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Symptom profile of severe mental illness and adverse health outcomes

Student thesis: Doctoral ThesisDoctor of Philosophy

Individuals with severe mental illness (SMI- schizophrenia, schizoaffective disorder and bipolar disorder) experience higher levels of morbidity and mortality than the general population. An important policy goal is to reduce this gap. Investigating the contributory role of physical illness is of key importance to unpacking the associations between SMI and detrimental outcomes, such as premature mortality and frequent hospital admissions. This thesis builds on recent advances to data extraction technologies to investigate the above.
• To describe the relative contributions of major disease groups to the gap in life expectancy between individuals with SMI and the general population.
• To describe the most common reasons for admission to non-psychiatric hospitals by individuals with SMI and the relative frequencies of these admissions compared to the general population.
• As a proof of principle for ascertaining meaningful symptom profiles from routine mental health record text fields, to describe the prospective association between number of recorded negative symptoms and mental healthcare outcomes (admission, duration of admission, and readmission) among individuals with schizophrenia.
• To describe the association with mortality and hospitalisation for each of six symptom dimensions (positive, negative, manic, disorganisation, catatonic and depressive) extracted from the clinical records of individuals with SMI.
Information for SMI cohorts were derived from the Clinical Record Interactive Search (CRIS), a de-identified electronic patient records data resource. Data on mortality were extracted using existing linkages between CRIS and death certification (Office for National Statistics). Life expectancy estimates were used to explain the contributions of specific causes of death to the gap. Using Hospital Episode Statistics data, frequencies of and causes for non-psychiatric hospital admissions in SMI were compared to those in the catchment general population. Symptoms within clinical record text fields were ascertained using a range of natural language processing algorithms, and were assessed for their associations with mortality and hospitalisation outcomes.
Natural causes accounted for 79.2% of lost life-years in women and 78.6% in men. Deaths from circulatory disorders accounted for more life-years lost in women than men (22.0% versus 17.4%, respectively), as did deaths from cancer (8.1% versus 0%), but the contribution from respiratory disorders was lower in women than men (13.7% versus 16.5%).
Commonest discharge diagnosis categories were urinary conditions, digestive conditions, unclassified symptoms, neoplasms, and respiratory conditions. SARs were raised for most major categories, except neoplasms where risk was significantly lower. Hospitalisation risks were specifically higher for poisoning and external causes, injury, endocrine/metabolic conditions, haematological, neurological, dermatological, infectious and non- specific (‘Z-code’) causes. The five commonest specific ICD-10 diagnoses at discharge were ‘chronic renal failure’ (N18), a non-specific code (Z04), ‘dental caries’ (K02), ‘other disorders of the urinary system’ (N39), and ‘pain in throat and chest’ (R07), all of which were higher than expected (SARs ranging 1.57–6.66).
Proof of concept analyses showed negative symptoms were associated with younger age, male gender and single marital status, and with increased likelihood of hospital admission (OR 1.24, 95% CI 1.10 to 1.39), longer duration of admission (β-coefficient 20.5 days, 95% CI 7.6–33.5), and increased likelihood of readmission following discharge (OR 1.58, 95% CI 1.28 to 1.95).
Cox regression analyses detected significant effect of positive (HR 1.08, 95% CI 1.03- 1.16), negative (HR 1.09 95% CI 1.02- 1.16) and catatonic (HR 1.09 95% CI 1.03- 1.16) symptoms on mortality with adjustment of age, sex, employment, marital status and ethnicity. Linear regression analyses detected significant effect of manic (β-coefficient 0.09, 95% CI 0.02- 0.15), catatonic (β-coefficient 0.08, 95% CI 0.02- 0.15) and depressive (β-coefficient 0.14,95% CI 0.08- 0.21) symptoms on admission to non-mental health hospitals.
Clinically, findings from this thesis confirm that SMI has a substantial negative impact on physical health, associated with increased mortality and morbidity. Of clinical relevance, the findings showed differences in burden depending on types of symptoms recorded in health records. From a policy context, the gap in life expectancy and increased non-psychiatric hospitalisation, accounted for by a broad range of causes, need to be addressed systematically. Interventions should focus on a whole system approach to improve health benefits for individuals of SMI.
Original languageEnglish
Awarding Institution
Award date2018


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