Diagnostic accuracy of first and early second trimester multiple biomarkers for prediction of gestational diabetes mellitus: a multivariate longitudinal approach

Background Gestational Diabetes Mellitus (GDM) is an underlying cause of maternal and newborn morbidity and mortality all around the world. Timely diagnosis of GDM plays an important role in reducing its adverse consequences and burden. This study aimed to determine diagnostic accuracy of multiple indicators in complete blood count (CBC) test for early prediction of GDM. Methods In this prospective cohort study, the data from 600 pregnant women was analyzed. In the study sample, the two-step approach was utilized for the diagnosis of GDM at 24–28 weeks of gestation. We also used the repeated measures of hemoglobin (Hb), hematocrit (Hct), fasting blood sugar (FBS) and red blood cell count (RBC) in the first and early second trimesters of pregnancy as the longitudinal multiple indicators for early diagnosis of GDM. The classification of pregnant women to GDM and non-GDM groups was performed using a statistical technique based on the random-effects modeling framework. Results Among the sample, 49 women (8.2%) were diagnosed with GDM. In the first and early second trimester of pregnancy, the mean HcT, Hb and FBS of women with GDM was significantly higher than non-GDMs (P < 0.001). The concurrent use of multiple longitudinal data from HcT, Hb, RBC and FBS in the first and early second trimester of pregnancy resulted in a sensitivity, specificity and area under the curve (AUC) of 87%, 70% and 83%, respectively, for early prediction of GDM. Conclusions In general, our findings showed that the concurrent use of repeated measures data on Hct, Hb, FBS and RBC in the first and early second trimester of pregnancy might be utilized as an acceptable tool to predict GDM earlier in pregnancy.

Life-course socioeconomic circumstances in acute, chronic and disabling pain among young employees: a double suffering

Background: Pain is known to be socioeconomically patterned and associated with disability. However, knowledge is scarce concerning life-course socioeconomic circumstances and pain among young adults. Our aim was to examine the associations of childhood and current socioeconomic circumstances with acute pain and chronic pain with low and high disability levels among young Finnish municipal employees. Methods: We analysed questionnaire data retrieved from the Young Helsinki Health Study ( n=4683) covering 18–39-year-old employees of the City of Helsinki, Finland. We included multiple indicators of childhood and current socioeconomic circumstances and examined their associations with acute pain and with chronic pain with low and high disability levels. The level of chronic pain-related disability was assessed by the chronic pain grade questionnaire. Multinomial logistic regression analyses were conducted with stepwise adjustments for sociodemographic, socioeconomic and health-related covariates. Results: Childhood and current socioeconomic disadvantage were associated with acute and chronic pain, particularly with chronic pain with high disability level. The strongest associations after adjustments for covariates remained between chronic pain with high disability level and low educational level (odds ratio (OR) 3.38, 95% confidence interval (CI) 2.18–5.24), manual occupation (OR 3.75, 95% CI 1.92–7.34) and experiencing frequent economic difficulties (OR 3.07, 95% CI 2.00–4.70). Conclusions: Pain is a common complaint that contributes to disability among young employees, particularly the most socioeconomically vulnerable. There is a socioeconomic gradient in both pain chronicity and the level of chronic pain-related disability. Life-course socioeconomic factors should be considered in pain-preventing strategies and in clinical practice.

New Estimates of Over 500 Years of Historic GDP and Population Data

Gross Domestic Product (GDP), GDP per capita, and population are central to the study of politics and economics broadly, and conflict processes in particular. Despite the prominence of these variables in empirical research, existing data lack historical coverage and are assumed to be measured without error. We develop a latent variable modeling framework that expands data coverage (1500 A.D--2018 A.D) and, by making use of multiple indicators for each variable, provides a principled framework to estimate uncertainty for values for all country-year variables relative to one another. Expanded temporal coverage of estimates provides new insights about the relationship between development and democracy, conflict, repression, and health. We also demonstrate how to incorporate uncertainty in observational models. Results show that the relationship between repression and development is weaker than models that do not incorporate uncertainty suggest. Future extensions of the latent variable model can address other forms of systematic measurement error with new data, new measurement theory, or both.

The Shadow Economy in Pakistan: An Analysis with MIMIC Model

This paper explicitly presents the estimation of the size and development of the shadow economy. The study examines the impact of multiple exogenous causes (observed variables) on the shadow economy (latent variable) and the size of the shadow economy influencing the performance of multiple macroeconomic indicators. In order to accomplish this econometric analysis, a MIMIC Model (Multiple Indicators Multiple Causes Model) is applied over the period 2011 to 2021. The results indicate that the tax burden, business freedom, unemployment rate, and Gross Domestic Product are the key driving forces of the shadow economy in Pakistan. The findings are potentially beneficial for the policymakers in identifying and dealing with the shadow economic activities as well as developing the strategies relevant to the economic policy.

Process-Outcome Relationships and Solution-Based Goal-Setting Interventions With Grandparent Caregivers

The present study examines the impact of change processes on outcomes in a solution-based thinking and goal-setting intervention for grandparents raising their grandchildren. We found that across the 6 program sessions there was stability and/or increases in the salience of hypothesized change processes, i.e., hopefulness about the future, solution-based thinking, positive thoughts about one's grandchild, multiple indicators of decisional personal goal-setting regarding one's own well-being and grandchild relationship quality. Indicators of change processes were for the most part, related to both post-program outcomes as well as to pre-post program outcome difference scores. Regression analyses suggested that change processes in many cases partially mediated pre-post primary program outcome scores. These data suggest that how grandmother caregivers think about themselves and their grandchildren and their approach to setting personal goals are key change processes explaining the impact of a solution-based, goal-setting intervention on them.

Multiple Indicators of Wastewater Contamination to Shellfish Farms Near a Tidal River

Wastewater contamination threatens the shellfish aquaculture industry by posing risks to public health. Multiple indicators of wastewater contamination, including fecal coliforms (fc), male-specific coliphage (MSC), dissolved nutrients, stable isotope ratios, and artificial sweeteners were analyzed to determine possible sources of wastewater to local shellfish farms. Samples were collected at a wastewater treatment plant outfall (WTPO), nonpoint residential, and agricultural areas of a tidal river, and tidal creek inflows adjacent to farms. To capture seasonal variation, we sampled under warm and cold, and wet and dry conditions. Fc ranged < 5–5250 CFU 100 mL−1, NH4+ concentrations ranged up to 9.58 μM, and δ15N ranged 1.4–7.8‰ across all sites and time periods. Fc and NH4+ were higher, and δ15N was lower in the cold wet period and near residential and agricultural areas. Acesulfame and sucralose concentrations ranged 0.004–0.05 μg L−1 and up to > 0.8 μg L−1, respectively, and did not correlate with other indicators but tended to be higher in residential areas and at the WTPO, supporting their value in differentiating human sewage from other sources. Shoreline disturbance during septic system upgrades may have inadvertently contributed bacterial indicators to shellfish farms. Overall, indicator source dominance depended on environmental conditions, with WTPO and residential sources conveying human-specific indicators to farms year-round, while agricultural and industrial sites contributed additional fc during cold wet periods. The use of multiple indicators will aid managers to detect and define wastewater sources, identify targets for monitoring or remediation, and manage shellfish areas in estuaries with a mosaic of land-derived wastewater sources.

Measurement Disparities in Frailty Among Kidney Transplant Patients: Impact of Differential Item Functioning

Frailty is commonly measured for clinical risk stratification during transplant evaluation and is more prevalent among older, non-White kidney transplant (KT) patients. However, group differences may be partially attributable to misclassification resulting from measurement bias (differential item functioning/DIF). We examined the extent that DIF affects estimates of age, sex, and race differences in frailty (physical frailty phenotype/PFP) prevalence among 4,300 candidates and 1,396 recipients. We used Multiple Indicators Multiple Causes with dichotomous indicators to assess uniform DIF in PFP criteria attributable to age (≥65vs.18-64 years), sex, and race (Black vs.White). Among candidates (mean age=55 years), 41% were female, 46% were Black, and 19% were frail. After controlling for mean frailty level, females were more likely to endorse exhaustion (OR=1.20,p=0.003), but less likely to endorse low activity (OR=0.83,p=0.01). Younger candidates were more likely to endorse weight loss (OR=1.30,p=0.005), exhaustion (OR=1.60,p&lt;0.001), and low activity (OR=1.80,p&lt;0.001). Black candidates were more likely to endorse exhaustion (OR=1.25,p&lt;0.001), but less likely to endorse weakness (OR=0.79,p&lt;0.001). Among recipients (mean age=54 years), 40% were female, 39% were Black, and 15% were frail. Younger recipients were more likely to endorse weight loss (OR=1.55,p=0.005) and low activity (OR=1.61,p=0.02); however, no DIF was detected by sex or race. Results highlight the impact of DIF for specific PFP measures by age, sex, and race among candidates, but only by age for recipients. Further research is needed to ascertain whether candidate- and/or recipient-specific thresholds to correct for DIF could improve risk prediction and equitable access to KT for older, female, and Black candidates.

Analysis of the Evolution of Time and Space Differences and Coordinated Development Degree of Tourism Economy Based on Regional Internet of Things Technology

From the time dimension, this paper analyzes the characteristics of the scale, industrial structure, employment flexibility, and comprehensive employment effects of regional tourism employment based on the three-level criteria of the scope of tourism employment based on the regional Internet of Things. From the spatial dimension, we take the regional city as the basic research unit, adopt multiple indicators, and conduct a comprehensive evaluation of the regional development of regional tourism employment through horizontal and vertical comparisons. This paper uses multiple linear regression analysis to establish the relationship between the development level of the county tourism economy and the influencing factors; in order of magnitude of influence, they are tourism resource endowment, location traffic conditions, and economic development. Using a combination of a single indicator and multiple indicators, the county tourism economy is evaluated and analyzed for differences in time and space. We select the total tourism revenue as an indicator and use methods such as range, standard deviation, coefficient of variation, and coefficient to analyze the time difference of the tourism economy in each county. We adopt the Granger causality test and other methods to analyze the factors affecting tourism employment in the area and the growth mode. Through the establishment of a structural model of the tourism employment growth dynamics system, causality test and other methods are adopted to analyze the regional tourism employment influencing factors and growth methods, and the results show that the regional tourism employment growth mode is an investment-driven tourism employment growth mode.