Prediction of Environmental Properties in Water–Soil–Air Systems for Phthalates

In this paper, environmentally key properties including aqueous solubilities (S _W), vapor pressures (V _P), sorption coefficients (K _OC), octanol–water (K _OW), octanol–air (K _OA) and air–water (K _AW) partition coefficients of 53 phthalates are studied by the quantitative structure-property relationship models by means of previously proposed Lu index. Reliable models are obtained to estimate Log S _W, solubility in air (Log S _A), Log K _OC and Log K _OW for phthalates with the correlation coefficients of estimations (R) being 0.9869, 0.9461, 0.8880 and 0.9836, and the standard errors being 0.44, 0.27, 0.52 and 0.40, respectively. The predictive ability of the constructed models is demonstrated by the correlation coefficients (R _CV) in the leave-one-out cross validation procedures being 0.9709, 0.9218, 0.8089 and 0.9784, and the corresponding standard errors (s _CV) being 0.47, 0.34, 0.67 and 0.47, respectively. The properties of K _AO and K _AW for the phthalates are calculated by the predicted S _W, S _A, and K _OW values.     

A Preparedness Model for Mother–Baby Linked Longitudinal Surveillance for Emerging Threats

Introduction

Public health responses often lack the infrastructure to capture the impact of public health emergencies on pregnant women and infants, with limited mechanisms for linking pregnant women with their infants nationally to monitor long-term effects. In 2019, the Centers for Disease Control and Prevention (CDC), in close collaboration with state, local, and territorial health departments, began a 5-year initiative to establish population-based mother–baby linked longitudinal surveillance, the Surveillance for Emerging Threats to Mothers and Babies Network (SET-NET).

Objectives

The objective of this report is to describe an expanded surveillance approach that leverages and modernizes existing surveillance systems to address the impact of emerging health threats during pregnancy on pregnant women and their infants.

Methods

Mother–baby pairs are identified through prospective identification during pregnancy and/or identification of an infant with retrospective linking to maternal information. All data are obtained from existing data sources (e.g., electronic medical records, vital statistics, laboratory reports, and health department investigations and case reporting).

Results

Variables were selected for inclusion to address key surveillance questions proposed by CDC and health department subject matter experts. General variables include maternal demographics and health history, pregnancy and infant outcomes, maternal and infant laboratory results, and child health outcomes up to the second birthday. Exposure-specific modular variables are included for hepatitis C, syphilis, and Coronavirus Disease 2019 (COVID-19). The system is structured into four relational datasets (maternal, pregnancy outcomes and birth, infant/child follow-up, and laboratory testing).

Discussion

SET-NET provides a population-based mother–baby linked longitudinal surveillance approach and has already demonstrated rapid adaptation to COVID-19. This innovative approach leverages existing data sources and rapidly collects data and informs clinical guidance and practice. These data can help to reduce exposure risk and adverse outcomes among pregnant women and their infants, direct public health action, and strengthen public health systems.

     

Bayesian bivariate subgroup analysis for risk–benefit evaluation

Subgroup analysis is a frequently used tool for evaluating heterogeneity of treatment effect and heterogeneity in treatment harm across observed baseline patient characteristics. While treatment efficacy and adverse event measures are often reported separately for each subgroup, analyzing their within-subgroup joint distribution is critical for better informed patient decision-making. In this paper, we describe Bayesian models for performing a subgroup analysis to compare the joint occurrence of a primary endpoint and an adverse event between two treatment arms. Our approach emphasizes estimation of heterogeneity in this joint distribution across subgroups, and our approach directly accommodates subgroups with small numbers of observed primary and adverse event combinations. In addition, we describe several ways in which our models may be used to generate interpretable summary measures of benefit-risk tradeoffs for each subgroup. The methods described here are illustrated throughout using a large cardiovascular trial (\(N = 9361\)) investigating the efficacy of an intervention for reducing systolic blood pressure to a lower-than-usual target.     

Mannosamine–biotin as a novel masking agent for coating IgG for immune response silencing and augmentation of antibody–antigen interaction

A variety of protein-coating procedures are used to modify proteins’ properties. The principle coating agent used is PEGylation, in which proteins are coated by conjunction to polyethylene glycol (PEG). In the present study, we describe a novel approach that makes use of small molecules with multifunctional groups as the protein-coating agent. The new coating molecule was produced by reacting two endogenous molecules, mannosamine and biotin, to form mannose–biotin adducts (MBA). hIgG was coated with MBA at various MBA/protein ratios. The immunogenicity of MBA-coated hIgG was tested in chickens. A dose-responsive effect of MBA/hIgG ratio on immune response suppression was detected, with an optimal masking effect at a 12:1 ratio. The immune response to MBA-coated hIgG was about eightfold lower than that to PEG-coated hIgG. MBA also increased antibody–antigen-binding affinity, and decreased recognition of the Fc domain of MBA-coated hIgG by Fc receptor and secondary antibodies. While the PEG molecule consists of inert repeating units of ethylene oxide with no additional functional group to allow for potentially desirable modifications, the MBA has several functional groups, including vicinal hydroxyls, which can easily be converted to active residues such as aldehydes or carboxyls. This may be of importance for developing passive immunizations or for achieving tolerance of the immune response to an immunogenic molecule or virus. In summary, we developed a new protein-coating molecule with the ability to mask foreign antigens and in the case of antibodies, to enhance activity.     

Reliability of hypothalamic–pituitary–adrenal axis assessment methods for use in population-based studies

Population-based studies have been hampered in exploring hypothalamic–pituitary–adrenal axis (HPA) activity as a potential explanatory link between stress-related and metabolic disorders due to their lack of incorporation of reliable measures of chronic cortisol exposure. The purpose of this review is to summarize current literature on the reliability of HPA axis measures and to discuss the feasibility of performing them in population-based studies. We identified articles through PubMed using search terms related to cortisol, HPA axis, adrenal imaging, and reliability. The diurnal salivary cortisol curve (generated from multiple salivary samples from awakening to midnight) and 11 p.m. salivary cortisol had the highest between-visit reliabilities (r = 0.63–0.84 and 0.78, respectively). The cortisol awakening response and dexamethasone-suppressed cortisol had the next highest between-visit reliabilities (r = 0.33–0.67 and 0.42–0.66, respectively). Based on our own data, the inter-reader reliability (r_s) of adrenal gland volume from non-contrast CT was 0.67–0.71 for the left and 0.47–0.70 for the right adrenal glands. While a single 8 a.m. salivary cortisol is one of the easiest measures to perform, it had the lowest between-visit reliability (R = 0.18–0.47). Based on the current literature, use of sampling multiple salivary cortisol measures across the diurnal curve (with awakening cortisol), dexamethasone-suppressed cortisol, and adrenal gland volume are measures of HPA axis tone with similar between-visit reliabilities which likely reflect chronic cortisol burden and are feasible to perform in population-based studies.     

Solid Fuel Use for Household Cooking: Country and Regional Estimates for 1980–2010

Background: Exposure to household air pollution from cooking with solid fuels in simple stoves is a major health risk. Modeling reliable estimates of solid fuel use is needed for monitoring trends and informing policy.Objectives: In order to revise the disease burden attributed to household air pollution for the Global Burden of Disease 2010 project and for international reporting purposes, we estimated annual trends in the world population using solid fuels.Methods: We developed a multilevel model based on national survey data on primary cooking fuel.Results: The proportion of households relying mainly on solid fuels for cooking has decreased from 62% (95% CI: 58, 66%) to 41% (95% CI: 37, 44%) between 1980 and 2010. Yet because of population growth, the actual number of persons exposed has remained stable at around 2.8 billion during three decades. Solid fuel use is most prevalent in Africa and Southeast Asia where > 60% of households cook with solid fuels. In other regions, primary solid fuel use ranges from 46% in the Western Pacific, to 35% in the Eastern Mediterranean and < 20% in the Americas and Europe.Conclusion: Multilevel modeling is a suitable technique for deriving reliable solid-fuel use estimates. Worldwide, the proportion of households cooking mainly with solid fuels is decreasing. The absolute number of persons using solid fuels, however, has remained steady globally and is increasing in some regions. Surveys require enhancement to better capture the health implications of new technologies and multiple fuel use.     

Annals Journal Club: Strategies for Reducing Potentially Avoidable Hospitalizations for Ambulatory Care–Sensitive Conditions

PURPOSE

Hospitalizations for ambulatory care–sensitive conditions (ACSCs) are seen as potentially avoidable with optimal primary care. Little is known, however, about how primary care physicians rate these hospitalizations and whether and how they could be avoided. This study explores the complex causality of such hospitalizations from the perspective of primary care physicians.

METHODS

We conducted semistructured interviews with 12 primary care physicians from 10 primary care clinics in Germany regarding 104 hospitalizations of 81 patients with ACSCs at high risk of rehospitalization.

RESULTS

Participating physicians rated 43 (41%) of the 104 hospitalizations to be potentially avoidable. During the interviews the cause of hospitalization fell into 5 principal categories: system related (eg, unavailability of ambulatory services), physician related (eg, suboptimal monitoring), medical (eg, medication side effects), patient related (eg, delayed help-seeking), and social (eg, lack of social support). Subcategories frequently associated with physicians’ rating of hospitalizations for ACSCs as potentially avoidable were after-hours absence of the treating physician, failure to use ambulatory services, suboptimal monitoring, patients’ fearfulness, cultural background and insufficient language skills of patients, medication errors, medication nonadherence, and overprotective caregivers. Comorbidities and medical emergencies were frequent causes attributed to ACSC-based hospitalizations that were rated as being unavoidable.

CONCLUSIONS

Primary care physicians rated a significant proportion of hospitalizations for ACSCs to be potentially avoidable. Strategies to avoid these hospitalizations may target after-hours care, optimal use of ambulatory services, intensified monitoring of high-risk patients, and initiatives to improve patients’ willingness and ability to seek timely help, as well as patients’ medication adherence.     

Statistical Methods for the Analysis of Time–Location Sampling Data

Time-location sampling (TLS) is useful for collecting information on a hard-to-reach population (such as men who have sex with men [MSM]) by sampling locations where persons of interest can be found, and then sampling those who attend. These studies have typically been analyzed as a simple random sample (SRS) from the population of interest. If this population is the source population, as we assume here, such an analysis is likely to be biased, because it ignores possible associations between outcomes of interest and frequency of attendance at the locations sampled, and is likely to underestimate the uncertainty in the estimates, as a result of ignoring both the clustering within locations and the variation in the probability of sampling among members of the population who attend sampling locations. We propose that TLS data be analyzed as a two-stage sample survey using a simple weighting procedure based on the inverse of the approximate probability that a person was sampled and using sample survey analysis software to estimate the standard errors of estimates (to account for the effects of clustering within the first stage [locations] and variation in the weights). We use data from the Young Men's Survey Phase II, a study of MSM, to show that, compared with an analysis assuming a SRS, weighting can affect point prevalence estimates and estimates of associations and that weighting and clustering can substantially increase estimates of standard errors. We describe data on location attendance that would yield improved estimates of weights. We comment on the advantages and disadvantages of TLS and respondent-driven sampling.     

Global Capacity for Emerging Infectious Disease Detection, 1996–2014

The speed with which disease outbreaks are recognized is critical for establishing effective control efforts. We evaluate global improvements in the timeliness of outbreak discovery and communication during 2010–2014 as a follow-up to a 2010 report. For all outbreaks reported by the World Health Organization’s Disease Outbreak News, we estimate the number of days from first symptoms until outbreak discovery and until first public communication. We report median discovery and communication delays overall, by region, and by Human Development Index (HDI) quartile. We use Cox proportional hazards regression to assess changes in these 2 outcomes over time, along with Loess curves for visualization. Improvement since 1996 was greatest in the Eastern Mediterranean and Western Pacific regions and in countries in the middle HDI quartiles. However, little progress has occurred since 2010. Further improvements in surveillance will likely require additional international collaboration with a focus on regions of low or unstable HDI.     

Rural–Urban Differences in End-of-Life Care: Implications for Practice

Most older adults with advanced illnesses express the wish to die at home. Home-based care from home health and hospice agencies makes this possible, but there are great geographic variations in utilization. Interviews and focus groups with key constituents in home health and hospice agencies across the 8-county region of Western New York State were used to explore how rural–urban location and agency type (home health or hospice) influence variations in end-of-life care. Emergent themes were: Rural-Urban Differences (geographic challenges, market forces, and programming issues) and End-of-life Care Issues (macrosocial, mezzosocial, and microsocial factors). Implications for social work practice are discussed.     

Hospital Care for Opioid use in Illinois, 2016–2019

The Journal of Behavioral Health Services & Research - This study analyzes trends in hospital emergency room visits and admissions for patients with opioid diagnoses seen at 214 hospitals in...     

National surveillance for meningococcal disease in Japan, 1999–2014

We summarize the epidemiology of Japanese meningococcal disease with serogroup distribution. One hundred seventy-eight meningococcal meningitis cases were reported from April 1999 to March 2013 to the national surveillance system. From April 2013, bacteremia was added to the condition of reporting invasive meningococcal disease (IMD). Since then, 59 IMD cases were reported by the end of 2014. Approximately two thirds of the cases were male and the median age was 56 years (range: 0–93 years). Only 3% of the cases were <5 years old. One third of reported cases were meningitis and the others were bacteremia. The annual incidence (2014) for IMD was 0.028 per 100,000 and case fatality rate (CFR) was 19%. Serogroup Y (42%) was the most dominant serogroup, followed by C (12%), B (7%) and W (3%). Even though the number of reported cases has increased after the amendment of reporting requirements, the incidence of IMD is still low in Japan. Underreporting may play a role in this low incidence. Improving on the limitations of the surveillance system is necessary to capture the true epidemiology and accurate serogroup distribution of IMD cases in Japan, which is essential for making effective recommendations on newly licensed vaccine.