There was a significant reduction in the diagnosis of many common physical and mental health conditions in Salford during the coronavirus lockdown, research suggests.
Electronic health records of approximately a quarter of a million people in Salford were analysed to identify the impact of Covid-19 on general practice (primary care) by a patient safety research centre between March 1st and May 31st.
Researchers found the biggest reductions were for mental health conditions and type 2 diabetes, as there were half the expected number of diagnoses.
For malignant cancer, the reduction was 16% for the time period analysed, but for the month of May there was a drop of 44%.
For circulatory system diseases such as stroke, heart failure and coronary heart disease, the study found a reduction in diagnoses of 43%.
The research, published in The Lancet Public Health, was conducted by the National Institute for Health Research Greater Manchester Patient Safety Translational Research Centre (NIHR GM PSTRC).
The study used 10 years' worth of data to create statistical models to give predicted levels of new diagnoses for the health conditions identified in general practice to be routine.
We were aware that GP practices have been reporting a drop in the number of patients seeking medical help since the start of the Covid-19 pandemic. Our research has revealed which conditions people are not seeking medical attention for. This means that, potentially, there are high numbers of people living with undiagnosed type 2 diabetes, mental health conditions and circulatory system failure.
The authors used routinely collected primary care data that was recorded in the Salford Integrated Record system between January 1 2010 and May 31 2020.
They extracted data on symptoms and observations, diagnoses, prescriptions, operations and procedures, laboratory tests, and other diagnostic procedures.
They then used computer modelling with data of monthly counts of first diagnoses of common conditions, and corresponding first prescriptions of medications indicative of these conditions.
These models were used to predict the expected numbers of first diagnoses and first prescriptions between March 1 and May 31.
This will have an impact individually on those patients - the longer a patient goes undiagnosed, the more complications they are likely to suffer.