Review Article - (2023) Volume 31, Issue 3
Received: 18-Feb-2022, Manuscript No. IPQPC-22-11853; Editor assigned: 21-Feb-2022, Pre QC No. IPQPC-22-11853(PQ); Reviewed: 07-Mar-2022, QC No. IPQPC-22-11853; Revised: 11-Mar-2022, Manuscript No. IPQPC-22-11853(R); Published: 18-Mar-2022, DOI: 10.36648/14791064.31.3.25
India has a population of 1.26 billion people in that three quarters live in rural areas. Approximately, in India 400 million people live on less than 1.25 US $ per day In spite of this, most Indians seek healthcare in private facilities. Due to many years of neglect, lower-level public healthcare facilities often suffer from a variety of problems, including worker absenteeism and dual public-private practice, low demand for their use, and shortages of supplies and staff. All these sustainable changes raises questions like, how the health care is delivered and utilized, combined with demands for expensive new technology and how the funds are mobilized. In case of utilization, access to public healthcare is central, in the performance of utilization of health care systems. In fact, the importance of service delivery for people has resulted in measurement of utilization and access having a prominent role in the health policy literature. Opinions about the access differs, whether the emphasis should be put more on describing characteristics of the providers or the actual process of care. However, access to health care can be elaborated by integrating demand and supply-side-factors. Many researchers, policy makers and practitioners, often pushed in confusion about the utilization, innovations in public health financing and about the better usage of Public Health care centers. The main obstacle to access Public health care center was the non-medical direct cost (travel cost) and the non-medical indirect cost (waiting time) incurred by the households especially in rural areas which mainly related to financial interventions. However, there are multiple factors in addressing the access costs alone. The rationale of this paper is to provide an overall framework of the various barriers to access Public health care center.
Sustainable; Literature; Utilization; Framework
Generally in health care systems, effectiveness, efficiency and equity are complementary to one another. Improving health effectiveness increases efficiency which creates opportunities for effectiveness and equity [1]. But in Indian health system effectiveness, efficiency and equity has become conflict to each other [2]. Maximizing effectiveness by allocating additional resources like providing hospital beds, increasing the number of PHC may conflict with efficiency i.e., the cost per hospital bed or other resources which will be high with respect to effectiveness [3]. This in case deemed unfair in terms of disparities or inequalities in accessing the health services. The measures taken in improving the access to health services also not up to the mark in accessing the medical care [4]. According to accessibility refers to ‘the usage of health services when there is a need [5]. In India, Public hospitals are known for low quality treatment, long waiting period, long distance, inconvenient location and inadequate facilities in public hospitals [6,7]. Hence utilization of Public health care centers depends upon the direct as well as the indirect cost incurred by the households in utilizing the Public health care centers.
Various dimensions in the access of public health care center
Barriers to accessing health services can stem from the demand side and/or the supply side [7]. In case of supply side, government had put enough efforts in bringing the public health care with maximum facilities like human man power and infrastructure. The focus of various health policies concentrates mainly on reducing supply side barriers. In this study, most important reasons for not seeking public health care centers were found to be demand factors. Demand-side determinants are factors influencing the capability or the facility to use health services by individual. According to framework, demand side analysis can be analyzed under four dimensions [6].
Demand side barriers
• Information of health care choices in rural areas
• Preference of households/cultural or community
Direct costs: Distance cost-long and short travel to facilities.
Indirect Cost: Opportunity cost-Need for the patient or a care taker to stay for long periods in order to seek care.
Waiting cost-Long time wait to avail the facility/to see the staff
This study attempts to augment the barriers in the rural areas of Karnataka in the utilization of public health care centers
Objective
• To study the demand for health care services provided by public hospitals
• To measure the impact of indirect cost on health-care-seeking behaviour
For the present study, the data was collected from four districts of Karnataka state. The districts were selected according to their development in terms of socio economic indicators. Of the four districts, Shimoga identified as good performing district (as given by National Commission on Population, GOI), Mandya identified as average performing district and two poor performing (Bijapur and Koppal) were selected for the study. In four districts, 14 villages were selected of which are 4 villages located within the radius of 5 to 10 Kms and 10 villages above 10 Kms away from the public health care center were selected. A total of 1404 samples were collected.
The survey data was analyzed in two stages
1. Chi-square tests to examine the association between demand for health care services and attributes like age, occupation, income level and education.
2. Factor analysis was used to explore the predominant factors affecting the access and equity in seeking Public health care centers followed by regression to evaluate the impact of indirect cost on utilization of public hospitals by rural households.
Socio economic characteristics of households
First step is to examine the socio economic characteristics and its illness in the selected districts of Karnataka morbidity pattern and its correlates in the state (Table 1).
| Socio-economic characteristics | District name | Total | ||||
|---|---|---|---|---|---|---|
| Bijapur | Koppal | Mandya | Shimoga | |||
| Age | 15-20 | 41 | 27 | 10 | 12 | 90 |
| 21-30 | 215 | 152 | 72 | 87 | 526 | |
| 31-40 | 142 | 78 | 74 | 63 | 357 | |
| 41-50 | 40 | 54 | 74 | 51 | 219 | |
| >50 | 68 | 38 | 57 | 49 | 212 | |
| 506 | 349 | 287 | 262 | 1404 | ||
| χ2 Value | 83.57 p<0.01 | |||||
| Gender | Male | 202 | 181 | 156 | 81 | 620 |
| Female | 304 | 168 | 131 | 181 | 784 | |
| Total | 506 | 349 | 287 | 262 | 1404 | |
| χ2 Value | 42.8 p<0.01 | |||||
| Marital | Married | 441 | 304 | 210 | 231 | 1186 |
| Unmarried | 45 | 33 | 47 | 11 | 136 | |
| Single | 6 | 1 | 8 | 0 | 15 | |
| Widowed | 14 | 6 | 15 | 20 | 55 | |
| Divorced | 0 | 5 | 7 | 0 | 12 | |
| Total | 506 | 349 | 287 | 262 | 1404 | |
| χ2 Value | 72.8 p<0.01 | |||||
| Education | Illiterate | 223 | 95 | 92 | 67 | 477 |
| Primary/middle | 136 | 164 | 98 | 111 | 509 | |
| 10th class/PUC | 73 | 49 | 56 | 39 | 217 | |
| >PUC | 74 | 41 | 41 | 45 | 201 | |
| Total | 506 | 349 | 287 | 262 | 1404 | |
| χ2 Value | 59.09 p<0.01 | |||||
| Monthly income | <4,000 | 182 | 91 | 60 | 11 | 344 |
| 4,000-6,000 | 224 | 148 | 138 | 151 | 661 | |
| 6,000-8,000 | 88 | 74 | 69 | 69 | 300 | |
| 8,000-10,000 | 9 | 35 | 14 | 14 | 72 | |
| >10,000 | 3 | 1 | 6 | 17 | 27 | |
| Total | 506 | 349 | 287 | 262 | 1404 | |
| χ2 Value | 154.9 p<0.01 | |||||
Table 1: Socio economic characteristics of households.
It was found that of the total 1404 households, most of the households are in the age group of 21-30 (526) followed by 31-40 (357). In case of gender Female was more comparative to men since females were more concern towards the health and they are the more responsible persons in the family. In rural areas, male dominates females, but in case of health, females were more particular about health than men. In marital status, most of selected households are married (1186) In case of education, Bijapur has the more number of illiterates than other districts. Koppal which declared as more poorly developed area (GOI, Karnataka 2013) has the maximum number of primary learners which illustrates that development of area does not depend only upon education.
Since the study is confined to rural areas, most of the households receive their family income in the range of 4000-6000. This is positively associated with the present study, since it targeted towards the lower income people. Hence the selection of sample with regard to income is significant.
Access-Health seeking behavior
The first part deals with the choice of health seeking behavior of the people and second part estimates the critical failure factors for the curative healthcare services at the rural areas of Karnataka.
i. Choice of health care services
Analysis of health seeking behavior of the people
Health seeking behavior is a significant pointer used to study the baseline realities of exiting healthcare services. It has been explained by using the information on choice of medical treatment for their illness. Present study analyzes the possibility of using the two hospitals public as well as private (Table 2).
| District_Name | Total | ||||
|---|---|---|---|---|---|
| Bijapur | Koppal | Mandya | Shimoga | ||
| Always | 114 | 195 | 66 | 81 | 456 |
| Very often | 341 | 113 | 146 | 107 | 707 |
| Sometimes | 51 | 41 | 75 | 64 | 231 |
| Rarely | 0 | 0 | 0 | 9 | 9 |
| never | 0 | 0 | 0 | 1 | 1 |
| 506 | 349 | 287 | 262 | 1404 | |
Table 2: Prefer to go for public hospitals.
Prefer to go for public hospitals
Table 2 explains the analysis of health seeking behaviour of the households to control the diseases. Of all the treatment actions taken, Koppal registered the maximum number of households seeking public hospitals always followed by Bijapur scoring 114. Quite often people from Bijapur (341) prefer to go for public hospitals. Very few households from Shimoga recorded to prefer public hospitals. This implies that rural households are eager to utilize the public hospitals than private hospitals (Graph 1 and 2).
Figure 1: Prefer to go for public hospitals.
Figure 2: Prefer to go for Private hospitals.
Table 3 explains the preference for private hospitals by the households. In all the selected study area, households wish to utilize the private hospitals sometimes but not always.
| District_Name | Total | ||||
|---|---|---|---|---|---|
| Bijapur | Koppal | Mandya | Shimoga | ||
| Always | 23 | 21 | 63 | 4 | 111 |
| Very often | 50 | 33 | 54 | 64 | 201 |
| Sometimes | 403 | 278 | 167 | 139 | 987 |
| Rarely | 30 | 17 | 3 | 55 | 105 |
| never | 0 | 0 | 0 | 0 | 0 |
| 506 | 349 | 287 | 262 | 1404 | |
Table 3: Prefer to go for private hospitals.
Bijapur has the maximum of 403 towards private hospitals followed by Koppal (278), Mandya (167) and Shimoga (139). Nearly 63 housholds from Mandya enrolled that they prefer to go for private health care always.
They are not ready to utilize public health care. Almost, out of 1404 households, 201 households prefer to go private hospitals very often. Of that, Shimoga has the maximum of 64.
The main reasons for utilization of private hospitals were:
• Medical treatment not appropriate for illness
• Non- availability of ime
• Health facility being far away from home
ii. Critical failure factors for the curative healthcare services
From the policy perspective, it is valuable to examine the health seeking behaviour across the varies predictors
KMO and Bartlett's Test for Access and Equity
This table shows two tests that indicate the suitability of the data for structure detection. As per Kaiser-Meyer-Olkin Measure of Sampling Adequacy high values (close to 1.0) generally indicate that a factor analysis can be done with the data. Table 4 shows value nearing to 1 (0.881) hence suited for factor analysis.
| Kaiser-Meyer-Olkin Measure of Sampling Adequacy. | 0.881 | |
| Bartlett's Test of Sphericity | Approx. Chi-Square | 13795.1 |
| Df | 55 | |
| Sig. | 0 | |
Table 4: KMO and Bartlett's Test for Access and Equity.
Bartlett's test of sphericity tests the hypothesis that the correlation matrix is an identity matrix, which indicates that the variables are unrelated and therefore unsuitable for structure detection. The significance value (0.000) was less than assumed value (.05). Small values (less than 0.05) of the significance level indicate the effectiveness of factor analysis. Hence Null hypotheis Ho was rejected and the data found suitable for factor analysis.
The factor analysis was conducted on different measures to purify the data.
All the 1404 responses of the surveyed data were examined using principal component factor analysis as the extraction technique and Varimax as the rotation method.
Only factors with Eigen value more than 1 were included in final solutions.
It was seen from Table 5 that only 3 factors have Eigen value more than 1.
| S.no | Initial Eigenvalues | Extraction Sums of Squared Loadings | Rotation Sums of Squared Loadings | ||||||
|---|---|---|---|---|---|---|---|---|---|
| Total | % of Variance | Cumulative % | Total | % of Variance | Cumulative % | Total | % of Variance | Cumulative % | |
| 1 | 5.833 | 53.028 | 53.028 | 5.833 | 53.028 | 53.028 | 5.772 | 52.471 | 52.471 |
| 2 | 1.73 | 15.731 | 68.759 | 1.73 | 15.731 | 68.759 | 1.698 | 15.44 | 67.911 |
| 3 | 1.354 | 12.313 | 81.072 | 1.354 | 12.313 | 81.072 | 1.448 | 13.161 | 81.072 |
| 4 | 0.546 | 4.961 | 86.033 | ||||||
| 5 | 0.455 | 4.138 | 90.171 | ||||||
| 6 | 0.296 | 2.686 | 92.858 | ||||||
| 7 | 0.224 | 2.038 | 94.896 | ||||||
| 8 | 0.202 | 1.833 | 96.728 | ||||||
| 9 | 0.185 | 1.679 | 98.408 | ||||||
| 10 | 0.126 | 1.144 | 99.552 | ||||||
| 11 | 0.049 | 0.448 | 100 | ||||||
| Extraction Method: Principal Component Analysis. | |||||||||
Table 5: Total Variance Explained for Access and Equity.
The factors under each variable were decided by Factor loadings followed by rotated factor matrix Table 6.
| Categorization of components | TVE | List of factors | RCMV |
|---|---|---|---|
| Infrastructure | 53.028 | Medicines | 0.659 |
| Doctors and Nurses | 0.948 | ||
| Treatments | 0.947 | ||
| Ambulance services | 0.881 | ||
| Diagnostic services | 0.874 | ||
| Spending a day for treatment | 0.908 | ||
| Physical accessibility | 15.731 | Distance bother s | 0.849 |
| Spend lot of time in travelling | |||
| 0.778 | |||
| Mobility | 12.313 | Spending on transportation | 0.953 |
Table 6: Failure factors for accessing Public health care centers.
Analysis shows that eleven factors reduced to three with different priorities for the preference of seeking public health care hospitals. Component 1 Infrastructure has the most priority factors with percentage of variance as 53.028%. Preference in seeking public health care hospitals depends upon the availability of human resources like doctors, nurses and health assistance. Also households expected ambulance services for emergency care. Apart from this, major extracted factor of the RCMV value (0.908) was towards spending the whole day for treatment in the hospital. Component 2 Physical accessibility (15.731%) In this component, access depends upon the closeness of health centers. It is evident from table 4.9 that distance bothers to reach health centers and spending or waiting for buses are the two main factors rotated from this component.
Component 3 Mobility. The third priority is then Mobility of the transportation. According to the survey, villages are provided with good transportation facilities. But there exists a mismatch between the bus schedule and PHC timings which creates transportation problem for the households. This shows that households seeks public hospitals when there is enough availability of human resources, medicines and diagnostics services, ambulance services and good transport facilities. It also evident to notice that, irrespective of distance, when there is availability of transportation; people are ready to go for public health centers
Factors influencing the utilization of Public Health care centers: Table 7 and Table 8 results regression model to find the factors influencing the utilization of public health care hospitals. The results indicate that the R-square of the model I is 0.292. This means that the model explains 59.5% of the variance in the utilization of public health care hospitals (i.e. the dependent variable). In other words the 11 independent variables explain 60% of the variations in the utilization of public health care services.
| Model | R | R Square | Adjusted R Square | Std. Errorof the Estimate |
| 1 | 0.546 a | 0.298 | 0.292 | 0.59528 |
Table 7: Model Summary.
| Model | Unstandardized Coefficients | Standd Coeffi | t | Sig. | ||
|---|---|---|---|---|---|---|
| B | Std. Error | Beta | ||||
| 1 | (Constant) | 1.381 | 0.109 | 12.692 | 0 | |
| Distance bother s | -0.095 | 0.031 | -0.091 | -3.015 | 0.003 | |
| Huge time in getting appointment | 0.1 | 0.028 | 0.098 | 3.504 | 0 | |
| Spending on transportation | -0.173 | 0.03 | -0.182 | -5.724 | 0 | |
| Availability of medicines in hospitals | 0.111 | 0.032 | 0.145 | 3.487 | 0.001 | |
| Availability of doctors and nurses in health care centers | 0.061 | 0.04 | 0.12 | 1.541 | 0.124 | |
| Spending a day for treatment | 0.113 | 0.039 | 0.223 | 2.927 | 0.003 | |
| Spend lot of time in travelling | -0.036 | 0.018 | -0.093 | -2.013 | 0.044 | |
| Treatment 24 × 7 provided | -0.027 | 0.031 | -0.05 | -0.89 | 0.374 | |
| Availability of ambulance services | 0.13 | 0.027 | 0.226 | 4.785 | 0 | |
| Health care centre provides hospital transport ambulance | 0.015 | 0.016 | 0.027 | 0.933 | 0.351 | |
| Health care centre provides diagnostic service | -0.03 | 0.022 | -0.053 | -1.392 | 0.164 | |
Table 8: Significant factors in accessing public health care centers.
Dependent variable: Prefer to go for PHCs
Factors namely emergency care (t=-4.785, p=0.000<0.05), availability of medicines (t=3.487, p=0.000<0.05) and distance bothers in seeking health care (t=3.504, p=0.000<0.05) are the three major predictor variables with highest positive impact with significance. Next 4 factors namely easy to reach (t=-3.015), transportation facility is good (t=-5.724), treatment is available at all time (t=-2.013) and diagnostic services (t=-1.392) shows negative impact for utilization of public health care centers with significance. The results indicate that availability of doctors and health care assistance shows insignificance with respect to other predictor variables. This illustrates that, households prefers to seek the public health in case of easy availability and accessibility.
Seeking health services for themselves or for someone in their household depends mainly on various demand and supply side factors. It is important to note that the cost of obtaining PHC and other health services to get the disease cured is the total expenditure incurred by the household. It implies that demand side of health service utilization is as pertinent as the supply side factor. From the above analysis, Factors like travel time, travel distances, transport costs, diagnostic and medicine cost shows the most significant critical factors acquired by the rural peoples.
Distance to health care facilities and access to transportation could significantly impact health care utilization. The distances to regional health care centers can often be more, especially in the most rural areas. A study by examined that distance to regular services was found to have negative significant with the number of visit to the public hospitals. Another important factor is the waiting cost. Spending the full day in hospitals for the treatment of health makes the households to lose their daily wages which in turn to be the Out Of Pocket expenditure for them. Hence cost is both a supply side and a demand side phenomenon. However, indirect cost like travel cost, waiting cost in hospitals in health care is only of value if the care is of high quality [8,9].
Infrastructure, physical accessibility and Mobility factors do have impact on the regular visit to the hospitals. Hence infrastructure, physical accessibility and Mobility are the three main critical failure factors for seeking the public health care facilities [10,11].
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Citation: Ganesh L (2022) Critical Failure Factors and its Impact in Accessing Rural Primary Health Care Hospitals-Special reference from Karnataka. Qual Prim Care Vol:30 No:1
Copyright: This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
