Introduction

Injuries are the outcome of an unintentional event, one of the major causes of most of the premature and sudden demise of individuals worldwide. (Begg et al., 2003; Krug, Sharma, & Lozano, 2000; WHO, 2018; Peden et al., 2002). More than five million people lose their lives due to injuries every year, accounting for 9 per cent of total deaths worldwide (WHO, 2014). It has also been estimated to be the fifth-largest cause of mortality worldwide by 2030 (WHO, 2008). Generally, various causes of injuries have been taken into account as an act of violence against others or oneself, such as road traffic crashes and accidents, burns, drowning, falls, assaults and poisoning, etc. (Peden et al., 2002). Road traffic accidents/injuries are the leading human tragedy, which involves human suffering in terms of hospitalisation, premature mortality, socio-economic costs, and loss of productivity due to disabilities (Ruikar, 2013). It is ranked the fourth leading cause of death worldwide, with more than 1.35 million shares in global deaths (Gumber, 2010; WHO, 2014, 2018; Reddy, 2016). At the same time, it is the ninth leading cause of loss of disability-adjusted life years (DALYs) and has an unacceptably mounting estimation both in high and low-income countries (Garg & Hyder, 2006a; Margie Peden et al., 2004). However, the rate of injuries is declining in high-income countries, but road traffic deaths are still high, especially among people with lower socio-economic backgrounds (WHO, 2018; Prinja et al., 2015).

On the contrary, low and middle-income countries (LMICs) are also experiencing a rapid increase in injuries, mainly due to road traffic crises (WHO, 2018; Prinja et al., 2015). In LMICs, road traffic death contributes 90 per cent of total fatalities and is the ninth leading cause of DALYs lost worldwide (Mathers et al., 2003; WHO, 2018). Further, the burden is excessively high in these countries, where unintentional injuries cause more than 80 per cent of the deaths, mainly in LMICs, due to their limited public healthcare facilities. (WHO, 2008). Also, in LMICs, poor infrastructure, especially roads, non-serviced vehicles, poor execution of driving codes, ignorance about traffic norms among people, etc., are standard practices and cause a drastic catastrophe (Borowy, 2013).

Like other LMICs, India faces one of the highest-burden of injuries, with the second most common cause of death and 30 per cent of years of life lost (YLL) (Dandona et al., 2017; Gururaj, 2005; Jagnoor et al., 2012; Mohan & Anderson, 2000; Prinja et al., 2016). Among all injuries, road traffic injuries are the leading cause of mortality and disability in India (Gururaj, 2005, 2008; Gururaj et al., 2016). The severity of these road traffic injuries is measured by the individuals killed or disabled in such accidents (Ravikumar, 2013). Every minute registers a road traffic accident, and in it, one dies every 3.6 min from such accidental injuries in the country. According to WHO, road traffic accidents are India’s sixth-leading cause of death, contributing to 16.6 per cent of all deaths (WHO, 2018). Annually, 1.5 lakh (0.15 million) people lose their lives due to road traffic injuries in India. Interestingly, with only one per cent of the world’s vehicles, India accounts for nearly 11 per cent of accidental-related death worldwide (GOI, 2019).

The number of motor vehicles, primarily the two-wheelers, is rapidly rising with the country’s economic and population growth (Kumar et al., 2008; Schmucker et al., 2011). At the same time, expanding the road network, rapid urbanisation and changing the nature of work culture are increasing the habits of motor vehicles among young people, contributing to the rising numbers of road traffic accidents and fatalities in the country (Ruikar, 2013). Also, it is the major contributor to socio-economic losses, hospitalisation costs, and disability burden and is one of the country’s responsible factors for productivity loss. In addition, road traffic accidents are unexpected events that occur suddenly and raise unforeseen treatment expenses for households (Gopalakrishnan, 2012; Stewart et al., 2016). The estimated cost of road traffic crashes and serious injuries are 7.5 per cent of India’s GDP or INR 12.9 lakh crore (the US$ 19.2 billion approx.) for 2016, according to the World Bank report 2019 (Bank, 2020). On the contrary, the country’s total health expenditure (both public and private) is only 3.84 per cent of the GDP (GOI, 2018). At the same time, the under-coverage of health insurance facilities uplifts the medical and non-medical expenditure and expands the country’s OOP expenditure on road traffic injuries (Pradhan et al., 2017; Sangar et al., 2018). The high OOP expenses impose a severe financial burden on individuals and push non-poor families into poverty and already poor into extreme poverty and indebtedness. (Pradhan et al., 2017; Srinivas Goli et al., 2018).

Theoretical background

Many studies have tried to analyse various aspects of accidental injuries and their impact on people in India. Some studies have focused explicitly on the consequences of the country’s road traffic accidents. In a study, Kumar et al. (2012) found that road traffic injuries significantly increase the OOP medical (including consultation fees, diagnostics, medicines, surgery, ambulance charges etc.) and non-medical (including food, transportation of the caretakers, phone charges, repair of the vehicle, legal and consultation expenses etc.) expenditures in urban India. For instance, the study calculated the country’s OOP medical and non-medical expenditure on road traffic injuries was US$ 169 and US$ 163, respectively, in 2004. Also, a study by Prinja et al. (2016) reported that the mean OOP expenditure was US$ 400 and US$ 388 due to hospitalisation on road traffic injuries in north India, respectively. Pradhan et al. (2017) concluded that a high treatment cost of accidental injuries leads to out-of-pocket (OOP) expenditure and has a catastrophic impact on households getting treatment, particularly in private care facilities in India. Findings further postulated that the odds of catastrophic health expenditure (CHE) were higher by 28 per cent if the person did not have any insurance coverage.

Further, Goli (2018) found that in 2014 the mean OOP expenditure on accidental injury, road traffic accidents and falls was INR 26,132 annually in India. Later in another study, Prinja et al. (2019) found that the mean OOP expenditure was US$ 263/INR 16,768 on road traffic injuries in north India. Despite it, various studies such as Gumber (1997), Gururaj (2005), Gururaj (2008), Gopalkrishna Gururaj et al. (2016), Singh (2017) and Tripathy et al. (2018) concluded that injuries are rising as a significant threat to public health in India. Findings of these studies showed that the mean OOP expenditure on accidental injuries/injuries is significantly high among wealthier households. While getting treatment in private healthcare facilities also has a catastrophic impact on households in the country.

Moreover, these studies argued that accidental injuries might vary according to age, sex, cultural differences, occupational status, and other associated circumstances. A study by Gururaj (2008) found that most of the fatalities of road traffic injuries are among men, particularly in the age groups of 15–44 years and among the poor economic sections of society. Overall, these studies tried to measure the burden of loss of life, productivity and efficiency due to disabilities and death and the social strain of road traffic injuries/accidental injuries.

Therefore, it is imperative to identify the correlations of OOP expenditure with accidental injuries and associated risk factors, which may help formulate policies to reduce accidental injury losses from a public health perspective. Many studies have analysed the accidental cases and their losses to the individuals and indisputably added satisfactory literature for policy formulations. Few studies, such as Kumar et al. (2012), Prinja et al. (2016) and Prinja et al. (2019), have targeted specific regions with limited sample sizes. In comparison, many of the studies, i.e., Pradhan et al. (2017), Tripathy et al. (2018), and Goli (2018), have employed nationally representative sample size data for their analysis. Also, Dandona et al. (2020) analysed the population-level trends systematically in deaths due to road injuries in India from 1990 to 2017. These studies have focussed on many aspects, such as incidence rate, risk factors, economic burden etc., concerned with accidental injuries, particularly road traffic accidents and explained their impact on households in India.

Nevertheless, a comprehensive analysis is needed to asset the literature on accidental injuries and road traffic accidents in India. The prior studies limit themselves to accidental injuries and only a few of them to road traffic accidents. Few have studied road traffic accidents and injuries separately, while others have calculated their impact collectively. Gururaj (2008) has examined the current scenario of road traffic deaths, injuries and disabilities in India, while Gumber (2010) has reviewed the costs and burden of injuries and different associated aspects in the analysis. However, Kumar et al. (2012) analysed the burden of OOP expenditure on road traffic injuries in urban India. In addition, Pradhan et al. (2017) have focused on OOP expenditure and CHE driven by accidental injuries, while Goli (2018) has covered India’s economic burden of road traffic accidents and injuries. These studies were conducted before 2020 using the nationally representative data source till 2014. The findings of these studies show that the burden of accidental injuries is continuously rising in terms of economic losses after injuries. In addition, a shift in the survey period from 2014 to 2017–18 induces us to look at the variations in the burden of road traffic accidents in the country. Also, changes in the survey period changed the size of the data and demographic variations. Hence, the intensity of the subject matter compels us to analyse the current scenario of accidental injuries and their impact on individuals in India. Given the literature gaps, the present study aims to measure the overall burden of accidental injuries in terms of the prevalence, associated OOP expenditure, and sources of finance to cope with the economic burden among different states and socio-economic classifications in India. We believe that the findings of this analysis will be helpful to the policymakers to amend or frame new interventions focused on preventing accidental injuries in the country.

Methods

Data

The analysis is based on cross-sectional data from the National Sample Survey Organization (NSSO), 75th Round (2017–18) on Key Indicators of Social Consumption in India: Health. The survey consists of a sample of 1,13,823 households comprising 5,55,115 individuals (NSSO, 2017–18). In inpatient care, a sample of 6624 individuals, out of 58,214 ailing persons, reported suffering from at least any incident of accidental injuries, road traffic accidents and falls during the recall period of 365-days. While on outpatient care, a sample of 626 individuals, out of 39,778 ailing sample persons, reported the same during the last 15-day recall period. In the study, the term ‘accidental injuries’ has been used to specify the various types of injuries, such as accidental injury, road traffic accidents, and falls, analysed in inpatient and outpatient care in India.

Methods

In this study, the headcount of accidental injuries (PAI) has been calculated, \(P_{{\mathrm{AI}}} = \frac{1}{N}\mathop {\sum}\nolimits_{i = 1}^n {{\mathrm{AI}}_i}\), where ‘N’ is the population size and ‘Ali’ is the number of individuals suffering from accidental injuries.

The study also estimated a certain percentage of the population spending out-of-pocket expenditure on accidental injuries more than a certain percentage of their total consumption expenditure (TCE). These percentages have been categorised into three threshold levels 20, 40 and 60 per cent of their TCE. It will analyse the fraction of the population spending more than the threshold of their TCE on accidental injuries. Population spending OOP as a percentage of total consumption expenditure (PAITCE) is given by: \({\rm{PAI}}_{{\rm{TCE}}}=\left({\sum \nolimits_{(i=1)}^n}{{\rm{PH}}_{{\rm{TCE}}}}\right)/\left({\sum \nolimits_{(i=1)}^n}{{\rm{AI}}_{i}}\right)*100\). In the equation, ‘PHTCE’ is the sum of population headcount spending OOP more than the threshold of their TCE, and ‘Ali’ is the number of individuals suffering from accidental injuries. The economic burden of accidental injuries among various socio-economic covariates has been measured as the average monthly per capita OOP health expenditure on accidental injury has been calculated as Per Capita OOP \(= \mathop {\sum}\nolimits_{i = 1}^n {{\mathrm{OOP}}_{{\mathrm{AI}}}/N}\). Where ‘OOPAI’ is the out-of-pocket expenditure on accidental injuries, and ‘N’ denotes the total population. Further, the average monthly OOP expenditure by the accidental injuries-affected people per patient has been measured as \({\mathrm{Per}}\,{\mathrm{Patient}}\,{\mathrm{OOP}} = \mathop {\sum}\nolimits_{i = 1}^n {{\mathrm{OOP}}_{{\mathrm{AI}}}} /\mathop {\sum}\nolimits_{i = 1}^n {{\mathrm{AI}_i}}\). Where ‘OOPAI’ is the out-of-pocket expenditure on accidental injuries, and ‘Ali’ is the number of individuals suffering from injuries.

Finally, the source of finance to cope with OOP expenditure on accidental injuries has also been measured in the study. The percentage of different sources of finance used to manage the OOP health expenditure on accidental injuries has been calculated \(Y = \mathop{\sum}\nolimits_{i = 1}^{n} {U/V} \,\ast\, 100\), where ‘Y’ is the percentage share of a source of finance, ‘U’ is the particular source of finance, and ‘V’ is the sum of all sources of finance.

Finally, the logistic regression analysis has been modelled to analyse the association between socio-economic covariates and the likelihood of suffering from accidental injuries in India. The mathematical modelling of logistic regression is given by: \(P/1 - P = B_0 + B_1X_1 + B_2X_2 + B_3X_3 + B_4X_4 + B_5X_5 + B_6X_6 + B_7X_7 + B_8X_8 + B_9X_9 + u_i \ldots \left( 1 \right)\). Where ‘P’ is the probability of occurrence of accidental injuries, 1 – P is the probability of not occurring accidental injuries, variables such as X1 – X9 refer to the independent variable, where X1 denotes the place of residence, X2 denotes sex, X3 denotes age, X4 denotes a religion, X5 denotes household’s size, X6 denotes social groups, X7 denotes the general education level of the individuals, X8 is the economic status of individuals through wealth quintile, X9 denotes the insurance status. Beta-coefficients such as B0 to B9 are the parameters to be estimated, and ‘ui’ is the random disturbance or error term.

A more meaningful interpretation of the results is made through the odds ratio. The odds ratio is obtained by taking the antilog of various slope coefficients. Eq. (1) can be written in the form of odds ratio as; \(\left( {\frac{{{p}}}{{1 - {{P}}}}} \right) = \frac{{1 + e^Z}}{{1 + e^{ - Z}}} = {{e}}^{{Z}} \ldots \left( 2 \right)\). Where \(\left( {\frac{{{p}}}{{1 - {{P}}}}} \right)\) = is the odds ratio of occurring accidental injuries, Z = B0 + B1X1 ……. + B9X9, eZ is the antilog of Z. By taking the natural log of Eq. (2) we obtain the logit function, written as; \({{L}} = {{{\mathrm{In}}}}\left( {\frac{{{P}}}{{1 - {{P}}}}} \right) = {{Z}} = {{B}}_{{{\mathrm{0}}}} + {{B}}_{{1}}{{X}}_{{{\mathrm{1}}}} + - - - + {{B}}_{{9}}{{X}}_{{{\mathrm{9}}}} \ldots \left( 3 \right)\). Where ‘L’ is the log of odds ratio, which is the logit model.

Variables of the study

Dependent variables

For analytical purposes, the individual’s indisposition due to accidental injuries in inpatient and outpatient care is the primary concern of this analysis. The key dependent variables in the study are the incidence of accidental injuries and the probability of fatalities among various socio-economic, demographic and health variables. We also examined the monthly per capita OOP expenditure on accidental injuries and compared the percentage of population spending OOP expenditure at different threshold levels (20, 40 and 60 per cent) of their total consumption expenditure (TCE) in India. Also, the source of finance to cope with OOP expenditure on accidental injuries has been measured in the study.

Explanatory variables

By considering the existing literature and availability of the different associated variables in the data source, we considered the various socio-economic, demographic and health covariates as explanatory variables for calculating the other aspects of accidental injuries in India. The analysis includes explanatory variables such as the place of residence (Rural/Urban), sex (Male, Female, and Transgender), age (0–14, 15–29, 30–59 and >60), education (Illiterate, Up to Primary, Secondary and Graduation and above), religious groups (Hindu, Muslims, and Others), social groups (STs, SCs, OBCs, and Others), economic status (Wealth quintiles such as Very Poor, Poor, Average, Rich and Very Rich), households’ size (Less than average and more than average). In Indian society, religion is one of the critical variables, broadly divided into Hindus, Muslims, Sikhs, Buddhism, Christianity, Jainism, Zoroastrianism, and many smaller religious groups. However, in this analysis, we have classified religion into three major categories, viz. Hindus, Muslims, and the remaining have been included in ‘other’ religious groups because of their small sample size in the total population. The individuals’ economic state has been calculated through monthly per capita consumption expenditure (MPCE) as a proxy of their income level. It has been ranked from very poor to a very rich one. The analysis categorises households’ sizes into less than average and more than average. Individuals’ status on insurance received or not has been classified into two categories. The level of care has been divided based on treatment received by individuals into three categories: public care, private care, and public and private care in the study (Table 1).

Table 1 Summary of the findings of different studies on accidental/road traffic injuries in India.
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Results of the study

Summary statistics

The details of sample persons and the respective estimated population in different demographic and socio-economic categories, i.e., place of residence, sex, age, religion, social classification, economic status, etc., have been given in Table 2. A total of 5,55,115 sample persons have been surveyed, which represents the 1,140,187,554 total population of the country. Individuals suffering from various ailments have been shown in two categories, i.e., inpatient and outpatient care. In inpatient care, a sample of 58,214 individuals reported suffering from multiple ailments during the survey period. Further, 6,624 individuals stated that they were affected by accidental injuries out of these individuals. While in outpatient care, a sample of 39,778 persons stated suffering from various ailments, of which only 626 individuals reported suffering from accidental injuries. Table 2 also details the sample description of each demographic and socio-economic variable.

Table 2 Summary statistics.
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Prevalence of accidental injuries in India

Table 3 shows that out of 1000 persons, 2.60 persons reported episodes of accidental injuries in inpatient care, which is 10.69 per cent of the country’s total ailing population and 88.54 per cent of the injured population. Furthermore, the findings show that the prevalence is comparatively highest among people residing in urban areas, male populations, 60+ aged people, educated up to secondary level, other religious groups, other social groups, less than average family members, and economically wealthy people in India. However, it is also high among people who receive insurance coverage and treatment in private hospitals. Also, out of the country’s total ailing population, the percentage of accidental injuries differs with some socio-economically and relatively vulnerable groups and illustrates different conclusions. The rate is highest among rural areas, between 15 and 29 age groups, STs, economically less well-off people, and people who didn’t receive insurance coverage than their respective correspondents in the study.

Table 3 Prevalence of accidental injuries in India (2017–18).
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Similarly, in outpatient care, the prevalence has been reported at 0.83 persons out of the country’s 1000 population, which is 1.11 per cent of the country’s total ailing population and 87.28 per cent of the injured population. Further, excluding residence, education level, social category, and wealth quintiles, the study found the highest prevalence similar to inpatient care on various socio-economic and demographic variables. It is highest among rural areas, illiterates, STs, and economically poor people in India. Also, variation has been observed in the percentage of accidental injuries from India’s total ailing population, as depicted in Table 3. However, out of 1000 persons, overall prevalence has been observed as low in outpatient care in India. It suggested that most accidental injuries need institutional medical treatment, resulting in inpatient care.

Level of out-of-pocket expenditure on accidental injuries in India

Table 4 shows that in inpatient care, the average per capita OOP expenditure on accidental injuries is INR 6.96 (US$ 0.11)Footnote 1 in the country. The average monthly per capita OOP expenditure of the injuries-affected population has also been calculated. On average, INR 2672.46 (US$ 41.06) has been spent monthly on inpatient care in India. The distinction between various socio-economic and demographic covariates has been found in the analysis. The expenditure is highest among males, urban areas, 60+ age groups, other religious groups, other social groups, highly educated people, economically wealthy people, and people who receive insurance and treatment in private hospitals. Subsequently, the study calculated OOP expenditure on accidental injuries as a share of the country’s total consumption expenditure (TCE) as 0.32 per cent.

Table 4 Level of out-of-pocket expenditure on accidental injuries in India (2017–18).
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On the other hand, the study has also measured OOP health expenditure on accidental injuries out of TCE of injuries-affected population. The analysis illustrates that the country’s injuries-affected population spends 108.47 per cent of TCE on accidental injuries. On various socio-economic and demographic variables, the share has been observed highest among males, among 60+ age groups, among educated up to secondary level, among SCs, among more than average households’ size, among economically poor groups, and getting treatment both in public and private healthcare facilities than their respective counterparts in India. Population spending OOP expenditure as a share of TCE at different threshold levels is 20 per cent, 40 per cent and 60 per cent, which have also been calculated in the study. Table 4 illustrates that at the 20 per cent threshold level of TCE, only 73.50 per cent population can spend OOP expenditure on accidental injuries. While at 40 per cent and 60 per cent threshold levels of TCE, it is 57.08 per cent and 46.38 per cent, respectively. The analysis shows that it declines with increasing the country’s consumption expenditure threshold.

Similarly, India’s overall average per capita OOP health expenditure on accidental injuries in outpatient care is INR 2.52 (US$ 0.04). Also, based on numerous socio-economic and demographic covariates, it is highest among rural areas, among males, among 60+ age group, among highly educated people, among other religious groups, among STs, among less than average households, among wealthy people, among people with insurance received status and getting treatment both in public and private hospitals in India. Further, we calculated the average monthly per capita OOP expenditure of the injuries-affected population simultaneously. Results show that, on average, INR 3041.64 (US$ 46.73) has been spent per month on outpatient care in India. Against inpatient care, it is highest in the urban area, rich wealth quintiles, highly educated people, 30–59 age groups and relatively weaker sections (socio-economically) such as females, STs, individuals not receiving insurance and getting treatment both in public and private care facilities than their respective counterparts in India.

Further, OOP expenditure as a share of the country’s TCE has been observed at 0.12 per cent on outpatient care. Also, based on various socio-economic and demographic variables, the proportion has been found highest among injuries-affected population inhabiting rural areas, 30–59 age groups, illiterates, other religious groups, STs, economically weaker sections, and people not receiving insurance from any sources and getting treatment both in public as well as private care facilities in India, respectively. Whereas the percentage of population spending OOP expenditure as a share of TCE at 20 per cent, 40 per cent, and 60 per cent threshold levels have been measured as 82.42 per cent, 60.04 per cent, and 52.44 per cent, respectively, in the study. Moreover, the study observed a similar trend of deterioration with an increase in threshold among various socio-economic and demographic variables.

Sources of finance to cope with out-of-pocket expenditure on accidental injuries in India

Table 5 shows that in inpatient care, 76.63 per cent of the accidental injuries-affected population uses savings/income as a first source to finance the injuries-derived expenditure in India. Subsequently, borrowings and other coping strategies contribute only 15.78 per cent and 7.48 per cent share, respectively. Also, a relatively less significant percentage of not employing any strategy has been found in the study. At the same time, savings/income with an 86.30 per cent share is the leading strategy to finance healthcare expenditure on outpatient care in India. Here, borrowings and other coping strategies contribute only 5.74 and 2.48 per cent to the total share of spending on accidental injuries. Further, the analysis clearly distinguishes different socio-economic and demographic variables by employing strategies to cope with the OOP expenditure on accidental injuries in India.

Table 5 Sources of finance to cope with out-of-pocket expenditure on accidental injuries in India (2017–18).
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In addition to saving/income, the share of borrowings as acute distress coping strategy in inpatient care is relatively highest among male, rural areas, 0–14 age groups, Hindu religious groups, less than average household size, SCs social groups, illiterates, economically poor people, a population who doesn’t receive insurance and are getting treatment in both public and private healthcare facilities in India. In outpatient care, it is highest among females, 30–59 age groups, Muslims, more than average family size, other social groups, illiterates, impoverished people, not receiving insurance assistance and getting treatment in public and private care facilities in the country. Figures also indicate that the percentage of Muslims using borrowings (23.87 per cent) as a source of finance is extremely high among all socio-economic and demographic variables in India. Further, in inpatient and outpatient care, the other coping strategies, such as selling assets and contributions by friends and relatives, are also high among males, urban people, Muslims, and illiterate people in the country. Although, the overall share of other coping strategies is highest at 7.48 per cent in inpatient care than the 2.48 per cent in outpatient care in India. While not reported, any source of finance is very significant and contributes to nearly 5.53 per cent share in cases of outpatient care in India.

Multivariable logistic regression analysis

Table 6 shows the likelihood of suffering from accidental injuries in India in inpatient and outpatient care. The findings describe that concerning rural areas, the possibility of suffering (0.87 OR and 0.91 OR) from accidental injuries, etc., has been observed relatively less in inpatient and outpatient care in the study. Similarly, compared to males, the odds of likelihood are 63 per cent (0.37 OR) and 61 per cent (0.39 OR) lower for females in the country. Concerning 0–14 age groups, the chances of suffering from accidental injuries show a rising trend with an increase in the number of ages in India. The probability is extremely high with the study’s 60+ age group in inpatient and outpatient care. While compared to Hindus, the likelihood of suffering is nearly 9 per cent (0.91 OR) and 10 per cent (0.90 OR) lower among Muslim religious groups in the country. In the case of social groups, the odds of likelihood are greater for OBCs (1.51 and 1.42 OR) concerning STs in the case of inpatient and outpatient care. Concerning illiterates, the likelihood of suffering from accidental injuries is rising with education status in inpatient care in India. This proportional relationship shows that the probability may be high among educated people, mainly due to mobility and the nature of work in India. At the same time, the lower likelihood of incidents in outpatient care may be due to the awareness among educated people to prefer hospitalisation after accidental episodes in the country. On an economic basis, occurring accidental injuries increase with the high wealth quintile of the individuals and are lower among individuals from large households in India. Further, concerning people who have not received insurance, the likelihood is (1.02 OR and 1.32 OR) relatively higher with those receiving insurance in case of suffering from injuries.

Table 6 Multivariate logistic regression of accidental injuries in India (2017–18).
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State-wise analysis of accidental injuries in India

Also, a state-wise analysis of accidental injuries has been illustrated in Table 7 and Figs. 1 and 2. The inter-state prevalence of per 1000 population and per capita OOP expenditure of the individuals suffering from accidental injuries have been given both in inpatient and outpatient care in India. The finding shows variation between the rate of prevalence and per capita OOP expenditure on injuries among states/Union Territories (UTs) in the study. In inpatient care, the lowest prevalence has been observed among many states/UTs such as Uttarakhand, Uttar Pradesh, Delhi, Madhya Pradesh, Gujrat, and many northeastern states in Meghalaya.

Table 7 State-wise prevalence rate and per capita OOP expenditure on accidental injuries in India (2017–18).
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Fig. 1: Graphical illustration of state-wise prevalence rate of accidental injuries and per capita OOP expenditure of the individuals suffering from accidental injuries in India 2017–18 (Inpatient care).
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A State-wise prevalence rate of accidental injuries in India. B State-wise per capita OOP expenditure of the individuals suffering from accidental injuries in India. Figures are based on the author’s calculations from NSSO 75th Round.

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Fig. 2: Graphical illustration of state-wise prevalence rate of accidental injuries and per capita OOP expenditure of the individuals suffering from accidental injuries in India 2017–18 (Outpatient care).
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A The state-wise prevalence rate of accidental injuries in India. B The State-wise per capita OOP expenditure of the individuals suffering from accidental injuries in India. Figures are based on the author’s calculations from NSSO 75th Round.

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Table 7 and Fig. 1 show the high prevalence rate in states/UTs such as Jammu & Kashmir, Himachal Pradesh, Punjab, West Bengal, Mizoram, and some southern states. Among them, the number of people suffering is highest in Kerala. However, per capita OOP expenditure on injuries shows no similar trend to the prevalence rate. The amount is highest with fewer suffering states such as Uttar Pradesh, Jharkhand, Andaman, and Nicobar states/UTs of India. Although, the lowest amount has been found in the Goa state of the country.

The analysis in outpatient care is depicted in Table 7 and Fig. 2. Findings reported that the prevalence rate is low in most of the country’s states, in the lowest rate has been found in Nagaland. At the same time, the highly suffered state/UT has been perceived in Chandigarh in the study. Similarly, injuries-affected populations’ per capita OOP expenditure has been relatively high among less affected states and vice versa. The lowest amount has been found on Lakshadweep Island and the highest in Chandigarh UTs of India.

Discussion

Overall, the study observed that accidental injuries continue to be a significant challenge to public health and policy in India. The study indicated that the prevalence of accidental injuries does not seem very high concerning the total population, but it imposes massive fatalities and economic burdens on the country’s inhabitants. The share of OOP expenditure on accidental injuries is substantially high, like some other ailments in India. Besides outpatient care, accidental injuries are very high in inpatient care in the country. It elucidates that hospitalisation is essential for healing accident-derived wounds and fractures instead of outpatient visits. These facts induce the need to expand better healthcare facilities and universal health insurance coverage to ensure sufferers’ quick recovery and financial protection. Despite the low incidence in outpatient care, the findings showed that the share of OOP expenditure is high compared to inpatient care in the country. For instance, by using public healthcare facilities, the monthly OOP expenditure is INR 793.01 (US$ 12.18) for inpatient care, while the same is INR 2185.99 (US$ 33.59) in the case of outpatient care. In simple accidental cases, people do not prefer hospitalisation, but the multiple visits for regular check-ups raise their health expenditure in outpatient care.

In the study, the mean monthly OOP expenditure of accidental injuries-affected population has been estimated as INR 2672 .46 (US$ 41.06) and INR 3041.64 (US$ 46.73) in inpatient and outpatient care, respectively. Whereas, in the previous studies, the OOP expenditure on road traffic accidents and injuries was estimated at US$ 827.6 per annum by Kumar et al. (2012) in urban India, which is also supported by the fact of the high burden of OOP expenditure on road traffic accidents and injuries in the country. Pradhan et al. (2017) and Srinivas Goli et al. (2018) estimated the level of OOP expenditure of INR 26,132 annually on road traffic/accidental injuries in 2014 using the national statistics. However, Prinja et al. (2019) measured the injuries-derived OOP expenditure of INR 17829.7 (the US$ 280) in public sector hospitals. They also found that the economic impact of injuries is significantly high both in terms of OOP expenditure and productivity loss in north India. After comparing the results with earlier estimations, we found that OOP expenditure on accidental injuries has grown over time. Although unlike earlier studies, our estimates have presented a monthly average, we also measured the OOP expenditure as a percentage share of their TCE. We found that the monthly OOP expenditure of the affected population is 108.47 per cent higher than the country’s TCE, which shows that health expenditure is more considerable than their consumption expenditure in India.

Moreover, in inpatient care, the prevalence rate and the share of OOP expenditure are significantly high among the country’s better-off sections. Similar trends of OOP expenditure have been found in outpatient care except for females, STs, and 30–59 age groups, which is substantially high in the country. More precisely, the working-age groups bear a significant burden of the economic costs of road traffic accidents (Elango et al., 2018). Findings also showed that the rich spend more on healthcare due to affordability. In contrast, a high share of OOP expenditure in private healthcare facilities is mainly because of the overpriced medical and treatment services. The OOP expenditure increases even after individuals receive insurance compensation. It reveals that backing with insurance coverage may induce people to prefer better healthcare facilities to reduce accidental fatalities. As a source of finance, though, saving/income has been observed the leading coping strategy by individuals. A significant share of the population is dependent on distressed sources of finance due to their poor economic conditions. Borrowings from various sources and selling household assets are the popular distressed sources of finance among poor individuals in the country. To pay the high OOP expenditure through distress sources in inpatient care is due to the unexpected hospitalisation after the accident episodes. Along with productivity loss due to accidental injuries, repayment of loans with interest rate and the selling of assets increases the loss of future income of the households.

Furthermore, the analysis found that among different states/UTs in inpatient care, there is no proportional relationship between accidental injury cases and the level of OOP expenditure. For instance, India’s top three states/UTs with accidental injuries are Kerala, Puducherry and Goa. In contrast, the monthly mean OOP expenditure on accidental injuries is significantly high in Andaman & Nicobar Island, Utter Pradesh and Tamil Nadu. However, in outpatient care, Chandigarh (UT) has secured the top rank in the incidence of accidental injuries and monthly mean OOP expenditure in the country. It indicates that Chandigarh is one of the most urbanised and developed cities in North India. The availability and affordability of advanced medical facilities could be the reason for high OOP expenditure. Himachal Pradesh is the second state with the highest OOP expenditure in India, irrespective of its incidence rate below the national average. Despite the low accidental injury cases, the state’s high share of OOP expenditure shows the elevated costs of accidental injuries. It may be because of the severity of the accidents due to the mountainous terrain and the lack of nearby better healthcare facilities in the state. Furthermore, lack of health insurance coverage is also a possible reason for this high OOP expenditure among different states/UTs in the country.

Recommendations, shortcomings, and way forward of the study

As discussed, road accidents and related fatalities are highly dependent on predictable risk factors, which are preventable and controlled to some extent. This study proposed the pre-episode and post-episode approaches to reduce the losses of accidental injuries in the country. In the pre-episode approach, reforms should be initiated at policy and infrastructural levels to reduce accidental injury. Better infrastructure facilities, especially road and transportation services, can significantly reduce accidental injury and fatalities. However, developing countries like India are growing towards the expansion of better infrastructural facilities across their territories. The transformation is changing the entire economic activities, where the nature of the job and shifting occupational structure are changing the lifestyle and leading to rapid urbanisation in these countries. As a result, the number of vehicles is increasing on the one hand, and man-made mistakes and negligence are growing as critical reasons on the other hand. Over-speeding accounts for the country’s maximum share of road accidents and road accident deaths. Along with better road facilities and awareness campaigns among people about road safety and transportation, the motor vehicle regulation act must be enacted strictly in the country. The provision of driving licence suspension must be followed strictly against the traffic rule violations such as overloading, over speed, drunken driving, using mobile phones while driving etc., to ensure road safety measures.

Although, the government has taken many initiatives to address the burden of accidental injuries. However, the key hurdles to addressing the burden are the lack of awareness about road safety measures, poor enforcement of traffic rules and poor coordination among different agencies such as public works, transportation, road traffic and safety etc. The Government of India has initiated a comprehensive Motor Vehicle (Amendment) Act-2019 to regulate the country’s road traffic accidents and injuries. This act is more wide-ranging and inclusive than its preceding motor vehicle acts. Both punishment and compensation have been effectively considered in this act. It does not provide the grant of licences and permits to motor vehicles only but also confirms the standards for motor vehicles and penalties for violating the conditions by the persons. Furthermore, it ensures a compulsory insurance scheme for all road users to treat injured persons as per the ‘golden hour scheme’. This scheme provides immediate and prompt medical care to the injured persons with the highest likelihood of preventing life during the period lasting 1 h following a traumatic injury. However, the data used in the study have been surveyed before the introduction of the new motor vehicle act 2019. Therefore, the present study doesn’t include any anticipated outcomes and shortcomings of the new motor vehicle act 2019 in the analysis.

On the contrary, the post-episode approaches to reduce the losses of accidental injuries, the country’s public healthcare system must establish advanced medical facilities, i.e., trauma centres and emergency medical treatment facilities within the approachable regions (Uthkarsh et al., 2016). However, the government has witnessed substantial progress in its population health and the availability of healthcare services in the last two decades (Srinivas Goli et al., 2018). Despite it, accidental injuries are still a significant cause of mortality and disability in the country (Kumar et al., 2012; Singh, 2017). A high financial burden of accidental injuries traps people in the vicious circle of poverty (Kumar et al., 2012; Mahal et al., 2010). Mainly, individuals in working-age groups, residing in rural inhabitants, STs, and the economically poor are the victims of accidental-related losses in the country. At the same time, low government spending on health is also a vital issue for the public healthcare system in India. Statistics show that India spends only a small amount (3.84 per cent of GDP) on healthcare compared to the other developing countries in the world (GOI, 2018). The share of public spending on healthcare is only 1.18 per cent, and more than 69 per cent of the total health expenditure is paid out-of-pocket in India (GOI, 2018). Therefore, expansion of the insurance coverage, increase in the healthcare spending as a share of GDP, and the establishment of advanced medical facilities are required to reduce the burden of accidental injuries in the country.

Conclusion

The study concludes that the high incidence of accidental injuries threatens people more than many other ailments in India. The rising number of motor vehicles, bad road conditions, changing occupational structure and rapidly growing urbanisation are the key reasons for road traffic accidents in the country. Although, citizens’ casual approach of not following the traffic norms cannot be denied as a reason for its mounting. Therefore, awareness campaigns can make positive results in reducing road traffic fatalities. Although the government has done much at policy levels, it still has a long way to go to provide any positive outcomes in road traffic accidents in the country (Jagnoor et al., 2020). In addition, a strict motor vehicle act, administrative and political determination, awareness campaigns through various media platforms, and diverse road lane systems and traffic norms can help reduce accidental losses in the country. Timely diagnosis and medical facilities can be helpful to ensure the reduction in the cost of post-road traffic fatalities. Even the healthcare practitioners and associated agents can take a lead role in reducing the fatalities due to accidental disasters by providing quick and required treatment without delay. Also, advanced healthcare facilities, mainly emergency services, must be set up in the approachable region throughout the country.

Further, the high share of OOP expenditure and productivity loss are escalating the burden of healthcare and compelling people to live in the vicious circle of poverty. It focuses on the country’s insufficient spending on the health systems and the under-coverage of insurance facilities in India. The dependency on borrowings reveals that people can still not pay health expenditures out of their resources, such as income and savings. Sometimes people sell their household assets to meet their essential health expenditures. Also, it reveals that people are still far from the mainstream to earn a decent amount from distinguished economic activities. At the same time, the high share of borrowings is making them destitute and compelling them to live in poverty and under the dominance of lenders. At the same time, selling assets reduces the probability of future income for households. Hence, it is needed to increase the public spending on healthcare services as a percentage share country’s GDP. Furthermore, comprehensive universal health insurance coverage for all, including all components of healthcare expenditure is the need of the hour.