DOI: 10.5061/dryad.866t1g22m
title: Effectiveness of screening and ultra-brief intervention for hazardous drinking in primary care: pragmatic cluster randomised controlled trial

description: Context Hazardous drinking affects about one in five primary‑care
 patients. The EASY study compared a ≤1‑minute ultra‑brief intervention
 (Ultra‑BI) with simplified assessment only (SAO) across 40 primary care
 clinics in Japan.  Objective To provide open access to datasets
 and SAS / R scripts needed to reproduce the published analyses and support
 future studies including independent participant data meta-analyses.
 Datasets and scripts description RawData.csv – unprocessed
 participant-level data at baseline, 12 weeks, and 24 weeks Baseline.xlsx
 –unprocessed participant-level data at baseline for SAS
 data_EASY_cluster_RCT_preprocessed.csv / .RData – processed versions of
 RawData.csv Baseline.xlsx –unprocessed participant-level data at
 baseline for SAS  Scr_Select.csv – flag indicating participants
 recruited at clinics that restricted screening to patients suspected of
 hazardous drinking Six analysis scripts and one CONSORT-diagram script Key
 variables  participant id, clinic id, allocation group, AUDIT-C score
 (0–12), total alcohol consumption (grams per 4 weeks), WHO drinking risk
 level (DRL), readiness-to-change score, sex, five-year age band,
 comorbidities, smoking status, visit type, and participant reported
 recognition of receiving advice. Reuse potential  The dataset and scripts
 enables full replication of the published analyses, and pooled effect
 estimates in individual participant data meta-analysis. Ethical
 consideration Public data sharing was conducted under IRB-approved written
 informed consent and public disclosure, with participants given the
 opportunity to decline data sharing. All data were de-identified prior to
 release, including the removal of direct identifiers and the aggregation
 or masking of potentially re-identifiable information.
description: # Effectiveness of screening and ultra-brief intervention for hazardous
 drinking in primary care: a pragmatic cluster randomised controlled trial
 Dataset DOI: [10.5061/dryad.866t1g22m](10.5061/dryad.866t1g22m) ## Dataset
 abstract This dataset contains de-identified, participant-level records
 from 1,136 adults (20–74 years) screened for hazardous drinking across 40
 primary-care clinics in Okayama, Hyogo, Osaka, and Hiroshima prefectures,
 Japan, between 29 June and 7 August 2023. Data are provided as rectangular
 tables in CSV, XLSX, and RData formats capturing baseline demographics,
 medical histories, readiness-to-change scales (1–4 ordinal items), AUDIT-C
 sub-scores (0–4) and total scores (0–12), and ethanol consumption amounts
 expressed in grams per 4-week period at baseline, 12 weeks, and 24 weeks.
 The deposit also includes a preprocessed analysis-ready table with derived
 indicators (e.g., per-protocol flag, intervention receipt percentage,
 patient-reported advice) and R scripts/SAS programs used to reproduce the
 trial tables. The files support reuse for evaluating alcohol screening and
 ultra-brief interventions, examining behavioural readiness trajectories,
 benchmarking cluster-randomised trial analyses, and developing replication
 or secondary analyses. All direct identifiers have been removed,
 participant and facility IDs are pseudonymised, and age is supplied as
 bands or integers with no dates, aligning with Dryad's human-subject
 data guidelines and the informed-consent provisions for public data
 sharing. Users should note that missing values appear as blank cells
 (import as `NA` in R) and that binary indicators consistently use `0 = No`
 / `1 = Yes` unless otherwise specified. ## Description of the data and
 file structure We collected these data during a two-arm cluster randomised
 controlled trial that evaluated an ultra-brief alcohol intervention
 (Ultra-BI) versus simplified assessment only (SAO) in routine primary-care
 settings. Forty outpatient clinics from urban, suburban, and rural areas
 of Okayama, Hyogo, Osaka, and Hiroshima prefectures were invited and
 randomly allocated (block design, computer-generated sequence) before
 recruitment began. Between 29 June and 7 August 2023, reception staff
 consecutively screened all attending patients aged 20–74 years for basic
 eligibility, obtained written informed consent, and administered a
 baseline questionnaire that included the AUDIT-C. Patients meeting
 hazardous-drinking thresholds received the Ultra-BI immediately or usual
 care, according to cluster assignment. Follow-up questionnaires capturing
 alcohol consumption and readiness to change lifestyle behaviours were
 distributed at 12 and 24 weeks by post (paper or QR-linked web form) with
 SMS reminders; non-responders were contacted by an independent survey
 company, which also double-entered and validated all screening and
 follow-up data. ### Dataset descriptions Unless noted otherwise, missing
 values in the CSV files are encoded as `.` (period). When importing to
 analysis software, treat `.` as `NA` and cast numeric fields accordingly.
 Blank cells in the XLSX workbook represent missing data. All participant
 and clinic identifiers are pseudonymised and consistent across files where
 the column exists. #### RawData.csv (1136 rows × 22 columns)
 Participant-level baseline and follow-up responses exported from the trial
 database in the original coding used for monitoring. * `id` — integer
 pseudonymous participant identifier. * `w0_PerProtocol` — string;
 `'1'` marks participants meeting the pre-specified per-protocol
 criteria (1130 rows), `'.'` indicates not assessed/not in the
 per-protocol set (3 rows). * `w0_Sex` — integer; `1 = Male`, `2 = Female`.
 * `w0_VisitHistory` — categorical text; `First visit`, `Routine
 appointments`, or `Visit as needed`; `'.'` = not recorded. *
 `w0_ConsideringDietChange` — categorical text with readiness options `No
 Improvement Needed`, `No intention to improve`, `Intending to improve`,
 `Already working on improvement`; `'.'` = missing. *
 `w0_ConsideringSmokingChange` — categorical text with options `Never
 smoked`, `Smoked but quit`, `Intending to improve`, `No intention to
 improve`; `'.'` = missing. * `w0_AUDIT1` — integer 1–4; Alcohol
 Use Disorders Identification Test (AUDIT-C) item 1 (drinking frequency). *
 `w0_AUDIT2` — string digits `0–4`; AUDIT-C item 2 (usual quantity).
 Convert to integer after treating `'.'` as missing. *
 `w0_AUDIT3` — string digits `0–4`; AUDIT-C item 3 (frequency of heavy
 drinking). `'.'` = missing. * `w0_ConsideringDrinkingChange` —
 ordinal code `1–5`: `1 = No Improvement Needed`, `2 = No intention to
 improve`, `3 = Interested but no intention to improve`, `4 = Intending to
 improve`, `5 = Already working on improvement`; `'.'` = missing.
 * `w0_Allocation` — integer cluster assignment (`0 = Simplified assessment
 only (SAO)`, `1 = Ultra-brief intervention (Ultra-BI)`). * `FacilityID` —
 integer string `1–40`; pseudonymised clinic identifier. *
 `w0_DrinkingAmountPer4weeks` — numeric stored as string; ethanol
 consumption in grams per 4-week period at baseline (`'.'` =
 missing). * `w0_SmokingStatus` — integer string; `1 = Never smoked`, `2 =
 Smoked but quit`, `3 = Current smoker`, `'.'` = missing. *
 `w12_ConsideringDietChange` — same categories as
 `w0_ConsideringDietChange`; `'.'` = missing. *
 `w12_ConsideringSmokingChange` — same categories as
 `w0_ConsideringSmokingChange`; `'.'` = missing. *
 `w12_ConsideringDrinkingChange` — ordinal code `1 = No Improvement
 Needed`, `2 = No intention to improve`, `3 = Intending to improve`, `4 =
 Already working on improvement`; `'.'` = missing. *
 `w12_DrinkingAmountPer4weeks` — numeric stored as string; grams of ethanol
 per 4 weeks at 12-week follow-up (`'.'` = missing,
 `'0'` indicates abstinent). * `w24_ConsideringDietChange` — same
 categories as `w0_ConsideringDietChange`; `'.'` = missing. *
 `w24_ConsideringSmokingChange` — same categories as
 `w0_ConsideringSmokingChange`; `'.'` = missing. *
 `w24_ConsideringDrinkingChange` — ordinal code with the same mapping as at
 12 weeks (`1 = No Improvement Needed` … `4 = Already working on
 improvement`); `'.'` = missing. * `w24_DrinkingAmountPer4weeks`
 — numeric stored as string; grams of ethanol per 4 weeks at 24-week
 follow-up (`'.'` = missing). ####
 data_EASY_cluster_RCT_preprocessed.csv (1133 rows × 24 columns)
 Analysis-ready participant-level dataset restricted to the per-protocol
 set and enhanced with derived variables. Values are cleaned and typed for
 immediate use. * `w0_PerProtocol` — numeric; `1` for the 1130 participants
 in the per-protocol set, `NA` for the 3 participants excluded from that
 set. * `w0_Sex` — categorical text; `Male` or `Female`. * `w0_Age` —
 character string five-year band (`20–29`, `30–39`, …, `70–74`). *
 `w0_VisitHistory` — categorical text; `First visit`, `Routine
 appointments`, `Visit as needed`, or missing (`NA`). *
 `w0_PH_Hypertension` — boolean; history of hypertension (`TRUE`/`FALSE`).
 * `w0_PH_Diabetes` — boolean; history of diabetes. * `w0_PH_Gout` —
 boolean; history of gout. * `w0_PH_Dyslipidemia` — boolean; history of
 dyslipidaemia. * `w0_PH_LiverDisease` — boolean; history of liver disease.
 * `w0_PH_DigestiveDisease` — boolean; history of digestive disease. *
 `w0_ConsideringDrinkingChange` — categorical text; `No Improvement
 Needed`, `No intention to improve`, `Interested but no intention to
 improve`, `Intending to improve`, `Already working on improvement`. *
 `w0_Allocation` — numeric; `0 = SAO`, `1 = Ultra-BI`. * `FacilityID` —
 character string `1–40`; pseudonymised clinic ID (matching `RawData.csv`).
 * `w0_AgeCont` — numeric; exact age in years (includes .5 where ages were
 rounded to half-years). * `w0_AUDIT_c` — numeric; AUDIT-C total score
 (0–12). * `w0_DrinkingAmountPer4weeks` — numeric; grams of ethanol per 4
 weeks at baseline. * `w0_SmokingStatus` — ordered factor; `Never smoked`,
 `Smoked but quit`, `Smoking`. * `invited` — categorical text describing
 clinic invitation coverage; values are `Invited &lt;50% of eligible
 patients`, `Invited about 60% of eligible patients`, `Invited about 70% of
 eligible patients`, `Invited about 80% of eligible patients`, `Invited
 about 90% of eligible patients`, `Invited ~100% of eligible patients`, or
 `Selected only patients likely to drink heavily`. *
 `w12_ConsideringDrinkingChange` — categorical text; `No Improvement
 Needed`, `No intention to improve`, `Intending to improve`, `Already
 working on improvement`. * `w12_DrinkingAmountPer4weeks` — numeric; grams
 of ethanol per 4 weeks at 12-week follow-up. *
 `w24_ConsideringDrinkingChange` — categorical text; same scale as at 12
 weeks. * `w24_DrinkingAmountPer4weeks` — numeric; grams of ethanol per 4
 weeks at 24-week follow-up. * `Received_Percentage` — numeric;
 within-clinic percentage (0–100) of participants who reported receiving
 the intervention (calculated for intervention clinics only; missing for
 control clinics). * `Received_patient_report` — boolean; `TRUE` if the
 participant reported receiving counselling/advice, otherwise `FALSE` or
 missing. #### data_EASY_cluster_RCT_preprocessed.RData R binary file
 containing a tibble named `df` with the same 1133 rows and 24 columns as
 `data_EASY_cluster_RCT_preprocessed.csv`. Variable types follow the
 descriptors above. #### Baseline.xlsx (1136 rows × 18 columns) Excel
 workbook with baseline questionnaire data in human-readable labels (all
 categorical responses are now supplied in English). String categories
 correspond to the coded variables in `RawData.csv`. * `id` — pseudonymous
 participant identifier (matches `RawData.csv`). * `Sex` — `Male` /
 `Female`. * `Age` — decade band (`20's`, `30's`, …,
 `70's`). * `VisitHistory` — clinic attendance description: `First
 visit`, `Routine appointments`, or `Visit as needed`. * `PH_Hypertension`
 — logical; history of hypertension (`TRUE`/`FALSE`). * `PH_Diabetes` —
 logical; history of diabetes. * `PH_Gout` — logical; history of gout. *
 `PH_Dyslipidemia` — logical; history of dyslipidaemia. * `PH_LiverDisease`
 — logical; history of liver disease. * `PH_DigestiveDisease` — logical;
 history of digestive disease. * `ConsideringDietChange` — readiness
 statements: `No Improvement Needed`, `No intention to improve`, `Intending
 to improve`, `Already working on improvement`. *
 `ConsideringSmokingChange` — readiness statements: `Never smoked`, `Smoked
 but quit`, `No intention to improve`, `Intending to improve`. * `AUDIT1` —
 integer 0–4; AUDIT-C item 1. * `AUDIT2` — integer 0–4; AUDIT-C item 2. *
 `AUDIT3` — integer 0–4; AUDIT-C item 3. * `ConsideringDrinkingChange` —
 readiness statements aligned with the numeric codes in `RawData.csv` (`No
 Improvement Needed`, `No intention to improve`, `Interested but no
 intention to improve`, `Intending to improve`, `Already working on
 improvement`). * `Allocation` — integer (`0 = SAO`, `1 = Ultra-BI`). *
 `FacilityID` — integer 1–40; pseudonymised clinic identifier. ####
 Scr_Select.csv (1133 rows × 2 columns) Link table flagging clinics that
 did not invite every eligible patient. * `id` — pseudonymous participant
 identifier (matches `RawData.csv` and `Baseline.xlsx`). *
 `w0_biased_invitation` — boolean; `TRUE` when the clinic reported
 selectively screening only patients suspected of hazardous drinking,
 `FALSE` for universal screening invitation. --- ## Abbreviations *
 AUDIT-C: Alcohol Use Disorders Identification Test, consumption items *
 WHO DRL: WHO Drinking-Risk Level (Low / Medium / High / Very high) *
 Ultra-BI: ultra-brief intervention * SAO: Simplified assessment only ##
 Code/software #### table1.r This script aggregates data for creating Table
 1 (Baseline characteristics per participant by allocation group) of the
 paper. #### stable1.r This script aggregates data for creating sTable 1
 (Baseline characteristics per cluster by allocation group) of the paper.
 #### table2_and_4.sas This script estimates the Local Average Treatment
 Effect (LATE) for creating Table 2 (Baseline characteristics per cluster
 by allocation group) of the paper. #### table2_sensitivity_analyses.r This
 script estimates the Local Average Treatment Effect (LATE) for creating
 Table 2 (Baseline characteristics per cluster by allocation group) of the
 paper. #### calculate_effect_sizes.r This script calculates Hedges’ g for
 outcome variables presented in Table 2 and 4. #### table3.r This script
 aggregates data for creating Table 3 (Proportion of WHO drinking risk
 level at follow-ups) of the paper. #### table5.r This script aggregates
 data for creating Table 5 ( Proportion of readiness to change drinking
 habits by category) of the paper. #### CONSORT_flowchart.r This script
 aggregates data for creating Figure 2 (CONSORT flow diagram of clusters
 and patients through the trial) of the paper. ## Access information Other
 publicly accessible locations of the data: * N/A Data was derived from the
 following sources: * N/A ## Human subjects data Public data sharing was
 conducted under IRB-approved written informed consent and public
 disclosure, with participants given the opportunity to decline data
 sharing. All data were de-identified prior to release, including the
 removal of direct identifiers and the aggregation or masking of
 potentially re-identifiable information.