Association of Asthma with Rheumatoid Arthritis: A Population-Based Case-Control Study.

BACKGROUND: TH1 and TH2 cells have counterregulatory relationships. However, the relationship between asthma, a TH2-predominant condition, and risk of systemic inflammatory diseases such as rheumatoid arthritis (RA), a TH1 condition, is poorly understood. OBJECTIVE: We aimed to determine whether asthma was associated with increased risks of incident RA among adults. METHODS: We conducted a retrospective population-based case-control study that examined existing incident RA cases and controls matched by age, sex, and registration year from the general population in Olmsted County, Minnesota, between January 2002 and December 2007. We performed comprehensive medical record reviews to ascertain asthma status using predetermined asthma criteria. The frequency of a history of asthma before the index date was compared between cases and controls. Logistic regression models were used to adjust for confounding factors. RESULTS: We enrolled 221 RA cases and 218 controls. Of the 221 RA cases, 156 (70.6%) were females, 207 (93.7%) were white, the median age at the index date was 52.5 years, and 53 (24.0%) had a history of asthma. Controls had similar characteristics except that 35 of 218 controls (16.1%) had a history of asthma. After adjustment for sex, age, smoking, body mass index, socioeconomic status, and comorbidity, asthma was significantly associated with increased risks of RA (adjusted odds ratio, 1.74; 95% CI, 1.05-2.90; P = .03). CONCLUSIONS: Despite the counterregulatory relationship between TH1 and TH2 cells, patients with asthma had a significantly higher risk of developing RA than healthy individuals

The predictive power of geographic health care utilization for unintentional fatal fall rates

Abstract Background Falls are the leading cause of fatal and nonfatal injuries among adults over 65 years old. The increase in fall mortality rates is likely multifactorial. With a lack of key drivers identified to explain rising rates of death from falls, accurate predictive modelling can be challenging, hindering evidence-based health resource and policy efforts. The objective of this work is to examine the predictive power of geographic utilization and longitudinal trends in mortality from unintentional falls amongst different demographic and geographic strata. Methods This is a nationwide, retrospective cohort study using the United States Centers for Disease Control (CDC) Web-based Injury Statistics Query and Reporting System (WISQARS) database. The exposure was death from an unintentional fall as determined by the CDC. Outcomes included aggregate and trend crude and age-adjusted death rates. Health care utilization, reimbursement, and cost metrics were also compared. Results Over 2001 to 2018, 465,486 total deaths due to unintentional falls were recorded with crude and age-adjusted rates of 8.42 and 7.76 per 100,000 population respectively. Comparing age-adjusted rates, males had a significantly higher age-adjusted death rate (9.89 vs. 6.17; p <  0.00001), but both male and female annual age-adjusted mortality rates are expected to rise (Male: + 0.25 rate/year, R2= 0.98; Female: + 0.22 rate/year, R2= 0.99). There were significant increases in death rates commensurate with increasing age, with the adults aged 85 years or older having the highest aggregate (201.1 per 100,000) and trending death rates (+ 8.75 deaths per 100,000/year, R2= 0.99). Machine learning algorithms using health care utilization data were accurate in predicting geographic age-adjusted death rates. Conclusions Machine learning models have high accuracy in predicting geographic age-adjusted mortality rates from health care utilization data. In the United States from 2001 through 2018, adults aged 85+ years carried the highest death rate from unintentional falls and this rate is forecasted to accelerate

LAION-5B: An open large-scale dataset for training next generation image-text models

Groundbreaking language-vision architectures like CLIP and DALL-E proved the utility of training on large amounts of noisy image-text data, without relying on expensive accurate labels used in standard vision unimodal supervised learning. The resulting models showed capabilities of strong text-guided image generation and transfer to downstream tasks, while performing remarkably at zero-shot classification with noteworthy out-of-distribution robustness. Since then, large-scale language-vision models like ALIGN, BASIC, GLIDE, Flamingo and Imagen made further improvements. Studying the training and capabilities of such models requires datasets containing billions of image-text pairs. Until now, no datasets of this size have been made openly available for the broader research community. To address this problem and democratize research on large-scale multi-modal models, we present LAION-5B - a dataset consisting of 5.85 billion CLIP-filteredimage-text pairs, of which 2.32B contain English language. We show successful replication and fine-tuning of foundational models like CLIP, GLIDE and Stable Diffusion using the dataset, and discuss further experiments enabled with an openly available dataset of this scale. Additionally we provide several nearest neighbor indices, an improved web-interface for dataset exploration and subset generation, and detection scores for watermark, NSFW, and toxic content detection