Delay of Detection Of COVID-19 Patients In Bangladesh; An Application To Cox Proportional Hazard Model

doi:10.21203/rs.3.rs-117305/v1

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Delay of Detection Of COVID-19 Patients In Bangladesh; An Application To Cox Proportional Hazard Model

https://doi.org/10.21203/rs.3.rs-117305/v1

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Objective: To delineate the survival rate of the patients with coronavirus disease 2019 (COVID-19) who did the diagnostic tests lately after the development of symptoms. The aim is to determine the socio-demographic risk factors associated with the delay of the detection of COVID-19 patients.

Methods: For this cross-sectional study, 300 patients were selected who were diagnosed as COVID-19 patients in the Molecular Biology Laboratory of Chittagong Medical College, Chattogram, Bangladesh. Data were collected from May to July 2020. Clinical characteristics were obtained from over phone interviews and laboratory diagnosis by Real-time Reverse Transcriptase Polymerase Chain Reaction (rRT-PCR). Cox proportional hazard model is applied to estimate risk factors affecting the delay of detection of COVID-19 patients.

Result: Female mortality rate was 44.9% higher compared to males, graduates died 32% more than undergraduates, unmarried peoples’ death rate were 56% more than married and those who were in traveling irregularly and in contact with symptomatic patients, were 86% more died than non-travelers.

Conclusion: Early diagnosis of COVID-19 can save a huge amount of lives and special attention should be emphasized on the significant explanatory variable.

Internal Medicine

COVID-19

survival analysis

delay of detection

Coronaviruses are significant pathogens of humans and animals that can cause diseases ranging from the common cold to fatal respiratory diseases. In the past two decades, two highly pathogenic human coronaviruses have emerged in two separate events. They are the severe acute respiratory syndrome (SARS-CoV) and the Middle East respiratory syndrome (MERS-CoV) [1, 2]. They caused lower respiratory tract infection as well as extrapulmonary manifestations, leading to thousands of patients with high mortality rates of up to 50%. In December 2019 a new strain of coronavirus, officially named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was first isolated from three patients with coronavirus disease 2019 (COVID-19) by the Chinese Center for Disease Control and Prevention [3, 4].

Recent epidemiological reports have provided evidence for person to person transmission of the SARS-CoV-2 in family and hospital settings [5, 6]. On 30 January 2020, the World Health Organization declared that the outbreak of SARS-Cov-2 constituted a public health emergency of international concern. As of 31 July 2020, the number of patients infected with SARS-CoV-2 has exceeded 2, 54,474 globally, and more than 6002 have now died of COVID-19.

Research illustrates that marked similarities exist between severe acute respiratory syndrome and COVID-19. A recent study reported a 79.5% genome sequence identity between SARS-CoV-2 and SARS-CoV, and SARS-Cov-2 was 96% identical in terms of whole-genome sequence to a bat coronavirus [7]. Clinical and pathological features of patients with COVID-19 have recently been reported, showing that the SARS-CoV-2 infection causes clusters of severe and even fatal pneumonia with clinical presentation greatly resembling that of SARS-CoV infection, associated with admission to intensive care units and high mortality [8].

To keep going with this economic breakdown, the members of maximum families went outside of their homes to earn money. Regular household necessities were bought by them too from markets in this pandemic situation. Besides, a so long period of home restrictions made them unsteady to breathe fresh air, as a result, affected by COVID-19. But initially, symptoms were mild, and especially the young generation was unwilling to maintain healthy hygiene and to follow restrictions. So, in our study, we tried to identify the hazard of late detection time of COVID-19 of those patients.

A cross-sectional study was conducted from May 2020 to July 2020 in the molecular biology laboratory of Chittagong Medical College, Chattogram with a total of 300 patients who are COVID-19 positive. Reverse Transcriptase-Polymerase Chain Reaction was done in the QuantStudio 5 Real-Time PCR instrument (96-Well 0.2 ml Block) by Sansure Biotech Novel Coronavirus (2019-nCoV) Nucleic Acid Diagnostic Kit (PCR-Fluorescence Probing).

Patients with a SARS-CoV-2 infection confirmed by an rRT-PCR of a nasopharyngeal and oropharyngeal combined sample within 3 days of onset of symptoms were included. Presence of symptoms, days of illness, contact with symptomatic patient /health personnel, types of PPE used, travelling history, supplemental oxygen therapy requirement, hospital admission history, ICU support requirement, co-morbid conditions, baseline investigations, and total treatment sheet were collected through interviewing over the phone.

The WHO website has provided several rRT-PCR protocols for detecting SARS-CoV-2 in different countries [9]. Here an automated system was repeated the amplification process for about 45 cycles until the viral cDNA could be detected, usually by a fluorescent signal from different channels such as FAM, ROX, and CY5. We considered the SARS-CoV-2 open reading frame-ORF 1ab and the specific conserved sequence of coding nucleocapsid protein N gene as the target regions which were designed for the conserved sequence of the double-target genes, to achieve detection of sample RNA through FAM and ROX signal changes respectively. Internal standard gene fragments (Rnase P) were detected by the CY5 signal. Here a single tube containing the necessary primers, probes, dNTPs, MgCl2, Rnasin, PCR buffer, RT enzyme, and Taq enzyme to run the entire RT-PCR reaction (24 tests/kit) was used. Positive internal control was fixed to monitor the presence of PCR inhibitors in test specimens and normal saline as a negative control.

After getting the rRT-PCR results, we sorted 300 SARS-CoV-2 positive patients for our study purpose; from those we got proper history and relevant data completely.

Cox proportional hazard model (1972) is well admired model used in survival analysis that can be used to assess the importance of various covariates in the survival time of individuals or objects through the hazard function. It makes us capable of estimating the relationship between the hazard rate and explanatory variables without having to make assumptions on the shape of the baseline hazard function. In our study, the cox proportion hazard model was used to analyze the factor affecting COVID-19 patients’ mortality due to late diagnosis by considering any extra heterogeneity present in the data. To evaluate the equality of the hazard function we used a log rank test (does not make any assumptions regarding the distribution of the data set) to test the null hypothesis: all the survival curves were the same. To explore the relationship among factors having multiple-categories the Bonferroni test was applied.

This study described 300 patients with confirmed COVID-19 from May 09, 2020 to July 12, 2020 in Molecular Biology Laboratory, Chittagong Medical College, Chattogram.

Table 1

Distribution of survival status along with the Log Rank test with the selected variables
Backgroud Characteristics	Catagories	Censored	Condition of patients (Died)	Log Rank (Mantel-Cox)	df	p-value
Sex	Female	87(29.0%)	3(1.0%)	7.675	1	0.006*
	Male	183(61.0%)	27(9.0%)
Education	Undergraduate	96(32.0%)	15(5.0%)	1.632	1	0.201
	Graduate	174(58.0%)	15(5.0%)
Occupation	Health personnel	16(5.3%)	2(0.7%)	5.764	3	0.124
	Service holder	49(16.3%)	2(0.7%)
	Student	57(19.0%)	3(1.0%)
	Others	148(49.3%)	23(7.7%)
Place of Residance	Urban	195(65.0%)	9(3.0%)	5.086	1	0.024*
	Rural	75(25.0%)	21(7.0%)
Marital Status	Unmarried	60(20.0%)	0(0.0%)	1.025	1	0.311
	Married	210(70.0%)	30(10.0%)
Age	0–18	20(6.7%)	1(0.3%)	1.221	3	0.748
	19–39	90(30.0%)	3(1%)
	40–59	94(31.3%)	14(4.7%)
	60 and above	66(22.0%)	12(4.0%)
Family member's affected or not	Not affected	135(45.0%)	21(7.0%)	11.688	1	0.001*
	Affected	135(45.0%)	9(3.0%)
Travelling History	Contact with symptomatic patient	114(42.7%)	3(1.1%)	17.788	2	0.000*
	Travelling regularly	90(33.7%)	9(3.4%)
	No travelling history	36(13.5%)	15(5.6%)
(*) Indicates variable is the significance level at % level of significance

In the above table, censored were those who were alive during the study period whether he/she was in isolation at home or the hospital. Total 1%female and 9% male were not alive at p = 0.006 level of significance. 0.7% of health professional as well as service holder died and in the case of the student, it is only 1% but the people related to the ‘others’ profession 7.7% died which is much more. Rural patients died 7%, where urban people were only 3% (p = 0.024). The death of married persons was 10%. Middle-age adults had a 4.7% death rate; on the contrary, only 0.3% of children died. Besides, 7% of death was from those groups of patients whose family members were not affected and it was significantly higher (p = 0.001) than those who had affected family members. Surprisingly 5.6% of patients were died (p = 0.000) those who had no travelling history.

Table 2

Estimated Hazard Ratio obtained from Cox proportional hazard model
Variables	Categories	B	SE	df	Sig.	Exp(B)	95.0% CI for Exp(B)
Variables	Categories	B	SE	df	Sig.	Exp(B)	Lower	Upper
Sex	Female	.371	.142	1	.009	1.449	1.097	1.915
Sex	Male			1		1.000
Education	Undergraduate			1		1.000
Education	Graduate	.275	.140	1	.050	1.316	1.000	1.732
Occupation	Health personnel	.022	.264	3	.933	1.022	.610	1.714
	Service holder	.101	.165		.543	1.106	.800	1.529
	Student	.303	.157		.054	1.353	.995	1.840
	Others					1.000
Place of Residance	Urban	.249	.137	1	.069	1.283	.981	1.678
Place of Residance	Rural			1		1.000
Marital Status	Unmarried	.444	.239	1	.064	1.559	.975	2.492
Marital Status	Married			1		1.000
Age	0–18			3		1.000
	19–39	.126	.260		.629	1.134	.681	1.890
	40–59	− .012	.162		.005	.988	.719	1.358
	60 and above	− .084	.162		.267	.920	.670	1.263
Family member's affected or not	Not affected	− .336	.122	1	.006	.715	.563	.908
Family member's affected or not	Affected			1		1.000
Travelling History	Contact with symptomatic patient	.621	.192	2	.001	1.860	1.277	2.710
	Travelling regularly	.591	.199		.003	1.807	1.223	2.669
	No travelling history					1.000

For testing the proportional hazard assumption, we checked whether the independent variables meet the proportional assumption by using a log-minus-log (LML) plot. The LML plot is a graph constructed by applying the log-log transformation of the survival function, in other words, it is a graph of the logarithm of time against the logarithm of the negative logarithm of the estimated survival function. Figure 1 we have seen that curves of each risk factor are not crossing but they are parallel. That means all the risk factors are well defined and satisfy the proportional hazard (PH) assumptions.

From Table 2, female mortality (hazard) rate was 44.9% higher [(p = 0.009) CI (9%, 91%)] compared to the male. The factor education was likely to have in more hazardous conditions; graduate entities were likely to 1.316 times higher [CI (1.000, 1.732)] risk compared to undergraduate. Students were 1.353 times more [CI (.995, 1.840)] in hazard than ‘others’ category.

The residents of urban areas were in 1.283 times [CI (.981, 1.678)] high-risk zone than the rural residents. Unmarried were 1.559 times [CI (.975, 2.492)] riskier for death than married persons. 19–39 year’s age group patients were 1.134 times [CI .681, 1.890)] more in hazardous context than 0–18 year’s age group.

Patients whose family members were not affected were .715 times [CI (.563, .908)] less in risk than those whose family members were affected. Besides, the patients who were in contact with symptomatic patients were 86% more in hazard [(p = 0.001) CI (28%, 71%)] than those who had no travelling history during this pandemic.

Delay of detection of COVID-19 plays a significant role in this survival analysis regarding the patient's status: he/she survived or not. By the cox proportional hazard model, we explore the hazard rate of several risk factors and explained with respect to the odds ratio. From this study, it has obvious that female (1.499 times) participants and graduates (1.316) are in more risky positions compare to other participants. The Students (1.353) are in more vulnerable positions, however, the service holders and the health and personnel are at low risk. The urban (1.283 times higher) affected, and the unmarried (1.559 times higher) affected, should be more careful as they both at high risk to befall in COVID-19. We should avoid traveling because the people who travel regularly are 1.807 times at high risk. Besides, without health personnel, Contact with symptomatic patients should be kept off. As the people who were in contact with symptomatic patients are at 1.86 times more risk. This research has some limitations. First, information regarding SARS CoV-2 is limited. Second, the information provided here is based on current evidence but may be modified as more information becomes available. Third, the sample size was small.

Late detection of COVID-19 can cause serious health hazards. Besides, its outcome is significantly worse than those who have comorbidities which concomitantly exacerbates the death of a patient. So, early earnestness to identify the SARS-CoV-2 virus and taking prompt treatment can save many lives.

COVID-19: Coronavirus Disease 2019; rRT-PCR: Real-time Reverse transcription-polymerase chain reaction; SARS-CoV-2: Severe Acute Respiratory Syndrome Coronavirus 2; MERS-CoV: Middle East Respiratory Syndrome Coronavirus; ICU: Intensive Care Unit; WHO: World Health Organization; PPE: Personal Protective Equipment; LML: Log-Minus-Log; CI: Confidence Interval; SE: Standard Error; df: Degrees of freedom; Exp(B): Odds ratio.

ETHICS APPROVAL AND CONSENT TO PARTICIPATE

Case record forms were approved by the Ethical Review Committee of the Chittagong Medical College and Hospital, Chattogram, Bangladesh, and written informed consent was obtained from each participant. No additional ethical approval is needed.

CONSENT FOR PUBLICATION

Our manuscript was approved the consent for publication from Department of Microbiology, Chittagong Medical College.

AVAILABILITY OF DATA AND MATERIAL

The data sets are available from the corresponding author on reasonable request.

CONFLICT OF INTEREST

The authors declare no conflict of interest.

FUNDING

This research received no external funding.

AUTHORS' CONTRIBUTIONS

Sujan Rudra: Database management and Statistical analysis; Shuva Das: Conceptualization, Writing - original draft preparation; Md. Ehsanul Hoque: Writing - review & editing, Supervision; Abul Kalam: Investigation and Validation; Mohammad Arifur Rahman: Methodology and Investigation; The authors read and approved the final manuscript.

ACKNOWLEDGMENTS

We thank all of the medical and ancillary hospital staff and the patients for consenting to participate. We would like to thanks the editors and referees whose constructive criticism led us to develop the presentation and maintain the quality of the paper. We would also like to thanks the Molecular Biology Laboratory of Chittagong Medical College, Chattogram, to give us almost all the opportunity for conducting the research.

AUTHOR’S INFORMATION

^1†Department of Statistics, University of Chittagong, -4331, Chattogram, Bangladesh. +8801919128128, [email protected] ; ²Department of Microbiology, Chittagong Medical College, -4203, Chattogram, Bangladesh. +8801717186475, [email protected] ; ³Department of Microbiology, Chittagong Medical College, -4203, Chattogram, Bangladesh. +8801819375563, [email protected] ; ⁴Department of Microbiology, Chittagong Medical College, -4203, Chattogram, Bangladesh.+8801822003975 ,[email protected] ; ⁵Department of Microbiology, Chittagong Medical College, -4203, Chattogram, Bangladesh. +8801874823177, [email protected]

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Delay of Detection Of COVID-19 Patients In Bangladesh; An Application To Cox Proportional Hazard Model

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Conclusion

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