UNASSIGNED: Studies for developing diagnostics and treatments for infectious diseases usually require observing the onset of infection during the study period. However, when the infection base rate incidence is low, the cohort size required to measure an effect becomes large, and recruitment becomes costly and prolonged. We developed a model for reducing recruiting time and resources in a COVID-19 detection study by targeting recruitment to high-risk individuals.
UNASSIGNED: We conducted an observational longitudinal cohort study at individual sites throughout the U.S., enrolling adults who were members of an online health and research platform. Through direct and longitudinal connection with research participants, we applied machine learning techniques to compute individual risk scores from individually permissioned data about socioeconomic and behavioral data, in combination with predicted local prevalence data. The modeled risk scores were then used to target candidates for enrollment in a hypothetical COVID-19 detection study. The main outcome measure was the incidence rate of COVID-19 according to the risk model compared with incidence rates in actual vaccine trials.
UNASSIGNED: When we used risk scores from 66,040 participants to recruit a balanced cohort of participants for a COVID-19 detection study, we obtained a 4- to 7-fold greater COVID-19 infection incidence rate compared with similar real-world study cohorts.
UNASSIGNED: This risk model offers the possibility of reducing costs, increasing the power of analyses, and shortening study periods by targeting for recruitment participants at higher risk.

UNASSIGNED: Clostridioides (Clostridium) difficile ranks first among the pathogens of hospital-acquired infections with hospital-based preventive strategies being only partially successful in containing its spread.
UNASSIGNED: We performed a spatial statistical analysis to examine the association between population characteristics and parameters of community healthcare practice and delivery with hospital-onset Clostridioides (Clostridium) difficile infection (HO-CDI), using data from the Medicare Hospital Compare, Medicare Provider Utilization Part D, and other databases. Among the areas with the highest HO-CDI rates (\"hot spots\"), we conducted a geographically weighted regression (GWR) to quantify the effect of the decrease in the modifiable risk factors on the HO-CDI rate.
UNASSIGNED: Percentage of population > 85 years old, community claims of antimicrobial agents and acid suppressants, and density of hospitals and nursing homes within the hospital service areas (HSAs) had a statistically significant association with the HO-CDI incidence (p < 0.001). The model including the community claims of antimicrobial agents and number of hospital centers per HSA km2 was associated with 10% (R2 = 0.10, p < 0.001) of the observed variation in HO-CDI rate. The hot spots were organized into 5 Combined Statistical areas that crossed state borders. The association of the antimicrobial claims and HO-CDI rate was as high as 71% in the Boston-Worcester-Providence area (R2 = 0.71, SD 0.19), with a 10% decrease in the rate of antimicrobial claims having the potential to lead to up to 23.1% decrease in the HO-CDI incidence in this area.
UNASSIGNED: These results outline the association of HO-CDI with community practice and characteristics of the healthcare delivery system and support the need to further study the effect of community and nursing home-based antimicrobial and acid suppressant stewardship programs in the rate of HO-CDI in geographic areas that may cross state lines.