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  • Open Access

Does spending matters? Re-looking into various covariates associated with Out of Pocket Expenditure (OOPE) and catastrophic spending on accidental injury from NSSO 71st round data

  • 1,
  • 1Email author,
  • 2 and
  • 3
Health Economics Review20177:48

https://doi.org/10.1186/s13561-017-0177-z

  • Received: 19 April 2017
  • Accepted: 27 October 2017
  • Published:

Abstract

Background

Accidental Injury is a traumatic event which not only influences physical, psychological, and social wellbeing of the households but also exerts extensive financial burden on them. Despite the devastating economic burden of injuries, in India, there is limited data available on injury epidemiology. This paper aims to, first, examine the socio-economic differentials in Out of Pocket Expenditure (OOPE) on accidental injury; second, to look into the level of Catastrophic Health Expenditure (CHE) at different threshold levels; and last, to explore the adjusted effect of various socio-economic covariates on the level of CHE.

Methods

Data was extracted from the key indicators of social consumption in India: Health, National Sample Survey Organisation (NSSO), conducted by the Government of India during January–June-2014. Logistic regression analysis was employed to analyse the various covariates of OOPE and CHE associated to accidental injury.

Findings

Binary Logistic analysis has demonstrated a significant association between socioeconomic status of the households and the level of OOPE and CHE on accidental injury care. People who used private health services incurred 16 times higher odds of CHE than those who availed public facilities. The result shows that if the person is covered via any type of insurance, the odd of CHE was lower by about 28% than the uninsured. Longer duration of stay and death due to accidental injury was positively associated with higher level of OOPE. Economic status, nature of healthcare facility availed and regional affiliation significantly influence the level of OOPE and CHE.

Conclusion

Despite numerous efforts by the Central and State governments to reduce the financial burden of healthcare, large number of households are still paying a significant amount from their own pockets. There are huge differentials in cost for the treatment among public and private healthcare providers for accidental injury. It is expected that the findings would provide insights into the prevailing magnitude of accidental injuries in India, the profile of the population affected, and the level of OOPE among households.

Keywords

  • Accidental
  • Socioeconomic
  • OOPE
  • Logistic
  • Economic burden

Introduction

Injuries1 are well acknowledged globally as a major cause of death and disability, and road traffic injuries (RTI)2 accounted for nearly more than 1 million deaths in 2015 [13]. As per WHO estimates, RTI, are predicted to be the fifth leading cause of death in all age groups by the year 2030. Injuries results into more annual deaths as compared to HIV, Malaria, and Tuberculosis combined [4]. Each year approximately 250 million people worldwide are affected by injury, and of these more than 300,000 people die [5]. The socioeconomic burden of injury is quiet significant which affects most adversely to the younger and most productive demographic segment of the population [6]. In particular, the burden is disproportionately in Low and Middle-Income Countries (LMICs) where more than 90% of the mortality occurs due to unintentional injuries, primarily due to Road Traffic Accidents (RTA)3 [7, 8]. Individuals who sustain injuries in LMICs are six times more likely to die than those in High-Income Countries (HICs), given the limited capabilities for trauma care in low-income settings [9].

Within LMICs, India faces one of the highest burdens of injuries. According to a study on the burden of diseases and injuries, 28% of years of life lost (YLL) in India are attributed to injuries [10]. In fact, injuries are the first cause of YLL among all causes of death in India. As per the World Health Organization (WHO), injury is the second leading cause of death in India, with deaths annually from RTA being among the highest in the world [4, 11]. During the past few years, with the growing pattern of industrialization and motorization, there has been an increase in the number of deaths from injuries [1214]. This is projected to escalate further since the country is undergoing rapid urbanization. Even though the burden of diseases in India has been aggravated by accidental injuries, limited information is available about the economic consequences associated with it. The direct cost involved in the accidental injury was found to be huge, which not only forces the households into the poverty trap due to high treatment cost and disability, but also imposes high economic as well as societal cost too [15, 16]. Estimating the cost of injuries is identified among the five priority items to address the global burden of unintentional injuries [17].

Background

Indian health system is characterized by low public spending, as major share of the health expenditure is borne by the households in form of out-of-pocket expenditures (OOPE)4 [1820]. In case of accidental injury, households have to bear heavy cost both in terms of direct as well as indirect health expenditure. The impoverishment of households in India due to OOPE and the catastrophic health expenditure (CHE)5 on health are well documented [16, 21, 22] and these are reported to be higher for injuries than for other ailments [17]. The majority of India’s citizens receive health care through a publicly funded system governed by the Ministry of Health. Data from public health facilities may provide some insights on injuries, however, many of the injuries could have been reported to non-public or private health care facilities or not been reported at all [23]. Further, hospital records, albeit helpful to understand patterns and mechanisms of injury retrospectively, have been found to be deficient in several settings [24]. Few previous works on a tertiary hospital had identified that fundamental information on patient demographics, circumstances under which accidents6 occur; treatment costs and modalities were frequently not recorded [25].

Despite the overwhelming burden of injuries, there is limited data on injury epidemiology and its outcomes in India. Mortality statistics, though used as an indicators of injury magnitude, may represent the ‘tip of the iceberg’ since non-fatal injuries exceed fatal injuries by up to 20 times [26, 27]. As such no nationwide data is available on the prevalence, demographic profile of the injured persons, treatment and outcome of the accidental injury in the country. Available studies are confined either to the single facility or they are limited to some specific category such as RTA, which fails in providing complete picture of the injury profile in India [24, 27]. Public health interventions and policies to reduce harm and prevent accidents would be effective only when they are designed for the right population, at the right time and in the right setting [28].

Since cost of injuries may vary according to age and gender, influenced by subtle variation of cultural lifestyle and behavioral pattern, knowledge of trauma epidemiology is essential for identifying the correlates of OOPE on accidental injuries and development of solutions from the public health viewpoint. Accidental injury involves higher expenses on seeking healthcare services and forces households to spend significantly higher amount from their own pockets leading to impoverishment. As per available information in the latest NSSO report (NSS KI(71/25.0, NSSO, 2014), [17] average medical expenditure per hospitalization case on accidental injury, is the fifth highest expenses borne by the households after OOPE on other major ailments such as cancer, cardio-vascular, genitourinary, and psychiatric and neurological diseases (Appendix 1). Though injury is an important cause of death in India, there is limited knowledge regarding the economic impact on households. Earlier, studies examining the economic impact of injury on households were based on either hospital settings or on the basis of limited samples. This is not adequate to explain the overall economic impact of injury on households. Given the limitations of previously reported studies, we undertook a study to determine the magnitude of injuries, their distribution and associated health care expenditure in India. The major objectives of this paper are: first, to describe the prevalence of accidental injury among the sampled population, second, to examine the socio-economic differentials in OOPE on accidental injury by taking both direct and indirect costs into account; third, to look into the level of CHE (5%, 10% and 15%) at various threshold levels due to accidental injury; fourth, to measure the adjusted effect of various covariates on the level of CHE. It is expected that the findings would provide insights into the prevailing magnitude of accidental injuries in India, indicate the epidemiological distribution, the profile of the population affected and correlates and identify the prevalence of catastrophic expenditure, if any. Such breadth of information could be used to inform the planning central to all facets of injury prevention.

Method

Data is used from the key indicators of social consumption in India: Health, National Sample Survey Organization (NSSO), conducted by the Government of India January–June-2014 i.e., the 71st round. The present study uses household schedule 25.0 questionnaires. The Socio-economic Survey (SES) rounds contain information on household’ssocial consumption of health on various heads such as, the proportion of ailing persons, spells of ailments and their treatment, rate of hospitalization, the cost of treatment-hospitalization and the cost of treatment–non-hospitalized. The recall periods of institutional expenses are for 365 days [17]. Here we have included cost under two heads i.e., direct and indirect. Direct cost includes doctor’s/surgeon’s fee (hospital staff/other specialists, medicines, diagnostic tests, bed charges, other medical expenses (attendant charges, physiotherapy, personal medical appliances, blood, and oxygen). Indirect cost includes the cost of transport for the patient, other non-medical expenses (in INR) incurred by the households (food, transport for others, expenditure on escort, lodging charges if any, etc.). The outcome variable for the study is OOPE on injury. The approach to measure OOPE for healthcare payments was adopted from Wagstaff and Doorslaer, in the World Bank document. In addition to the medical and non-medical expenditure, information was also available on the household’s socio-economic and demographic characteristics.

Variables under study

This paper explains the association between the HE and socio demographic variables and how this changes across caste, education and other SES variables. The independent variables (relating to of the individuals and households) in the study are: age (less than 15, 15–29, 30–59 and 60+); sex (male and female); level of education (illiterate, up to primary, up to secondary, graduation and above); place of residence (urban/rural); social group (Scheduled Caste (SC), Scheduled Tribe (ST), Other Backward Caste (OBC) and Others); religious affiliation (Hindu, Muslims and Others); household size (1–3 members, 4–6 and 7+ members); economic status (Poorest, Poorer, Middle, Richer and Richest); level of care (public/private); insurance coverage (covered/not covered); source of financing (income/savings, borrowings and others); survival status (Dead/Alive); duration of stay (1–2 days, 3–7 days, 8–14 days and 14+ days); and, region (north, northeast, east, central, west, south).

First, descriptive analysis was done to assess the socio-economic and demographic profile of the persons with accidental injury. Second, we calculated socio-economic differentials in OOPE (with 95% CI) on the accidental injury. Third, CHE was calculated at different threshold levels (5%, 10%, and 15%). Last, Binary logistic regression analysis was employed to explore the relative effect of socio-economic and demographic characteristics on the level of CHE for injury in India.

Logistic regression can be used to predict a dependent variable on the basis of independents, and to determine the percent of the variance in the dependent variable explained by the independents; to rank the relative importance of independents; to assess interaction effects, and to understand the impact of covariates. Logistic regression applies maximum likelihood estimation after transforming the dependent into a logit variable (the natural log of the odds of the dependent occurring or not). So, logistic regression estimates the probability of certain event, whether occurring or not. The multiple logistic models can be noted as:
$$ \ln \left(\frac{p}{1-p}\right)=\alpha +{\beta}_1{x}_1+{\beta}_2{x}_2+{\beta}_3{x}_3+\dots {\beta}_i{x}_i+e $$

Where, p is the probability of occurrence of multimorbidity, p(у = 1);β 1 β 2, β 3,… β i refer to the beta coefficients;x 1 x 2 x 3 ….x i Refer to the independent variables and e is the error term. STATA 12 is used to analyze the data. We have also looked into the problem of endogeneity, which may not arise in this case due to following reasons. As we all know the problem of endogeneity occurs due to measurement errors and omitted variable bias. The measurement errors could be due to recall bias and other similar issues related to the explanatory variables. This problem is addressed in a large sample here. Further, the measurement errors will not arise here as whatever is true for higher caste for instance, will hold good for the lower caste or whatever is true for lower education will also hold good for higher education. It is because the explanatory variables are not systematically different.

Results

Socio-demographic profile of the respondents with accidental injury

Socio-economic and demographic characteristics of the persons with accidental injury7 (Table 1) indicates that about 48% belonged to the age group of 30–59 years, 24% were in age group of 15–29, 16% were aged 60+ and 12% were in the age group of less than 15 years. About 56% belong to rural background. Nearly 73% of the persons with accidental injury were males. About, 24% sampled population were illiterate, 26% had studied up to primary, 39% up to secondary, and 12% had studied up to graduation and above. Majority of the persons belonged to OBC group, followed by Others, SCs and STs. Nearly 80% persons belonged to the Hindu community, 12% were Muslims and 8% were of other religious groups.
Table 1

Socio-economic and demographic characteristics of persons with accidental injury, India, 2014

Co-variates

Percent

Number

Age

 less than 15

11.9

482

 15–29

24.3

985

 30–59

47.7

1934

 60+

16.1

654

Sex

 Male

73.3

2973

 Female

26.7

1082

Education levela

 Illiterate

24.1

941

 Up to primary

25.9

1011

 Up to secondary

38.6

1509

 Graduation and above

11.5

449

Residence

 Rural

55.8

2264

 Urban

44.2

1791

Caste

 Scheduled Tribes

9.5

387

 Scheduled Castes

17.9

724

 Other Backward Classes (OBC)

39.3

1595

 Others

33.3

1349

Religion

 Hindu

79.6

3227

 Muslim

12.2

493

 Others

8.3

335

Household size

 1–3 member

21.0

853

 4–6 members

57.6

2335

 7 + members

21.4

867

Wealth quintile

 Poorest

15.0

608

 Poorer

20.9

845

 Middle

19.4

788

 Richer

23.0

935

 Richest

21.7

879

Level of care

 Public

41.2

1670

 Private

58.8

2385

Insurance coveragea

 Not covered

79.2

3098

 Covered

20.8

812

Reimbursement status

 Received

4.3

174

 Not received

95.7

3881

Survival status

 Alive

96.4

3910

 Dead

3.6

145

Duration of stay

 1–2 days

24.3

985

 3–7 days

39.4

1599

 8–14 days

17.0

688

 14+ days

19.2

783

Source of financing

 Household income and saving

69.6

2826

 Borrowings

23.0

933

 Other sources

7.3

296

Regionsb

 North

15.5

628

 Northeast

7.7

312

 East

17.2

697

 Central

22.4

909

 West

13.1

531

 South

24.1

978

Total

100

4055

Source: Authors’ estimates from NSSO survey, 2014

aBased on 3910 cases

b North: J& K Himachal Pradesh, Punjab, Chandigarh, Haryana, Delhi, Rajasthan; Northeast: Sikkim, Arunachal Pradesh,, Nagaland, Manipur, Mizoram, Tripura, Meghalaya, Assam; East: Bihar, West Bengal, Jharkhand, Odisha; Central: Uttar Pradesh, Chhattisgarh, Madhya Pradesh; West: Gujrat, Daman and Diu, Dadar and Nagar Haveli, Maharashtra, Goa; South: Andhra Pradesh, Karnataka, Lakshadweep, Kerala, Tamil Nadu, Pondicherry, Andaman and Nicobar, Telangana

About 59% people were availing private facilities; others depended on public care. Insurance coverage for accidental injury was low since only 21% were covered by any type of insurance scheme; about 79% were not covered by any insurance scheme. Nearly 96% of the households had not received any reimbursement from the insurance providers. Survival status indicates that 96% people who suffered from accidental injury survived while 4% died. Nearly 39% sampled population stayed in the hospital for an average of 3–7 days, followed by other categories. A major source of financing for accidental injury was from household income and savings; nearly 70% financing was from own pocket while 23% resorted to borrowings and 7% managed financing from other sources. Regional distribution of the sampled population, who had faced accidental injuries, was higher in the Southern (24%) region followed by Central (23%), East (17%), Northern region (16%) and others. However the share of accidental injuries out of the total sampled general population by selected socio-economic and demographic covariates was not that higher (1.21% out of general population) (Appendix 2).

Socio-economic differentials in the level of OOPE

Table 2 shows the socioeconomic differentials in OOPE. We have first analyzed the pattern of OOPE (medical, transportation and non-medical), without taking into consideration the total amount of reimbursement from insurance. Later, for examining the effect of reimbursement on the total OOPE, the amount reimbursed was deducted from the total expenditure on accidental injury care. The share of mean OOPE (without considering reimbursement amount) was higher for medical expenses (INR 24916) followed by non-medical expenditure (INR 1909) and transportation (INR 906), in the overall accidental injury expenditure (INR 27731). People in the age group of 60+ had spent more under all heads i.e., medical care (INR 30567), non-medical (INR 2376) and transportation (INR 1097), in comparison to other age groups. Similarly, spending on accidental injury among males was higher than for females under all the heads.
Table 2

Socio-economic differentials in OOPE (in INR), India, 2014

Co-variates

Medical

Transport

Non-medical

Total (Including reimbursement amount)

Total (Excluding reimbursement amount)

Age

 less than 15

12,822

589

1234

14,646

14,165

 15–29

24,535

937

1908

27,380

25,782

 30–59

26,606

914

1938

29,457

27,224

 60+

30,567

1097

2376

34,040

33,309

Sex

 Male

26,173

988

2094

29,255

27,542

 Female

21,337

674

1380

23,391

22,116

Education levela

 Illiterate

18,782

785

1568

21,135

20,849

 Up to primary

20,182

906

1832

22,920

22,248

 Up to secondary

28,415

878

2084

31,377

29,367

 Graduation and above

38,660

930

2000

41,589

34,851

Residence

 Rural

21,930

979

1863

24,772

24,127

 Urban

30,691

765

1997

33,453

30,008

Caste

 Scheduled Tribes

12,589

762

1774

15,126

14,917

 Scheduled Castes

19,978

791

1822

22,591

21,537

 Other Backward Classes (OBC)

24,130

891

1872

26,894

26,073

 Others

31,507

1030

2042

34,579

31,243

Religion

 Hindu

26,126

927

2014

29,066

27,597

 Muslim

18,250

779

1234

20,263

17,858

 Others

23,429

909

1987

26,325

24,752

Household size

 1–3 member

25,892

846

1903

28,640

25,909

 4–6 members

23,716

890

1827

26,433

24,887

 7 + members

27,871

1025

2185

31,081

30,486

Wealth quintile

 Poorest

15,579

1116

1524

18,219

18,086

 Poorer

18,355

770

1462

20,587

20,446

 Middle

18,460

746

1570

20,775

19,110

 Richer

25,312

799

2023

28,134

27,113

 Richest

47,496

1217

3008

51,720

46,467

Level of care

 Public

7014

746

1426

9186

8869

 Private

36,261

1008

2215

39,483

37,071

Insurance coveragea

 Not covered

24,466.00

881.00

1861

27,208

27,049

 Covered

25,565.00

826.00

1936

28,327

21,722

Reimbursement status

 Received

66,720

1223

22,167

70,159

24,691

 Not received

23,392

895

1897

26,184

26,184

Survival statusa

 Dead

29,365

1794

2605

33,764

33,661

 Alive

24,723

868

1878

27,469

25,805

Duration of stay

 1–2 days

4858

354

400

5613

5422

 3–7 days

18,638

842

1320

20,801

19,143

 8–14 days

32,199

1181

2433

35,813

34,170

 14+ days

60,380

1595

4833

66,809

63,385

Source of financing

 Household income and saving

20,163

727

1528

22,419

21,157

 Borrowings

35,378

1260

2780

39,400

36,973

 Other sources

29,573

1231

2225

33,031

31,418

Regionsb

 North

23,729

1046

1990

26,765

24,196

 Northeast

8970

683

1329

10,982

10,641

 East

19,256

1038

1555

21,850

19,776

 Central

25,560

816

1671

28,048

27,654

 West

35,611

905

1928

38,443

36,187

 South

24,520

836

2231

27,586

26,203

Total

24,916.

906

1909

27,731

26,132

Source: Authors’ estimates from NSSO survey, 2014

a Based on 3910 cases

b North: J& K Himachal Pradesh, Punjab, Chandigarh, Haryana, Delhi, Rajasthan; Northeast: Sikkim, Arunachal Pradesh,, Nagaland, Manipur, Mizoram, Tripura, Meghalaya, Assam; East: Bihar, West Bengal, Jharkhand, Odisha; Central: Uttar Pradesh, Chhattisgarh, Madhya Pradesh; West: Gujrat, Daman and Diu, Dadar and Nagar Haveli, Maharashtra, Goa; South: Andhra Pradesh, Karnataka, Lakshadweep, Kerala, Tamil Nadu, Pondicherry, Andaman and Nicobar, Telangana

Those who were educated till graduation or above were spending the highest on medical, transportation and non-medical heads compared to others. People residing in the urban areas were spending higher than their rural counterparts on medical, non-medical, and in terms of overall expenses. Other caste people were spending higher than their OBC, SC and ST counterparts under all the heads. Results indicate that Hindus were spending more than others and Muslims in accidental injury cases. People belonging to the richest wealth quintile were spending maximum under all the heads, followed by others. Minimum expenditure was among people who belong to the poorest economic status. Similarly, expenses were higher if the accidental injury care was sought from private facility and with access to some sort of insurance coverage. Average expenditure under all the heads was higher for deceased persons in comparison to those who had survived accidental injury. With the lengthening of stay duration in the hospital, OOPE increased at a significant rate. The level of OOPE was higher among households who met their healthcare financing need through borrowings [29]. Results also indicate regional variations in the mean OOPE - spending was higher in the Western region, followed by Central and Southern regions. Lowest level of OOPE was recorded among the Northeastern states.

Finally, we presented the socio-economic differentials in OOPE excluding the amount of reimbursement. Results suggest that the mean OOPE was marginally lower for households who received reimbursement (INR 26,132), compared to their counterparts (INR 27731). The reimbursed amount was higher among elderly population, males, households educated above graduation, residing in the urban areas, and belonging to the others as caste group. Similarly, Muslims, households with 1–3 members, higher economic section, seeking private care covered via insurance schemes and received reimbursement, stayed in the hospital for more than 14 days, received higher reimbursement, which marginally reduced the burden of OOPE as compared to other categories. However, even after receiving reimbursement from insurance companies, the households were paying a significant amount from their own pockets for seeking care for accidental injury.

Level of catastrophic health expenditure on accidental injury care

Table 3 shows the CHE at 5%, 10% and 15% level for accidental injury care. Results indicate that at 5% level, CHE was highest among categories -persons in the age group of 15–29 years, males, attained education up to graduation or above, residing in the rural areas, belongs to SC and Other social group, other religious communities, household size up to 1–3 members and belongs to the poorest section of the population.
Table 3

CHE at 5, 10 and 15% level, India, 2014

Co-variates

5%

10%

15%

Age

 less than 15

70.1

48.1

34.6

 15–29

75.0

56.9

45.1

 30–59

71.9

55.2

44.6

 60+

72.5

58.2

49.0

Sex

 Male

74.4

56.7

45.7

 Female

67.5

51.4

40.6

Education levela

 Illiterate

71.3

56.7

45.9

 Up to primary

71.6

53.4

41.5

 Up to secondary

73.1

54.8

44.5

 Graduation and above

75.8

57.8

44.8

Residence

 Rural

75.2

57.8

46.3

 Urban

69.2

52.1

41.7

Caste

 Scheduled Tribes

69.2

47.2

34.1

 Scheduled Castes

73.1

57.1

46.2

 Backward Classes

72.7

54.9

44.8

 Others

73.1

57.0

45.4

Religion

 Hindu

72.8

56.1

45.6

 Muslim

69.5

51.5

41.7

 Others

73.7

52.8

34.6

Household size

 1–3 member

77.3

62.3

52.2

 4–6 members

71.7

53.9

42.5

 7 + members

69.9

52.0

41.1

Wealth quintile

 Poorest

79.1

66.1

54.6

 Poorer

74.5

57.2

47.6

 Middle

74.7

56.1

43.5

 Richer

69.1

52.2

41.9

 Richest

67.9

48.4

37.0

Level of care

 Public

52.2

32.8

23.4

 Private

86.7

71.0

58.8

Insurance coveragea

 Not covered

73.9

56.3

45.0

 Covered

68.1

51.3

40.8

Survival status

 Dead

68.2

55.8

48.2

 Alive

72.7

55.2

44.1

Duration of stay

 1–2 days

44.0

22.0

13.4

 3–7 days

74.5

53.6

39.5

 8–14 days

86.7

73.9

64.0

 14+ days

91.8

84.0

75.4

Source of finance

 Household income and saving

66.1

47.4

36.2

 Borrowings

89.7

75.3

64.5

 Other sources

79.7

66.8

57.7

Regions

 North

69.1

51.4

39.9

 Northeast

70.1

43.9

29.1

 East

72.8

55.6

44.6

 Central

74.0

57.7

47.5

 West

79.8

64.7

53.3

 South

69.9

53.7

43.7

Total

72.5

55.3

44.3

Source: Authors’ estimates from NSSO survey, 2014

aBased on 3910 cases

Similarly, those who had availed care from private providers, not covered by insurance, survived after the accidental injury, financed from the borrowings, stayed in the hospital more than 14 days, belongs to the western regions, also incurred higher CHE at 5%. At the 10% level, also similar pattern was observed regarding CHE except for a few exceptions such as, older population of 60+ years, households who belong to the Hindu religious community and persons who died after the accidental injury, who incurred higher CHE. At the 15% level, similar trends were recorded in terms of CHE as observed at 10%, on accidental injury except for one exception; here CHE was more concentrated among persons who were illiterate. Rest of the variables showed similar results.

Results from the logistic regression analysis

Table 4 shows the results of the Binary logistic regression analysis on the related covariates of the determinants of accidental injury at 5%, 10% and 15% levels of CHE. Results reveal that odds of CHE at 5% are about 33% lower for females as compared to males. Educational level shows that persons who are educated up to primary have significantly lower odds of CHE. Odds of CHE for people who were educated up to graduation or above were about two times higher than those who were illiterate. The occurrence of CHE was lower by 44% for Muslims as compared to Hindus. Odds of CHE were significantly lower by 79% and 68% for the richest and richer section respectively, compared to the poorest. People who used private health services incurred 16 times higher odds of CHE than those who availed public facilities. The result shows that if the person is covered via any type of insurance, the odd of CHE was lower by about 28% than the uninsured. Duration of stay is an important determinant of CHE. Results indicate that odds of CHE for accidental injury care was about 34 times higher for those who stayed in the hospital for more than 14 days, as compared to those who stayed for 1–2 days. The analysis shows that odds of CHE at 10% and 15% are about two times higher for people who belong to the age group of 60+ compared to less than 15 years age group. Similarly, odds of catastrophic spending were nearly two times higher for people who had education up to graduation or above. Odds of CHE at 10% and 15% level was 89% and 87% lower for the richest section of the population as compared to the poorest group. Similarly, the odds of CHE was 14 (10% level) and 12 times (15% level) higher in private care in comparison to those who have availed care from government facilities. Odds of CHE at 10% and 15% were 55% and 70% lower for those who survived the accident than the deceased. Duration of stay significantly influences the level of CHE. Odds of CHE at 10% were about 56 times and at 15% it was 60 times higher, for those who stayed in the hospital for more than 14 days, in comparison to those who stayed only for 1–2 days. The results indicate that odds of CHE were three times higher (at 10% CHE) and two times higher (at 15% CHE) for those who borrowed for accidental injury care, than other counterparts. Odds of CHE were nearly 61% lower for the southern region people at 10% CHE and 62% lower at 15% CHE than others.
Table 4

Results from logistic regression analysis, India, 2014

Co-variates

5%

10%

15%

 

OR

95% CI

OR

95% CI

OR

95%CI

Age

 less than 15

         

 15–29

1.25

[0.73

2.13]

2.02*

[1.01

4.04]

1.74*

[0.94

3.22]

 30–59

0.92

[0.58

1.47]

1.73*

[0.94

3.22]

1.72*

[1.00

2.96]

 60+

1.01

[0.59

1.73]

2.16*

[1.06

4.43]

2.20*

[1.12

4.31]

Sex

 Male

         

 Female

0.67*

[0.47

0.96]

0.90

[0.66

1.22]

0.98

[0.72

1.32]

Education level*

 Illiterate

         

 Up to primary

0.70*

[0.47

1.05]

0.88

[0.59

1.30]

1.04

[0.70

1.56]

 Up to secondary

1.21

[0.77

1.89]

1.18

[0.74

1.89]

1.59*

[1.00

2.55]

 Graduation and above

1.90*

[1.04

3.45]

2.19**

[1.23

3.89]

2.10*

[1.19

3.71]

Residence

 Rural

         

 Urban

0.80

[0.58

1.11]

0.86

[0.63

1.16]

0.93

[0.69

1.26]

Caste

 Scheduled Tribes

         

 Scheduled Castes

0.71

[0.38

1.33]

1.05

[0.45

2.45]

0.94

[0.50

1.77]

 Backward Classes

0.94

[0.52

1.70]

1.43

[0.61

3.32]

1.25

[0.67

2.34]

 Others

1.03

[0.56

1.89]

1.82

[0.79

4.21]

1.54

[0.81

2.94]

Religion

 Hindu

         

 Muslim

0.56*

[0.35

0.89]

0.74

[0.49

1.11]

0.80

[0.53

1.21]

 Others

1.01

[0.51

2.00]

0.98

[0.60

1.60]

0.73

[0.43

1.23]

Household size

 1–3 member

         

 4–6 members

0.95

[0.65

1.38]

0.84

[0.60

1.18]

0.88

[0.62

1.24]

 7 + members

0.74

[0.46

1.20]

0.73

[0.46

1.15]

0.75

[0.49

1.17]

Wealth quintile

 Poorest

         

 Poorer

0.72

[0.44

1.19]

0.45**

[0.28

0.72]

0.59*

[0.38

0.91]

 Middle

0.61*

[0.36

1.03]

0.26***

[0.15

0.45]

0.24***

[0.14

0.41]

 Richer

0.32***

[0.18

0.54]

0.24***

[0.14

0.43]

0.29***

[0.17

0.48]

 Richest

0.21***

[0.12

0.37]

0.11***

[0.06

0.20]

0.13***

[0.07

0.22]

Level of care

 Public

         

 Private

16.38***

[11.62

23.08]

14.41***

[10.34

20.09]

11.91***

[8.57

16.57]

Insurance coverage*

 Not covered

         

 Covered

0.72*

[0.51

1.03]

0.85

[0.62

1.17]

0.78

[0.55

1.11]

Survival status

 Dead

         

 Alive

0.65

[0.30

1.41]

0.45*

[0.21

0.97]

0.30**

[0.15

0.61]

Duration of stay

 1–2 days

         

 3–7 days

6.90***

[4.80

9.91]

6.74***

[4.65

9.76]

7.57***

[5.23

10.94]

 8–14 days

21.23***

[13.20

34.16]

25.18***

[16.10

39.39]

23.73***

[15.42

36.53]

 14+ days

33.87***

[19.68

58.28]

56.26***

[35.46

89.25]

59.85***

[37.97

94.34]

Source of finance

 Household income and saving

         

 Borrowings

4.61***

[3.02

7.04]

3.44***

[2.48

4.77]

2.33***

[1.69

3.22]

 Other sources

3.29**

[1.56

6.93]

2.22**

[1.22

4.05]

2.06**

[1.20

3.55]

Regions

 North

         

 Northeast

1.73*

[0.91

3.31]

1.23

[0.64

2.37]

0.84

[0.44

1.59]

 East

1.43

[0.87

2.35]

0.80

[0.50

1.30]

0.66*

[0.42

1.05]

 Central

1.01

[0.64

1.60]

0.73

[0.46

1.14]

0.71

[0.45

1.11]

 West

0.86

[0.47

1.59]

0.58*

[0.35

0.96]

0.57*

[0.35

0.92]

 South

0.52**

[0.34

0.80]

0.39***

[0.26

0.57]

0.38***

[0.25

0.59]

Source: Authors’ estimates from NSSO survey, 2014

aBased on 3910 cases, *significant at 10% level, **significant at 5% level, ***significant at 1% level

Discussion

Accidental injury is one of the important causes of deaths in India. There has been a sharp increase in cases of accidental injury due to heavy industrialization and a disproportionate rise in the number of vehicles [30]. As per our information, this study is a pioneering attempt to explore various socio- economic and demographic covariates of catastrophic expenditure on accidental injury in India. This study indicates the catastrophic economic impact on households due to accidental injury. Expenditure under all heads i.e., both direct and indirect costs, has provided a wide-ranging perspective on the cost associated with accidental injury. We have used data on household expenditure rather than annual income, to determine the nature of catastrophic expenditure by using 5%, 10% and 15% threshold levels out of the total household expenditure. In literature, when total expenditure is used as a denominator, most widely used threshold is 10% [3134]. It shows an approximate threshold at which the household is forced to sacrifice their other basic needs and are forced to sell their productive assets, resort to borrowings and become impoverished [35]. We have used binary logistic regressions to examine the intensity of catastrophic payment due to accidental injury.

In the present study, nearly half of the sampled population who suffered from accidental injuries was in the age group of 30–59 years (48%). Various studies from India and across the globe has also noted similar finding in their studies [11, 3639]. Various reasons can be cited for the higher number of accidents among the above-mentioned groups of the population. This age group represents the most productive group, which contributes significantly to the income of the households or the family [34, 40]. Accidental injuries significantly affect more to the productive age group between 30 and 59 years [13, 14, 41, 42]. However, fragility contributed little to the excess accidental injury risk under the age of 30–59 years population [43].

Similarly, males were more prone to accidental injury as compared to females. Males are more vulnerable to accidental injuries, and are more often the victims of accidental injury in comparison to females [44, 45]. Females were involved in fewer road accident deaths as compared to their male counterparts [46, 47]. Also, as men are often the sole income earners in many families, they have to travel often. Women may typically not be in jobs that have a higher risk of accidental injury. The other reasons could be that males are more aggressive and careless than their female counterparts [11, 39]. Generally females are considered to be risk averters and are more cautious. Males are also more mobile due to their job profiles and sometimes are the sole breadwinners in the family, resulting into higher number of road fatalities, and in turn higher catastrophic spending at all the threshold levels i.e., 5%, 10% and 15%. Women seem to be less associated with accidental injury, as women mostly walk, do not hold a valid driving license and have lower participation in the workforce [48, 49].

It was also observed that percentage share of accidental injuries in the rural areas was comparatively higher than urban areas. Rural areas had more road fatalities (56%) than urban areas (44%). They lack in terms of basic infrastructural facilities such as, roads, traffic lights and traffic operators. The accidents are more severe, resulting into higher casualties in rural locations [11, 23, 50]. Rural areas also lack in terms of basic facilities to treat emergency and serious cases of accidental injuries. In case of severe accidental cases or emergencies, referral is provided to the patients to the nearest city hospitals after providing basic first aid treatment [47, 5153]. Inadequate or non-availability of the health infrastructure causes higher catastrophic spending to the households at all threshold levels (5%, 10% and 15%).

Majority of the population sought care from private facilities, were not insured, and even if insured, did not receive reimbursement from the insurance providers [40, 54]. More than half of the population sought care from private providers. Reasons can be cited in terms of availability of better infrastructure and proper care in the private facilities as compared to the government units. Studies also indicate that casualties are more in public hospitals in comparison to private facilities [55, 56]. This may be due to the infrastructural lags which are either not properly functional or absent in public facilities. Accountability can be another cited factor for the larger number of deaths in public facilities. The problem arises when these super specialty public hospitals, which are the last hope for the poor and helpless rural families, are totally mismanaged. Regarding the coverage of accidental insurance, in developing countries like India, more than 96% of victims of road fatalities have no definable source of health insurance at the time of accident [57]. Limited number of studies on community-based health insurance in LMICs also shows that lack of insurance can lead to higher level of OOPE and result into higher CHE [15, 27, 58, 59].

The study shows a direct and proportional relationship between spending on accidental injury and age. The socio-economic differentials in the level of OOPE indicates that spending on accidental injury was higher among the elderly population i.e., in the age group of 60+ population, and least among the less than 15 years of age group. CHE was higher for the elderly population, perhaps due to various reasons due to health conditions deteriorating in the older age and recovery from the accidental injury being a lengthy process. Elder population is also more prone to the accident related deaths and in many instances also suffers from post-accidental disabilities as compared to others [60, 61]. As per a study, drivers aged above 60, showed serious accidental injury rates twice as high as those of the 30–59-year-old group [43]. Various probable reasons has been cited such as age related physical mobility, ability to manage complex situations such as, at the intersections, turning out of the parking spaces, vision related difficulty especially in nights [6264].

Gender was an important determinant of spending on accidental injury as spending among males was seen to be higher than females [65]. Literature also indicates that accidents involving males are more serious and harmful. Often, males are the sole earners in many families, so injury related disabilities may hamper the overall economic situation of the family [41, 66]. Similarly, those who were either formally educated or having education up to graduation or above, were spending more on accidental injury than others. It may be due to better awareness of road traffic laws and regulations, knowledge of traffic signs, or related fines [47, 67, 68]. However, our experience in the Indian context seems to show that impact of education on accidental injury is still a debatable issue. Some studies also indicate that road traffic-related knowledge was not correlated with formal education [69, 70]. So, a general conclusion cannot be drawn from this study and more studies are required to establish this fact.

The spending was more on accidental injury in the urban area. Literature also highlights that the density and severity of crash accidents was higher in the urban areas than the rural, which may result into higher spending [50, 71]. Other factors may also aggravate the spending on accidental injuries such as, better facilities and higher cost of treatment. Other studies also support our finding that the spending on accidental injury in the urban areas was comparatively higher than the rural areas [52, 72]. However, level of CHE was higher in the rural areas as compared to urban. The gap between rich and poorer section of the households was significantly higher, as the richer section of the population was spending a considerably higher amount on accidental injury than their counterparts from other categories [22, 73]. Poorer people are unable to afford costly treatment procedures and they avail care from public facilities; else, they have to endure distress financing [74]. A few studies also indicate some interesting findings such as, people from the higher income group may assign a higher priority to the perceived value of time and comparatively lower weight to the real cost or fines. Richer or richest individuals may, therefore, drive faster, which would increase their chances of being involved in an accident [73].

Accidental injury care from private facilities is quite expensive and unaffordable. Sometimes private centers lengthen the duration of stay in the hospital, resulting in higher level of OOPE for people. Another notable finding of the study was, as the length of hospital stay increases, the expenses incurred for the treatment also rises. Other studies have also shown similar results, which may be due to the difference in the severity of accidents or type of hospital [75]. There is a clear cut private and public divide in terms of OOPE [52]. Present study also supports the general notion that generally treatment for the accidental injuries in private hospitals involves high household OOPE and CHE as compared with public hospitals. Our study indicates that the cost of treatment for accidental injury in private hospitals was four times higher than public providers. The level of CHE was significantly higher in the private facilities. There was huge iceberg in terms of cost and in the treatment procedures of the public and private hospitals. In spite of these cost differentials there is a general opinion that RTA cases are better managed in private health care facilities. In case of lack of ability to pay, poorer section of the households admits the patients in the government hospital. However, they still need to pay a lump sum amount from their income, which may affects their daily way of living in many ways [7577]. Deaths due to road casualties were very less and maximum people survived from these injuries. Those who survived accidental injury incurred a lower level of OOPE than the deceased. Literature also shows that [78, 79] the cost of inpatient care for the decedents was higher in comparison to that of survivors. Insured population incurred a higher level of OOPE than the uninsured population on the treatment of the accidental injury; so, merely having access to insurance was not helpful in reducing OOPE. No doubt, those who received reimbursement from the insurance were in a better position, as level of OOPE was comparatively lower for them. However, in the Indian context, only a handful of the population has access to any sort of insurance coverage. A large segment of the uninsured population is unable to manage the expenditure and, is forced to take discharge from the care units. In both ways they have to bear double burden either in form of wage loss if they choose rest/care and cost to avail the healthcare facility or else they have to compromise with the wages they could have earned in perfect functional/physical condition. All these factors further increase the economic burden on accidental injury cases and on their families [80].

Major source of financing for accidental injury was from the income or savings of the households. The levels of OOPE and CHE significantly increase in the case of road fatalities. Those who have resorted to borrowings were incurring higher OOPE, resulting into catastrophic spending and impoverishment. Because of this added expenditure, households are forced to opt for borrowings or taking loans to manage the shortfall. Spatial distribution of accidental injury shows a higher concentration of the cases in the southern states, followed by central and east. However, the levels of OOPE and CHE were higher in the western, central and southern regions in comparison to the other parts of the country. Indian Roads are getting more vulnerable beyond any surprise due to increase in motorization, accompanied by urbanization, and higher population density [59, 67, 81]. Various reasons can be cited such as tremendous growth in the level of income, GDP, industrialization and economic development and population density in these states [82, 83]. Increased urbanization and population growth causes an increase in pedestrian activity often accompanied by higher pedestrian fatalities [84, 85]. This observation is consistent with our findings.

Binary logistic regression analysis also shows similar trends in OOPE and CHE. Level of CHE was lower for females, illiterate households, Muslims, poorest section of the population, in public facilities, for insured population, shorter stay duration (1–2 days), using savings/income or other sources for financing accidental injury, and for the southern regions. The CHE increases for the elderly population aged 60+, males, uninsured population and for those who seek private care and also resort to borrowings as a source of financing.

Conclusion

Indian healthcare system is the most privatized system where people still prefer big and private hospitals for trauma and accidental injury care. Due to the existence of unofficial dual healthcare delivery system in India, supply of the services depends upon the economic status of the population. Infrastructural issues such as negligence, understaffing, availability of equipment’s and manpower are the major reason behind increased casualties, and poor delivery of healthcare facilities in case of accidental injury. There is an immediate need to strengthen the public healthcare system and proper regulation is required for bringing the private healthcare sector into a well-regulated national health-care system. Special trauma care units can be set in the rural areas and can be managed appropriately to reduce the deaths and injuries from RTA. Proper awareness should be provided for avoiding accidental injuries. This can be done through mass campaigning and media promotion programs. Special target should be laid on the group of males in the age group of 30–59 years who are more prone to accidents. Safety measures should be adopted while using any type of vehicle on the roads. There is also immediate need to relook at the existing legal penalties for those who drink and drive or use mobile phones while driving. Factors responsible for regional variations in the accidental injury can be further examined in the Indian context. Majority of the people avoid renewal of third party insurance of the vehicles which later on proves to be detrimental for both health and financial situations. There should be some type of reinforcement measures for rechecking of third party insurance for vehicles.

The resultant financial burden caused due to RTA or accidental injury on the households is so huge that it cannot be ignored and little has been said and done to anodyne this pain. Hence, there is an urgent need for affordable health insurance policies to be made available by the government for the economically weaker sections, and also increased awareness among people regarding the availability of the insurance facility. This study provides further evidence for the need to strengthen the public health system in India in order to reduce the burden of OOPE on households caused due to accidental injury. Disabilities caused due to accidental injuries may affect adversely the individuals, households and overall economy. To confirm some of the findings (role of education), more evidences are required at micro level to find out how education influences the risk attitude, exposure and knowledge.

Footnotes
1

“Injury” refers to damage to the body produced by energy exchanges that have relatively sudden discernible effects [3]

 
2

A road traffic injury is a fatal or non-fatal injury incurred as a result of a collision on a public road involving at least one moving vehicle [2].

 
3

Similar as accidental injury

 
4

Household out-of-pocket expenditureon health comprise cost-sharing, self-medication and other expenditure paid directly by private households, irrespective of whether the contact with the health care system was established on referral or on the patient’s own initiative [20]

 
5

Health spending is taken to be catastrophic when a household must reduce its basic expenditure over a period of time to cope with health costs, but there is no consensus on the threshold proportion of household expenditure [74]

 
6

An accident, also known as an unintentional injury, is an undesirable, incidental, and unplanned event that could have been prevented had circumstances leading up to the accident been recognized, and acted upon, prior to its occurrence [3].

 
7

It includes accidental injury, road traffic accidents (RTA) and falls.

 

Abbreviations

CHE: 

Catastrophic health expenditure

HICs: 

High-Income Countries

LMICs: 

Low and Middle-Income Countries

NSSO: 

National Sample Survey Organisation

OBC: 

Other Backward Caste

OOPE: 

Out of pocket expenditure

RTA: 

Road Traffic Accidents

SC: 

Scheduled Caste

SES: 

Socio-economic Survey

ST: 

Scheduled Tribe

WHO: 

World Health Organization

YLL: 

Years of life lost

Declarations

Acknowledgements

The authors are grateful to the Department of Humanities and Social Sciences, National Institute of Technology (NIT), Rourkela for their support and encouragement, which has helped in improving this paper. The authors also thank the journal editor and reviewers for their insightful comments which greatly helped improve this paper.

Funding

We received no funding support to undertake this study.

Availability of data and materials

This paper uses data from the unit level information from 71st round of the NSSO, conducted under the aegis of the Ministry of Statistics and Program Implementation, Government of India. For more details see the report “India - Social Consumption: Health, NSS 71st Round: Jan - June 2014” by NSSO, Ministry of Statistics and Program Implementation, Government of India, New Delhi: http://mail.mospi.gov.in/index.php/catalog/161/related_materials

Authors’ contributions

Design of the study: JP, RD SP and SKR. Wrote the Paper: JP, RD and SP. Analysed the data: JP and RD with the help of SP and SKR. Finalizing the article: RD, JP, SP and SKR. All authors read and approved the final manuscript.

Authors’ information

JP is assistant professor, and RD is Senior Research Fellow (SRF), in Health Economics with the Department of Humanities and Social Sciences, National Institute of Technology (NIT), Rourkela, Odisha. SP is Director of Regional Medical Research Centre, Bhubaneswar, Odisha. SKR is associate Professor, with Indian Institute of Public Health (IIPH)–PHFI, Bhubaneswar, Odisha. Research interests of the authors include healthcare financing, specifically health equity and inequality perspective in health economics.

Ethics approval and consent to participate

N.A. As this study uses secondary data so no consent to participate is required by the authors.

Consent for publication

We have bought this data to use in our research and data is also available in public domain.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors’ Affiliations

(1)
Department of Humanities and Social Sciences, National Institute of Technology, Rourkela, Orissa, 769 008, India
(2)
Regional Medical Research Centre, Bhubaneswar, Odisha, 751023, India
(3)
Indian Institute of Public Health-PHFI, Bhubaneswar, Odisha, 751024, India

References

  1. Reddy KS. Global burden of disease study 2015 provides GPS for global health 2030. Lancet. 2016;388(10053):1448–9.View ArticlePubMedGoogle Scholar
  2. World Health Organization. Injuries and violence: the facts 2014. http://apps.who.int/iris/bitstream/10665/149798/1/9789241508018_eng.pdf?ua=1&ua=1&ua=1.
  3. World Health Organization. The world health report 2002: reducing risks, promoting healthy life. World Health Organization, 2002. Available at: www.who.int/iris/handle/10665/42510.
  4. World Health Organization. Violence, Injury Prevention, World Health Organization. Global status report on road safety 2013: supporting a decade of action: World Health Organization; 2013. http://www.who.int/violence_injury_prevention/road_safety_status/2013/en/.
  5. Singh A, Tetreault L, Kalsi-Ryan S, Nouri A, Fehlings MG. Global prevalence and incidence of traumatic spinal cord injury. Clin Epidemiol. 2014;6:309–31.PubMedPubMed CentralGoogle Scholar
  6. Viner RM, Coffey C, Mathers C, Bloem P, Costello A, Santelli J, Patton GC. 50-year mortality trends in children and young people: a study of 50 low-income, middle-income, and high-income countries. Lancet. 2011;377(9772):1162–74.View ArticlePubMedGoogle Scholar
  7. Chandran A, Hyder AA, Peek-Asa C. The global burden of unintentional injuries and an agenda for progress. Epidemiologic reviews. 2010;32(1):110–20.Google Scholar
  8. Stewart BT, Yankson IK, Afukaar F, Medina MCH, Cuong PV, Mock C. Road traffic and other unintentional injuries among travelers to developing countries. Med Clin N Am. 2016;100(2):331–43.View ArticlePubMedPubMed CentralGoogle Scholar
  9. Norton R, Kobusingye O. Injuries. N Engl J Med. 2013;368(18):1723–30.View ArticlePubMedGoogle Scholar
  10. Haagsma JA, Graetz N, Bolliger I, Naghavi M, Higashi H, Mullany EC, et al. The global burden of injury: incidence, mortality, disability-adjusted life years and time trends from the global burden of disease study 2013. Inj Prev. 2016;22(1):3–18.Google Scholar
  11. Ruikar M. National statistics of road traffic accidents in India. J Ortop Traumatol Rehabil. 2013;6(1):1.View ArticleGoogle Scholar
  12. Kim DH, Chung YN, Park YS, Min KS, Lee MS, Kim YG. Epidemiologic impact of rapid industrialization on head injury based on traffic accident statistics in Korea. J Korean Neurosurg Soc. 2016;59(2):149–53.View ArticlePubMedPubMed CentralGoogle Scholar
  13. Rastogi D, Meena S, Sharma V, Singh GK. Causality of injury and outcome in patients admitted in a major trauma center in North India. Int J Crit Illn Inj Sci. 2014a;4(4):298.View ArticlePubMedPubMed CentralGoogle Scholar
  14. Rastogi D, Meena S, Sharma V, Singh GK. Epidemiology of patients admitted to a major trauma centre in northern India. Chin J Traumatol. 2014b;17(2):103–7.PubMedGoogle Scholar
  15. Mohanan M. Causal effects of health shocks on consumption and debt: quasi-experimental evidence from bus accident injuries. Rev Econ Stat. 2013;95(2):673–81.View ArticlePubMedPubMed CentralGoogle Scholar
  16. Prinja S, Jagnoor J, Chauhan AS, Aggarwal S, Nguyen H, Ivers R. Economic burden of hospitalization due to injuries in North India: a cohort study. Int J Environ Res Public Health. 2016;13(7):673.View ArticlePubMed CentralGoogle Scholar
  17. National Sample Survey Office. Key Indicators of Social Consumption in India: Health, (NSSO 71st Round, January–June 2014), Ministry of Statistics and Programme Implementation(MOSPI),Government of India (GOI), 2015. http://mail.mospi.gov.in/index.php/catalog/161/download/1949.
  18. Pradhan J, Dwivedi R. Do we provide affordable, accessible and administrable health care? An assessment of SES differential in out of pocket expenditure on delivery care in India. Sex Reprod Healthc. 2017;11:69–78.View ArticlePubMedGoogle Scholar
  19. Singh P, Kumar V. The rising burden of healthcare expenditure in India: a poverty nexus. Soc Indic Res. 2017;133(2):741–62.Google Scholar
  20. Organization for Economic Cooperation and Development, and World Health Organization. DAC guidelines and reference series on poverty and health. Paris: Organization for Economic Co-operation and Development & WHO; 2003. Available at: http://www.oecd.org/dac/povertyreduction/dacguidelinesonpovertyandhealth.htm. Accessed 1 January, 2016.
  21. Jayakrishnan T, Jeeja MC, Kuniyil V, Paramasivam S. Increasing out-of-pocket health care expenditure in India-due to supply or demand. PharmacoEconomics. 2016;1(105):1–6.Google Scholar
  22. Xu K, Evans DB, Carrin G, Aguilar-Rivera AM, Musgrove P, Evans T. Protecting households from catastrophic health spending. Health Aff. 2007a;26(4):972–83.View ArticleGoogle Scholar
  23. Gururaj G, Uthkarsh PS, Rao GN, Jayaram AN, Panduranganath V. Burden, pattern and outcomes of road traffic injuries in a rural district of India. Int J Inj Control Saf Promot. 2016;23(1):64–71.View ArticleGoogle Scholar
  24. Bhalla, K., Khurana, N., Bose, D., Navaratne, K. V., Tiwari, G., & Mohan, D. (2016). Official government statistics of road traffic deaths in India under-represent pedestrians and motorised two wheeler riders. Injury prevention, injuryprev-2016.Google Scholar
  25. Pal R, Agarwal A, Galwankar S, Swaroop M, Stawicki SP, Rajaram L, Menon G. The 2014 Academic College of Emergency Experts in India's INDO-US Joint Working Group (JWG) white paper on“ developing trauma sciences and injury care in India”. Int J Crit Illn Inj Sci. 2014;4(2):114.Google Scholar
  26. Mahajan N, Aggarwal M, Raina S, Verma LR, Mazta SR, Gupta BP. Pattern of non-fatal injuries in road traffic crashes in a hilly area: a study from Shimla, North India. Int J Crit Illn Inj Sci. 2013;3(3):190.View ArticlePubMedPubMed CentralGoogle Scholar
  27. Mohan D, Tiwari G, Mukherjee S. Urban traffic safety assessment: a case study of six Indian cities. IATSS Res. 2016;39(2):95–101.View ArticleGoogle Scholar
  28. Raina P, Sohel N, Oremus M, Shannon H, Mony P, Kumar R, et al. Assessing global risk factors for non-fatal injuries from road traffic accidents and falls in adults aged 35–70 years in 17 countries: a cross-sectional analysis of the prospective urban rural epidemiological (PURE) study. Inj Prev. 2016;22(2):92–8.View ArticlePubMedGoogle Scholar
  29. Balarajan Y, Selvaraj S, Subramanian SV. Health care and equity in India. Lancet. 2011;377(9764):505–15.View ArticlePubMedPubMed CentralGoogle Scholar
  30. Dandona R. Making road safety a public health concern for policy-makers in India. Natl Med J India. 2006;19(3):126–33.Google Scholar
  31. Pradhan M, Prescott N. Social risk management options for medical care in Indonesia. Health economics. 2002;11(5):431–46.Google Scholar
  32. Ranson MK. Reduction of catastrophic health care expenditures by a community-based health insurance scheme in Gujarat, India: current experiences and challenges. Bulletin of the World Health Organization. 2002;80(8):613–21.Google Scholar
  33. Wagstaff A, Doorslaer EV. Catastrophe and impoverishment in paying for health care: with applications to Vietnam 1993–1998. Health economics. 2003;12(11):921–33.Google Scholar
  34. World Health Organization. Gender and road traffic injuries. Gender and health information sheet. Geneva: World Health Organization; 2002. http://apps.who.int/iris/bitstream/10665/68887/1/a85576.pdf. Last access 24/03/2016.
  35. Russell S. The economic burden of illness for households in developing countries: a review of studies focusing on malaria, tuberculosis, and human immunodeficiency virus/acquired immunodeficiency syndrome. The American journal of tropical medicine and hygiene. 2004;71(2_suppl):147–55.Google Scholar
  36. Ghosh PK. Epidemiological study of the victims of vehicular accidents in Delhi. J Indian Med Assoc. 1992;90(12):309–12.PubMedGoogle Scholar
  37. Jha N, Srinivasa DK, Roy G, Jagdish S, Minocha RK. Epidemiological study of road traffic accident cases: a study from South India. Indian J Community Med. 2004;29(1):20–4.Google Scholar
  38. Palmer KT, Harris EC, Coggon D. Chronic health problems and risk of accidental injury in the workplace: a systematic literature review. Occupational and environmental medicine. 2008.Google Scholar
  39. Shaker RH, Eldesouky RS, Hasan OM, Bayomy H. Motorcycle crashes: attitudes of the motorcyclists regarding riders’ experience and safety measures. J Community Health. 2014;39(6):1222–30.View ArticlePubMedGoogle Scholar
  40. Kelly M, Strazdins L, Ellora TD, Khamman S, Seubsman SA, Sleigh AC. Thailand's work and health transition. International Labour Review. 2010;149(3):373–86.View ArticlePubMedPubMed CentralGoogle Scholar
  41. Al-Maniri AAN, Al-Reesi H, Al-Zakwani I, Nasrullah M. Road traffic fatalities in oman from 1995 to 2009: evidence from police reports. Int J Prev Med. 2013;4(6):656.PubMedPubMed CentralGoogle Scholar
  42. Oginni FO, Ugboko VI, Ogundipe O, Adegbehingbe BO. Motorcycle-related maxillofacial injuries among Nigerian intracity road users. J Oral Maxillofac Surg. 2006;64(1):56–62.View ArticlePubMedGoogle Scholar
  43. Meuleners LB, Harding A, Lee AH, Legge M. Fragility and crash over-representation among older drivers in Western Australia. Accid Anal Prev. 2006;38(5):1006–10.View ArticlePubMedGoogle Scholar
  44. Eurostat. Healthcare statistics, 2015. http://ec.europa.eu/eurostat/statistics-explained/index.php/Healthcare_statistics. Accessed 15 July, 2016.
  45. Harris CR, Jenkins M, Glaser D. Gender differences in risk assessment: why do women take fewer risks than men? Judgm Decis Mak. 2006;1(1):48.Google Scholar
  46. El-Menyar A, El-Hennawy H, Al-Thani H, Asim M, Abdelrahman H, Zarour A, et al. Traumatic injury among females: does gender matter? J Trauma Manag Outcomes. 2014;8(1):8.View ArticlePubMedPubMed CentralGoogle Scholar
  47. Karbakhsh, M., Zandi, N. S., Rouzrokh, M., & Zarei, M. R. (2009). Injury epidemiology in Kermanshah: the National Trauma Project in Islamic Republic of Iran.Google Scholar
  48. Santamariña-Rubio E, Pérez K, Olabarria M, Novoa AM. Gender differences in road traffic injury rate using time travelled as a measure of exposure. Accid Anal Prev. 2014;65:1–7.View ArticlePubMedGoogle Scholar
  49. Communist Party of China Central Committee. Implementation plan for the recent priorities of the health care system reform. Beijing: Government of the People’s Republic of China; 2009. p. 2009–11.Google Scholar
  50. Kalediene R, Starkuviene S, Petrauskiene J. Social dimensions of mortality from external causes in Lithuania: do education and place of residence matter? Soz Praventivmed. 2006;51(4):232–9.View ArticlePubMedGoogle Scholar
  51. Garg RH. Who killed Rambhor?: the state of emergency medical services in India. J Emerg Trauma Shock. 2012;5(1):49.View ArticlePubMedPubMed CentralGoogle Scholar
  52. Kumar GA, Dilip TR, Dandona L, Dandona R. Burden of out-of-pocket expenditure for road traffic injuries in urban India. BMC Health Serv Res. 2012;12(1):285.View ArticlePubMedPubMed CentralGoogle Scholar
  53. Ministry Of Health and Family Welfare (MoHFW) (2005). Financing and delivery of health care services in India, background papers of the national commission on macroeconomics and health, government of India, New Delhi. 2005.Google Scholar
  54. Ekman B. Community-based health insurance in low-income countries: a systematic review of the evidence. Health Policy Plan. 2004;19(5):249–70.View ArticlePubMedGoogle Scholar
  55. Saksena P, Xu K, Elovainio R, Perrot J. Health services utilization and out-of-pocket expenditure at public and private facilities in low-income countries. World Health Rep. 2010;20:20.Google Scholar
  56. Srivastava NM, Awasthi S, Agarwal GG. Care-seeking behavior and out-of-pocket expenditure for sick newborns among urban poor in Lucknow, northern India: a prospective follow-up study. BMC Health Serv Res. 2009;9(1):61.View ArticlePubMedPubMed CentralGoogle Scholar
  57. Chalya PL, Mabula JB, Dass RM, Mbelenge N, Ngayomela IH, Chandika AB, Gilyoma JM. Injury characteristics and outcome of road traffic crash victims at Bugando Medical Centre in Northwestern Tanzania. J Trauma Manag Outcomes. 2012;6(1):1.View ArticlePubMedPubMed CentralGoogle Scholar
  58. Murray CJ, Vos T, Lozano R, Naghavi M, Flaxman AD, Michaud C, et al. Disability-adjusted life years (DALYs) for 291 diseases and injuries in 21 regions, 1990–2010: a systematic analysis for the global burden of disease study 2010. Lancet. 2013;380(9859):2197–223.View ArticleGoogle Scholar
  59. Nantulya VM, Reich MR. The neglected epidemic: road traffic injuries in developing countries. BMJ. 2002;324(7346):1139.View ArticlePubMedPubMed CentralGoogle Scholar
  60. Health Promotion England (HPE) (2000). Avoiding slips, trips and broken hips, fact sheet-2, Department of Trade and Industry, http://webarchive.nationalarchives.gov.uk/+/http:/www.dti.gov.uk/homesafetynetwork/pdffalls/accidents.pdf. 2000.Google Scholar
  61. Kumar, A., Verma, A., Yadav, M., & Srivastava, A. K. (2011). Fall: the accidental injury in geriatric population.Google Scholar
  62. Hakamies-Blomqvist LE. Fatal accidents of older drivers. Accid Anal Prev. 1993;25(1):19–27.View ArticlePubMedGoogle Scholar
  63. Holland C, Hill R. The effect of age, gender and driver status on pedestrians’ intentions to cross the road in risky situations. Accid Anal Prev. 2007;39(2):224–37.View ArticlePubMedGoogle Scholar
  64. Ryan GA, Legge M, Rosman D. Age related changes in drivers’ crash risk and crash type. Accid Anal Prev. 1998;30(3):379–87.View ArticlePubMedGoogle Scholar
  65. Al-Balbissi AH. Role of gender in road accidents. Traffic Inj Prev. 2003;4(1):64–73.View ArticlePubMedGoogle Scholar
  66. Broyles RW, Clarke SR, Narine L, Baker DR. Factors contributing to the amount of vehicular damage resulting from collisions between four-wheel drive vehicles and passenger cars. Accident Analysis & Prevention. 2001;33(5):673–8.Google Scholar
  67. Bener A, Burgut HR, Sidahmed H, AlBuz R, Sanya R, Khan WA. Road traffic injuries and risk factors. Calif J Health Promot. 2009;7(2):92–101.Google Scholar
  68. Borrell C, Plasencia A, Huisman M, Costa G, Kunst A, Andersen O, Gadeyne S. Education level inequalities and transportation injury mortality in the middle aged and elderly in European settings. Inj Prev. 2005;11(3):138–42.View ArticlePubMedPubMed CentralGoogle Scholar
  69. Grimm M, Treibich C. Why do some motorbike riders wear a helmet and others don’t? Evidence from Delhi, India. Transp Res A Policy Pract. 2016;88:318–36.View ArticleGoogle Scholar
  70. Hung KV. Education influence in traffic safety: a case study in Vietnam. IATSS Res. 2011;34(2):87–93.View ArticleGoogle Scholar
  71. Zwerling C, Peek-Asa C, Whitten PS, Choi SW, Sprince NL, Jones MP. Fatal motor vehicle crashes in rural and urban areas: decomposing rates into contributing factors. Inj Prev. 2005;11(1):24–8.View ArticlePubMedPubMed CentralGoogle Scholar
  72. Archer, J., & Vogel, K. (2000). The traffic safety problem in urban areas. Research Report.Google Scholar
  73. Marmot M. The influence of income on health: views of an epidemiologist. Health Aff. 2002;21(2):31–46.View ArticleGoogle Scholar
  74. Xu K, Evans DB, Kawabata K, Zeramdini R, Klavus J, Murray CJ. Household catastrophic health expenditure: a multicountry analysis. Lancet. 2003;362(9378):111–7.View ArticlePubMedGoogle Scholar
  75. Mandal BK, Yadav BN. Pattern and distribution of pedestrian injuries in fatal road traffic accidental cases in Dharan, Nepal. J Nat Sci Biol Med. 2014;5(2):320.View ArticlePubMedPubMed CentralGoogle Scholar
  76. Gururaj G. Road traffic deaths, injuries and disabilities in India: current scenario. Natl Med J India. 2008;21(1):14.PubMedGoogle Scholar
  77. Reddy GM, Negandhi H, Singh D, Singh A. Extent and determinants of cost of road traffic injuries in an Indian city. Indian J Med Sci. 2009;63(12):549.View ArticlePubMedGoogle Scholar
  78. Ladusingh L, Pandey A. The high cost of dying. Econ Polit Wkly. 2013;48(11):45.Google Scholar
  79. Peymani P, Heydari ST, Hoseinzadeh A, Sarikhani Y, Moafian G, Aghabeigi MR, et al. Epidemiological characteristics of fatal pedestrian accidents in Fars Province of Iran: a community-based survey. Chin J Traumatol. 2012;15(5):279–83.PubMedGoogle Scholar
  80. World Health Organization. World health assembly resolution 58.33. Sustainable health financing, universal coverage and social health insurance. Geneva: World Health Organization; 2005. Available from: http://apps.who.int/medicinedocs/documents/s21475en/s21475en.pdf. Accessed 17 Nov 2010
  81. Ameratunga S, Hijar M, Norton R. Road-traffic injuries: confronting disparities to address a global-health problem. Lancet. 2006;367(9521):1533–40.View ArticlePubMedGoogle Scholar
  82. Kopits E, Cropper M. Traffic fatalities and economic growth. Accid Anal Prev. 2005;37(1):169–78.View ArticlePubMedGoogle Scholar
  83. Paulozzi LJ, Ryan GW, Espitia-Hardeman VE, Xi Y. Economic development's effect on road transport-related mortality among different types of road users: a cross-sectional international study. Accid Anal Prev. 2007;39(3):606–17.View ArticlePubMedGoogle Scholar
  84. Surender J. Pattern of injuries in fatal road traffic accidents in Warangal area. J Indian Acad Forensic Med. 2013;35(1):55–9.Google Scholar
  85. World Health Organization. Injuries, Violence Prevention Department. The injury chart book: A graphical overview of the global burden of injuries. World Health Organization; 2002. http://apps.who.int/iris/bitstream/10665/42566/1/924156220X.pdf.

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