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. 2024 Apr 4;19(4):e0297909. doi: 10.1371/journal.pone.0297909

Face mask use in the city of Chennai, India: Results from three serial cross-sectional surveys, 2021

Murali Sharan 1,*,#, Manikandanesan Sakthivel 2,#, Polani Rubeshkumar 1, Ramya Nagarajan 1, Vettrichelvan Venkatasamy 1, Sendhilkumar Muthappan 1, Mohankumar Raju 2, Joshua Chadwick 1, Kalyani S 2, Navaneeth S Krishna 1, Mogan Kaviprawin 2, Pavan Kumar Gollapalli 2, Srinath Ramamurthy 2, Parasuraman Ganeshkumar 1, Murugesan Jagadeesan 3, Prabhdeep Kaur 1
Editor: Vijayaprasad Gopichandran4
PMCID: PMC10994359  PMID: 38574080

Abstract

Introduction

The Government of Tamil Nadu, India, mandated wearing face masks in public places to combat the COVID-19 pandemic. We established face mask surveillance and estimated the prevalence of appropriate mask use (covering the nose, mouth, and chin) in the slums and non-slums of Chennai at different time points in 2021.

Methods

We conducted three serial cross-sectional surveys in the outdoors and indoors of Chennai in March, July, and October 2021. We observed the mask wearing among 3200 individuals in the outdoors and 1280 in the indoors. We divided the outdoor and indoor locations into slums and non-slums. In October 2021, we also surveyed 150 individuals from each of the 11 shopping malls in the city. We calculated the proportions and 95% confidence interval (95%CI) for the appropriate mask use in the outdoor, indoor, and malls by age, gender, region, and setting (slum and non-slum).

Results

We observed 3200 individuals in the outdoor and 1280 individuals in the indoor setting, each from a slum and non-slum, during the three rounds of the study. In outdoor and indoors, males comprised three-fourths and middle-aged individuals were half the study population. Mask compliance changed significantly with time (p-value <0.001). Males consistently demonstrated better compliance in all rounds. The south region had the highest mask compliance in slums indoors and outdoors in rounds 4 and 5. Young adults had the highest mask compliance in both outdoor slums and non-slums in all rounds. Overall mask compliance in shopping malls was 57% (95% CI: 48–65).

Conclusion

The mask compliance in Chennai outdoors during the COVID-19 pandemic was less than 50%, with variations across time points by gender, age groups, and geographical locations. We must develop more effective communication strategies for older age groups and crowded indoor settings.

Introduction

The SARS-CoV-2 virus transmits from an infected individual to a susceptible individual via droplet, aerosol, or fomite transmission [1,2]. However, the risk for COVID-19 transmission may depend on population density, ventilation, face mask usage, total contact duration, and type of activity (talking, singing, etc.) [3]. Face mask usage showed a high protection rate of 70% against SARS-CoV-2 infection [4]. Considering this available evidence, public health agencies, including World Health Organisation (WHO), the U.S. Centers for Disease Control and Prevention (CDC), and various governments across the world, advocated the use of face masks in all public settings as the prime mitigation strategy [5–7]. High-Income Countries used compliance to face mask use as an ecological indicator in understanding the waxing and waning of the COVID-19 waves [8].

Chennai is the capital city of Tamil Nadu, a southern state of India. Chennai is also the fourth largest metropolitan city in the country, spread over an area of 426 sq. km and a population of nearly 8 million (2020). During the COVID-19 pandemic, Chennai recorded 5,66,147 cases and 8,588 deaths till December 25, 2021 [9]. In alignment with the guidelines issued by the Ministry of Health & Family Welfare (MoHFW), the Government of Tamil Nadu issued mandates for wearing face masks in public places. Violators could be charged a fine of Rs. 500 [10,11].

In 2020, the Greater Chennai Corporation (GCC) and ICMR NIE established the mask compliance surveillance system in Chennai. The team conducted two survey rounds in 2020 and documented the mask compliance in the outdoor locations, indoor places, and malls of Chennai City. GCC used the report for public health advocacy through mass media and law enforcement authorities [12]. We continued our periodic surveillance to understand the change in the behaviour of Chennai City’s citizens with changing waves of the COVID-19 pandemic. We estimated the compliance with appropriate mask usage in the outdoor and indoor locations of slums and non-slums of Chennai, Tamil Nadu, India, at three different time points in 2021, and compare the trend with the two rounds in 2020. During the last survey in 2021, we documented the mask compliance in the city’s shopping malls, in addition to outdoor location and indoor spaces.

Methods

Design & setting

We conducted serial cross-sectional surveys among the residents of Chennai to quantify compliance to face mask usage. We previously completed two rounds in 2020 and published elsewhere [12]. We conducted round 3 at the start of the second COVID-19 wave from March 20 to 26, 2021 to document the mask compliance in rise of a pandemic wave. We conducted round 4 from July 8 to 10, 2021 at the decline of the second COVID-19 to see if the mask wearing pattern has changed. We conducted the last (fifth) round towards the end of the year 2021, from October 30 to November 1, 2021 to document how the residents adhered to the mask-wearing guidelines when the number of COVID-19 cases were low.

Chennai is a cosmopolitan city divided into 15 zones and 200 wards. The city has around 45,000 streets stratified as Slum and Non-slum areas, mapped and documented for administrative purposes by the Greater Chennai Corporation (GCC). The population density was higher in the slums than in the non-slums. From time to time, the state’s public health department released guidelines about compliance with face mask use in public places [10,13].

Sample size and sampling

Round 1 of the Chennai mask study reported compliance to face mask use in outdoor locations as 36% [12]. Considering this, we calculated the sample size for the three-time points in this study with a 95% confidence interval, 5% absolute precision, and a design effect of 4.5 as 1600. Considering that we will observe 50 individuals from each street, we needed 32 streets each from slums and non-slums from among the 45,000 streets in Chennai. We selected the 32 streets by simple random sampling using Microsoft Excel [14].

We calculated the sample size separately for the indoor locations. Round 2 of the Chennai mask study documented indoor mask compliance as 11% [12]. Using this prevalence, we calculated the sample size with 95% confidence, 5% absolute precision, and a design effect of 4.25 as 640. We chose the indoor locations from the 64 outdoor streets already chosen for the outdoor survey. Thus, we observed 20 individuals in the indoor locations from each of these 64 locations for the indoor survey.

Round 2 of the Chennai mask study reported 57% compliance to face masks in the Chennai malls [12]. Using this proportion, we calculated the sample size with a 95% confidence interval, 5% absolute precision, and a design effect of 4.5 in Open Epi version 3.1 to be 1695 [15]. However, considering that this is a follow-up of round 2 and due to feasibility, we decided to sample 150 individuals from each mall, summing to 1650 individuals in total. We selected the locations using a simple random technique using random number allocation in Microsoft Excel [14].

Operational definitions

"Mask" was defined as any cloth mask, medical mask, or N95 respirator worn over the face. We defined appropriate mask use as wearing a mask that covers the nose, mouth, and chin [12]. For ease of observation and recording, we grouped the individuals into four age groups: Children or adolescents, young adults, middle age, and old age. We defined streets in residential or commercial areas and bus stations as outdoor public places. We defined closed public settings open to the people as indoor public settings. Most of the indoor public settings in Chennai lacked personnel who would insist on the use of face masks in their shop/establishment. We included all such locations (e.g., grocery shops, vegetable shops, pharmacies, religious places, and apparel stores) as indoor public places. We excluded areas with security personnel who insisted on wearing a mask before entry, and food courts, eateries, liquor shops, and tea/coffee cafes from our observation. Shopping malls, however, had personnel who would insist on mask compliance and hence were included as a separate setting.

Study procedure

We trained five teams of two data collectors in the operational definitions and conducted simulation exercises to observe face mask compliance directly [8,9]. We aimed to reduce the inter-observer variation through this training and simulation exercise. The technical team mapped the selected locations using Google Maps and shared it with the data collection teams. The geographical spread of the three survey rounds is shown in Fig 1. Upon reaching the specific tagged location, the stationary data collector would observe the individuals crossing from right to left direction, one individual at a time, and record the individual’s mask compliance. We maintained this directional rule to minimize possible duplication. We included pedestrians, bicycle riders, motorcycle riders, autorickshaws, and bus passengers, excluding individuals traveling in a car, and riders wearing a helmet. We repeated this process until the sample size for that location was completed. Individuals in the defined indoor areas and malls were also sampled similarly.

Fig 1. Study sites of mask survey, Chennai, Tamil Nadu, India, 2021.

Fig 1

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We obtained the spatial data to construct the map from the following open-access sources: https://data.opencity.in/dataset/gcc-ward-information [16].

We observed the participants from a distance to avoid the Hawthorne effect. Data collectors recorded age group, gender, and compliance to mask usage as worn appropriately, inappropriately, or with no mask. We recorded the data in an Open Data Kit (ODK), an Android-based mobile application with pictorial cues to reduce the survey time. Observers collected data using their mobile phones, and we received the data in our secured Institutional server. We used the same sampling list and methodology for all three rounds and in malls (round 5 alone) reported in this paper.

Data analysis

We explored four study settings in the present study–outdoor slums, indoor slums, outdoor non-slums, and indoor non-slums. We summarised the exposure variables age group, gender, and geographical region in Chennai as the frequency with proportions. We summarised the appropriate mask compliance in each of the four settings as proportion and 95% confidence interval (CI). We also computed the mask compliance for each exposure group separately for slum and non-slum in each round. We used the chi-square test to compare mask compliance across the different age groups during each round for the four study settings. We performed a similar analysis for gender and geographical regions in Chennai.

We combined the results of the previously published rounds 1 and 2 into our dataset [17] to compare mask compliance across the five rounds within each setting using chi-square for trend.

We estimated the mask compliance in malls of Chennai during round 5 of the present study. We summarised the mask compliance in each exposure category with its 95% CI. We also used the chi-square test to compare the mask compliance within each exposure category. We performed this analysis separately for slums and non-slums.

We generated the required visualizations to compare the mask compliance across the study groups in the three rounds. We analyzed the data using Survey set analysis in STATA SE (version 17.0) software (StataCorp LLC, College Station, TX, USA) for the study [18]. A p-value less than 0.05 was considered significant.

Results

We observed 3200 individuals in the outdoor and 1280 individuals in the indoor setting, each from a slum and non-slum, during the three rounds of the study. In indoor and outdoor settings, males constituted three-fourths of the study population. Nearly half of the study population was middle-aged individuals in indoor and outdoor settings. Over one-third (36%) of the indoor and outdoor study population in each round belonged to the northern region of Chennai, followed by central (33%) and south (31%).

Trends in mask compliance at various stages of the pandemic

Round 4 reported the highest mask compliance of all rounds (non-slum outdoor: 47% [95% CI: 43–53] vs. slum outdoor 41% [95% CI: 34–47], non-slum indoor 33% [95% CI: 27–40] vs. slum indoor 24% [95% CI: 18–31]). Mask compliance was the lowest at round 3, with the compliance in slums indoors reaching the minimum of all times at 11% [95% CI: 8–16] (Fig 2).

Fig 2. Compliance with appropriate usage of face masks at various time points during the COVID-19 pandemic in the city of Chennai, India.

Fig 2

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The increase or decrease in mask compliance at various survey rounds was not due to chance. There was a significant change in mask compliance with time (p-value <0.001) (Table 1).

Table 1. Compliance with appropriate face mask usage within each study setting (outdoor/indoor & slum/non-slum) in Chennai compared across the five rounds.

OUTDOOR
Round Time Period Slum Non-Slum
N n (%) 95% CI p-value for trend # N n (%) 95% CI p-value for trend #
1 Oct 2020* 1800 497 (28) 23–33 <0.001 1800 643 (36) 31–41 0.002
2 Dec 2020* 1600 460 (29) 24–35 1600 561 (35) 31–39
3 Mar 2021 1600 344 (22) 17–27 1600 435 (27) 23–33
4 Jul 2021 1600 646 (41) 34–47 1600 767 (47) 43–53
5 Oct–Nov 2021 1600 518 (32) 28–37 1600 563 (35) 29–41
INDOOR
Round Time Period Slum Non-Slum
N n (%) 95% CI p-value for trend # N n (%) 95% CI p-value for trend #
2 Dec 2020* 640 72 (11) 8–15 <0.001 640 67 (10) 8–16 <0.001
3 Mar 2021 640 72 (11) 8–16 640 104 (16) 13–21
4 Jul 2021 640 153 (24) 18–31 640 212 (33) 27–40
5 Oct–Nov 2021 640 92 (14) 9–22 640 135 (21) 16–27
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*Results from rounds 1 & 2 of the Chennai Mask Study (12).

#Chi-square for trend.

Mask compliance & gender

Mask compliance in the male ranged from 11% (indoor slum–round 3 [95% CI: 8–17]) to 48% (outdoor non-slum–round 4 [95% CI: 43–53]). Whereas among females, mask compliance ranged from the lowest 11% (indoor slum–round 3 [95% CI: 6–19]) to 47% (outdoor non-slum–round 4 [95% CI: 40–56]) (S1 and S2 Tables in S1 File). Males consistently showed higher mask compliance in rounds 3 and 4 in all groups (Fig 3). However, in round 5, females demonstrated better compliance in all settings except outdoor slums, where compliance was lower than men (29% versus 33%). (S1 Table in S1 File).

Fig 3. Compliance with appropriate usage of face masks in the various study settings by gender over the three rounds, Chennai, India.

Fig 3

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Mask compliance & geographical regions

The south region consistently showed the highest mask compliance in slums indoors and outdoors in surveys 4 and 5. In outdoor non-slums, we observed a mixed pattern. In round 3, the south showed the highest, while in round 4, it was north, and in round 5, it was central (Fig 4). However, in all three rounds, the central region consistently showed higher mask compliance in the indoor non-slums (Fig 4).

Fig 4. Compliance with appropriate usage of face masks in the various study settings by geographical regions over the three rounds, Chennai, India.

Fig 4

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Mask compliance & age groups

Young adults showed the highest mask compliance in both outdoor slums and non-slums. Middle-aged individuals closely followed this. Children/ adolescents showed the lowest mask compliance in round 3 in both slum and non-slum outdoors, which slowly improved over time. In the indoor setting, children demonstrated poor compliance in round 3, which increased consistently to the highest in round 5 (Fig 5).

Fig 5. Compliance with appropriate usage of face masks in the various study settings by age groups over the three rounds, Chennai, India.

Fig 5

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Mask compliance in shopping malls (Round 5)

Overall mask compliance in the shopping malls was 57% (95% CI: 48–65) (S1 File). Women (60% [95% CI: 53–67]) showed higher compliance than men (54% [95% CI: 44–64]) in the malls (p-value: 0.061). Older age group individuals had the highest compliance (63% [95% CI: 47–77], followed by children (60% [95% CI: 43–76]). The difference in compliance among the age groups was statistically significant (p-value: 0.039).

Discussion

The study summarises three repeat surveys to assess compliance with appropriate mask use in Chennai over 2020 and 2021. We also compared the results with the previous two surveys in the same setting using the same methodology [12]. Although the law mandated using face masks in outdoor and closed indoor places, mask compliance remained below 50% outdoors and 33% indoors across the surveys. The present study also documented the low mask compliance in the city’s shopping malls during the last survey round. We demonstrated the changes in mask-wearing behavior with time by geographical region, gender, and age group.

The study documented the changes in mask-wearing patterns of the community with changes in overall reported cases of COVID-19. The Government introduced the COVID-19 vaccines in early 2021 [19]. The introduction of the COVID-19 vaccine coincided with the start of the second wave, predominantly driven by the delta variant of SARS-CoV2 [20]. With newer strains in circulation and the poor uptake of COVID-19 vaccines initially, mask use remained the primary mitigation strategy against COVID-19. We documented the highest-ever mask use in the Chennai population during the second wave (July 2021). The present study showed increased mask compliance with increased cases and vice versa. This increase in compliance could be due to the frequent mention of mask use and the increase in cases and deaths frequently mentioned in social and mass media. Evidence from China suggests that mass media could influence health behaviours [21]. In the United States, during the early phase of the Pandemic, the release of CDC recommendations to wear masks led to a 12% improvement in the overall reported mask usage [22]. Although the rules and enforcement were in place in our setting, mask use never exceeded 50% and declined as the number of cases decreased. The only exception was shopping malls where compliance was higher, possibly due to consistent monitoring for masks at the shopping mall [23].

We observed that mask compliance in the outdoor locations was higher than the indoors in all repeat surveys. Literature deems outdoor open spaces as low risk, while indoor closed spaces could be potential high-risk areas for transmission of SARS-Cov2 [24]. This swapping of the intended mask compliance in the population could be due to social appearance anxiety [25]. Law enforcement constantly monitored outdoor locations, which could have led to higher compliance. The health promotion messages need to emphasize the high risk of transmission in indoor settings during respiratory illness outbreaks.

We observed that compliance with appropriate mask use was higher among the males than the females in the outdoor locations in most rounds. On the contrary, females had higher mask use in indoor settings. Our results were consistent with a survey from the US, where women entering retail stores during the Pandemic reported higher mask compliance than men [26–28].

In the present study, young adults showed higher mask compliance than other age groups in outdoor settings. A modeling study combined the results of four separate datasets from surveys conducted in high-income settings among MTURK employees, indicating that the older age group is less likely to wear face masks than the young, which also agrees with our study setting [29]. A study from two Swiss hospitals documented the perception of their employees for wearing masks. The young wore masks due to the perceived risk and also for social preferences, while the old wore masks due to only the perceived risk [30]. Studies have proved a positive association between higher self-related risk perception and better face mask compliance [31].

The present study documented higher compliance to appropriate mask use at all time points in the non-slum settlements than in the slum settlements. The non-slum population might have perceived the risk of COVID-19 better hence the consistently high compliance with face masks [32,33]. Also, the lack of social acceptance in non-slum settings could have increased compliance [31]. The southern region of Chennai, which is the hub for information technology companies, showed higher compliance when compared to that of the central and northern areas [32]. The level of education and social acceptance might play an essential role in mask-use behaviours.

Our study was a rapid and low-cost methodology to monitor mask compliance in public places by direct in-person observation which is one of the recommended methods for measuring compliance to safe behaviours. Our study was able to give mask compliance in different environments in Chennai city–Outdoors, Indoors, and also in Shopping Malls. Our study had some limitations too. In the present study, we anticipate possible inter-observer variation in identifying the individual’s age group. But through vigorous training, simulation exercises, and observation rules, we have tried to keep this bias to the minimum.

Conclusion & recommendation

The mask compliance in Chennai outdoors during the COVID-19 pandemic was less than 50%, with variations across time points by gender, age groups, and geographical locations. Although the COVID-19 pandemic has subsided, the survey findings provide essential insights into mask-use behavior. They are relevant to plan interventions during the resurgence of COVID-19 or other respiratory disease epidemics. We must set realistic expectations about adopting mask use and develop more effective communication strategies. Communication should focus on wearing the mask in crowded indoor settings, and monitoring should be improved in indoor environments. The intensified communication campaigns should focus on older age groups at higher risk and slum areas.

Supporting information

S1 File. Tables of face mask compliance in different settings (outdoor, indoor, and shopping malls) and exposure categories (age group, gender, and region) in Chennai, India.

(PDF)

pone.0297909.s001.pdf (128.5KB, pdf)

Acknowledgments

We thank the Greater Chennai Corporation health staff for providing the necessary support for this project. We acknowledge the contribution of Ramya Kumaraguruparan, Lavanya Ayyasamy, Dharsikaa T, Vijayaprabha Radhakrishnan, Murali Mohan Muni krishnaiah, Suresh Arunachalam, Punita Muni krishna Gandhi, Prakash Marappan, and Ezhil Pounraj for their valuable contributions during the data collection of this study. The authors are grateful for the excellent support provided by Alby John Varghese, Manish Narnaware, and Gagandeep Singh Bedi for this study.

Data Availability

The datasets generated and analyzed during the current study have been deposited in the Mendeley repository at https://data.mendeley.com/datasets/vkv4cwhh7n (DOI: 10.17632/vkv4cwhh7n.1).

Funding Statement

This study was supported by the Intramural fund of ICMR-National Institute of Epidemiology, Chennai, India.

References

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29 Sep 2023

PONE-D-23-17287Face mask use in the city of Chennai, India: Results from three serial cross-sectional surveys, 2021PLOS ONE

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Reviewer #1: Thank you for the opportunity to review the article titled "Face mask use in the City of Chennai, India: Results from three serial cross-sectional surveys, 2021". The authors have tried to assess face mask usage during the COVID-19 pandemic in Chennai. I believe that the manuscript could benefit from the following suggestions:

1. Kindly rewrite the abstract, as the methods mentioned in the abstract is confusing, and please stick to the abstract guidelines specific for Plos One

2. Line 73, which reads "We also documented mask compliance in the city's shopping malls for the third time point (round 5) alone" is not appropriately placed and is confusing. How many rounds were conducted? 3 or 5? Please be specific and modify the statement.

3. Why mention about rounds 1 and 2 from 2020 in this study? when the objective is to estimate compliance with appropriate mask usage at three different time points in 2021?

4. Why were these specific time periods chosen for sampling? the justification for the final round is provided, please provide the rationale and justification for conducting the survey at specific time periods of 2021

5. Why were 32 streets chosen? and how were the 32 chosen from 45,000? what sampling strategy was followed?

6. Pharmacies and religious places had "no entry restrictions?" and thus considered indoor settings.? not even in pharmacies? a personal would insist on mask usage?

7. "We excluded areas with security personnel who insisted on wearing a mask before entry.. Then why include Malls? which mandates mask usage before entry?

8. How was it possible to record the mask usage and compliance among motorcycle riders, who wear helmets?

9. How did the estimates calculate for the population inside the moving bus? how did the data collector collect this information? Were they stationed in the street or inside the bus?

10. Table 1: Round 3 is March 2020? or 2021?

11. When the objective is not to compare with the 2020 estimates, I feel there is no necessity to compare with round 1 and 2 estimates..

12. Please check the referencing style.. eg 2,10,13 are not in Vancouver

13. Please provide clearer images, preferably in TIFF format

14. Why did the study observe higher compliance during July 2021, while the peak was in March - April 2021?

Reviewer #2: I would like to express my gratitude for the opportunity to review the manuscript titled "Face mask use in the City of Chennai, India: Results from three serial cross-sectional surveys, 2021". The study highlights the importance of developing more effective communication strategies on appropriate mask use for older age groups and addressing compliance issues in crowded indoor settings to enhance public health measures during a pandemic. The following are some suggestions that I've considered for the manuscript:

1. The line number 71, which reads “We estimated the compliance with appropriate mask usage in the outdoor and indoor locations of slums and non-slums of Chennai, Tamil Nadu, India, at three different time points in 2021.We also documented mask compliance in the city's shopping malls for the third time point (round 5) alone.” Please modify the statements for clarification and justify the inclusion of shopping malls in the survey.

2. Also, line number 118 states, “We excluded areas with security personnel who insisted on wearing a mask before entry” please justify the inclusion of shopping malls in the survey as these areas were with strict enforcement for wearing masks.

3.As the objective was to estimate the compliance with appropriate mask usage at three different time points in 2021, justify the comparison of findings from the round 1 and 2 surveys of 2020 in the current study.

4.Line 109 mention, Mask" was defined as any cloth mask, medical mask, or N95 respirator worn over the face. We described appropriate mask use as wearing a mask covering the nose, mouth, and chin and inappropriate use as wearing the mask either below the nose or mouth. When the individual did not wear a mask, we considered it no mask. Cite reference for the same.

5.Line 130, which reads, “We included pedestrians, bicycle riders, motorcycle riders, autorickshaws, and bus passengers, excluding individuals traveling in a car.” How was it feasible/possible to record the mask usage and compliance of helmet-wearing motorcycle riders? as well as the bus passengers inside the bus?

6.Line 145, “We classified the mask compliance in each of the four settings into three categories: appropriate, inappropriate, and no mask, and computed the proportion and 95% confidence interval (CI). But the findings on ‘inappropriate use’ and ‘no mask use’ category were not mentioned.

7.Line 127 states, “Upon reaching the specific tagged location, the data collector would observe the individuals crossing from right to left direction, one individual at a time, and record the individual’s mask compliance. We maintained this directional rule to minimize possible duplication. Also mention the status of the observer as it may lead to duplication of the observation - whether stationary observer measured adherence by passersby, or a mobile observer measured adherence in stationary people.

8.For the south region, the compliance was low in round 1 and 2 whereas it was high in round 3-5, comparing with other regions of the city. Justify the findings.

9.Please check for the year in Table 1 for Round 3. Was it March 2020 or 2021?

10.Please check the reference numbers 2,10 and 13 for its styling with reference to the journal guidelines.

11. Mention about the generalizability of the study findings? Also mention the strengths and limitations.

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Reviewer #1: No

Reviewer #2: No

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PLoS One. 2024 Apr 4;19(4):e0297909. doi: 10.1371/journal.pone.0297909.r002

Author response to Decision Letter 0

1 Jan 2024

Thank you, Reviewers, for all the comments.

We have tried our best to include and answer all these comments.

Response to Reviewers is added as a separate file also.

Attachment

Submitted filename: Response to Revieweres 2.pdf

pone.0297909.s002.pdf (248.2KB, pdf)

Decision Letter 1

Vijayaprasad Gopichandran

16 Jan 2024

Face mask use in the city of Chennai, India: Results from three serial cross-sectional surveys, 2021

PONE-D-23-17287R1

Dear Dr. Sharan,

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Vijayaprasad Gopichandran

Academic Editor

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Additional Editor Comments (optional):

Reviewers' comments:

Acceptance letter

Vijayaprasad Gopichandran

27 Mar 2024

PONE-D-23-17287R1

PLOS ONE

Dear Dr. Sharan,

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Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Supplementary Materials

    S1 File. Tables of face mask compliance in different settings (outdoor, indoor, and shopping malls) and exposure categories (age group, gender, and region) in Chennai, India.

    (PDF)

    pone.0297909.s001.pdf (128.5KB, pdf)
    Attachment

    Submitted filename: Response to Revieweres 2.pdf

    pone.0297909.s002.pdf (248.2KB, pdf)

    Data Availability Statement

    The datasets generated and analyzed during the current study have been deposited in the Mendeley repository at https://data.mendeley.com/datasets/vkv4cwhh7n (DOI: 10.17632/vkv4cwhh7n.1).

    Articles from PLOS ONE are provided here courtesy of PLOS

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