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.
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.
Other: attendance by ambulance crew
hear, attendance, convey
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
University of Lincoln
Lincoln, United Kingdom
Investigator: Graham Law, PhD
Contact: 07905008828
glaw@lincoln.ac.uk
Graham Law, PhD
07905008828
glaw@lincoln.ac.uk
Sam Lewis
samlewis@lincoln.ac.uk
Graham Law, PhD, Principal Investigator
University of Lincoln