Healthcare – Why Facility Wise Data on CD and NCD is Important for India?

Picture Reference - https://elements.envato.com/medical-doctors-at-the-conference-RABX5Q3 

 

Communicable diseases (CD) are illnesses caused by viruses or bacteria that people spread to one another through contact with contaminated surfaces, bodily fluids, blood products, insect bites, or through the air (Peter F. Edemekong; Ben Huang, 2022, National Library of Medicine). Non-communicable diseases (NCD) are diseases that are not spread through infection or through other people, but are typically caused by unhealthy behaviours (ifrc.org). 

The sum of mortality and morbidity is called the “burden of disease” (BoD) by researchers, and can be measured by a metric called “Disability Adjusted Life Years” (DALYs). DALYs are standardized units to measure lost health. They help compare the burden of different diseases in different countries, populations, and times (Max Roser, Hannah Ritchie and Fiona Spooner, 2016 and 2024, ourworldindata.org). 

Global burden of diseases study examines 333 health conditions and 84 risk factors. This, and other similar studies, classify the burden of diseases in three broad groups: Communicable, noncommunicable, and injuries. Within communicable diseases are included the following conditions: Diseases of infectious etiology (diarrhea, pneumonia, tuberculosis, HIV being the most common ones), all maternal and neonatal deaths (irrespective of the cause), and all deaths due to nutritional deficiencies (deaths due to under-nutrition). Within the broad group of NCDs are included the following conditions: Cardiac conditions, diabetes, cancers, chronic pulmonary diseases, mental health conditions, and many others. Injuries include injuries due to trauma, drowning, poisonings, and bites. 

While for purposes of an epidemiological study, it is useful to bucket the conditions in manageable entities, it is important to understand that these are not neat groups. For example, origins of adult diseases such as hypertension and diabetes often lie in maternal, fetal, and early childhood malnutrition. Also, it is being now realized that many of the so called NCD such as cancer cervix and hepatocellular cancers are caused by infectious agents, which primarily cause communicable diseases. Chronic Obstructive Pulmonary Diseases (COPDs) are also often a result of or are exacerbated by chest infections such as bacterial pneumonias and past tuberculosis. Similarly, rheumatic heart disease is classified as NCD, but it has its origins in streptococcal throat infection, an infectious disease. 

According to the Lancet Global Burden of Disease Study in 2016, NCDs contributed to 61.8% of all deaths, while the communicable diseases contributed to 27.5% of all the deaths. A state makes an epidemiological transition when the disease burden due to NCDs (including injuries) is greater than that due to communicable diseases (including maternal-newborn). By 2016, all states in India had made this epidemiological transition. In contrast, by 1990, only two states - Kerala and Goa and union territories other than Delhi had made the transition. (Ref for above paragraphs - Mohan P, Mohan SB, Dutta M.; J Family Med Prim Care. 2019 Feb;8(2):326-329. doi: 10.4103/jfmpc.jfmpc_67_19. PMID: 30984632; PMCID: PMC6436242). 

India is facing the challenge of “Building strong primary health care systems to address double burden of disease (CD and NCD)”. In some way, the double burden is also observed from the following data visualizations, which collectively compare some of the prominent countries in Asia. For the analysis purpose, I have considered data of top five Asian Countries by GDP (as per data from DEC 2022). 

Graph – 1: CD and NCD Deaths (per Lakh Population) in selected Asian Countries during 1980 - 2021 

 

 

 

Graph – 2: Population (in Billions) for selected Asian Countries during 1950 - 2021 

 

 

 

Graph – 3: GDP (in Trillion USD) of selected Asian Countries during 1960 - 2022 

 

 

Going through the above three graphs, following points can be inferred: - 

1. 1) Nearly, during the last four decades, India has fared poor in reducing numbers of deaths due to CD. But, has done much poorly in reducing numbers of deaths due to NCD (Ref – Graph – 1). 

2. 2) However, India underwent through a phase of steep increase in population with moderate increase in GDP (Graph – 2 and Graph - 3).  

Probably, only China is the other country (in the world), which has faced a similar situation of consistently surging population as India (Graph – 2 and Graph - 3). Anyhow, China could still do far much better than India in managing CD and NCD (Graph – 1, Graph – 2 and Graph - 3). The better performance of China could be attributable to massive economic growth seen in the country during the period under consideration (say last five decades).  

In relation to this comparative discussion, it may be noted that during all these years, India was under democratic regime, while China was under communist regime. Academic literature is convinced that there are not enough evidence to suggest any relationship between democracy and economic development (Helliwell, John F. “Empirical Linkages Between Democracy and Economic Growth.” British Journal of Political Science 24, no. 2 (1994): 225–48.; Gerring, John, Philip Bond, William T. Barndt, and Carola Moreno. “Democracy and Economic Growth: A Historical Perspective.). Focused reforms related to economic liberalization was initiated in China in 1978 and in India in 1991.  

It turns out, that during initial decades after liberalization year (1978), China was able to capitalize on sharp and sustained increase in productivity due to increased  worker efficiency (IMF Working Paper 96/75, "Why Is China Growing So Fast?" by Zuliu Hu and Mohsin S. Khan of the IMF's Research Department. Rozlyn Coleman). India on the other hand seems to have been able to take only a limited mileage after liberalization of economy in 1991 (Graph - 3). The weak quality of Indian institutions is increasingly (considered as) a problem, and without better institutions, India may not be able to maximize its gains from liberalization (Swaminathan S. Anklesaria Aiyar, 2016, “Twenty‐​Five Years of Indian Economic Reform”). 

Those interested in further comparative on two interesting neighboring countries (India and China) on some more health indicators to understand performances of these two countries in a bit more detail may refer to APPENDIX – A. 

3. 3) When it comes to health data for India, it is obvious that India needs to improve on healthcare services with an intent to reduce burden of both CD and NCD. Digitization of healthcare services (under ABDM – Ayushman Bharat Digital Mission Program) may soon make it possible to record, store and analyze (on near real time basis) the healthcare data (made available from both public facilities and private facilities). Most of the captured data will be made available at facility level (some sensitive data, which may have risk of revealing unwanted details – of (say) individuals or groups may be available at higher levels – say block level / district level). Broad classification of diagnosis-data as CD, NCD and Injury cases at every facility (say for simplicity - above PHCs – Primary Health Care Centers in the hierarchy of healthcare facilities) should be easily available to policymakers and to the development sector professionals.  

Analysis of causes for prevalence of CD and NCD to facility level can be done from available data (related to various aspects of healthcare). Digitization may also make it possible to do aggressive monitoring till facility level (facility by facility monitoring - say above PHCs) to accelerate efforts to bring changes on prevalence of CD and NCD. 

Facility level supervision is also important in relation to preparing facilities to face upcoming challenges of climate change. Climate change has already increased the occurrence of diseases in some natural and agricultural systems, but in many cases, outcomes depend on the form of climate change and details of the host-pathogen system. Future work must continue to anticipate and monitor pathogen biodiversity and disease trends in natural ecosystems and identify opportunities to mitigate the impacts of climate-driven disease emergence. (Sonia Altizer et al. ,Climate Change and Infectious Diseases: From Evidence to a Predictive Framework.Science341,514-519(2013)). This may be best managed in a decentralized and localized way at facility levels supervised through central monitoring and control. 

Therefore, going forward all the health facilities may need to be mapped with impacts of climate change likely to be expected in the vicinity of facilities (Owrangi, A.M., Lannigan, R. & Simonovic, S.P. Mapping climate change-caused health risk for integrated city resilience modeling. Nat Hazards 77, 67–88 (2015)). Thus, facility wise monitoring may also help in preparing individual facilities for upcoming changes to profile of patient-load due to climate change induced health problems. 

In view of the above discussions, using hypothetical data (of facility-wise CD and NCD), an attempt to develop high level classification of facilities based on profile of CD-NCD patients has been attempted. Some broad and indicative directives to handle the situation by different types of classified facilities has also been suggested. Hypothetical data used to show distribution of CD and NCD cases in 15 imaginary high-level facilities (say of the level of District Hospitals (or equivalent Private Hospital or Medical College Hospital) of a relatively underdeveloped states in India) is tabulated under APPENDIX – B.  The following graph (Graph - 8), on plotting the hypothetical data (as given in APPENDIX – B), along the following two axis are analyzed under the following points. 

Graph – 8: Classification of Facilities based on Patients with CD and NCD Cases 

 

 

 

Table – 1: Suggested Directives based on Classification of Facilities 

 

 

 

1. 1) Assumptions for the analysis are as under: - 

1. a) CD and NCD Cases Observed in Facilities are considered as independent variables. 

2. b) Other than variance of CD and NCD all other factors in selected facilities are expected to be equal. 

3. c) The Facilities taken under consideration are representative of occurrences of CD and NCD cases in the geography (say boundary of related Municipality) 

4. d) All of the cases reach to private or government facilities and report on medical conditions when health related complaints are experienced. 

5. e) Patient follow medical advice given by recognized professional healthcare experts in facilities. 

6. f) The suggested directives are very generic (for just illustration of a way to look into complicated issue of health service provisioning) and before taking action, much more detailed facility wise, geography wise socio-economic condition wise health indicators may need to be considered. 

7. g) Classification of Patients as CS or NCD is accurate and based on diagnosis by authentic and authorized healthcare professional. 

2. 2) In Graph – 8, facility wise cases of CD and NCD as registered in the imaginary facilities with imaginary data (as shown in APPENDIX – B) are plotted with CD in X-Axis and NCD in Y Axis. The numbers of cases of CD and NCD plotted are the average monthly cases diagnosed in respective hospital for a given time period (say JAN 2022 – MAY 2024). Even though in this blogpost, representative higher level facility wise CD and NCD data are plotted. Similar analysis can be taken-up for geographical regions (like Blocks, Districts, Division), once complete digitization of all the facilities under a geography is achieved as this may be more useful to health management team (or health administrative offices). 

3. 3) Type A Facilities: Classification of Region A in Graph -8 represents facilities with High CD and High NCD cases diagnosed for the given time period on monthly basis. 

4. 4) Type B Facilities: Classification of Region B in Graph -8 represents facilities with Low CD and High NCD cases diagnosed for the given time period on monthly basis. 

5. 5) Type C Facilities: Classification of Region C in Graph -8 represents facilities with Low CD and Low NCD cases diagnosed for the given time period on monthly basis. 

6. 6) Type D Facilities: Classification of Region D in Graph -8 represents facilities with High CD and Low NCD cases diagnosed for the given time period on monthly basis. 

7. 7) In Table – 1 (as above), an attempt to suggest directives for immediate actions based on Type of Facilities (as defined in points 3 to 6 above). These are broad and indicative suggestions articulated to assist Health Sector Management with one of the possible ways to organize thoughts for addressing challenges of CD and NCD prevalence. Suggestions are briefly discussed under following headings: - 

1. A) Institutional Healthcare 

2. B) Community Healthcare 

3. C) Insurance Cover  

4. D) Request to Local Municipality / Local Body 

 

APPENDIX – A

 

APPENDIX – A: Some more illustrations of Performance of China and India on Healthcare 

May have a look at the following graphs in relation to some of the selected countries in Asia (including India and China). 

Graph – 4: Health Expenditure as percent of GDP of selected Asian Countries during 2000 - 2022 

 

 

 

 

Graph – 5: Government Health Expenditure as percent of Total Health Expenditure in selected Asian Countries during 2000 - 2022 

 

 

 

 

Graph – 6: Health Expenditure per Capita in USD for selected Asian Countries during 2000 - 2022 

 

 

Graph – 7: Out-of-Pocket Health Expenditure as Percentage of Total Health Expenditure for selected Asian Countries during 2000 - 2022 

 

 

 

 

It turns out that since 2000, Health Expenditure (w.r.t. GDP) in China has been far more than India (Graph – 4). This indicates that people in China (and government of China) care more about health and preventive health in comparison to India.  

But, interestingly, percentage values for Government Health Expenditure (with respect to Total Health Expenditure) were close for both India and China until year 2000 (Graph – 5). However, in the years following 2000, China outperformed India on this particular indicator. Similarly, the two countries were almost at the same levels on the indicator “Health Expenditure per Capita” around the year 2000 but China kept on surging ahead on this parameter in the following years (Graph – 6) and it was able to do so with decreasing contribution of Out-of-Pocket expenditure (Graph – 7) during last decade. 

It seems Chinese government spent more on healthcare out of the revenues generated through ongoing surge in economic growth. It may be noted that China observed surge in economic growth since mid-nineties, which eventually started to get significantly high from around year 2000 (refer – Graph – 3). Probably this (increased spending on healthcare) was giving synergic effect to the growth of Chinese economy as the 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 - eurohealthobservatory.who.int)  

In a nutshell, Graph – 5 (growing health spending), Graph -6 (growing health spending), Graph – 2 (consistently increasing population) and Graph – 7 (which shows sharp decline in Out-of-Pocket expenditure by Chinese residents in last decade) collectively indicate that healthcare services availed through public funding has been effective in China, despite consistently rising population. This doesn’t seem to have happened for India so far. 

 

APPENDIX – B

APPENDIX – B: Facility Wise Monthly Average of CD and NCD Patients during a given Time Period (example - JAN 2022 – MAY 2024) 

 

Monthly Average of CD and NCD Patient Visits Respective Facilities for Period JAN 2022 - MAY 2024
Facilities	CD (Average Numbers)	NCD (Average Numbers)
F1	4310	13942
F2	17762	2899
F3	9186	12290
F4	10917	7855
F5	11634	1053
F6	15751	6520
F7	7603	1367
F8	6681	16702
F9	19705	14537
F10	15016	10679
F11	14208	15735
F12	17066	7149
F13	18711	13643
F14	13076	2806
F15	7728	12727

 

  

 

/*******************************/