The goals of this investigation were to 1) identify and measure exposures inside homes of individuals with chemical intolerance (CI), 2) provide guidance for reducing these exposures, and 3) determine whether our environmental house calls (EHCs) intervention could reduce both symptoms and measured levels of indoor air contaminants.

CI is an international public health and clinical concern, but few resources are available to address patients’ often disabling symptoms. Numerous studies show that levels of indoor air pollutants can be two to five (or more) times higher than outdoor levels. Fragranced consumer products, including cleaning supplies, air fresheners, and personal care products, are symptom triggers commonly reported by susceptible individuals.

A team of professionals trained and led by a physician/industrial hygienist and a certified indoor air quality specialist conducted a series of 5 structured EHCs in 37 homes of patients reporting CI.

We report three case studies demonstrating that an appropriately structured home intervention can teach occupants how to reduce indoor air exposures and associated symptoms. Symptom improvement, documented using the Quick Environmental Exposure and Sensitivity Inventory Symptom Star, corresponded with the reduction of indoor air volatile organic compounds, most notably fragrances. These results provide a deeper dive into 3 of the 37 cases described previously in Perales et al. (2022).

We address the long-standing dilemma that worldwide reports of fragrance sensitivity have not previously been confirmed by human or animal challenge studies. Our ancient immune systems’ ‘first responders’, mast cells, which evolved 500 million years ago, can be sensitized by synthetic organic chemicals whose production and use have grown exponentially since World War II. We propose that these chemicals, which include now-ubiquitous fragrances, trigger mast cell degranulation and inflammatory mediator release in the olfactory-limbic tract, thus altering cerebral blood flow and impairing mood, memory, and concentration (often referred to as ‘brain fog’). The time has come to translate these research findings into clinical and public health practice.

DSM-5 differentiates avoidant/restrictive food intake disorder (ARFID) from other eating disorders (EDs) by a lack of overvaluation of body weight/shape driving restrictive eating. However, clinical observations and research demonstrate ARFID and shape/weight motivations sometimes co-occur. To inform classification, we: (1) derived profiles underlying restriction motivation and examined their validity and (2) described diagnostic characterizations of individuals in each profile to explore whether findings support current diagnostic schemes. We expected, consistent with DSM-5, that profiles would comprise individuals endorsing solely ARFID or restraint (i.e. trying to eat less to control shape/weight) motivations.

We applied latent profile analysis to 202 treatment-seeking individuals (ages 10–79 years [M = 26, s.d. = 14], 76% female) with ARFID or a non-ARFID ED, using the Nine-Item ARFID Screen (Picky, Appetite, and Fear subscales) and the Eating Disorder Examination-Questionnaire Restraint subscale as indicators.

A 5-profile solution emerged: Restraint/ARFID-Mixed (n = 24; 8% [n = 2] with ARFID diagnosis); ARFID-2 (with Picky/Appetite; n = 56; 82% ARFID); ARFID-3 (with Picky/Appetite/Fear; n = 40; 68% ARFID); Restraint (n = 45; 11% ARFID); and Non-Endorsers (n = 37; 2% ARFID). Two profiles comprised individuals endorsing solely ARFID motivations (ARFID-2, ARFID-3) and one comprising solely restraint motivations (Restraint), consistent with DSM-5. However, Restraint/ARFID-Mixed (92% non-ARFID ED diagnoses, comprising 18% of those with non-ARFID ED diagnoses in the full sample) endorsed ARFID and restraint motivations.

The heterogeneous profiles identified suggest ARFID and restraint motivations for dietary restriction may overlap somewhat and that individuals with non-ARFID EDs can also endorse high ARFID symptoms. Future research should clarify diagnostic boundaries between ARFID and non-ARFID EDs.

To implement and evaluate a point-of-care (POC) molecular testing platform for respiratory viruses in congregate living settings (CLS).

Prospective quality improvement study.

Seven CLS, including three nursing homes and four independent-living facilities.

Residents of CLS.

A POC platform for COVID-19, influenza A and B, and respiratory syncytial virus was implemented at participating CLS from December 1, 2022 to April 15, 2023. Residents with respiratory symptoms underwent paired testing, with respiratory specimens tested first with the POC platform and then delivered to an off-site laboratory for multiplex respiratory virus panel (MRVP) polymerase chain reaction (PCR) as per standard protocol. Turn-around time and diagnostic accuracy of the POC platform were compared against MRVP PCR. In an exploratory analysis, time to outbreak declaration among participating CLS was compared against a convenience sample of 19 CLS that did not use the POC platform.

A total of 290 specimens that underwent paired testing were included. Turn-around time to result was significantly shorter with the POC platform compared to MRVP PCR, with median difference of 36.2 hours (interquartile range 21.8–46.4 hours). The POC platform had excellent diagnostic accuracy compared to MRVP PCR, with area under the curve statistic of .96. Time to outbreak declaration was shorter in CLS that used the POC platform compared to CLS that did not.

Rapid POC testing platforms for respiratory viruses can be implemented in CLS, with high diagnostic accuracy, expedited turn-around times, and shorter time to outbreak declaration.

Preventing psychiatric admissions holds benefits for patients as well as healthcare systems. The Clinical Global Impression-Severity (CGI-S) scale is a 7-point measurement of symptom severity, independent of diagnosis, which has shown capability of predicting risk of hospitalisation in schizophrenia. Due to its routine use in clinical practice and ease of administration, it may have potential as a transdiagnostic predictor of hospitalisation.

To investigate whether early trajectories of CGI-S scores predict risk of hospitalisation over a 6 month-follow-up period.

A retrospective cohort study was conducted, analysing Electronic Health Record (EHR) data from the NeuroBlu Database (Patel et al. BMJ Open 2022;12:e057227). Patients were included if they had a psychiatric diagnosis and at least 5 recorded CGI-S scores within a 2-month period, defined as the ‘index’ period. The relationship between early CGI-S trajectories and risk of hospitalisation was investigated using Cox regression. The analysis was adjusted for age, gender, race, number of years in education, and psychiatric diagnosis. Early CGI-S trajectories were estimated as clinical severity (defined as the mean CGI-S score during the index period) and clinical instability (defined as a generalised Root Mean Squared Subsequent Differences of all CGI-S scores recorded during the index period). The primary outcome was time to psychiatric hospitalisation up to 6 months following the index period. Patients who had been hospitalised before or within the index period were excluded.

A total of 36,914 patients were included (mean [SD] age: 29.7 [17.5] years; 57.3% female). Clinical instability (hazard ratio: 1.09, 95% CI 1.07-1.10, p<0.001) and severity (hazard ratio: 1.11, 95% CI 1.09-1.12, p<0.001) independently predicted risk of hospitalisation. These associations were consistent across all psychiatric diagnoses. Patients in the top 50% of severity and/or instability were at a 45% increased risk of hospitalisation compared to those in the bottom 50% (Figure 1).

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Early CGI-S trajectories reflecting clinical severity and instability independently predict risk of hospitalisation across diagnoses. This risk was compounded when instability and severity were present together. These results have translation potential in predicting individuals who are at high risk of hospitalisation and could benefit from preventative strategies to mitigate this risk.

E. Palmer Employee of: Holmusk, M. Taquet Consultant of: Holmusk, K. Griffiths Employee of: Holmusk, S. Ker Employee of: Holmusk, C. Liman Employee of: Holmusk, S. N. Wee Employee of: Holmusk, S. Kollins Employee of: Holmusk, R. Patel Grant / Research support from: National Institute of Health Research (NIHR301690); Medical Research Council (MR/S003118/1); Academy of Medical Sciences (SGL015/1020); Janssen, Employee of: Holmusk