Digital mental health and employment - Lessons from the Australian experience

Background and objectives: This paper reviews Australia's take-up of digital mental health interventions, including some specific reference to their application in relation to employment. Use of these interventions in Australia was already significant. Under COVID-19 they have exploded. The Australian experience offers useful lessons for European and other countries, and these are summarised. Methods: This paper presents a narrative review of key texts, resources, policies and reports, from government and other sources. It also presents data reflecting the take-up of digital mental health services and the employment of people with a mental illness in Australia. Results: We present data showing the explosion in uptake of digital mental health services. There is very limited evidence about the impact of these services in improving employment outcomes for people with a mental illness in Australia. The Government has moved to make them a permanent feature of mental health care, in addition to traditional face-to-face care. Conclusion: Key lessons emerge from the Australian experience, including the need for target clarity;the importance of blending digital services into broader frameworks of mental health care;the need for quality and safety standards to be developed and applied to digital services;and the need for better evaluation of the outcomes of digital interventions in the workplace. The digital mental health genie is out of the bottle. New capacity for evaluation of the outcomes of digital mental health services is vital to ensure value and quality of such investments. © 2022 Asociación Universitaria de Zaragoza para el Progreso de la Psiquiatría y la Salud Mental

Identifying UK travellers at increased risk of developing pneumococcal infection: a novel algorithm.

BACKGROUND: In 2016, the travel subcommittee of the UK Joint Committee on Vaccination and Immunisation (JCVI) recommended that 13-valent PCV (PCV13) could be offered to travellers aged over 65 years, visiting countries without infant PCV immunization programmes. This study aimed to identify, collate and review the available evidence to identify specific countries where UK travellers might be at an increased risk of developing pneumococcal infection. The data were then used to develop an algorithm, which could be used to facilitate implementation of the JCVI recommendation. METHODS: We conducted a systematic search of the published data available for pneumococcal disease, PCV vaccine implementation, coverage data and programme duration by country. The primary data sources used were World Health Organization databases and the International Vaccine Access Centre Vaccine Information and Epidemiology Window-hub database. Based on the algorithm, the countries were classified into 'high overall risk', 'intermediate overall risk' and 'low overall risk' from an adult traveller perspective. This could determine whether PCV13 should be recommended for UK adult travellers. RESULTS: A data search for a total of 228 countries was performed, with risk scores calculated for 188 countries. Overall, 45 countries were classified as 'high overall risk', 86 countries as 'intermediate overall risk', 57 countries as 'low overall risk' and 40 countries as 'unknown'. CONCLUSION: To our knowledge this is the first attempt to categorize the risk to UK adult travellers of contracting pneumococcal infection in each country, globally. These findings could be used by national travel advisory bodies and providers of travel vaccines to identify travellers at increased risk of pneumococcal infection, who could be offered PCV immunization.

ONCE AT THE EPICENTER OF THE PANDEMIC, ELMHURST HOSPITAL LEADING EQUITY FOR DIABETES CARE

STATEMENT OF PROBLEM/QUESTION: Improve diabetes quality and equity during the pandemic. DESCRIPTION OF PROGRAM/INTERVENTION: Elmhurst hospital center is one of 11 acute care hospitals in the NYC H+H network, the largest public health care system in the United States. Elmhurst hospital was at the epicenter of the COVID 19 pandemic in March 2020;providing care for immigrant, uninsured and underinsured patients, including more than 5000 patients with diabetes. As NYC emerged from the first surge, the primary care clinic assessed and addressed care gaps. Difficulties in accessing in-person visits, lab, and the social and economic impact on patients added to the challenges of managing diabetes during the pandemic. Disproportionately burdened were ethnic and racial minorities. An assessment of the diabetes outcome revealed that the control rate of diabetes defined as Hemoglobin A1c (A1c) less than 8% dropped by 8% compared to the pre-pandemic rate. There was a 4% difference in diabetes outcomes between the insured and uninsured patient populations.The clinic leadership implemented a collaborative care model. The collaborative care model consists of clinical pharmacists, registered nurses, a diabetes education-certified dietitian, and the population health team that provides outreach and data analytics support. Patients referred to the collaborative team by the primary care provider who assists in setting the treatment plan and goals. The collaborative care team screens every patient for barriers and social needs, provides diet education and a self-management plan. Each patient receives medication management in either the RN led treat-totarget clinic or by the clinical pharmacist. The referral criteria follow an algorithm based on the A1c level of control, number of medications, and the use of injectables. Cases are discussed weekly in collaboration with the primary care provider. The level of care is adjusted to address patient needs. The team determines the number of visits and time between visits based on the clinical progress. Visits are conducted in person and virtually;tailored to the patient's ability to use telehealth. MEASURES OF SUCCESS: Diabetes control improved by 10% in 8 months. The gap between insured and uninsured was reduced from 4% to 1% at the end of the study project period. FINDINGS TO DATE: A team-based approach using risk stratification that incorporates clinical outcomes and patient social barriers led to significant improvement in diabetes outcomes and closed the inequity gap. KEY LESSONS FOR DISSEMINATION: - High-quality diabetes care requires a multi-disciplinary team approach. - Treat-to-target RN visits improved access and equity in diabetes care. - Clinical Risk algorithms must incorporate social barriers. - Team-based approaches require continuous training and evaluation, with team members empowered for decision making.

COVID-19 NUTRITION AND DISEASE RISK ASSESSMENTS PREDICT MORTALITY IN VETERANS AFFAIRS MEDICAL CENTER ICU

Background: COVID-19 pandemic significantly increases morbidity and mortality in vulnerable veteran populations. This observation cohort study evaluates nutrition risk factors associated with mortality in COVID-19 patients admitted to VA Level 1A complexity ICU. Methods: 508 COVID-19 patients requiring VAMC ICU admission 02/2020-07/2021 were retrospectively reviewed. We hypothesized that Covid19 may cause significant malnutrition. Survivors and mortality cohorts were assessed with five nutrition and physiology risk assessment algorithms: APACHE II, SOFA, ASA, NRI and GNRI, and co-morbidities: age, gender, race, obesity, diabetes, hypertension, coronary disease, and malnutrition. Results: Mortality: 111 of 508 patients (21.85%). Low admission NRI (45.27±2.1) and GNRI (60.73) indicated malnutrition in all patients. Both scores lower in mortality vs. survivors (NRI:42.45±4.1 vs. 46.06±3.49, p≤0.004) (GNRI: 56.4±12.56vs 61.9±25.88, p≤0.003). Nutritional markers lower in mortality vs. survivors: Albumin (2.9±0.5 vs. 3.4±0.6, p≤0.001), total lymphocyte count (1.23±1.8 vs. 2.5±1.9, p≤0.005), Hematocrit (33.2±9.9 vs 38.9±7.4, p≤0.01.) Physiology risk assessment scores were higher in mortality vs survivors: SOFA (4.24±1.73 vs. 2.54±0.88, p≤0.0005), APACHE II (12.3±4.64 vs. 7.19±2.48, p≤0.0002), and ASA (3.86±0.53 vs. 2.9±0.75, p≤0.0003). Comorbidities were higher in mortality vs. survivor: age (72.3±9.5 vs 67.8±12.9y/o, p≤0.001), obesity (87.39% vs. 42.06%, p≤0.04), diabetes (58.5% vs. 47.6%, p≤0.05), Hypertension (34.2% vs 11.58%, p≤0.04), Coronary disease (61.26% vs. 32.74%, p<0.002). No mortality difference between genders: (21.86% male, 21.74% female, p=NS), or races:(21.48%AA, 22.08% W, 22.08 UNK. p=NS) Conclusion: All COVID-19 patients were malnourished on ICU admission. Malnutrition low NRI score, high risk assessment scores, with comorbidities directly predict COVID-19 mortality risk regardless of gender or race. Low NRI scores indicate need for nutritional support to critically ill COVID-19 patients.