People with neurodevelopmental conditions such as autism, intellectual disabilities (ID) and attention deficit hyperactivity disorder (ADHD) tend to have more health problems and a shorter life expectancy than people without a neurodevelopmental condition. Receiving too many or too few medications may contribute to this. For example, high cholesterol can increase the chance of heart attacks and strokes. Some mental health medications can increase cholesterol levels. On the other hand, statins help reduce cholesterol but it is possible that they are not offered to people with neurodevelopmental conditions when they should be .
We will use UK healthcare records to understand changes in prescriptions of commonly prescribed medications, and the conditions they are prescribed for, over time for people with neurodevelopmental conditions.
By comparing diagnoses, prescriptions, and later records of side effects and health outcomes, we will try to determine whether there seems to be over or under prescription of certain medications for people with neurodevelopmental conditions.
As each single method of studying this question is imperfect, we will compare the results from a range of approaches and see if they give consistent answers.
We hope this work will help in understanding the inequalities in health outcomes for people with neurodevelopmental conditions, to provide better healthcare and reduce side effects from medications.
Objective & Rationale:
To understand whether there are inequalities in healthcare, prescribing, and associated outcomes for those a neurodevelopmental condition compared to those without. This should help to empower patients and professionals to make informed medication and service design decisions.
Aims:
i) Describe time trends in commonly co-occurring conditions and associated prescriptions for people with neurodevelopmental conditions;
ii) Assess whether adverse health outcomes, including medication effects, are more likely to be recorded for people with neurodevelopmental conditions;
iii) Triangulate evidence from multiple methods to strengthen causal inference
Study design: Descriptive and comparative cohort study
Setting: UK CPRD primary care records linked to Hospital Episodes Statistics (HES) for diagnoses and hospitalisation data, Office of National Statistics (ONS) death records, and sociodemographic data.
Primary exposures:
(i) Lifetime diagnosis of a neurodevelopmental condition, including autism, intellectual disability and attention deficit hyperactivity disorder
Primary outcomes:
(i) Commonly co-occurring mental and physical health conditi ons (e.g. depression, high cholesterol)
(ii) Prescriptions for common psychotropic (e.g. antidepressants, antipsychotics) and physical health medications (e.g. lipid lowering agents).
(iii) Potential adverse medication effects (e.g. obesity with antipsychotic use).
(iv) All-cause and cause-specific mortality.
Methods including main statistical tests:
1) Descriptive statistics for each medication group, including prevalence and incidence of prescriptions and their indications, by age group, calendar year, and diagnostic status. Also patterns of prescription such as time to discontinuation assessed using confounder adjusted cumulative incidence.
2) Calculation of the risk of adverse health outcomes, including medication effects , at multiple lengths of follow up, in addition to the cumulative incidence, for the groups defined by the combination of medication prescription and neurodevelopmental diagnosis. We will calculate the risk differences and relative risk using confounder adjusted regression models.
3) Triangulating results from multiple methods within the cohort, such as target trial emulation, propensity score matching, negative controls.
1) Commonly co-occurring mental and physical health diagnoses
2) Prescriptions of commonly prescribed medications, including antidepressants, antipsychotics and lipid lowering medications
3) Subsequent adverse health outcomes, including medication effects, for example in the case of antipsychotics these would include dyslipidaemia, obesity, hypertension and diabetes.
4) All-cause mortality; cause-specific mortality
Paul Madley-Dowd - Chief Investigator - University of Bristol
Aws Sadik - Corresponding Applicant - University of Bristol
Antonio Fernández Pardiñas - Collaborator - Cardiff University
Dheeraj Rai - Collaborator - University of Bristol
Golam Khandaker - Collaborator - University of Bristol
Paul Madley-Dowd - Collaborator - University of Bristol
HES Accident and Emergency;HES Admitted Patient Care;HES Outpatient;ONS Death Registration Data;Patient Level Index of Multiple Deprivation;Practice Level Index of Multiple Deprivation