Recent estimates suggest that between 15 per cent and 30 per cent of the English population suffer from more than one medical condition, a problem known as multi-morbidity. This is an expensive problem for the NHS, because patients in this state of health take up more than half of GP consultations and hospital admissions and receive more than three-quarters of prescriptions issued.
A report “The Marmot review 10 years on” published in 2020 looked at trends in health inequalities in England and reported large differences in health by patient status, such as age and wealth, and these differences are getting bigger. We know that multi-morbidity is more common amongst women, older people and people from poorer areas, but we know little about the inequalities in access to healthcare that exist amongst middle-aged and older people with multi-morbidity. Our research will attempt to fill this gap in the knowledge.
We will study:
1) Whether there is inequality in the use of health services between middle-aged and older people with multi-morbidity across a measure of wealth known as the "index of multiple deprivation”.
2) Whether there is inequality in the times that people have to wait for health services and the cost associated
3) Whether the COVID-19 pandemic has worsened inequality in the use of health services and waiting times.
This will help policymakers to target resources and show that efforts to improve the health of people in poorer areas are likely to have economic as well as health benefits.
This project aims to improve understanding of the relationships between socioeconomic status and i) use of healthcare services; ii) access to primary and secondary care, by middle-aged and older people with multiple chronic conditions.
We focus on people aged 50 and over since multi-morbidity is more prevalent in later life. We define multi-morbidity as occurring when a patient has two or more of 37 health conditions identified in a University of Cambridge study. We will use electronic records from CPRD linked with HES and IMD data to examine relationships between the number of health conditions in each database, patients’ socioeconomic status and their use of and access to healthcare services and cost. We will conduct multivariate analyses of the linked data to examine the relationship between the number of health conditions, patients’ socioeconomic status and use of healthcare services. The diseases defining multimorbidity will be extracted using Read Codes from CPRD, as well as diagnoses and procedures from A&E, Outpatients, and Inpatients HES databases.
As a proxy for primary care access, we will estimate, using a dynamic regression (time variant) analysis, the number of A&E attendances (consultant led 24-hour service) recorded in a specified time-period, for patients who self-refer to A&E and are discharged. This requires the use of HES A&E data to determine which patient visits were necessary A&E attendances, and which visits could have been handled through primary care.
Using multivariate analysis and Poisson models, we will estimate the numbers of consultations and tests in primary care, visits to A&E departments, outpatient appointments, and hospital admissions for groups of patients who suffered a postponed/cancelled appointment before and during Covid-19.
This will help policymakers target resources and show that efforts to improve the health of people in poorer areas are likely to have economic as well as health benefits.
1. Utilisation of health services i.e. primary care consultations, A&E visits, hospital outpatient attendances (and separately for chronic conditions), hospital admissions, by number of morbidities and socioeconomic status and across time, and the respective cost.
2. Waiting times in A&E and waiting times for outpatient consultations and for elective admissions by number of morbidities and socioeconomic status.
3. The number of A&E attendances, controlling for use of primary care, by socioeconomic status and presence of multi-morbidity.
4. The use of primary care measured in terms of the number of consultations by type of consultation, prescriptions, diagnostic tests, referrals to outpatient clinics during the pandemic, and admissions, by socioeconomic status and morbidity status in patients whose hospital appointments were cancelled/delayed.
5. Mortality without distinguishing the causes.
Catia Nicodemo - Chief Investigator - University of Oxford
Catia Nicodemo - Corresponding Applicant - University of Oxford
Clare Bankhead - Collaborator - University of Oxford
Cynthia Wright Drakesmith - Collaborator - University of Oxford
Emma Parry - Collaborator - Keele University
Joan Madia - Collaborator - University of Oxford
Laia Bosque Mercader - Collaborator - University of Oxford
Margaret Smith - Collaborator - University of Oxford
Raphael Wittenberg - Collaborator - University of Oxford
Rhys Thomas - Collaborator - University of Oxford
Stavros Petrou - Collaborator - University of Oxford
Stuart Redding - Collaborator - University of Oxford
Subhashisa Swain - Collaborator - University of Oxford
HES Accident and Emergency;HES Admitted Patient Care;HES Outpatient;ONS Death Registration Data;Patient Level Index of Multiple Deprivation;Practice Level Rural-Urban Classification