Lived experiences of transgender forced migrants and their mental health outcomes: systematic review and meta-ethnography.

BACKGROUND: Owing to multiple, complex and intersecting health inequities, systemic oppression and violence and discrimination in their home countries, some transgender people are forced to migrate to countries that offer them better legal protection and wider social acceptance. AIMS: This review sought to explore and understand the multiple factors that shape the mental health outcomes of transgender forced migrants (TFMs). METHOD: We systematically searched nine electronic databases for multidisciplinary literature (PROSPERO ID: CRD42020183062). We used a meta-ethnographic approach to synthesise data. We completed a quality appraisal and developed a socio-ecological model to draw together our findings. RESULTS: We retrieved 3399 records and screened titles, abstracts and full text to include 24 qualitative studies in this review. The synthesis identified individual survival strategies and factors in interpersonal, organisational and societal environments that contributed to profound deprivation and mental distress in TFMs. Pervasive and persistent violence and discrimination, economic exclusion, barriers to healthcare and a dependency on legal documentation were identified as key factors leading to poor mental health outcomes. Sources of resilience included community acceptance and support, being granted asylum, societal affirmation of gender, fulfilment of basic rights and healthcare access. Individual strategies for survival, such as hope and having purpose in life, were important in bringing relief from distress. CONCLUSIONS: Improved communication and knowledge about the unique needs and concerns of TFMs through interventions at the individual, interpersonal, organisational and societal levels are necessary to improve mental health outcomes.

Agreement between self-reported and researcher-measured height, weight and blood pressure measurements for online prescription of the combined oral contraceptive pill: an observational study.

OBJECTIVES: To compare agreement between self-reported height, weight and blood pressure measurements submitted to an online contraceptive service with researcher-measured values and document strategies used for self-reporting. DESIGN: An observational study. SETTING: An online sexual health service which provided the combined oral contraceptive pill, free of charge, to users in Southeast London, England. PARTICIPANTS: Between August 2017 and August 2019, 365 participants were recruited. PRIMARY AND SECONDARY OUTCOME MEASURES: The primary outcome, for which the study was powered, was the agreement between self-reported and researcher-measured body mass index (BMI) and blood pressure measurements, compared using kappa coefficients. Secondary measures of agreement included sensitivity, specificity and Bland Altman plots. The study also describes strategies used for self-reporting and classifies their clinical appropriateness. RESULTS: 327 participants fully described their process of blood pressure measurement with 296 (90.5%) classified as clinically appropriate. Agreement between self-reported and researcher-measured BMI was substantial (0.72 (95% CI 0.42 to 1.0)), but poor for blood pressure (0.06 (95% CI -0.11 to 0.23)). Self-reported height and weight readings identified 80.0% (95% CI 28.4 to 99.5) of individuals with a researcher-measured high BMI (≥than 35 kg/m2) and 9.1% (95% CI 0.23 to 41.3) of participants with a researcher-measured high blood pressure (≥140/90 mm Hg). CONCLUSION: In this study, while self-reported BMI was found to have substantial agreement with researcher-measured BMI, self-reported blood pressure was shown to have poor agreement with researcher-measured blood pressure. This may be due to the inherent variability of blood pressure, overdiagnosis of hypertension by researchers due to 'white coat syndrome' or inaccurate self-reporting. Strategies to improve self-reporting of blood pressure for remote prescription of the combined pill are needed.

Can natural language processing models extract and classify instances of interpersonal violence in mental healthcare electronic records: an applied evaluative study.

OBJECTIVE: This paper evaluates the application of a natural language processing (NLP) model for extracting clinical text referring to interpersonal violence using electronic health records (EHRs) from a large mental healthcare provider. DESIGN: A multidisciplinary team iteratively developed guidelines for annotating clinical text referring to violence. Keywords were used to generate a dataset which was annotated (ie, classified as affirmed, negated or irrelevant) for: presence of violence, patient status (ie, as perpetrator, witness and/or victim of violence) and violence type (domestic, physical and/or sexual). An NLP approach using a pretrained transformer model, BioBERT (Bidirectional Encoder Representations from Transformers for Biomedical Text Mining) was fine-tuned on the annotated dataset and evaluated using 10-fold cross-validation. SETTING: We used the Clinical Records Interactive Search (CRIS) database, comprising over 500 000 de-identified EHRs of patients within the South London and Maudsley NHS Foundation Trust, a specialist mental healthcare provider serving an urban catchment area. PARTICIPANTS: Searches of CRIS were carried out based on 17 predefined keywords. Randomly selected text fragments were taken from the results for each keyword, amounting to 3771 text fragments from the records of 2832 patients. OUTCOME MEASURES: We estimated precision, recall and F1 score for each NLP model. We examined sociodemographic and clinical variables in patients giving rise to the text data, and frequencies for each annotated violence characteristic. RESULTS: Binary classification models were developed for six labels (violence presence, perpetrator, victim, domestic, physical and sexual). Among annotations affirmed for the presence of any violence, 78% (1724) referred to physical violence, 61% (1350) referred to patients as perpetrator and 33% (731) to domestic violence. NLP models' precision ranged from 89% (perpetrator) to 98% (sexual); recall ranged from 89% (victim, perpetrator) to 97% (sexual). CONCLUSIONS: State of the art NLP models can extract and classify clinical text on violence from EHRs at acceptable levels of scale, efficiency and accuracy.

Cognitive Impairments in Schizophrenia: A Study in a Large Clinical Sample Using Natural Language Processing.

Background: Cognitive impairments are a neglected aspect of schizophrenia despite being a major factor of poor functional outcome. They are usually measured using various rating scales, however, these necessitate trained practitioners and are rarely routinely applied in clinical settings. Recent advances in natural language processing techniques allow us to extract such information from unstructured portions of text at a large scale and in a cost effective manner. We aimed to identify cognitive problems in the clinical records of a large sample of patients with schizophrenia, and assess their association with clinical outcomes. Methods: We developed a natural language processing based application identifying cognitive dysfunctions from the free text of medical records, and assessed its performance against a rating scale widely used in the United Kingdom, the cognitive component of the Health of the Nation Outcome Scales (HoNOS). Furthermore, we analyzed cognitive trajectories over the course of patient treatment, and evaluated their relationship with various socio-demographic factors and clinical outcomes. Results: We found a high prevalence of cognitive impairments in patients with schizophrenia, and a strong correlation with several socio-demographic factors (gender, education, ethnicity, marital status, and employment) as well as adverse clinical outcomes. Results obtained from the free text were broadly in line with those obtained using the HoNOS subscale, and shed light on additional associations, notably related to attention and social impairments for patients with higher education. Conclusions: Our findings demonstrate that cognitive problems are common in patients with schizophrenia, can be reliably extracted from clinical records using natural language processing, and are associated with adverse clinical outcomes. Harvesting the free text from medical records provides a larger coverage in contrast to neurocognitive batteries or rating scales, and access to additional socio-demographic and clinical variables. Text mining tools can therefore facilitate large scale patient screening and early symptoms detection, and ultimately help inform clinical decisions.

A natural language processing approach for identifying temporal disease onset information from mental healthcare text.

Receiving timely and appropriate treatment is crucial for better health outcomes, and research on the contribution of specific variables is essential. In the mental health domain, an important research variable is the date of psychosis symptom onset, as longer delays in treatment are associated with worse intervention outcomes. The growing adoption of electronic health records (EHRs) within mental health services provides an invaluable opportunity to study this problem at scale retrospectively. However, disease onset information is often only available in open text fields, requiring natural language processing (NLP) techniques for automated analyses. Since this variable can be documented at different points during a patient's care, NLP methods that model clinical and temporal associations are needed. We address the identification of psychosis onset by: 1) manually annotating a corpus of mental health EHRs with disease onset mentions, 2) modelling the underlying NLP problem as a paragraph classification approach, and 3) combining multiple onset paragraphs at the patient level to generate a ranked list of likely disease onset dates. For 22/31 test patients (71%) the correct onset date was found among the top-3 NLP predictions. The proposed approach was also applied at scale, allowing an onset date to be estimated for 2483 patients.

Birth, attitudes and placentophagy: a thematic discourse analysis of discussions on UK parenting forums.

BACKGROUND: The post-partum consumption of the placenta by the mother (placentophagy) has been practiced since the 1970s in the global North and is seemingly increasing in popularity. Maternal placentophagy is not known to have been practiced in any other time period or culture, despite being near-ubiquitous in other placental mammals. An in-depth qualitative exploration as to the reasons for the practice, its increasing popularity and how it is narratively incorporated into discourses surrounding "ideal" natural and medical births are given in this paper. METHODS: 1752 posts from 956 users across 85 threads from the parenting forums Mumsnet and Netmums were identified for inclusion. A thematic discourse analysis was performed using NVivo. RESULTS: Three main themes were identified: women recounted predominantly positive attitudes towards their own experiences of placentophagy, and they were respectful of others' views and experiences; some had negative views, particularly around the concept of disgust, but again, they were respectful of others' experiences. By far the most common method of consumption of the placenta was encapsulation. CONCLUSIONS: This paper identifies the motivation for placentophagy to almost universally be for medical benefits, most commonly the prevention or treatment of post-natal depression (PND). Whilst disgust is a common reaction, discussion of risks is rare, and positive experiences outweigh negative ones. The increasing popularity of the practice is ascribed in part to the comparative palatability of encapsulation and the use of the internet to share resources and remove barriers. Parenting forums are important spaces to negotiate normative birth practices, including placentophagy, and act to build communities of women who value personal experience over medical evidence and highly value personal choice and bodily autonomy. Placentophagy is discussed in terms of its relation to natural and medical births with arguments being made using both discourses for and against the practice. This paper argues that placentophagy is practiced as a resistance to medicalisation as an assertion of control by the mother, whilst simultaneously being a medicalised phenomenon itself.