Development and validation across trimester of the Prenatal Eating Behaviors Screening tool.

Rapid screening tools are useful for identifying at-risk patients and referring them for further assessment and treatment, but none exist that consider the unique medical needs of pregnant women with eating disorders (EDs). There is a need for a rapid, sensitive, and specific screening tool that can be used to identify a potential ED in pregnancy. We started with a set of 25 questions, developed from our qualitative work along with other ED screening tools, and tested on a development (n = 190) and validation sample (n = 167). Statistical analysis included factor analysis and logistic regressions with ROC curves. Development and validation samples were combined for trimester analysis (n = 357). Refining the tool to 12 items demonstrated strong internal reliability (development alpha = 0.95, validation alpha = 0.91). With correlated errors, questions demonstrated acceptable CFA fit (development: GFI: 0.91, RMSEA: 0.10, NNFI: 0.95; validation: GFI: 0.85, RMSEA: 0.14, NNFI: 0.86). Similar fits were seen by trimester: first trimester n = 127, GFI: 0.89, RMSEA: 0.12, NNFI: 0.94; second trimester n = 150, GFI: 0.83, RMSEA: 0.14, NNFI: 0.88; third trimester n = 80, GFI: 0.99, NNFI: 0.99. Validation against current ED diagnosis demonstrated acceptable sensitivity and specificity using a cutoff of 39 (development sensitivity = 80.7%, specificity = 79.7%, OR = 16.42, 95% CI: 7.51, 35.88; validation sensitivity = 69.2%, specificity = 86.5%, OR: 17.43, 95% CI: 6.46, 47.01). Findings suggest the PEBS tool can reliably and sensitively detect EDs across pregnancy trimesters with 12 questions. A further implication of this work is to reduce health and mental health treatment disparities through this standard and rapid screening measure to ensure early identification and treatment.

All-cause and cause-specific mortality following non-traumatic spinal cord injury: evidence from a population-based cohort study in Switzerland.

STUDY DESIGN: Observational cohort study. OBJECTIVE: To benchmark all-cause and cause-specific mortality following NTSCI to the general population (GP). SETTING: Specialized rehabilitation centers in Switzerland. METHODS: Longitudinal data from the Swiss Spinal Cord Injury (SwiSCI) Medical Record study were probabilistically linked with cause of death (CoD) information from the Swiss National Cohort. Standardized mortality ratios (SMRs) were estimated for all-cause and cause-specific mortality. Competing risk frameworks were used to estimate the probability of death due to specific CoD. RESULTS: One thousand five hundred and one individuals were admitted for first rehabilitation with NTSCI between 1990-2011; CoD information was available for 454 individuals of the 525 individuals that died. Overall, the mortality rate for persons with NTSCI was 1.6 times greater than that of the GP. Deaths due to cardiovascular disease (39.8%), neoplasms (22%), and infection (9.9%) were most often reported. Individuals with an SCI due to a vascular etiology indicated the greatest burden of mortality from infection compared with the GP (SMR 5.4; 95% CI, 3.1 to 9.2). CONCLUSIONS: Cause-specific SMRs varied according to etiology. This supports the need for targeted clinical care and follow-up. Cardiovascular disease, neoplasms, and infection, emerged as main causes of death following NTSCI and should thus be targets for future research and differential clinical management approaches.

Survival after non-traumatic spinal cord injury: evidence from a population-based rehabilitation cohort in Switzerland.

STUDY DESIGN: Observational cohort study. OBJECTIVE: To investigate survival and life expectancy after NTSCI in Switzerland according to etiology. SETTING: Specialized rehabilitation centers in Switzerland. METHODS: Longitudinal data from the Swiss Spinal Cord Injury (SwiSCI) medical records study were used. Adjusted hazard ratios (HRs) and life expectancies were estimated using flexible parametric survival modeling. RESULTS: One thousand four hundred and fifty individuals were admitted to first rehabilitation for NTSCI between 1990 and 2011, contributing 6137 cumulative person-years at risk and 528 deaths. With reference to persons with a degenerative disc disorder, the HR for mortality in individuals with NTSCIs from infections was 1.42 (95% CI 0.99-2.04), while risk in those with NTSCIs from vascular disorders was 1.28 (95% CI 0.97-1.68). Mortality risk was most pronounced in individuals with NTSCIs from malignant neoplasms (HR 6.32, 95% CI 4.79-8.34). Exemplified for males with an attained age of 60 years, a malignant etiology was associated with 1.7 life years remaining (LYR), as compared to 10.1 LYR for non-malignant etiologies. Males with an attained age of 60 years and a degenerative disc etiology were estimated to have 12.9 LYR. CONCLUSIONS: This study contributes an evidence base for risk factors of mortality after NTSCI, reducing a considerable knowledge gap in survival after NTSCI. Survival and life expectancy estimates were highly differential between etiological groups, indicating a need for a heterogeneous clinical approach and dynamic health-care provisions for this growing population.

Prevalence and associated factors of pain in the Swiss spinal cord injury population.

STUDY DESIGN: Population-based, cross-sectional. OBJECTIVES: To determine pain prevalence and identify factors associated with chronic pain in individuals with spinal cord injury (SCI) living in Switzerland. SETTING: Swiss SCI Cohort Study (SwiSCI). METHODS: Pain characteristics were assessed using an adapted version of the International SCI Pain Basic Data Set, adding one item of the SCI Secondary Conditions Scale to address chronic pain. Pain prevalence was calculated using stratification over demographic, SCI-related and socioeconomic characteristics; odds ratios (adjusted for non-response) for determinants of severity of chronic pain were calculated using stereotype logistic regressions. RESULTS: Pain (in the past week) was reported by 68.9% and chronic pain by 73.5% (significant 36.9%) of all participants (N=1549; 28% female). Most frequently reported pain type was musculoskeletal (71.1%). Back/spine was the most frequently reported pain location (54.6%). Contrasting the 'significant' to the 'none/mild' category of chronic pain, adjusted odds ratios were 1.54 (95% CI: 1.18-2.01; P<0.01) for women (vs men); 6.64 (95% CI: 3.37-11.67; P<0.001) for the oldest age group 61+ (vs youngest (16-30)); 3.41 (95% CI: 2.07-5.62; P<0.001) in individuals reporting severe financial hardship (vs no financial hardship). Individuals reporting specific SCI-related health conditions were 1.41-2.92 (P<0.05) times more likely to report chronic pain as 'significant' rather than 'none/mild' compared with those without the respective condition. CONCLUSIONS: Pain is highly prevalent in individuals with SCI living in Switzerland. Considered at risk for chronic pain are women, older individuals and individuals with financial hardship and specific secondary health conditions. Longitudinal studies are necessary to identify predictors for the development of pain and its chronification.

Stochastic simulation of successive waves of COVID-19 in the province of Barcelona.

Analytic compartmental models are currently used in mathematical epidemiology to forecast the COVID-19 pandemic evolution and explore the impact of mitigation strategies. In general, such models treat the population as a single entity, losing the social, cultural and economical specificities. We present a network model that uses socio-demographic datasets with the highest available granularity to predict the spread of COVID-19 in the province of Barcelona. The model is flexible enough to incorporate the effect of containment policies, such as lockdowns or the use of protective masks, and can be easily adapted to future epidemics. We follow a stochastic approach that combines a compartmental model with detailed individual microdata from the population census, including social determinants and age-dependent strata, and time-dependent mobility information. We show that our model reproduces the dynamical features of the disease across two waves and demonstrates its capability to become a powerful tool for simulating epidemic events.