Indian Journal of Public Health

ORIGINAL ARTICLE
Year
: 2022  |  Volume : 66  |  Issue : 3  |  Page : 239--244

Burden of COVID-19: DALY and productivity loss for Karnataka, India


Shashank D Shindhe1, Suhas Bhat2, Surekha B Munoli3,  
1 Senior Engineer-Data Science, Altimetrik India Private Limited, Bangalore, Karnataka, India
2 Assistant Professor, Department of Statistics, CHRIST (Deemed to be University), Bangalore, Karnataka, India
3 Professor, Department of Statistics, Karnatak University, Dharwad, Karnataka, India

Correspondence Address:
Suhas Bhat
Assistant Professor, Department of Statistics, CHRIST (Deemed to be University), Bangalore - 560 029, Karnataka
India

Abstract

Background: COVID-19 is a pandemic that is devastating the world right now quelling over 2.5 million people worldwide. Similarly, in India and its largest southern state Karnataka, the coronavirus is responsible for around 161,000 and 12,449 deaths, respectively. These numbers capture the havoc caused by this novel coronavirus, but fail to discern the complete picture. Objectives: Broadly, this study aimed to study the mortality, morbidity, and the economic issues inflicted by the COVID-19 in the state of Karnataka. Specifically, the study used publically available epidemiological data to study both mortality and morbidity by means of disability-adjusted life years (DALYs). Furthermore, the study aimed at estimating the permanent losses to the state gross domestic product (SGDP) due to the pandemic. Materials and Methods: Publicly available epidemiological data are used from selected sources and DALYs are computed. The permanent loss to the SGDP is estimated using the human capital approach. Results: The total DALYs for Karnataka are computed to be 22,506 of which 22,041 correspond to mortality and remaining correspond to morbidity. Financially, Karnataka lost around 208 years of productive years of lives costing around ₹590 million rupees to the SGDP. Conclusions: It is found that major burden of COVID-19 during study period is due to mortality. Morbidity accounts for around 2% of the total DALYs. Males are the most affected by the mortality and also the morbidity. With respect to loss in productivity, the losses due to premature mortality of COVID-19 amounted to ₹590 million.



How to cite this article:
Shindhe SD, Bhat S, Munoli SB. Burden of COVID-19: DALY and productivity loss for Karnataka, India.Indian J Public Health 2022;66:239-244


How to cite this URL:
Shindhe SD, Bhat S, Munoli SB. Burden of COVID-19: DALY and productivity loss for Karnataka, India. Indian J Public Health [serial online] 2022 [cited 2022 Dec 3 ];66:239-244
Available from: https://www.ijph.in/text.asp?2022/66/3/239/356620


Full Text



 Introduction



COVID-19 is a respiratory disease that is an ongoing pandemic. SARS-CoV-2 is evidenced to be spreading from person-person through both direct contacts such as person-to-person contact, droplet spread, and/or indirect contacts such as airborne transmission through contaminated objects.

The respiratory distress induced by SARS-CoV-2 is mild to moderate in majority of the cases, but it is observed to be inducing sever to critical symptoms such as hypoxia, respiratory failure, shock, and multiorgan failure in significant number of cases.[1] It is also observed that in notable number of the cases, the infected did not seem to develop, perceive, and report any symptoms.[2],[3] COVID-19 is also found to be causing long term damages to the organs even after recovery. This is known as long COVID.[4],[5]

Although majority of people who have contracted the disease recover without any intensive medical care, enough number of people experience conditions that require a special treatment. Severe-to-critical cases have been observed among people with underlying chronic medical conditions related to vital organs such as heart, lungs, kidney, and liver.[6]

The first case was identified in Wuhan, China, in December of 2019 and since then it has infected over 100 million people worldwide and is responsible for over 2.5 million deaths.[7] As evidenced by numerous studies, the severity and outcome of the infection is a function of gender, age, ethnicity, comorbidity, and many other factors.

The impact of pandemic is always more than what meets the eye. It has been established that severe-to-critical cases reported among males, elders, pregnant women, people with comorbidities,[7] minority ethnic groups,[8] and economically weaker sections.[9] The aggravators of are not only limited to morphology, genetics and medical conditions but also include socioeconomic factors. It is also observed that these factors not only aggravated the disease but also resulted in mortality.

The number of deaths in a given region is never an accurate measure of mortality. A fitting measure of mortality should consider the chance of mortality as opposed to just the frequency of mortality. One such measure is case fatality rate (CFR). The regions with high CFR are considered to be regions that are worst hit by the pandemic. The negativities of the pandemic is not only the loss of life but also the accompanied burden of the disease on the economy, mental health,[10] and societal changes in the region.[8],[9]

CFR, however, efficient cannot ascertain the effects of a disease on the socioeconomic stability of a region. CFR alone will only present us with a limited perspective of the negative impact of the pandemic. That is, there is always a need for more sophisticated measures that can clear the clutter.

Hence, this work focuses on measures like DALY and the cost of permanent loss of productivity to the GDP due to COVID-19 on the study region, Karnataka, a large state in southern India. Karnataka is a key player in various aspects of development. The advance estimates (in the absence of COVID-19) of Gross State Domestic Product of Karnataka for the year 2019–2020 was ₹1201,031 Crores with a growth of 6.8%. This study also focused on the loss to the GDP of the state due to premature mortality caused by COVID-19. According to the information provided by the government of Karnataka, as on 24th February 2021, there were 6062 active cases in the state after the first ever case was detected on 8th of March 2020. The total number of infections in the state till 24th February 2021 stands at 930,465 out of which 12,303 have succumbed to either the infection or to the resulting complications.[11],[12]

 Materials and Methods



Source of the data

The way in which COVID-19 differs from previous pandemics is not only the availability of immense real time data on the number of infections, mortality rate, and comorbidities but also the boundless access to this information to the general public. This setting has prompted the administrations around the world to implement and advice measures that keep the infection from spreading along with creating a sense of self-preservation among the populations.

In India, the daily information on the pandemic is published by government (MoFHW)[13] and nongovernment (Worldometer,[7] Covid19.org[11]) organizations alike. For this study, the data is retrieved from COVID-19 information portal of Karnataka government.[12] The data regarding the individual patients were retrieved from the 8th March, 2020 to 21st July, 2020. The data are truncated based on the availability of complete information and nature of death. Life expectancy at various quinquennial age groups is obtained from SRS based abridged life table released by Registrar General and Census Commissioner of India.[14]

For computing permanent loss of productivity and its cost to the GDP of the study area, information regarding labour force participation rate (LFPR) and unemployment rate (UER) are retrieved from selected sources.[15] The projected population of Karnataka for the year 2020 is obtained from report on population projections released by government of India.[16] The average minimum annual wages (MAWs) for the Karnataka are deduced from the minimum wage notification released by the labor commissioner office of the state.[17] The estimates of the SGDP are obtained from the report of the economic survey released by the state.[18]

Methods

DALY

In this study, the GBD concept introduced by Murray and Lopez[19] is used to assess the impact of COVID-19 on the study area. The Global Burden of Disease employs disability adjusted life years (DALYs) which is a summary measure that combines the mortality and morbidity components of a disease. It measures the health gaps in a population affected by a disease as the difference between the current situation and a hypothetical situation where every individual of a population lives up to the standard life expectancy.

DALY = YLL (Mortality) + YLD (Mortality) (1)

Where

YLL = Number of deaths × Life expectancy

at the age of death (2)

YLD = Number of cases × duration till

recovery of death × disability weight (3)

The disability weights (DW) are societal preferences for different health states and are scaled from 0 (good health) to 1 (worst health). The disability weights for different health conditions are given in the GBD study.[20] However, currently there is no DW for COVID-19 is available. Yet, the DW for lower respiratory infection (0.133) can be used for computation of DALY since it presents with similar case definition of COVID-19.[21]

More elaborate modifications to (2) and (3) are employed. The modifications given below are aimed at adjusting for the value of life depending on age (age weighting), and value of life depending on time (social discounting).[21],[22],[23]

[INLINE:1]

and

[INLINE:2]

Where a is the age at death, is the social discount rate generally set at 3%, β is the age weighting constant generally set at 4%, K is the age modulating factor (0: No weighting, 1: Weighting), C is the adjustment constant for age weighting which is set at 0.1658, L is the life expectancy at the age of death, M is the number of death, N is the number of cases and DW is the disability weight.

Permanent productivity loss

The human capital approach is employed to compute the permanent productivity loss (PPL) due to the pre-mature mortality of COVID-19. The computations of PPL are carried out based on the methodology given in Pearce et al.[24] and Nurchis et al.[21] That is, for computation of total PPL (TPPL) only the deaths among working ages (15–60) are considered. The working ages are divided into 9 age groups according to the availability of LFPR and UER viz. 15–20, 20–25,…, 55–60 and PPL for every individual in an age group (i) is computed as:

[INLINE:3]

Where, is the working age group, is the productive years of life lost due to premature mortality, is the annual adjusted wage and is the discount rate for individual's future annual wages. The parameters of (4) are obtained as:

PYLL = Retirement age – Age at death,

AAW = MAW × LEPR × (1 – UER)

and r = 4.25%.

Then, TPPL is computed as the sum of the products of IPPL and number of deaths for each age group as:

[INLINE:4]

 Results



YLL and YLD

For the case when K = 0, for males, the total YLL was 17,499 while the total YLD equaled 235. Whereas for females, the YLL was at 9032 and the YLD equaled 140. The situation was not different even if age weighting is considered. For K = 1, the total YLL for males and females were at 14,522 and 7,519, respectively. The YLDs for males and females were at 290 and 175, respectively. [Table 1] displays the YLL, YLD and DALY for males and females, respectively.{Table 1}

It can also be noted that, during the period of study, the highest YLL and YLD were recorded for the males in the age groups 45–50 and 25–30, respectively. In case of females, the most losses in life years and life with the disability were recorded among people in 55–60 and 25–30 age groups. [Figure 1] compare the YLL and YLD for males and females when K = 1.{Figure 1}

DALY

The total DALY for the study region as on 21st July, 2020 due to COVID-19 was 22,506 which is 0.3180 per 1000 population. Of these DALY, males contributed 14,813 (65.82%) and females attributed remaining 7693 DALY. The DALY per 1000 population was also more in male population at 0.4128 as compared to 0.2205 in females. The highest DALYs are observed in males belonging to the age bracket 45–50. In case of females, the highest DALY is recorded among females aged between 55 and 60 years. The age specific DALYs for K = 1 are presented in [Figure 2].{Figure 2}

Losses in productivity

[Table 2] display the TPPL for different age groups. According to the [Table 2], as of the cut-off date, considering the deaths among the working age groups, the productive years of life lost was estimated to be around 208 years. Assuming a minimum wage earning, this resulted in a loss of around ₹590 Million to the SGDP due to loss of productivity caused by premature mortality. Its impact on SGDP is around 0.005%. The highest loss in productivity is attributed to the working age group of 45–50, which was estimated at around ₹171 million, about 0.0014% of the SGDP.{Table 2}

 Discussion



In this study, DALY is used to ascertain the burden of COVID-19 on Karnataka. DALY as a metric of burden of disease is a validated method employed immensely across the globe since its inception in the 1990s. The computations DALY can be standardized based on the population size, so that the estimates are comparable.

According to the results of the current study, the major burden of COVID-19 is due to the premature mortality resulting from the infection. It is observed that 97.94% of the DALY can be attributed to YLL and a mere 2.06% of the burden of COVID-19 is attributed to YLD.

Furthermore, it is observed that males are experiencing more than half of the burden of COVID-19 contributing around 65% of the total DALY. The same finding in other studies across the world further supports the argument. Further, middle aged males are found to be contributing more toward DALY as compared to any other section of the population. This is evidenced by the fact that highest DALY is for males aged between 45 and 50 years.

The DALY per 1000 population of the state is estimated at 0.3394, which is less than the DALY of 2.01/1000 population of Italy as of 28th April 2020[21] and greater than the DALY of 0.0493/1000 population of South Korea as of 24th April, 2020.[25]

The in the study area as of the cut-off date was at 22,041 years, of which males contributed around 65% of total YLL. The highest YLL were observed in males aged 45–50 and in females aged 55–60. The YLL per million males and females are 431.66 and 230.08, respectively. That is, males have more burden due to mortality as compared to females.

Also, these initial estimates of YLL are lower than Italy: 120,814 (as on 28th April, 2020),[21] United States of America (USA): 245,246 (as on 28th May, 2020) and New York state, USA: 94,265 (as on 29th May, 2020) but greater than Germany: 10,865 (as on 27th May, 2020)[26] and South Korea: 2271 (as on 24th April, 2020).[25]

Hence, the YLL estimates from the region imply that the burden of COVID-19 in Karnataka is lower as compared to New York state which has lower population but greater than Germany and Korea with comparable population sizes. When compared with the YLL per death, the YLL per death for Karnataka was at 11.78 (1870 deaths) against New York's 3.10 (30,340 deaths),[26] Germany's 1.27 (8533 deaths),[26] Italy's 4.40 (27,428 deaths),[21] and South Korea's 9.46 (240 deaths).[25] This might imply that the average age at death in Karnataka is lower than South Korea, Italy, New York and Germany.

As far as YLD is concerned, highest YLD of 43 among males and 28 among females are observed for the same age group of 25–30. The YLD per 1000,000 males and females for the Karnataka are estimated to be 8.62 and 5.35, respectively. Therefore, males have greater YLD as compared to females implying that morbidity is high in males as compared to females.

With respect to loss in productivity, the working population of the Karnataka lost around 208 years of potential life resulting in around ₹590 million losses to the state up till 21st July, 2020 which is around 0.05% of the SGDP. This is less than the Italy's loss of EUR 300 Million up till 28th April, 2020.[21]

Limitations

This study is a novel analysis of the burden of COVID-19 for the study region. It used publically available information about the infections of COVID-19 published by the government of Karnataka and contained demographic information of the people infected by the coronavirus.

The estimates provided here are based on the limited information. That is, with more recent data the numbers quoted in the current study may be dragged upward. A more recent study, on DALY of Maharashtra, India can be referred in this regards.[27] Furthermore, It is vital to note that, this data contained the infected that were identified either because of the symptomatic nature of the infection or were results of testing of the suspected cases. Hence, the true number of infections and subsequent estimates are impossible to obtain. Furthermore, these estimates are based on the number of cases recorded during the initial stages of the pandemic and do not reflect the full burden of COVID-19.

Even though the study sheds light on the burden of disease that was physically observed among the identified cases, it does not cover the burden of sequelae resulting from the coronavirus infection such as the mental health issues caused by the infection,[10] Cardio-vascular complications solely due to COVID-19,[28] Large-vessel stroke among infected youth.[29]

Furthermore, all the estimates discussed in this work quantified only the tip of the ice burg and do not present a comprehensive picture of the actual situation. This is due to the lack of epidemiological and socioeconomic information on all the COVID-19 infected cases ever identified in the study region.

 Conclusions



The novel coronavirus is an emerging threat that is posing questions of stability in front of societies. This pandemic has affected lives and livelihoods of billions of people and is continuing to wreak havoc in every corner of the world.

Apart from the mortality aspect of COVID-19, it is also important to identify and quantify the burden caused by the infection on societies and economies. Hence, this work presented measures that are sophisticated than simple measures of mortality and focused on the burden of disease caused by the mortality along with the morbidity stemming from the infection using methods that are referenced in scientific literature and are endorsed by the WHO.[30]

It is found from the study that the burden of COVID-19 is higher among males as compared to females and it is more among the elderly. This pandemic has also resulted in a loss of ₹590 million to the SGDP of Karnataka. These estimates, apart from helping in understanding the burden of the pandemic, are also helpful in materializing targeted health and economic policies to deal with emergencies like this.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.

References

1Clinical Management Protocol: COVID-19, Government of India, MoHFW, Directorate General of Health Services (EMR Division); 3rd July, 2020. Available from: https://www.mohfw.gov.in/pdf/UpdatedClinicalManagementProtocolforCOVID19dated03072020.pdf. [Last accessed on 2021 Mar 21].
2Nogrady B. What the data say about asymptomatic COVID infections. Nature 2020;587:534-5.
3Gao Z, Xu Y, Sun C, Wang X, Guo Y, Qiu S, et al. A systematic review of asymptomatic infections with COVID-19. J Microbiol Immunol Infect 2021;54:12-6.
4COVID-19: Long-Term Effects of COVID-19. Centers for Disease Control and Prevention; 13 November, 2020. Available from: https://www.cdc.gov/coronavirus/2019-ncov/long-term-effects.html. [Last accessed on 2021 Mar 24].
5Summary of COVID-19 Long Term Effects: Emerging Evidence and Ongoing Investigation. COVID-19 Literature Report Team; 01 September, 2020. Available from: https://www.doh.wa.gov/Portals/1/Documents/1600/coronavirus/SummaryCOVIDLong%20termHealthEffects9-1-2020.pdf. [Last accessed on 2021 Mar 24].
6Bhat S, Kolla R, Shindhe SD, Munoli SB. Mortality among the early cases of COVID-19 and its association with key comorbidities in a large state in south India. Int J Community Med Public Health 2020;7:3882-6.
7Coronavirus Update (live): Worldometer. Available from: https://worldometers.info/coronavirus/. [Last accessed on 2021 Mar 25].
8Laurencin CT, McClinton A. The COVID-19 pandemic: A call to action to identify and address racial and ethnic disparities. J Racial Ethn Health Disparities 2020;7:398-402.
9Dorn AV, Cooney RE, Sabin ML. COVID-19 exacerbating inequalities in the US. Lancet 2020;395:1243-4.
10Galea S, Merchant RM, Lurie N. The mental health consequences of COVID-19 and physical distancing: The need for prevention and early intervention. JAMA Intern Med 2020;180:817-8.
11Covid19.Org. Crowd Sourced Initiative. Available from: https://www.covid19india.org [Last accessed on 2021 Mar 25].
12Covid-19 Information Portal. Government of Karnataka, India. Available from: https://covid19.karnataka.gov.in. [Last accessed 2021 Mar 25].
13Covid-19 Information Portal. Government of India. Available from: https://www.mygov.in/covid-19. [Last accessed 2021 Mar 25].
14Registrar General and Census Commissioner of India. SRS Based Abridged Life Tables 2014-18. Office of the Registrar General & Census Commissioner, India Ministry of Home Affairs, Government of India; September, 2020.
15Centre for Monitoring Indian Economy. Unemployment in India – A Statistical Profile; May-August, 2020. Available from: https://www.cmie.com. [Last accessed 2021 Mar 25].
16National Commission on Population. Report on Population Projections for India and States 2011-2036. National Commission on Population, Ministry of Health and Family Welfare; November, 2019. Available from: https://nhm.gov.in. [Last accessed 2021 Mar 25].
17Labour Commissioner Office. Report on Minimum Rates of Wages for the Year 2019-20. Labour Commissioner Office, Government of Karnataka; June, 2020. Available from: https://karmikaspandana.karnataka.gov.in. [Last accessed 2021 Mar 25].
18Planning, Programme Monitoring and Statistics Department. Report on 42nd Economic Survey of Karnataka 2019-20. Government of Karnataka, India; March, 2020.
19Murray CJ, Lopez AD. Evidence-based health policy-lessons from the global burden of disease study. Science 1996;274:740-3.
20Global Burden of Disease Collaborative Network. Global Burden of Disease Study 2017 (GBD 2017) Disability Weights; 2018.
21Nurchis MC, Pascucci D, Sapienza M, Villani L, D'Ambrosio F, Castrini F, et al. Impact of the burden of COVID-19 in Italy: Results of disability-adjusted life years (DALYs) and productivity loss. Int J Environ Res Public Health 2020;17:4233.
22Krahn M, Gafni A. Discounting in the economic evaluation of health care interventions. Med Care 1993;31:403-18.
23Devleesschauwer B, Havelaar AH, Maertens de Noordhout C, Haagsma JA, Praet N, Dorny P, et al. Calculating disability-adjusted life years to quantify burden of disease. Int J Public Health 2014;59:565-9.
24Pearce A, Sharp L, Hanly P, Barchuk A, Bray F, de Camargo Cancela M, et al. Productivity losses due to premature mortality from cancer in Brazil, Russia, India, China, and South Africa (BRICS): A population-based comparison. Cancer Epidemiol 2018;53:27-34.
25Jo MW, Go DS, Kim R, Lee SW, Ock M, Kim YE, et al. The burden of disease due to COVID-19 in Korea using disability-adjusted life years. J Korean Med Sci 2020;35:e199.
26Mitra AK, Payton M, Kabir N, Whitehead A, Ragland KN, Brown A. Potential years of life lost due to COVID-19 in the United States, Italy, and Germany: An old formula with newer ideas. Int J Environ Res Public Health 2020;17:4392.
27Vasishtha G, Mohanty SK, Mishra US, Dubey M, Sahoo U. Impact of COVID-19 infection on life expectancy, premature mortality, and DALY in Maharashtra, India. BMC Infect Dis 2021;21:343.
28Zhu H, Rhee JW, Cheng P, Waliany S, Chang A, Witteles RM, et al. Correction to: Cardiovascular complications in patients with COVID-19: Consequences of viral toxicities and host Immune response. Curr Cardiol Rep 2020;22:36.
29Oxley TJ, Mocco J, Majidi S, Kellner CP, Shoirah H, Singh IP, et al. Large-vessel stroke as a presenting feature of Covid-19 in the young. N Engl J Med 2020;382:e60.
30World Health Organization, the Global Health Observatory: Disability-Adjusted Life Years (DALYs). Available from: https://www.who.int/data/gho/indicator-metadata-registry/imr-details/158.. [Last accessed 2021 Mar 25].