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Comparative Assessment of Economic Burden of Disease in Relation to Out of Pocket Expenditure

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Abstract

Background: The economic costs associated with morbidity pose a great financial risk on the population. Household's over-dependence on out-of-pocket (OOP) health expenditure and their inability to cope up with the economic costs associated with illness often push them into poverty. The current paper aims to measure the economic burden and resultant impoverishment associated with OOP health expenditure for a diverse set of ailments in India. Methods: Cross-sectional data from National Sample Survey Organization (NSSO) 71st Round on “Key Indicators of Social Consumption: Health” has been employed in the study. Indices, namely the payment headcount, payment gap, concentration index, poverty headcount and poverty gap, are defined and computed. The measurement of catastrophic burden of OOP health expenditure is done at 10% threshold level. Results: Results of the study reveal that collectively non-communicable diseases (NCDs) have higher economic and catastrophic burden, individually infections rather than NCDs such as Cardio Vascular Diseases and cancers have a higher catastrophic burden and resultant impoverishment in India. Ailments such as gastro-intestinal, respiratory, musco-skeletal, obstetrics, and injuries also have a substantial economic burden on population and push them below the poverty line. Results also show that despite the pro-poor concentration of infections, their economic burden is more concentrated among the wealthier consumption groups. Conclusion: The study concludes that universal health coverage through adequate provision of pooled resources for health care and community-based health insurance is critical to reduce the economic burden and impoverishment related to OOP health expenditure. Measures should also be instituted to insulate people from economic burden on morbidity, especially the NCDs.
BRIEF RESEARCH REPORT
published: 29 January 2019
doi: 10.3389/fpubh.2019.00009
Frontiers in Public Health | www.frontiersin.org 1January 2019 | Volume 7 | Article 9
Edited by:
Obinna E. Onwujekwe,
University of Nigeria, Nigeria
Reviewed by:
Chhabi Lal Ranabhat,
Yonsei University, South Korea
Natasa Djordjevic,
University of Kragujevac, Serbia
*Correspondence:
Ramna Thakur
ramna@iitmandi.ac.in
Specialty section:
This article was submitted to
Health Economics,
a section of the journal
Frontiers in Public Health
Received: 05 July 2018
Accepted: 10 January 2019
Published: 29 January 2019
Citation:
Sangar S, Dutt V and Thakur R (2019)
Comparative Assessment of
Economic Burden of Disease in
Relation to Out of Pocket Expenditure.
Front. Public Health 7:9.
doi: 10.3389/fpubh.2019.00009
Comparative Assessment of
Economic Burden of Disease in
Relation to Out of Pocket
Expenditure
Shivendra Sangar, Varun Dutt and Ramna Thakur*
School of Humanities and Social Sciences, Indian Institute of Technology Mandi, Mandi, India
Background: The economic costs associated with morbidity pose a great financial
risk on the population. Household’s over-dependence on out-of-pocket (OOP) health
expenditure and their inability to cope up with the economic costs associated with illness
often push them into poverty. The current paper aims to measure the economic burden
and resultant impoverishment associated with OOP health expenditure for a diverse set
of ailments in India.
Methods: Cross-sectional data from National Sample Survey Organization (NSSO)
71st Round on “Key Indicators of Social Consumption: Health” has been employed in
the study. Indices, namely the payment headcount, payment gap, concentration index,
poverty headcount and poverty gap, are defined and computed. The measurement of
catastrophic burden of OOP health expenditure is done at 10% threshold level.
Results: Results of the study reveal that collectively non-communicable diseases
(NCDs) have higher economic and catastrophic burden, individually infections rather than
NCDs such as Cardio Vascular Diseases and cancers have a higher catastrophic burden
and resultant impoverishment in India. Ailments such as gastro-intestinal, respiratory,
musco-skeletal, obstetrics, and injuries also have a substantial economic burden on
population and push them below the poverty line. Results also show that despite
the pro-poor concentration of infections, their economic burden is more concentrated
among the wealthier consumption groups.
Conclusion: The study concludes that universal health coverage through adequate
provision of pooled resources for health care and community-based health insurance
is critical to reduce the economic burden and impoverishment related to OOP health
expenditure. Measures should also be instituted to insulate people from economic burden
on morbidity, especially the NCDs.
Keywords: economic burden, impoverishment, ailments, infections, morbidity
Sangar et al. Impoverishment Impact of Ailments
BACKGROUND
The economic costs associated with morbidity pose a great
financial risk on population (1). Direct spending on health
care discourages people from using health care services and
encourages them to postpone their health care needs (2,3). In
the majority of LMICs, limited resources for health care and lack
of protection against catastrophic health spending have led to the
over-reliance on OOP health expenditure (4).
Household’s overly dependence on out-of-pocket (OOP)
health expenditure and their inability to cope up with the
economic costs of illness often push them into poverty (5,6).
Households faced with this situation face enormous financial
liability and are devoid of adequate means for other essential
needs such as food and education (7,8). Further, rural dwelling,
low socioeconomic status and outpatient care also contribute to
the increasing economic burden of illness (9,10).
There are few studies which have talked about this issue by
taking into consideration either NCDs or infectious diseases
without analyzing its impact on impoverishment (6,11,12).
There is no study which has analyzed the monetary burden and
impoverishment impact of different sets of NCDs and infectious
diseases separately. Therefore, this study aims at filling this gap
in the literature by analyzing the economic burden and resultant
impoverishment due to OOP health expenditure for a diverse set
of ailments by employing the recent health expenditure survey in
India.
MATERIALS AND METHODS
Data
The current study employed a nationally representative data
from National Sample Survey Organization (NSSO), 71st Round
(2004) on Key indicators of social consumption: Health (13).
The survey comprises 65,932 sample households consisting
a population of 0.33 million persons. The survey adopted a
stratified multistage sample design, using census villages for the
rural areas and urban blocks for the urban areas as the first-
stage units (FSUs), and households as the second-stage units.
The reference period for inpatient and outpatient care is 365
and 15 days, respectively. For the analysis, the OOP health
expenditure for inpatient and outpatient care is converted into
monthly figures and added together to get the total OOP health
expenditure. In this study, OOP health expenditure is calculated
by deducting the amount of reimbursement from total health
expenditure. Respective sample weights have been applied in the
calculation of the results. For the analysis, we have taken 16
groups of different ailments (13). The analysis is done on Stata
14 (14).
Methods
Measuring the Economic Burden of Morbidity
The economic burden of morbidity has been calculated in terms
of headcount and payment gap (15). Headcount (HC) measures
the percentage of population incurring OOP health expenditure.
It is measured by HC =1
NPN
i=1ni, where N is the sample size,
HC is the headcount and n is number of persons incurring OOP
health expenditure. The payment gap is explained as the share
of OOP health expenditure in total consumption expenditure is
given by G=H
TCE 100, S is the payment gap, H is the OOP
health expenditure, TCE is the total consumption expenditure.
Measurement of catastrophic burden of OOP health expenditure
(headcount and payment gap) is done at 10% threshold level of
TCE which has been a standard benchmark in the literature (15,
16). Catastrophic headcount is a fraction of the population whose
OOP health expenditure as a proportion of TCE exceed the given
threshold. Whereas, catastrophic payment gap measures the
average degree by which OOP health expenditure as a proportion
of TCE exceeds the threshold level. The concentration index has
been used to determine whether the poor incur more OOP health
expenditure or the rich (17). Concentration index CEand CO
(for headcount and payment gap, respectively) as given by the
following formula: CI =p1L2p2L1+p2L3p3L2+...+
(pt1LtptLt1), Where CI is the concentration index, ptis
the cumulative percentage of the population ranked by monthly
consumption expenditure in group t, Ltis the corresponding
health variable.
Measuring the Impoverishment Impact of Morbidity
Poverty headcount impact measures the fraction of population
falling below the poverty line due to OOP health expenditure.
The poverty impact in terms of headcount is measured as PIHC =
HCPost HCPre , where PIHC is the poverty headcount impact,
ZPre be the pre-payment poverty line. Then PPre =1 if x <
ZPre HCPre =1
NPN
i=1PPre, PPre is the pre-payment poverty
headcount, HCPost and HCPost are the post and pre-payment
poverty headcount. Poverty impact in terms of gap measures
the average shortfall due to OOP health expenditure from the
existing poverty line. It is given as PIG=GPost GPre , where
PIGis the poverty gap impact, gPre is the pre-payment gap, that
is equal to x—ZPre if x <ZPre, and zero otherwise, GPre =
1
NPN
i=1gPre , GPost and GPre are the post and pre-payment
poverty gap.
RESULTS
Economic Burden of Morbidity in India
Table 1 reports the economic and catastrophic burden of
OOP health expenditure incurred on different ailments.
Although, collectively NCDs have higher economic burden,
in case of individual ailments a significant proportion of
population reported OOP health expenditure in case of
infections followed by respiratory, CVDs, musco-skeletal,
gastro-intestinal, psychiatric, and injuries. Although in lesser
proportions, OOP health expenditure is also reported in other
categories of ailments. Similarly, the payment gap as share of
OOP health expenditure in TCE is also higher among infections
as compared to other ailments. Similar to the economic burden,
the catastrophic burden reported at 10% threshold level is
relatively higher in case of infections than other ailments.
However, collectively NCDs have higher catastrophic impact on
the population. In case of infections, the negative value of C.IE
and the positive value of C.IOreveal that despite its pro-poor
concentration, it is the wealthier consumption groups which
Frontiers in Public Health | www.frontiersin.org 2January 2019 | Volume 7 | Article 9
Sangar et al. Impoverishment Impact of Ailments
TABLE 1 | Economic burden of morbidity in India.
Type of ailments Population
reporting OOP (%)
C.I*
EPayment gap (%) C.I**
OCatastrophic
headcount 10%
Catastrophic
payment gap 10%
Infections 14.1 (13.5 to 14.7) 0.034 (0.055 to 0.014) 2.2 (2.1 to 2.4) 0.058 (0.039 to 0.076) 6.6 (6.2 to 7.0) 1.2 (1.0 to 1.3)
Cancers 0.4 (0.3 to 0.5) 0.282 (0.243 to 0.322) 0.4 (0.3 to 0.5) 0.441 (0.410 to 0.472) 0.3 (0.2 to 0.4) 0.3 (0.2 to 0.4)
CVDs 5.3 (5.0 to 5.7) 0.339 (0.224 to 0.452) 1.7 (1.5 to 1.9) 0.524 (0.455 to 0.593) 2.8 (2.5 to 3.0) 1.0 (0.9 to 1.1)
Injuries 2.3 (2.1 to 2.4) 0.124 (0.081 to 0.166) 0.8 (0.7 to 0.9) 0.293 (0.260 to 0.325) 1.3 (1.2 to 1.4) 0.6 (0.5 to 0.7)
Respiratory 6.0 (5.6 to 6.5) 0.027 ( to 0.010 to 0.065) 1.0 (0.8 to 1.3) 0.130 (0.100 to 0.159) 2.4 (2.1 to 2.7) 0.6 (0.4 to 0.7)
Gastro–intestinal 4.2 (4.0 to 4.5) 0.055 (0.014 to 0.095) 1.0 (0.8 to 1.1) 0.201 (0.173 to 0.229) 2.3 (2.1 to 2.6) 0.7 (0.5 to 0.8)
Blood Disorders 0.7 (0.5 to 0.8) 0.113 (0.014 to 0.239) 0.2 (0.1 to 0.3) 0.239 (0.166 to 0.311) 0.4 (0.3 to 0.5) 0.1 (0.08 to 0.2)
Endocrine 0.7 (0.5 to 0.8) 0.113 (0.009 to 0.233) 0.2 (0.1 to 0.3) 0.239 (0.166 to 0.312) 0.4 (0.3 to 0.5) 0.1 (0.08 to 0.2)
Psychiatric 2.7 (2.4 to 3.0) 0.075 (0.026 to 0.124) 0.8 (0.7 to 0.9) 0.267 (0.230 to 0.303) 1.4 (1.3 to 1.6) 0.5 (0.4 to 0.6)
Eye 1.1 (1.0 to 1.3) 0.088 (0.015 to 0.159) 0.2 (0.1 to 0.3) 0.201 (0.147 to 0.256) 0.5 (0.4 to 0.6) 0.1 (0.06 to 0.02)
Ear 0.3 (0.2 to 0.4) 0.167 (0.035 to 0.298) 0.1 (0.02 to 1.7) 0.237 (0.125 to 0.349) 0.2 (0.1 to 0.3) 0.1 (0.01 to 1.9)
Skin 1.1 (1.0 to 1.2) 0.028 (0.060 to 0.116) 0.2 (0.1 to .0.3) 0.124 (0.058 to 0.190) 0.5 (0.4 to 0.6) 0.1 (0.08 to 1.1)
Musco–skeletal 4.6 (4.3 to 5.0) 0.010 (0.005 to 0.016) 1.0 (0.09 to 1.1) 0.265 (0.230 to 0.299) 2.4 (2.1 to 2.7) 0.6 (0.5 to 0.7)
Genito–urinary 1.6 (1.4 to 1.8) 0.214 (0.153 to 0.274) 0.7 (0.6 to 0.8) 0.293 (0.256 to 0.332) 1.1 (1.0 to 1.2) 0.4 (0.3 to 0.5)
Obstetrics 1.1 (1.0 to 1.2) 0.003 (0.004 to 0.002) 0.2 (0.1 to 0.3) 0.120 (0.069 to 0.171) 0.5 (0.4 to 0.6) 0.1 (0.04 to 1.9)
Others 1.1 (1.0 to 1.3) 0.195 (0.118 to 0.0.271) 0.3 (0.2 to 0.4) 0.352 (0.292 to 0.412) 0.5 (0.4 to 0.6) 0.2 (0.1 to 0.3)
Total 47.3 (46.4 to 48.4) 0.064 (0.053 to 0.075) 11.0 (10.5 to 11.5) 0.251 (0.239 to 0.262) 23.7 (22.9 to 24.4) 7.8 (7.3 to 8.2)
The figures are based on author’s calculations from NSSO 71st Round. Values in parentheses are 95% confidence interval.
*C.IEis the concentration index for headcount.
**C.IOis the concentration index for payment gap.
TABLE 2 | Poverty impact of morbidity in India.
Type of
ailments
Poverty impact
headcount (%)
Poverty impact
(Number)
Poverty impact gap
(INR)
Infections 2.1 (1.9–2.3) 23,543,134 14.9 (0.18) (13.6–16.3)
Cancers 0.2 (0.1–0.3) 2,242,203 1.2 (0.01) (0.8–1.5)
CVDs 0.8 (0.7–0.9) 8,968,813 5.0 (0.06) (4.4–5.6)
Injuries 0.5 (0.4–0.7) 5,605,508 3.9 (0.05) (3.4–4.5)
Respiratory 0.6 (0.5–0.8) 6,726,610 5.5 (0.07) (4.1–6.9)
Gastro–
intestinal
0.8 (0.7–0.9) 8,968,813 5.9 (0.07) (5.0–6.9)
Blood Disorders 0.1 (0.07–1.3) 1,121,102 1.0 (0.01) (0.6–1.3)
Endocrine 0.5 (0.4–0.6) 5,605,508 2.6 (0.03) (2.1–3.1)
Psychiatric 0.5 (0.4–0.6) 5,605,508 3.8 (0.05) (3.3–4.3)
Eye 0.2 (0.09–0.3) 2,242,203 0.8 (0.01) (0.5–1.1)
Ear 0.1 (0.02–0.07) 1,121,102 0.3 (0.01) (0.1–0.5)
Skin 0.1 (0.01–0.3) 1,121,102 1.0 (0.01) (0.8–1.3)
Musco–skeletal 0.7 (0.6–0.9) 7,847,711 5.6 (0.07) (4.5–7.0)
Genito 0.4 (0.3–0.5) 4,484,406 2.8 (0.03) (2.1–3.4)
Obstetrics 0.2 (0.1–0.3) 2,242,203 1.1 (0.01) (0.7–1.5)
Others 0.2 (0.1–0.3) 2,242,203 1.3 (0.02) (0.9–1.7)
Total 8.0 (7.6–8.4) 89,688,129 56.7 (0.70) (53.8–59.9)
The figures are based on author’s calculations from NSSO 71st Round. Values in
parentheses are 95% confidence interval. INR, Indian National Rupee. INR has been
converted into Euro for the year 2014.
spends more on the treatment. However, in case of NCDs,
especially CVDs and cancer, the positive values of CIEand CIO
reveal a pro-rich concentration of headcount as well as OOP
health expenditure.
Poverty Impact of Morbidity in India
Table 2 presents the poverty impact of OOP health expenditure
incurred on different ailments in terms of headcount and
payment gap. Poverty impact in terms of headcount is highest
in case of infections, followed by CVDs and gastro-intestinal,
musco-skeletal, respiratory, and injuries. Similarly, the poverty
impact in terms of payment gap is also significantly higher in
infections. It shows that the average shortfall from the poverty
line is higher in case of infections than other ailments. Ailments
consisting of gastro, musco-skeletal, respiratory, CVDs, and
injuries also have higher poverty gap impact. Some other ailments
such as skin, blood disorders, eye and ear also marginally
contribute toward impoverishment.
DISCUSSION
Overall, the results of the study reveal that NCDs such as CVDs,
cancers, etc. have the higher catastrophic burden and resultant
impoverishment in India. Although individually CVDs have a
significant economic burden and high poverty impact, it is less
than infections. Ailments such as gastro-intestinal, respiratory,
musco-skeletal, obstetrics and injuries also have a substantial
economic burden on population and push them below the
poverty line. Infections have higher poverty impact because the
population affected with the same is more concentrated around
the poverty line. A smaller increase in OOP health expenditure
pushes the larger proportion of population below the poverty
line.
Although it is true that the burden of NCDs is increasing
in India and cumulatively they have higher catastrophic burden
but it is the infectious diseases which push more people into
Frontiers in Public Health | www.frontiersin.org 3January 2019 | Volume 7 | Article 9
Sangar et al. Impoverishment Impact of Ailments
the quagmire of poverty (18). Studies from other LMICs reveal
that the economic burden and resultant impoverishment due
to OOP health expenditure has been relatively high in case of
NCDs such as CVDs, cancer, diabetes and stroke (19,20). Further,
many countries in Africa have higher incidence of catastrophic
health expenditure due to infectious disease like Malaria and
Tuberculosis (21,22). In LMICs, lack of access to health
services, poor quality of care and high user charges contribute
to higher OOP health expenditure (12). Further, inadequate
public spending on health care and poor implementation of
publicly financed health insurance schemes (PFHIs) have further
accentuated the problem of health care financing in India
(23,24).
The high catastrophic burden and resultant impoverishment
associated with morbidity highlight the need for a better financial
protection mechanism in India, particularly for the poor and
vulnerable. Universal health coverage (UHC) is regarded as a
critical path for improving the health outcomes and providing
financial protection against the catastrophic health expenditure
(25). It is a comprehensive health system approach that helps
to provide improved access to health care services which
significantly improves the health outcomes (26). UHC can be
achieved through a matured health system that can provide
sufficient and continuous pooled resources for health (27). Apart
from improving access to health care services, policy makers
must focus on extending quality care, especially to poor families
(28). LMICs can implement community-based health insurance
(CBHI) which can go a long way in achieving UHC (29).
The future plan of the research will be to measure the
economic burden of ailments at different threshold levels. Along
with-it different sources of finance used to cope up with OOP
health expenditure for different ailments will also be studied.
A comparative analysis with previous data rounds may also be
useful.
DATA AVAILABILITY STATEMENT
This paper is based on anonymized survey data collected by
the National Sample Survey organization (NSSO), a department
of the Ministry of Statistics and Programme Implementation,
Government of India. Data is available in the public domain.
The data is already available in publicly available repositories
to individuals both at national and international level through
http://www.mospi.gov.in/
AUTHOR CONTRIBUTIONS
RT and SS conceived the idea with inputs from VD. SS
performed the statistical analysis and prepared the initial
draft of the manuscript. RT and VD assisted in the revision
of the manuscript. All authors read and approved the final
manuscript.
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Front Public Health (2017) 5:250. doi: 10.3389/fpubh.2017.00250
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Frontiers in Public Health | www.frontiersin.org 5January 2019 | Volume 7 | Article 9
... Policy makers need to design and strictly implement suitable health system financing policies that will provide financial risk protection to households. diseases) but used data from a previous round of National Sample Survey (NSS) 2004 and did not report financial burden on households for outpatients and hospitalised cases separately [6]. ...
... expenditure (OOPE) on health represents 63.2% of the total health expenditure (THE), and health insurance expenditure is only around 7.6% of current health expenditure [4]. The overall burden of these diseases coupled with low public health spending, high OOPE and lack of protection against catastrophic health expenditure (CHE) could lead to devastating effects on human lives in India [5,6]. An estimated 32-39 million people are pushed into poverty due to health care expenditure in India each year [7][8][9]. ...
... Our analysis indicates that health care burden was almost five times higher for hospitalisation care and two times higher for OPD care if patients sought treatment in private health facilities rather than public health facilities. The impoverishment effect among the cancer patients irrespective of type of treatment was significantly higher if patients sought the treatment in a private health facility rather than a public health facility, which is consistent with findings from previous studies [6]. ...
Article
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Background In India, more than two-thirds of the total health expenditure is incurred through out-of-pocket expenditure (OOPE) by households. Morbidity events thus impose excessive financial risk on households. The Sustainable Development Goals Target 3.8 specifies financial risk protection for achieving universal health coverage (UHC) in developing countries. This study aimed to estimate the impact of OOPE on catastrophic health expenditure (CHE) and impoverishment effects by types of morbidity in India. Methods Data came from the 75th round of the National Sample Survey (NSS) on the theme ‘Social consumption in India: Health’, which was conducted during the period from July 2017 to June 2018. For the present study, 56,722 households for hospitalisation, 29,580 households for outpatient department (OPD) care and 6285 households for both (OPD care and hospitalisation) were analysed. Indices, namely health care burden, CHE, poverty head count ratio and poverty gap ratio using standard definitions were analysed. Results Households with members who underwent treatment for cancers, cardiovascular diseases, psychiatric conditions, injuries, musculoskeletal and genitourinary conditions spent a relatively high amount of their income on health care. Overall, 41.4% of the households spent > 10% of the total household consumption expenditure (HCE) and 24.6% of households spent > 20% of HCE for hospitalisation. A total of 20.4% and 10.0% of households faced CHE for hospitalisation based on the average per capita and average two capita consumption expenditure, respectively. Health care burden, CHE and impoverishment was higher in households who sought treatment in private health facilities than in public health facilities. Conclusion Our study suggests that there is an urgent need for political players and policymakers to design health system financing policies and strict implementation that will provide financial risk protection to households in India.
... Our findings are convergent with previous literature which revealed that likelihood of incurring catastrophic payments and distressed financing in India was inordinately large for NCDs. The incidence was more exacerbated for rural residents vis-à-vis urban counterparts [32,33], which was also found in our study (see Figs. 1 and 2, Additional file 1: Appendix). Furthermore, results were in tandem with evidence demonstrating that poor households were less able to cope with healthcare costs compared to their affluent counterparts [18,[34][35][36]. ...
Article
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Background Financing for NCDs is encumbered by out-of-pocket expenditure (OOPE) assuming catastrophic proportions. Therefore, it is imperative to investigate the extent of catastrophic health expenditure (CHE) on NCDs, which are burgeoning in India. Thus, our paper aims to examine the extent of CHE and impoverishment in India, in conjunction with socio-economic determinants impacting the CHE. Methods We used cross-sectional data from nationwide healthcare surveys conducted in 2014 and 2017–18. OOPE on both outpatient and inpatient treatment was coalesced to estimate CHE on NCDs. Incidence of CHE was defined as proportion of households with OOPE exceeding 10% of household expenditure. Intensity of catastrophe was ascertained by the measure of Overshoot and Mean Positive Overshoot Indices . Further, impoverishing effects of OOPE were assessed by computing Poverty Headcount Ratio and Poverty Gap Index using India’s official poverty line. Concomitantly, we estimated the inequality in incidence and intensity of catastrophic payments using Concentration Indices . Additionally, we delineated the factors associated with catastrophic expenditure using Multinomial Logistic Regression. Results Results indicated enormous incidence of CHE with around two-third households with NCDs facing CHE. Incidence of CHE was concentrated amongst poor that further extended from 2014(CI = − 0.027) to 2017–18(CI = − 0.065). Intensity of CHE was colossal as households spent 42.8 and 34.9% beyond threshold in 2014 and 2017-18 respectively with poor enduring greater overshoot vis-à-vis rich (CI = − 0.18 in 2014 and CI = − 0.23 in 2017–18). Significant immiserating impact of NCDs was unraveled as one-twelfth in 2014 and one-eighth households in 2017–18 with NCD burden were pushed to poverty with poverty deepening effect to the magnitude of 27.7 and 30.1% among those already below poverty on account of NCDs in 2014 and 2017–18 respectively. Further, large inter-state heterogeneities in extent of CHE and impoverishment were found and multivariate analysis indicated absence of insurance cover, visiting private providers, residing in rural areas and belonging to poorest expenditure quintile were associated with increased likelihood of incurring CHE. Conclusion Substantial proportion of households face CHE and subsequent impoverishment due to NCD related expenses. Concerted efforts are required to augment the financial risk protection to the households, especially in regions with higher burden of NCDs.
... Therefore, meta-analysis to determine the effect of various factors associated with CHE could not be conducted. Sangar et al., (2019) found that headcount for CHE was more among richer sections as compared to the poorer ones. Chauhan et al., (2018) found that the odds of CHE were higher for lower income quartile patients (OR: 5.6, 95% CI: 2.6-12.4, ...
Article
Objective: The aim of this systematic review is to determine pooled estimates of out-of-pocket (OOPE) and catastrophic health expenditure (CHE), correlates of CHE, and most common modes of distress financing on the treatment of selected non-communicable disease (cancer) among adults in India. Methods: PubMed, Scopus and Embase were searched for eligible studies using strict inclusion and exclusion criteria. Data was extracted and pooled estimates using random effects model of meta-analysis were determined for different types of costs. Forest plots were created and heterogeneity among studies was checked. Results: The pooled estimate of direct OOPE on inpatient and outpatient cancer care were 83396.07 INR (4405.96 USD) (95% CI = 44591.05-122202.0) and 2653.12 (140.17 USD) INR (95% CI = -251.28-5557.53), respectively, total direct OOPE was 47138.95 INR (2490.43 USD) (95% CI = 37589.43-56690.74), indirect OOPE was 11908.50 INR (629.15 USD) (95% CI=-5909.33-29726.31) and proportion of individuals facing CHE was 62.7%. However, high heterogeneity was observed among the studies. Savings, income, borrowing money and sale of assets were the most common modes of distress financing for cancer treatment. Conclusion: Income- and treatment-related cancer policies are needed to address the evidently high and unaffordable cancer treatment cost. Economic studies are needed for estimating all types of costs using standardised definitions and tools for precise estimates. Robust cancer database/registries and programs focusing on affordable cancer care can reduce the economic burden and prevent impoverishment.
... The majority of the publicly provided insurance schemes offer coverage for these conditions. The ailments associated with hearing difficulties/impairment are the most common morbidities along with the skin-related conditions which can marginally contribute towards impoverishment if not covered via any insurance schemes (Sangar et al., 2019). ...
Article
Purpose This paper aims to draw theoretical insight from Sen’s capability-approach and attempts to examine the effectiveness of health-insurance-schemes in reducing out-of-pocket-expenditure (OOPE) and catastrophic-health-expenditure (CHE) in India. Design/methodology/approach Data were extracted from the National-Sample-Survey-Organization, 71st round on Health-2014. Generalized-linear-regression-model was used to investigate the impact of social-protection-schemes on OOPE and CHE. Findings A notable segment of the Indian population is still not covered under any health-insurance-schemes. The majority of the insured population was covered by publicly-financed-health-insurance-schemes (PFHIs), with a trivial-share of private-insurance. Households from 16–59 age-group, urban, literate, richest, southern-regions, using private-facilities and having ear and skin ailments have reported higher insurance coverage. Reimbursement was higher among elderly, literates, middle-class, central-regions, using private-facilities/insurance and for infections. Access to PFHIs significantly reduces the risk of OOPE and CHE. Unavailability of reimbursement exposes the population to a higher risk of CHE. Research limitations/implications Being a study based on secondary data sources, its applicability may vary as per the other social indicators. Practical implications Extending insurance-coverage alone cannot answer the widespread inequalities in health care. Rather, an efficiently managed reimbursement-mechanism could condense OOPE and CHE by enhancing the capability of the population to confront the undue financial burden. Social implications Extending the health-insurance-coverage to the entire population requires a better understanding of the underlying-dynamics and health-care needs and must make health-care affordable by enhancing the overall capability. Originality/value This research brings a theoretical and conceptual analysis for improving the health-insurance coverage among the community as a public health strategy.
... Social isolation is an additional stressor to an already highly stressful world environment and people's extensive fear of the novel COVID-19 pandemic threat (Bavel et al., 2020;LeDoux, 2012;Mobbs et al., 2015). In addition, social distancing included full lockdowns in many countries, as well as in Israel, with dramatic economic effects (Anser et al., 2020;Sangar et al., 2019). Adverse local and global economic impacts, in addition to drastic personal income reduction, may be detrimental to people's psychological health and general well-being (Xiao et al., 2020). ...
Article
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The recent COVID-19 pandemic led to uncertainty and severe health and economic concerns. Previous studies indicated that owning a companion animal, such as a dog or a cat, has benefits for good mental health. Interactions with animals may help with depression and anxiety, particularly under stress-prone conditions. Human-animal interactions may even improve peer-to-peer social relationships, as well as enhance feelings of respect, trust, and empathy between people. Interestingly, it has also been shown that stress and poor well-being of dog owners negatively affect the well-being of their companion animals. However, a dramatic increase in dog abandonment could potentially occur due to COVID-19 related health, economic and social stresses, as well as due to the inconclusive reports of companion animals being potential COVID-19 carriers. Such a scenario may lead to high costs and considerable public health risks. Accordingly, we hypothesized that the COVID-19 pandemic, and the related social isolation, might lead to dramatic changes in human-dog bidirectional relationships. Using unique prospective and retrospective datasets, our objectives were to investigate how people perceived and acted during the COVID-19 pandemic social isolation, in regards to dog adoption and abandonment; and to examine the bidirectional relationship between the well-being of dog owners and that of their dogs. Overall, according to our analysis, as the social isolation became more stringent during the pandemic, the interest in dog adoption and the adoption rate increased significantly, while abandonment did not change. Moreover, there was a clear association between an individual's impaired quality of life and their perceptions of a parallel deterioration in the quality of life of their dogs and reports of new behavioral problems. As humans and dogs are both social animals, these findings suggest potential benefits of the human-dog relationships during the COVID-19 pandemic, in accordance with the One Welfare approach that implies that there is a bidirectional connection between the welfare and health of humans and non-human animals. As our climate continues to change, more disasters including pandemics will likely occur, highlighting the importance of research into crisis-driven changes in human-animal relationships.
... Social isolation is an additional stressor to an already highly stressful world environment and people's extensive fear of the novel COVID-19 pandemic threat (Bavel et al., 2020;LeDoux, 2012;Mobbs et al., 2015). In addition, social distancing included full lockdowns in many countries, as well as in Israel, with dramatic economic effects (Anser et al., 2020;Sangar et al., 2019). Adverse local and global economic impacts, in addition to drastic personal income reduction, may be detrimental to people's psychological health and general well-being (Xiao et al., 2020). ...
Preprint
The recent COVID-19 pandemic led to uncertainty and severe health and economic concerns, which may have impacted human-dog relationships. Our objectives were to investigate how people perceived and acted during the COVID-19 pandemic social isolation, in regards to dog adoption and abandonment; and to examine the bidirectional relationships between dog owners’ well-being to that of their dogs. Overall, according to our analysis, the stricter the social isolation became during the pandemic, the interest in dog adoption as well as adoption rate increased significantly, while abandonment did not change. Moreover, there was a clear association between individuals’ impaired quality of life and their perceptions of poorer life quality of their dogs as well as the development of new behavioral problems. These findings suggest potential benefits for human-dog relationship during the COVID-19 pandemic, in compliance with the One Welfare approach.
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Introduction Even in a country with a tax-based healthcare financing system, health insurance can play an important role, especially in the management of chronic diseases with high disease and economic burden such as Type 2 Diabetes Mellitus (T2DM). The insurance coverage among T2DM patients in Malaysia is currently unclear. The aim of this study was to determine the insurance status of T2DM patients in public and private healthcare facilities in Malaysia, and the association between this status and patients’ sociodemographic and economic factors. Methods A cross-sectional study among T2DM patients seeking inpatient or outpatient treatment at a public tertiary hospital (Hospital Canselor Tuanku Muhriz) and a private tertiary hospital (Universiti Kebangsaan Malaysia Specialist Centre) in Kuala Lumpur between August 2019 and March 2020. Patients were identified via convenience sampling using a self-administered questionnaire. Data collection focused on identifying insurance status as the dependent factor while the independent factors were the patients’ sociodemographic characteristics and economic factors. Results Of 400 T2DM patients, 313 responded (response rate, 78.3%) and 76.0% were uninsured. About 69.6% of the respondents had low monthly incomes of <RM5000. Two-thirds of participants (59.1%) spent RM100–500 for outpatient visits whilst 58.5% spent <RM100 on medicines per month (RM1 = USD0.244). Patients who visited a private facility had five times more likely to have insurance than patients who visited a public facility. Participants aged 18–49 years with higher education levels were 4.8 times more likely to be insured than participants aged ≥50 years with low education levels (2 times). Conclusions The majority of T2DM patients were uninsured. The main factors determining health insurance status were public facilities, age of ≥ 50 years, low education level, unemployment, and monthly expenditure on medicines.
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Background The transition from Millennium Development Goals to Sustainable Development Goals 2030 has yet again emphasized on the unfinished agenda of achieving efficient and equitable health systems and universal health coverage. With trifling public spending on healthcare and insignificant insurance coverage, India has recorded 55 million people descending below the poverty line in one year due to healthcare payments. Not only are these health-related payments impoverishing but also the burden of seeking care is disproportionately skewed towards deprived population groups. Against this backdrop, the present paper examines the inequality in health status, utilization of health care services, and financial risk protection. The study is further complemented by assessing who benefits from public subsidies across different economic quintiles and as per epidemiological transition level (ETL) of states. Methods This study used data nationwide National Sample Survey 75th round data. We perform the Benefit Incidence Analysis (BIA) using concentration indices, concentration curve, and poor-rich equity ratio to measure horizontal and vertical inequity and analyze the redistribution dimension to understand which population segment benefits more from public subsidies. Results Findings suggest that high out of pocket (OOP) spending on inpatient care, especially in the private sector resulted in lower utilization of health care services especially among marginalized communities. Seeking care for marginalized sub-groups is dilapidating in two major ways-, on one hand, it exhausts all their income/savings for the treatment. On the other, as a result of incapacitation, families lose their daily income. The present analysis finds a higher unmet need for treatment in the poor, and the reasons commonly reported were pertaining to affordability, availability and accessibility. The results also showed that low ETL states faced a higher percentage of catastrophic expenditure vis-à-vis other states, thereby; stressing health system reforms beyond the “one-size-fits-all” strategy. Conclusions It is imperative that both Central and State Governments should work together to strengthen the public healthcare system to ensure accessibility and quality of care. Central Government's Pradhan Mantri Jan Arogya Yojana (PM-JAY) health insurance program is a positive step forward to address the healthcare needs of deprived population subgroups.
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Coronavirus epidemic can push millions of people in poverty. The shortage of healthcare resources, lack of sanitation, and population compactness leads to an increase in communicable diseases, which may increase millions of people add in a vicious cycle of poverty. The study used the number of factors that affect poverty incidence in a panel of 76 countries for a period of 2010–2019. The dynamic panel GMM estimates show that the causes of death by communicable diseases, chemical-induced carbon and fossil fuel combustion, and lack of access to basic hand washing facilities menace to increase poverty headcounts, whereas, an increase in healthcare expenditures substantially decreases poverty headcounts across countries. Further, the results show the U-shaped relationship between economic growth and poverty headcounts, as economic growth first decreases and later increase poverty headcount due to rising healthcare disparities among nations. The causality estimates show that lack of access to basic amenities lead to increase of communicable diseases including COVID-19 whereas chemical-induced carbon and fossil fuel emissions continue to increase healthcare expenditures and economic growth in a panel of selected countries. The rising healthcare disparities, regional conflicts, and public debt burden further ‘hold in the hand’ of communicable diseases that push millions of people in the poverty trap.
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India launched the ‘Rashtriya Swasthya Bima Yojana’ (RSBY) health insurance scheme for the poor in 2008. Utilising 3 waves (1999-2000, 2004-05 and 2011-12) of household level data from nationally representative surveys of the National Sample Survey Organisation (NSSO) (N=346,615) and district level RSBY administrative data on enrolment, we estimated causal effects of RSBY on out-of-pocket expenditure. Using ‘difference-in-differences’ methods on households in matched districts we find that RSBY did not affect the likelihood of inpatient out-of-pocket spending, the level of inpatient out of pocket spending or catastrophic inpatient spending. We also do not find any statistically significant effect of RSBY on the level of outpatient out-of-pocket expenditure and the probability of incurring outpatient expenditure. In contrast, the likelihood of incurring any out of pocket spending (inpatient and outpatient) rose by 30% due to RSBY and was statistically significant. Although out of pocket spending levels did not change, RSBY raised household non-medical spending by 5%. Overall, the results suggest that RSBY has been ineffective in reducing the burden of out-of-pocket spending on poor households.
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Several publicly financed health insurance schemes have been launched in India with the aim of providing universalizing health coverage (UHC). In this paper, we report the impact of publicly financed health insurance schemes on health service utilization, out-of-pocket (OOP) expenditure, financial risk protection and health status. Empirical research studies focussing on the impact or evaluation of publicly financed health insurance schemes in India were searched on PubMed, Google scholar, Ovid, Scopus, Embase and relevant websites. The studies were selected based on two stage screening PRISMA guidelines in which two researchers independently assessed the suitability and quality of the studies. The studies included in the review were divided into two groups i.e., with and without a comparison group. To assess the impact on utilization, OOP expenditure and health indicators, only the studies with a comparison group were reviewed. Out of 1265 articles screened after initial search, 43 studies were found eligible and reviewed in full text, finally yielding 14 studies which had a comparator group in their evaluation design. All the studies (n-7) focussing on utilization showed a positive effect in terms of increase in the consumption of health services with introduction of health insurance. About 70% studies (n-5) studies with a strong design and assessing financial risk protection showed no impact in reduction of OOP expenditures, while remaining 30% of evaluations (n-2), which particularly evaluated state sponsored health insurance schemes, reported a decline in OOP expenditure among the enrolled households. One study which evaluated impact on health outcome showed reduction in mortality among enrolled as compared to non-enrolled households, from conditions covered by the insurance scheme. While utilization of healthcare did improve among those enrolled in the scheme, there is no clear evidence yet to suggest that these have resulted in reduced OOP expenditures or higher financial risk protection.
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The provision of affordable health care is generally considered a fundamental goal of a welfare state. In addition to its role in maintaining and improving the health status of individuals and households, it impacts the economic prosperity of a society through its positive effects on labor productivity. Given this context, this paper assesses socioeconomic-differentials in the impact of out-of-pocket-health-expenditure (OOPHE) on impoverishment in China and India, two of the fastest growing economies of the world. The paper uses data from the World Health Organisation's Study on Global Ageing and Adult Health (WHO SAGE), and Bivariate as well as Multivariate analyses for investigating the socioeconomic-differentials in the impact of out-of-pocket-health-expenditure (OOPHE) on impoverishment in China and India. Annually, about 7% and 8% of the population in China and India, respectively, fall in poverty due to OOPHE. Also, the percentage shortfall in income for the population from poverty line due to OOPHE is 2% in China and 1.3% in India. Further, findings from the multivariate analysis indicate that lower wealth status and inpatient as well as outpatient care increase the odds of falling below poverty line significantly (with the extent much higher in the case of in-patient care) due to OOPHE in both China and India. In addition, having at least an under-5 child in the household, living in rural areas and having a household head with no formal education increases the odds of falling below poverty line significantly (compared to a head with college level education) due to OOPHE in China; whereas having at least an under-5 child, not having health insurance and residing in rural areas increases the odds of becoming poor significantly due to OOPHE in India.
Article
Importance Health insurance is effective in preventing financial hardship from unexpected major health care events. However, it is also essential to assess whether vulnerable patients, particularly those from low-income families, are adequately protected from longitudinal health care costs for common chronic conditions such as atherosclerotic cardiovascular disease (ASCVD). Objective To examine the annual burden of total out-of-pocket health expenses among low-income families that included a member with ASCVD. Design, Setting, and Participants In this cross-sectional study of the Medical Expenditure Panel Survey from January 2006 through December 2015, all families with 1 or more members with ASCVD were identified. Families were classified as low income if they had an income under 200% of the federal poverty limit. Analyses began December 2017. Main Outcomes and Measures Total annual inflation-adjusted out-of-pocket expenses, inclusive of insurance premiums, for all patients with ASCVD. We compared these expenses against annual family incomes. Out-of-pocket expenses of more than 20% and more than 40% of family income defined high and catastrophic financial burden, respectively. Results We identified 22 521 adults with ASCVD, represented in 20 600 families in the Medical Expenditure Panel Survey. They correspond to an annual estimated 23 million or 9.9% of US adults with a mean (SE) age of 65 (0.2) years and included 10.9 million women (47.1%). They were represented in 21 million or 15% of US families. Of these, 8.2 million families (39%) were low income. The mean annual family income was $57 143 (95% CI, $55 377-$58 909), and the mean out-of-pocket expense was $4415 (95% CI, $3735-$3976). While financial burden from health expenses decreased throughout the study, even in 2014 and 2015, low-income families had 3-fold higher odds than mid/high–income families of high financial burden (21.4% vs 7.6%; OR, 3.31; 95% CI, 2.55-4.31) and 9-fold higher odds of catastrophic financial burden (9.8% vs 1.2%; OR, 9.35; 95% CI, 5.39-16.20), representing nearly 2 million low-income families nationally. Further, even among the insured, 1.6 million low-income families (21.8%) experienced high financial burden and 721 000 low-income families (9.8%) experienced catastrophic out-of-pocket health care expenses in 2014 and 2015. Conclusions and Relevance One in 4 low-income families with a member with ASCVD, including those with insurance coverage, experience a high financial burden, and 1 in 10 experience a catastrophic financial burden due to cumulative out-of-pocket health care expenses. To alleviate economic disparities, policy interventions must extend focus to improving not only access, but also quality of coverage, particularly for low-income families.