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



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.
published: 29 January 2019
doi: 10.3389/fpubh.2019.00009
Frontiers in Public Health | 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
Ramna Thakur
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
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
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
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
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).
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
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
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 =
i=1gPre , GPost and GPre are the post and pre-payment
poverty gap.
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
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Sangar et al. Impoverishment Impact of Ailments
TABLE 1 | Economic burden of morbidity in India.
Type of ailments Population
reporting OOP (%)
EPayment gap (%) C.I**
headcount 10%
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
Poverty impact
headcount (%)
Poverty impact
Poverty impact gap
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)
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.
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
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
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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
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
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
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
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Conflict of Interest Statement: The authors declare that the research was
conducted in the absence of any commercial or financial relationships that could
be construed as a potential conflict of interest.
Copyright © 2019 Sangar, Dutt and Thakur. Thisis an open-access article distributed
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author(s) and the copyright owner(s) are credited and that the original publication
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Frontiers in Public Health | 5January 2019 | Volume 7 | Article 9
... 1 Among NCDs, cancer related deaths are increasing very rapidly and almost 9.3 million cancer related deaths were recorded in the year 2018, which is further expected to rise to 16.3 million by the year of 2040. 2 A recent study conducted in India has revealed that collectively NCDs have higher economic and catastrophic burden of which, cancers have a higher catastrophic burden and resultant impoverishment in India. 3 Cancer care expenses includes expenditure for investigations, treatment, traveling, loss of productivity due to cancer disability, potential life years lost due to premature cancer deaths and other miscellaneous expenditures (like food, accommodation, bribe, etc). 4 Among various diseases, cancer has highest Out of Pocket Expenditure (OOPE) due to its chronicity and expensive treatment. ...
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Background: The expenses that the patient or the family pays directly to the health care provider, without a third-party (insurer or State) is known as 'Out of Pocket Expenditure' (OOPE). These expenses could be medical and non-medical. About 150 million people face financial catastrophe every year due to health care payments and cancer is one of the leading causes of high OOPE. Objectives: This study was conducted to estimate the OOPE among cancer patients and to determine the OOPE in relation to type of cancer and treatment modality. Methodology: A cross sectional study was conducted at a tertiary care centre in Hyderabad during August and September,2022 with a total study population of 400 cancer patients. After consenting the participants, data was collected via face-to-face interview using a semi structured questionnaire. Results: The mean OOPE per patient was found to be $1032.65 (₹84,643.20). This includes the medical and non-medical costs. Leukaemia was found to have the highest OOPE amongst all cancers followed by colon cancer. Similarly, radiotherapy + surgery was found to have the highest OOPE followed by chemotherapy + radiotherapy + surgery. Conclusion And Interpretation- This study is unique in its way that no other study has considered OOPE for different cancers in single research. We would like to highlight the quantification of OOPE among various types of cancers and its variation based on treatment modality used. It is necessary that future government initiatives consider the importance of mitigating the OOPE along with provision of cancer care.
... This study used monthly household consumption expenditure as a proxy variable for household income which has been used in numerous earlier studies (Joe & Rajpal, 2018;Kastor & Mohanty, 2018;Sangar et al., 2019). This is a preferred measure since it is less prone to disparity and bears fewer chances of being under reported or over reported when related to income (Kastor & Mohanty, 2018). ...
Majority of people in low- and middle-income countries with mental illness do not receive healthcare, leading to chronicity, suffering and increased costs of care. This study estimated the out-of-pocket expenditure (OOPE), catastrophic health expenditure (CHE), and poverty impact due to mental illness in India. Data was acquired from the 76th round data of the National Sample Survey (NSS) on the theme ‘Persons with Disabilities in India Survey’, July–December 2018. Data of 6,679 persons who reported mental illness during the survey was included for analysis. OOPE, CHE, poverty impact and state differentials of healthcare expenditure on mental illness were analysed using standard methods. In total, 18.1% of the household’s monthly consumption expenditure was spent on healthcare on mental illness. About 59.5% and 32.5% of the households were exposed to CHE based on 10% and 20% thresholds, respectively. About 20.7% of the households were forced to become poor from non-poor due to treatment care expenditure on mental illness. Our study suggests the critical need to accelerate on various measures for early diagnosis and management of mental health issues along with financial risk protection for reducing financial impact of healthcare expenditure on mental illness among households in India.
... Households who are dependent on OOP healthcare payment and who are unable to cope with the economic implications of illness are frequently pushed into poverty. Households in this scenario incur more financial obligations and lack the resources to meet other basic requirements such as food and education [7]. ...
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Background: Financial hardship (of health care) is a global and a national priority area. All people should be protected from financial hardship to ensure inclusive better health outcome. However, financial hardship of healthcare has not been well studied in Ethiopia in general and in Debre Tabor town in particular. Therefore, this study aimed to assess the incidence of financial hardship of healthcare and associated factors among households in Debre Tabor town. Methods: Community based cross sectional study was conducted, from May 24/2022 to June 17/2022, on 423 (selected through simple random sampling) households. Financial hardship was measured through catastrophic (using 10% threshold level) and impoverishing (using $1.90 poverty line) health expenditures. Patient perspective bottom up and prevalence based costing approach were used. Indirect cost was estimated through human capital approach. Bi-variable and multiple logistic regressions were used. Results: The response rate was 95%. The mean household annual healthcare expenditure was Ethiopian birr 12050.64 ($227.37). About 37.1% (95%CI: 32, 42%) of the households spend catastrophic health expenditure with a 10% threshold level and 10.4% of households were impoverished with $1.90 per day poverty line. Being old, with age above 60, (AOR: 4.21, CI: 1.23, 14.45), being non-insured (AOR: 2.19, CI: 1.04, 4.62), chronically ill (AOR: 7.20, CI: 3.64, 14.26), seeking traditional healthcare (AOR: 2.63, CI: 1.37. 5.05) and being socially unsupported (AOR: 2.77, CI: 1.25, 6.17) were statistically significant factors for catastrophic health expenditure. Conclusion: The study showed that significant number of households was not yet protected from financial hardship of healthcare. The financial hardship of health care is stronger among the less privileged populations: non-insured, the chronically diseased, the elder and socially unsupported. Therefore, financial risk protection strategies should be strengthened by the concerned bodies.
... Between 2005 and 2016, impoverishment incidence climbed from 1.1% to 1.5%, 1.4% to 2.0%, and 1.7% to 1.5% among non-NCD-only, NCD-only, and both NCD and non-NCD-affected households, respectively, or 0.4 to 0.5 million, 0.3 to 0.6 million, and 0.4 to 0.5 million people, respectively, at the population level. The current (2016) impoverishment incidence among NCD-affected households in Bangladesh (2.0%) is higher than in Nepal (1.3%) but lower than in India (5.4%), while the rate among families without NCDs (1.5%) was lower than in both countries (Nepal: 1.7%; India: 2.1%) [48,49]. Further impoverishment was a more severe problem than impoverishment in Bangladesh, though it fell from 10.8% to 7.3%, 8.0% to 6.7%, and 7.8% to 5.3% among non-NCD-only, NCD-only, and both NCD and non-NCD-affected households, respectively, or 4.3 to 2.6 million, 1.8 to 2.2 million, and 2.0 to 1.9 million individuals, respectively. ...
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Background Demographic and epidemiological transitions are changing the disease burden from infectious to noncommunicable diseases (NCDs) in low- and middle-income countries, including Bangladesh. Given the rising NCD-related health burdens and growing share of household out-of-pocket (OOP) spending in total health expenditure in Bangladesh, we compared the country’s trends and socioeconomic disparities in financial risk protection (FRP) among households with and without NCDs. Methods We used data from three recent waves of the Bangladesh Household Income and Expenditure Survey (2005, 2010, and 2016) and employed the normative food, housing (rent), and utilities method to measure the levels and distributions of catastrophic health expenditure (CHE) and impoverishing effects of OOP health expenditure among households without NCDs (i.e. non-NCDs only) and with NCDs (i.e. NCDs only, and both NCDs and non-NCDs). Additionally, we examined the incidence of forgone care for financial reasons at the household and individual levels. Results Between 2005 and 2016, OOP expenses increased by more than 50% across all households (NCD-only: USD 95.6 to 149.3; NCD-and-non-NCD: USD 89.5 to 167.7; non-NCD-only: USD 45.3 to 73.0), with NCD-affected families consistently spending over double that of non-affected households. Concurrently, CHE incidence grew among NCD-only families (13.5% to 14.4%) while declining (with fluctuations) among non-NCD-only (14.4% to 11.6%) and NCD-and-non-NCD households (12.9% to 12.2%). Additionally, OOP-induced impoverishment increased among NCD-only and non-NCD-only households from 1.4 to 2.0% and 1.1 to 1.5%, respectively, affecting the former more. Also, despite falling over time, NCD-affected individuals more frequently mentioned prohibiting treatment costs as the reason for forgoing care than the non-affected (37.9% vs. 13.0% in 2016). The lowest quintile households, particularly those with NCDs, consistently experienced many-fold higher CHE and impoverishment than the highest quintile. Notably, CHE and impoverishment effects were more pronounced among NCD-affected families if NCD-afflicted household members were female rather than male, older people, or children instead of working-age adults. Conclusions The lack of FRP is more pronounced among households with NCDs than those without NCDs. Concerted efforts are required to ensure FRP for all families, particularly those with NCDs.
... Comparing financial risk protection by disease area across countries permits consideration of which factors are common across contexts and which factors are due to particularities of health systems, including their financing and access policies. A number of recent single-country studies have assessed CHE by disease area (Mahal et al., 2010;Engelgau et al., 2011;Smith-Spangler et al., 2012;Verguet et al., 2016;Selvaraj et al., 2018;Kastor and Mohanty, 2018;Sangar et al., 2019), but such analyses have not yet been conducted in a large number of countries, limiting any capacity to make conclusions about systematic differences in CHE by disease area across health systems. Many estimates of CHE due to NCDs have focused on a subset of NCD conditions (e.g. ...
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The growing burden of non-communicable diseases (NCDs) in low- and middle-income countries may have implications for health system performance in the area of financial risk protection, as measured by catastrophic health expenditure (CHE). We compare NCD CHE to the CHE cases caused by communicable diseases across health systems to examine whether: 1) disease burden and CHE are linked, 2) NCD CHE disproportionately affects wealthier households, and 3) whether the drivers of NCD CHE differ from the drivers of communicable disease CHE. We used the Study on Global Aging and Adult Health survey, which captured nationally representative samples of 44,089 adults in China, Ghana, India, Mexico, Russia, and South Africa. Using two-part regression and random forests, we estimated out-of-pocket spending and CHE by disease area. We compare the NCD share of CHE to the NCD share of disability-adjusted life years (DALYs), or years of life lost to disability and death. We tested for differences between NCDs and communicable diseases in the out-of-pocket costs per visit and the number of visits occurring before spending crosses the CHE threshold. NCD CHE increased with the NCD share of DALYs except in South Africa, where NCDs caused more than 50% of CHE cases but only 19% of DALYs. A larger share of households incurred CHE due to NCDs in the lowest than the highest wealth quintile. NCD CHE cases were more likely to be caused by five or more health care visits relative to communicable disease CHE cases in Ghana (p = 0.003), India (p = 0.004), and China (p = 0.093). Health system attributes play a key mediating factor in how disease burden translates into financial risk protection by disease. Health systems must target the specific characteristics of CHE by disease area to bolster financial risk protection as the epidemiological transition proceeds.
... This study used yearly household consumption expenditure as a proxy variable for household income as used in numerous earlier studies (Joe & Rajpal, 2018;Kastor & Mohanty, 2018;Sangar, Dutt, & Thakur, 2019). As the NSS gives the monthly per capita consumer expenditure, this was converted to a yearly expenditure by using a factor of 12 . ...
Background: Drowning is a global public health challenge, with significant burden in low- and middle-income countries. There are few studies exploring nonfatal drowning, including the economic and social impacts. This study aimed to quantify unintentional drowning-related hospitalization in India and associated healthcare expenditure. Method: Unit level data on unintentional drowning-related hospitalization were obtained from the 75th rounds of the National Sample Survey of Indian households conducted in 2018. The outcome variables were indices of health care cost such as out of pocket expenditure (OOPE), health care burden (HCB), catastrophic health expenditure (CHE), impoverishment, and hardship financing. Descriptive statistics and multivariate analysis were conducted after adjusting for inflation using the pharmaceutical price index for December 2020. The association of socio-demographic characteristics with the outcome variable was reported as relative risk with 95% CI and expenditure reported in Indian Rupees (INR) and United States dollars (USD). Results: 174 respondents reported drowning-related hospitalization (a crude rate of 15.91-31.34 hospitalizations per 100,000 population). Proportionately, more males (63.4%), persons aged 21-50 years (44.9%) and rural dwelling respondents (69.9%) were hospitalized. Drowning-related hospitalization costs on average INR25,421 ($345.11USD) per person per drowning incident. Costs were higher among older respondents, females, urban respondents, and longer lengths of hospital stays. About 14.4% of respondents reported hardship financing as a result of treatment costs and 9.0% of households reported pushed below the poverty line when reporting drowning-related hospitalization. Conclusions: Drowning can be an economically catastrophic injury, especially for those already impacted by poverty. Drowning is a significant public health problem in India. Investment in drowning prevention program will reduce hospitalization and economic burden. Practical applications: This study provides support for investment in drowning prevention in India, including a need to ensure drowning prevention interventions address the determinants of health across the lifespan.
... 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]. ...
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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.
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Background Financial risk protection (FRP), defined as households’ access to needed healthcare services without experiencing undue financial hardship, is a critical health systems target, particularly in low- and middle-income countries (LMICs). Given the remarkable growth in FRP literature in recent times, we conducted a scoping review of the literature on FRP from out-of-pocket (OOP) health spending in LMICs. The objective was to review current knowledge, identify evidence gaps and propose future research directions. Methods We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines to conduct this scoping review. We systematically searched PubMed, Scopus, ProQuest and Web of Science in July 2021 for literature published since 1 January 2015. We included empirical studies that used nationally representative data from household surveys to measure the incidence of at least one of the following indicators: catastrophic health expenditure (CHE), impoverishment, adoption of strategies to cope with OOP expenses, and forgone care for financial reasons. Our review covered 155 studies and analysed the geographical focus, data sources, methods and analytical rigour of the studies. We also examined the level of FRP by disease categories (all diseases, chronic illnesses, communicable diseases) and the effect of health insurance on FRP. Results The extant literature primarily focused on India and China as research settings. Notably, no FRP study was available on chronic illness in any low-income country (LIC) or on communicable diseases in an upper-middle-income country (UMIC). Only one study comprehensively measured FRP by examining all four indicators. Most studies assessed (lack of) FRP as CHE incidence alone (37.4%) or as CHE and impoverishment incidence (39.4%). However, the LMIC literature did not incorporate the recent methodological advances to measure CHE and impoverishment that address the limitations of conventional methods. There were also gaps in utilizing available panel data to determine the length of the lack of FRP (e.g. duration of poverty caused by OOP expenses). The current estimates of FRP varied substantially among the LMICs, with some of the poorest countries in the world experiencing similar or even lower rates of CHE and impoverishment compared with the UMICs. Also, health insurance in LMICs did not consistently offer a higher degree of FRP. Conclusion The literature to date is unable to provide a reliable representation of the actual level of protection enjoyed by the LMIC population because of the lack of comprehensive measurement of FRP indicators coupled with the use of dated methodologies. Future research in LMICs should address the shortcomings identified in this review.
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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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Background: There are substantial differences in long term health outcomes across countries, particularly in terms of both life expectancy at birth (LEAB) and healthy life expectancy (HALE). Socio-economic status, disease prevention approaches, life style and health financing systems all influence long-term health goals such as life expectancy. Within this context, universal health coverage (UHC) is expected to influence life expectancy as a comprehensive health policy. The aim of the study is to investigate this relationship between Universal Health Coverage (UHC) and life expectancy. Method: A multi-country cross-sectional study was performed drawing on different sources of data (World Health Organization, UNDP-Education and World Bank) from 193 UN member countries, applying administrative record linkage theory. Descriptive statistics, t-tests, Pearson correlations, hierarchical linear regressions were utilized as appropriate. Result: Global average healthy life years was shown to be 61.34 ± 8.40 and life expectancy at birth was 70.00 ± 9.3. Standardized coefficients from regression analysis found UHC (0.34), child vaccination (Diphtheria Pertussis Tetanus−3: 0.17) and sanitation coverage (0.31) were associated with significantly increased life expectancy at birth. In contrast, population growth was associated with a decrease (0.29). Likewise, unit increases in child vaccination (DPT 3), sanitation and UHC would increase healthy life expectancy considerably (0.18, 0.31, and 0.40 respectively), whereas the same for population growth reduces healthy life expectancy by 0.28. Conclusion: Universal Health Coverage (UHC) is a comprehensive health system approach that facilitates a wide range of health services and significantly improves the life expectancy at birth and healthy life expectancy. This study suggests that specific programs to achieve UHC should be considered for countries that have not seen sufficient gains in life expectancy as part of the wider push to achieve the Sustainable Development Goal (SDG).
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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.
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Background: Achieving universal health coverage (UHC) requires health financing systems that provide prepaid pooled resources for key health services without placing undue financial stress on households. Understanding current and future trajectories of health financing is vital for progress towards UHC. We used historical health financing data for 188 countries from 1995 to 2015 to estimate future scenarios of health spending and pooled health spending through to 2040. Methods: We extracted historical data on gross domestic product (GDP) and health spending for 188 countries from 1995 to 2015, and projected annual GDP, development assistance for health, and government, out-of-pocket, and prepaid private health spending from 2015 through to 2040 as a reference scenario. These estimates were generated using an ensemble of models that varied key demographic and socioeconomic determinants. We generated better and worse alternative future scenarios based on the global distribution of historic health spending growth rates. Last, we used stochastic frontier analysis to investigate the association between pooled health resources and UHC index, a measure of a country's UHC service coverage. Finally, we estimated future UHC performance and the number of people covered under the three future scenarios. Findings: In the reference scenario, global health spending was projected to increase from US$10 trillion (95% uncertainty interval 10 trillion to 10 trillion) in 2015 to $20 trillion (18 trillion to 22 trillion) in 2040. Per capita health spending was projected to increase fastest in upper-middle-income countries, at 4·2% (3·4-5·1) per year, followed by lower-middle-income countries (4·0%, 3·6-4·5) and low-income countries (2·2%, 1·7-2·8). Despite global growth, per capita health spending was projected to range from only $40 (24-65) to $413 (263-668) in 2040 in low-income countries, and from $140 (90-200) to $1699 (711-3423) in lower-middle-income countries. Globally, the share of health spending covered by pooled resources would range widely, from 19·8% (10·3-38·6) in Nigeria to 97·9% (96·4-98·5) in Seychelles. Historical performance on the UHC index was significantly associated with pooled resources per capita. Across the alternative scenarios, we estimate UHC reaching between 5·1 billion (4·9 billion to 5·3 billion) and 5·6 billion (5·3 billion to 5·8 billion) lives in 2030. Interpretation: We chart future scenarios for health spending and its relationship with UHC. Ensuring that all countries have sustainable pooled health resources is crucial to the achievement of UHC. Funding: The Bill & Melinda Gates Foundation.
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With the ongoing demographic and epidemiological transition, cancer is emerging as a major public health concern in India. This paper uses nationally representative household survey to examine the overall prevalence and economic burden of cancer in India. The age-standardized prevalence of cancer is estimated to be 97 per 100,000 persons with greater prevalence in urban areas. The evidence suggests that cancer prevalence is highest among the elderly and also among females in the reproductive age groups. Cancer displays a significant socioeconomic gradient even after adjusting for age-sex specifics and clustering in a multilevel regression framework. We find that out of pocket expenditure on cancer treatment is among the highest for any ailment. The average out of pocket spending on inpatient care in private facilities is about three-times that of public facilities. Furthermore, treatment for about 40 percent of cancer hospitalization cases is financed mainly through borrowings, sale of assets and contributions from friends and relatives. Also, over 60 percent of the households who seek care from the private sector incur out of pocket expenditure in excess of 20 percent of their annual per capita household expenditure. Given the catastrophic implications, this study calls for a disease-based approach towards financing such high-cost ailment. It is suggested that universal cancer care insurance should be envisaged and combined with existing accident and life insurance policies for the poorer sections in India. In concluding, we call for policies to improve cancer survivorship through effective prevention and early detection. In particular, greater public health investments in infrastructure, human resources and quality of care deserve priority attention.
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Background There are different models for community-based health insurance (CBHI), and in Nepal, among them, the government and the local communities (co-ops) are responsible for operating the CBHI models that are in practice.AimsThe aim of this study is to compare the outcomes in relation to benefit packages, population coverage, inclusiveness, healthcare utilization, and promptness of treatment for the two types of CBHI models in Nepal.Methods This study was an observational and interactive descriptive study using the concurrent mixed approach of data collection, framing, and compilation. Quantitative data were collected from records, and qualitative data were collected from key informants in all (1) CBHI groups. Unstructured questionnaires, observation checklists, and memo notepads were used for data collection. Descriptive statistics and the Mann–Whitney U test were used when appropriate. Ethically, written informed consent was obtained from the respondents who participated in the study, and they were told that they could withdraw from the study anytime.ResultsThe study revealed the following: new enrolment did not increase in either group; however, the healthcare utilization rate did (Government 107% and co-ops 137%), while the benefit packages remained almost same for both groups. Overall, inclusiveness was higher for the government group. For the CBHI co-ops, enrollment among the religious minority and the discount negotiated with the hospitals for treatment were significantly higher, and the promptness in reaching a hospital was significantly faster (p < 0.05) than that in the government-operated CBHI.Conclusion Findings indicate that CBHI through co-ops would be a better model because of its lower costs and ability to enhance self-responsiveness and the overall health system. Health insurance coverage is the most important component to achieve universal health coverage.
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Background Cardiovascular disease poses a great financial risk on households in countries without universal health coverage like Ethiopia. This paper aims to estimate the magnitude and intensity of catastrophic health expenditure and factors associated with catastrophic health expenditure for prevention and treatment of cardiovascular disease in general and specialised cardiac hospitals in Addis Ababa. Methods and findings We conducted a cross-sectional cohort study among individuals who sought cardiovascular disease care in selected hospitals in Addis Ababa during February to March 2015 (n=589, response rate 94%). Out-of-pocket payments on direct medical costs and direct non-medical costs were accounted for. Descriptive statistics was used to estimate the magnitude and intensity of catastrophic health expenditure within households, while logistic regression models were used to assess the factors associated with it. About 27% (26 .7;95% CI 23.1 to 30.6) of the households experienced catastrophic health expenditure, defined as annual out-of-pocket payments above 10% of a household’s annual income. Family support was the the most common coping mechanism. Low income, residence outside Addis Ababa and hospitalisation increased the likelihood of experiencing catastrophic health expenditure. The bottom income quintile was about 60 times more likely to suffer catastrophic health expenditure compared with the top quintile (adjusted OR=58.6 (16.5–208.0), p value=0.00). Of those that experienced catastrophic health expenditure, the poorest and richest quintiles spent on average 34% and 15% of households’ annual income, respectively. Drug costs constitute about 50% of the outpatient care cost. Conclusions Seeking prevention and treatment services for cardiovascular disease in Addis Ababa poses substantial financial burden on households, affecting the poorest and those who reside outside Addis Ababa more. Economic and geographical inequalities should also be considered when setting priorities for expanding coverage of these services. Expanded coverage has to go hand-in-hand with implementation of sound prepayment and risk pooling arrangements to ensure financial risk protection to the most needy.
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Background: An adequate amount of prepaid resources for health is important to ensure access to health services and for the pursuit of universal health coverage. Previous studies on global health financing have described the relationship between economic development and health financing. In this study, we further explore global health financing trends and examine how the sources of funds used, types of services purchased, and development assistance for health disbursed change with economic development. We also identify countries that deviate from the trends. Methods: We estimated national health spending by type of care and by source, including development assistance for health, based on a diverse set of data including programme reports, budget data, national estimates, and 964 National Health Accounts. These data represent health spending for 184 countries from 1995 through 2014. We converted these data into a common inflation-adjusted and purchasing power-adjusted currency, and used non-linear regression methods to model the relationship between health financing, time, and economic development. Findings: Between 1995 and 2014, economic development was positively associated with total health spending and a shift away from a reliance on development assistance and out-of-pocket (OOP) towards government spending. The largest absolute increase in spending was in high-income countries, which increased to purchasing power-adjusted $5221 per capita based on an annual growth rate of 3·0%. The largest health spending growth rates were in upper-middle-income (5·9) and lower-middle-income groups (5·0), which both increased spending at more than 5% per year, and spent $914 and $267 per capita in 2014, respectively. Spending in low-income countries grew nearly as fast, at 4·6%, and health spending increased from $51 to $120 per capita. In 2014, 59·2% of all health spending was financed by the government, although in low-income and lower-middle-income countries, 29·1% and 58·0% of spending was OOP spending and 35·7% and 3·0% of spending was development assistance. Recent growth in development assistance for health has been tepid; between 2010 and 2016, it grew annually at 1·8%, and reached US$37·6 billion in 2016. Nonetheless, there is a great deal of variation revolving around these averages. 29 countries spend at least 50% more than expected per capita, based on their level of economic development alone, whereas 11 countries spend less than 50% their expected amount. Interpretation: Health spending remains disparate, with low-income and lower-middle-income countries increasing spending in absolute terms the least, and relying heavily on OOP spending and development assistance. Moreover, tremendous variation shows that neither time nor economic development guarantee adequate prepaid health resources, which are vital for the pursuit of universal health coverage. Funding: The Bill & Melinda Gates Foundation.
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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.