Adults with Severe Mental Illness (SMI) are twice as likely as adults without to die with cancer before their 75th birthday.(1) It has been suggested that this inequality is contributed to by reduced uptake of cancer screening services, leading to delayed diagnoses.(2–7) In 2018, people with SMI were 18% more likely not to have participated in breast screening, 20% more likely not to have participated in cervical screening and 30% more likely not to have participated in bowel screening within the recommended time period than people without SMI.(8)
This study will expand on the existing evidence by measuring the inequalities in screening broken down by a range of additional health related characteristics. These additional characteristics will include the type of SMI the adult has been diagnosed with, their ethnicity, the deprivation of the community in which they live, the region of England in which they live, as well as their smoking status, body mass index (BMI), age and gender (where appropriate). We will also consider looking at drug and alcohol consumption.
The data will come from a large sample of adults registered with GPs in England, and will cover the period up to September 2020.
The research should offer those planning, commissioning and providing cancer screening valuable information with which to improve the quality and targeting of their services for adults with SMI. Ultimately this could result in fewer deaths from cancer among adults with SMI.
Adults with SMI are twice as likely as adults without SMI to die with cancer before their 75th birthday. (1). It has been suggested that this inequality is contributed to by reduced uptake of cancer screening services, delayed cancer diagnosis, treatment choices and adherence to treatment plans. (2–7)
Clinical Practice Research Datalink (CPRD) Aurum data will be used to explore associations between cancer screening, SMI diagnoses and a range of person, place and behaviour related variables in England. It will also be used test the null hypotheses that people in England with different SMIs are as likely as people without SMI to participate in cancer screening services, once other non-SMI factors are adjusted for. The analysis will be conducted on a cross section of data from September 2020.
The hypothesis tests will be completed using three multiple logistic regression models. The outcome variables will be participation in bowel, breast and cervical screening. The primary exposure variable will be a previous diagnosis of SMI (split into three categories of SMI, and a reference category of no SMI). Models will be constructed separately for bowel, breast and cervical screening. Three categories of confounding variables will be added to each model: person based (age, gender and ethnicity), place based (deprivation and region) and healthy behaviour based (smoking status and body mass index (BMI)). Alcohol and drug consumption may also be considered. Null hypotheses will be rejected where the confidence intervals around the SMI diagnosis coefficients do not overlap with 1.
This research may contribute to improved cancer screening services and health outcomes for adults with SMI. If the null hypotheses are rejected, it offers adequate evidence that merely targeting non-SMI characteristics is unlikely to fully address the SMI based inequalities. It would provide an evidence-based justification for SMI targeting of the screening services.
The study will include both descriptive and analytical components, and each will measure slightly different outcomes.
Descriptive statistics:
• The proportion (with confidence intervals) of adults with and without SMI (separately) who had participated in bowel, breast and cervical screening (separately) in the period up to September 2020, split by:
o Index of multiple deprivation (IMD) quintile
o Patient ethnicity
o Region of England of the GP practice where the patient was registered
o Smoking status (current smoker or not)
o BMI status (greater or less than 30)
o Age group (for cervical screening)
o Gender (for bowel screening)
o Alcohol and drug consumption may also be considered
• The proportion (with confidence intervals) of adults who had participated in bowel, breast and cervical screening (separately) in the period up to September 2020 split by category of SMI (bipolar disorder, psychosis, schizophrenia or no SMI)
• The proportion of females aged between 60 and 64 who had participated in 0, 1, 2 or all three of the screening programmes
• The number of females age between 50 and 64 who had participated in 0, 1 or both of breast and cervical screening
• The number of females aged between 60 and 70 who had participated in 0, 1 or both of bowel and breast screening
Analytical statistics:
Multiple logistic regression models will be used to measure:
• Participation in bowel screening
• Participation in breast screening
• Participation in cervical screening
The outcomes will be summarised and presented by their odds. Odds ratios and their confidence intervals will be estimated for participation given a diagnosis of bipolar disorder, psychosis and schizophrenia respectively against a reference category of no SMI.
Odds ratios and confidence intervals will also be estimated for each of the additional confounding variables.
Non-affective psychosis diagnostic codes will be included. Therefore we excluded conditions such as organic psychoses or drug- induced psychoses.
The analysis includes 6 months period in addition to the standard recall period for each cancer screening programme which will allow for some delay due to pandemic restrictions. We expect that the impact of pandemic on access to cancer screening will apply to the SMI and non-SMI. We will compare our cancer screening participation rates with what would be expected and caveat our results accordingly.
Alex Jones - Chief Investigator - Office for Health Improvement and Disparities
Gabriele Price - Corresponding Applicant - Office for Health Improvement and Disparities
Cam Lugton - Collaborator - Office for Health Improvement and Disparities
Danny Yip - Collaborator - Office for Health Improvement and Disparities
Elizabeth Barley - Collaborator - University of Surrey
Jianhe Peng - Collaborator - Office for Health Improvement and Disparities
Julia Verne - Collaborator - Department of Health & Social Care
Lanre Segilola - Collaborator - Office for Health Improvement and Disparities
Robert Kerrison - Collaborator - University of Surrey
Cam Lugton - Collaborator - Office for Health Improvement and Disparities
Robert Kerrison - Collaborator - University College London ( UCL )
Patient Level Index of Multiple Deprivation;Practice Level Index of Multiple Deprivation