Social Insurance Literacy of Dutch Workers Receiving Disability Benefits and its Associations with Socio-Economic Characteristics

Purpose This study explores the concept social insurance literacy (SIL) and corresponding questionnaire (SILQ) among workers receiving disability benefits and the comprehensibility of the social security institute (SSI), and examines associations with socio-economic characteristics. Methods 1753 panel members of the Dutch SSI were approached to complete the SILQ-NL37. This measure was based on the original SILQ. The SILQ-NL37 contains domains for obtaining, understanding and acting upon information for both individual SIL and system comprehensibility. A higher score means better SIL or comprehensibility. Data on age, gender, education, living situation, Dutch skills and time receiving disability benefits were also collected. With k-means clustering, groups with adequate and limited SIL were created. Associations with socio-economic characteristics were examined with independent t-tests and linear regression analyses for both the total scores and within domain scores. Cronbach α and Spearman rho’s indicated measurement properties were good to acceptable for the SILQ-NL37. Results Thirty-five percent of the 567 participants were in the group with limited SIL. Higher individual SILQ-NL37 scores were associated with having a partner (p = 0.018) and northeastern living region (p = 0.031). Higher scores for obtaining (p = 0.041) and understanding (p = 0.049) information were associated with female sex, and for acting on information with younger age (p = 0.020). People with limited Dutch skills (p = 0.063) and a partner (p = 0.085) rated system comprehensibility higher. Conclusions According to the SILQ-NL37 scores, about 35% of the panel members have limited ability to obtain, understand and act upon social insurance systems information. Limited SIL is associated with several socio-economic factors. Future researches should study the concept in a more representative sample, and in different countries and social insurance contexts.

Enhancing Disability Determination Decision Process Through Natural Language Processing

In this article, a new approach is introduced that makes use of the valuable information that can be extracted from a patient's electronic healthcare records (EHRs). The approach employs natural language processing and biomedical text mining to handle patient's data. The developed approach extracts relevant medical entities and builds relations between symptoms and other clinical signature modifiers. The extracted features are viewed as evaluation features. The approach utilizes such evaluation features to decide whether an applicant could gain disability benefits or not. Evaluations showed that the proposed approach accurately extracts symptoms and other laboratory marks with high F-measures (93.5-95.6%). Also, results showed an excellent deduction in assessments to approve or reject an applicant case to obtain a disability benefit.

“Why can’t I Have the Office Jobs?”: Immigrant Latinx Transgender Peoples’ Experiences with Seeking Employment

Research has documented the experiences of transgender people in seeking employment. To date, no scholarship has explored the experiences of immigrant Latinx transgender people seeking employment in the United States. Using an intersectionality framework, the present study aimed to uncover the experiences of immigrant Latinx transgender people as they sought employment in the United States. A community sample of 18 immigrant Latinx transgender people from a large metropolitan city in Florida engaged in semi-structured interviews. Thematic analysis revealed five themes related to participants’ experiences seeking employment, including: (1) discrimination, (2) limited options, (3) positive experiences, (4) momentary de-transition, and (5) disability benefits as financial relief. Future directions such as exploring ways in which immigrant Latinx transgender people resist discrimination while seeking job opportunities are discussed. Implications for practice and advocacy such as advocating for equitable employment policies that acknowledge the intersectional experiences of this community are presented.

Under-recognition of medically unexplained symptom conditions among US Veterans with Gulf War Illness

Objective Conditions defined by persistent “medically unexplained” physical symptoms and syndromes (MUS) are common and disabling. Veterans from the Gulf War (deployed 1990–1991) have notably high prevalence and disability from MUS conditions. Individuals with MUS report that providers do not recognize their MUS conditions. Our goal was to determine if Veterans with MUS receive an ICD-10 diagnosis for a MUS condition or receive disability benefits available to them for these conditions. Methods A chart review was conducted with US Veterans who met case criteria for Gulf War Illness, a complex MUS condition (N = 204, M = 53 years-old, SD = 7). Three coders independently reviewed Veteran’s medical records for MUS condition diagnosis or service-connection along with comorbid mental and physical health conditions. Service-connection refers to US Veterans Affairs disability benefits eligibility for conditions or injuries experienced during or exacerbated by military service. Results Twenty-nine percent had a diagnosis of a MUS condition in their medical record, the most common were irritable colon/irritable bowel syndrome (16%) and fibromyalgia (11%). Slightly more Veterans were service-connected for a MUS condition (38%) as compared to diagnosed. There were high rates of diagnoses and service-connection for mental health (diagnoses 76% and service-connection 74%), musculoskeletal (diagnoses 86%, service-connection 79%), and illness-related conditions (diagnoses 98%, service-connection 49%). Conclusion Given that all participants were Gulf War Veterans who met criteria for a MUS condition, our results suggest that MUS conditions in Gulf War Veterans are under-recognized with regard to clinical diagnosis and service-connected disability. Veterans were more likely to be diagnosed and service-connected for musculoskeletal-related and mental health conditions than MUS conditions. Providers may need education and training to facilitate diagnosis of and service-connection for MUS conditions. We believe that greater acknowledgement and validation of MUS conditions would increase patient engagement with healthcare as well as provider and patient satisfaction with care.

Differences in Work Disability Duration for Immigrants and Canadian-Born Workers in British Columbia, Canada

This study aimed to investigate differences in work disability duration among immigrants (categorized as economic, family member or refugee/other classification upon arrival to Canada) compared to Canadian-born workers with a work-related injury in British Columbia. Immigrants and Canadian-born workers were identified from linked immigration records with workers’ compensation claims for work-related back strain, connective tissue, concussion and fracture injuries requiring at least one paid day of work disability benefits between 2009 to 2015. Quantile regression investigated the relationship between immigration classification and predicted work disability days (defined from injury date to end of compensation claim, up to 365 days) and modeled at the 25th, 50th and 75th percentile of the distribution of the disability days. With a few exceptions, immigrants experienced greater predicted disability days compared to Canadian-born workers within the same injury cohort. The largest differences were observed for family and refugee/other immigrant classification workers, and, in particular, for women within these classifications, compared to Canadian-born workers. For example, at the 50th percentile of the distribution of disability days, we observed a difference of 34.1 days longer for refugee/other women in the concussion cohort and a difference of 27.5 days longer for family classification women in the fracture cohort. Economic immigrants had comparable disability days with Canadian-born workers, especially at the 25th and 50th percentiles of the distribution. Immigrant workers’ longer disability durations may be a result of more severe injuries or challenges navigating the workers’ compensation system with delays in seeking disability benefits and rehabilitation services. Differences by immigrant classification speak to vulnerabilities or inequities upon arrival in Canada that persist after entry to the workforce and warrant further investigation for early mitigation strategies.

Linking Free Text Documentation of Functioning and Disability to the ICF With Natural Language Processing

Background: Invaluable information on patient functioning and the complex interactions that define it is recorded in free text portions of the Electronic Health Record (EHR). Leveraging this information to improve clinical decision-making and conduct research requires natural language processing (NLP) technologies to identify and organize the information recorded in clinical documentation.Methods: We used natural language processing methods to analyze information about patient functioning recorded in two collections of clinical documents pertaining to claims for federal disability benefits from the U.S. Social Security Administration (SSA). We grounded our analysis in the International Classification of Functioning, Disability, and Health (ICF), and used the Activities and Participation domain of the ICF to classify information about functioning in three key areas: mobility, self-care, and domestic life. After annotating functional status information in our datasets through expert clinical review, we trained machine learning-based NLP models to automatically assign ICF categories to mentions of functional activity.Results: We found that rich and diverse information on patient functioning was documented in the free text records. Annotation of 289 documents for Mobility information yielded 2,455 mentions of Mobility activities and 3,176 specific actions corresponding to 13 ICF-based categories. Annotation of 329 documents for Self-Care and Domestic Life information yielded 3,990 activity mentions and 4,665 specific actions corresponding to 16 ICF-based categories. NLP systems for automated ICF coding achieved over 80% macro-averaged F-measure on both datasets, indicating strong performance across all ICF categories used.Conclusions: Natural language processing can help to navigate the tradeoff between flexible and expressive clinical documentation of functioning and standardizable data for comparability and learning. The ICF has practical limitations for classifying functional status information in clinical documentation but presents a valuable framework for organizing the information recorded in health records about patient functioning. This study advances the development of robust, ICF-based NLP technologies to analyze information on patient functioning and has significant implications for NLP-powered analysis of functional status information in disability benefits management, clinical care, and research.

Linking Free Text Documentation of Functioning and Disability to the ICF with Natural Language Processing

Background: Invaluable information on patient functioning and the complex interactions that define it is recorded in free text portions of the Electronic Health Record (EHR). Leveraging this information to improve clinical decision-making and conduct research requires natural language processing (NLP) technologies to identify and organize the information recorded in clinical documentation. Methods: We used NLP methods to analyze information about patient functioning recorded in two collections of clinical documents pertaining to claims for federal disability benefits from the U.S. Social Security Administration (SSA). We grounded our analysis in the International Classification of Functioning, Disability and Health (ICF), and used the ICF's Activities and Participation domain to classify information about functioning in three key areas: Mobility, Self-Care, and Domestic Life. After annotating functional status information in our datasets through expert clinical review, we trained machine learning-based NLP models to automatically assign ICF codes to mentions of functional activity. Results: We found that rich and diverse information on patient functioning was documented in the free text records. Annotation of 289 documents for Mobility information yielded 2,455 mentions of Mobility activities and 3,176 specific actions corresponding to 13 ICF-based codes. Annotation of 329 documents for Self-Care and Domestic Life information yielded 3,990 activity mentions and 4,665 specific actions corresponding to 16 ICF-based codes. NLP systems for automated ICF coding achieved over 80% macro-averaged F-measure on both datasets, indicating strong performance across all ICF codes used. Conclusions: NLP can help to navigate the tradeoff between flexible and expressive clinical documentation of functioning and standardizable data for comparability and learning. The ICF has practical limitations for classifying functional status information in clinical documentation, but presents a valuable framework for organizing the information recorded in health records about patient functioning. This study advances the development of robust, ICF-based NLP technologies to analyze information on patient functioning, and has significant implications for NLP-powered analysis of functional status information in disability benefits management, clinical care, and research.

ЕSTIMATION OF SOCIO-ECONOMIC BURDEN OF CHRONIC OBSTRUCTIVE PULMONARY DISEASE FOR A 5-YEAR PERIOD: A REGIONAL ASPECT

The aim of the study was to estimate the economic damage by COPD, including direct medical and non-medical costs and indirect costs associated with premature deaths of working-age individuals.Materials and methods. First, estimation of the economic COPD burden in Astrakhan region (AR) was carried out using the clinical and economic analysis of the "cost of illness" (COI). Direct medical costs of inpatient, outpatient, ambulance and emergency medical care, as well as direct non-medical costs associated with the disability benefits payments, were taken into account. Indirect costs were defined as economic losses from undelivered products due to premature deaths of working-age individuals.Results. From 2015 to 2019, the economic COPD burden in AR amounted to 757.11 million rubles in total, which is equivalent to 0.03% of the gross regional product covering a five-year period of the study. Direct medical and non-medical costs totaled 178.02 million rubles. In the structure of direct medical expenses, expenses for inpatient, as well as ambulance and emergency medical care during the study period, increased by 92.5% and 45.5%, respectively. While the costs for the outpatient care decreased by 31.9%, the increase in direct non-medical costs associated with the disability benefits payments, increased by 5.1% (2019). Indirect losses amounted to 579.09 million rubles.Conclusion. The structure of the main damage is dominated by indirect losses in the economy associated with premature deaths of working-age individuals. In the structure of direct medical costs, inpatient care costs prevailed. These studies indicate the need to continue an advanced analysis of the economic burden of COPD, as well as to optimize the treatment and prevention of the exacerbations development of this disease.