Global spending on health more
than doubled in real terms over the past two decades, reaching US$ 8.5 trillion
in 2019, or 9.8% of global GDP. But it was unequally distributed, with high
income countries accounting for approximately 80%. Health spending in
low-income countries was financed primarily by out-of-pocket spending (OOPS;
44%) and external aid (29%), while government spending dominated in high income
countries (70%). (Global
Spending on Health: Public Spending on the Rise – 2021, WHO)
In India, Health is a state
subject. The delivery of (public) health care largely rests with the states of
the Federal Union. The allocation of funds to health-sector (for public
healthcare service delivery) inter-alia is dependent on the overall resource
availability with the government, competing sectoral priorities, and also the
absorptive capacity of the system (ref – PIB – Press
Information Bureau, GoI). However, prioritizing (public) health spending is
more of a policy choice than a result of government fiscal capacity (Global
Spending on Health: Public Spending on the Rise – 2021, WHO).
In India, NITI Aayog (Government of India) evaluates
states and UTs on Health Performance and generates a Health Index Score. In
doing so, the Aayog takes into consideration the huge diversity amongst the
Indian federations. Therefore, it evaluates health of population under broad
administrative territories based on the following classifications (ref – NITI Aayog): -
a)
Large States - Haryana, Rajasthan,
Jharkhand, Andhra Pradesh, Assam, Telangana, Maharashtra, Karnataka, Jammu and
Kashmir, Chhattisgarh, Himachal Pradesh, Gujarat, Madhya Pradesh, Punjab,
Kerala, Tamil Nadu, Odisha, Uttarakhand, Uttar Pradesh, Bihar.
b)
Small States – Tripura, Manipur, Mizoram,
Nagaland, Meghalaya, Goa, Sikkim, Arunachal Pradesh.
c)
Union Territories (UTs) - D & N
Haveli, Chandigarh, Daman & Diu, Puducherry, Lakshadweep, A&N lslands,
Delhi.
A relative ranking of Indian federations
(under above classified categories) is also prepared based on respective Health
Index Score (HIS – we may refer as “NITI Aayog Health Index” or “NITI Health
Index” in this article). Further details on NITI-Index can be found in the
APPENDIX – A. In this article, we may prefer to adhere with the above
classification in doing analysis and in doing comparative visualizations
(wherever appropriate).
It may be noted that based on the
availability of data, we have considered the time-period for this (data-visualization
based) analysis as that of financial year 2019-20 (April 2019 to March 2020). Parameters
used in this analysis do not change rapidly and therefore, the analysis may
hold good enough for developing an understanding in larger context (even in
current scenario). Sources of data sets used in this analysis are given in
APPENDIX – B. Going with the limitation of the available datasets, we have to
accommodate some minor deviations from (the above mentioned) reference period for
the following parameter (which should be within acceptable range): -
·
‘Per Capita Net State Domestic Product at
Current Prices in INR’ for the year 2020-21.
With the above background,
let us try to compare following two aspects of public healthcare service
delivery for Indian States through data visualization: -
a.
Expenditure in Public Healthcare and
b.
Quality of Health.
We need to quantify the above two aspects of public healthcare service delivery. For this, we assume that NITI Health Index quantifies Quality of Health in a state and that government data available for “Public Expenditure on Healthcare” can be directly used for quantifying Expenditure in Public Healthcare for any state. We will prefer to compare these parameters separately for Large States, Small States and UTs and will use logarithmic scale for much pronounced visualization. The states are plotted against following parameters in X and Y axes: -
a) X
– Axis represents: Public Expenditure on Health (2019-20) in Crores per LAKH
Population in logarithmic scale
a.
Median Value for this parameter is represented
by Vertical Dotted Orange Line.
b) Y
– Axis represents: NITI Health Index
Score for 2019-20 in logarithmic scale
a.
Median Value for this parameter is represented
by Horizontal Dotted Orange Line.
c)
Data Sources – The sources of data are detailed
under APPENDIX – B.
Figure – 2: Expenditure and NITI Health Index for
Small States
Going through the above
visualizations (which is based on the available datasets), the following observations
are noted: -
1. Separate
data representations for large states, small states and UTs can be observed in
Figures – 1,2 and 3 (as above). The data set for large states is better in
terms of numbers in comparison to the other two categories. Dataset for UTs is
very less as most of the UTs do not have expenditure details recorded in the
available dataset.
2. Median
Value for Public Health Expenditure is seen significantly higher for Small
States category in comparison to the other two categories. One of the possible
reasons for this observed hire values of public health expenditure could be the
composition of Small States. A closer look at the list of small states shown in
figure – 2 reveals that seven out of the eight states are North-Eastern (NE)
states (call them seven sisters or call the six sisters and one brother
(Sikkim)). These hilly states are mostly rural and thinly populated territories
with difficult terrains, with lower levels of infrastructure and with
under-developed transportation facilities. It is widely accepted fact that natural
terrain, location and transportation are significant factors of efficiency in
efficiency of rural public goods (Chunyan
He, Li Peng, Shaoquan Liu, Dingde Xu, Peng Xue, 2016, Factors influencing the efficiency of rural
public goods investments in mountainous areas of China —— Based on micro panel
data from three periods, Journal of Rural Studies,ISSN 0743-0167). Thus,
for similar levels of quality of services spending on public healthcare is much
higher in NE region of India. For example, in 2014-15, major States spent
anywhere between Rs 617 and Rs 2,026 per capita on health and allied subjects.
Less populated, hilly or small Indian States spent between Rs 2,289 and Rs
7,409 per person (Srinath
et al, A Qualitative and Quantitative Analysis of Public Health Expenditure in
India: 2005-06 to 2014-15, Working Paper 2018-01, Takshila Institution).
3. Quality
(NITI Health Index) appears to improve with Expenditure (public health expenditure)
in general. This trend looks dominant for large states and for UTs. However, small
states do not look to be falling in line with the trend. The observation is
also evident from correlation between Quality and Expenditure for the three
cases: -
a.
Correlation Coefficient for Quality and
Expenditure (Large States) = 0.50 (Moderate)
b.
Correlation Coefficient for Quality and
Expenditure (Small States) = 0.17 (Low)
c.
Correlation Coefficient for Quality and
Expenditure (UTs) = 0.94 (High)
4. In
the large states category, it is observed that the distribution of public health
expense amongst states are cluttered around median value of 15.11 Crores per
Lakh of population. Comprehensively looking at the graph, insists that going beyond
the median value in public health expenditure may be important accelerator for likelihood
of improvement in quality of health.
5. In
the large states category, some states with higher expenditure are observed to have
lower NITI Health Index score and some states with lower expenditure are
observed to be having higher NITI Health Index score. To be specific,
Maharashtra, Punjab and Karnataka are spending less (below median expense in
public health) in comparison to Assam, Haryana, Uttarakhand (UK) and Rajasthan
(above median expense in health). However, Maharashtra, Punjab and Karnataka
have a better NITI Health Index score in comparison to Assam, Haryana,
Uttarakhand (UK) and Rajasthan. This could be considered an anomaly.
Let us speculate to find a
possible explanation for this anomaly. It is well-known that NITI Health Index
does a generic assessment for any Indian state on the quality of health (of
residing population) following a scientific methodology. Thus, it includes health
outcomes achieved through efforts made by means of private as well as public healthcare
service providers in a given state.
However, (in the above
visualization) we have used data only for public health expenditure (which is
available with us). Therefore, a likelihood is high that exclusion of the private
spending (made by the individuals / trusts / NGOs in population) could the
possible reason for the anomaly. Let us investigate this aspect in the
following sections of this article.
OOPE (Out of Pocket Expenditure) happens to be the dominant private healthcare expenditure in India (refer – figure – 4 below), therefore, we may have preferred to opt to compare the OOPE data (say - OOPE per lakh population) for states showing anomalous behavior in the (above visualized) graphs for improving our understanding. However, we could not find this dataset (for OOPE for Indian States for 2020) in attempts made through internet surfing. Therefore, let us substitute OOPE with readily available government data for Per-Capita Income and try to find an explanation for the (above) observed anomaly. However, by taking this parameter into consideration, we assume that income distribution across all the states is nearly same (say - with similar proportions of high-middle-low income households) and also, we assume that there exists a reasonable elasticity in availing of healthcare services (so, the more per-capita income, the higher the paying capacity and proportionally people alter spending on healthcare). So to say, we will go with the widely accepted fact that ‘the larger the per capita income, the greater the expenditure on health’ (NLiS, WHO).
In view of the discussions in the
last point (refer – point - 5 above), let us take the analysis further from
this point by introducing the third dimension to the above visualization illustrating
Expenditure and Quality (in figures – 1,2 and 3). This third dimension (as
discussed) is the income of individuals in the respective states expressed as “Per
Capita Net State Domestic Product at Current Prices for 2020-21 (Base Year
2011-12) in Lakhs (INR)”. This will broadly represent the proportion of private
expenditure in healthcare by people in the state and will be shown in the above
graphs the third dimension represented by the size of the circular points
(bubbles – used as markers for states in the above graphs). Thus, the larger
the circle/bubble representing a state, the more is the per capita income of
that state (and more is the private health spending). With this new dimension
introduced in our previous graphs (given in figures 1,2 and 3), let us redraw
the above three graphs (as figures 4,5 and 6) and try to look for possibilities
of deeper analysis. (For Data Source – Refer – APPENDIX – B).
Figure – 5: Expenditure (X Axis), NITI Health Index (Y-Axis) and Per-Capita Income in Lakhs of INR (Size of Bubble) for Large States
Figure – 6: Expenditure (X Axis), NITI Health Index
(Y-Axis) and Per-Capita Income in Lakhs of INR (Size of Bubble) for Small
States
Going through the newly created
visualizations as above (based on the available datasets), the following observations
can be noted: -
A.
A broad trend in favor of improvement in Quality
(NITI Health Index) with increase in Income (Per-Capita Income representing
private healthcare expenditure) is observed in large states, in small states
and UTs (from graphical observation). Thus, the anomaly (observed under point –
4 above) appears to be better understood now. Under large state category,
Maharashtra, Punjab and Karnataka are spending less (below median expense in
public health) in comparison to Assam, Haryana, Uttarakhand (UK) and Rajasthan
(above median expense in health) but are doing better in Quality (NITI Health
Index) because income of population in these states are relatively higher (in
comparison) and therefore, individuals (in the population) appear to be
spending more on healthcare through OOPE, eventually improving the average
health of population in entire state.
B.
With inclusion of private expenditure to the
public expenditure on healthcare, the likelihood of getting much more
pronounced linear relationship between Quality (expressed as NITI Health Index Score)
and Expenditure (Total Health Expenditure – private expense + public expense)
is high. The related correlations of Quality (NITI Health Index) with Income
(per-capita income representing private expenditure) are as under: -
a.
Correlation Coefficient for Quality and Income
(Large States) = 0.74 (High)
b.
Correlation Coefficient for Quality and Income
(Small States) = 0.27 (Low)
c.
Correlation Coefficient for Quality and Income
(UTs) = 0.53 (Moderate)
Comparatively,
correlation between “Quality of Health” and “Public Expenditure” is less in
comparison to correlation between “Quality of Health” and “Private Expenditure”
for larger states. This is vice-a-versa for UTs.
However,
the small states fail to show any significant correlation with respect to
either public healthcare expenditure or private healthcare expenditure. And, one
of the the possible reasons may not be much difficult to guess looking into the
fact that out of the 8 small states shown in the graph, 7 are the famous
Seven-Sisters of North East India and the eighth one is Goa on the western
coast of India. In all these seven north-eastern states, Christian Missionaries
started their medical mission the early 20th century (IJHSSS, ISSN: 2349-6959 (Online),
ISSN: 2349-6711 (Print), Volume-I, Issue-I, July 2014, Published by Scholar
Publications, Karimganj, Assam, India, 788711, Website: http://www.ijhsss.com) and
have been actively rendering healthcare services to the local tribes since then.
Statistics reveal that 85 percent of the health care institutions run by
Christian Church / Mission are in the villages (Rev. Dr. S. M. John
Kennedy SJ,"Christian Contribution to Indian Education", 2018, www.sxcejournal.com, Research and Reflections
on Education ISSN 0974-648X Vol.17 No.6 January-March 2018). Provisioning of
healthcare service delivery by these missionaries run by Christian Churches may
not get accounted for either in public or private expenditure. Interestingly, just
like seven NE states, Christian Missionaries were active in setting up medical
institutions and shaping healthcare in the eighth small state of Goa since the
times, when the state was a colony of Portugal. Numerous evidence about
contributions of missions in establishment of medical institutions and in putting
efforts to improve healthcare in and around Goa during nineteenth and twentieth
centuries can be found in literature (Cristiana
Bastos, Doctors of the Empire: Medical School of Goa and its Narratives, 2001).
At this point, I may like to put-up a personal note about Christian
Missionaries in India, I feel these missionaries were NGOs (NGOs – Non-Government
Organizations), which operated independently without being part of the
ecosystem of the government (during pre-independent India and more so after
independence of India in 1947). In doing so, they could create a space for
themselves in the minds of people as credible organizations dedicated to their profession
with the sole intent to improve healthcare and education in different parts of
India.
C.
Considering all the above discussions, we may conclude
that as indicated by the available data for Indian States, Quality of Health
(as reflected in NITI Health Index) of Indian states is correlated to Expenditure*
(Public and Private) on healthcare.
* The
Expenditure includes private expenditure (including contributions made by
Missions / NGOs / Charitable Trusts) and public expenditure in healthcare by
government.
D.
Now, let us turn our attention to those states
which need the most attention under Large State category. These are Bihar,
Madhya Pradesh (MP) and Uttar Pradesh (UP). Following are some observations for
these states: -
i.
From figure – 5, it is clear that these three
states are lower in Quality of Health and lower in Expenditure (Private - due
to lower income as evident from size of respective bubbles in Figure-5 and
Public – due to lower public expenditure as evident from positioning of
respective bubbles in Figure-5).
ii.
Even though health literacy in India has been low
(Tribune
India Survey, 2019). Good health (for all) is a necessity, which no society
may fail to acknowledge. Covid-19 pandemic of 2020 has increased political
significance of health and healthcare related issues. Therefore, respective governments
(in these states) may need to consider improving the quality of health as a
priority.
iii.
Population in these three states are expected to
increase in decades to come going by birth-rate and death-rate trends (these
states are amongst the states with higher birth rates and lower death rates).
This can be seen from the following graphs (refer: Figure – 8 and Figure – 9).
Therefore, the load on healthcare service delivery is expected to continuously
increase in the years to come. Accordingly, the healthcare system is required
to improve at an accelerated pace.
iv.
Now, there is a clear correlation between health
and wealth (wealth here means economy represented by size of GDP). This
correlation cannot (clearly and decisively) determine the direction of
causality between the two. However, health does matter for wealth of
individuals, and that the direction of the causality between the two goes from
health to wealth, though it is not very strong. ("Kobylinski
and Tyrowicz" 2019 "On the Relation Between Health and Income: A
Cross-Country Analysis"; Central European Economic Journal
5(1):256-2695(1):256-269).
v.
Further, time and again it has been established
that health and the economy are inextricably linked and there is evidence
which, whilst not always apparent or obvious, shows that investing in health
and health systems is clearly beneficial for achieving economic objectives. (Ref
- https://eurohealthobservatory.who.int/themes/observatory-programmes/health-and-economy)
vi. From B and C above, we can assume that there exists a mutual synergy between health and economic development. This means bi-directional incremental efforts to improve health and economy over a significant period of time may yield prosperity to a territory. Social leadership in these states may develop a cohesive social consensus on healthcare and inclusive economic development as top priorities in order to politically influence governments for effective, articulated and consistent efforts these two aspects.
vii. Going with the above considerations, we are assuming a workable model to do a turn-around in health of these states may start with substantial increase in health expenditure (private, public and donor (NGOs, Charity)) and simultaneously promotion of prospects for commercial value generation at all levels business (through rendering support for – say - Small Scale Industries, Ease of Doing Business, Entrepreneurship, regulation for Just and Fair Market, commercial value generation friendly taxation, ease of availability of business-loans, etc.) by the respective governments.
Figure – 8: State Wise
Decadal Birth Rate
Figure – 9: State Wise
Decadal Death Rate
E.
Finally, let us have a closure look at the state
of Bihar. This choice is purely personal, and the reason is simple, because I
am presently posted in the capital of this state (Patna) with rich historical
importance. A further analysis with respect to per-capita-income in Bihar has
been attempted in the following points: -
i.
The GSDP (Gross State Domestic Product) of Bihar
may need much more attention. In order to understand the magnitude of the
problem, comparative GSDP of some of the selected states (can be considered as peers)
are given in the figure below for five successive years starting from 2017-18 (figure
– 10). GSDP of Bihar has been consistently very low during the mentioned
period.
ii.
There could be a possibility that Bihar (or for
that matter even MP and UP) may have been driven into a ‘low income trap’
analogous to ‘middle income trap’ coined by the World Bank in 2007 (looking
into economic performance of some of the countries in Latin America and Middle
East). The World Bank also provided a policy framework for countries trapped in
‘middle income trap’ called 3i Strategy to reach ‘high income’ status. The 3i
strategy insisted on sequenced and progressively sophisticated mix of policies.
In ‘1i’ phase – it is suggested to increase investment, in ‘2i’ phase – it is
suggested to shift gear to investment and infusion and in ‘3i’ phase – it is
suggested to focus on investment, infusion and innovation. If deemed
appropriate, this framework of the World Bank could be suitably considered by
the policy makers, advisors and strategist in the state of Bihar.
iii. Another related concept from development economics could be the Poverty Trap. A poverty trap is created when an economic system requires a significant amount of capital to escape poverty (ref - Investopedia). Taking the poverty trap view on under-development, a poverty trap graph has been attempted (ref – figure 11). This graph shows GSDP of Bihar for a given year (Year) in X-Axis and following Year (Year + 1) in Y-Axis. A 45-degree line indicating a plot for GSDP value for any Year is equal to GSDP value for the following year (Year + 1) is also drawn in red. Since, Bihar GSDP has consistently been above this 45-degree line, possibly, it can be suggested that GSDP of Bihar has been consistently increasing and has not gone below this 45-degree line for many consecutive years (indicating that Bihar has not been into the Poverty Trap in recent years with respect to GSDP).
Figure – 11: State Wise GSDP (Gross State Domestic Product) for Bihar for 2017-18 to 2021-22 plotted for mentioned “Year” against GSDP in the following Year (Year + 1)
APPENDIX – A: NITI AAYOG Health Index
NITI Aayog Health Index Score
(NITI-HIS) - NITI Aayog and MoH&FW are spearheading the Health Index
initiative. Under this initiative, (based on a defined framework) the NITI
Aayog evaluates states (and UTs) and generates Health Index Scores for
respective states (ref – NITI
Aayog). This index is grouped under following three classifications
(detailed as above): -
·
Large States
·
Small States
·
Union Territories (UTs)
The criteria for evaluation under
the framework of Health Index Score takes into account the following domains
(with weighted subdomains details can be referred at - NITI Aayog): -
·
Domain 1 – Health Outcomes
·
Domain 2 – Governance And Information
·
Domain 3 – Key Inputs / Processes
NITI-HIS could be one of the
additional dimensions to the previous comparative positioning of states (and
UTs) on the basis of Community, Primary and Secondary healthcare.
APPENDIX – B (SOURCES OF DATA)
1. Population - Source – Downloaded from ESRI Web Site accessed on 05 JUL 2024 22:00 IST -https://policymaps.esri.in/datasets/1e89e73ebbac436d8f34f1d2bd845eec/explore?location=22.537486%2C82.767700%2C2.85
2. Public Expenditure on Health - RBI Web Site accessed on 05 JUL 2024 22:00 IST - https://m.rbi.org.in/Scripts/PublicationsView.aspx?id=22088
3. NITI Aayog Health Index 2021: Key Highlights - https://social.niti.gov.in/hlt-ranking and https://social.niti.gov.in/hlt-ranking and https://social.niti.gov.in/hlt-ranking/?round=4
4. State Wise Per Capita Income accessed on 05 JUL 2024 22:00 IST - https://pib.gov.in/PressReleasePage.aspx?PRID=1942055
5. Yearly State Wise GSDP accessed on 04 AUG 2024 10:14 IST - https://www.rbi.org.in/scripts/AnnualPublications.aspx?head=Handbook+of+Statistics+on+Indian+States (taken from - National Statistics Office, Ministry of Statistics and Programme Implementation, Government of India)
6.
Source:
Decadal Death and Birth Rate – Office of the Registrar General of India,
Ministry of Home Affairs
Link as accessed on 06 AUG 2024 - https://www.indiabudget.gov.in/economicsurvey/doc/stat/tab82.xlsx
Note: Andhra Pradesh includes
Telangana for the year 2009 and Jammu & Kashmir includes Ladakh for the
year 2019, Birth rate & death rate per 1000 population and IMR is infant
deaths per 1000 live births. Dadra & Nagar Haveli also includes the data
for Daman & Diu for the year 2020.
7.
Out
of Pocket Expenditure – Press Information Bureau, Government of India
Link
as as accessed on 07 AUG 2024 - https://pib.gov.in/PressReleseDetailm.aspx?PRID=1894902
8. Data Table used for Data Visualization and Data Analysis can be accessed at – Data Table
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