Distancia-Covid Survey: First Report

Introduction

This report provides preliminary results from the Distancia-Covid Survey launched on 14 May 2020 under the CSIC-funded project “Impacto de las medidas de distanciamiento social sobre la expansión de la epidemia de Covid-19 en España”. It relies on the survey responses received from the launch date through 10 June 2020. This period encompasses the vast majority of the responses received to date, and it corresponds to the time during which Spain was transitioning away from the extensive restrictions on mobility and social contacts that had been put into place with the state of alarm decreed on 14 March 2020. The state of alarm lasted until 21 June 2020 and Spanish territories were moving, at varying rates, through the three phases of the de-escalation process during the survey period analyzed here.

Survey Design

The survey was designed by the Distancia-Covid team in order to better understand changing patterns of human mobility and social contacts in Spain in the context of the Covid-19 pandemic. Many of the questions draw on the approach taken by the POLYMOD study (Mossong et al. 2008; Prem, Cook, and Jit 2017), and were developed in coordination with researchers in other countries working on similar surveys related to social mixing (Del Fava et al. 2020; Feehan and Mahmud 2020; Perrotta et al. 2020).

The survey was distributed in Spanish, Catalan, Galician, Basque, and English using Kobo Toolbox¹. Respondents accessed the survey at https://distancia-covid.csic.es/encuesta and it remains available at present at that URL. Respondents are able to access the survey questions only if they first provide informed consent.

The sampling design was non-random, based entirely on people self-selecting into the respondent pool by connecting to the survey URL online. The survey URL was distributed through press releases, Twitter, Whatsapp, and other channels by members of the project team and institutional press offices, and it appears to have propagated through digital networks reasonably well, reaching all provinces in Spain and a relatively wide segment of the population (see further below).

As of 10 June 2020 there were 4390 valid submissions. Initial data cleaning was done to improve the interpretability of variable names and generate additional variables calculated from the original ones. Among other things, an imputed usual postal code variable was created based on the two postal code questions in the survey, which asked respondents to list their current and usual postal codes. The imputed variable takes the value of the usual postal code when this has been provided. When it has not been provided it takes the value of the current postal code on the assumption that these are the same in these cases. In addition, province variables were created based on the first two digits of the postal code responses.

Descriptive Statistics

This section provides descriptive statistics of the 4390 survey submissions received as of 10 June 2020.

Temporal Distribution

Most survey submissions were made soon after the survey was released. Figure 1 shows the submission time pattern on a histogram with the data aggregated in 1-hour bins. As can be seen, there were several waves of submissions, with the largest occurring on the day the survey was released and then a later, much smaller wave on 25 and 26 May. There is also a clear daily cycle of submissions, which drop off at night (as one would expect).

Figure 1: Histogram of survey start times binned by the hour.

Geographic Distribution

Based on the imputed usual postal code variable, survey respondents appear to have had their usual places of residence distributed across Spain, with at least one respondent in each province. (This mostly also corresponded to their current places of residence, although 382 respondents listed different current and usual postal codes, and of these, 203 are in different provinces.)

In absolute terms, most respondents reported their usual places of residence in Madrid or Barcelona, as shown in the right panel of Figure 2. Relative to the province residential populations (taken from the padron), the greatest sampling fraction is from Girona, followed by Toledo, Bizkaia, Barcelona, and Castellon, as shown in the left panel of Figure 2.

Figure 2: Province sampling fractions. Percentage of each province’s residential population sampled (left) and number sampled (right). Province populations are based on padron.

Age and Gender

The survey respondents also represented a broad cross-section of ages, ranging from 18 (the requirement for participation) up to 92. The mean age of respondents was 46, with an interquartile range of (36, 57). There were both male and female respondents in nearly every age group, and 63% of respondents identified themselves as female, 35% as male, and 2% declined to respond to the gender question. Figure 3 provides a population pyramid of male and female respondents.

Figure 3: Age and gender distribution of survey respondents.

Education

The survey asked respondents to report their highest level of education, divided in to four levels. Submissions were received from people reporting all four levels, with most reporting undergraduate or graduate level. Figure 4 shows reported education levels by gender.

Figure 4: Distribution of respondent education levels by gender.

Work

The survey also asked respondents to report their “occupation or type of work” as well as “the activity of the establishment in which [they] work.” The distribution of responses to the occupation question is shown in Figure 5, with the labels on the x-axis corresponding to the following response options:

• military: Military occupations; Armed forces
• directors: Directors and managers; Business and Public Administration Management
• scientists: Scientific and intellectual technicians and professionals
• support techs: Support Technicians and Professionals
• admin: Accounting, Administrative, and Other Office Employees; Administrative type employees
• caterers: Catering, personal, protection and trade vendor workers
• skilled agricultural: Skilled workers in the agricultural, livestock, forestry and fishing sectors; Skilled workers in agriculture and fishing
• skilled manufacturers: Artisans and skilled workers in manufacturing and construction industries (except facilities and machinery operators; Artisans and skilled workers in manufacturing, construction, and mining industries, except facilities and machinery operators
• machinery operators: Plant and machinery operators and assemblers
• unskilled: Elementary occupations; Unskilled workers
• other: Other

Figure 5: Distribution of respondent occupations by gender.

The distribution of responses to the work activity question is shown in Figure 6, with the labels on the x-axis corresponding to the following response options:

• Agriculture: Agriculture, forestry and fishing
• Food: Food, textile, leather, wood and paper industry
• Extractive: Extractive industries, oil refining, chemical, pharmaceutical, rubber and plastics industries, electricity, gas, steam and air conditioning supply, water supply, waste management. Metallurgy
• Construction: Construction of machinery, electrical equipment and transport material. Industrial installation and repair
• Building: Building
• Wholesale: Wholesale and retail trade and its facilities and repairs. Auto repair, hospitality
• Transportation: Transportation and storage. Information and communications
• Financial: Financial intermediation, insurance, real estate activities, professional, scientific, administrative and other services
• Public: Public administration and education
• Health: Health activities
• Other services: Other services
• Other: Other

Figure 6: Distribution of respondent activities by gender.

Country of Birth

Most respondents reported that they were born in Spain (93%). Of those who reported being born outside Spain, the top 5 countries of birth were Argentina (14% of non-natives), Italy (9% of non-natives), Germany (7% of non-natives), the UK (5% of non-natives), and France (5% of non-natives), as shown below in Figure 7.

Figure 7: Country of birth by gender for respondents born outside Spain. (Only those countries with at least 5 respondents are shown.)

Continuing to work

In response to the survey question about current work status, 43% of respondents reported that they were working entirely remotely, while 31% reported that they were not working, 13% reported that they were working partially remotely, and 12% reported that they were working in person. Of those who reported working remotely partially or totally, 25% reported that they were also doing so before the confinement. Of those who reported currently working in-person, 91% reported taking extra precautions at work.

Figure 8: Responses to question: Are you continuing to work during the lockdown?

ICT Resources

Nearly all respondents reported owning or living with someone who owns an information and communication technology (ICT) device, with personal computers being most prevalent, followed by smart phones and then tablets (Figure 9). Respondents mostly reported multiple devices. Most (>60%) of respondents also reported being constantly connected to the internet, and most of the rest reported being connected several times per day (Figure 10).

Figure 9: Proportion of respondents reporting that they or someone they live with owns particular ICT devices.

Figure 10: Distributiuon of responses to the question about how many times per day respondents connect to the internet.

Trips out of home

As one way of assessing levels of mobility, respondents were asked about the trips they had taken out of their dwellings during the past week. Figure 11 shows the distribution of number of trips reported. Less than 5% of respondents reported having not left their dwellings at all, while 80% reported having gone out between 1 and 7 times. The mode of the distribution was 7 trips, presumably because many people were going out once per day during the confinement period (or were using this as a rough estimate of what they had done).

Figure 11: Number of trips out of dwelling during past week.

Respondents were also asked about the farthest distance they had traveled on any of these trips as well as all of their destinations and safety precautions. The distributions of responses are shown in Figures 12, 13, and 14. Nearly 80% of respondents reported having traveled less than 10 km from their home and nearly 40% reported having traveled less than 1 km. The most frequent destination was stores, followed by public spaces and workplaces. Nearly all respondents reported taking some sort of safety precaution, with masks, social distancing, and handwashing being the most frequent.

Figure 12: Farthest distance travelled out of home during past week.

Figure 13: Destinations of trips during past week.

Figure 14: Precautions employed on trips taken during past week.

Households

An important source of information about social mixing comes from the sizes and age structures of people’s households (defined here as the group of people with whom they were residing at the time of the survey submission). Figure 15 shows the number of co-residents reported by each respondent by autonomous community/city. Figure 16 shows the age-structure of respondents and co-residents.

Figure 15: Distribution of co-residents by autonomous community/city.

Figure 16: Mean number of co-residents by respondent and co-resident age group.

Out-of-home contacts

Relevant social mixing also occurs outside the home. Respondents were asked to report the number and ages of the people with whom they had contact on the previous day. Following the POLYMOD approach, contacts were defined for respondents as: “EITHER a two-way conversation with three or more words in the physical presence of another person, OR physical skin-to-skin contact (for example a handshake, hug, kiss or contact sports).” The distribution of the reported numbers of contacts is shown in Figure 17 and the matrix of reported contacts by age group is shown in Figure 18.

Figure 17: Distribution of out-of-home contacts by autonomous community/city.

Figure 18: Mean number of out-of-home contacts by respondent and contact age group.

Population Estimates

The project team is now using multilevel regression with poststratification (MRP) (Zhang et al. 2014; Downes et al. 2018; Park, Gelman, and Bafumi 2004) to make population-level estimates from the survey data. Some preliminary results are offered here but the models on which they relay are still being tuned and improved. These figures should, therefore, be treated with caution. We focus here on social mixing patterns, both in and out of home, because of the obvious relevance to understanding Covid-19 dynamics.

Households

Starting with the number of co-residents each respondent reported, we estimate a population-level distribution of co-resident counts for people aged 20 and over. This is shown in Figure 19. Adding age groups, we estimate the matrix shown in Figure 20.

Figure 19: Estimated distribution of co-residents by autonomous community/city. Estimates are limited to population aged 20 and over.

Figure 20: Estimated mean number of co-residents by age groups. Estimates are limited to population aged 20 and over.

Out-of-Home Contacts

For out-of-home contacts we use the survey responses to estimate the distribution and age-structured contact matrix for the population aged 20 and over.

Probability of Any Contact

Since a large number of respondents reported no out-of-home contacts at all on the previous day, we start by simply estimating the probability of any out-of-home contact. Figure 21 shows the estimated probabilities and the 90% credible intervals for these estimates for each province.

Figure 21: Estimated probability of having any out-of-home contact on the previous day for each province. Estimates are limited to population aged 20 and over. They are indicated by the points with 90% credible intervals shown by the lines.

Number of Contacts

Figure 22 shows the estimated distribution of the number of out-of-home contacts for the total population aged 20 and over. Preliminary fits suggest that this closely follows a zero-inflated exponential distribution. Figure 22 provides distributions each age group. In addition to again showing a high proportion of zeros (i.e. many people had no contacts), this distributions have longer tails for the older age groups than for younger ones. This might be the result of parts of the older populaton having needs, such as trips to pharmacies or to medical centers, that required greater contacts. Or it might be the result of differences in percieved risk or risk-aversion. Finally, Figure 24 shows the estimated age-structured contact matrix for this population.

Figure 22: Distribution of estimated number of contacts per person. Estimates are limited to population aged 20 and over.

Figure 23: Distribution of estimated number of contacts per person by 5-year age group of the reference person. Estimates are limited to population aged 20 and over. Panels are labelled by the lower age of each group.

Figure 24: Estimated mean number of out-of-home contacts by age group. Estimates are limited to population aged 20 and over.

Acknowledgements

Special thanks to Ane Calvo, Jose A. Costoya, and Manuel Pereira, for translating the survey into Basque and Galician, and to Wiebke Weber, Dennis Feehan, Ayesha Mahmud, Emilio Zagheni, and Jorge Cimentada for suggestions and feedback on the survey design.

References

Del Fava, Emanuele, Jorge Cimentada, Daniela Perrotta, André Grow, Francesco Rampazzo, Sofia Gil-Clavel, and Emilio Zagheni. 2020. “The Differential Impact of Physical Distancing Strategies on Social Contacts Relevant for the Spread of Covid-19.” medRxiv. https://doi.org/10.1101/2020.05.15.20102657.

Downes, Marnie, Lyle C Gurrin, Dallas R English, Jane Pirkis, Dianne Currier, Matthew J Spittal, and John B Carlin. 2018. “Multilevel Regression and Poststratification: A Modeling Approach to Estimating Population Quantities From Highly Selected Survey Samples.” American Journal of Epidemiology 187 (8): 1780–90. https://doi.org/10.1093/aje/kwy070.

Feehan, Dennis, and Ayesha Mahmud. 2020. “Quantifying Interpersonal Contact in the United States During the Spread of Covid-19: First Results from the Berkeley Interpersonal Contact Study.” medRxiv. https://doi.org/10.1101/2020.04.13.20064014.

Mossong, Joël, Niel Hens, Mark Jit, Philippe Beutels, Kari Auranen, Rafael Mikolajczyk, Marco Massari, et al. 2008. “Social contacts and mixing patterns relevant to the spread of infectious diseases.” Edited by Steven Riley. PLoS Medicine 5 (3): 0381–91. https://doi.org/10.1371/journal.pmed.0050074.

Park, David K., Andrew Gelman, and Joseph Bafumi. 2004. “Bayesian Multilevel Estimation with Poststratification: State-Level Estimates from National Polls.” Political Analysis 12 (4): 375–85. https://doi.org/10.1093/pan/mph024.

Perrotta, Daniela, André Grow, Francesco Rampazzo, Jorge Cimentada, Emanuele Del Fava, Sofia Gil-Clavel, and Emilio Zagheni. 2020. “Behaviors and Attitudes in Response to the Covid-19 Pandemic: Insights from a Cross-National Facebook Survey.” medRxiv. https://doi.org/10.1101/2020.05.09.20096388.

Prem, Kiesha, Alex R. Cook, and Mark Jit. 2017. “Projecting social contact matrices in 152 countries using contact surveys and demographic data.” Edited by Betz Halloran. PLoS Computational Biology 13 (9): e1005697. https://doi.org/10.1371/journal.pcbi.1005697.

Zhang, X., J. B. Holt, H. Lu, A. G. Wheaton, E. S. Ford, K. J. Greenlund, and J. B. Croft. 2014. “Multilevel Regression and Poststratification for Small-Area Estimation of Population Health Outcomes: A Case Study of Chronic Obstructive Pulmonary Disease Prevalence Using the Behavioral Risk Factor Surveillance System.” American Journal of Epidemiology 179 (8): 1025–33. https://doi.org/10.1093/aje/kwu018.

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1. https://www.kobotoolbox.org/↩︎