Asthma and eczema are common allergic diseases. Asthma can make breathing difficult, and eczema causes red, itchy skin. Existing evidence has linked asthma and eczema to other diseases, including heart disease and broken bones. Links between asthma/eczema and other diseases may may be due to the biological mechanisms of asthma and eczema themselves, the side effects of the drugs used to manage eczema/asthma, or perhaps something related to having asthma and eczema (like reduced ability to exercise, due to breathing difficulties in asthma or sweat increasing itch in eczema, or longterm sleep problems due to itch).
We need to understand better how and why other mental and physical diseases group together with common allergic conditions, to prevent the development of multiple health conditions (multimorbidity) and improve the health of people with asthma and eczema. This work is particularly important now as there have been major advances in the drugs used in allergic diseases, these new drugs may reduce the development of other health conditions. However, if we do not know how and why people with asthma/eczema get other health conditions we do not know who would particularly benefit from these new drugs.
Our study will use information from people with asthma and eczema collected when people see general practitioners with identifers removed. We identify groups of other diseases that tend to cluster together in people with these common allergic conditions. We will also try to identify what drives the development of multiple other diseases in people with asthma and eczema.
Our study aims to characterise known and novel clusters of multimorbidity (known and novel) in common allergic disorders (asthma and atopic eczema).
We will identify anonymised records of adults with asthma or eczema in CPRD-GOLD using algorithms based on morbidity codes in primary care data. We will apply data-driven techniques to identify disease clusters (multimorbidity, defined using Read code chapters) in people with common allergic diseases, including previously described morbidity (e.g., cardiovascular diseases, fractures). This exploratory research will offer insights into pathways to multimorbidity, potentially allowing us to identify novel disease clusters not previously linked to allergic conditions (i.e., non-random clustering), highlighting avenues for subsequent research.
We will use logistic regression with individual level data to derive associations (Odds Ratios) between comorbidities. By modelling individuals together (people with asthma and atopic eczema separately [cases] matched on age, sex and practice with individuals without these conditions [control]) and including a comorbidity variable as the outcome (e.g. cardiovascular) and another comorbidity variables as exposures (e.g. respiratory, renal, etc.). By including an interaction term between exposure comorbidity and individual case-control status, we will test if associations between comorbidities varies across groups. We will visualise associations between comorbidities as networks in each patient group and identify clusters of highly associated comorbidities within networks. Clusters will be reviewed by subject-matter experts and patients for clinical plausibility. Clusters might suggest temporally-ordered sequences of diagnoses that we will visualise to illustrate disease progression over time. Logistic models can be adapted to estimate associations of sequence of events (e.g. A -> B) by defining appropriately event B as the outcome and only preceding A events as exposures. Sequential associations can be visualised using directed networks. We will compare networks and clusters between groups with a differential analysis of the edges and comparing overall network metrics (centrality, connectivity, etc.).
Our final outcomes summaries are associations between comorbidities- multiple health conditions including allergic and non-allergic morbidity (with and without directionality) and patterns of comorbidity clusters, which we will compare between exposed and unexposed groups that can vary depending on asthma and atopic eczema status. To find these clusters, we will use population-level metrics derived from regression analyses on individual-level data.
Sinead Langan - Chief Investigator - London School of Hygiene & Tropical Medicine ( LSHTM )
Amy Mulick - Corresponding Applicant - London School of Hygiene & Tropical Medicine ( LSHTM )
Alasdair Henderson - Collaborator - London School of Hygiene & Tropical Medicine ( LSHTM )
Aziz Sheikh - Collaborator - University of Edinburgh
David McAllister - Collaborator - University of Glasgow
David Prieto-Merino - Collaborator - London School of Hygiene & Tropical Medicine ( LSHTM )
Dorothea Nitsch - Collaborator - London School of Hygiene & Tropical Medicine ( LSHTM )
Jennifer Quint - Collaborator - Imperial College London
Kathryn Mansfield - Collaborator - London School of Hygiene & Tropical Medicine ( LSHTM )
Liam Smeeth - Collaborator - London School of Hygiene & Tropical Medicine ( LSHTM )
Ronan Lyons - Collaborator - Swansea University
Spiros Denaxas - Collaborator - University College London ( UCL )
Patient Level Index of Multiple Deprivation;Practice Level Index of Multiple Deprivation