Abstract
Introduction:
COVID is a new disease; understanding the transmission dynamics and epidemiological characteristics may help in developing the effective control measures. The study is done 1. To determine the various factors influencing the acquisition of COVID-19 infection among high-risk contacts 2. To estimate the secondary attack rate among high-risk contacts 3. To determine the factors in COVID index cases influencing their secondary attack rate.
Methodology:
Unmatched case control study was conducted from March to August 2020 among 139 COVID index cases in Madurai district from March–May (Reference period) and their 50 COVID positive (cases), 551 COVID negative (controls) high-risk contacts. Case investigation form* and contact tracing Proforma*were used to collect data. Chi-square test and independent sample t test were used to find out the association. Univariate* and Multivariate logistic regression* were used to predict the risk of various factors in acquisition of COVID infection with the help of adjusted and unadjusted odds ratio. P value < 0.05 was considered statistically significant.
Results:
Male contacts (P = 0.005, OR = 2.520), overcrowding (P = 0.007, OR = 3.810), and duration of exposure to index case (for 4-7 days P = 0.014, OR = 2.902, for >7 days P = 0.001, OR = 6.748 and for > 12 hours/day P = 0.000, OR = 5.543) were significant factors predicted to be associated with acquisition of COVID infection among high-risk contacts. Reproductive number (R0)* estimated was 1.3. Secondary attack rate (SAR)* estimated among high-risk contacts was 8.32%. Index cases whose outcome was death (P = 0.026); symptomatic index cases (P = 0.000), cases with fever (P = 0.001); sorethroat (P = 0.019); breathlessness (P = 0.010); cough (P = 0.006) and running nose (P = 0.002) had significantly higher mean SAR than their counterparts.
Conclusion:
Contacts with above said risk factors who were found to be more prone to infection could be given special focus to prevent the transmission in them.
Keywords: Case investigation form, contact tracing proforma, multivariate logistic regression, reproductive number, secondary attack rate, univariate logistic regression
Key Messages: According to our study findings; • Factors related to acquisition of COVID infection among high-risk contacts are presence of Overcrowding, increase in duration of exposure and Symptomatic status of Index cases.; • Knowledge on these risk factors may throw light in controlling future pandemic diseases following similar mode of transmission.; • Preventive measures like social distancing must be emphasized in the community as well as in the households having symptomatic family member.; • Mass screening of the community for the symptoms like fever, cough, sore throat, running nose, and breathlessness at the primary level help in early diagnosis and isolation of affected individuals.
Introduction
COVID -19 was first reported in Wuhan city, China in late December 2019[1] and was declared as Public Health Emergency of International Concern (PHEIC) on January 30, 2020[2] and as Pandemic on March 11, 2020.[3] As on 15/1/2021, 93.1 million people worldwide,[4] 10.5 million Indians,[5] 829,000 people in Tamil Nadu, and 20,788 in Madurai were affected.[6] Basic Reproduction number (R0) was estimated to be around 1.4-6.49 with mean of 3.28.[7] In INDIA, R0 was around 1.83 in April, 1.27 in May after lockdown,[8] and around 1.405 in Tamil Nadu.[9] The risk of acquisition of COVID infection was higher among adult and old-age contacts compared to children,[10,11] higher among female contacts and contacts of symptomatic cases and spouses of index cases,[10] and higher among household contacts.[11]
COVID-19 is the emerging disease with unknown epidemiological characteristics and transmission dynamics. Understanding them may help in developing and evaluating the effective control measures; it helps the physician at primary level to take preventive measures against those factors involved in the disease spread, and also helps in contact tracing and containment at the primary level. Studies on this area is scarce, hence the need of the present study.
Objective
To determine the various factors influencing the acquisition of COVID-19 infection among high-risk contacts
To estimate the secondary attack rate among high-risk contacts
To determine the factors in COVID index cases influencing their secondary attack rate.
Methodology
This unmatched case control study was conducted in Madurai district, in southern part of Tamil Nadu, with the population of 3,038,252. From March to May 2020 (our reference period), one Government tertiary care hospital and four private hospitals accommodated COVID positive cases.
The study was conducted from March to August 2020 for a duration of 6 months. Madurai district was in complete lockdown till May 31, 2020 and relaxed thereafter, leading to transmission of COVID infection through multiple sources; hence it was difficult to trace the definite source of infection for all positive cases. So, we limited our reference period from March to May 2020. The study population was divided into three categories: (1) INDEX COVID CASES (2) CASES, and (3) CONTROLS.
Covid positive
Patients who were confirmed for COVID 19 infection through RT-PCR.
Index covid cases
The first case in a family or a workplace that came to the attention of the investigator and also unlinked, that is those without contact with known lab-confirmed COVID cases.[12]
High-risk contact
Those (1) living in the same household as the confirmed case (2) touched body fluids of confirmed case without personal protective equipments (PPE) (3) had direct physical contact with body of confirmed case including physical examination without PPE (4) touched or cleaned the linen, clothes or dishes of confirmed cases (5) anyone in close proximity within 1m of confirmed case without precaution (6) passenger in close proximity within 1m of a convergance with symptomatic person who later tested positive for COVID 19 for more than 6 hours.[13,14]
Cases
High-risk contacts of index cases who were lab-confirmed swab positive through RT-PCR.
Controls
High-risk contacts of index cases who were lab-confirmed swab negative through RT-PCR.
Ethical clearance was bought from Institutional Ethical Committee and patient’s confidentiality was maintained throughout the study. Out of 268 cases in MADURAI till May 2020, 208 were index cases. On contact tracing, 946 high-risk contacts were identified. Excluding the nonresponders, we arrived at the sample size of 139 index cases with 601 high-risk contacts. Out of 601 high-risk contacts, number of cases were 50 and number of controls were 551 with the ratio of 1:11. COVID-19 positive line list updated by state bulletin were obtained on a daily basis from the month of March by our department. For all those cases telephonic interview was conducted by a team of trained personals in our department, which included Assistant Professors, Post Graduates, and interns. Case Investigation Form (CIF) approved by National Centre for Disease Control (NCDC),[15] New Delhi was used to collect data for all positive cases. CIF form had questions to collect data regarding their (1) age, sex, occupation, (2) symptoms, initial symptoms, date of onset of symptoms, (3) health care facilities visited, notification by healthcare facility (4) place and date of admission and discharge date, (5) co-morbidities, (6) contact history,v(7) travel history, and (8) swab details. High-risk contacts for all the positive cases were traced along with co-ordination with Deputy Director of Health Services and were mobilized for swab test. We obtained their swab results from Microbiology department. For all those high-risk contacts we collected data using Contact tracing proforma designed by NCDC. It had questions to collect data on age, sex, duration of exposure to index cases in days and hours of exposure per day, type of contact, and their relationship to index cases. Data on co-morbidity, overcrowding were also collected through telephonic interview. Presence of overcrowding[12] was based on following criterias:
•PERSONS PER ROOM:
1 Room >2 Persons
2 Rooms >3 Persons
3 Rooms >5 Persons,
4 Rooms >7 Persons,
5 Rooms >10 Persons
• SEX SEPARATION:
Two persons over nine years of age not husband and wife of opposite sexes were oblique to sleep in the same room.
For those whose phones were under switched off or not reachable or not attending were called again for the maximum of three times at an interval of 1 week till we could reach them on phone. Those who could not be reached even after three attempts were excluded from our study. If either an index case or their high-risk contact didn’t respond, we excluded the index case along with their contacts. In case of severe cases, data were obtained from the primary care givers. All the collected data were analyzed using SPSS software version 21.
Descriptive statistics were used to find out the frequencies and percentages. Association between various risk factors (categorical) and acquisition of COVID infection was found using Chi-SQUARE test. Unadjusted Odds ratio was found out using BINARY LOGISTIC REGRESSION. P value < 0.05 was considered to be significant. All the significant factors were put in Multivariate logistic regression and adjusted odds ratios were calculated to predict the risk of various factors in acquisition of COVID infection. Independent sample t test was used to find the mean difference of secondary attack rate of index cases between two independent groups. “CORRELATION AND REGRESSION” was used to find the association between two continuous variables.
Secondary Attack Rate (SAR)[12] was defined as the number of exposed person developing the disease within the range of incubation period following exposure to primary case.
SAR = (Number of exposed persons developing disease within the range of incubation period × 100)/Total number of exposed or susceptible contacts
The basic Reproduction number (R0)[12] describes the number of people an infected person is likely to spread the disease. It says the epidemic size R0 < 1 indicates that an epidemic is arrested.
R0= (Total number of primary cases + Number of high-risk contacts tested positive)/Total number of primary cases.
Results
Among 601 high-risk contacts traced, 50 were cases and 551 were controls. Majority of the high-risk contacts that is 359 (59.7%) were in the age group 15–48 years and also 64% (32) of cases and 59.3% (327) of controls were in that age group. About 52.57% of high-risk contacts were females and maximum of the cases were males (66%); 67.22% had overcrowding in which 11.1% were cases. Most of the cases (66%) and controls (54.44%) had exposure to the index cases for 4-7 days. Majority of cases (44%) had exposure to index case from 7–12 hours per day. Most of the controls (48.45%) had < 6 hours of exposure per day. Only 8% of high-risk contacts had any one of the co-morbidities. About 86% of Cases and 92.55% of controls were nondiabetics, whereas 98% of cases and controls were normotensive. Household contacts (86.68%) form greater cluster of total high-risk contacts.
Table 1 shows factors influencing acquisition of COVID-19 among Cases and Controls. Chance of acquisition of COVID 19 infection is higher in the age group of 15-48 years (64%) compared to other age groups. But this was not statistically significant. Among male contacts 11.6% were cases but only 5.4% of female contacts were cases (OR = 2.303). About 67.2% of Contacts had history of overcrowding in which 11.1% developed disease (OR = 4.813), it was statistically significantly higher than those without overcrowding. Proportion of contacts developing disease increased as duration of exposure with index case increased. About 9.9% contacts who had exposure between 4 and 7 days and 25.6% of contacts who had exposure >7 days developed disease but only 3.1% of contacts exposed <4 days developed disease and these differences were statistically significant. Among diabetic contacts, 14.6% developed disease whereas 7.8% of nondiabetic contacts developed disease, but that difference was not statistically significant. About 6.7% of hypertensive and 8.4% of normotensive contacts developed disease but the difference was statistically insignificant. Among different relations, proportion of children of index cases who developed disease (9.8%) was more than all other relations (spouse-4.5%, parent-8.4%, friends-9.1%, others-8.6%) but the difference between groups was not statistically significant. Secondary attack rate among work place contacts, that is the proportion of workplace contacts developed disease was 15.4% and it was higher than other type of contacts (SAR for household contacts = 8.1%, SAR for friends who came into contact with COVID index case = 9.1%, SAR for healthcare worker contacts = 0%). Workplace contacts had two times higher risk of acquiring disease than household contacts, however it was not statistically significant.
Table 1.
Factors influencing acquisition of COVID-19 between Cases and Controls (n=601)
| Factors | Cases n=50 | Controls n=551 | Total n=601 | Chi-Square P | P by Binary Logistic Regression | Unadjusted odds ratio (95% Confidence Interval) |
|---|---|---|---|---|---|---|
| Age | ||||||
| <=14 YRSa | 9 (7.4%) | 112 (92.6%) | 121 (100%) | - | ||
| 15-48 YRS | 32 (8.9%) | 327 (91.1%) | 359 (100%) | 0.814 | 0.616 | 1.218 (0.564-2.630) |
| >48 | 9 (7.4%) | 112 (9`2.6%) | 121 (100%) | 1.000 (0.383-2.613) | ||
| Sex | ||||||
| Male | 33 (11.6%) | 252 (88.4%) | 285 (100%) | 0.006* | 0.007* | 2.303 (1.253-4.233) |
| Femalea | 17 (5.4%) | 299 (94.6%) | 316 (100%) | - | ||
| Overcrowding | ||||||
| Absenta | 5 (2.5%) | 192 (97.5%) | 197 (100%) | - | ||
| Present | 45 (11.1%) | 359 (88.9%) | 404 (100%) | 0.000* | 0.001* | 4.813 (1.879-12.327) |
| Duration of Exposure | ||||||
| 1-3 Daysa | 7 (3.1%) | 222 (96.9%) | 229 (100%) | - | ||
| 4-7 Days | 33 (9.9%) | 300 (90.1%) | 333 (100%) | 0.000* | 0.000* | 3.489 (1.515-8.031) |
| >7 Days | 10 (25.6%) | 29 (74.4%) | 39 (100%) | 0.005* | 10.936 (3.863-30.956) | |
| Hours of Exposure/day | ||||||
| <=6 Hrsa | 16 (5.7%) | 267 (94.3%) | 283 (100%) | - | ||
| 7-12 Hrs | 22 (8%) | 254 (92%) | 276 (100%) | 0.000* | 0.000* | 1.445 (0.742-2.815) |
| >12 Hrs | 12 (28.6%) | 30 (71.4%) | 42 (100%) | 0.000* | 6.675 (2.887-15.435) | |
| Diabetes | ||||||
| Absenta | 43 (7.8%) | 510 (92.2%) | 553 (100%) | - | ||
| Present | 7 (14.6%) | 41 (85.4%) | 48 (100%) | 0.101 | 0.108 | 2.025 (0.857-4.785) |
| Hypertension | ||||||
| Absenta | 49 (8.4%) | 537 (91.6%) | 586 (100%) | 0.814 | 0.815 | - |
| Present | 1 (6.7%) | 14 (93.3%) | 15 (100%) | 0.783 (0.101-6.079) | ||
| Co-morbidity status | ||||||
| Absenta | 43 (7.8%) | 509 (92.2%) | 552 (100%) | 0.115 | 0.121 | - |
| Present | 7 (14.3%) | 42 (85.7%) | 49 (100%) | 1.973 (0.836-4.655) | ||
| Relationship | ||||||
| Spousea | 4 (4.5%) | 85 (95.5%) | 89 (100%) | - | - | |
| Children | 16 (9.8%) | 147 (90.2%) | 163 (100%) | 0.711 | 0.945 | 2.313 (0.749-7.144) |
| Parent | 8 (8.4%) | 87 (91.6%) | 95 (100%) | 0.384 | 1.954 (0.567-6.731) | |
| Friends | 4 (9.1%) | 40 (90.9%) | 44 (100%) | 0.490 | 2.125 (0.506-8.933) | |
| Others | 18 (8.6%) | 192 (91.4%) | 210 (100%) | 0.225 | 1.992 (0.655-6.064) | |
| Type of contact | ||||||
| Householda | 42 (8.1%) | 479 (91.9%) | 521 (100%) | 0.442 | - | - |
| Friends | 4 (9.1%) | 40 (90.9%) | 44 (100%) | 0.811 | 1.140 (0.389-3.342) | |
| Workplace | 4 (15.4%) | 22 (84.6%) | 26 (100%) | 0.198 | 2.074 (0.683-6.299) | |
| Healthcare | 0 (0%) | 10 (100%) | 10 (100%) | 0.999 | 0.000 |
*Statistically significant (P<0.05). aReference category
SAR among high-risk contacts was estimated to be 8.32%, which indicates that 8.32% of total high-risk contacts had a chance of developing COVID infection. R0 was calculated as 1.3, which denotes that one index case can pass virus to approximately 1.3 of high-risk contact.
Factors that were statistically significant in univariate analysis were analyzed through Multivariate regression and the unadjusted odds ratios were calculated. Table 2 shows multivariate analysis of various factors in acquisition of COVID-19. Factors significant in Chi-square and univariate analysis were also significant in multivariate analysis. Male contacts were having 2.52 times higher risk than female contacts in acquisition of COVID infection. Contacts with overcrowding had 3.81 times higher risk than their counterpart. Taking contacts with <4 days of exposure as reference group, the risk of acquisition of COVID infection was 2.9 times higher in contacts with 4–7 days of exposure and 6.74 times higher in those exposed for >7 days. Those contacts with exposure >12 hours per day had 5.54 times more risk of acquiring COVID infection compared to those exposed to <6 hours per day.
Table 2.
Adjusted Odds Ratio For Factors Influencing Acquisition Of COVID-19 between Cases And Controls Using Multivariate Logistic Regression (n=601)
| Factors | P | Adjusted odds ratio (95% Confidence Interval) |
|---|---|---|
| Sex | ||
| Femalea | - | - |
| Male | 0.005* | 2.520 (1.321-4.806) |
| Overcrowding | ||
| Absenta | - | - |
| Present | 0.007* | 3.810 (1.434-10.125) |
| Duration of exposure in days | ||
| 1-3 Daysa | - | - |
| 4-7 Days | 0.014* | 2.902 (1.237-6.808) |
| >7 Days | 0.001* | 6.748 (2.282-19.960) |
| Duration of exposure in hours/day | ||
| <=6 Hrsa | - | - |
| 7-12 Hrs | 0.709 | 1.144 (0.566-2.311) |
| >12 Hrs | 0.000* | 5.543 (2.227-13.797) |
*Statistically significant (P<0.05). aReference category
Table 3 shows the Association of Index Cases factors with their Secondary Attack Rate. About 103 (74.1%) of 139 index cases were males. Mean SAR of male and female Index cases were 0.19% and 0.27, respectively. This difference in mean was not statistically significant in independent sample T test. About 73.4% of index cases were asymptomatic and 26.6% were symptomatic. Mean SAR of symptomatic cases was higher than asymptomatic index cases, which was statistically significant. Common symptoms reported in index cases were fever (15.15%), cough (14.38%), sore throat (5.03%), running nose (7.19%), nausea and vomiting (0.71%), breathlessness (6.47%), headache (2.15%), diarrhea (1.43%), and pain (4.31%). Means of SAR of people with the above mentioned symptoms were higher than those without those respective symptoms. But the mean difference was statistically significant only for fever (P = 0.001), sore throat (P = 0.014), cough (P = 0.006), breathlessness (P = 0.010), and running nose (P = 0.002). Mean SAR of Index cases with and without co-morbidities was nearly same. Most common co-morbidity was diabetes. Mean of SAR of diabetic patients was higher than that of nondiabetics and this association was statistically significant. Going through outcomes of 139 index cases, 98.6% were discharged successfully and 1.4% were deceased. Mean SAR of deceased cases was 41.6 and this was statistically significantly higher than mean SAR of those discharged (8.37).
Table 3.
Association of Index case factors with their Secondary Attack Rate (n=139)
| Factors | Frequency (%) | Mean (SAR) | Standard deviation | t | P |
|---|---|---|---|---|---|
| Outcome | |||||
| Death | 2 (1.44%) | 41.428 | 2.020 | ||
| Discharge | 137 (98.56%) | 8.375 | 20.638 | -2.257 | 0.026* |
| Sex | |||||
| Male | 103 (74.11%) | 0.1942 | 0.397 | ||
| Female | 36 (25.89%) | 0.2778 | 0.454 | -1.046 | 0.297 |
| Fever | |||||
| Absent | 118 (84.9%) | 0.1695 | 0.376 | ||
| Present | 21 (15.1%) | 0.4762 | 0.511 | -3.243 | 0.001* |
| Sore throat | |||||
| Absent | 132 (94.97%) | 0.1970 | 0.399 | ||
| Present | 7 (5.03%) | 0.5714 | 0.534 | -2.377 | 0.019* |
| Nausea/Vomiting | |||||
| Absent | 138 (99.29%) | 0.2101 | 0.408 | ||
| Present | 1 (0.71%) | 1.000 | -1.925 | 0.056 | |
| General Weakness | |||||
| Absent | 136 (97.85%) | 0.2132 | 0.411 | ||
| Present | 3 (2.15%) | 0.333 | 0.577 | -0.497 | 0.620 |
| Breathlessness | |||||
| Absent | 130 (93.53%) | 0.1923 | 0.395 | ||
| Present | 9 (6.47%) | 0.5556 | 0.527 | -2.605 | 0.010* |
| Headache | |||||
| Absent | 136 (97.85%) | 0.2059 | 0.405 | ||
| Present | 3 (2.15%) | 0.6667 | 0.577 | -1.931 | 0.056 |
| Cough | |||||
| Absent | 119 (85.65%) | 0.1765 | 0.382 | ||
| Present | 20 (14.38%) | 0.450 | 0.510 | -2.809 | 0.006* |
| Diarrhea | |||||
| Absent | 137 (98.57%) | 0.2117 | 0.410 | ||
| Present | 2 (1.43%) | 0.500 | 0.707 | -0.980 | 0.329 |
| Running nose | |||||
| Absent | 129 (92.81%) | 0.1860 | 0.390 | ||
| Present | 10 (7.19%) | 0.600 | 0.516 | -3.152 | 0.002* |
| Pain | |||||
| Absent | 133 (95.69%) | 0.2030 | 0.403 | ||
| Present | 6 (4.31%) | 0.50 | 0.547 | -1.736 | 0.085 |
| Symptom Status | |||||
| Absent | 106 (76.86%) | 0.1415 | 0.350 | ||
| Present | 33 (23.14%) | 0.4545 | 0.505 | -4.005 | 0.000* |
| Hypertension | |||||
| Absent | 131 (94.25%) | 0.2061 | 0.406 | ||
| Present | 8 (5.75%) | 0.375 | 0.517 | -1.124 | 0.263 |
| Diabetes mellitus | |||||
| Absent | 125 (89.93%) | 0.192 | 0.395 | ||
| Present | 14 (10.07%) | 0.428 | 0.513 | -2.057 | 0.042* |
| Co-morbidity Status | |||||
| Absent | 112 (80.58%) | 0.2054 | 0.405 | ||
| Present | 27 (19.42%) | 0.2593 | 0.446 | -0.608 | 0.545 |
*Statistically significant (P<0.05). SAR - Secondary Attack Rate
Table 4 shows Correlation of age and duration of hospital stay of index case with their Secondary Attack Rate. Age and duration of hospital stay were not statistically correlated with SAR.
Table 4.
Correlation of age and duration of hospital stay of index case with their Secondary Attack Rate (n=139)
| Factors | Pearson’s correlation coefficient “r” | P |
|---|---|---|
| Age | -0.531 | 0.596 |
| Duration of hospital stay | 0.217 | 0.828 |
Discussion
In this study, out of 139 Index cases and 601 high-risk contacts, the factors such as male gender, presence of overcrowding, increased duration of exposure to index case were significantly associated with risk of acquiring COVID-19 infection among the high-risk contacts. SAR was significantly higher in index cases with symptoms like fever, sore throat, breathlessness, cough, running nose, index case with Diabetes mellitus, and in dead cases than their respective counterparts.
In our study, R0 was calculated to be 1.36. This is comparatively lower than in developed nations, a study done in China estimated Ro to be 8.21 in USA and 7.9 in Wuhan, China.[16] In another article published by Mohana Basu, Ro in India was 1.379[8] and by Marimuthu,[9] R0 in Tamil Nadu was 1.405 at the end of May month. Various other studies conducted in Tamil Nadu revealed R0 to be around three in Madurai without lockdown and one with lockdown.[17] These findings were similar to our study.
Our study shows that male contacts were affected more than female contacts which was similar to a study done by Republic of Korea.[18] But this was in contradiction to other studies like meta-analysis published by AIIMS, Delhi,[10] COVID study by ICMR,[19] and a study done by Liu et al. in China,[20] where female contacts were found to be highly affected. In a study done by Bi et al.,[21] male and female contacts were equally affected. Male contacts in our study might have spent most of their time outdoors even during the period of lockdown, making them more prone to COVID-19 infection. The present study forms the base for future researches to find out, if there are any biological and behavioral factors making male contacts more susceptible in acquiring the infection rather than female contacts.
In our study, Overcrowding increases the risk of acquiring the COVID-19 infection among contacts, which was similar to another study conducted by Ahmad et al.[22] across US countries and another study by Azuma et al.[23] showing 3C of Crowded spaces, Close contacts, and Closed space with poor ventilation resulting in higher secondary infection. As we know, poor housing conditions like overcrowding is associated with the spread of infection through aerosol and droplets; the risk of COVID-19 infection that spreads through aerosol and droplets also increases. The risk of acquisition of COVID-19 infection among high-risk contacts increases as their duration of exposure to the index cases increases. This is similar to the study conducted by Public health England Transmission group.[24] Increase in duration of the exposure may increase the viral load in the exposed individual leading to development of infection and disease manifestation.
The SAR of household contacts in our study was 8.1%. This is similar to the study done by Centres of Disease control and prevention, Republic of South Korea[18] (7.56%) and slightly lower than a study done by Arnedo-Pena et al.[25] from Spain and (11.1%), by Wilkinson et al.[26] from Canada (14.7%) and by Li et al.[27] (16.3%), Jing et al.[28] (12.4%) from China, and slightly higher than the study done by Ng et al.[29] (5.9%). Among the different relationships, children of index cases were more affected (9.8%) than others. But in previous studies done by AIIMS Delhi,[10] Li et al.[27] and Sun et al.[30] from China and Madewell et al.,[31] spouses were more affected.
No association was found between age of the contacts and the risk of acquiring COVID infection in our study, which is similar to the study done by Ng et al.[29] whereas Spencer EA et al.[32] in their study found out that increased age of the close contacts was associated with increased risk of infection.
Age of the index case was positively correlated to their SAR in the study done by Arnedo-Pena et al.[25] in Spain. This is in contrast to our study where we found no significant correlation between age and SAR. Mean SAR of symptomatic index cases were higher (0.45%) than asymptomatic cases (0.14%). This is similar to studies done by Qiu et al.[33] and by Burke in US.[34] Index cases with fever, cough, and breathlessness were having high mean SAR, which is in line with studies done by Liu et al.,[20] Luo et al.,[35] Lin et al.,[36] and Hu et al.[11] Cough and running nose had higher chances of shedding droplets in room surroundings, which may influence the spread of COVID infection to their contacts and hence secondary attack rate might be high. Symptoms like fever, breathlessness, and sore throat may reflect the severity of the disease in index cases and this may result in higher SAR due to high viral load. Index cases who were diabetic had high SAR. SAR of deceased index cases (41.2%) was higher than that of discharged; this might be because of the increase in the severity of the disease. This is similar with other studies done by Shah et al.[37] in western India, Luo et al.,[35] and Liu et al.[20]
It was a community-based study done in the entire Madurai district where we included all type of cases (i.e. both incidence and prevalence cases and cases with all severity) along with their contacts. Hence, common biases in case control study like incidence bias, prevalence bias, and berkesonian bias had been minimized in our study. All the retrospective data collected were about the factors that happened recently within a short time limit, thus reducing the recall bias. Inspite of these strengths, we also had few limitations. Some data were collected retrospectively through phone conversation and so it was difficult to understand some subjective perceptions of our study participants. Only RT-PCR swab positive cases were considered as COVID positive cases and CT positive clinical COVID/suspect cases were not included in our study.
Conclusions
Overcrowding increases the risk of acquisition of COVID infection, hence preventive measures like social distancing must be emphasized in the community as well as in the households. Symptomatic and severe cases spread the infection more than asymptomatics and mild cases, mass screening of the community at the primary level for the symptoms like fever, cough, sore throat, running nose, and breathlessness help in early diagnosis and isolation. Health education of the public must also be carried out to emphasize the role of self isolation if they have any of the above said symptoms, so that disease transmission and the magnitude of the pandemic can be reduced.
Key points
Risk of acquisition is more among male contacts
As duration of exposure increases, risk of acquiring infection also increases
Cases with respiratory symptoms spread the disease more than those without symptoms
As severity of disease increases, severe covid-19 cases spread the disease more than mild cases.
Financial support and sponsorship
Nil.
Conflicts of interest
There are no conflicts of interest.
Acknowledgments
We thank our Dean Dr. J. Sangumani, MD (General Medicine), D. Diab, Madurai Medical college and Government Rajaji Hospital, Madurai for his constant support throughout our study.
References
- 1.Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet. 2020;395:497–506. doi: 10.1016/S0140-6736(20)30183-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Who.int. Statement on the second meeting of the International Health Regulations (2005) Emergency Committee regarding the outbreak of novel coronavirus (2019-nCoV) 2020. [Last accessed on 2021 Feb 10]. Available from: https://www.who.int/news/item/30-01-2020-statement-on-the-second-meeting-of-the-international-health-regulations-(2005)-emergency-committee-regarding-the-outbreak-of-novel-coronavirus-(2019-ncov)
- 3.WHO Director-General's opening remarks at the media briefing on COVID-19-11 March 2020. [Last accessed on 2021 Jan 22]. Available from: https://www.who.int/director-general/speeches/detail/who-director-general-s-opening-remarks-at-the-media-briefing-on-COVID-19---11-march-2020 .
- 4.Coronavirus Update (Live):98,180,233 Cases and 2,102,588 Deaths from COVID-19 Virus Pandemic-Worldometer. [Last accessed on 2021 Jan 22]. Available from: https://www.worldometers.info/coronavirus/
- 5.Coronavirus (COVID-19) Tracker:Find India's day wise Cases, Deaths and Recovered. NDTV.com. [Last accessed on 2021 Jan 22]. Available from: https://www.ndtv.com/coronavirus/india-COVID-19-tracker .
- 6.Daily Bulletin –StopCoronaTN. [Last accessed on 2021 Jan 22]. Available from: https://stopcorona.tn.gov.in/daily-bulletin/
- 7.Katul GG, Mrad A, Bonetti S, Manoli G, Parolari AJ. Global convergence of COVID-19 basic reproduction number and estimation from early-time SIR dynamics. PLoS One. 2020;15:e0239800. doi: 10.1371/journal.pone.0239800. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Mohana Basu. The Print. [Last accessed on 2020 May 08]. R0 at 1.27 but experts say India yet to hit COVID peak, lockdown alone can't end pandemic. Available from: https://theprint.in/india/r0-at-1-27-but-experts-say-india-yet-to-hit-COVID-peak-lockdown-alone-cant-end-pandemic/416595/ [Google Scholar]
- 9.Marimuthu S, Joy M, Malavika B, Nadaraj A, Asirvatham ES, Jeyaseelan L. Modelling of reproduction number for COVID-19 in India and high incidence states. Clin Epidemiol Glob Health. 2021;9:57–61. doi: 10.1016/j.cegh.2020.06.012. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.The household secondary attack rate of SARS-CoV-2:A rapid review. AIIMS COVID Information Portal. [Last accessed on 2021 Jan 25]. Available from: https://COVID.aiims.edu/the-household-secondary-attack-rate-of-sars-cov-2-a-rapid-review/ [Google Scholar]
- 11.Hu P, Ma M, Jing Q, Ma Y, Gan L, Chen Y, et al. Retrospective study identifies infection related risk factors in close contacts during COVID-19 epidemic. Int J Infect Dis. 2021;103:395–401. doi: 10.1016/j.ijid.2020.12.011. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Park K. Park's Textbook of Preventive and Social Medicine. M/s Banarsidas Bhanot. Jabalpur India. (25th ed) 2019 [Google Scholar]
- 13.Standard Operating Procedures SOPs:National Centre for Disease Control (NCDC) [Last accessed on 2021 Jan 25]. Available from: https://www.ncdc.gov.in/index1.php?lang=1&level=1&sublinkid=691 &lid=542 .
- 14.Case definitions:National Centre for Disease Control (NCDC) [Last accessed on 2021 Jan 25]. Available from: https://www.ncdc.gov.in/index1.php?lang=1&level=1&sublinkid=634&lid=547 .
- 15.Revised Case Investigation Form:National Centre for Disease Control (NCDC) [Last accessed on 2021 Feb 15]. Available from: https://ncdc.gov.in/showfile.php?lid=530 .
- 16.Zhu H, Huang J, Liu X. Base reproduction number of COVID-19:Statistic analysis. medRxiv. 2020 doi:10.1101/2020.09.26.20202010. [Google Scholar]
- 17.Ameex Technologies. COVID-19 Impact Prediction in Tamil Nadu with Mathematical Modeling. Ameex Technologies. [Last accessed on 2021 Jan 25]. Published May 11, 2020. Available from: https://www.ameexusa.com/blogs/COVID-19-impact-prediction-tn-with-mathematical-modeling . [Google Scholar]
- 18.Coronavirus Disease-19:Summary of 2,370 Contact Investigations of the First 30 Cases in the Republic of Korea. [Last accessed on 2021 Feb 07]. Available from: https://ophrp.org/journal/view.php?doi=10.24171/j.phrp . 2020.11.2.04. [DOI] [PMC free article] [PubMed]
- 19.ICMR COVID Study Group, COVID Epidemiology and Data Management Team, COVID Laboratory Team, VRDLN Team, Priya et al. Laboratory surveillance for SARS-CoV-2 in India:Performance of testing and descriptive epidemiology of detected COVID-19, January 22-April 30, 2020. Indian J Med Res. 2020;151:424–37. doi: 10.4103/ijmr.IJMR_1896_20. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Liu T, Liang W, Zhong H, He J, Chen Z, He G, et al. Risk factors associated with COVID-19 infection:A retrospective cohort study based on contacts tracing. Emerg Microbes Infect. 2020;9:1546–53. doi: 10.1080/22221751.2020.1787799. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Bi Q, Wu Y, Mei S, Ye C, Zou X, Zhang Z, et al. Epidemiology and transmission of COVID-19 in 391 cases and 1286 of their close contacts in Shenzhen, China:A retrospective cohort study. Lancet Infect Dis. 2020;20:911–9. doi: 10.1016/S1473-3099(20)30287-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Ahmad K, Erqou S, Shah N, Nazir U, Morrison AR, Choudhary G, et al. Association of poor housing conditions with COVID-19 incidence and mortality across US counties. PLoS One. 2020;15:e0241327. doi: 10.1371/journal.pone.0241327. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Azuma K, Yanagi U, Kagi N, Kim H, Ogata M, Hayashi M. Environmental factors involved in SARS-CoV-2 transmission:Effect and role of indoor environmental quality in the strategy for COVID-19 infection control. Environ Health Prev Med. 2020;25:66. doi: 10.1186/s12199-020-00904-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.PHE:Factors contributing to risk of SARS-CoV2 transmission in various settings, 26 November 2020 GOV.UK. [Last accessed on 2021 Feb 07]. Available from: https://www.gov.uk/government/publications/phe-factors-contributing-to-risk-of-sars-cov2-transmission-in-various-settings-26-november-2020 . [Google Scholar]
- 25.Arnedo-Pena A, Sabater-Vidal S, Meseguer-Ferrer N, et al. COVID-19 secondary attack rate and risk factors in household contacts in Castellon (Spain):Preliminary report. Rev Enf Emerg. 2020;19:64–70. [Google Scholar]
- 26.Wilkinson K, Chen X, Shaw S. Secondary attack rate of COVID-19 in household contacts in the Winnipeg health region, Canada. Can J Public Health. 2021;112:12–6. doi: 10.17269/s41997-020-00451-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Li W, Zhang B, Lu J, Liu S, Chang Z, Peng C, et al. Characteristics of household transmission of COVID-19. Clin Infect Dis. 2020;71:1943–6. doi: 10.1093/cid/ciaa450. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Jing Q-L, Liu M-J, Zhang Z-B, Fang L-Q, Yuan J, Zhang A-R, et al. Household secondary attack rate of COVID-19 and associated determinants in Guangzhou, China:A retrospective cohort study. Lancet Infect Dis. 2020;20:1141–50. doi: 10.1016/S1473-3099(20)30471-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Ng OT, Marimuthu K, Koh V, Pang J, Linn KZ, Sun J, et al. SARS-CoV-2 seroprevalence and transmission risk factors among high-risk close contacts:A retrospective cohort study. Lancet Infect Dis. 2021;21:333–43. doi: 10.1016/S1473-3099(20)30833-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Sun WW, Ling F, Pan JR, Cai J, Miao ZP, Liu SL, et al. [Epidemiological characteristics of COVID-19 family clustering in Zhejiang Province] Zhonghua Yu Fang Yi Xue Za Zhi. 2020;54:625–9. doi: 10.3760/cma.j.cn112150-20200227-00199. [DOI] [PubMed] [Google Scholar]
- 31.Madewell ZJ, Yang Y, Longini IM, Jr, Halloran ME, Dean NE. Household transmission of SARS-CoV-2:A systematic review and meta-analysis. JAMA Netw Open. 2020;3(12):e2031756. doi: 10.1001/jamanetworkopen.2020.31756. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Spencer EA HC. Risk of transmission in COVID-19 among close contacts. The Centre for Evidence-Based Medicine. [Last accessed on 2021 Jan 22]. Published July 6, 2020. Available from: https://www.cebm.net/study/COVID-19-risk-of-transmission-in-COVID-19-among-close-contacts/
- 33.Qiu X, Nergiz AI, Maraolo AE, Bogoch II, Low N, Cevik M. Defining the role of asymptomatic and pre-symptomatic SARS-CoV-2 transmission-A living systematic review. Clin Microbiol Infect. 2021;27:511–9. doi: 10.1016/j.cmi.2021.01.011. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Burke RM, Midgley CM, Dratch A, Fenstersheib M, Haupt T, Holshue M, et al. Active monitoring of persons exposed to patients with confirmed COVID-19-United States, January-February 2020. MMWR Morb Mortal Wkly Rep. 2020;69:245–6. doi: 10.15585/mmwr.mm6909e1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Luo L, Liu D, Liao X, Wu X, Jing Q, Zheng J, et al. Contact settings and risk for transmission in 3410 close contacts of patients with COVID-19 in Guangzhou, China:A prospective cohort study. Ann Intern Med. 2020;173:879–87. doi: 10.7326/M20-2671. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Lin Q, Zhao S, Gao D, Lou Y, Yang S, Musa SS, et al. A conceptual model for the coronavirus disease 2019 (COVID-19) outbreak in Wuhan, China with individual reaction and governmental action. Int J Infect Dis. 2020;93:211–6. doi: 10.1016/j.ijid.2020.02.058. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Shah K, Desai N, Mavalankar D. Secondary attack rate in household contacts of COVID-19 paediatric index cases:A study from Western India. J Public Health (Oxf) 2021;43:243–5. doi: 10.1093/pubmed/fdaa269. [DOI] [PMC free article] [PubMed] [Google Scholar]