (1) = 19.90, p < 0.001
Yes	725 (20.6%)	4,349 (17.8%)
No	2,799 (79.4%)	20,079 (82.2%)
BMI§	28.5337 (41.37)	27.6046 (33.43)	t-test = 1.275, p = 0.203
Non-prescription medications in the last 2 weeks			χ² (1) = 389.66, p < 0.001
Yes	728 (20.7%)	2,333 (9.6%)
No	2,796 (79.3%)	22,095 (90.4%)
Have any-chronic diseases			χ²1) = 573.77, p < 0.001
Yes	2,240 (69.2%)	11,642 (47.7%)
No	1,084(30.8%)	12,786 (52.3%)
Hypertension			χ² (1) = 49.67, p < 0.001
Yes	690 (19.6%)	3,668 (15%)
No	2,834 (80.4%)	20,753 (85%)
Back musculoskeletal system disorders			χ² (1) = 495.24, p < 0.001
Yes	901 (25.6%)	2,892 (11.8%)
No	2,623 (74.4%)	21,533 (88.1%)
Rheumatismal joint disease			χ² (1) = 296.05, p < 0.001
Yes	629 (17.8%)	2,150 (8.8%)
No	2,890 (82%)	22,272 (91.2%)
Gastric ulcer			χ² (1) = 265.90, p < 0.001
Yes	538 (15.3%)	1,775 (7.3 %)
No	2,983 (84.6%)	22,649 (92.7%)
Diabetes			χ² (1) = 11.99, p < 0.003
Yes	322 (9.1%)	1,885 (7.7%)
No	3,200 (90.8%)	22,540 (92.3%)
Cancer			χ² (1) = 1.047, p = 0.306
Yes	21 (0.6%)	184 (0.8%)
No	3,503 (99.4 %)	24,244 (99.2%)
Have mental health problems			χ² (1) = 93.42, p < 0.001
Yes	453 (12.9%)	1,948 (8.0%)
No	3,071 (87.1%)	22,480 (92%)

Note: †TL: Turkish Liras.

‡Other for workings statue means cannot work.

§Since BMI is continuous here values indicate mean (standard deviation), and a t-test is applied.

Reasons of Avoiding

The 2012 health survey of TurkStat asks the avoider to select one of eight pre-identified reasons which defines best her/his cause and if neither defines the other option could be preferred.

According to our analysis, the main reason for avoiding health care services in Turkey is found as high costs, almost one-third of participants (31.7% of 3,524) pointed out that they could not afford health services (Figure 1). Second, having not enough time raises as a crucial constraint to getting health services, 22% of the participant acknowledged that they could not take time because of work or caring for children or other dependents. When we further investigated the data see that among the participants who choose ‘having not enough time’ for a reason of avoiding 60% have work and 32% were occupied with house duties; and 99.4% of the ones who are occupied with house duties are women.

Figure 1.

Distribution of the Reasons for Avoiding (%).

Among the reasons for avoiding health care, organisational factors like difficulty of having treatment by policlinics or other reasons due to health organisations (7.7%, n = 273) and fear of medical treatment or surgery (7.7%, n = 271) take the third slot and followed by the distance of health institution or having no means of transportation (6.6%, n = 231), late appointment date (4.3%, n = 153), having nobody to accompany (3.5%, n = 122). Finally, only 0.9% of the participants (n = 31) indicate that family or relatives gives no permission or think it unnecessary to get medical care. Consequently, all these eight reasons together explain almost 84% of the avoiding behaviour.

Gender and Avoiding Tendency

Now, we examine whether the avoiders’ distribution with respect to gender changes under the different reasons for avoiding health care or remains the same. The distributions are given in Table 2. We should indicate that the rank of the avoiding reasons does not change by gender, that is high cost has the first, the family avoids has the last place. However, the distribution according to gender among avoiders significantly changes for three avoiding reasons: having nobody to accompany and distance to health institutions are severe problems, especially for females and fear threats for men.

Table 2.

Distributions of the Reasons for Avoiding, by Gender.

Gender		Avoiders	Reasons of Avoiding
Gender		Avoiders	H. Cost	Not Time	Org. Factor	Fear	Distance	Late Appoint.	No Accompany	Family Avoids	Other
Male	n	1,401	399	324	122	134	72	61	22	13	241
Male	%	39.80%	35.80%	41.90%	44.70%	49.40%	31.20%	39.90%	18.00%	41.90%	45.30%
Female	n	2,123	717	450	151	137	159	92	100	18	291
Female	%	60.20%	64.20%	58.10%	55.30%	50.60%	68.80%	60.10%	82.00%	58.10%	54.70%
Total	n	3,524	1,116	774	273	271	231	153	122	31	532
Total	%	100.00%	100.00%	100.00%	100.00%	100.00%	100.00%	100.00%	100.00%	100.00%	100.00%

Discussion

According to our findings for the Turkey case, we observed that avoiders are more likely to have a health problem at least for 6 months, to have a problem restricting their daily lives, and to have pain. This is beyond feeling unhealthy the avoiders are actually unhealthier physically. Indeed, it was observed that 69.2% of avoiders have any chronic diseases and avoiders are significantly more likely to have any chronic diseases. With respect to Crisis Decision Theory people who have negative health conditions are the ones who may have already realised their need to inquire about medical care and perceived seeking care as a feasible option but did not expect the gains of looking for care to compensate the costs (Sweeny, 2008; Taber et al., 2015). Actually, besides high costs, thoughts of dying, fear of treatment, negative attitudes toward physicians and negative provider experiences have been indicated among important reasons for avoiding medical care in the literature under chronic illness (Iskandarsyah et al., 2013; Love, 1991; Moser et al., 2006; Reynolds et al., 2013; Rogers et al., 2011; Smith et al., 2018). Indeed, we figured out that among the reasons for avoiding health care in Turkey, fear of medical treatment is the third most cited reason a long with organisational factors and followed by late appointment dates. Also in the literature, all unfavourable provider experiences and negative evaluations of seeking medical care are among the crucial reasons for avoidance (Byrne, 2008; Kannan & Veazie, 2014; Reynolds et al., 2013; Taber et al., 2015).

We should note that according to our findings, having cancer does not affect the tendency of being an avoider, as there was no statistical difference in the percentage of having cancer between avoiders and non-avoiders in Turkey. The data do not allow us to differentiate according to the type of cancer. Further studies are necessary to examine the avoiding or delaying behaviour of cancer patients in Turkey.

Conclusions

In Turkey, the avoiders constitute a significant portion of the population (12.6% of all 15+ aged nationally representative sample). Indeed, according to our findings since the avoiders are significantly more likely to have worse perceived health, negative emotions, serious health problems and chronic diseases, the policymakers should be aware that avoiders would bring significant burdens to the health system unless interventions coping with patient delay/avoidance take place. In Turkey, interventions combating healthcare avoidance behaviours in at-risk populations such as female, financially disadvantaged, illiterate, middle-aged, working and obese populations should be developed. As it has been noted that when people avoid health care deemed necessary limits the success of the medical treatment at later stages of the disease and makes it more costly for all actors in the healthcare sector including the patients, healthcare providers and payers. Indeed, this avoidance attitude may even lead to catastrophic health expenditures for households.

In future studies, we will identify the predictors of avoiding visiting a doctor for each reason of avoidance by constructing logit models and also examine the avoiding behaviour of the patients when they need to be hospitalised following a recommendation from a doctor, that is the avoiding behaviour at the utilisation level will be considered for Turkey.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Ethical Issues

Not required. The authors used Turkish Statistical Institute’s 2012 Health Survey Data records based on official data.

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

Notes

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