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Friday, July 03, 2020

Legal and regulatory framework for laboratory testing in India: A case study for Covid-19

By Harleen Kaur, Ameya Paleja, and Siddhartha Srivastava.

Testing is central to understanding the spread of the SARS-CoV-2 virus at an individual & population level and designing suitable interventions (Shah, 2020). As of June 23, 2020, India has the fourth-largest number of SARS-CoV-2 cases worldwide. This is despite having conducted only 119 tests per million people. In comparison, the United States and Russia, countries with more cases than India have conducted 1518 and 2074 tests per million respectively. While India has somewhat improved its testing rate since the early stages of the SARS-CoV-2 pandemic (21 per million on April 24), we are still unable to test in adequate numbers. In this blog, we study the reasons behind insufficient testing rates in India by reviewing the legal environment for regulating medical testing.

The Indian diagnostics industry is dominated by the private sector. The legal framework for regulation of private labs is set up under the Clinical Establishments Act, 2010. The issues of non-standardisation of service quality and supplier-induced demands are prevalent in the industry (Competition Commission of India, 2018). Therefore, these labs have been functioning under market-led and self-imposed norms. The government did not depend on this regulatory framework during the SARS-CoV-2 pandemic. Instead, it granted unchecked discretionary power to the Indian Council of Medical Research (ICMR) to regulate the testing strategy. Under the regulatory framework set up by the ICMR, the private lab network is not being utilised optimally for SARS-CoV-2 testing. For instance, the private sector accounts for about 70% of the health care market in India. As of June 22, 2020, only 27% of all labs approved for SARS-CoV-2 testing in India are private labs. In this article, we argue that; i) the private labs are governed by a weak regulatory framework that has allowed market failure to persist in the diagnostics sector in India, and ii) the testing strategy mandated by the ICMR for SARS-CoV-2 pandemic has led to poor outcomes with respect to the participation of private labs. Hence, there is an immediate requirement for reviewing the powers of ICMR for managing the testing strategy and a long term requirement for rethinking the present regulatory framework for labs.

Concerns about market failure in the field of medical testing

A market failure occurs when the free market is unable to obtain efficient economic outcomes. Of the four types of market failures, viz; externalities, asymmetric information, market power, and public goods, the diagnostics sector in India is seen to be affected primarily by information asymmetry. Information asymmetry or information inequality occurs when one party such as a physician possesses much greater information than the other, a patient (Arrow, 1963). During a pandemic, testing becomes a crucial part of a nation's public health strategy and hence, the public goods element of market failure also comes into play. For instance, testing data is a public good in as much as it is useful to understand the spread of the disease in an area that helps the government to design public health policies, and sharing of such data by the government affects behavioral changes in individuals.

As a result of information asymmetry, the field of medical testing in India faces the recurring issue of quality control and standardisation of services. For instance, practices such as hiring unqualified professionals, using sub-standard equipment, and proxy digital signatures have become prevalent in the industry in the absence of effective regulation. In extreme cases, there have been instances of private labs disbursing 300-400 diagnostic reports within a matter of hours, often without conducting any testing at all.

The free market does not solve the issues of market failure on its own and requires state intervention. This can be done through effective regulation of the market either by itself or through State coercion. We now study the existing regulatory framework for labs in India and its limitations.

Regulation of diagnostic labs

Health care is a state subject under the Indian Constitution. This means that in the usual course of events, states have exclusive powers to make laws concerning different aspects of health care such as diagnostic laboratories. Article 249 of the Constitution provides exceptional powers to the union government to make laws on state subjects in the national interest. For such matters, the states retain the power to accept or reject the union law. The Clinical Establishment Act has been passed by the union government under this provision and 11 states have enacted it as of now. However, there are two difficulties with the law which have created a gap between aspiration and outcome. First, under our constitutional arrangement the Clinical Establishments Act is only applicable to those states that choose to adopt it, and only 11 states have adopted this law. Second, the law has serious difficulties in design and implementation.

In the 11 states where the Act is present and implemented, the regulatory function is limited to granting registration to labs and maintaining a register of clinical establishments. The labs interact with the regulatory authority only at the time of registration when they submit evidence of having complied with the prescribed standards for registration to the regulatory authority. Once a permanent registration is granted, there is no mechanism to review the functioning of the labs or provide grievance redressal to patients under the Act. If a person starts a lab without registration, the maximum punishment under the law is a monetary penalty of rupees five lakhs.

Other than the Clinical Establishements Act, private labs have to comply with the standard regulatory requirements under the state Shops an Establishments Act (relating to hours of work, cleanliness, holidays, etc.) and obtain registration under the provisions of the Biomedical Waste Management Rules, 2016. Additionally, diagnostic kits and reagents used by labs are defined as 'drugs' under the Drugs and Cosmetics Act, 1940, and therefore have to be approved by the Central Drugs Standard Control Organisation (CDSCO).

We see that there is effectively no legal framework for regulating private labs in India. The labs only comply with allied regulatory requirements such as disposal requirements for biomedical waste and approval of diagnostic kits under the Drugs and Cosmetics Act. Given this regime, two mechanisms namely accreditation and public procurement have sought to fill the regulatory void in the diagnostics industry.

Alternative methods of regulation

In the absence of an overarching law that assures the quality of clinical establishments, private labs have turned to voluntary accreditation for establishing credibility in the vast diagnostics market. Accreditation of labs is not mandatory in India. The National Accreditation Board for Testing and Calibration Laboratories (NABL), an autonomous body under the Quality Council of India, prescribes accreditation criteria for various kinds of labs. Of the estimated 100,000-110,000 labs present in India, around 4000 have NABL accreditation. Some labs prefer obtaining certifications from international accreditation bodies in addition to obtaining NABL accreditation. Accreditation helps in assuring the quality of labs to the public as well as the government.

The second method to ensure quality standards and avoid market failure is public procurement. The government has dealt with the absence of a regulatory framework in the past by using contractual mandates to avail the services of private labs. The standards expected from these labs are contractually specified by the government while entering into public-private partnership (PPP) agreements for diagnostics. For instance, the union government under the National Health Mission (NHM) has a Free Diagnostics Services Initiative which contains detailed requirements from diagnostic/pathology labs. NABL accreditation is one of the common requirements for private labs to participate in such government programmes.

To compensate for weak regulation under the Clinical Establishments Act, voluntary accreditation by the NABL and public procurement through PPP agreements have acted as alternative strategies for regulation. These alternatives help in reducing information asymmetry and assuring the quality of services to the public and could have played an important part in the regulation of the labs for SARS-CoV-2. Yet, we find that the government strategy for medical labs for SARS-CoV-2 is based on a command-and-control approach under ICMR.

Regulation of medical labs for SARS-CoV-2

Under the existing regulatory framework, private labs did not have to follow any criteria or adhere to any standards before starting a new/novel test, such as the SARS-CoV-2 test. This means that patients would have been able to get SARS-CoV-2 tests done in any private lab offering the test using reagents/test kits approved by the CDSCO and having a valid bio-waste and other local licenses.

The lack of a regulatory framework led to confusion regarding the role of private labs in the response to the SARS-CoV-2 pandemic. As a result, the government set up an emergency regulatory framework for the SARS-CoV-2 crisis using provisions of the Epidemic Diseases Act, 1987, and the Disaster Management Act, 2005. Using these laws, it appointed the Indian Council of Medical Research (ICMR) as the apex decision-making body for India's diagnostic testing strategy through the MoHFW (see notifications here and here).

The Epidemics Act authorises the state governments to take exceptional measures and prescribe regulations to contain the spread of a dangerous epidemic disease. It lists a set of basic subjects for which regulations may be made such as travel restrictions, examination and quarantine of suspected cases, and inspections of any ship or vessel leaving or arriving at any port of call. The role of the union government under this law is limited to managing epidemic diseases at ports.

The Disaster Management Act contains an administrative framework for disaster management. Section 6 of the Act sets up the National Disaster Management Authority (NDMA) as a nodal body for disaster management. Any directions issued by the NDMA and the union government must be followed by the Union Ministries, State Governments and State Disaster Management Authorities. The SARS-CoV-2 pandemic has been notified as a disaster under this Act. Under this, the government has passed various directives on different aspects of the SARS-CoV-2 response using the umbrella clauses of this legislation such as section 6(2)(i) (The NDMA may lay down the policies, plans and guidelines for disaster management) and Section 10(2)(l) (The National Executive Committee may lay down guidelines or give directions to union ministries, state governments and state authorities for responding to the disaster) have been invoked to respond to the SARS-CoV-2 crisis.

Using the powers granted to it by the government, the ICMR has placed severe restrictions on private labs to test for SARS-CoV-2. These restrictions include requiring approvals from ICMR for lab facilities, commercial testing kits, and cost-capping for testing. We now study the ICMR decisions on testing strategy in detail to understand its role in the testing outcomes for SARS-CoV-2.

The role of the ICMR

The ICMR has been responsible for the regulation of public labs under a 2012 scheme called the Viral Research and Diagnostic Laboratories (VRDL) network under the MoHFW. The scheme was initiated to increase government capacity for the timely detection of emerging/re-emerging viral diseases. The VRDL labs were exclusively responsible for testing in the initial phase of the SARS-CoV-2 pandemic in India.

The initial advisories issued by the ICMR contained no mention of private labs and focused only on directing public labs to undertake SARS-CoV-2 testing. At the time, some state governments explicitly banned private labs from testing as per their regulations issued under section 2 of the Epidemic Diseases Act, 1897. For instance, the Delhi Epidemic Diseases COVID-19 Regulations, 2020 and the Bihar Epidemic Diseases COVID-19 Regulations, 2020 contain the following provision on testing of potential SARS-CoV-2 cases by private laboratories:

"No private laboratory has been authorised to take samples for COVID-19 in the State. All such samples will be collected as per the guidelines of the Government of India..."

Subsequently, the ICMR issued guidelines for private labs to undertake SARS-CoV-2 testing on March 21, 2020. Since then, the ICMR has been responsible for approving private labs to test for SARS-CoV-2. The ICMR conducts checks on the capability of private labs to test for SARS-CoV-2 and updates the list of approved private labs regularly. It also issues detailed guidelines for other aspects of testing such as procurement of reagents, evaluation of commercial testing kits, etc. In doing so, it has usurped the regulatory functions of existing statutory regulators such as the CDSCO, as well as voluntary bodies like the NABL. For instance, while diagnostic kits for SARS-CoV-2 are considered "drugs" and should be approved by the CDSCO, they also require validation by the ICMR. Similarly, NABL approved private labs are required to get a mandatory clearance from ICMR for SARS-CoV-2 testing. This means that while NABL has accredited 278 labs for RT PCR RNA testing for SARS-CoV-2, the ICMR has approved 258 of these labs for testing as of June 21, 2020. The ICMR does not document the rationale or process of performing these regulatory functions. The Epidemics Act and the Disaster Management Act do not require the ICMR to adhere to minimum standards of accountability, transparency, and public engagement. The invocation of these laws to empower the ICMR means that there is no coherent or intellectually defensible framework for reviewing the ICMR's actions during the pandemic except that the basic rule of law principles are followed by it.

Building state capacity for regulation is a gradual process that requires backing by a comprehensive legal framework (Roy et al, 2018). ICMR was abruptly thrust into a role for which it did not have the required organisational or procedural capacity. Hence, it compensated for the lack of a regulatory framework by issuing strict command and control orders. We see that after being appointed as the government regulator for the testing strategy for SARS-CoV-2, the ICMR barred all private labs from testing unless approved by it. Given that the labs are already approved by NABL, the rationale for re-approval for testing of private labs by ICMR was never shared. Additionally, ICMR started regulation of reagents, test-kits and costs of tests. This has had an adverse impact on the testing outcomes as seen below.

Implications of regulation of private labs by ICMR

ICMR has been responsible for advising on the SARS-CoV-2 testing strategy for the country. The restrictive policies by the ICMR have led to the inaccessibility of the tests for a vast population. As a result, various courts in India are being involved to challenge such policies.

In April, the Supreme court heard the issue of cost-capping of lab testing for SARS-CoV-2 by ICMR and ordered that the tests shall be free for persons falling under government schemes such as Ayushman Bharat or any other category of economically weaker section of the society as notified by the government. The ICMR cost-cap of INR 4500 per test for private labs was not examined by the court in this petition, but it emphasised on the need for affordable tests to the population.

The Delhi high court reviewed the cost fixed by ICMR for the procurement of rapid testing kits in April. It held that the costs at which ICMR procured the kits had an unduly high profit-margin for the vendors and ordered the cost per kits to be reduced from INR 600 to INR 400. Furthermore, the kits procured by ICMR were later found to be faulty. The court criticised the government and ICMR for low testing of SARS-CoV-2 cases in another order dated June 18, 2020. It ordered the government to review ICMR policies on labs such as the protocol for sample collection, approval of labs, data sharing by labs, and costs per test through an existing government committee.

The Gujarat high court is monitoring the state response to SARS-CoV-2 under a suo-motu writ petition. Under this petition, in an order dated May 29, 2020, the court modified the ICMR guidelines on testing for different categories of patients as it found the patient categories to be non-exhaustive. The court has also decided to review the rationale behind the ICMR SARS-CoV-2 testing strategy.

The ICMR has been criticised for its advisories on the evolving SARS-CoV-2 testing strategy by experts. For instance, its restrictions on the usage of RT-PCR and rapid antigen testing are seen to be unreasonable as the testing capacity has been increasing over time. Additionally, the issue of lack of transparency in sharing testing data and its regulatory procedure makes ICMR decisions difficult to understand and implement.

The ICMR policies regarding the testing strategy for SARS-CoV-2 are restrictive for private labs. This is indicative of a trust-deficit between ICMR and the labs. The ICMR regulatory strategy to reduce this trust-deficit is to micromanage every aspect of testing sought to be done by the private labs. This has led to lower participation of such labs in testing for SARS-CoV-2 and issues of unavailability of tests to the public.

Conclusion

The bulk of the health care services in India are provided by the private sector despite the presence of public health care facilities (Hooda, 2015). Recognising the growth and demand of the private sector, the policy framework in health has gradually shifted from the government providing health care services to being a financier of these services (Patnaik et. al, 2018). Recently, the Indian government conceded before the Supreme Court that the testing capacity of the public sector for SARS-CoV-2 is insufficient.

In this article, we studied the regulatory framework with respect to medical laboratories in India. We find that in the regular course of events, the Clinical Establishment Act, 2010, and the rules thereunder are responsible for such regulation. Issues with the adoption and implementation of this Act leave the sector effectively unregulated. Despite the presence of some alternative methods of regulation, the regulatory gap in the diagnostic sector persists. Therefore, there is a need for a comprehensive law to deal with the market failure of information asymmetry and public goods. However, the enactment of such a law is a long-term deliberative process and should not be attempted in the face of a pandemic.

For SARS-CoV-2 testing, the government has deviated from the existing course of minimal intervention in regulating private labs to regulating every aspect of testing through the ICMR. Government laboratories set up under the VRDL framework were initially the exclusive bodies allowed to test for SARS-CoV-2. While private labs have now been allowed to test for SARS-CoV-2, they are still heavily regulated by the ICMR. The rationale for this approach has not been provided. We believe such an approach is unsuitable for managing the SARS-CoV-2 pandemic. Using the broad powers given to it, the ICMR has reduced the capacity for testing in India by introducing prescriptive testing guidelines, licensing requirements, and cost-capping. This has resulted in non-utilisation of a bulk of the testing capacity for SARS-CoV-2 in India so far. Therefore, we suggest that the power given to the ICMR for SARS-CoV-2 regulation be minimised by specifically disallowing any duplication of regulatory functions already being performed by bodies such as CDSCO and NABL. Further, for the powers delegated to ICMR for regulating the testing strategy, due process requirements such as documenting the rationale, public consultation, sharing of public data should be mandated by the government to increase the accountability of ICMR.

References and further reading:

Arrow, 1963: Kenneth J. Arrow, Uncertainty and the welfare economics of medical care The American Economic Review, December 1963.

Nandraj, 2012: Sunil Nandraj, Unregulated and Unaccountable: Private Health Providers, Economic and Political Weekly, January, 2012.

Srinivasan, 2013: Sandhya Srinivasan, Clinical Establishments Act, 2010 Regulation and the Medical Profession, Economic and Political Weekly, 19 January, 2013.

Hooda, 2015: Shailendra Kumar Hooda, Private Sector in Health Care Delivery Market in India: Structure, Growth and Implications, Institute for Studies in Industrial Development, Working Paper 185, December, 2015.

Patnaik et. al, 2018: Ila Patnaik, Shubho Roy, and Ajay Shah, The rise of government funded health insurance in India, NIPFP Working Paper Series, No. 231, 21 May 2018.

Roy et al, 2018: Shubho Roy, Ajay Shah, B. N. Srikrishna, and Somasekhar Sundaresan, Building State capacity for regulation in India NIPFP Working Paper Series, No. 237, 3 August, 2018.

Competition Commission of India, 2018, Policy Note: Making markets work for affordable health care, Competition Commission of India, October, 2018.

Kelkar and Shah, 2019: Vijay Kelkar and Ajay Shah, In service of the Republic: The Art and Science of Economic Policy, Penguin Allen lane, December 2019.

Shah, 2020: Ajay Shah, More testing: From concept to implementation, The Leap Blog, 06 April, 2020.


Ameya Paleja is a molecular biologist and science blogger based in Hyderabad. Harleen and Siddhartha are researchers at NIPFP. The authors are thankful to Ajay Shah, Renuka Sane, Amrita Agarwal, Smriti Parsheera, Shubho Roy, Anand Prakash, Arjun Sinha, and three anonymous referees for their valuable comments.

Wednesday, July 01, 2020

Covid-19 and Corporate India

by Aakriti Mathur and Rajeswari Sengupta.

India is dealing with a massive shock in the form of the Covid-19 pandemic. The first case was reported in India on 30 January, 2020. By middle of March the disease had begun spreading rapidly across the country. To prevent the spread of the virus the Indian government announced a nationwide lockdown on 24 March. The pandemic and the lockdown affected nearly all firms and sectors of the economy; however, there are likely to be significant heterogeneities.

We propose a novel approach to identify firms that may have had greater exposure to the pandemic even before it assumed serious proportions in India, by virtue of, for example, their connections to other affected countries, among others. These firms may have fared worse when the lockdown was announced. We also examine the pre-pandemic balance sheet characteristics that may have worsened the impact of the lockdown on some firms compared to the others.

Analysing earnings call reports

We propose the use of earnings call transcripts as an important source of information for gauging a firm's fundamental exposure to the pandemic.

Earnings calls typically follow the presentation of a firm's quarterly results. These calls are attended by senior management of the firm (for example, the CEO, CFO, MD, etc), who present short prepared remarks, and then open the floor to questions from analysts. This implies that the calls are more spontaneous as compared to say the firm's annual report, because the senior management answers questions on the fly from the audience. These reports therefore convey not just fundamental financial information, but also analysts' and managers' opinion about the firm (Borochin et al., 2018).

A significant part of the literature focuses on the tone and sentiment of these reports and their implications for stock market returns, trading volumes (Frankel et al., 1999; Bushee et al., 2003, 2004; Brown et al., 2004), and options pricing (Borochin et al., 2018). Our work closely relates to two recent papers, Hassan et al (2020) and Ramelli and Wagner (2020). Both these studies use the information contained in earnings call transcripts. Hassan et al (2020) focus on globally listed firms, and study whether firms that were more exposed to previous disease outbreaks such as SARS and MERS were better prepared for the 2020 pandemic, and therefore had higher equity returns, than those who were not. Ramelli and Wagner (2020) analyse characteristics of US firms that explain both their stock market performance between January and March 2020 and their discussions of Covid-19 in the earnings transcripts.

Unlike these papers, we use the information in earnings call reports, to measure fundamental exposure of Indian firms to the pandemic. We are interested in using this information to study the equity market performance of the firms around the largest, most stringent lockdown announced in the world (at the time). Our analysis complements earlier work by Sane and Sharma (2020) who calculated the liquidity cover of listed firms in India in the face of large revenue shocks during the pandemic and Bansal et al. (2020) who also examine variations in the market valuation of firms on account of firm-specific characteristics during the pandemic. In this work, we take a more holistic view of firm-level vulnerabilities, examining the fundamental exposure to the pandemic, as well as the role of financial flexibilities, including liquidity. We also focus on one specific event -- the 24 March lockdown -- in order to obtain greater precision.

We focus on earnings calls conducted by firms in January and February 2020, when the case load was still low in India but the pandemic had begun spreading in other countries. These are calls discussing the income statements of October-December, 2019 (Q3 FY20) and January-March, 2020 (Q4 FY20) respectively, of the Indian financial year.

When India reported its first case of the Covid-19 pandemic on 30 January, 2020, close to 7,700 people had been infected all over the world, the majority being in China. Other countries such as the US, Australia, Germany, Japan, South Korea, UAE and HongKong had started reporting Covid-19 cases. By end February, it had morphed into a full blown public health crisis. The total number of infections globally had risen to more than 83,000, with a death toll of more than 2,800. While the disease was spreading rapidly in countries such as Italy, South Korea, France, the US and Iran, these were still early days of the pandemic in India which had less than 10 confirmed cases.

Focusing on the call reports of Jan-Feb 2020 enables us to analyse the firms' exposure to the pandemic at a time when the disease was still at a nascent stage in India unlike say March when the spread of the pandemic had begun affecting most firms. It also allows for easier identification of firm exposure because it is not muddled by domestic policy interventions. For example, there were only 13 Covid-19 related notifications issued by the Indian government in February, compared to 266 in March, as listed by PRS Legislative Research. Hence, from March onwards, the stock market performance of all firms was likely to be affected by these interventions over and above firm-specific concerns around the disease itself.

We start with a sample of the largest listed firms on the Nifty500 index of the National Stock Exchange (NSE) of India. Of the 500 firms in the index, we have access to the call reports of 196 firms in January-February 2020, and of 90 firms in April-May 2020.

Which firms had exposure to Covid-19 in Jan-Feb 2020?

We interpret the number of times a firm mentioned Covid-19 related words in its call reports as an indicator of its exposure to the pandemic. Accordingly, we count the number of times Covid-19 and related words (such as "coronavirus", "pandemic", "ncov", "sarscov", "epidemic" etc) are mentioned in the quarterly earnings call reports of the 196 firms in January-February 2020, and also of the 90 firms in April-May 2020 for the sake of comparison. We briefly summarise our findings below.

  • Only one-third of the firms in our Jan-Feb sample mention Covid-19 or related words. The average number of times these words are mentioned is three. Only three of the firms discussing the pandemic are in the financial services sector.
  • All 90 firms in the Apr-May 2020 sample mention Covid-19 or related words, demonstrating the extensive spread of the disease by this time. The average occurrence of the words per report is ten times higher, close to 31. This reflects our earlier concern that from March onwards, all firms had become exposed to the pandemic.
  • Even in Jan-Feb 2020, there were sector-wise heterogeneities in Covid-19 discussions, as shown in figure 1 below.
  • The occurrences of Covid-19 related words were higher in those sectors which presumably have more fundamental exposure to the pandemic, for example in the form of supply-chains with China or other early-affected countries. Some of these sectors are pharmaceuticals, consumer goods, automobile, chemicals etc.
  • Firms in health care services, financial services, media and entertainment, power and telecom industries either did not mention or mentioned much less pandemic related words during this period in their call reports. With the possible exception of health care, these sectors were likely to be affected due to indirect exposure to the pandemic.

Figure 1: Sector-wise occurrences of Covid-19 related words in Jan-Feb call reports

We also look at the firms that mentioned "supply", "demand" and "uncertainty" related words in context of the Covid-19 discussion in their call reports. These are likely to be the most common channels of disruption faced by the firms during the pandemic. In results not reported here we find that firms in sectors with higher than average mentions of Covid-19 related words also had higher than average mentions of "supply" related words in the sentences where Covid-19 was discussed. For firms in the services sector, mentions of "demand" related words in the context of the disease were higher. For all the sectors having higher than average mentions of Covid-19 related words, we also find significantly higher mentions of "uncertainty" related words in the context of the pandemic.

This preliminary analysis gives us an idea of which firms and sectors had greater exposure to the pandemic as early as Jan-Feb 2020 when the disease still hadn't spread in India.

For subsequent analysis, we consider the firms that mention Covid-19 in Jan-Feb 2020 as our "treated" sample and those that did not discuss the pandemic as our "control" sample. A relevant question to ask is how similar are the "treated" and "control" samples in terms of their key balance sheet characteristics. Using annual data from the pre-pandemic period (ending in March 2019) from the Prowess database of CMIE, we compare the two sets of firms in size, age, profit, foreign exchange earnings, inventories, cash balances etc. For ease of comparison, we drop the three firms that are in the financial services sector and that mentioned pandemic related words in the Jan-Feb call reports. As shown in table 1 below, we do not find any major difference between these two groups of firms, except that the "treated" firms are on average older and hold higher inventories than the "control" group firms.

Table 1: Summary statistics for non-financial firms: Data as of March 31, 2019
No. of firms with no COVID mentions in Jan-Feb 2020No. of firms with COVID mentions in Jan-Feb 2020
9660
VariableMean of firms with no COVID mentionsMean of firms with COVID mentions
Age3543.7
Log Size11.1911.13
Leverage (Debt/Assets) 0.150.16
PBDITA/Total Sales0.260.23
FX Earnings/Total income0.300.27
Cash and Bank balance/Total Assets0.070.05
Trade Receivables/Total Assets0.140.13
Inventories/Total Assets0.090.12
Operating Expenses/Total Income0.760.78

What kind of exposure did firms have to the pandemic in Jan-Feb, 2020?

We next analyse the context within which the firms discussed the pandemic in their call reports, for example, references to supply-chains, demand disruptions, or uncertainty due to the pandemic and so on. This will give us a sense of the kind of exposure the firms may have had to the pandemic in the early part of 2020.

We use the techniques applied in Mathur and Sengupta (2019). For every firm's call report, we first isolate the sentences that contain Covid-19 related words. There are 176 sentences in total for the Jan-Feb 2020 reports. Then, we create a word cloud with the most frequently occurring words in these sentences, after stripping out stop-words and other uninformative words.

The word cloud for Q3 FY19-20 reports is shown in figure 2. The size of each word is directly proportional to its frequency in the sentences. We plot the 50 most frequently occurring words. All the coronavirus related words, which are the most common words in these sentences by construction, are not plotted here for ease of comprehension.

  • The words "china" and "impact" occur most frequently indicating that firms were talking about the origin of the coronovirus disease and its effect.
  • We also see words related to areas where the impact of the pandemic was potentially anticipated or the expected transmission channels of the disease such as "earnings", "shipping", "pharma", "macro", "supply chain", "trade", "logistics", "imports", "demand", "supply", "prices" etc.

Figure 2: Word clouds of sentences with Covid-19 related words in Jan-Feb call reports

Which firms were more affected by the 24 March lockdown announcement?

The 24 March lockdown in India was regarded as one of the most severe lockdowns in the world, based on data from the Oxford COVID-19 Government Response Tracker. All transport services, except those for essential personnel, were suspended, in addition to all educational, commercial, and private establishments (see here). The lockdown affected all sectors of the Indian economy. The stock market reacted negatively overall. This is not surprising, since stock prices reflect changes in expected future cash flows and/or discount rates. However it is possible that some firms were more affected than the others depending on their exposure to the pandemic as well as pre-pandemic characteristics.

To measure the differential, cross-sectional responses of firms to the lockdown announcement, we use high-frequency stock market data and an event study methodology. We have two main hypotheses.

Our primary hypothesis is that firms that were more exposed to the pandemic and mentioned Covid-19 in their earnings call reports in Jan-Feb 2020 (the "treated" group) fared worse than the "control" group when the lockdown was announced.

  • If investors believe that firms who discussed Covid-19 and its implications for their businesses early on in the year are more exposed to the virus, for example due to supply chains with China, or factories in badly-affected countries like Italy, then they would revise their expectations of future profitability downwards in response to the lockdown. Therefore, we would see that treated firms as a whole perform worse than control firms.
  • If investors believe that early discussions of the pandemic implied that these firms were better prepared to weather the storm, then their returns would be better than those that seemed to have been caught "off-guard". We hypothesise that the former is likelier than the latter, since it is not clear how firms could have unilaterally prepared for the over-arching extent of the shock (such as to demand disruptions) just a couple of months in advance.

Our second hypothesis is that low-profitability firms with higher share of foreign exchange earnings, higher share of inventories, greater dependence on trade credit and higher operating expenses should have witnessed lower stock market returns when the lockdown was announced, compared to more domestically oriented firms which were more profitable, were holding lower inventories, had lower dependence on trade credit and also lower operating expenses.

Estimation strategy

We use a difference-in-difference strategy to estimate the impact of the lockdown event on firms' stock market returns. We consider all "treated" firms as one group by using a dummy ("Covid dummy"). Our dependent variable is the cumulative abnormal stock market returns (CARs) for each firm over a window of (-1, +2) days around the lockdown event, i.e. between 23 March (Monday) and 26 March (Thursday). To obtain these abnormal returns, we estimate a market model (i.e. controlling for movements in the Nifty50 index), as shown in the equation below, over a period of 81 days prior to March 24. More specifically our window starts 91 days prior to the lockdown and stops 11 days before the lockdown. The model specification is:

$$\text{Daily firm returns}_{firm,t} = \alpha + \beta~\text{Daily Nifty50 returns}_{t} + \epsilon$$

The advantage of using a tight window around the event is that it better accounts for anticipation effects and other confounding factors. We use a cross-sectional ordinary least squares regression shown in the equation below, to regress the firm-specific CARs on the "Covid dummy" and on a host of balance sheet variables. Among the regressors, of particular interest is the "Covid dummy" which tells us the difference in CARs between the "treated" and the "control" firms. Other regressors include the balance sheet variables shown in table 1 above as well as dummy variables for the sectors that the firms belong to. Firm level annual balance sheet variables are as of March 31, 2019.

$$\text{CARs around event window}_{firm} = \alpha_{sector} + \beta~\text{Covid Dummy}_{firm} + \\ \log(age)_{firm} + \log(size)_{firm} + Controls_{firm} + \epsilon $$

Results

We summarise our results in Figure 3. In panel (a) we plot the results from our baseline model (model 1) which includes only age and size of firms, and the sector dummies, over and above the Covid dummy. We also plot the results from models 2 to 6 where in addition to the Covid dummy, age, size, and sectors, we sequentially add the regressors of interest: profit, FX earnings, inventories, operating expenses, and trade receivables. We also investigate the role of cash, leverage, borrowing composition, and tangible assets. Here we only report results that are significant at 90% confidence interval. We list our main findings below.

  • In all our specifications, the stock returns of "treated" firms, i.e. those that mentioned Covid-19 in their call reports early on in 2020, significantly underperform (at the 90% confidence level or more) the "control" firms. On average, returns of the "treated" firms are roughly 3.5 percentage points lower.
  • We find that the equity returns of more profitable firms outperformed those of less profitable ones by 9 percentage points (model 3). Higher profitability implies higher ability to withstand large revenue shortfalls.
  • Firms with a higher share of foreign exchange earnings in their total income performed worse. They were likely to be more affected due to supply and demand disruptions in the rest of the world.
  • Firms with high inventories saw 24 percentage points lower returns. High share of inventories in total assets might make it difficult for firms to get rid of their inventories once an economywide lockdown is announced yet they would have had to incur the costs of maintaining these inventories which makes them worse off than firms with lower share of inventories (Banerjee et al., 2020).
  • Firms with higher pre-pandemic trade credit reliance saw significantly lower abnormal returns. This is likely because in a broad-based crisis such as this one, credit markets are likely to freeze along both extensive and intensive margins. Thus, rolling over existing trade credit as well as obtaining new supply of trade credit would be difficult. (Banerjee et al., 2020).
  • Firms with higher pre-pandemic operating expenses also fared worse once the lockdown was announced. Operating expenses are typically short term expenses. In absence of steady revenues in a lockdown, firms would depend on credit from the financial system to meet these expenses. During a crisis if the financial system is unwilling to offer short term credit (Sengupta and Vardhan, 2020), then these firms are likely to witness lower stock returns.

In figure 3, panel (b), we plot the coefficients on the sector dummies from the baseline model 1, with only age and size included as controls. Automobiles is the benchmark sector. We find that stock returns of more consumer facing sectors (textiles, media and entertainment) and those that rely on supply chains (metals, and oil and gas) did particularly badly when the lockdown was announced. On the other hand, healthcare services in particular outperformed as compared to automobiles.

Figure 3, panel (A): Explaining cumulative abnormal returns around first lockdown (24 March, 2020)

Figure 3, panel (B): Sector dummies from baseline regression

In a nutshell, we find that when the nationwide lockdown was announced on 24 March, firms who mentioned Covid-19 in their earnings calls in early-2020 and hence were more exposed to the pandemic, fared worse than firms who did not discuss the pandemic. This result holds when we account for the sectors and key balance sheet characteristics of the firms.

As discussed in Fahlenbrach et al.(2020), less financially flexible firms are less able to withstand large negative shocks to their revenues, which translates to worse equity market performance. Lower cash, lower profitability, lower diversification in earnings (e.g. higher reliance on foreign exchange revenues) or in borrowing sources (e.g. higher reliance on trade credit) can all be considered indicators of low financial flexibility. In other words, firms that had lower financial flexibility in the pre-pandemic period were worse affected when the lockdown was announced.

We further find that controlling for mentions of "supply" and "demand" related words (not shown here) in the firms' call reports -- which may account for the nature of their exposure to the pandemic -- does not change the results qualitatively, and makes them stronger in some specifications.

Firms with more cash holdings reported higher returns on average around the lockdown announcement, but this effect is not significant (hence, not reported here). In further tests, we find some evidence of non-linearities. Firms with above-median cash holdings significantly outperform their counterpart.

Conclusion

Using the informational content of earnings call reports of some of the largest, non-financial firms in India we throw light on the firms and sectors that may have been more exposed to the pandemic as early as January and February 2020 when as per the official statistics, the disease had still not spread in India. We find that these firms were also worse affected by the announcement of a nationwide lockdown in March compared to firms that were presumably less exposed to the pandemic early on.

Our results highlight the kind of firms that are likely to be more affected when a crisis such as the ongoing one hits the economy. Firms with lower profits, higher share of foreign exchange earnings, higher share of inventories, greater dependence on trade credit and higher operating expenses fared worse on the stock market when the lockdown was announced.

References

Banerjee, R., Illes, A., Kharroubi, E., and Serene, JM. (2020). COVID-19 and corporate sector liquidity, BIS Bulletin No. 10, April, 2020.

Bansal, A., Gopalakrishnan B., Jacob, J., and Srivastava, Pranjal. (2020). When the Market Went Viral: COVID-19, Stock Returns, and Firm Characteristics,
Available at SSRN (June 21, 2020).

Borochin, P.A., Cicon, J.E., DeLisle, R.J., and Price, S.M. (2018). The effects of conference call tones on market perceptions of value uncertainty, Journal of Financial Markets, 40(2018), April, 2018, pp.75--91.

Bushee, B.J., Matsumoto, D.A., Miller, G.S., 2003. Open versus closed conference calls: The determinants and effects of broadening access to disclosure,. Journal of Accounting and Economics, 34(1-3), January, 2003, pp.149--180.

Bushee, B.J., Matsumoto, D.A., Miller, G.S., 2004. Managerial and investor responses to disclosure regulation: the case of Reg FD and conference calls, . The Accounting Review, 79(3), July, 2004, pp.617--643.

Fahlenbrach, R., Rageth K., and Stulz, R. (2020). How valuable is financial flexibility when revenue stops? Evidence from the COVID-19 crisis, NBER Working Papers 27106, May, 2020.

Frankel, R., Marilyn J., and Douglas, S. (2020). An empirical examination of conference calls as a voluntary disclosure medium, Journal of Accounting Research, 37(1), Spring, 1999, pp.133--150.

Hale, T., Webster, S., Petherick, A., Phillips, T., and Kira, B. (2020). Oxford COVID-19 Government Response Tracker, Blavatnik School of Government.

Hassan, A, T. et al (2020). Firm-level exposure to epidemic diseases: COVID-19, SARS, and H1N1, NBER Working Papers 26971, April, 2020.

Mathur, A., and Sengupta, R. (2019). Analysing monetary policy statements of the Reserve Bank of India, IHEID Working Papers 08-2019, May, 2019.

Ramelli, S., and Wagner, A.F. (2019). Feverish stock price reactions to COVID-19, Swiss Finance Institute Research Paper No. 20-12, Forthcoming Review of Corporate Finance Studies, March, 2020.

Sane, R., and Sharma, A. (2020). Holding their breath: Indian firms in an interruption of revenue, The Leap Blog, 03 April, 2020.

Sengupta R., and Vardhan, H. (2020). Policymaking at a time of high risk-aversion Ideas for India, 06 April, 2020.


Aakriti Mathur is a PhD candidate at The Graduate Institute (IHEID), Geneva. Rajeswari Sengupta is an Assistant Professor of Economics at IGIDR, Mumbai.

Wednesday, June 24, 2020

Skepticism about measurement: Hospital beds edition

by Shubho Roy.

The Covid 19 pandemic has motivated many studies based on data about the disease and the response. However, measurement in India is often weak. There is a need for greater caution before using such data. In this article, we look closely at one such issue: estimating health care capacity based on hospital beds.

The health care infrastructure response to Covid 19 has been to ramp up the number of hospital beds. However, severe and critical care patients in India may often need ventilators and ICU beds. There are no measures available about the number of ventilators or ICU beds in India. Researchers have taken to guesswork in order to address this gap. How reasonable are these estimates?

Extrapolation from the count of hospital beds

It makes sense to use hospital beds as a standardised measure of hospital capacity. Infrastructure, equipment and manpower standards for hospitals have been built on a per bed basis. The number of nurses, doctors, equipment, and even floor space is a function of the number of beds in the hospital. For example, according to the Indian government, a hospital should ideally have 80 to 85 sq m of plinth area per bed; there should be a toilet for every six beds; and one operation theatre for every 50 beds in the general ward. This makes estimating the availability of health facilities easier. Count the number of hospital beds in a country, and you have a sense of the overall health care capacity. Both the World Health Organization and the World Bank track the number of hospital beds per 1000 population as a measure of health care capacity.

Estimates

If we work within such quantification of hospital capacity based on the number of beds, how many ventilators and ICU beds might be present in India? The central government formulated the IPH Standards in 2012, to improve capacity in government health services (run mostly by the state governments). The standards for district-hospitals (at pg.5) requires 300 district-hospital beds per million population. IPH standards for district-hospitals states that five to ten per cent of the total beds in a district-hospital should be ICU beds (See page 25) and each ICU bed should have a ventilator (amongst other equipment). If the entire country were to be up to IPHS standards, there should be 416,189 district hospital beds and between 41,618 to 20,809 ventilators in the country (in the government system).

In reality, the numbers will probably be lower than the standards. Experts have tried to estimate the availability of hospital beds, ICUs and ventilators for the present epidemic. Rajagopalan and Choutagunta have estimated the availability of hospital beds (both in government and private sector) in various Indian states. Singh et al. use a 2008 paper by Yeolekar and Mehta, which estimates that there are around 5-8% ICU beds in government hospitals. Singh et al. assume that around 50% of the ICU beds have a ventilator. This gives them a range of 35,699 to 57,119 ventilators for the entire country (in the government system). Similarly, Kapoor et al. estimate that there should be around 35,699 ICU beds and 17,850 ventilators for the country.

For U.P., they estimate 3,813 ICU beds and 1,907 ventilators.

The ground reality in Uttar Pradesh

How do the government aspirations and expert estimates stack up against the ground reality? In 2019, India’s supreme audit institution (the CAG), carried out a performance audit of Hospital Management in Uttar Pradesh. U.P. has 75 districts and 174 district hospitals (in 2018). The CAG covered seven districts (out of 75) for its audit of 16 government hospitals. In addition, three out of 11 district-hospitals of Lucknow were audited (See Table 45 at pg. 93). The seven districts which were fully covered by the audit (for district-hospitals) are distributed across the five administrative regions of U.P. The CAG found that the reality was far away from the aspiration or estimates.

The seven districts have a population of 25.9 million. As per IPH standards, they should have 7,700 district-hospital beds. The CAG found 2,275 beds, a shortfall of over 70%. If the districts follow IPH standards, there should be 385 to 770 ICU beds for the ideal 7,700 district-hospital beds. Even if IPH standards were maintained on a base of 2,275 beds, there should have been 113 to 228 ICU beds in the sampled district hospitals. The CAG found that ` 10 out of the 11 district hospitals had no ICU beds’. Only the Gorakhpur district hospital had 13 ICU beds (3% of its total beds). In short, of the seven districts examined by the CAG, there were 13 ICU beds, all located in one district.

Even where there were ICU beds, the CAG found shortages of equipment considered essential for an ICU bed (as per IPH standards). The CAG noted:

audit observed that only six High-end Monitors were available against the requirement of 14, seven Infusion pumps were available against the requirement of 14, while Ventilators, Ultrasound for invasive procedures and Arterial Blood Gas (ABG) analysis machine were not available at all in D.H. Lucknow. Similarly, in D.H. Gorakhpur, there were no Ventilators, Infusion Pumps, Ultrasound for invasive procedures and ABG analysis machine.

(pg. 34 of the CAG Report)

In the seven districts where the CAG audited district hospitals, the CAG found no ventilators. Even in the district-hospitals in Lucknow (outside the seven districts), 2% of the beds were ICU, and there were no ventilators.

The CAG report only covers seven districts out of U.P.’s 75 districts. U.P. is one of India’s poorest states. The government of U.P. also operates some super-speciality hospitals where facilities might be better, but they will be few. These seven districts constitute 11% of U.P.’s population. While the findings may not be representative of India, they are not inconsequential. The gap between central government standards, expert estimations, and reality is vast. Table 1 shows the gap between these numbers for the 11 districts of U.P.

Target and reality gap between hospital beds, ICUs and ventilators
Measurement approach Hospital Beds ICU Beds Ventilators
IPH standards 7,700 385 - 770 385 - 770
Expert estimation (from UP numbers) 8,339 419 210
If existing beds maintained ICU ratio 2,275 113 - 228 113 - 228
CAG findings (Reality) 2,275 13 0

Goodhart’s law

Why is there such a large discrepancy between the IPH standards, expert estimates and the reality observed by the CAG? We may conjecture that Goodhart’s Law is at work. Goodhart’s law states: “When a measure becomes a target, it ceases to be a good measure”.

For too long, the academic and policy literature, in India, has emphasised one metric: the number of beds available in government hospitals. Press articles regularly criticize the government for India’s low bed to population ratio (See here, here and here). An easy way out for politicians and officials is to look good in such measurement, while skimping on other elements of health care. Between 2014-2018 the Central Government spent Rupees 8.5 billion for the country out of which Rupees 1.5 billion was spent in U.P., under the National Health Mission, to upgrade facilities in state government hospitals. The result was a rapid expansion in the number of beds (the measure which health policy makers are sensitive to), and not much else.

This problem is not limited to hospital beds. It extends to other parts of the health sector. The central government operates a detailed database called the Health Management Information System. As an example, Smriti Sharma shows that there are significant discrepancies in the database. Numbers which portray the system in poor light are under-reported while the numbers which show the health system positively are inflated.

Using the hospital bed measure to estimate the availability of health care capacity is misleading. Even when the government sets up new facilities, measurement is being done on the basis of new beds. Till April 11, the government had set aside 100,000 hospital beds and 11,500 ICU beds in 586 hospitals. On May 15, the Maharashtra government planned to set aside another 100,000 beds, just in Mumbai with an additional 1,000 ICU beds. In thinking about the situation in health care, this is not enough information. We need to know the facilities and personnel that will be available for these beds.

A general philosophy in India is to be very careful about using data. Researchers need to gain confidence in the quality of the measurement process. This is particularly critical where the agency which performs a function is also the source of data about the same function. When the underlying data is weak, no amount of cleverness in statistics can rescue the distortion of our view of what is going on.

References

Assessing Healthcare Capacity in India. Shruti Rajagopalan and Abishek Choutagunta, Mercatus Working Paper, Mercatus Center at George Mason University, Arlington, VA, April 2020.

Covid 19 in India: State-wise estimates of current hospital beds, intensive care unit (ICU) beds and ventilators, Geetanjali Kapoor, Aditi Sriram, Jyoti Joshi, Arindam Nandi, and Ramanan Laxminarayan, Center for Disease Dynamics, Economics and Policy, Princeton University April 2020.

Covid 19 | Is India’s health infrastructure equipped to handle an epidemic?, Prachi Singh, Shamika Ravi and Sikim Chakraborty, Up Front, Brookings, March 24, 2020.

Covid-19 in India in the coming months: The puzzles faced by leaders of health care organisations, Ajay Shah, The Leap Blog, June 2020.

Hospital Management in Uttar Pradesh, Comptroller and Auditor General of India, 2019.

Problems of the Health Management Information System (HMIS): the experience of Haryana., Smriti Sharma, The Leap Blog, June 2016.

Prudent public health intervention strategies to control the coronavirus disease 2019 transmission in India: A mathematical model-based approach., Sandip Mandal, Tarun Bhatnagar, Nimalan Arinaminpathy, and Anup Agarwal Indian Journal of Medical Research. 2020 10.4103/ijmr.IJMR_504_20.

 

The author is a researcher at the University of Chicago and would like to thank Renuka Sane and Rajeswari Sengupta for their valuable inputs.

Monday, June 15, 2020

Covid-19 in India in the coming months: The puzzles faced by leaders of health care organisations

by Ajay Shah.

Peering into the next six months

How might the pandemic play out in India in coming months? There are newspaper reports about some important statistical evidence from ICMR about the spread of Covid-19 in 70 districts of India (caveat). Based on antibody testing, it appears that about a third of the people in containment zones in some large cities had antibodies in late April. We can cautiously expect significant progress towards herd immunity, in containment zones, by today, i.e. mid-June. Recent stories from Dharavi in Bombay are consistent with such an argument (while also being a testimony to the public health capability of the municipal authorities).

In most of India, however, the picture is quite different. E.g. while about a third of the people in the containment zones in Bombay had antibodies in end-April, the fraction of persons in Bombay as a whole who had antibodies is small. In most of India, the bulk of the epidemic lies in the future.

There is a public health problem (how to slow down the spread of the disease) and there is a health care problem (how to care for the people who get sick). In this article, we focus on the health care problem. For the leadership of health care organisations in most of India, this is an extremely important moment, when they need to plan for this coming surge. In this article, we think about the pandemic from their point of view. A given facility might appear to be relatively unruffled today, but it is important to envision the coming surge of demand for health care, and to lay the groundwork for faring better at the peak of the pandemic in the catchment of the facility. What are the issues, and what are the potential actions that can be taken?

About 70 per cent of health care in India is in the private sector. In this article, we place ourselves in the shoes of the leaders of health care organisations of all kinds, but we have an accent on private organisations as this is where the bulk of the action will lie.

Equipment

Oxygen therapy is a key element. There has been a lot of talk, internationally, about ventilators. However, from the viewpoint of both efficacy and cost, ventilators are a poor solution. The skill required of health care workers, to use a ventilator, is substantial and this will limit scale up. It is more useful to develop a strategy that involves oxygen cylinders and oxygen concentrators. The former is associated with the problem of managing the supply chain for oxygen cylinders. All hospital beds should be equipped with oxygen ports.

In many hospitals, there is a need to introduce physical isolation and establish a dedicated wing in which Covid-19 patients will be treated.

Beds in the ICU are a scarce resource. It is useful to establish `step down beds', where patients exiting the ICU can be safely placed, when they require a high standard of care but no longer require to be in the ICU. This will improve the extent to which the ICU is available to the patients who need it most.

Internal management

The medical community in each city needs to debate and agree on the clinical protocols that will be put into play, that are feasible and cost-effective under their local conditions. This will reduce fumbling and recrimination in the surge. Conversations and documents around rules of triaging will help.

Economies of scale and cost reductions can be obtained by establishing `eICUs', where a central command centre has skilled staff which monitors the data coming in from a remote ICU. This is a more feasible path to scaling up ICU capacity, particularly in places when the skilled staff in ICUs is hard to find.

When the surge comes, the management processes of the hospital will be tested. Every element of the process requires analysis from the viewpoint of coping with a surge environment. Enhancing non-medical staff and processes, ahead of time, will help cope with the surge.

Health care workers

A key problem concerns health care workers (HCWs), who face the risk of high dose exposure to the virus. While some HCW are driven to serve the community, many may retreat from work when the surge gathers momentum. At precisely the time when the most capacity is required, the capacity could degrade, thus increasing the chances of an organisational rout.

The leadership needs to undertake many measures which will be fair to HCW and reinforce their commitment to hold the ranks:

  1. It is penny wise, pound foolish, to skimp on the quality and quantity of PPE. If a few ward boys get sick, word of this will leak to other ward boys. For ward boys to feel safe, their training and consumables have to be of high quality.
  2. HCW and their families need to be reassured that there will be ample effort on giving them treatment if required.
  3. PPE and training is required not just in the ICU but also for the primary care providers, who are the first point of contact for patients when they reach the facility.
  4. Periodic antibody testing for all HCW will be particularly useful: (a) In assessing the extent to which infection and immunity has come about, (b) Generate metrics of the class of situations where new infections are coming about and feed back to process improvements, and (c) Increase the confidence of HCW as a stream of process improvements are visible, and when it is seen that the infection rate and severity of the disease is low.

Community initiatives

These elements (equipment, management, HCW) constitute a reasonable work plan to gear up for the surge. But many or most hospitals today are beset with difficulties. In the best of time, their management bandwidth was limited. Covid-19 has induced a financial crisis with a decline in non-Covid revenues, and the Indian financial system is not able to engage effectively with most hospitals. The thin capabilities have been adversely affected by the retreat of HCW. The puzzle lies in finding the energy and resources to actually pull off a significant amount of preparatory work.

There are many problems which are hard to address at the level of one hospital. Consider a city like Nagpur. There is significant value in constructing a Coalition of hospitals and of the local business community, which can work towards many initiatives -- without any government involvement -- which will reduce the damage caused to the city from the epidemic. Examples of such collaborative initiatives are :

  1. Nagpur requires facts, through random sampling, about the state of infection and antibodies in Nagpur. The weekly or monthly construction of these facts is vital for health care organisations to know the planning horizon that they face, before the surge. The citizenry requires these facts to make decisions about the economic and social activities that are safe. The Indian state does not produce this information. Better planning by health care organisations is good for them and for the citizenry and economy of Nagpur. It would be valuable if such a work program can be put together by the Coalition.
  2. The Coalition can collaborate with the medical testing industry to establish capacity, and negotiate bulk rates.
  3. The Coalition can establish a process of discussion and drafting of appropriate clinical protocols which can then by used by all HCW in the city.
  4. When an individual requires health care or a bed, there is chaos during the surge, with patients running around across multiple facilities looking for spare capacity. The Coalition should establish a shared information system and call centre for patients to use. This will reduce the operational overheads and queues outside facilities. This will increase bed utilisation and improve the allocation of facilities based on the condition of the patient.
  5. The Coalition can pool resources to do bulk buying and inventory management on medical supplies such as PPE or oxygen cylinders, and dynamically respond to the shortages of consumables that are discovered at future dates.
  6. There has been significant friction between health care organisations and the government. The Coalition could be more effective in addressing inappropriate behaviour of various arms of the Indian state. The Coalition is a natural locus for addressing bad behaviour by some health care actors, and can head off such problems so as reduce the probability of the state getting involved.
  7. The Coalition can be more effective in overcoming the frictions faced by hospitals in empanelment with the various government sponsored health insurance schemes (GSHIS) and address frictions associated with dealing with health insurance companies.
  8. Some health care organisations may falter in their commitment to stay in this fight. The Coalition will be valuable in exerting peer pressure, and in helping transmit management knowledge to some organisations who are sitting on the fence.

To the extent that the health care problem in a city like Nagpur is worked out well, individuals will feel more safe, and will get back to working and consuming, thus bolstering the economy. If the health care system gets crushed, there will be greater reticence on the part of the citizenry to spend or work, and the economy will be more adversely affected. Supply chains will get disrupted if there is a lockdown in the future in Nagpur. There is thus ample self interest which should drive the business community, and the health care community, to come together, and expend financial and management resources on building such a Coalition.

There are severe financial problems in many health care organisations today, as the traditional revenue stream has dried up as a consequence of fearful households. The leadership of many health care organisations is firefighting a financial crisis, which is exerting a tax upon their management bandwidth, at a time when they should primarily be working on laying the groundwork for the surge. The Indian financial system works poorly and is not able to perform its role, of efficiently supplying capital. There is a need for owners to bring in equity capital to alleviate this problem. It is also in the self interest of business interests in a given city, to offer loans to health care organisations, so as to diminish the organisational rout of health care organisations in the surge, which would adversely impact upon the economy of the city.

Conclusion

The bulk of the discussions surrounding Covid-19 in India are focused on public policy. But state capacity in India is low, and we should have low expectations for what the state can do. As the de-lockdown progresses, the pandemic will accelerate. We are now at the last barricade: Health care.

How things work out in 2020 will now be shaped by the sagacity and leadership qualities of the senior managers of health care organisations across the country. There are about 10,000 important hospitals in India, and about 50,000 key persons who make up the leadership of these hospitals. All eyes are on the actions of these 50,000 people, which will have an impact upon millions of lives. Seldom has so much depended on so few.

In this article, we have shown some areas of planning and preparedness that are required in health care organisations. All large hospitals in the country, private or public, need to plan for the surge. A key theme we have emphasised is negotiation and collaboration between private persons. The Indian state is generally not able to usefully intermediate in the interactions between private persons. A key feature of the way forward lies in organising communities, in privately negotiated local solutions. It is in the best interests of the citizenry and the health care community of (say) Nagpur to take their future in their own hands, to plan their best way forward.

Saturday, June 13, 2020

Information about COVID-19 in India

By Natasha Agarwal and Harleen Kaur.

The presence of timely and reliable data enables informed decision-making by government organisations and individuals. When a machine-readable dataset is released on a website, it is non-rival, and thus has characteristics of a public good. There is a case for state financing or production of information. As Carl Malamund says, "Government information is a form of infrastructure, no less important to our modern life than our roads, electrical grid or water systems". Open Data Governance (ODG) are structured datasets produced by government institutions that are released in a machine-readable format. These datasets contain information such as statistics, plans, maps, environmental data, spatial data, materials of agencies, ministries, parliamentary data, budgetary data, and laws.

Governments across the globe have been actively opening their data through national and regional data transparency portals recognising the need for making data available to the public. The process is informed by ODG principles. There are three main reasons for opening government data; increasing transparency, releasing the social and commercial value of the data, and to encourage participatory governance (Attard et al. (2015)). As an example, the COVID-19 pandemic is best controlled through behavioral changes by each individual. To support such changes, the governments need to open their data about the pandemic at an individual and community level.

The ODG principles defining best practices of data sharing include; i) identifying and publishing high-value datasets in a standardised format (such as a directory of medical professionals, tests conducted and results and information about surveillance), ii) adopting open data scheme protocol to share human and machine-readable, non-proprietary format and include universal resource identifier and linked data to provide access, iii) removing barriers to data access such as requirements of establishing an account, of proving identity, or payments for data access, and iv) making information available in perpetuity by not deleting/changing data permanently.

In this article, we examine the information systems on COVID-19 in India from the viewpoint of these issues in the design of a high performance statistical system.

Data.gov.in and its limitations

In India, an open data policy the National Data Sharing and Accessibility Policy (NDSAP) was announced in 2012 to open government data to the public by following ODG principles.

The policy requires all ministries, departments, subordinate bodies, organisations, and autonomous bodies of the Indian Government to share all publicly generated non-sensitive data in both human-readable and machine-readable formats. The data is disseminated through a common government data platform deployed and managed by the National Informatics Centre (NIC), Ministry of Communications and Information Technology. It mandated that datasets be periodically updated by government agencies along with comprehensive meta-data which enables data discovery and access through departmental portals.

Furthermore, NDSAP requires the Department of Information Technology (DIT) to publish guidelines to implement NDSAP. The implementation guidelines provide details of the data contribution process including; the role and responsibilities of the data controller, approval, publishing process for catalogs and resources, and management of published datasets.

In compliance with NDSAP, India's national data transparency website, data.gov.in was launched in 2012. Accordingly, data.gov.in provides a unified catalog of datasets allowing users to browse the dataset catalog, view the meta-data associated with each dataset, comment on and rank various datasets, download available datasets, submit suggestions and queries on the published dataset, and submit a request for those that are not available yet (Chattapadhyay (2013)).

Despite the comprehensiveness of the policy and the accompanying guidelines, agencies have responded predictably, i.e. they neither comply with NDSAP nor with the implementation guidelines. As a result, data.gov.in contains issues such as the absence of databases, duplicate datasets, lack of follow-up, or meta-data (Agarwal (2016) and Buteau et al. (2015)). The terms 'policy document' and 'guidelines' which are often used in India are ineffective in that they do not constrain the executive. Hence, these documents amount to exhortations that have little impact on the incentives of officials in favour of greater opacity, reduced work, or gaining power through the control of data.

Ministry of Health and Family Welfare (MoHFW) and COVID-19 data

We examine the data in the public domain emanating from MoHFW during the ongoing COVID-19 pandemic. To understand the availability of resources for healthcare, we searched for a directory of healthcare providers (both institutions and individuals). The latest hospital directory available on data.gov.in was for 2016 and the latest data for the number of registered allopathic doctors and dental surgeons was available for the year 2013.

The MoHFW is disseminating limited data on the spread of COVID-19 through the data.gov.in portal. For example, as of 1st June 2020, the data reported under mygov.in (not in data.gov.in) contains information on three variables namely (i) total number of persons infected with COVID-19; (ii) COVID-19 infected persons who have been cured/discharged/migrated; and (iii) COVID-19 infected persons who have died. The state-wise distribution of these three variables is available for a given date "T = Today". This data cannot be downloaded. The meta-data for this information is also not available. On the other hand, the data.gov.in only releases daily factsheets in a pdf format summarising this data.

The dissemination of COVID-19-related data by the MoHFW has problems. It gathers detailed COVID-19-related data from the National Centre for Disease Control (NCDC) (surveillance data from the field) and Indian Council of Medical Research (ICMR) (data through the testing laboratory network), which is not reflected in data.gov.in.

The NCDC, under the Integrated Disease Surveillance Project (IDSP), consists of union, state, and district-level units responsible for the surveillance of infectious diseases in India. Although it releases weekly outbreak reports notifying the status of infectious diseases in India, the reports are available only on its website and not integrated on data.gov.in. On the COVID-19 pandemic, the weekly outbreak report dated 10th-16 February, 2020 was the latest available report under IDSP as of 8 June, 2020.

Similarly, ICMR, the designated body under the National Disaster Management Act to coordinate the testing strategy for COVID-19 has been releasing its data through its website and not through data.gov.in. Through its website, ICMR releases information on two parameters, the total number of samples tested for COVID-19 over time, and in the last 24 hours.

Therefore, data.gov.in is not being utilised by the union government agencies for releasing information. Individuals and researchers interested in the government data on the pandemic have to access information available in different silos according to their skills and knowledge. Moreover, none of the information shared is available in a machine-readable or standardised format. This leads to a weak information base on Covid-19 available to the public and to researchers, which hampers the decision making of individuals on the appropriate care that they should take, and hampers policymaking by government organisations for want of data and research.

Data disseminated by state governments

The union agencies are not the only government source on COVID-19 information. We now study the data dissemination protocols for COVID-19 as followed by the states.

We could not find state data on COVID-19 on the data.gov.in website. As a result, the following information was collected through individual COVID-19 portals set up by the states. Table 1 shows that there is heterogeneity in reporting across states. The information shared by the states is classified into three categories; "state-level", "district-level" and "individual-level".

Parameters

Delhi

Kerala

Maharashtra

Gujarat

Karnataka

Madhya Pradesh

State-level data

Total COVID-19 confirmed cases

Y

Y

Y

Y

Y

Y

Active cases

Y

Y

Y

Y

Y

Y

Total COVID-19 tests conducted

N

Y

N

Y

Y

N

Hospitalisation status of positive cases

Y

Y

N

N

Only ICU patients

N

Isolated/ quarantined patients

Y

Y

N

Y

Y

N

Total recovered patients

Y

Y

Y

Y

Y

Y

Total deaths

Y

Y

Y

Y

Y

Y

District-level data

Number of people under observation

N

Y

N

N

Y

N

Number of quarantined/ isolated people

N

Y

N

N

Y

N

Individual-level data

Age

N

Y

N

N

N

N

Gender

N

N

N

N

Y

N

Comorbidity

N

Y

N

N

N

N

Table 1: State-level reporting parameters for COVID-19 (As of 9 June, 2020)

Table 1, placed above, shows the data sharing protocol for COVID-19 in selected states. We may point out a few facts that influence the interpretation of this table:

  1. Data as of 10th June, 2020. Sources: Delhi, Kerala, Maharashtra, Gujarat, Karnataka and Madhya Pradesh.

  2. Maharashtra, Gujarat, and Karnataka share information about the same parameters at the State and District level. The information depicted here is about parameters in addition to the duplicate information.

  3. In the studied states, Gujarat and Delhi inform about the number of patients on ventilators at the state level. However, the information on available hospital beds and ventilators in Delhi is shared under a separate website, https://coronabeds.jantasamvad.org/.

  4. District-level information in Kerala is available for patients hospitalised, symptomatic patients hospitalised, the chronology of positive cases, and hotspots. No other states releases data on these parameters.

  5. Karnataka is the only state which shared anonymised patient data related to their travel history, district, and location of isolation. It also has a dedicated patient case number for individual patients for whom information is shared.

  6. Madhya Pradesh had a dedicated website for individual-level data which was discontinued from 11th May 2020 onwards following the raising of privacy concerns over social media.

We find that in most states, the baseline data includes overall state data about testing rates, persons infected, deaths, and recovery data. However, some states provide additional information such as the number of COVID-19 tests conducted, the number of isolated/quarantined persons, the counts of patients on ventilators, and stable patients. While some states like Maharashtra report data at the district level along with the overall state data, others like Karnataka share information at the individual level. There is a high variation in the type of data shared by the states. For instance, at an individual level, Karnataka reports anonymised information about the demographic details in addition to the baseline data. On the other hand, Madhya Pradesh used to share the name and addresses of the suspected COVID-19 patients to the public while reporting individual-level data. Similarly, Kerala, Maharashtra, and Gujarat report their data at the district level. Kerala reports its surveillance data which is not reported by Maharashtra, and Gujarat. Some states provide daily reports in English, while others do not. For example, Gujarat provides daily reports only in Gujarati.

Most states disseminate data through their COVID-19 websites. However, some resort to reporting through social media. For example, the Maharashtra government website on COVID-19 does not provide information other than that reported in table 1. However, the Maharashtra government has been releasing daily reports providing COVID-19-related information across age, gender, comorbidities amongst other variables through Twitter. While twitter can amplify the transmission of information in a public statistical system, it should not supplant the foundational systems. Data disseminated through a tweet cannot be traced to any government website. Besides, there is inconsistency in the reports shared by the Maharashtra government through twitter. For example, the report dated 22nd April 2020 provides for district-wise distribution of COVID-19 cases in Maharashtra which is not available in the report dated 1st April 2020. The data is a "delete-tweet" away from not being available.

There is also variation in the data sharing format. Most state governments provide data in human-readable formats like pdf. However, some state governments provide some data in machine-readable formats. For example, district-wise data on variables available on dashboard for Gujarat which contains the total number of cases tested for COVID-19, positive cases, patients recovered, people under quarantine, and total deaths can be exported to a csv document. Nevertheless, demographic details of COVID-19 patients or data patients on ventilator/stable, are only available in daily reports in pdf format.

We find that the states do not share their COVID-19 data through the data.gov.in framework. Users have to look for multiple information sources about COVID-19 data to access this data. Within the framework of stand-alone websites providing information, there are two concerns. The first concern is the lack of standardised parameters for information releasing. For instance, few states share the hospitalisation status and the availability of beds which would be useful for the general public in case of emergency. The second concern is the quality of data shared by the states. As discussed, most states share human-readable data and not machine-readable, downloadable data. Meta-data is not available for any state studied making it difficult to interpret. Moreover, the lack of data standardisation makes data non-interoperable. The state-level historical information is unavailable for most states. Therefore, not all data shared by the states is permanent.

Difficulties of CoVID-19 data release seen elsewhere in the world

So far, we have documented variation in what data is being released, and how the same is disseminated, in India. This is a global concern for COVID-19. We map the data reported by selected countries in table 2 below. We find that countries are using two forms of data distribution methods. These are daily updates and dashboards. While daily updates are usually pdf documents, dashboards provide progress of COVID-19 over time. The type of information shared by countries can broadly be classified according to the level of data as "country-level" and "individual-level". Country-level data consists of aggregate information such as the total number of tests conducted, the total number of COVID-19 positive patients, the number of patient hospitalised and deaths, etc. Some countries also share aggregate surveillance data which consists of information about individuals isolated, quarantined, and contact traced. At an individual level, we see a wide variation of data shared by the countries. While India does not provide individual-level data through its Ministry of Health, other countries share demographic information such as age, gender, race/ethnicity, and occupation. A comparison of data disclosed by selected countries is shared in table 2.

Country Daily updates (DU) or Dashboard (DB) Total Number of tests conducted Total Number of COVID-19 +ve patients Total Number of patients hospitalised Total Number of deaths Surveillance data Individual level data
Age Gender Race/ Ethnicity Occupa-tion

India

DU and DB

Y

Y

N

Y

N

N

N

N

N

USA

DU and DB

Y

Y

N

Y

Y

Y

N

Y

N

UK

DU and DB

Y

Y

N

Y

N

Y

Y

Y

Y

South Korea

DU and DB

Y

Y

N

Y

Y

Y

N

N

N

Singapore

DU and DB

Y

Y

Y

Y

Y

N

N

N

N

Canada

DB

Y

Y

Y

Y

Y

Y

Y

N

N

Australia

DU and DB

Y

Y

Y

Y

Y

Y

Y

N

N
Table 2: Country-level data parameters for COVID-19 (As of 10 May, 2020)

It can be seen from the above table that most countries report testing data (information about the number of tests conducted), and the number of positive cases and deaths. At the national level, India only reports these minimum consistent variables. Some countries report more variables to the public. For instance, the US, South Korea, Singapore, Canada, and Australia report surveillance data in varying details. A few countries like Canada share their database in a downloadable format. This includes information about quarantined and isolated individuals and details about contact tracing and source of infection. Singapore, Canada, and Australia also report data on the number of cases hospitalised. The UK has recently started reporting information about COVID-19 deaths, disaggregated into deaths inside and outside hospitals. Individual-level data such as age, gender, race/ethnicity, and occupation, is visible in some countries, as is the case in some states (though not the union government) in India. The US releases data about age and race, while the UK releases information about age, gender, race, and occupation. South Korea releases age details for only severe cases and Singapore releases individual-level data only in the event of the death of the individual. Canada releases data about age and pre-existing conditions of the individuals and Australia releases information about age and gender.

Therefore, we find that data release for COVID-19 has issues of lack of standardisation and inter-operability globally. In India, the union and state governments have important deficiencies.

Implications for India

India's existing data infrastructure does not meet the demands of a public health emergency. The implications of this are multifaceted. For example, amid the COVID-19 pandemic, the government had to create a Covid19-warriors dashboard that provides information on doctors, nurses, ASHA workers, and others who could be deployed for immediate response. If data.gov.in had worked well, then the government would have had this information already.

Likewise, the problem of inaccurate databases highlighting data discrepancies in reporting COVID-19 infected persons could have been avoided. An available database infrastructure in data.gov.in would have avoided the need for ICMR to evolve its own data-dissemination method in the middle of the COVID19 pandemic. Besides, the problem of collecting, processing, and releasing COVID-19 data with other databases would have been eased. For example, if the existing data infrastructure had data collection and reporting standards across space like district names with their respective codes, then it would not only be easy to collect the data but also facilitate easier collation with other datasets for enabling interoperability.

Conclusion

In the present article, we highlighted one element of the public health response, the issue of data release by the Indian government authorities for COVID-19. We show that the statistical system for disease surveillance dissemination in India is in a need of reform.

The ODG platform in India, data.gov.in, can play an important role in strengthening India's public health data infrastructure. To realise the utility of public data, a data protocol framework with a legally enforceable mandate on the government is required, as is seen in countries like the US. The principles of standardising, anonymising, interoperability, meta-data release, and grievance redressal in the event of non-release should be in this legal framework.

For the union government, a data.gov.in which utilises the sound principles of OGD release could become a better foundation for data release, and thus improve India's response to an epidemic. State and city governments could choose to use the services of data.gov.in or build their own systems. An indicative list of the essential components of such a portal (as seen in NDSAP and ODG principles) are provided below:

  1. Standardising data release: Standardisation of reported variables such as reporting unit, disease data, language, individual, and community-level data is required. Elements that go towards this include geotagging and coding of hospitals/labs and the adoption of International Classification of Diseases (ICD) for diagnosis and treatment of diseases.

  2. Ensuring privacy: Privacy is a fundamental right in India (Supreme Court of India (2017)). Despite this, states like Madhya Pradesh and Karnataka were seen to be disseminating personally identifiable information of suspected COVID-19 patients. The government would need to adopt various tools at its disposal to protect these rights at an individual and community level. These tools include tagging appropriate data, incorporating principles of Privacy by design (PBD), anonymising and utilising appropriate fiduciary principles (Cavoukian (2011) and Bailey and Goyal (2019)).

  3. Interoperability: Facilitating systems interoperability by incorporating common formats, software standards, and semantic interoperability by incorporating e-governance standards so that the meaning of data is not lost across data silos is required (Wright et al. (2010)).

  4. Adopting an open data scheme: Legislators need to create the frameworks through which the executive is required to release meta-data, and release data in a machine-readable format.

  5. Setting up governance framework: Union, state, and city governments have legitimate authority on how they organise their work, but greater consistency and predictability for API-based access is desirable.

References

Attard et al. (2015): Judie Attard, Fabrizio Orlandi, Simon Scerri, and Sören Auer, A systematic review of open government data initiatives, Government Information Quarterly, 2015.

Chattapadhyay (2013): Sumandro Chattapadhyay, Towards an Expanded and Integrated Open Government Data Agenda for India, IDRC Digital Library.

Agarwal (2016): Natasha Agarwal, Open Government Data: An Answer to India's Growth Logjam, SSRN, 16 August, 2016.

Buteau et al. (2015): Sharon Buteau, Aurelie Larquemin and Jyoti Prasad Mukhopadhyay, Open data and applied socio-economic research in india: An overview, IFMR Working Paper, 27 May, 2015.

Supreme Court of India (2017): Justice K.S. Puttaswamy v. Union of India, 2017 (10) SCC 1.

Cavoukian (2011): Ann Cavoukian, Privacy by design: The seven foundational principles, Information and Privacy Commissioner of Ontario, 2011.

Wright et al. (2010): Glover Wright, Pranesh Prakash Sunil Abraham, Nishant Shah, Open government data study: India, The Centre for Internet and Society, 2010.

Bailey and Goyal (2019): Rishab Bailey and Trishee Goyal, Fiduciary relationships as a means to protect privacy: Examining the use of the fiduciary concept in the Draft Personal Data Protection Bill, 2018, Data Governance Network, 2019.

 

Natasha Agarwal is an independent research economist. Harleen Kaur is a researcher at NIPFP. The authors are thankful to Ajay Shah and two anonymous referees for their valuable comments and inputs on the article.