DOI: 10.5061/dryad.31zcrjds1
title: Data for: A behaviourally-informed chatbot increases vaccination in Argentina

description: This repository contains the dataset used for the paper 'A
 behaviourally informed chatbot increases vaccination rates in Argentina
 more than a one-way reminder'. The data originates from
 administrative databases collected by the Ministry of Health of the
 Republic of Argentina and the Ministry of Health of the Province of Chaco,
 Argentina. The original data sources are (1) Nomivac: a complete dataset
 of all COVID-19 vaccinations across the country; (2) Pasaporte Chaco: the
 Chaco province's online services phone application; (3) Chaco's
 0800 help line: a database from a phone helpline established by the
 provincial Ministry of Health to address citizens' queries on
 COVID-19 vaccinations and (4) SUMAR: the Argentinian public subsidised
 healthcare system. The data have been processed to anonymise identity
 numbers for public availability and to create variables suitable for
 regression analysis.
description: This is administrative data collected by Argentina's
 Ministry of Health. It encompasses four constituent datasets: three phone
 number databases and Nomivac (briefly explained in the Data Sources
 section of the Materials and Methods). The data has been anonymized for
 public availability and processed to generate variables suitable for
 regression analysis.
description: # Data for: A behaviourally-informed chatbot increases vaccination in
 Argentina This repository hosts the dataset employed for the 'A
 behaviourally-informed chatbot increases vaccination rates in Argentina
 more than a one-way reminder' paper's analysis. The paper
 discusses an intervention involving a new chatbot service to explore if
 two-way interactive messaging outperforms one-way reminders. We conducted
 a pre-registered randomised controlled trial involving 249,705
 participants in Argentina and recorded vaccinations using Ministry of
 Health data. The repository includes two data files:
 'brown2024_analysis_data.rds' features individual-level data of
 all trial participants, including basic demographic data, Covid-19
 vaccination history, assignment to an experimental arm, a summary of
 chatbot interaction, and outcome measures: binary variables indicating
 whether the participant received their next Covid-19 vaccine dose within
 four weeks. The data has been de-identified. Information on age has been
 coded into bins or ranges (18-29, 30-49, 50+) and information on gender
 has been set as a binary variable (0, 1).
 '20230505_vaccinated_per_day.csv' depicts the demand for
 COVID-19 vaccines in the Chaco province from February 2021 to December
 2022. It records the daily total of COVID-19 vaccine doses delivered
 during this period in Chaco. These data were sourced from Nomivac, a
 comprehensive dataset of all COVID-19 vaccinations nationwide. ##
 Description of the data and file structure ### 1. File
 “brown2024\_analysis\_data.rds” * randomisation_allocation_name: Name of
 treatment group * current_dose: Number of doses of COVID-19 vaccine
 received * next_dose: Number of next dose of COVID-19 to be received *
 random_number_date: Date of message delivery, assigned at random to
 individuals in the control group. See materials and methods for details *
 outcome_latest_date: Date of most recent dose of COVID-19 vaccine received
 * dose_X_date: Date of X dose of COVID-19 vaccination *
 vaccinated_message_sent: Whether a message was successfully delivered to
 the individual * session_current_level: Level of the message flow where
 the user stopped interacting with the chatbot * session_current_branch:
 Branch within the level of the message flow where the user stopped
 interacting with the chatbot * session_selected_hc: Option number chosen
 by the user when asked to select a health centre, out of a list of a
 maximum of 9 options * session_reminder_date: The date for which the
 reminder message was programmed * session_reminder_time: Time of day
 chosen by the user to get vaccinated * session_initial_response: Users’
 answer to the initial message sent by the chatbot * session_reminder_sent:
 Whether a reminder message was successfully sent to the user *
 history_message_code: Type of initial message sent to the user (“control”
 is the one-way message; “inicial” is the chatbot’s first message) *
 history_created: Timestamp of the first message successfully delivered to
 the user * history_timestamp_read: Timestamp of the first message read by
 the user * message_date: This variable is used to indicate the date that
 each individual entered the trial. For individuals in the chatbot and
 one-way message reminder groups, it is the date that the message was sent.
 For the control group, this is the date that we randomly pre-assigned each
 participant to enter the trial (random_number_date). See materials and
 methods for details * had_new_dose: Participant received an additional
 dose of the COVID-19 vaccine after being randomly assigned to the
 treatment arm * had_new_dose_after_message: Participant received a vaccine
 within 4 weeks of entering the trial * had_new_dose_after_message_2w:
 Participant received a vaccine within 2 weeks of entering the trial *
 had_new_dose_after_message_6w: Participant received a vaccine within 6
 weeks of entering the trial * n_doses_after_entering_trial: Number of
 doses within four weeks of entering the trial * primary_outcome:
 had_new_dose as a factor variable * age: Age range of participant *
 gender_numeric: Gender of the participant, coded as 1 or 0 for
 de-identification purposes * period_since_last_dose: Length of time
 between the first value of message_date in the whole sample and the last
 dose date before trial ### 2. File “20230505\_vaccinated\_per\_day.csv” *
 delivery_date: Date of vaccination * count: Total number of residents of
 Chaco province who received a COVID-19 dose on a given day