Summary
Data analysis suggest that States of Indian Union, which are having high Per Capita Net State Domestic Product (PC-NSDP) are having higher Public Healthcare Expenditure (PHE). And, those States with higher PHE are putting greater efforts to provide public healthcare services beyond the community and primary healthcare (that is at secondary and tertiary levels of healthcare services).
State Wise ‘Public Expenditure on Health (2019-20) in Crores’ representing efforts on provisioning of public healthcare services is compared with following indicators: -
a. ‘Per Capita Net State Domestic Product at Current Prices in INR’ for the year 2020-21 representing economic development.
b. State Wise ‘Number of ASHA (31 MAR 2020)’ representing Community Healthcare.
c. State Wise ‘Number of Government Hospitals’ for 2021 representing Primary Healthcare.
d. State Wise ‘Number of Bed Availability in Public Facilities’ for 2021 representing Secondary (and above) Healthcare.
In India, Health is a state subject. The delivery of (public) health care largely rests with the states of the Federal Union. The healthcare system of the country involves a three-tier system with Sub Health Centre (Urban and Rural), Primary Health Centre (Urban and Rural) and Community Health Centre (Urban and Rural) as the three pillars of Primary Health Care System in India.
As per the established norms, a PHC in rural areas is to be established for a population of 20,000 (in hilly and tribal areas) and 30,000 (in plains) and Sub Centre for a population of 5,000 (in plain) and 3000 (in hilly and tribal area) subsequently, Community Health Centre for a population of 1,20,000 (in plain) and 80,000 (in hilly and tribal area).
Similarly, District Hospital (DH), Sub-District Hospital (SDH) and First Referral Unit-community Health Centers provide secondary care services. (ref - PIB Delhi, 2023).
Health systems are often organized in a "hub-and-spoke" arrangement, with a large district hospital (the hub) having more and better-trained personnel and better equipment than more peripheral clinics (the spokes). The district is, therefore, used as a generic term for an administrative unit often comprising a population of 100,000 to 1 million people for whom one tier of local government is typically responsible. The shared administrative boundaries and frequent proximity of district hospitals to district political administrations often result in the district hospital's involvement in the much wider tasks of district health management and public health. (English M, Lanata CF, Ngugi I, et al. The District Hospital. In: Jamison DT, Breman JG, Measham AR, et al., editors. Disease Control Priorities in Developing Countries. 2nd edition. Washington (DC): The International Bank for Reconstruction and Development / The World Bank; 2006. Chapter 65.)
The National Health Policy, 2017 recommends two beds per 1000 population. It is therefore proposed that the provision of one bed per 1000 population is an ‘Essential’ norm for every district while two beds per 1000 is a target they should aspire towards ‘Desirable’. The ‘Essential’ number of beds in a district should be provided through the public health system of tertiary care (Medical Colleges), secondary care (DH, SDH and selected CHCs) and primary care (PHCs and remaining CHCs). However, while calculating the patient-bed ratio in a district, it should primarily rely on the facilities from PHC to DH since tertiary care facilities (Medical Colleges) do not cater only to the district where it is located, but to other districts too. (Indian Public Health Standards Sub District Hospital and District Hospital, Volume I, 2022)
The above hierarchical institutional backbone for public healthcare can be theoretically reduced to three elementary healthcare services these are community healthcare, primary healthcare and secondary healthcare (includes tertiary healthcare). Therefore, in this blogpost, we will be following three broad categories of healthcare service provisioning (we call tier-category of public healthcare service delivery structure), which we may call PSC (an acronym for following meaning of P, S and C): -
Community Outreach Health Care Services of Public Healthcare System (Community - C)
Primary Health Care Services of Public Healthcare System (Primary - P)
Secondary & Tertiary Health Care Services of Public Healthcare System (Secondary - S)
We believe that an understanding of efforts being made by respective (state) governments across these three parameters (PSC) may certainly throw some light on structured system designed for provisioning of public healthcare service delivery.
In the following points, we have tried to briefly explain the above-mentioned tier-categories and also tried to find a way to broadly measure efforts (in respective tier-categories) for analysis purpose: -
Community Health Care Services (Community) – Community health programs improve access to preventive healthcare services, engage citizens in care decisions, and seek lower medical costs (ref – publichealth.tulane.edu).
ASHAs (Accredited Social Health Activists) are trained to work as an interface between the community and the public health system (ref - NHM). Therefore, in the context of Indian States & UTs, we will assume that “Number of ASHA Workers” represents efforts in Community Health Care in quantitative terms (for analysis purpose).
However, for a much better quantified comparison of Community-Efforts across diverse states, instead of “Number of ASHA Workers”, we may better consider “No of ASHA per LAKH Population” of respective states.
Primary Health Care Services (Primary) - Primary health care enables health systems to support a person’s health needs – from health promotion to disease prevention, treatment, rehabilitation, palliative care and more. This strategy also ensures that health care is delivered in a way that is centered on people’s needs and respects their preferences. (ref - WHO)
In context of Indian States & UTs, we will assume that “Number of Government Hospitals” broadly represents efforts in Primary Health Care quantitatively. In support of this assumption, following points may be considered: -
Almost all the Government Hospitals at least provide primary health care services.
Government Hospitals, which are at higher level(s) in the hierarchy of public health service delivery facilities are associated with a proportionate number of lowest-level facilities called sub-center (SCs). These sub centre also provide primary health services. A Sub-Health Centre (Sub-Centre) is the most peripheral and first point of contact between the primary health care system and the community (NHM).
However, for a much better comparison of Primary-Efforts across diverse states, instead of “Number of Government Hospitals”, we may better consider “Number of Government Hospitals per LAKH Population” in respective states.
Secondary Health Care Services (Secondary) – Secondary care is specialist care provided on an ambulatory or inpatient basis, usually following a referral from primary care. (ref – WHO)
In context of Indian States & UTs, we will assume that “Available Number of Beds” represents efforts towards Secondary Health Care quantitatively. This may include all specialized healthcare including tertiary healthcare.
However, for better comparison of Secondary-Efforts across diverse states, instead of “Number of Government Hospitals”, we may better consider “No of Bed Availability per LAKH (Population) in Public Facilities” in respective states.
Having defined the three tier categories for our analysis (as above), let us try to add more dimensions to the above datasets and thereafter try to do exploration for further detailing into efforts on public healthcare expenditure. These additional dimensions are discussed in the points below: -
Economic Produce expressed as Per Capita Net State Domestic Product (PC-NSDP): - Per Capita Net State Domestic Product (PC-NSDP) is a measure that represents the average economic output per person in a specific state, taking into account the net value of goods and services produced within the state's borders. It is calculated by dividing the Net State Domestic Product (NSDP) by the population of the state. The per capita measure provides an indication of the average economic well-being of the residents in a particular state. (ref - testbook.com)
Here's the formula for calculating Per Capita Net State Domestic Product:
PC-NSDP = NSDF/Population
Per Capita Net State Domestic Product (PC-NSDP) could be another aspect, which may be considered during analysis of state-wise public healthcare efforts.
Per Capita Public Expenditure on Health (Expense): - Health expenditure includes all expenditures for the provision of health services, family planning activities, nutrition activities and emergency aid designated for health, but it excludes the provision of drinking water and sanitation. (ref – WHO)
Per capita total expenditure on health - This indicator is defined as the per capita total expenditure on health, expressed at the average exchange rate for that year in US$. It shows the total expenditure on health relative to the beneficiary population, expressed in US$ to facilitate international comparisons. (ref – WHO)
For ease of representation, we may express the Expenditure on Health per Lakh Population (per hundred thousand people) instead of per capita.
To sum up, we will go ahead with our analysis based on the following indicators: -
State Wise ‘Number of ASHA (31 MAR 2020)’ representing Community Healthcare.
State Wise ‘Number of Government Hospitals’ for 2021 representing Primary Healthcare.
State Wise ‘Number of Bed Availability in Public Facilities’ for 2021 representing Secondary (and above) Healthcare.
‘Per Capita Net State Domestic Product at Current Prices in INR’ for the year 2020-21 representing economic development.
State Wise ‘Public Expenditure on Health (2019-20) in Crores’ representing efforts on provisioning of public healthcare services.
It may be noted that based on the availability of data, we have considered the time-period for this visualization-based analysis as that of financial year 2020-21 (April 2020 to March 2021). Data set of some of the parameters used in this analysis may not refer precisely to the chosen financial year (2020 – 2021) but may be representing adjacent financial years. However, usually these parameters do not undergo massive changes so frequently and therefore, the analysis may hold good enough for developing a broad understanding in larger context. Datasets used for analysis along with corresponding sources are given in APPENDIX – A. A brief on NITI-Aayog Health Index Score id given in APPENDIX – B.
So, after setting up the context with long paragraphs (as above), let us start visualization-based analysis in pointwise manner: -
Plot of “Public Healthcare Expenditure” with “Per Capita Net State Domestic Product”
X-Axis – Public Expenditure on Health (2019-20) in Crores per LAKH Population
Y-Axis – Per Capita Net State Domestic Product at Current Prices for 2020-21 (Base Year 2011-12) in Lakhs (INR)
Scale – Logarithmic
Illustrations – Median values of X and Y Axis (in Columbia Blue) with a trend line (in black)
Data – All states available in dataset
Needless to reiterate the widely accepted fact that natural terrain, location and transportation are significant factors of efficiency in delivery of public goods (especially rural public goods). Mostly, mountainous areas are considered as difficult terrains with low accessibility and lesser infrastructural support (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, it can be assumed that for similar levels of quality of services spending on public healthcare is much higher in hilly states (Indian states with significantly high hilly region or mountainous terrain). In support of this, the following example could be cited - 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). Probably similar considerations may also be the rationale for having separate standards for numbers SCs (Sub-Centers), PHCs (Primary Health Centers) and CHCs (Community Health Centers) with respect to population (like say -- a PHC in rural areas is to be established for a population of 20,000 (in hilly and tribal areas) and 30,000 (in plains) -- ) by GoI (Government of India). Therefore, we may try to repeat analysis for non-hilly states (states with significantly higher area of plains rather than mountains) in this analysis and also in subsequent analysis (we may undertake). This is to weed out special cases from generic consideration. Our list of Hilly States is given in APPENDIX – C.
Plot of “Public Healthcare Expenditure” with “Per Capita Net State Domestic Product” (Excludes Hilly States)
X-Axis – Public Expenditure on Health (2019-20) in Crores per LAKH Population
Y-Axis – Per Capita Net State Domestic Product at Current Prices for 2020-21 (Base Year 2011-12) in Lakhs (INR)
Scale – Logarithmic
Illustrations – Median values of X and Y Axis (in Columbia Blue) with a trend line (in black)
Data – All states available in dataset (Excludes Hilly States)
Figure – 2: Plot of “Public Healthcare Expenditure” with “Per Capita Net State Domestic Product” (Excluding Hilly States)
Observations on “Public Healthcare Expenditure” (PHE) with “Per Capita Net State Domestic Product” (PC-NSDP) graphs as above (Figure – 1 and Figure – 2): -
Median public health expenditure is generally less for Indian states. India as a country also spends relatively less on Public Healthcare. The same is reflected in India’s position in Country Wise Government Health Expenditure data from World Bank. (refer – APPENDIX - H)
In general, Public Healthcare Expenditure is higher for the States having higher values of Per Capita Net State Domestic Product. The trend appears to be marginally steeper for Indian States, when hilly states (that is states with large hilly areas) are excluded from the list of states.
Prevalence of common diseases are not less for Indian states with lesser PC-NSDP. Therefore, there is no reason to believe that these states (with lesser PC-NSDP) need less healthcare services. (refer – APPENDIX – G)
In India healthcare expense is either covered by the Government or born by patients as OOPE (Out of Pocket Expenditure). GHE (Government Health Expenditure) and OOPE (Out of Pocket Expenditure) are the dominant expenses on healthcare by Indians. (refer APPENDIX – E)
Following the trends of given distribution of income and wealth (refer APPENDIX - J), it may be reasonable to assume that low PC-NSDP may mean lower capacity of population to afford OOPE. Therefore, the states with low PC-NSDP can be assumed to have a larger population within their respective jurisdictions, which are dependent on GHE. State Governments and Central Government are expected to put collective efforts to promote distributive justice for citizens residing in different parts of the country (refer – APPENDIX - D). However, the disparity in public spending across the states can be considered to indicate that an effective redistribution of resources in relation to healthcare service provisioning is not happening (as reflected through visualizations of the available datasets).
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. (Refer - https://eurohealthobservatory.who.int/themes/observatory-programmes/health-and-economy). Therefore, disparity in public healthcare spendings (with respect to PC-NSDP) could have restrictive influence on growth prospects of the states, which are already having low PC-NSDP.
Further, the modern growth framework assumes that the long-term growth rate depends on governmental actions, such as taxation, maintenance of law and order, provision of infrastructure services, protection of intellectual property rights, and regulations of international trade, financial markets, and other aspects of the economy. The government therefore has great potential for good or ill through its influence on the long-term rate of growth. (refer – APPENDIX - F)
Considering the above points (a through g), it seems that for the states with low PC-NSDP, there is an urgent need for pumping funds to support healthcare services to support health of the population and to accelerate human capital development (which becomes basis for economic growth).
In relation to point (h), to make arrangement for required funds for improving healthcare services an approach is elaborated in the following points: -
The Process – The process to get required attention and funds for healthcare for Indian states in general and for states with PC-NSDP in particular may need an urgent attention from society, political circles and from the government. A hypothetical process is articulated through the following figure and subsequent elaborations of the components: -
Figure – 3: Process for Generating Funds for Public Healthcare Services
Social Efforts for Healthcare Services and Associated Challenges
Efforts to build consensus for a model of development with absolute no-tolerance for any development activity at the cost of generic public health, environmental degradation or general wellbeing of people may be difficult to build through advocacy and deliberations in Indian states with low PC-NSDP.
One of the major challenges faced in this pursuit could be compulsions of carrying on economic development for livelihood of huge population (with significantly higher percentage of population falling under unskilled workforce), even at the cost to health of population. Such compulsions may be met with short term perspectives keeping larger perspective and long-term goals of all round development in focus. This is so because, in the long run, good health and wellbeing of populations turns out to be a critical factor for economic growth. This is evident from the following findings. “The environmental stress has had a high cost on income and health from the derived reduced form, a 1 percent increase in the air pollution index leads to a decrease of about 8 percent in the per capita income, a decrease of about 0.7 percent in the life expectancy, and an increase of about 19 percent in the number of cases of respiratory diseases.” (A L Nagar, Amit Shovon Ray, Aparna Sawhney, Sayan Samanta; “The Interface between Economic Development, Health and Environment in India: An Econometric Investigation”; 2008 - Working Paper 2008-56).
Political Efforts for Healthcare Services and Associated Challenges
It is particularly important to keep the healthcare service provisioning as a dominant issue in the political spectrum through continuous political discourse at state and at central government levels.
However, the challenge is that the benefits of healthcare services in terms of returns (as improved health and improved economy) may be difficult to measure and may take long span of time to see the results. It is usually difficult to sustain something in the realm of politics, without perceptible results in quick succession.
Government Interventions and Associated Challenges
The Union Government may attempt to do distributive justice when allocating funds for central schemes to the states in the health sector. State governments may put all the possible efforts into generating funds for healthcare through all possible means (Grants, Loans, Donations, CSR (Corporate Social Responsibility) and all other means).
Transparency in monitoring and controlling public health services provisioning may be helpful in assuring better services. Engaging civil society and other interest groups (concerned with health equity or sustainable development) to support government in this endeavor may also be considered by the governing agencies.
In India’s version of federalism, health policymaking has been influenced by four mutually interacting sources: international public health discourses; Indian government programmes and policies; civil society organizations concerned with health; and the political economy of the different states and their associated political regimes. Public health issues sometimes achieve a high policy profile at the government of India, but very rarely do so at state level. This divergence provides fertile spaces for negotiation and conflict. (Jeffery, R. (2021). Health policy and federalism in India. Territory, Politics, Governance, 10(1), 67–85.).
With a massive mandate, systematic fiscal decentralization may be instrumental in making a difference on the ground. Exploiting large differences in the size of the tax base across regions, it is found that fiscal decentralization processes that attribute a greater tax power to lower government tiers, besides reducing inefficiencies of healthcare policies, seem to be effective in reducing also within-regional disparities in health outcomes. However, the degree of economic development – on which depends the actual fiscal autonomy from Central government – significantly affects the effectiveness of these reforms and highlights the importance to take properly into account the specific features of the context where the decentralization of power is implemented. (Di Novi, Cinzia and Piacenza, Massimiliano and Robone, Silvana and Turati, Gilberto, How Does Fiscal Decentralization Affect Within-Regional Disparities in Well-Being? Evidence from Health Inequalities in Italy (June 30, 2015). University Ca' Foscari of Venice, Dept. of Economics Research Paper Series No. 21/WP/2015). But, decentralization of government machinery is not that easy. Although Decentralization potentially provides significant opportunities for effective rural growth, its execution is often plagued by a variety of issues. This is evident from implementation challenges of Panchayati Raj in India. (reference - Parvez Shahid Ali, Research Scholar, Department of Political Science, Ranchi University, Ranchi.“ Democratic Decentralisation in India and Challenges of Rural Governance”, 2022)
Now, let us attempt to plot “Public Healthcare Expenditure” with comparative efforts of state governments on Community, Primary and Secondary / Tertiary healthcare services.
Plot of “Public Healthcare Expenditure” with “Number of ASHA Workers per LAKH Population”
X-Axis – Public Expenditure on Health (2019-20) in Crores per LAKH Population
Y-Axis – Number of ASHA (31 MAR 2020) per LAKH Population (quantitatively representing Community Healthcare Efforts by States)
Scale – Logarithmic
Illustrations – Median values of X and Y Axis (in Columbia Blue) with a trend line (in black)
Data – All states available in dataset
Figure – 4: Plot of “Public Healthcare Expenditure” with “Number of ASHA per LAKH Population”
Plot of “Public Healthcare Expenditure” with “Number of ASHA Workers per LAKH Population” (Excludes Hilly States)
X-Axis – Public Expenditure on Health (2019-20) in Crores per LAKH Population
Y-Axis – Number of ASHA (31 MAR 2020) per LAKH Population (quantitatively representing Community Healthcare Efforts by States)
Scale – Logarithmic
Illustrations – Median values of X and Y Axis (in Columbia Blue) with a trend line (in black), which appears to have been influenced by a few outliers
Data – All states available in dataset (Excludes Hilly States)
Plot of “Public Healthcare Expenditure” with “Number of ASHA Workers per LAKH Population” (Excludes Hilly States and Outliers)
X-Axis – Public Expenditure on Health (2019-20) in Crores per LAKH Population
Y-Axis – Number of ASHA (31 MAR 2020) per LAKH Population (quantitatively representing Community Healthcare Efforts by States)
Scale – Logarithmic
Illustrations – Median values of X and Y Axis (in Columbia Blue) with a trend line (in black)
Outlier States – Chhattisgarh (CG), Delhi, Puducherry and Tamil Nadu (TN)
Data – All states available in dataset (Excludes Hilly States and Outliers)
Plot of “Public Healthcare Expenditure” with “Number of Government Hospitals per LAKH Population”
X-Axis – Public Expenditure on Health (2019-20) in Crores per LAKH Population
Y-Axis – Number of Government Hospitals (2021) per LAKH Population (quantitatively representing Primary Healthcare efforts by States)
Scale – Logarithmic
Illustrations – Median values of X and Y Axis (in Columbia Blue) with a trend line (in black)
Data – All states available in dataset
Plot of “Public Healthcare Expenditure” with “Number of Government Hospitals per LAKH Population” (Excludes Hilly States)
X-Axis – Public Expenditure on Health (2019-20) in Crores per LAKH Population
Y-Axis – Number of Government Hospitals (2021) per LAKH Population (quantitatively representing Primary Healthcare Efforts by States)
Scale – Logarithmic
Illustrations – Median values of X and Y Axis (in Columbia Blue) with a trend line (in black)
Data – All states available in dataset (Excludes Hilly States)
Plot of “Public Healthcare Expenditure” with “Number of Bed Availability per LAKH Population in Public Facilities”
X-Axis – Public Expenditure on Health (2019-20) in Crores per LAKH Population
Y-Axis – Number of Bed Availability per LAKH Population in Public Facilities (2021) (quantitatively representing secondary and above Healthcare Efforts by States)
Scale – Logarithmic
Illustrations – Median values of X and Y Axis (in Columbia Blue) with a trend line (in black)
Data – All states available in dataset
Plot of “Public Healthcare Expenditure” with “Number of Bed Availability per LAKH Population in Public Facilities” (Excludes Hilly States)
X-Axis – Public Expenditure on Health (2019-20) in Crores per LAKH Population
Y-Axis – Number of Bed Availability per LAKH Population in Public Facilities (2021) (quantitatively representing secondary and above Healthcare Efforts by States)
Scale – Logarithmic
Illustrations – Median values of X and Y Axis (in Columbia Blue) with a trend line (in black)
Data – All states available in dataset (Excludes Hilly States)
A trend line is a line that minimizes the distance between each point in a scatter plot and the line itself. Therefore, in points 6 through 12 (as above), the trend line shown be considered as a generic indicator of directionality at a broad level. This is a mathematically calculated line based on the points (as they appear) on the scatter plot. Thus, the related graphs be understood comprehensively with due attention to positioning of all the points shown in scatter plots using trend line only as a broad guidance on directionality.
Points 6, 7 and 8 (as above) suggest that comparing “Public Healthcare Expenditure” with “Number of ASHA Workers per LAKH Population” and considering “Number of ASHA Workers per LAKH Population” to be quantifying the efforts by state for community healthcare services, the following observations can be made: -
Removing Hilly States and a few outliers (from the plot as shown), even states with lower PC-NSDP are nearly able to do at par with states with higher PC-NSDP in relation to community healthcare efforts.
Points 9 and 10 (as above) suggest that comparing “Public Healthcare Expenditure” with “Number of Government Hospitals per LAKH Population” and considering “Number of Government Hospitals per LAKH Population” to be quantifying the efforts by state for provisioning of Primary Healthcare Services, the following observations can be made: -
Many states are clustered around the median values of “Public Healthcare Expenditure” and “Number of Government Hospitals per LAKH Population”. Remaining states are cluttered all around the plot area without no specific patterns visible in this comparative plots.
However, a closure look at figure – 8 (under point – 10 above) may reveal that to some extent increase in “Public Healthcare Expenditure” is seen increasing with “Number of Government Hospitals per LAKH Population” only till near to intersection of median lines (of these two parameters) but this relation does not carry far beyond this point (intersection of median lines).
Points 11 and 12 (as above) suggest that comparing “Public Healthcare Expenditure” with “Number of Bed Availability per LAKH Population in Public Facilities” and considering “Number of Bed Availability per LAKH Population in Public Facilities” to be quantifying the efforts by state for provisioning of Secondary and Tertiary Healthcare Services, the following observations can be made: -
Clear pattern of “Public Healthcare Expenditure” increasing with “Number of Bed Availability per LAKH Population in Public Facilities” emerges.
Interestingly those states, which are falling under high “Public Healthcare Expenditure” and high “Number of Bed Availability per LAKH Population in Public Facilities” broadly are the states having higher literacy rates. State wise literacy rate of Indian states is shown under APPENDIX – I (of this post).
So, now here is a question. Could this mean that to make arrangement of funds for improving healthcare services at secondary and tertiary levels, the more literate states are more successful in executing the process given in Point – 4 (i) as above (that is, so to say these states have harmonized Social, Political and Government efforts in taking public health service provisioning beyond primary care)? – A ‘yes’ to this question is at the best a possibility and may need a thorough investigation and research to get conclusive on this.
Going through points 14 through 16 (as above), a positive correlation between “Public Healthcare Expenditure” and “Number of Bed Availability per LAKH Population in Public Facilities” is observed. Does this mean that interventions with larger impact at lower expenditure get preference in delivery of public health services? May or may not be true but there is evidence that the low-cost health interventions that have large‐scale effects on population health make health investments a promising policy tool for growth in developing countries. In addition, higher priority could be given to tackling widespread “neglected” diseases—that is, diseases with low mortality burdens that are not priorities from a pure health perspective, but that do have substantial effects on productivity. (Population Health and Economic Growth, Bloom and Canning, Working Paper - 2008)
Going through points 14 through 16 (as above), public health service delivery seems to be working better for community healthcare and primary healthcare in comparison to secondary (and tertiary) healthcare on health equity across Indian states. Does this mean that root case for this could be the scarcity of sophisticated products and specialized services required for delivery of healthcare services (beyond primary care). If so, would it be possible to improve the situation on ground by promoting quick technology transfer, encouraging fast scale-up of healthcare services and improving operational excellence of agencies involved in healthcare service delivery? A further investigation into this may be required before deciding a take on this question.
In a nutshell, the above discussions indicate that the States of Indian Union, which are having high Per Capita Net State Domestic Product (PC-NSDP) are having higher Public Healthcare Expenditure (PHE). And, those States with higher PHE are putting greater efforts to provide public healthcare services beyond the community and primary healthcare (that is to provision secondary and tertiary healthcare).
This analysis is just an attempt to highlight some of the observations from data available in public domain on public healthcare services. Healthcare service delivery is a complex issue dealing with science & technology to operations to policies to politics to the prevailing economic scenario to preferred notion of justice, notion of social welfarism and many many more such aspects from various disciplines. Therefore, health and healthcare issues need a much more large and representative forums to discuss issues in larger interest of society.
APPENDIX – A: DATA USED FOR ANALYSIS WITH SOURCES OF DATA
Data-Sheet: Data used in this analysis can be accessed at the following link: -
https://1drv.ms/x/c/4e64d4338e9cebf7/EfAWVdl013JAjgBxnPaivmEBSM17QqeX-ismVPUlCHH3uA?e=EVWhlI
Population (Projected Population of Indian States in 2020) - Source – Web Site accessed on 16 NOV 2024 16:21 IST - https://uidai.gov.in/images/state-wise-aadhaar-saturation.pdf
Public Expenditure on Health (2019-20) - RBI Web Site accessed on 05 JUL 2024 22:00 IST - https://m.rbi.org.in/Scripts/PublicationsView.aspx?id=22088
State Wise Per Capita Income accessed on 05 JUL 2024 22:00 IST - https://pib.gov.in/PressReleasePage.aspx?PRID=1942055
Number of Government Hospitals – Downloaded from RBI Web Site accessed on 05 JUL 2024 22:00 IST - https://m.rbi.org.in/Scripts/PublicationsView.aspx?id=22087
Availability of Bed in Public Facilities - Downloaded from RBI Web Site accessed on 05 JUL 2024 22:00 IST - https://m.rbi.org.in/Scripts/PublicationsView.aspx?id=22087
Number of ASHAs – Copied from Press Information Bureau (PIB) site accessed on 05 JUL 2024 22:00 IST - https://pib.gov.in/Pressreleaseshare.aspx?PRID=1606212
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
APPENDIX – B: BREIF ON NITI AYOG HEALTH INDEX AS MENTIONED IN DATA-SHEET
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 Health Index): -
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 – C: HILLY STATES
However, in this blogpost, we may consider the following states as hilly state Jammu and Kashmir, Ladakh, Uttarakhand, Himachal Pradesh, Arunachal Pradesh, Manipur, Meghalaya, Mizoram, Nagaland, Sikkim and Tripura. Assam and West Bengal are left out considering significant higher agricultural land (LOK SABHA, UNSTARRED QUESTION NO. 2416, TO BE ANSWERED ON THE 3RD AUGUST, 2021, "IRRIGATED AGRICULTURAL LAND").
APPENDIX – D: DISTRIBUTIVE JUSTICE IN HEALTHCARE
Distributive justice is concerned with the fair distribution of the burdens and benefits of social cooperation among diverse persons with competing needs and claims. In order to provide a basis for resolving conflicting claims to social goods, a theory of distributive justice must set out an account of political justification specifying the weight to be assigned to various kinds of considerations relevant to political justice (e.g., moral norms, social practices, claims of entitlement or desert, efficiency arguments) and providing an acceptable description of the standpoint from which judgments are formed, noting, in particular, the amount and kind of information available, the most appropriate decision rule (e.g., unanimity, majority vote, plurality), the moral psychology assigned to persons who occupy the standpoint, and the (ir)relevance of threat advantage to judgments formed from this standpoint. The theory must employ these resources to justify an account of a just distribution of social goods, determining, in the process, the priority to be assigned to considerations such as claims of right, entitlement, efficiency, fairness, and community.
Since a theory of distributive justice therefore develops an account of the practical implications of sets of fundamental intuitions regarding (1) the nature of political justification and (2) the relative weight to be assigned to central concerns relevant to the justice of distributions, an adequate description of such a theory must describe and assess not merely the principle or set of principles proposed to regulate the distribution of goods, but also the account of justification that underlies the theory and the weight assigned by the theory to relevant fundamental concerns. (A. Kaufman, in Encyclopedia of Applied Ethics (Second Edition), 2012)
Distributive justice refers to the fair and appropriate distribution of benefits, risks and costs within a society. In a medical context, this requires patients with similar cases to be treated in a similar manner, and for there to be overarching equality of access to finite health resources. Distributive justice is a derivative of the broader principle of justice, which is one of the four biomedical ethics pillars described by Beauchamp and Childress as underpinning modern medical practice, along with beneficence, non‐maleficence and autonomy (Beauchamp, Tom L., and James F. Childress. Principles of biomedical ethics. Edicoes Loyola, 1994.). (Reference - NIH)
Distributive Justice in Healthcare focuses on the fair allocation of scarce medical resources and access to quality medical care. It asks how healthcare resources should be distributed throughout a population to ensure everyone has an equitable opportunity to maintain or regain their health. This principle goes beyond providing care but aims to ensure that everyone, regardless of income, background, or location, can achieve good health outcomes.
Distributive Justice in Healthcare doesn't prescribe a single "one-size-fits-all" solution. Instead, it revolves around several core principles that guide the fair allocation of scarce resources. Here are some fundamental principles:
Equity: This principle emphasizes that everyone deserves a fair chance at good health, regardless of their social or economic standing. Resources should be distributed based on need, ensuring those with greater needs receive a larger share.
Need: This principle prioritizes allocating resources based on the severity of a patient's condition and the effectiveness of available treatments.
Equality of opportunity: This principle ensures everyone has equal access to preventive care, screenings, and early intervention programs to prevent health problems from escalating.
APPENDIX – E: OOPE IN INDIA
Out-of-Pocket Expenditure (OOPE) in healthcare refers to the money people pay directly from their own pockets for medical services, such as doctor visits, medicines, and hospital stays.
OOPE forces low-income families to spend a large portion of their earnings or savings on healthcare.
This financial burden can push families into poverty, create debt, and make it harder for them to afford other essentials like food and education.
In India, GHE (Government Health Expenditure) and OOPE (Out of Pocket Expenditure) are dominant healthcare expenditures. This is depicted in the following figure.
References to this APPENDIX are: -
Press Release Page of Ministry of Health and Family Welfare, GoI - https://pib.gov.in/PressReleaseIframePage.aspx?PRID=2058791
An article on “Decline in Out-of-Pocket Expenditure (OOPE) in Health in India” published by NEXTIAS - https://www.nextias.com/ca/current-affairs/11-11-2024/decline-in-out-of-pocket-expenditure-oope-in-health-in-india
APPENDIX – F: GROWTH FRAMEWORKS
In the 1960s, growth theory consisted mainly of the neoclassical model, as developed by Ramsey (1928), Solow (1956), Swan (1956), Cass (1965), and Koopmans (1965). One feature of this model, which has been exploited seriously as an empirical hypothesis only in recent years, is the convergence property. The lower the starting level of real per capita gross domestic product (GDP) the higher is the predicted growth rate.
The initial wave of the new research — Ramer (1986), Lucas (1988), Rebelo (1991) — built on the work of Arrow (1962), Sheshinski (1967), and Uzawa (1965) and did not really introduce a theory of technological change. In these models, growth may go on indefinitely because the returns to investment in a broad class of capital goods, which includes human capital, do not necessarily diminish as economies develop.
The incorporation of R&D theories and imperfect competition into the growth framework began with Romer (1987, 1990) and includes significant contributions by Aghion and Howitt (1992) and Grossman and Helpman (1991, Chapters 3 and 4). Barro and Sala-i-Martin (1995, Chs. 6, 7) provide expositions and extensions of these models. In these settings, technological advance results from purposive R&D activity, and this activity is rewarded, along the lines of Schumpeter (1934), by some form of ex-post monopoly power. If there is no tendency to run out of ideas, then growth rates can remain positive in the long run. The rate of growth and the underlying amount of inventive activity tend, however, not to be Pareto optimal because of distortions related to the creation of the new goods and methods of production. In these frameworks, the long-term growth rate depends on governmental actions, such as taxation, maintenance of law and order, provision of infrastructure services, protection of intellectual property rights, and regulations of international trade, financial markets, and other aspects of the economy. The government therefore has great potential for good or ill through its influence on the long-term rate of growth.
Reference to this APPENDIX: -
Health and Economic Growth, Robert J. Barro , Harvard University, 2013 ( https://ftp.aefweb.net/WorkingPapers/w572.pdf)
APPENDIX – G: STATE WISE DISTRIBUTION OF SELECTED DISEASES IN INDIA
Burden of TB (Tuberculosis)
Reference – Ni-kshay Portal as accessed on 20 NOV 2024 (link - https://reports.nikshay.in/reports/tbnotification )
Prevalence of Diabetes
Reference – Pradeepa R, Mohan V. Epidemiology of type 2 diabetes in India. Indian J Ophthalmol. 2021 Nov;69(11):2932-2938. doi: 10.4103/ijo.IJO_1627_21. PMID: 34708726; PMCID: PMC8725109. (link - https://pmc.ncbi.nlm.nih.gov/articles/PMC8725109/#R25 as accessed on 20 NOV 2024); Data taken from the works by: -
Anjana RM, Pradeepa R, Deepa M, Datta M, Sudha V, Unnikrishnan R, et al. Prevalence of diabetes and prediabetes (impaired fasting glucose and/or impaired glucose tolerance) in urban and rural India: Phase I results of the Indian Council of Medical Research–INdia DIABetes (ICMR–INDIAB) study. Diabetologia. 2011;54:3022–7. doi: 10.1007/s00125-011-2291-5. [DOI] [PubMed] [Google Scholar]
Anjana RM, Deepa M, Pradeepa R, Mahanta J, Narain K, Das HK, et al. Prevalence of diabetes and prediabetes in 15 states of India:results from the ICMR-INDIAB population-based cross-sectional study. Lancet Diabetes Endocrinol. 2017;5:585–96. doi: 10.1016/S2213-8587(17)30174-2. [DOI] [PubMed] [Google Scholar]
APPENDIX – H: HEALTHCARE – COUNTRY WISE EXPENDITURE & GOVERNMENT EXPENDITURE
Domestic general government health expenditure per capita, PPP (current international $) Sorted by Values for Year 2021 (World Bank data accessed on 20 NOV 2024).
Domestic general government health expenditure per capita, PPP (current international $) Sorted by Values for Year 2021 (World Bank data accessed on 20 NOV 2024).
APPENDIX – I: LITERACY RATE OF STATES IN INDIA IN 2021
(Reference - Manzoor, Shaista & Qayoom, Kahkashan & Rafiqui, Aroos. (2023). Present Status of Women Education in India.)
APPENDIX – J: INCOME INEQUALITY IN INDIA 1922 - 2023
“…inequality declined post-independence till the early 1980s, after which it began rising and has skyrocketed since the early 2000s. Trends of top income and wealth shares track each other over the entire period of our study. Between 2014-15 and 2022-23, the rise of top-end inequality has been particularly pronounced in terms of wealth concentration. By 2022-23, top 1% income and wealth shares (22.6% and 40.1%) are at their highest historical levels and India’s top 1% income share is among the very highest in the world.”
State Wise GINI Coefficient for Indian States 2022-23
(Reference - https://www.mospi.gov.in/major-state-wise-gini-coefficient-total-consumption-expenditure-2022-23 )
