The IDeaS initiative: pilot study to assess the impact of rare diseases on patients and healthcare systems.

Background: Rare diseases (RD) are a diverse collection of more than 7-10,000 different disorders, most of which affect a small number of people per disease. Because of their rarity and fragmentation of patients across thousands of different disorders, the medical needs of RD patients are not well recognized or quantified in healthcare systems (HCS).

Methodology: We performed a pilot IDeaS study, where we attempted to quantify the number of RD patients and the direct medical costs of 14 representative RD within 4 different HCS databases and performed a preliminary analysis of the diagnostic journey for selected RD patients.

Current and Future Projections of Amyotrophic Lateral Sclerosis in the United States Using Administrative Claims Data.

Background: Various methodologies have been reported to assess the real-world epidemiology of amyotrophic lateral sclerosis (ALS) in the United States. The aim of this study was to estimate the prevalence, incidence, and geographical distribution of ALS using administrative claims data and to model future trends in ALS epidemiology.

Methods: We performed a retrospective analysis of deidentified administrative claims data for >100 million patients, using 2 separate databases (IBM MarketScan Research Databases and Symphony Health Integrated DataVerse [IDV]), to identify patients with ALS.

Impact of socio-demographic variables on antenatal services in eastern Uttar Pradesh, India.

We investigated the impact of socio-demographic variables on antenatal care (ANC) utilization and the low birth weight of a child. Data were collected from 300 pregnant females. Only 22.

Use of machine learning techniques in the development and refinement of a predictive model for early diagnosis of ankylosing spondylitis.

Objective: To develop a predictive mathematical model for the early identification of ankylosing spondylitis (AS) based on the medical and pharmacy claims history of patients with and without AS.

Methods: This retrospective study used claims data from Truven databases from January 2006 to September 2015 (Segment 1) and October 2015 to February 2018 (Segment 2). Machine learning identified features differentiating patients with AS from matched controls; selected features were used as inputs in developing Model A/B to identify patients likely to have AS.

Impact of phosphorous on biochemical changes in Hordeum vulgare L. in mixed cropping with chickpea.

Multiple cropping (i.e. intercropping or mixed cropping) plays an important role in agriculture because of the effective utilization of resources, significantly enhancing crop productivity compared with that of monocultured crops.