This is an open access article under the terms of the Creat ive Commo ns Attri bution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

This is an open access article under the terms of the Creat ive Commo ns Attri bution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Background and purpose: Although psychiatric diagnoses are recognized in idiopathic dystonia, no previous studies have examined the temporal relationship between idiopathic dystonia and psychiatric diagnoses at scale. Here, we determine rates of psychiatric diagnoses and psychiatric medication prescription in those diagnosed with idiopathic dystsuponia compared to matched controls. Methods:A longitudinal population-based cohort study using anonymized electronic health care data in Wales (UK) was conducted to identify individuals with idiopathic dystonia and comorbid psychiatric diagnoses/prescriptions between 1 January 1994 and 31 December 2017. Psychiatric diagnoses/prescriptions were identified from primary and secondary health care records.Results: Individuals with idiopathic dystonia (n = 52,589) had higher rates of psychiatric diagnosis and psychiatric medication prescription when compared to controls (n = 216,754, 43% vs. 31%, p < 0.001; 45% vs. 37.9%, p < 0.001, respectively), with depression and anxiety being most common (cases: 31% and 28%). Psychiatric diagnoses predominantly predated dystonia diagnosis, particularly in the 12 months prior to diagnosis (incidence rate ratio [IRR] = 1.98, 95% confidence interval [CI] = 1.9-2.1), with an IRR of 12.4 (95% CI = 11.8-13.1) for anxiety disorders. There was, however, an elevated rate of most psychiatric diagnoses throughout the study period, including the 12 months after dystonia diagnosis (IRR = 1.96, 95% CI = 1.85-2.07). Conclusions:This study suggests a bidirectional relationship between psychiatric disorders and dystonia, particularly with mood disorders. Psychiatric and motor symptoms in dystonia may have common aetiological mechanisms, with psychiatric disorders potentially forming prodromal symptoms of idiopathic dystonia.

The contributions of colleagues and students to the work presented in this thesis are explicitly detailed below: Chapter 3: Damien Le'Goff and Rebecca Owens assisted with field work, DNA extraction and PCR procedures Chapter 4: Dr. Ed Dudley ran GC-MS analysis Chapter 5: Dr. Suzy Moody collaborated on experimental setup procedures. Eoin O'Connor, Maynooth University, undertook protein extraction, peptide preparation and ran LC-MS/MS and protein sequence identification analyses. Dr. Ed Dudley ran GC-MS analysis.

Background Measurement of multimorbidity in research is variable, including the choice of the data source used to ascertain conditions. We compared the estimated prevalence of multimorbidity and associations with mortality using different data sources. Methods A cross-sectional study of SAIL Databank data including 2,340,027 individuals of all ages living in Wales on 01 January 2019. Comparison of prevalence of multimorbidity and constituent 47 conditions using data from primary care (PC), hospital inpatient (HI), and linked PC-HI data sources and examination of associations between condition count and 12-month mortality. Results Using linked PC-HI compared with only HI data, multimorbidity was more prevalent (32.2% versus 16.5%), and the population of people identified as having multimorbidity was younger (mean age 62.5 versus 66.8 years) and included more women (54.2% versus 52.6%). Individuals with multimorbidity in both PC and HI data had stronger associations with mortality than those with multimorbidity only in HI data (adjusted odds ratio 8.34 [95% CI 8.02-8.68] versus 6.95 (95%CI 6.79-7.12] in people with ≥ 4 conditions). The prevalence of conditions identified using only PC versus only HI data was significantly higher for 37/47 and significantly lower for 10/47: the highest PC/HI ratio was for depression (14.2 [95% CI 14.1–14.4]) and the lowest for aneurysm (0.51 [95% CI 0.5–0.5]). Agreement in ascertainment of conditions between the two data sources varied considerably, being slight for five (kappa < 0.20), fair for 12 (kappa 0.21–0.40), moderate for 16 (kappa 0.41–0.60), and substantial for 12 (kappa 0.61–0.80) conditions, and by body system was lowest for mental and behavioural disorders. The percentage agreement, individuals with a condition identified in both PC and HI data, was lowest in anxiety (4.6%) and highest in coronary artery disease (62.9%). Conclusions The use of single data sources may underestimate prevalence when measuring multimorbidity and many important conditions (especially mental and behavioural disorders). Caution should be used when interpreting findings of research examining individual and multiple long-term conditions using single data sources. Where available, researchers using electronic health data should link primary care and hospital inpatient data to generate more robust evidence to support evidence-based healthcare planning decisions for people with multimorbidity.