Comparability of online out-of-pocket tools from Australian private health funds

Privately insured patients face highly variable out-of-pocket (OOP) costs for inpatient admissions. Three of Australia’s largest private health insurance (PHI) funds have therefore developed online OOP cost estimator tools for various procedur

The burden of childhood atopic dermatitis in the primary care setting: a report from the Meta-LARC Consortium

Background: Little is known about the burden of AD encountered in U.S. primary care practices and the frequency and type of skin care practices routinely used in children. Objectives: To estimate the prevalence of AD and allergic comorbidities in children 0-5 years attending primary care practices in the U.S. and to describe routine skin care practices used in this population. Design: A cross-sectional survey study of a convenience sample of children under the age of 5 attending primary care practices for any reason. Setting: Ten primary care practices in five U.S. states.Results: Amongst 652 children attending primary care practices, the estimated prevalence of ever having AD was 24 % (95% CI= 21-28) ranging from 15% among those under the age of one to 38% among those aged 4- 5 years. The prevalence of comorbid asthma was higher among AD participants compared to those with no AD, 12% and 4%, respectively (p less than 0.001). Moisturizers with high water:oil ratios were most commonly used (i.e., lotions) in the non-AD population, whereas moisturizers with low water:oil content (i.e. ointments) most common when AD was present. Conclusions: Our study found a large burden of AD in the primary care practice setting in the U.S. The majority of households reported skin care practices in children without AD that may be detrimental to the skin barrier such as frequent bathing and the routine use of moisturizers with high water: oil ratios. Clinical trials are needed to identify which skin care practices are optimal for reducing the significant risk of AD in the community

A systematic review of complex system interventions designed to increase recovery from depression in primary care

BACKGROUND: Primary care is being encouraged to implement multiprofessional, system level, chronic illness management approaches to depression. We undertook this study to identify and assess the quality of RCTs testing system level depression management interventions in primary care and to determine whether these interventions improve recovery. METHOD: Searches of Medline and Cochrane Controlled Register of Trials. 'System level' interventions included: multi-professional approach, enhanced inter-professional communication, scheduled patient follow-up, structured management plan. RESULTS: 11 trials met all inclusion criteria. 10 were undertaken in the USA. Most focussed on antidepressant compliance. Quality of reporting assessed using CONSORT criteria was poor. Eight trials reported an increase in the proportion of patients recovered in favour of the intervention group, yet did not account for attrition rates ranging from 5 to 50%. CONCLUSION: System level interventions implemented in the USA with patients willing to take anti-depressant medication leads to a modest increase in recovery from depression. The relevance of these interventions to countries with strong primary care systems requires testing in a randomised controlled trial

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

SARS-CoV-2-specific nasal IgA wanes 9 months after hospitalisation with COVID-19 and is not induced by subsequent vaccination

BACKGROUND: Most studies of immunity to SARS-CoV-2 focus on circulating antibody, giving limited insights into mucosal defences that prevent viral replication and onward transmission. We studied nasal and plasma antibody responses one year after hospitalisation for COVID-19, including a period when SARS-CoV-2 vaccination was introduced. METHODS: In this follow up study, plasma and nasosorption samples were prospectively collected from 446 adults hospitalised for COVID-19 between February 2020 and March 2021 via the ISARIC4C and PHOSP-COVID consortia. IgA and IgG responses to NP and S of ancestral SARS-CoV-2, Delta and Omicron (BA.1) variants were measured by electrochemiluminescence and compared with plasma neutralisation data. FINDINGS: Strong and consistent nasal anti-NP and anti-S IgA responses were demonstrated, which remained elevated for nine months (p < 0.0001). Nasal and plasma anti-S IgG remained elevated for at least 12 months (p < 0.0001) with plasma neutralising titres that were raised against all variants compared to controls (p < 0.0001). Of 323 with complete data, 307 were vaccinated between 6 and 12 months; coinciding with rises in nasal and plasma IgA and IgG anti-S titres for all SARS-CoV-2 variants, although the change in nasal IgA was minimal (1.46-fold change after 10 months, p = 0.011) and the median remained below the positive threshold determined by pre-pandemic controls. Samples 12 months after admission showed no association between nasal IgA and plasma IgG anti-S responses (R = 0.05, p = 0.18), indicating that nasal IgA responses are distinct from those in plasma and minimally boosted by vaccination. INTERPRETATION: The decline in nasal IgA responses 9 months after infection and minimal impact of subsequent vaccination may explain the lack of long-lasting nasal defence against reinfection and the limited effects of vaccination on transmission. These findings highlight the need to develop vaccines that enhance nasal immunity. FUNDING: This study has been supported by ISARIC4C and PHOSP-COVID consortia. ISARIC4C is supported by grants from the National Institute for Health and Care Research and the Medical Research Council. Liverpool Experimental Cancer Medicine Centre provided infrastructure support for this research. The PHOSP-COVD study is jointly funded by UK Research and Innovation and National Institute of Health and Care Research. The funders were not involved in the study design, interpretation of data or the writing of this manuscript

Utilising Big Data in the search for low-value health care

Low-value health care provides little benefit relative to its cost. Australian policy makers, health care payers and providers want to reduce its use due to unnecessary costs and harms. These decisions, however, need to be informed by the measurement of low-value care in the context of Australia’s mixed public-private health care system. This thesis investigated low-value procedures using routinely collected data and direct measures. Direct measures use patient- or episode-level clinical information to distinguish low-value from appropriate care. Based on our review of the literature, we introduced a framework to classify direct measures as providing either a service-centric or a patient-centric result. The Choosing Wisely campaign publishes clinician-endorsed ‘do-not-do’ recommendations, and provides a source of potential direct measures. We screened 824 recommendations, and found only a small proportion measurable in a hospital-claims data set. We used these and other recommendations to develop 21 measures applicable to private health insurance claims from 376,354 patients (approximately 7% of the Australian privately insured population). There were 14,662 patients with at least one of the 21 procedures in 2014 (the service-centric result, according to our framework). Of these patients, 20.8 to 32.0% had a low-value procedure according to a narrow (more specific) and broad (more sensitive) set of measures. We extended this investigation to all payer types using the New South Wales (NSW) Admitted Patient Data Collection, and generally found higher proportions and volumes of low-value procedures in the private sector. In 2014-15, 40.3% of all low-value procedures in NSW state were for privately insured patients in private hospitals (relative to 35.6% of all procedures). Despite the limited scope of health care captured by these measures, the work in this thesis has already led to several policy-focussed projects informing governments and payers on low-value care

Health service research definition builder: An R Shiny application for exploring diagnosis codes associated with services reported in routinely collected health data

Many administrative health data-based studies define patient cohorts using procedure and diagnosis codes. The impact these criteria have on a study’s final cohort is not always transparent to co-investigators or other audiences if access to the research data is restricted. We developed a SAS and R Shiny interactive research support tool which generates and displays the diagnosis code summaries associated with a selected medical service or procedure. This allows non-analyst users to interrogate claims data and groupings of reported diagnosis codes. The SAS program uses a tree classifier to find associated diagnosis codes with the service claims compared against a matched, random sample of claims without the service. Claims are grouped based on the overlap of these associated diagnosis codes. The Health Services Research (HSR) Definition Builder Shiny application uses this input to create interactive table and graphics, which updates estimated claim counts of the selected service as users select inclusion and exclusion criteria. This tool can help researchers develop preliminary and shareable definitions for cohorts for administrative health data research. It allows an additional validation step of examining frequency of all diagnosis codes associated with a service, reducing the risk of incorrect included or omitted codes from the final definition. In our results, we explore use of the application on three example services in 2016 US Medicare claims for patients aged over 65: knee arthroscopy, spinal fusion procedures and urinalysis. Readers can access the application at https://kelsey209.shinyapps.io/hsrdefbuilder/ and the code at https://github.com/kelsey209/hsrdefbuilder

Health service research definition builder: An R Shiny application for exploring diagnosis codes associated with services reported in routinely collected health data.

Many administrative health data-based studies define patient cohorts using procedure and diagnosis codes. The impact these criteria have on a study's final cohort is not always transparent to co-investigators or other audiences if access to the research data is restricted. We developed a SAS and R Shiny interactive research support tool which generates and displays the diagnosis code summaries associated with a selected medical service or procedure. This allows non-analyst users to interrogate claims data and groupings of reported diagnosis codes. The SAS program uses a tree classifier to find associated diagnosis codes with the service claims compared against a matched, random sample of claims without the service. Claims are grouped based on the overlap of these associated diagnosis codes. The Health Services Research (HSR) Definition Builder Shiny application uses this input to create interactive table and graphics, which updates estimated claim counts of the selected service as users select inclusion and exclusion criteria. This tool can help researchers develop preliminary and shareable definitions for cohorts for administrative health data research. It allows an additional validation step of examining frequency of all diagnosis codes associated with a service, reducing the risk of incorrect included or omitted codes from the final definition. In our results, we explore use of the application on three example services in 2016 US Medicare claims for patients aged over 65: knee arthroscopy, spinal fusion procedures and urinalysis. Readers can access the application at https://kelsey209.shinyapps.io/hsrdefbuilder/ and the code at https://github.com/kelsey209/hsrdefbuilder