Home About us Editorial board Ahead of print Current issue Archives Submit article Instructions Subscribe Contacts Login 
  • Users Online: 156
  • Home
  • Print this page
  • Email this page


 
 Table of Contents  
ORIGINAL ARTICLE
Year : 2020  |  Volume : 27  |  Issue : 4  |  Page : 293-301

Epidemiological determinants of COVID-19 infection and mortality: A study among patients presenting with severe acute respiratory illness during the pandemic in Bihar, India


Department of Community and Family Medicine, All India Institute of Medical Sciences, Patna, Bihar, India

Date of Submission13-Sep-2020
Date of Decision16-Sep-2020
Date of Acceptance12-Oct-2020
Date of Web Publication04-Nov-2020

Correspondence Address:
Dr. Bijit Biswas
Department of Community and Family Medicine, All India Institute of Medical Sciences, Phulwarisharif, Patna - 801 507, Bihar
India
Login to access the Email id

Source of Support: None, Conflict of Interest: None


DOI: 10.4103/npmj.npmj_301_20

Rights and Permissions
  Abstract 


Objectives: The study was designed to explore epidemiological characteristics, determinants of COVID-19 infection development and mortality of patients presenting with severe acute respiratory illness (SARI) to a tertiary care health facility of Bihar. Methods: This was an observational record-based study, longitudinal in design. Data of 281 SARI patients who have attended All India Institute of Medical Sciences, Patna, Bihar, India during 25th April 2020, till 12th July 2020 (16 weeks) were used for the study. Results: Out of 281 study participants, 95 (33.8%) were detected to have COVID-19 and 42 (14.9%) died. Among COVID-positive study subject's death rate was 28.4%. In the multivariable logistic regression analysis; increasing age (adjusted odds ratio [AOR] = 1.02 [1.00–1.03]), gender (males) (AOR = 2.51 [1.27–4.96]), presenting symptom (cough) (AOR = 2.88 [1.46–5.70]), co-morbidity (hypothyroidism) (AOR = 4.59 [1.45–14.56]) and delay between symptom onset and admission (>2 days) (AOR = 2.46 [1.19–5.07]) were significant predictors of COVID-19 infection among study participants adjusted with other co-morbidities (diabetes and hypertension). Similarly, place of residence (outside Patna district) (AOR = 2.38 [1.03–5.50]), co-morbidity (diabetes) (AOR = 3.08 [1.12–8.50]), intensive care unit (ICU) requirement at admission (yes) (AOR = 9.47 [3.98–22.52]) and COVID status (positive) (AOR = 6.33 [2.68–14.96]) were significant predictors of death among the study participants whereas place of residence (outside Patna district) (AOR = 4.04 [1.33–12.28]) and ICU requirement at admission (yes) (AOR = 7.22 [2.54–20.52]) were attributes affecting death of COVID-positive study participants. Conclusion: Risk of COVID-19 infection among the study participants was high. Age, gender and co-morbidities increased the risk of infection. COVID-19 infection negatively impacted the treatment outcome of the study participants. Age, co-morbidity and ICU requirement were the other attributes affecting mortality.

Keywords: COVID-19, diabetes, intensive care, mortality, severe acute respiratory illness


How to cite this article:
Agarwal N, Biswas B, Lohani P. Epidemiological determinants of COVID-19 infection and mortality: A study among patients presenting with severe acute respiratory illness during the pandemic in Bihar, India. Niger Postgrad Med J 2020;27:293-301

How to cite this URL:
Agarwal N, Biswas B, Lohani P. Epidemiological determinants of COVID-19 infection and mortality: A study among patients presenting with severe acute respiratory illness during the pandemic in Bihar, India. Niger Postgrad Med J [serial online] 2020 [cited 2020 Nov 24];27:293-301. Available from: https://www.npmj.org/text.asp?2020/27/4/293/299918




  Introduction Top


COVID-19 is rapidly spreading its roots to the different parts of the world, affecting billions of lives.[1],[2] The pandemic is causing social and economic disruptions and overwhelming health-care systems. Different strategic public health and social measures (PHSM) have been implemented by the affected countries to slow its spread. Some of these PHSMs are restriction in domestic and international travel, social gatherings, closing of schools, offices, religious places, etc.[3],[4] As the pandemic is disrupting social and economic activities, there was a need for implementation of effective surveillance measures to elicit even a smaller change in the disease epidemiology. Therefore, World Health Organisation suggested influenza-like illness surveillance at the community level and severe acute respiratory illness (SARI) surveillance at the facility level to enable policymakers to keep track on the pandemic. Based on the findings of this surveillance, economic and social activities may be allowed to minimise the detrimental effects of the pandemic.[3],[4],[5]

SARI, by definition, is an acute respiratory illness (ARI) requiring hospitalisation with measured body temperature ≥38°C or history of fever along with cough; onset within the past ~ 10 days.[6] Novel coronavirus may present with varying degrees of severity, namely mild, moderate or severe. The spectrum of the severest form of the disease includes acute respiratory distress syndrome (ARDS), severe pneumonia, septic shock, etc., Early recognition of suspected patients with SARI allows for timely initiation of vigilant clinical management and enforce infection prevention and control measures to prevent further individual and geographical spread.[7]

India started SARI surveillance from the very beginning of the pandemic. Although very few evidence exists on epidemiological determinants of COVID infection and mortality of SARI patients, especially in Indian settings.[8],[9] Knowledge of epidemiological characteristics and determinants of COVID infection is crucial in terms of planning interventions to reduce transmission risk to their contacts, geographical limitation and health logistics planning to treat SARI patients. On the other hand, knowledge of predictors of mortality of SARI patients may help in early identification of risk factors for its minimisation. Thus, the current study was planned to explore epidemiological characteristics, determinants of COVID-19 infection development and mortality of patients presenting with SARI to a tertiary care health facility of Bihar, situated in the eastern part of India.


  Methods Top


This was an observational record-based study, longitudinal in design. SARI patients who have attended All India Institute of Medical Sciences (AIIMS), Patna, Bihar, India between 25th April and 12th July, 2020 (16 weeks) were the study participants. AIIMS-Patna is one of the centres of excellence in terms of medical education and treatment in India. During the study period, it was catering both COVID and non-COVID patients. Later, it was designated as COVID dedicated hospital by the Government of Bihar. Data sources for the study were SARI patients line list of AIIMS, Patna. There were in total of 281 SARI patients who have attended AIIMS-Patna during the study period. Data of all these 281 SARI patients were included in the study using the complete enumeration method. Ethical clearance of the Institutional Ethics Committee (IEC) of AIIMS-Patna (Ref. No.-AIIMS/Pat/IEC/2020/523 dated 7th August 2020) was taken before conducting the research. Informed written consent of the study participants could not be taken as it is a retrospective data analysis of routinely collected data. During analysis and drafting of the manuscript, their confidentiality was assured.

The SARI line list dataset was maintained and updated daily from 25th April 2020, as per government directives.[10] The SARI line list dataset contained following variables: case registration number (a unique number assigned to a case on arrival), age (in completed years), sex (male, female), place of residence (native district), whether patient a health-care worker (yes, no), contact history with a positive case (yes, no), travel history (yes, no), whether quarantined before (yes, no), presenting symptoms (i.e., fever, cough, breathing difficulty etc.), co-morbidity (yes, no), if co-morbid name of co-morbidity (i.e., hypertension, diabetes, hyperthyroidism, chronic obstructive pulmonary disease, asthma, cancer etc.), whether required intensive care unit (ICU) at admission (yes, no), COVID status (positive, negative) and treatment outcome (death, discharge).

Some definitions used in the study were as follows:

COVID status

For determining this nasopharyngeal or oropharyngeal swab was taken using the standard operating procedure by a trained otolaryngologist. Then, the sample was subjected to real-time polymerase chain reaction test to determine COVID status of the study participants.

Severe acute respiratory illness

Those who presented with an ARI requiring hospitalisation with measured body temperature ≥38°C or history of fever along with cough; onset within the last ~10 days were considered as having SARI.[7]

Statistical analysis

Data were analysed using IBM SPSS (Chicago, USA) (version 22). At first, bivariate analysis was performed using the Chi-square test and independent samples t-test to find out significant associates of COVID status and mortality of the study participants. This was followed by bivariate logistic regression analysis to find out the strength of the association of COVID status and mortality with their significantly associated attributes. Finally, statistically associated variables in bivariate analysis were entered into the multivariable logistic regression model using forced entry method. Model fit was assessed using the Hosmer-Lemeshow test. This was done to find out multivariable associates of COVID status and mortality of the study participants. The minimum acceptable confidence level was α = 0.95 for all statistics, and the maximum acceptable significance level was P < 0.05.


  Results Top


Among the study participants, 95 (33.8%) were found to be COVID positive, while 42 (14.9%) of them eventually died. Overall, the mortality rate was about three times higher in COVID positives (28.4%) compared to others (8.1%). The trend of different outcomes of the study participants attending the current healthcare facility during the study period is depicted in [Figure 1].
Figure 1: Trend of different outcomes of patients with Severe Acute Respiratory Illness attending the study setting: n =281

Click here to view


Most of the study participants belonged to Patna district (54.1%) followed by Saran (4.6%) and Bhojpur (3.9%). Considering COVID positivity, study participants who belonged to Kishanganj district had a cent percent COVID positivity followed by Darbhanga (85.7%) and Siwan (75.0%). In contrast, those who belonged to Patna district showed 36.2% COVID positivity rate. Geographical distribution of the proportion of COVID positivity as per native district is depicted in [Figure 2]. Considering the mortality rate, study participants who belonged to Lakhisarai registered cent percent mortality followed by Arwal, Vaishali, Siwan and Madhubani, which have shown a 50% mortality rate each. Notably, study subjects who belonged to Patna district have shown 8.6% mortality rate. Geographical distribution of the proportion of mortality as per their native district is depicted in [Figure 3].
Figure 2: Map of Bihar showing the distribution of the proportion of COVID infection among the study participants as per their native districts: n = 281

Click here to view
Figure 3: Map of Bihar showing the distribution of proportional mortality rate of the study participants as per their native districts: n= 281

Click here to view


The mean age of the study participants was 42.5 ± 18.4 years (range: 0–90 years) while the mean age of the COVID-positive study participants was 47.7 ± 15.9 years (range: 13–83 years). More than two-thirds (69.8%) of the study participants were males whereas, among COVID positive study participants, it was about four-fifth (83.2%). Among all the study participants 6.4% were health-care workers, while 6.4% and 3.2% had a history of contact with a positive case and travel, respectively. Fever was the most common presenting symptom, followed by cough and breathing difficulty. Two-fifth (39.5%) of the study participants reported having co-morbidities with diabetes (14.2%) being the most common co-morbidity followed by hypertension (13.8%) and hypothyroidism (5.3%). The median delay between symptom onset and admission in the current health-care facility was 5 days with interquartile range of 2–7 days. About one-fourth of them (28.8%) required ICU support at the time of admission. In univariate analysis, age, gender, presenting symptom (cough), co-morbidities (diabetes, hypertension and hypothyroidism), delay between symptom onset and admission and ICU requirement was significantly associated with COVID status of the study participants. Similarly, age, place of residence (outside Patna district), presenting symptom (breathing difficulty), co-morbidity (diabetes), ICU requirement at the time of admission and COVID status was significantly associated with treatment outcome of the study participants. Considering the treatment outcome of COVID positive study participants; place of residence and ICU requirement at the time of admission were significant associates of it [Table 1], [Table 2], [Table 3].
Table 1: Distribution of the study participants as per their background characteristics and COVID status (n=281)

Click here to view
Table 2: Distribution of the study participants as per their background characteristics and treatment outcome (n=281)

Click here to view
Table 3: Distribution of COVID-positive study participants as per their background characteristics and treatment outcome (n=95)

Click here to view


In the multivariable logistic regression analysis; increasing age (adjusted odds ratio [AOR] = 1.02 [1.00–1.03]), gender (males) (AOR = 2.51 [1.27–4.96]), presenting symptom (cough) (AOR = 2.88 [1.46–5.70]), co-morbidity (hypothyroidism) (AOR = 4.59 [1.45–14.56]) and higher delay between symptom onset and admission (>2 days) (25th percentile) (AOR = 2.46 [1.19–5.07]) were significant predictors of COVID-19 infection among study subjects adjusted with other co-morbidities (diabetes and hypertension). Overall, the model explained 22.5% variability of the outcome variable with predictive accuracy rate of 69.4%, whereas a non-significant Hosmer Lemeshow test (P = 0.599) indicated model fit. Similarly, place of residence (outside Patna district) (AOR = 2.38 [1.03–5.50]), co-morbidity (diabetes) (AOR = 3.08 [1.12–8.50]), ICU requirement at admission (yes) (AOR = 9.47 [3.98–22.52]) and COVID status (positive) (AOR = 6.33 [2.68–14.96]) were significant predictors of death among the study participants, whereas place of residence (outside Patna district) (AOR = 4.04 [1.33–12.28]) and ICU requirement at admission (yes) (AOR = 7.22 [2.54–20.52]) were attributes affecting death of COVID-positive study participants [Table 4], [Table 5], [Table 6].
Table 4: Univariate and multivariable logistic regression analysis showing predictors of COVID status among the study participants (n=281)

Click here to view
Table 5: Univariate and multivariable logistic regression analysis showing predictors of mortality of the study participants (n=281)

Click here to view
Table 6: Univariate and multivariable logistic regression analysis showing predictors of mortality of the COVID-positive study participants (n=95)

Click here to view



  Discussion Top


This study was observational record-based which aimed to explore epidemiological characteristics, determinants of infection (COVID-19) development and treatment outcome of patients presenting with SARI to a tertiary care health facility of Bihar, situated in eastern India. In this study, we found 33.8% COVID positivity among the study participants which was similar with the prior study in New Delhi[9] (39.0%) but quite high compared to the study done by Gupta et al.[8] (1.8%). The possible reason of difference could be Gupta et al.[8] analysed the data of SARI patients from 15th February to 2nd April 2020, which was the early stage of the COVID-19 pandemic in India when COVID-19 cases in the country were quite low itself compared to the present date. Although considering the case fatality rate, the New Delhi study[9] reported it to be 28.0% which was almost twice as much compared to our observations (14.9%). The reason could be the difference in study population characteristics (i.e., disease severity, ICU care requirement etc.). For example, proportion of patients that required ICU care in the New Delhi study[9] was reported to be 37.5% which was quite higher compared to our observations (28.8%).

We found that with the increase of age, the risk of infection and mortality increases in the study participants irrespective of their COVID status. It was similar to the findings of a study in Brazil by Niquini et al.[11] which reported a higher proportion of COVID infection in SARI patients with increasing age. The findings were also in line with the findings of Gupta et al.[8] which reported higher COVID positivity in the age group of 40–69 years compared to others. The reason could be due to the increase in chances of having co-morbidities (especially non-communicable diseases like diabetes) with increasing age which are known risk factors for COVID-19 infection and prognosis.[12],[13] Concerning gender, males were found to be more prone to COVID infection, although this did not hold for mortality. The study conducted in Brazil[11] and a prior Indian Council of Medical Research (ICMR) laboratory surveillance[14] and Gupta et al.[8] reported similar findings. The reason could be higher exposure in males compared to females owing to their several outdoor activities (i.e., job related, marketing of household goods, etc.).

In the present study, those who resided outside the Patna district were at higher risk of mortality compared to others. However, COVID positivity was reported to be higher among those who resided in Patna district. This may be because as AIIMS Patna is situated in Patna district, study participants who were residing outside Patna district had to travel long distance and time in order to reach the institute. On the other hand, those who resided in Patna district by virtue of easy accessibility they seeked healthcare from AIIMS Patna despite having a relatively milder form of the disease compared to others. Distance and travel time to a health-care facility are established predictors of mortality, especially in patients needing urgent medical attention.[15],[16] The other possible reason could be as AIIMS-Patna is a centre of excellence and referral institute for most of the hospitals in the region, patients with a relatively severe form of the disease and worst prognosis are usually referred to the institute to seek specialist healthcare.

Considering presenting symptoms, those who reported cough as one of the presenting symptoms were more at risk of being diagnosed with COVID-19, while those who presented with breathing difficulty were more at risk of mortality. This was in concordance with the findings of a prior report of ICMR COVID study group[14] conducted on 1,021,518 individuals which reported cough as the most common symptom among those who reported being COVID positive. Considering co-morbidity, diabetes emerged as a significant risk factor for both COVID infection and mortality, whereas those who had hypertension and hyperthyroidism were more at risk of infection. This was similar to the findings of a New Delhi study by Aggarwal et al.[9] conducted among 82 SARI patients which reported diabetes and hypertension as significant associates of primary composite outcome (admission to an ICU, the use of mechanical ventilation or death). The reasons for variability of findings could be, the difference in study population (varying degree of severity of cases) and the outcome variable (determined associates of primary composite outcome) which were unlike us.

We observed that those who required ICU at admission were more at risk of mortality. The possible cause could be due to the relatively higher severity of the disease, poor prognosis of these study participants compared to others. Considering COVID status, those who were COVID positive had 6.3 times higher odds of mortality compared to others. This may be because as nCoV-19 is a new virus causing SARI, there are limited effective treatment options for SARI associated with n-CoV-19 infection compared to others.[17],[18]

In limitations, most of the data were self-reported by the study participants, so there may be reporting and social desirability-related biases. Second, as it was an institute-based study and complete enumeration method was followed for inclusion, so chances of Berksonian bias was there, and generalisability of the findings of the study to other tertiary health-care facilities is limited. Finally, as the study is based on a routinely collected data, specific other probable predictors of mortality in the study population (i.e., awareness regarding the disease, obesity, addiction profile, clinical course of the disease, laboratory parameters, etc.) could not be assessed due to non-availability of those data.


  Conclusion Top


Risk of COVID-19 infection among the study participants was high. Age, gender, co-morbidities and higher delay between symptom onset and admission increased the risk of infection. COVID-19 infection negatively impacts treatment outcome of the study participants. Age, co-morbidity and ICU requirement was found to be the other attributes affecting mortality. Through SARI surveillance, identification of certain risk factors such as age, sex, co-morbidity, presenting symptom, distance from health-care facility, intensive care requirement and COVID status in SARI patients may be useful in determining their prognosis early and therefore may help in averting mortality among them. SARI surveillance may also serve as a useful tool in determining geographical spread and trend of the disease in case of infectious disease pandemics like COVID-19.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
  References Top

1.
World Health Organisation. Coronavirus Disease (COVID-19): Situation Report – 2017. Available from: https://www.who.int/docs/default-source/coronaviruse/situation-reports/20200814-covid-19-sitrep-207.pdf?sfvrsn=2f2154e6_2. [Last accessed on 2020 Aug 14].  Back to cited text no. 1
    
2.
Coronavirus Update (Live): 21,355,685 Cases and 763,367 Deaths from COVID-19 Virus Pandemic Worldometer. Available from: https://www.worldometers.info/coronavirus/. [Last accessed on 2020 Aug 15].  Back to cited text no. 2
    
3.
World Health Organisation. Surveillance Strategies for COVID-19 Human Infection. Available from: https://apps.who.int/iris/bitstream/handle/10665/332051/WHO-2019-nCoV-National_Surveillance-2020.1-eng.pdf?sequence=1&isAllowed=y. [Last accessed on 2020 Aug 18].  Back to cited text no. 3
    
4.
World Health Organization. Operational Considerations for COVID-19 Surveillance Using GISRS. Available from: https://apps.who.int/iris/bitstream/handle/10665/331589/WHO-2019-nCoV-Leveraging_GISRS-20200.1-eng.pdf?sequence=1&isAllowed=y. [Last accessed on 2020 Aug 18].  Back to cited text no. 4
    
5.
World Health Organization. Critical Preparedness, Readiness and Response actions for COVID-19. Available from: https://www.who.int/publications-detail-redirect/critical-preparedness-readiness-and-response-actions-for-covid-19. [Last accessed on 2020 Aug 18].  Back to cited text no. 5
    
6.
Ministry of Health and Family Welfare, Government of India. Guidelines on Clinical Management of COVID-19. Available from: https://www.mohfw.gov.in/pdf/GuidelinesonClinicalManagementof COVID1912020.pdf. [Last accessed on 2020 Aug 18].  Back to cited text no. 6
    
7.
National Centre for Disease Control, Government of India. Guidelines on Clinical Management of Severe Acute Respiratory Illness (SARI) in Suspect/Confirmed Novel Coronavirus (nCoV) Cases. Available from: https://ncdc.gov.in/WriteReadData/l892s/96997299691 580715786.pdf. [Last accessed on 2020 Aug 18].  Back to cited text no. 7
    
8.
Gupta N, Praharaj I, Bhatnagar T, Vivian Thangaraj JW, Giri S, Chauhan H, et al. Severe acute respiratory illness surveillance for coronavirus disease 2019, India, 2020. Indian J Med Res 2020;151:236-40.  Back to cited text no. 8
[PUBMED]  [Full text]  
9.
Aggarwal A, Shrivastava A, Kumar A, Ali A. Clinical and epidemiological features of SARS-CoV-2 patients in SARI ward of a tertiary care centre in New Delhi. J Assoc Physicians India 2020;68:19-26.  Back to cited text no. 9
    
10.
Health Department, Government of Bihar. Regrading Severe Acute Respiratory Illness (SARI) Cases and Influenza-like Illness Surveillance. Available from: https://prsindia.org/files/covid19/notifications/1864.BR_ILI_SARI_Surveillance_Apr_6.pdf. [Last accessed on 2020 Aug 18].  Back to cited text no. 10
    
11.
Niquini RP, Lana RM, Pacheco AG, Cruz OG, Coelho FC, Carvalho LM, et al. Description and comparison of demographic characteristics and comorbidities in SARI from COVID-19, SARI from influenza, and the Brazilian general population. Cad Saude Publica 2020;36:e00149420.  Back to cited text no. 11
    
12.
Fang L, Karakiulakis G, Roth M. Are patients with hypertension and diabetes mellitus at increased risk for COVID-19 infection? Lancet Respir Med 2020;8:e21.  Back to cited text no. 12
    
13.
Marhl M, Grubelnik V, Magdič M, Markovič R. Diabetes and metabolic syndrome as risk factors for COVID-19. Diabetes Metab Syndr 2020;14:671-7.  Back to cited text no. 13
    
14.
ICMR COVID Study Group, COVID Epidemiology & Data Management Team, COVID Laboratory Team, VRDLN Team. Laboratory surveillance for SARS-CoV-2 in India: Performance of testing & descriptive epidemiology of detected COVID-19. Indian J Med Res 2020;151:424-37.  Back to cited text no. 14
    
15.
Beal EW, Mehta R, Hyer JM, Paredes A, Merath K, Dillhoff ME, et al. Association between travel distance, hospital volume, and outcomes following resection of cholangiocarcinoma. J Gastrointest Surg 2019;23:944-52.  Back to cited text no. 15
    
16.
Tansley G, Schuurman N, Bowes M, Erdogan M, Green R, Asbridge M, et al. Effect of predicted travel time to trauma care on mortality in major trauma patients in Nova Scotia Can J Surg 2019;62:123-30.  Back to cited text no. 16
    
17.
Zhang JJ, Lee KS, Ang LW, Leo YS, Young BE. Risk factors of severe disease and efficacy of treatment in patients infected with COVID-19: A systematic review, meta-analysis and meta-regression analysis. Clin Infect Dis. 2020 May 14:ciaa576. doi: 10.1093/cid/ciaa576. Epub ahead of print. PMID: 32407459; PMCID: PMC7239203.  Back to cited text no. 17
    
18.
Siemieniuk RA, Bartoszko JJ, Ge L, Zeraatkar D, Izcovich A, Kum E, et al. Drug treatments for covid-19: Living systematic review and network meta-analysis. BMJ 2020;370:m2980.  Back to cited text no. 18
    


    Figures

  [Figure 1], [Figure 2], [Figure 3]
 
 
    Tables

  [Table 1], [Table 2], [Table 3], [Table 4], [Table 5], [Table 6]



 

Top
 
 
  Search
 
Similar in PUBMED
   Search Pubmed for
   Search in Google Scholar for
 Related articles
Access Statistics
Email Alert *
Add to My List *
* Registration required (free)

 
  In this article
Abstract
Introduction
Methods
Results
Discussion
Conclusion
References
Article Figures
Article Tables

 Article Access Statistics
    Viewed341    
    Printed0    
    Emailed0    
    PDF Downloaded46    
    Comments [Add]    

Recommend this journal


[TAG2]
[TAG3]
[TAG4]