Clinical trial NCT04474444: Ambulance Calls for Substance Use and Alcohol in a Pandemic (ASAP)
Official title: Ambulance Calls for Substance Use and Alcohol in a Pandemic (ASAP): Exploring Attendance at Incidents Involving Substance and/or Alcohol Use During COVID-19 Pandemic
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Alcohol Use, Unspecified
Substance Use
Intoxication Alcohol
Emergencies
COVID-19 Pandemic
Treatment
Lead sponsor: University of Lincoln
Sponsor class: OTHER
First posted: 2020-07-16
Enrollment: 55000
Sex: ALL
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: CASE_ONLY
Time perspective: RETROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: All people who ring 999 or 111 in an emergency.

Brief summary: The Primary objective is to explore ambulance service attendance at incidents involving alcohol and/or substance use over the period of the pandemic lockdown, and the following months. This will be to determine prevalence and explore factors such as patient gender, age, ethnicity or location. Analysis will examine the calls over the course of the year prior to the lockdown, and then compare this to the period of lockdown and following months.

Detailed description: The Primary objective is to explore ambulance service attendance at incidents involving alcohol and/or substance use over the period of the pandemic lockdown, and the following months. This will be to determine prevalence and explore factors such as patient gender, age, ethnicity or location. Analysis will examine the calls over the course of the year prior to the lockdown, and then compare this to the period of lockdown and following months.

A time series analysis will be conducted to examine the calls over the course of the year prior to the lockdown, and then compare this to the period of lockdown and following months. This will use the 'Interrupted Time Series' (ITS) approach. To explore this regression models will be built that examine the causal models for attendance prior to the pandemic and compared to the lockdown time frame.

A multivariable regression model will be built. Initially a Directed Acyclic Graph (DAG) will allow the identification of confounders and exposures relevant to the model. A logistic regression model will be used to calculate the relative risk of call during lockdown compared to the data prior to lockdown. The model will be fit using p\<0.05 as the definition of statistical significance.

Descriptive statistics, trend analysis and predictive analysis will be conducted on the data set to determine trends across time, factors that predict patients requiring ambulance attendance, and factors that predict treatment outcomes. Missing data will be examined for systematic bias, and where found to be missing at random will be excluded from analysis. Where not missing at random, sensitivity analysis will be conducted.

Analysis will examine covariates. Age will be defined as single year continuous variable and examined in categories such as 5-year age groups. Ethnicity will be categorised as groups, such as black, Asian, other minority and mixed ethnic groups will be explored. Census data such as the deprivation, rurality, income, employment, disability and education will look at the decile as defined.

Eligibility criteria: Inclusion Criteria:

* Patient of any age
* Patient requested ambulance attendance between 23rd March 2019 and 22nd March 2021
* The patient record is held by East Midlands Ambulance Service
* Patient records that have recorded a clinical impression related to alcohol and substance use will be included in the data set alongside a word search in the free text response box for the following words/phrases: narcotic, spice, mamba, alcohol, substance use, drug use, illicit drug, overdose, intoxication, intoxicated, drunk, high.

Exclusion Criteria:

* The patient record was outside of the indicated date range.
* The patient record is not accessible via EMAS

Primary outcomes:
measure: Attendances for alcohol and drug use
timeFrame: Full data set 23/03/2019 compared to 22/03/2021 to look at interruption (lockdown) in the time series.
Secondary outcomes:
Other outcomes:
Locations:
city: Lincoln
country: United Kingdom
facility: University of Lincoln
Study officials:
affiliation: University of Lincoln
name: Graham Law, PhD
role: PRINCIPAL_INVESTIGATOR
References: