The burden of COVID-19 infection in a rural Tamil Nadu community

The coronavirus disease (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) began in early 2020; there are now over 200 million cases worldwide and over 4.3 million deaths [1]. In India, the world’s second most populous nation, there was a rapid rise in cases from the onset of the pandemic in March 2020, mostly from the urban areas, yet there was relatively little information on the infection rate in the rural areas [2, 3]. Concerns were expressed at an early stage that COVID-19 infection could be spreading unchecked in rural areas of India, where 70% of the population reside, because of disparities in access to and compliance with preventive measures and inadequate health information systems [4]. While spread of the virus in India was initially traceable to known sources of infection (such as international travellers), community transmission soon became established, with no clear identifiable source of the infection [5]. The second wave of the pandemic in India, around March to June 2021, had a deadly impact, with more virulent strains of the virus and more young people affected [6]. Available evidence shows that there is much variation in transmission rates from region to region in India, depending upon factors including the susceptibility of the population, population density, and feasibility and uptake of COVID-19 preventive measures [7, 8]. As of August 13th 2021, there have been over 32.1 m cases of COVID-19 reported in India with over 430,000 deaths [9].

In the early stages of the pandemic (May–June 2020), the Indian Council of Medical Research (ICMR) undertook a cross sectional seroprevalence survey in selected districts, stratified by incidence rate; it reported a pooled adjusted seroprevalence of 0.73% (0.34–1.13%) amongst adults residing in both rural and urban areas [10]. The second seroprevalence survey undertaken by ICMR between August 17 and September 22, 2020 examined the prevalence in rural and urban areas separately; it reported a pooled prevalence of 7.1% (95% CI: 6.2–8.2) with 4.4% in rural populations [11].

With increasing rates of COVID-19 infection in the country, the Government of India (GoI) initiated multiple preventive measures, in coordination with the state governments. These included lockdown, active airport screening, quarantining, recommendations for ‘work from home’, closure of educational institutions, limiting public transport, public awareness campaigns, and active case detection with contact tracing in most regions [12]. At the time of our study (commencing June 2020) there were lockdown conditions; after July 2020 these were eased, but some restrictions for large gatherings, recreation and religious gatherings remained. Schools in rural Tamil Nadu had little online provision, and remained closed longer than many city schools. Compliance with the use of masks was not high in these rural areas and, at village level, social distancing wasn’t well-observed. COVID-19 vaccines hadn’t been introduced in India during our study period.

The GoI also launched a nation-wide testing programme for symptomatic individuals using RT-PCR tests, which was done through district hospitals and primary health care centers. The measures were largely in line with the WHO’s Strategic Preparedness and Response Plan for COVID-19 [13]. While this initiative increased the access to detection of cases in the rural areas, there were problems with implementation, and there was limited information available about the burden of the disease among rural communities.

To address this information gap, we undertook a study that examined the burden of infection in a rural population of the Vellore district, Tamil Nadu. We:

We combined our results with secondary data to produce estimates of total numbers of infections in our population, and infection-to-case ratios (ICRs), a measure of how well cases might predict actual infections [14]. We also examined the effect of socio-demographic characteristics and other risk factors on COVID-19 test-positivity in both study components.

To date there have been few studies reporting on the burden of the COVID-19 pandemic in rural Indian communities. Our study found that in a sample of our target population who met at-risk criteria for COVID-19, over half tested positive for the disease, confirming that the pandemic has become established in rural populations, despite their relative isolation. Further, our study is one of the first to examine COVID-19 seroprevalence in a rural area of India—this is crucial to understanding the likely extent of herd immunity developing and strategies for the containment of the pandemic [27].

Factors associated with test-positivity in our fever clinic survey were male gender, older age, higher SES, social interaction and presence of diabetes. This reflects the rather complex epidemiological profile of risk factors for COVID-19 and has been seen in other studies in the Indian population [28]. While lower income/deprived life circumstances may be more conducive to disease transmission, higher SES individuals may be more mobile, and more likely to come in contact with people from areas of India with high levels of COVID-19 (such as large cities). Nevertheless, in terms of impact, it is lower SES communities which are the most vulnerable, with most studies suggesting that COVID-19 is likely to worsen poverty and health disparities [29]. In our seroprevalence studies, we were not able to replicate the observed associations with test-positivity, but were limited by small numbers.

Only 3 (1.6%) out of the 182 fever clinic participants had died when we undertook telephone follow-up, and 16 (8.8%) had suffered a severe illness. This case fatality rate (CFR) is low in comparison with other international studies [30]; national data in India show CFRs among men of 2.9% and 3.3% among women [31]. It may indicate that, while the epidemic has taken hold in our rural population, its impact in terms of prolonged, severe illness and death, may be lower than in other, seemingly less vulnerable populations. However, while we are confident we captured all events in our fever clinic follow-up processes, our numbers are small. Also, CFRs are prone to bias; our fever clinic patients were symptomatic, and capable of travelling to the testing facilities at RUHSA. They were also a young population—even compared to population level data on Indian average age of COVID-19 infection, which show lower ages than in most western countries [41]. Hence, national, and international comparisons of CFRs should be treated with caution. Nevertheless, at a national level, India’s low case-fatality rates (particularly before the second wave) have attracted significant praise for the country’s public health efforts [32]. Numerous contributing factors have been proposed, including India’s younger population, cross-reactive immunity from other coronavirus infections in the past and environmental factors such as sunlight and vitamin D [33]. Cautious interpretation of available data on deaths and case fatality rates has, nevertheless, been urged—along with the avoidance of any degree of complacency [34]. Importantly, the Indian Government, despite the severe challenges of the pandemic’s second wave, has maintained its commitment to widespread testing and vaccine rollout.

Our seroprevalence findings showed a cumulative incidence of 2.2 percent by the end of August, increasing to 22 percent by November 2020, with an infection- to case-ratio of 33. This finding contrasts with the ICMR surveys which, by the end of September, showed lower seroprevalence (4.4% in rural populations) but, in the early stage of the pandemic, showed an ICR of 81.6 (95% CI: 48.3–141.4) [10, 11]. A seroprevalence study in Mumbai showed a rate of 54.1 percent in slums and 16.1 percent in non-slum areas of the city [35]. The estimates of seroprevalence were lower in this rural population compared to cities and urban slums in India. The factors underlying these differences are likely to be multifactorial, and include lower population density in our population and natural social distancing compared to cities and urban slums [36]. Our estimate of 33 infections for every confirmed case is commensurate with other studies which have examined ICR [14]—confirming that much infection remains clinically undetected in our study community, as in other parts of India.

Our estimates of seroprevalence of COVID 19 infection rates in rural India add to a growing body of international data. A pooled, worldwide estimate of 3.38% (95% CI 3.05–3.72%) was published in August 2020, but there was significant variation from country to country – for example, 5.27% (3.97–6.57%) in Northern Europe; 2.02% (1.56–2.49%) in Eastern Asia; and 1.45% (0.95–1.94%) in South America [37]. Regional and international comparisons do, however, need to be interpreted with caution—they are dependent on many factors, including timing of the surveys, population sampling and coverage. About one-third of household contacts of known cases were sero-positive—high in comparison to many other international studies [38]; in the presence of community control measures, transmission within households is thought to account for about 70% of COVID-19 infections [39].

There are a number of limitations to our study; crucially, it was undertaken before the pandemic’s severe second wave in India (March–June 2021)—and this has changed some of the patterns of disease transmission [40]. Our ‘fever clinic’ survey was prone to sampling bias; villagers self-selected in response to community health workers making them aware of the study and the possibility of being tested. Travel to the clinic at RUHSA meant time away from work or the household; although travel costs were reimbursed, it is possible that those with more severe symptoms, or who were more financially challenged, were less likely to attend. Nevertheless, basic demographics of clinic attendees were similar to the wider RUHSA catchment population, and all villagers who met the criteria were strongly encouraged to attend for testing. The information we collected on attendees was based on self-report, with no external validation, and many study participants had low levels of education. Nevertheless, the interviewers were trained health workers with extensive experience in interacting with lower income, rural, low-health-literacy patients, using a range of strategies tailored to this population—so we have reasonable confidence in the accuracy of the data.

The surveys were conducted over discrete time periods, and there were complex background factors which are likely to have affected our results, including the timing of lockdowns and other social-distancing measures and the transmission rate of the virus (Rt) both within and outside the study region. This was shown in Tamil Nadu to vary over time and by district—likely reflecting changes in both the access to testing and compliance to preventive measures as well as the effectiveness of contact-tracing efforts [41]. Our household seroprevalence study was limited by poor response—we found a general reluctance for asymptomatic household member to provide samples, possibly relating to stigma from the illness. It did, however, suggest significant spread of the virus within the households, in keeping with international literature [39]. While we didn’t have sufficient numbers to examine within-household spread by family member characteristics, other studies have found children and adolescents to be less susceptible to COVID-19 infection but more infectious than older individuals [42]. There was disparity between some of the different tests for sero-positivity; we classified any positive test as COVID-19 sero-positive, noting that, worldwide, it’s thought we are likely to be under-, rather than over-estimating prevalence with available tests [43].

Despite its limitations, our study has provided some important insights on how the COVID-19 pandemic is playing out in a remote, rural lower-middle-income country (LMIC) population. LMIC countries are highly vulnerable to the COVID-19 pandemic, and it’s likely that additional support will be needed for communities similar to our study population which will require innovative policies to achieve sustainability and development [44]. The pandemic poses particular challenges for these communities due to the paucity of testing services, weak surveillance systems and limited access to medical care [4]. Our study population at least had the benefits of outreach health services from Christian Medical College and RUHSA—giving it advantages over other similar populations in India. The impacts of this pandemic, and especially the lockdown strategy, are multi-dimensional. Ideally, the most vulnerable populations should be systematically identified and targeted for support [45]. There are many calls for the government to assist these vulnerable communities as they meet the challenges of the pandemic [46]. COVID-19 vaccination started in India on 16th Jan 2021 but is limited to health care professionals and frontline workers at this stage—it will take some time before it reaches the rural general population. Once it does, a thorough understanding of how the pandemic is playing out in India’s lower income, rural populations will be vital in achieving efficacious and equitable national coverage [47]—for example, the vaccination threshold to achieve herd immunity may differ from populations in other regions of India.

The COVID-19 pandemic is having a significant impact on lower income, rural communities in India. We’ve shown that a simple approach using community health workers, a screening instrument, and a community-based testing clinic can readily identify COVID-19 infections in a rural population. While our data suggest low rates of death and severe illness, it is vital that ongoing efforts to control the pandemic do not overlook these vulnerable populations. It is also important to examine whether available data are capturing the full impact of the disease in groups which may not be adequately represented in existing studies in India—such as the elderly, and those with multiple co-morbidities. Ongoing monitoring through seroprevalence surveys will help in predicting future patterns of the pandemic in rural communities.