This documentation describes the fine-resolution contingency tables produced by grouping US COVID-19 Trends and Impact Survey (CTIS) individual responses by various self-reported demographic features.
These contingency tables provide granular breakdowns of COVID-related topics such as vaccine uptake and acceptance. Compatible tables are also available for the UMD Global CTIS for more than 100 countries and territories worldwide, through UMD’s website.
These tables are more detailed than the coarse aggregates reported in the COVIDcast Epidata API, which are grouped only by geographic region. Individual response data for the survey is available, but only to academic or nonprofit researchers who sign a Data Use Agreement, whereas these contingency tables are available to the general public.
Our Data and Sampling Errors documentation lists important updates for data users, including corrections to data or updates on data processing delays.
We currently provide data files at several levels of geographic and temporal aggregation. The reason for this is that each file is filtered to only include estimates for a particular group if that group includes 100 or more responses. Providing several levels of granularity allows us to provide coverage for a variety of use cases. For example, users who need the most up-to-date data or are interested in time series analysis should use the weekly files, while those who want to study groups with smaller sample sizes should use the monthly files. Because monthly aggregates include more responses, they have lower missingness when grouping by several features at a time.
Files contain all time periods for a given period type-aggregation type combination.
The included files provide estimates for various metrics of interest over a period of either a full epiweek (or MMWR week, a standardized numbering of weeks throughout the year) or a full calendar month.
Note: If a survey item was introduced in the middle of an aggregation period, derived indicators will be included in aggregations for that period but will only use a partial week or month of data.
At the moment, only nation-wide and state groupings are available.
Facebook only invites users to take the survey if they appear, based on attributes in their Facebook profiles, to reside in the 50 states or Washington, DC. Puerto Rico is sampled separately as part of the international version of the survey. If Facebook believes a user qualifies for the survey, but the user then replies that they live in Puerto Rico or another US territory, we do not include their response in the aggregations.
The aggregates are filtered to only include estimates for a particular group if that group includes 100 or more responses. Especially in the weekly aggregates, many of the state-level groups have been filtered out due to low sample size. In such cases, files that group by a single demographic of interest will likely provide more coverage.
“Rollup” files containing all time periods for a given period type-aggregation type combination have names of the form:
Unless noted otherwise, the time period is always a complete month
monthly) or epiweek (
the geographic level responses were aggregated over.
aggregation_type is a
concatenated list of other grouping variables used, ordered alphabetically.
Values for variables used in file naming align with those within files as
specified in the column section below.
Within a CSV, the first few columns store metadata of the aggregation:
||Survey geography (“US”)|
||Date (yyyyMMdd) of first day of time period used in aggregation, in the Pacific time zone (UTC - 7)|
||Date of last day of time period used in aggregation|
||Month or week number|
||Geography type (“state”, “nation”)|
||Concatenated list of grouping variables, ordered alphabetically|
||Country name (“United States”)|
||Three-letter ISO country code (“USA”)|
||GADM level 0 ID|
||State name; “Overall” if aggregation not grouped at the state level|
||GADM level 1 ID|
||State FIPS code;
||County name; “Overall” if aggregation not grouped at the county level|
||County FIPS code;
||Date on which estimates were generated|
These are followed by the grouping variables used in the aggregation, ordered alphabetically, and the indicators. Each indicator reports four columns (unrounded):
val_<indicator name>: the main value of interest, e.g., percent, average, or count, estimated using the survey weights to better match state demographics
se_<indicator name>: the standard error of
sample_size_<indicator name>: the number of survey responses used to calculate
represented_<indicator name>: the number of people in the population that
val_<indicator name>represents over all days in the given time period. This is the sum of survey weights for all survey responses used.
All aggregates using the same set of grouping variables appear in a single CSV.
Grouping variables (including region) will be missing (
NA) to represent
respondents who provided one or more responses to survey items used for
indicators (e.g., vaccine uptake) but who did not provide a response to the
survey item used for the particular grouping variable. For example, if
grouping by gender, we would report the groups: male, female, other, and
respondents who did not provide a response to the gender question.
For a given respondent group (25-34 year old healthcare workers in Nebraska,
e.g.) sample size can vary by indicator because of the survey display logic.
For example, all respondents are asked if they have received a COVID-19
vaccination (item V1), but only those who say they have are asked how many
doses they have received (item V2). This means that the sample size for V2 is
smaller than that for V1. Because indicators are censored
individually, it is possible that V1-derived indicators will be reported for a
given group while V2-derived indicators are not. In this case, the V2-derived
indicator columns will be marked as missing (
NA) for that group.
The files contain weighted estimates of the percent of respondents who fulfill one or several criteria. Estimates are broken out by state, age, gender, race, ethnicity, occupation, and health conditions.
We plan to expand the list of indicators based on research needs; if you have a public health or research need for a particular variable not included in our current tables please contact us at email@example.com.