Increased risk of second primary malignancy (SPM) in papillary thyroid cancer (PTC) has been reported. Here, we present the most updated incidence rates of second primary malignancy from original diagnosis of PTC by using the data from the Surveillance, Epidemiology, and End Results. In this cohort, 3,200 patients developed SPM, a substantially higher number than in the reference population of 2,749 with observed to expected ratio (O/E) of 1.16 (95% CI; 1.12–1.21). Bone and joint cancer had the highest O/E ratio of 4.26 (95% confidence interval [CI] 2.33–7.15) followed by salivary gland (O/E 4.15; 95% CI 2.76–6.0) and acute lymphocytic leukemia (O/E 3.98; 95% CI 2.12–6.8). Mean age at the diagnosis of SPM was 64.4 years old. Interestingly, incidence of colorectal cancer was lower in thyroid cancer survivors compared to general population (large intestine O/E 0.3; 95% CI 0.06–0.88, rectum O/E 0.6; 95% CI 0.41–0.85); however, this was not observed in patients who underwent radiation therapy. The incidence of SPM at all sites was higher during 2000–2012 compared to 1992–1999 (O/E 1.24 versus 1.10). Surprisingly, patients with micropapillary cancer had higher incidence of SPM than counterparts with a larger tumor in radiation group (O/E of 1.40 versus 1.15). O/E of all cancers were higher in males compared to females with O/E of 1.41 versus 1.17 during the period of 2000–2012. Diagnosis of PTC before age 50, especially at age 30–34, was associated with higher incidence of overall SPM (age 30–34; O/E 1.43; 95% CI; 1.19–1.71). Efficient monitoring strategies that include age at the time of thyroid cancer diagnosis, exposure to radiation, gender, and genetic susceptibility may successfully detect SPM earlier in the disease course. This is especially important given the excellent prognosis of the initial thyroid cancer itself.

BackgroundSafety climate is an important marker of patient safety attitudes within health care units, but the significance of intra-unit variation of safety climate perceptions (safety climate strength) is poorly understood. This study sought to examine the standard safety climate measure (percent positive response (PPR)) and safety climate strength in relation to length of stay (LOS) of very low birth weight (VLBW) infants within California neonatal intensive care units (NICUs).MethodsObservational study of safety climate from 2073 health care providers in 44 NICUs. Consistent perceptions among a NICU’s respondents, i.e., safety climate strength, was determined via intra-unit standard deviation of safety climate scores. The relation between safety climate PPR, safety climate strength, and LOS among VLBW (< 1500 g) infants was evaluated using log-linear regression. Secondary outcomes were infections, chronic lung disease, and mortality.ResultsNICUs had safety climate PPRs of 66 ± 12%, intra-unit standard deviations 11 (strongest) to 23 (weakest), and median LOS 60 days. NICUs with stronger climates had LOS 4 days shorter than those with weaker climates. In interaction modeling, NICUs with weak climates and low PPR had the longest LOS, NICUs with strong climates and low PPR had the shortest LOS, and NICUs with high PPR (both strong and weak) had intermediate LOS. Stronger climates were associated with lower odds of infections, but not with other secondary outcomes.ConclusionsSafety climate strength is independently associated with LOS and moderates the association between PPR and LOS among VLBW infants. Strength and PPR together provided better prediction than PPR alone, capturing variance in outcomes missed by PPR. Evaluations of NICU safety climate consider both positivity (PPR) and consistency of responses (strength) across individuals.

Objective To develop a nurse staffing prediction model and evaluate deviation from predicted nurse staffing as a contributor to patient outcomes. Data sources Secondary data collection conducted 2017‐2018, using the California Office of Statewide Health Planning and Development and the California Perinatal Quality Care Collaborative databases. We included 276 054 infants born 2008‐2016 and cared for in 99 California neonatal intensive care units (NICUs). Study design Repeated‐measures observational study. We developed a nurse staffing prediction model using machine learning and hierarchical linear regression and then quantified deviation from predicted nurse staffing in relation to health care‐associated infections, length of stay, and mortality using hierarchical logistic and linear regression. Data collection methods We linked NICU‐level nurse staffing and organizational data to patient‐level risk factors and outcomes using unique identifiers for NICUs and patients. Principal findings An 11‐factor prediction model explained 35 percent of the nurse staffing variation among NICUs. Higher‐than‐predicted nurse staffing was associated with decreased risk‐adjusted odds of health care‐associated infection (OR: 0.79, 95% CI: 0.63‐0.98), but not with length of stay or mortality. Conclusions Organizational and patient factors explain much of the variation in nurse staffing. Higher‐than‐predicted nurse staffing was associated with fewer infections. Prospective studies are needed to determine causality and to quantify the impact of staffing reforms on health outcomes.

APIs and Hispanics residing in more ethnic neighborhoods and individuals residing in lower income neighborhoods require more extensive preventive efforts tailored toward their unique risk factor profiles. The current race/ethnicity-specific geographic analysis can be extended to other states to inform priorities for HCC targeted prevention at the subcounty level, eventually reducing HCC burden in the country.