Trajectories of psychological adaptation to marital breakup after a long-term marriage

While the negative effects of divorce on well-being are well documented in research literature, the large individual differences in psychological adaptation over time are still not well understood. This is especially the case for marital breakup after long-term marriage, which is still a neglected research topic. Against this background, the aim of the present contribution is to shed light on the various trajectories of psychological adaptation to marital breakup after a long-term relationship. Data stem from a longitudinal survey study, which is part of the Swiss National Centre of Competence in Research ‘LIVES – Overcoming vulnerability: life course perspectives’ (funded by the Swiss National Science Foundation). Our analyses are based on results of an exploratory latent profile analysis performed at the first assessment in 2012 among 308 divorced participants aged 45 – 65 years, who divorced after an average of 25 years of marriage (Perrig-Chiello, Hutchison, & Morselli, 2014). Five different groups regarding psychological adaptation to marital breakup (i.e. life satisfaction, depression, hopelessness, subjective health, and mourning) were identified. They were composed of two larger groups of individuals that adapted quite well or very well (“average copers”, n=151 and “resilients”, n=90) and of three smaller groups with major difficulties to adjust to the new situation (“vulnerables”, n= 18; “malcontens”, n= 37 and “resigned ones”, n=12). Clusters differed statistically significant regarding personality variables, time since separation, current relationship status, and financial situation. In the present contribution, we want to investigate the course of adaptation of the five classes two years later by using latent transition analysis. Furthermore, we aim to examine which variables in terms of personality, relationship status, variables of the context of the separation and socio-demographic variables are crucial for change or stability in levels of adaptation in the different classes. The evaluation of the trajectories of adaptation to this critical life event and the identification of variables that enhance the adaptation over time is essential for developing more differentiated measures in counselling as well as intervention techniques in clinical and social services.

Simple measures of climate, soil properties and plant traits predict national-scale grassland soil carbon stocks

Soil carbon (C) storage is a key ecosystem service. Soil C stocks play a vital role in soil fertility and climate regulation, but the factors that control these stocks at regional and national scales are unknown, particularly when their composition and stability are considered. As a result, their mapping relies on either unreliable proxy measures or laborious direct measurements. Using data from an extensive national survey of English grasslands, we show that surface soil (0–7 cm) C stocks in size fractions of varying stability can be predicted at both regional and national scales from plant traits and simple measures of soil and climatic conditions. Soil C stocks in the largest pool, of intermediate particle size (50–250 μm), were best explained by mean annual temperature (MAT), soil pH and soil moisture content. The second largest C pool, highly stable physically and biochemically protected particles (0·45–50 μm), was explained by soil pH and the community abundance-weighted mean (CWM) leaf nitrogen (N) content, with the highest soil C stocks under N-rich vegetation. The C stock in the small active fraction (250–4000 μm) was explained by a wide range of variables: MAT, mean annual precipitation, mean growing season length, soil pH and CWM specific leaf area; stocks were higher under vegetation with thick and/or dense leaves. Testing the models describing these fractions against data from an independent English region indicated moderately strong correlation between predicted and actual values and no systematic bias, with the exception of the active fraction, for which predictions were inaccurate. Synthesis and applications. Validation indicates that readily available climate, soils and plant survey data can be effective in making local- to landscape-scale (1–100 000 km2) soil C stock predictions. Such predictions are a crucial component of effective management strategies to protect C stocks and enhance soil C sequestration.

An empirical evaluation of the Random Forests classifier models for variable selection in a large-scale lung cancer case-control study

Random Forests™ is reported to be one of the most accurate classification algorithms in complex data analysis. It shows excellent performance even when most predictors are noisy and the number of variables is much larger than the number of observations. In this thesis Random Forests was applied to a large-scale lung cancer case-control study. A novel way of automatically selecting prognostic factors was proposed. Also, synthetic positive control was used to validate Random Forests method. Throughout this study we showed that Random Forests can deal with large number of weak input variables without overfitting. It can account for non-additive interactions between these input variables. Random Forests can also be used for variable selection without being adversely affected by collinearities. ^ Random Forests can deal with the large-scale data sets without rigorous data preprocessing. It has robust variable importance ranking measure. Proposed is a novel variable selection method in context of Random Forests that uses the data noise level as the cut-off value to determine the subset of the important predictors. This new approach enhanced the ability of the Random Forests algorithm to automatically identify important predictors for complex data. The cut-off value can also be adjusted based on the results of the synthetic positive control experiments. ^ When the data set had high variables to observations ratio, Random Forests complemented the established logistic regression. This study suggested that Random Forests is recommended for such high dimensionality data. One can use Random Forests to select the important variables and then use logistic regression or Random Forests itself to estimate the effect size of the predictors and to classify new observations. ^ We also found that the mean decrease of accuracy is a more reliable variable ranking measurement than mean decrease of Gini. ^

Household health assessment and associated water sample analysis in the Miches watershed of the Dominican Republic

Background. Diarrhea and malnutrition are the leading causes of mortality for children age one to four in the Dominican Republic. Communities within the Miches watershed lack sanitation infrastructure and water purification systems, which increases the risk of exposure to water-borne pathogens. The purpose of this cross-sectional study was to analyze health information gathered through household interviews and to test water samples for the presence of diarrheagenic pathogens and antibiotic-resistant bacteria within the Miches watershed. Methods. Frequency counts and thematic analysis were used to investigate Human Health Survey responses and Fisher's exact test was used to determine correlation between water source and reported illness. Bacteria cultured from water samples were analyzed by Gram stain, real-time PCR, API® 20E biochemical identification, and for antibiotic resistance. Results. Community members reported concerns about water sources with respect to water quality, availability, and environmental contamination. Pathogenic strains of E. coli were present in the water samples. Drinking aquifer water was positively-correlated with reported stomach aches (p=0.04) while drinking from rivers or creeks was associated with the reported absence of “gripe” (cold or flu) (p=0.01). The lack of association between reported illnesses and water source for the majority of variables suggested that there were multiple vehicles of disease transmission. Antibiotic resistant bacteria were isolated from the water samples tested. Conclusions. The presence of pathogenic E. coli in water samples suggested that water is at least one route of transmission for diarrheagenic pathogens in the Miches watershed. The presence of antibiotic-resistant bacteria in the water samples may indicate the proliferation of resistance plasmids in the environment as a result of antibiotic overuse in human and animal populations and a lack of sanitation infrastructure. An intervention that targets areas of hygiene, sanitation, and water purification is recommended to limit human exposure to diarrheagenic pathogens and antibiotic-resistant organisms. ^

A retrospective analysis of patients referred for tuberculosis testing at Parkland Hospital, Dallas, TX

Tuberculosis (TB) is an infectious disease of great public health importance, particularly to institutions that provide health care to large numbers of TB patients such as Parkland Hospital in Dallas, TX. The purpose of this retrospective chart review was to analyze differences in TB positive and TB negative patients to better understand whether or not there were variables that could be utilized to develop a predictive model for use in the emergency department to reduce the overall number of suspected TB patients being sent to respiratory isolation for TB testing. This study included patients who presented to the Parkland Hospital emergency department between November 2006 and December 2007 and were isolated and tested for TB. Outcome of TB was defined as a positive sputum AFB test or a positive M. tuberculosis culture result. Data were collected utilizing the UT Southwestern Medical Center computerized database OACIS and included demographic information, TB risk factors, physical symptoms, and clinical results. Only two variables were significantly (P<0.05) related to TB outcome: dyspnea (shortness of breath) (P<0.001) and abnormal x-ray (P<0.001). Marginally significant variables included hemoptysis (P=0.06), weight loss (P=0.11), night sweats (P=0.20), history of homelessness or incarceration (P=0.15), and history of positive skin PPD (P=0.19). Using a combination of significant and marginally significant variables, a predictive model was designed which demonstrated a specificity of 24% and a sensitivity of 70%. In conclusion, a predictive model for TB outcome based on patients who presented to the Parkland Hospital emergency department between November 2006 and December 2007 was unsuccessful given the limited number of variables that differed significantly between TB positive and TB negative patients. It is suggested that a future prospective cohort study should be implemented to collect data on TB positive and TB negative patients. It may be possible that a more thorough prospective collection of data may lead to clearer comparisons between TB positive and TB negative patients and ultimately to the design of a more sensitive predictive model for TB outcome. ^

A study of nurses' perceptions of and participation in the resolution of treatment dilemmas for critically ill newborns

This study was an exploratory investigation of variables which are associated with neonatal intensive care nurses' perceptions of and participation in life-sustaining treatment decisions for critically ill newborns. The primary purpose of the research was to examine the extent to which assessment of infants' physical and mental prognoses, parents' preferences regarding treatment, and legal consequences of non-treatment influence nurses' recommendations about life-saving treatment decisions for handicapped newborns. Secondly, the research explored the extent and nature of nurses' reported participation in the resolution of treatment dilemmas for these critically ill newborns. The framework of the study draws upon the work of Crane (1977), Blum (1980), and Pearlman (1982) who have explored the sociological context of decision-making with critical care patients.^ Participants in the study were a volunteer sample of eighty-three registered nurses who were currently working in neonatal intensive care units in five large urban hospitals in Texas. Data were collected through the use of intensive interviews and case study questionnaires. Results from the study indicate that physical and mental prognoses as well as parent preferences and concerns about legal liability are related to nurses' treatment recommendations, but their levels of significance vary according to the type of handicapping condition and whether the treatment questions are posed in terms of initiating aggressive therapy or withdrawing aggressive therapy.^ The majority of nurses reported that the extent of their participation in formal decision-making regarding handicapped newborns was fairly minimal although they provide much of the definitive data used to make decisions by physicians and parents. There was substantial evidence that nurse respondents perceive their primary role as advocates for critically ill newborns, and believe that their involvement in the resolution of treatment dilemmas should be increased. ^

Selection bias and the cross-validation of regression models for prediction

Strategies are compared for the development of a linear regression model with stochastic (multivariate normal) regressor variables and the subsequent assessment of its predictive ability. Bias and mean squared error of four estimators of predictive performance are evaluated in simulated samples of 32 population correlation matrices. Models including all of the available predictors are compared with those obtained using selected subsets. The subset selection procedures investigated include two stopping rules, C$\sb{\rm p}$ and S$\sb{\rm p}$, each combined with an 'all possible subsets' or 'forward selection' of variables. The estimators of performance utilized include parametric (MSEP$\sb{\rm m}$) and non-parametric (PRESS) assessments in the entire sample, and two data splitting estimates restricted to a random or balanced (Snee's DUPLEX) 'validation' half sample. The simulations were performed as a designed experiment, with population correlation matrices representing a broad range of data structures.^ The techniques examined for subset selection do not generally result in improved predictions relative to the full model. Approaches using 'forward selection' result in slightly smaller prediction errors and less biased estimators of predictive accuracy than 'all possible subsets' approaches but no differences are detected between the performances of C$\sb{\rm p}$ and S$\sb{\rm p}$. In every case, prediction errors of models obtained by subset selection in either of the half splits exceed those obtained using all predictors and the entire sample.^ Only the random split estimator is conditionally (on $\\beta$) unbiased, however MSEP$\sb{\rm m}$ is unbiased on average and PRESS is nearly so in unselected (fixed form) models. When subset selection techniques are used, MSEP$\sb{\rm m}$ and PRESS always underestimate prediction errors, by as much as 27 percent (on average) in small samples. Despite their bias, the mean squared errors (MSE) of these estimators are at least 30 percent less than that of the unbiased random split estimator. The DUPLEX split estimator suffers from large MSE as well as bias, and seems of little value within the context of stochastic regressor variables.^ To maximize predictive accuracy while retaining a reliable estimate of that accuracy, it is recommended that the entire sample be used for model development, and a leave-one-out statistic (e.g. PRESS) be used for assessment. ^

THE RELATIONSHIP BETWEEN DEGREE OF BLOOD PRESSURE REDUCTION AND MORTALITY AMONG HYPERTENSIVES IN THE HYPERTENSION DETECTION AND FOLLOW-UP PROGRAM

The relationship between degree of diastolic blood pressure (DBP) reduction and mortality was examined among hypertensives, ages 30-69, in the Hypertension Detection and Follow-up Program (HDFP). The HDFP was a multi-center community-based trial, which followed 10,940 hypertensive participants for five years. One-year survival was required for inclusion in this investigation since the one-year annual visit was the first occasion where change in blood pressure could be measured on all participants. During the subsequent four years of follow-up on 10,052 participants, 568 deaths occurred. For levels of change in DBP and for categories of variables related to mortality, the crude mortality rate was calculated. Time-dependent life tables were also calculated so as to utilize available blood pressure data over time. In addition, the Cox life table regression model, extended to take into account both time-constant and time-dependent covariates, was used to examine the relationship change in blood pressure over time and mortality.^ The results of the time-dependent life table and time-dependent Cox life table regression analyses supported the existence of a quadratic function which modeled the relationship between DBP reduction and mortality, even after adjusting for other risk factors. The minimum mortality hazard ratio, based on a particular model, occurred at a DBP reduction of 22.6 mm Hg (standard error = 10.6) in the whole population and 8.5 mm Hg (standard error = 4.6) in the baseline DBP stratum 90-104. After this reduction, there was a small increase in the risk of death. There was not evidence of the quadratic function after fitting the same model using systolic blood pressure. Methodologic issues involved in studying a particular degree of blood pressure reduction were considered. The confidence interval around the change corresponding to the minimum hazard ratio was wide and the obtained blood pressure level should not be interpreted as a goal for treatment. Blood pressure reduction was attributed, not only to pharmacologic therapy, but also to regression to the mean, and to other unknown factors unrelated to treatment. Therefore, the surprising results of this study do not provide direct implications for treatment, but strongly suggest replication in other populations. ^

Age -period -cohort analysis: An extension of Cox's lifetable regression with time -dependent covariates

It is well known that an identification problem exists in the analysis of age-period-cohort data because of the relationship among the three factors (date of birth + age at death = date of death). There are numerous suggestions about how to analyze the data. No one solution has been satisfactory. The purpose of this study is to provide another analytic method by extending the Cox's lifetable regression model with time-dependent covariates. The new approach contains the following features: (1) It is based on the conditional maximum likelihood procedure using a proportional hazard function described by Cox (1972), treating the age factor as the underlying hazard to estimate the parameters for the cohort and period factors. (2) The model is flexible so that both the cohort and period factors can be treated as dummy or continuous variables, and the parameter estimations can be obtained for numerous combinations of variables as in a regression analysis. (3) The model is applicable even when the time period is unequally spaced.^ Two specific models are considered to illustrate the new approach and applied to the U.S. prostate cancer data. We find that there are significant differences between all cohorts and there is a significant period effect for both whites and nonwhites. The underlying hazard increases exponentially with age indicating that old people have much higher risk than young people. A log transformation of relative risk shows that the prostate cancer risk declined in recent cohorts for both models. However, prostate cancer risk declined 5 cohorts (25 years) earlier for whites than for nonwhites under the period factor model (0 0 0 1 1 1 1). These latter results are similar to the previous study by Holford (1983).^ The new approach offers a general method to analyze the age-period-cohort data without using any arbitrary constraint in the model. ^

Making genetic biodiversity measurable : a review of statistical multivariate methods to study variability at gene level

Measures of agro-ecosystems genetic variability are essential to sustain scientific-based actions and policies tending to protect the ecosystem services they provide. To build the genetic variability datum it is necessary to deal with a large number and different types of variables. Molecular marker data is highly dimensional by nature, and frequently additional types of information are obtained, as morphological and physiological traits. This way, genetic variability studies are usually associated with the measurement of several traits on each entity. Multivariate methods are aimed at finding proximities between entities characterized by multiple traits by summarizing information in few synthetic variables. In this work we discuss and illustrate several multivariate methods used for different purposes to build the datum of genetic variability. We include methods applied in studies for exploring the spatial structure of genetic variability and the association of genetic data to other sources of information. Multivariate techniques allow the pursuit of the genetic variability datum, as a unifying notion that merges concepts of type, abundance and distribution of variability at gene level.

IBAMar DATABASE: 4 decades of oceanographic sampling on the Western Mediterranean Sea

IBAMar (http://www.ba.ieo.es/ibamar) is a regional database that puts together all physical and biochemical data obtained by multiparametric probes (CTDs equipped with different sensors), during the cruises managed by the Balearic Center of the Spanish Institute of Oceanography (COB-IEO). It has been recently extended to include data obtained with classical hydro casts using oceanographic Niskin or Nansen bottles. The result is a database that includes a main core of hydrographic data: temperature (T), salinity (S), dissolved oxygen (DO), fluorescence and turbidity; complemented by bio-chemical data: dissolved inorganic nutrients (phosphate, nitrate, nitrite and silicate) and chlorophyll-a. In IBAMar Database, different technologies and methodologies were used by different teams along the four decades of data sampling in the COB-IEO. Despite of this fact, data have been reprocessed using the same protocols, and a standard QC has been applied to each variable. Therefore it provides a regional database of homogeneous, good quality data. Data acquisition and quality control (QC): 94% of the data are CTDs Sbe911 and Sbe25. S and DO were calibrated on board using water samples, whenever a Rossetta was available (70% of the cases). All CTD data from Seabird CTDs were reviewed and post processed with the software provided by Sea-Bird Electronics. Data were averaged to get 1 dbar vertical resolution. General sampling methodology and pre processing are described in https://ibamardatabase.wordpress.com/home/). Manual QC include visual checks of metadata, duplicate data and outliers. Automatic QC include range check of variables by area (north of Balearic Islands, south of BI and Alboran Sea) and depth (27 standard levels), check for spikes and check for density inversions. Nutrients QC includes a preliminary control and a range check on the observed level of the data to detect outliers around objectively analyzed data fields. A quality flag is assigned as an integer number, depending on the result of the QC check.

Abundance and sizes of European oak species (Quercus petraea (Matt.) Liebl., Quercus robur L.) in dependence of neighborhood and environmental factors

Quercus robur L. (pedunculate oak) and Quercus petraea (Matt.) Liebl. (sessile oak) are two European oak species of great economic and ecological importance. Even though both oaks have wide ecological amplitudes of suitable growing conditions, forests dominated by oaks often fail to regenerate naturally. The regeneration performance of both oak species is assumed to be subject to a variety of variables that interact with one another in complex ways. The novel approach of this research was to study the effect of many ecological variables on the regeneration performance of both oak species together and identify key variables and interactions for different development stages of the oak regeneration on a large scale in the field. For this purpose, overstory and regeneration inventories were conducted in oak dominated forests throughout southern Germany and paired with data on browsing, soil, and light availability. The study was able to verify the assumption that the occurrence of oak regeneration depends on a set of variables and their interactions. Specifically, combinations of site and stand specific variables such as light availability, soil pH and iron content on the one hand, and basal area and species composition of the overstory on the other hand. Also browsing pressure was related to oak abundance. The results also show that the importance of variables and their combinations differs among the development stages of the regeneration. Light availability becomes more important during later development stages, whereas the number of oaks in the overstory is important during early development stages. We conclude that successful natural oak regeneration is more likely to be achieved on sites with lower fertility and requires constantly controlling overstory density. Initially sufficient mature oaks in the overstory should be ensured. In later stages, overstory density should be reduced continuously to meet the increasing light demand of oak seedlings and saplings.

Physical, biological and chemical characteristics of Canadian and Danish lakes during summer

1. Winter temperatures differ markedly on the Canadian prairies compared with Denmark. Between 1 January 1998 and 31 December 2002, average weekly and monthly temperatures did not drop below 0 °C in the vicinity of Silkeborg, Denmark. Over this same time, weekly average temperatures near Calgary, Alberta, Canada, often dropped below -10 °C for 3-5 weeks and the average monthly temperature was below 0 °C for 2-4 months. Accordingly, winter ice conditions in shallow lakes in Canada and Denmark differed considerably. 2. To assess the implications of winter climate for lake biotic structure and function we compared a number of variables that describe the chemistry and biology of shallow Canadian and Danish lakes that had been chosen to have similar morphometries. 3. The Danish lakes had a fourfold higher ratio of chlorophyll-a: total phosphorus (TP). Zooplankton : phytoplankton carbon was related to TP and fish abundance in Danish lakes but not in Canadian lakes. There was no significant difference in the ratio log total zooplankton biomass : log TP and the Canadian lakes had a significantly higher proportion of cladocerans that were Daphnia. These differences correspond well with the fact that the Danish lakes have more abundant and diverse fish communities than the Canadian lakes. 4. Our results suggest that severe Canadian winters lead to anoxia under ice and more depauperate fish communities, and stronger zooplankton control on phytoplankton in shallow prairie lakes compared with shallow Danish lakes. If climate change leads to warmer winters and a shorter duration of ice cover, we predict that shallow Canadian prairie lakes will experience increased survivorship of planktivores and stronger control of zooplankton. This, in turn, might decrease zooplankton control on phytoplankton, leading to 'greener' lakes on the Canadian prairies.

Model results in NetCDF format of North African vegetation-precipitation in early and mid-Holocene climate simulated by CCSM3-DGVM//

The present study analyses the sign, strength, and working mechanism of the vegetation-precipitation feedback over North Africa in middle (6 ka BP) and early Holocene (9 ka BP) simulations using the comprehensive coupled climate-vegetation model CCSM3-DGVM (Community Climate System Model version 3 and a dynamic global vegetation model). The coupled model simulates enhanced summer rainfall and a northward migration of the West African monsoon trough along with an expansion of the vegetation cover for the early and middle Holocene compared to the pre-industrial period. It is shown that dynamic vegetation enhances the orbitally triggered summer precipitation anomaly by approximately 20% in the Sahara-Sahel region (10-25° N, 20° W-30° E) in both the early and mid-Holocene experiments compared to their fixed-vegetation counterparts. The primary vegetation-rainfall feedback identified here operates through surface latent heat flux anomalies by canopy evaporation and transpiration and their effect on the mid-tropospheric African easterly jet, whereas the effects of vegetation changes on surface albedo and local water recycling play a negligible role. Even though CCSM3-DGVM simulates a positive vegetation-precipitation feedback in the North African region, this feedback is not strong enough to produce multiple equilibrium climate-ecosystem states on a regional scale.

Tabulation of multiple responses

Although multiple response questions are quite common in survey research, Stata's official release does not provide much possibility for an effective analysis of multiple response variables. For example, in a study on drug addiction an interview question might be, "Which substances did you consume during the last four weeks?" The respondents just list all the drugs they took if any, e.g., an answer could be "cannabis, cocaine, heroin" or "ecstasy, cannabis" or "none", etc. Usually, the responses to such questions are held as a set of variables and, therefore, cannot be easily tabulated. I will address this issue and present a new module to compute one- and two-way tables of multiple responses. The module supports several types of data structure, provides significance tests, and offers various options to control the computation and display of the results.