Trust on Health Insurance Providers: Scale Validation and Determining Factors in Indian Context

 

P. Shobanapriya ¹, Dr. P. Easwaran ²

 

¹ Research Scholar, Karpagam Academy of Higher Education, Coimbatore, Tamil Nadu, India

² Associate Professor, Department of Commerce, Karpagam Academy of Higher Education, Coimbatore, Tamil Nadu, India

 

 

1. INTRODUCTION

The Indian government's efforts to reform the health system and combat poverty have recently been cited as a significant component Planning Commission and United Nations (2011), La Forgia and Nagpal (2012). The expansion of the "Universal Health Care for All by 2020" campaign Reddy et al. (2014) to 2030 represents a significant step in the Indian Health Care Reform Devadasan et al. (2014). Universal health insurance is a necessity in order to accomplish this objective Bennett et al. (2010), Reddy et al. (2011) and it has been suggested as a potential way to reduce health inequalities and out-of-pocket( OOP(out of pocket) expenses. In a country with such a diverse population as India, there are several interlinked government, commercial, and community health insurance systems. The 2017 update to the (NHP) report continues the importance of having fully accessible and free access to the right Sharma (2015).

What factors have influenced Indian policymakers 'decisions regarding health insurance? The effects of 1) the burden of illness, (2) low government health spending, (3) private health spending, particularly OOP (Out-Of-Pocket) spending, and (4) existing gaps in all types of insurance coverage Ahlin et al. (2016). The country is in the transition period in terms of healthcare because the population's level of illness from dietary deficiencies and infections is unprecedented, just like the elites 'pretentious revolution, which includes a lack of physical activity, a high intake of carbohydrates and fat, and an abundance of tobacco Horton (2005). On the other hand, poor and marginalized populations are attracted to the least amount of health care Maria (2021), Subramanian et al., (2021). Whose aspirations in the era of scientific advancement are nothing more than hopeful favours according to a little more than a false healthcare system?

 

2. SIGNIFICANCE OF STUDY

Healthcare spending is rising, especially in terms of OOP payments made by the sick Dror et al. (2008), Kao et al. (1998), Shahrawat and Rao (2012), because there is still a deficiency in the funds allocated for these services. Additionally, it has resulted in a decline in the standard of care at public hospitals Rao and Choudhury (2012). In response to this, even the least educated and poor sections of society choose to use public transportation Bhatia and Cleland (2001), Madhukumar et al. (2012). Private facilities have grown to 80% of outpatient and 60% of inpatient services Sharma (2015). When is the most urgent time to purchase health insurance?

For people in the low to middle income bracket, health insurance, such as indemnity or managed care, can serve as a safety net to reduce the risk of financial ruin as a result of unanticipated health care costs (Karajan, 2012, Reshmi et al. (2007). However, just a small percentage of the population is covered by this type of health insurance.

 

3. RATIONALE OF STUDY

Trust appears to be a major factor in the use, requirements, and enrolment of health insurance. It can be simply defined as the recognition of a vulnerable state in the belief that the trustee would safeguard the trustor's best interests Akafu et al. (2023). Goold, Hardin, and others have it between organizations and between people. It places trust in institutions 'standards, fundamental values, and procedures rather than people. The underlying tenets Pirson (2009) are the absence of any information about upcoming health conditions and the possibility that the risk may be anticipated will be passed on to others in advance. In exchange for the insurer agreeing to pay the insurer's waiting losses, a premium will be paid to transfer this risk. The enrolment criteria are determined by initial trust, which serves as the foundation for enrollment in an insurance plan (Eseta, Sinkie, Ozawa and Walker (2009), and Eseta). A large portion of people may never trust health insurance as an asset, as suggested by Ahlin and others. Trust yet travels when the insurer shares medical information with the patient, when treatment is accessed while being hospitalized, and when dependence is demonstrated during the reimbursement waiting period. We recognize the value of fostering trust in both the protection of the insured's services and the insurance company's survival Pirson (2009). It makes the enrolment process as well as the policy-holder process simpler. Despite being pertinent in all circumstances, LMICs have received surprisingly little attention regarding the impact of trust in the provision of healthcare Gilson (2006). There aren't many, if any, of the available ways to assess the level of trust between policyholders of health insurance in the British setting. Some measures fall short, in the opinion of development issues and lack of prior theory. In the few studies that examined institutional trust in medicine Blendon et al. (1998), only single-dimensional measures of trust were used. The factors that might affect trust are also undermined in the united context.

 

4. OBJECTIVES OF STUDY

1)     To validate Patients’ Trust in Health Insurers scale in the Indian context

2)     To compare the factors that influence the trust on the health insurer

 

5. REVIEW OF LITERATURE

The rising demand for health insurance in India is a result of bad lifestyle choices, air pollution, inhaling poisonous gases, and financial difficulties due to paying for medical treatment and hospitalization, according to Sethi and Bhatia (2023). Additionally, most workplaces today offer their employees access to health insurance, which raises the requirement for the latter. As a result of recent research, it has become apparent that trust can be used to determine a health insurance provider's performance Asmamawu, Besley, Dror, and Schneider, Schneider, 2014). According to a study conducted in West Africa, providers 'poor treatment quality had a negative effect on participation in the community insurance plan as a result of a loss of trust Criel and Waelkens (2003). Additionally, household surveys from various nations frequently reveal that insured members are concerned about whether the companies 'premiums will be used wisely Besley (1995).

Trust as a variable has been thoroughly investigated, whether it is enrolling or staying in the same job for a while for some years as an insured individual. Students who studied distrust was a barrier and trust in the provider was a facilitator De Allegri et al. (2006), Ozawa& Walker, Schneider) were also able to identify this. Further, studies have highlighted the importance of distrust: low community involvement Basaza et al. (2008), complaints from customers that the plan failed to fulfil its promises Criel and Waelkens (2003), and low community involvement as a barrier to the scheme's viability Kyomugisha et al. (2009). Participants in both the study claimed they enrolled because other nearby residents had joined or renewed, creating a trust for the insurance plan.

Nair compared the ratings of both personal and public insurance claimants. In the public sector, than in the counterparts, satisfaction with resolving disputes was more favorable. In India, Kumar examined the role of insurance in the provision of funding for health care and cited it as a necessary means of coordinating resources, ensuring risk protection, and providing insurance services. Dror and others examined the willingness of poor and economic citizens in India to enrol in a health insurance program. It revealed that those who were covered were more willing to pay off their insurance than those who were not. Yadav and Sudhakar (2017) conducted an investigation into private factors that influence Indian consumers 'purchasing decisions for health insurance policies. Knowledge, tax benefits, macroeconomic stability, risk coverage, and other factors were major factors in a policyholder's choice to purchase health insurance, according to Yadav and Sudhakar (2017). Only a small number of variables had a positive and negative correlation when a study examined the relationship between the household head's age and the purchase of health insurance Chankova et al. (2008), De Allegri et al. (2006), Gnawali et al. (2009), Gumber (2001), Mathiyazaghan (1998), Panda et al. (2014). According to Subramanian et al. (2013), the ability to purchase health insurance is positively influenced by income, indicating that higher income levels improve access to health insurance. Individual and family characteristics are the main factors in purchasing health insurance, according to another study on industrialized nations Karan et al. (2014)

 

6. RESEARCH METHODOLOGY

6.1. Study Area, Period and Population

In the Coimbatore, Tamil Nadu district, the survey was conducted in June 2023. The district's northern region serves as the state's hub for various textile industries. There are 69 towns and six sub districts in the area. The location was chosen in accordance with convenience. The study's population, according to the 2011 Census, was 1,050 and 721 people.

 

6.2. Study Design, Sampling Technique and Sample Size

The methodology was founded on a cross-sectional, community-based study design that used health insurance policyholders. The list of members who had insurance through a public or private company was made people. Because there isn't enough evidence to show how many insurance holders are in the chosen area, the population size was left unknown. Three of the six were arbitrarily selected using a lottery technique based on a multistage sampling technique. Following this, two towns were selected to serve as clusters, serving as representatives of each sub district. Eventually, the members were chosen from the six towns using a computer-generated, straightforward strange sampling technique. Respondents were contacted over the phone and given requested for their consent to participate. Each sub district had 60 members, and if a member was unavailable after two phone calls, the next member would be added. The sample size was 180 overall.

 

6.3. Instrument and Data Collection Procedure

A questionnaire was created to evaluate the validity of the Patients 'Trust in Health Insurers scale, which was created and validated by Beiyao Zheng, Mark A. In relation to India, Hall, Elizabeth Dugan, Kristin E. Kidd, and Douglas Levine, 2002. Section A contained the demographic variables as well as the gender, age, and academic qualification, while section B contained the original scale intended to be validated here because it is believed to be one of the most effective medical research tools to meet the survey study's objectives. The researcher has adopted the standard scale found in the study paper, Beyigo et al., "Developing a Scale to Measure Patients 'Trust in Health Insurers." 11 items spread across 5 dimensions make up as. With reverse scoring for items with poorly worded answers, the response categories are strongly agreed (SA- 5), agree (A-4), neutral (N-3), Disagree ( DA-2), and strongly disagree( SDA-1).

A conceptual framework was used to create the scale based on broad philosophical and empirical literature reviews. Fidelity, competence, honesty, and confidentiality are the four intersecting criteria for health insurance institutes. Prior to this, there were 19 items in total, which included Global Trust, which was included as the five dimension. The authors approved 11 items across the five dimensions with Cronbach's alpha of 0/ 0/ 0. 9 following the validation process. The investigators used the data as a major source and were well-versed in the technique and spoken local language fluently. The ultimate sample size was 180 and each data collection took 20 minutes on average.

 

6.4. Ethical considerations

All research participants were informed about the investigation's objectives in advance of the interview, and they obtained informed dental consent. No personal information was used in the data collection questionnaire; instead, a unique code for each individual was used to signify the study and preserve participants 'confidentiality. Data was kept in both hard copies and gentle copies in safe locations. The collected data was accessible only to the research team, and all data sharing procedures were carried out in compliance with established social and legal standards.

 

7. DATA ANALYSIS

Table 1

Table 1 Demographics: Gender
Options	Frequency	Percentage
Male	90	60
Female	60	40
Total	150	100

 

Figure 1

Figure 1Gender Distribution

Gender plays a significant role in a respondent's statistical profile. Some governments, economies, corporations, and other organizations place a high value on gender equality in policy making. A sample of 150 people is included in the statistical data on gender. The majority of respondents are male, with 60% (90 people), and 40% (60 people) are female. This indicates that male participants are more likely to have a fairly skewed gender distribution. The presence of females, however, also has a major impact, which suggests a pretty equal distribution of the two. Nevertheless, the gender profile exhibits good inclusivity, with male participation exhibiting a significantly higher level.

Table 2

Table 2 Demographics: Age
Options	Frequency	Percentage
Below 25 Years	50	33.33
25 to 40 Years	70	46.67
Above 40 Years	30	20.00
Total	150	100

 

Figure 2

Figure 2 Age Distribution

 

The respondents 'age distribution, which totals 150 people, is indicated in the statistical data table. Respondents between 25 and 40 years make up the sample's 46.67% ( 70 respondents), making up the largest sample. This suggests that the majority of people are middle-career or younger adults. Respondents under 25 years old make up 33.33% of the second-largest sample, which represents younger, probably first-career participants. Respondents over the age of 40 make up 20% of the sample, which indicates a relatively smaller proportion of older adults. Nevertheless, the demographics of participants aged between middle and old seem to be biased according to the data.

Table 3

Table 3 Demographics: Education
Options	Frequency	Percentage
SSC	20	13.33
HSC	30	20.00
Graduate	60	40.00
Post-Graduate	40	26.66
Total	150	100

Figure 3

Figure 3 Demographics- Education

 

The education credentials of 150 respondents are revealed in the demographic data on education. 40% of the sample, or 40% of the respondents, are graduates, which indicates that a sizable portion of the sample has completed their academic studies. The second-largest group of respondents, with 40 respondents, representing a significant proportion of people with graduate degrees, suggests a high level of education. Respondents with Secondary School Certification( SSLC) account for 20% of respondents, compared to only 13.33% of those with Higher Secondary Certification( HSC)( 30 respondents). Nevertheless, the sample size suggests a predominately grad and postgraduate-educated sample.

Table 4

Table 4 Work Experience
Options	Frequency	Percentage
Below 5 Years	40	26.67
5 to 10 Years	50	33.33
11 to 15 Years	30	20.00
16 to 20 Years	20	13.33
Above 20 Years	10	6.67
Total	150	100

 

Figure 4

Figure 4 Work Experience

The demographics of the 150 respondents 'work experience indicate how long they have been employed. The majority of respondents (50) are mid-level professionals with between five and ten years of experience. With 40 respondents, the second-largest sample is comprised of people with less than five years of experience, which suggests some notable participation from relatively fresh professionals. Respondents aged 16 to 20 years, or 13.33%, make up 20% of respondents, followed by those aged 16 to 20. The smallest category, which is made up of only 6.7% of respondents over 20 years old, has a chance of getting to know them, which is surprising because only those with the most experience gathering have a chance. The survey results, in general, suggest that the majority of respondents are quick-to-mid-career professionals.

According to statistical standards, the data was incorporated into AMOS v27 and SPSS v23 (Black, et al., 2001). 2010). In terms of respondents 'ages, 91 (51% males and 89 (49% females) were older than 40, 108 (60%) were graduates, and 72 (40%) were non-renews in terms of their educational status. In light of this, the sample sizes were used to evaluate the suitability of the previous requirement for CFA. After that, CFA and reliability were discovered. To compare the demographic data they used to assess the effect of their trust in health insurance providers, independent samples were used.

 

8. RESULTS AND DISCUSSION

Based on descriptive statistics, the test of normality, CFA, and the test's reliability score, the test results are elaborated. The samples 'descriptive statistics, which describe and summarise the data's general characteristics as well as distribution, were examined. Respondents had a median of 43 on the scale of 42.02, a standard deviation of 7.859, and a skewness of -.185 and a 0. 181 regular error for skewness and kurtosis of- were present. The common error of kurtosis is .360 and is 924. So, the obtained skewness and kurtosis values are lower than 1.96 (95% confidence interval) and 2.58 (99% confidence interval).

As a result of the comparison between the sample data input into the system and the probability distribution of a hypothetical data and the assumption of normality, the Kolmogorov-Smirnov Goodness of Fit test was conducted Aslam (2020). The distribution is regular, as evidenced by the p value of 0.05. In order to test whether the study's normality assumptions were true Aslam (2020), a Shapiro-Wilk test was also conducted. The result is that the p= 0.00001, or 0.05, is normal.

Objective 1: The requirement for using KMO Test and BTS as a prerequisite for CFA was carried out as a subsequent test. The KMO value is 0. 964-6, which is regarded as being extremely adequate, and is therefore higher than 0. 0 and also much closer. The significance of BTS for homogeneity of variance (22= 272.550, p 0.00001), which serves as a precondition for CFA analysis Hair et al. (2010), Tabachnick and Fidell (2007), is established Hair et al. (2010).

Table 5

Table 5 KMO and Bartlett’s test
KMO Value		0.964
BTS	Approx. Chi-Square Df Sig.	2702.55 55 0.000

 

A factor structure of the set of studied variables is verified using CFA statistical analysis. It will be simpler for the investigators to test the hypothesis if there is a connection between the discovered variables and the underlying hidden constructs, according to Suhr (2006), which suggests. It enables factor, variance, and relationship analysis of latent constructs Hill and Hughes (2007). When constructs are measured using multiple items, when the scale items have a linear relationship to the scale total or its average, and when the investigator is aware which item measures which domain Hunter (1982), CFA is a useful statistical tool for determining the validity of evidence.

Kline (2023), both stated that the goal of CFA is to verify the factor structure of a group of well-established variables, which is the five-factor organized Likert type scale in this case. CFA is satisfactory because it was created using priority theory and a framework and does not require an EFA. CFA is also the apt analysis to be done in this relatively new scale developed in 2002 to assess its validity and reliability in the Indian context because the fitness indices estimates of the model are taken into account here Hurley et al. (1997).

Because the classic scale has a second item under two dimensions, including Fidelity and Confidentiality, the dimensions were removed before being analyzed for CFA because the uni-item dimensions are incompatible with the CFA process. Figure 1 summarizes the model's fitness estimates based on the responses of 180 respondents to the remaining 9 three-dimensional aspects. The loading factors for the nine items exceeded the industry standard of 0. 5. According to Dai and Ng, 2008, the model is adequate because it provides good overall estimates and their values in accordance with the desired benchmarks.

Figure 5

Figure 5 The Fitness Estimates of the Model

 

Table 6 displays the model's indices. CMIN/DF=2.124, i .e., 3 benchmark values and a CFI of. GFI =.96, i .e., >0,95 benchmark value. 944, i .e., >0, benchmark value, and AGFI=. 894, i .e., 0-1 benchmark value, and RMSEA=. P value .000(0.05.0), and 0(0,1 benchmark value). The ultimate CFA model created includes three domains and nine items, and we can see that the values are consistent with the appropriate benchmark values for each. Honesty: 1, 7, 17, Competence: 4, 9, and Global Trust: 11, 14, 16, and 18 Table 3 are the final nine items.

The finalized scale Table 6 contains nine items and three factors, and has a reliability score of 0.964 as Cronbach's alpha. It has a higher degree of reliability and validity, and it can be used to assess the trust of policyholders in Indian settings.

Table 6

Table 6 List of Items Retained in Validated Scale
Item No.	Factor	Item
1	Honesty	You think the people at XXX are completely honest
2	Honesty	If someone at XXX made a serious mistake, you think they would try to hide it
3	Honesty	If you have a question, you think XXX will give a straight answer
4	Competence	As far as you know, the people at XXX are very good at what they do
5	Competence	You feel like you have to double check everything XXX does
6	Global trust	You worry there are a lot of loopholes in what XXX covers that you don't know about
7	Global trust	You believe XXX will pay for everything it is supposed to, even really expensive treatments
8	Global trust	If you got really sick, you are afraid XXX might try to stop covering you altogether
9	Global trust	All in all, you have complete trust in XXX

Objective 2: To compare the factors that influence the trust on the health insurer.

Ho1 Trust perceived by health insurance policy holder on the insurance provider have difference towards Gender

Ho2 Trust perceived by health insurance policy holder on the insurance provider have difference towards Age

Ho3 Trust perceived by health insurance policy holder on the insurance provider have difference Educational qualification

Table 7

Table 7 Summary Findings of Descriptive Statistics of Policy Holder’s Trust to Gender, Age and Educational Qualification
Measures	P value	CMIN/ DF	RMR	RMSEA	GFI	AGFI	PCFI	IFI	CFI
Result	0.000	2.124	0.038	.079	0.944	0.894	0.503	0.923	0.986
Benchmark	<0.05	<3	<0.08	<0.1	>0.90	0 -1	>0.8	>0.90	>0.95

 

Educational qualification

Variables	N	Mean	SD	SED
Gender
Male	91	43.41	7.81	0.819
Female	89	40.6	7.69	0.815
Age
Age above 40 years	84	44.23	6.73	0.734
Age below 40 years	96	40.08	8.28	0.846
Educational qualification
Graduates	108	43.58	7.58	0.730
Non-graduates	72	39.67	7.72	0.910

 

With respect to trust by the policy holder’s, the demographic variable gender has N=91, Mean=43.41, SD=7.81 and SED=0.819 for Males and N= 89, Mean= 40.60, SD= 7.69 and SED= 0.815 for females. This goes on to show that male respondents had more trust than their counterparts in this sample. With regards to age, above 40 years has N=84, Mean=44.23, SD=6.73 and SED= 0.734, and N= 96, Mean= 40.08, SD= 8.28 and SED= 0.846 for below 40 years. Here, we notice respondents with more than 40 years of age held more trust than the below 40 years respondents. Similarly, with regards to educational qualification N=108, Mean= 43.58, SD= 7.58 and SED= 0.730 was for graduates and above, and N=72, Mean= 39.67, SD=7.72 and SED= 0.910 for non-graduates. The respondents who were graduates showcased more trust than the non-graduates.

Table 8

Table 8 Gender, Age and Educational Status of Respondents
	Levene's Test for		t-test for Equality of Averages
Equality of Variances
	F	Sig.	t	df	Sig. (2-tailed)
Variance	Gender
Equal variances assumed	0.099	0.753	2.432	178	2.811
Age
Equal variances not assumed	7.127	0.008	3.698	177.05	0
Educational qualification
Equal variances assumed	0.337	0.562	3.369	178	0.001

 

Table 5 displays the results of Lopez's test, which indicate that the data is not statistically significant at the required level of confidence, i .e., that it was found to be gender specific. Related variance was so used as a t-test for interpretation by the investigator in this circumstance. Also, if t calc> t tab is selected, the void hypothesis cannot be rejected. The gender T-value is 2. 432, which is higher than the accepted 1. 96. So, Ho1 there is no obvious difference between the policyholder's perception of trust in terms of gender at the insurance company. This eliminates the gender gap between policyholders and a large portion of the trust in health insurance providers.

Similar to the Levene's test signature, policyholders 'age, etc. value is 0.008, or less than 0.008. Therefore, the data are thought to be significant. The age group's t-value is 3. 698, which is higher than the accepted 1. 96( t calc> t tab), making the variable as significant as the sig value falls below 0. 0.05. The zero hypothesis is therefore rejected because equal variance is always assumed below. Ho2 There is no discernible difference between the policyholders 'perceptions of the insurance company and the policyholders' ages. In other words, we know how crucial it is for policyholders to have faith in health insurance companies.

The Levene's test signature demonstrates the educational credentials of policyholders in relation to the credibility of health insurance providers. Because the value is 0. 562, and the value is greater than 0.000, the data is assumed to be non-significant and have a comparable variance. The graduate group's t-value, which is higher than the accepted value of 1.96, indicates that the variable is significant( 0.006) and that it is more prevalent than those in the above- and below-graduate groups. Due to the rejection of the zero hypothesis, a comparable level of variance is assumed. Based on the rejected educational credentials, there is no obvious difference between the level of trust a policyholder has in the insurance company. Due to this, a policy holder's level of education significantly influences the trust they place in insurance companies.

 

9. CONCLUSION

Trust is essential to any kind of communication between people and businesses because it has the power to influence significant outcomes. In terms of health policy, interactions between patients and policyholders and increased transparency in the insurance sector have grown tremendously. Health insurers work to build trust among the common public to avoid the strict regulations that come with losing confidence. In the end, a method to evaluate the effectiveness of health insurers can be used as both a management tool and a research study's tool to measure and improve efficiency. The researchers have validated the Scale to Measure Patients 'Trust in Health Insurers in an Indian setting, according to Beiyao Zheng and al., from 2002. When used in standard populations, the multi-factorial scale used in this study has sufficient clinical characteristics to be used with all types of health insurance. Because the samples were collected from both private and public insurance holders as well as managed care and indemnity, the validated scale can be used in non-homogenous areas without restrictions. Because the three socioeconomic factors have proven to be important, consideration should be given where necessary to increase the number of insurers and improve the company's performance.

 

CONFLICT OF INTERESTS

None. 

 

ACKNOWLEDGMENTS

None.

 

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