Study of association of covid 19 disease cases outcome with relation to sociodemographic factors in tertiary care centre in Mumbai, Maharashtra, India

 

 

Abstract              Background: In India uptill now total corona cases are 31,144,229 and death were 4,14,141 and recovered from covid are 30,308,456. The study was carried out in tertiary care centre in Mumbai area. Patients was admitted in hospital after Covid 19 infection. The study was carried out to see the association between Covid 19 patients; there outcome after admission whether discharged or death with their sociodemographic factor like sex, age and residential address. As the disease is deadly and research is still going on in many areas like treatment, vaccination. This study has given a small idea, the proportion of cases there outcome and association with sociodemographic factors. Methods: The present study is record based Hospital study. In six months period total Covid patients admitted in ward are taken for the study. The collected data was analysed using SPSS 15.0. Chi square test of significance was applied. P < 0.05 was considered as statistically significant. Results: A total of 100 patients were in this study. Total patients after covid infection out of which 50 were discharged and 50 succumbed death (Selective sampling). The association of outcome (Death and discharged) is highly significant with age factor but association is not found significant with sex and residential address i.e. resident of Mumbai or out of Mumbai. Conclusion: The present study shows the pattern discharged patients and death among the admitted patients. As age advances death among the patients are on higher side but no association with sex or residential address, they don’t be contributing factors in outcome of the disease.

Keywords: Socio-demographic, Covid, chi square test.

 

INTRODUCTION

World has facing large fatality in all age groups due to covid 19, The country like India faced loss not only in terms of economy but also in human lives. The presentation of patients with COVID-19 requiring hospitalisation differs considerably. Severe acute respiratory syndrome corona virus is likely to have a higher potential for respiratory pathogenicity, leading to more respiratory complications and to higher mortality, in-hospital mortality is higher; however, low patient numbers limit this finding. These findings highlight the importance of appropriate preventive measures for COVID-19, as well as the need for a specific vaccine and treatment. Another potential explanation for the higher mortality of COVID-19 is that the sudden influx of patients over a short period of time created medical structural constraints, and care teams were led to prioritise patients based on clinical status and prognosis.¹The most common symptoms of COVID-19 are fever, cough and difficulty in breathing. Some patients may have aches and pains, nasal congestion, runny nose, sore throat or diarrhea. These symptoms are usually mild and begin gradually. Some people become infected but don’t develop any symptoms and don't feel unwell. Most people (about 80%) recover from the disease without needing special treatment. With cases rising steadily, all sections of our society must play a role if we are to stop the spread of this disease and the frontline health worker has the responsibility, the reach and the influence within the community. In January 2020 the World Health Organization (WHO) declared the outbreak of a new corona virus disease. India has seen it’s first COVID-19 case in Kerala on 30th January 2020.² In India uptill now total corona cases are 31,144,229 and death were 4,14,141 and recovered from covid are 30,308,456. Mumbai is the second most populous city in India after Delhi and the seventh most populous city in the world with a population of 19.98 million. As per Indian government population census of 2011, Mumbai was the most populous city in India with an estimated city proper population of 12.5 million living under Municipal Corporation of Greater Mumbai. Mumbai is the centre of the Mumbai Metropolitan Region, the sixth most populous metropolitan area in the world with a population of over 23.64 million.³ Mumbai's population density is of 20,634 people per square kilometres (sq. km.) provides a happy hunting ground for communicable diseases such as covid-19. In Mumbai more than 60% of COVID19 cases are from most dense wards .^4-6The study was carried out to see the association between covid 19 patients; there outcome after admission. As the disease is deadly and research is still going on in many areas like treatment, vaccination. This study has given a small idea, the proportion of cases there outcome and association with sociodemographic factors.

 

METHODS

Study design: Record based Hospital study. ⁷

Study setting: Tertiary care centre in Mumbai The present study is record based Hospital study. The record of patients admitted in covid ward and their details were studied. In six months period total Covid patients admitted in ward are taken for the study. There details were studied. Outcome like death, discharged and their association with sociodemographic characteristic like age, sex and residential address were studied. The collected data was analysed using SPSS 15.0. Chi square test of significance was applied. P < 0.05 was considered as statistically significant. Contribution to above factor and risk is estimated with odd ratio and relative risk.

Study sampling: The primary data of COVID19 is collected from Ministry of Health and Family Welfare ⁸, COVID19 INDIA,⁹ and local administrative sites. The collected data is cleaned and processed into spreadsheets and arranged in the required manner. The study population is of patient admitted in ward. Selective purposive sampling is taken. Total 100 patients admitted in ward and there outcome is studied, out of 100, 50 patients were discharged and 50 succumbed death. No randomization was undertaken. Outcome assessment was undertaken as part of routine health care, therefore no blinding of any sort was attempted. However, study investigators had no involvement in outcome assessment.

Study period: Six months.

Tools: Their sociodemographic characters were studied and outcome. Association of outcome with sociodemographic character is studied and how it affects the outcome of patients is studied with help of likelihood ratios and risk ratios.

Procedure: Information studied on the basis of records of patients on Hospital record.

Data analysis: Data was entered in Microsoft excel 2007 and analysed using SPSS 15 software. Chi square test was used for statistical analysis. P <0.05 was considered statistically significant.

 

RESULTS

A total of 100 patients were studied in this study. The proportion of Discharged is 50 (i.e.50%) and Died patients were 50(i.e.50%). Age group of patients were from less than ten to less than 90. Mean age group of Died patients is from 40 to 80 years. Discharged patients were mainly from younger age group i.e.10 to 40 years. The association of age group with the outcome of disease is statistically significant (P < 0.05).[Table no.1, 1(a)]. In the present study, proportion of male is more as compare to female i.e. 62%, but no association is found between sex of admitted patients and outcome of patients. (P >0.05) There is lower odds of association between sex and outcome of disease. [Table no.2, 2(a), 2(b)]. The patients in this mostly from residents of Mumbai i.e. 83% and non residents were 17% , it may be because of the locality of hospital; which is centrally located in Mumbai region. Their is no significant association is found between residents of patients and their outcome of the disease. (The chi-square statistic is 3.4727. The p-value is .062389, at p < .05). [Table no.3, 3(a)]. There are lower odds of association between residents of patients and outcome of disease; overall value is less than one. For both the groups i.e. outcome of the disease value is less than one (upper limit 0.114 and lower limit 1.088) and for residence also value less than 1 (upper limit 0.705and lower limit 1.012) [Table no.3, 3(b)]

DISCUSSION

In a study done in US county level, by Monita Karmakar et al.,¹⁰ significant association has found between sociodemographic factors and incidence and moratality of covid 19 cases since Covid 19 outbreak. In present study like Monita Karmarkar study association found with age, which is highly significant. In study done in Brazil, done by I. T. Peres et al. ¹¹.; like our present study this study also showing independent association with high in-hospital mortality after adjusting for sex, age, level of education, region of residence. (odds ratio = 1.15; 95% confidence interval = 1.09–1.22). This study period coincides with present study i.e. Feb 2020 to August 2020. In the present study proportion of Male is more affected in terms of outcome of study same findings were observed in the study done by the Nikunj Kansara, Ashok B. Nandapurkar et al.¹²; the results showed that male gender has a 1.6 times higher risk of mortality than females even after adjusting for age, symptoms, and co morbidities, male gender was associated with an increased risk of mortality from COVID‑19 after adjusting for age and co morbidities. Study has done between the period of April 2020- July 2020 coincides with our present study. Another retrospective case series including 238 patients who hospitalized for COVID-19 at Hospital Universitario Clı´nico San Cecilio (Granada, Spain) either death or discharged; done by Mario Rivera-Izquierdo, Marı´a del Carmen Valero-Ubierna et al. ¹³showed that affected patients were males (55.0%, n = 131). The age range was 24 to 97 years with a mean of 64.7. Most patients (60.5%) were 60 years old or more, and the largest age stratum was 70 to 79 years; findings were to the present study were males were more affected than female.

 

CONCLUSION

The present study shows the pattern of discharged patients and death among the admitted patients. As age advances death among the patients are on higher side, they are significantly associated with outcome of disease; But no association with sex or residential address, they were not be contributing factors in outcome of the disease. Further studies need to be carried out to assess the impact of Sociodemographic factors on outcome of diseases among the different regions. Health education, vaccination are the key factors uptill now to combat the covid 19 pandemic.

 

REFERENCES

1. Lionel Piroth et al., Comparison of the characteristics, morbidity, and mortality of COVID-19 and seasonal influenza: a nationwide, population-based retrospective cohort study. The lancet, respiratory ; December 17, 2020, S2213-2600(20)30527-01.https://doi.org/10.1016
2. Ministry of family and welfare. Guidelines of the School Health Programme. Available from mohfw.nic.inFacilitatorGuideCOVID19_27%20March.pdf. Accessed 16 MAY 2021.
3. B. G. Kodge et al. A review on current status of COVID19 cases in Maharashtra state of India using GIS: a case study. Spat. Inf. Res. (2021) 29(2):223–229.
4. Wikipedia. Data on Mumbai City. Available from: https://en.wikipedia.org/wiki/Mumbai. Accessed 12 April 2021.
5. Wikipedia. Data on Geography of Mumbai City. Available from: https://en.wikipedia.org/wiki/Geography_of_Mumbai. . Accessed 12 April 2021
6. Wikipedia. Data of Mumbai. Census. Available from: https://censusindia.gov.in/maps/Town_maps/mum_muni_corp.sec.aspx_. Accessed 12 April 2021
7. Mahajan B.K.; Sampling, Methods In Biostatistics; 6th Edition; New Delhi Jaypee Brothers Medical Pub. Ltd ; Pg 93.
8. Ministry of family and welfare. Department of Health and family welfare. Data on covid19. Statistic. Available from https://mohfw.gov.in . Accessed 16 February 2021
9. Government of India. Corona outbreak in India. Available from https://www.covid19india.org/. Accessed 16 February 2021
10. Monita Karmakar et al., Public Health Association of Social and Demographic Factors With COVID-19 Incidence and Death Rates in the US. JAMA Network Open. 2021;January 29, 2021; ;4(1)
11. I. T. Peres et al. Sociodemographic factors associated with COVID-19 in-hospital mortality in Brazil. Public Health.RSPH. Vol 192, March 2021, Pages 15-20.
12. Nikunj Kansara, Ashok B. Nandapurkar et al. Prediction of Mortality by Age and Multi‑Morbidities among Confirmed COVID-19 Patients: Secondary Analysis of Surveillance Data in Pune, Maharashtra, India; Indian Journal of Public Health. Volume 65(1): January-March 2021;64-66.
13. Mario Rivera-Izquierdo et al. Sociodemographic, clinical and laboratory factors on admission associated with COVID- 19 mortality in hospitalized patients: A retrospective observational study. June 25, 2020 4 (12).

 

 

 

 

 

 

Policy for Articles with Open Access
Authors who publish with MedPulse International Journal of Anesthesiology (Print ISSN:2579-0900) (Online ISSN: 2636-4654) agree to the following terms:
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
Authors are permitted and encouraged to post links to their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.