The late Neandertal supraorbital fossils from Vindija Cave,Croatia: a biased sample?

The late Neandertal sample from Vindija (Croatia) has been described as transitional between the earlier Central European Neandertals from Krapina (Croatia) and modern humans. However, the morphological differences indicating this transition may rather be the result of different sex and/or age compositions between the samples. This study tests the hypothesis that the metric differences between the Krapina and Vindija supraorbital samples are due to sampling bias. We focus upon the supraorbital region because past studies have posited this region as particularly indicative of the Vindija sample's transitional nature. Furthermore, the supraorbital region varies significantly with both age and sex.We analyzed four chords and two derived indices of supraorbital torus form as defined by Smith & Ranyard (1980, Am. J. phys. Anthrop.93, pp. 589-610). For each variable, we analyzed relative sample bias of the Krapina and Vindija samples using three sampling methods. In order to test the hypothesis that the Vindija sample contains an over-representation of females and/or young while the Krapina sample is normal or also female/young biased, we determined the probability of drawing a sample of the same size as and with a mean equal to or less than Vindija's from a Krapina-based population. In order to test the hypothesis that the Vindija sample is female/young biased while the Krapina sample is male/old biased, we determined the probability of drawing a sample of the same size as and with a mean equal or less than Vindija's from a generated population whose mean is halfway between Krapina's and Vindija's. Finally, in order to test the hypothesis that the Vindija sample is normal while the Krapina sample contains an over-representation of males and/or old, we determined the probability of drawing a sample of the same size as and with a mean equal to or greater than Krapina's from a Vindija-based population. Unless we assume that the Vindija sample is female/young and the Krapina sample is male/old biased, our results falsify the hypothesis that the metric differences between the Krapina and Vindija samples are due to sample bias.     

The influence of subjective sleep quality on the association between eveningness and depressive symptoms

Increasing evidence suggests that eveningness is associated with increased risk for depression. Eveningness, however, is also associated with poor sleep quality and the unique role of eveningness in depressive symptomatology remains to be elucidated. The goal of the current study, therefore, was to examine the inter-relationships between eveningness, subjective sleep quality and depressive symptoms in healthy participants free of current or previous depression and sleep disorder. Here, 167 healthy participants (mean age 24.16, 129/38 females/males) completed the reduced Morningness–Eveningness Questionnaire (rMEQ), the Pittsburgh Sleep Quality Index (PSQI) and the Centre for Epidemiological Studies Depression Scale (CES-D). Bootstrap mediation analysis for a simple mediation model including rMEQ, PSQI and CES-D was applied. Eveningness was associated with increased depressive symptoms and mediation analysis showed that this relationship was partly mediated by sleep quality. Our results suggest that indicators of depression observed in evening-type individuals cannot be attributed exclusively to disturbed sleep. We suggest that interventions that target both sleep quality and dysfunctional cognitive styles would be optimal to promote well-being in evening-type individuals.     

Using Inverse Probability Bootstrap Sampling to Eliminate Sample Induced Bias in Model Based Analysis of Unequal Probability Samples

In ecology, as in other research fields, efficient sampling for population estimation often drives sample designs toward unequal probability sampling, such as in stratified sampling. Design based statistical analysis tools are appropriate for seamless integration of sample design into the statistical analysis. However, it is also common and necessary, after a sampling design has been implemented, to use datasets to address questions that, in many cases, were not considered during the sampling design phase. Questions may arise requiring the use of model based statistical tools such as multiple regression, quantile regression, or regression tree analysis. However, such model based tools may require, for ensuring unbiased estimation, data from simple random samples, which can be problematic when analyzing data from unequal probability designs. Despite numerous method specific tools available to properly account for sampling design, too often in the analysis of ecological data, sample design is ignored and consequences are not properly considered. We demonstrate here that violation of this assumption can lead to biased parameter estimates in ecological research. In addition, to the set of tools available for researchers to properly account for sampling design in model based analysis, we introduce inverse probability bootstrapping (IPB). Inverse probability bootstrapping is an easily implemented method for obtaining equal probability re-samples from a probability sample, from which unbiased model based estimates can be made. We demonstrate the potential for bias in model-based analyses that ignore sample inclusion probabilities, and the effectiveness of IPB sampling in eliminating this bias, using both simulated and actual ecological data. For illustration, we considered three model based analysis tools—linear regression, quantile regression, and boosted regression tree analysis. In all models, using both simulated and actual ecological data, we found inferences to be biased, sometimes severely, when sample inclusion probabilities were ignored, while IPB sampling effectively produced unbiased parameter estimates.     

Alcoholgebruik en depressieve symptomen bij ouderen: Resultaten van de Longitudinal Aging Study Amsterdam

The aim of this research is to investigate the often assumed relation between alcohol use and depressive symptoms among older men and women. For this study, a subsample of 2119 participants of the Longitudinal Aging Study Amsterdam, aged 65 to 85 years at baseline, was followed over time and visited in their homes in 1992, 1995, 1998 and 2002. Depressive symptoms are assessed with the Centre for Epidemiologic Studies Depression Scale (CES-D). Alcohol use is measured with questions about the frequency and quantities of alcohol use. A relation between depressive symptoms and alcohol use could not be demonstraded in a population based sample of older drinkers. Only older heavily drinking men with higher levels of depressive symptoms, higher levels of anxiety, and more chronic diseases at baseline significantly reduced the number of glasses consumed per week from 26 to 14 in the ten years of follow-up. Heavily drinking women do not reduce the level of alcohol intake during follow-up. Public prevention strategies are needed to make older heavy drinking women and men who are still in relatively good health aware of the potential risks of excessive alcohol use.     

Estimating the probability that the sample mean is within a desired fraction of the standard deviation of the true mean

The use of small sample sizes in human and primate evolutionary research is commonplace. Estimating how well small samples represent the underlying population, however, is not commonplace. Because the accuracy of determinations of taxonomy, phylogeny, and evolutionary process are dependant upon how well the study sample represents the population of interest, characterizing the uncertainty, or potential error, associated with analyses of small sample sizes is essential. We present a method for estimating the probability that the sample mean is within a desired fraction of the standard deviation of the true mean using small (n < 10) or very small (n ≤ 5) sample sizes. This method can be used by researchers to determine post hoc the probability that their sample is a meaningful approximation of the population parameter. We tested the method using a large craniometric data set commonly used by researchers in the field. Given our results, we suggest that sample estimates of the population mean can be reasonable and meaningful even when based on small, and perhaps even very small, sample sizes.     

Modeling Systematic Change in Stopover Duration Does Not Improve Bias in Trends Estimated from Migration Counts

The use of counts of unmarked migrating animals to monitor long term population trends assumes independence of daily counts and a constant rate of detection. However, migratory stopovers often last days or weeks, violating the assumption of count independence. Further, a systematic change in stopover duration will result in a change in the probability of detecting individuals once, but also in the probability of detecting individuals on more than one sampling occasion. We tested how variation in stopover duration influenced accuracy and precision of population trends by simulating migration count data with known constant rate of population change and by allowing daily probability of survival (an index of stopover duration) to remain constant, or to vary randomly, cyclically, or increase linearly over time by various levels. Using simulated datasets with a systematic increase in stopover duration, we also tested whether any resulting bias in population trend could be reduced by modeling the underlying source of variation in detection, or by subsampling data to every three or five days to reduce the incidence of recounting. Mean bias in population trend did not differ significantly from zero when stopover duration remained constant or varied randomly over time, but bias and the detection of false trends increased significantly with a systematic increase in stopover duration. Importantly, an increase in stopover duration over time resulted in a compounding effect on counts due to the increased probability of detection and of recounting on subsequent sampling occasions. Under this scenario, bias in population trend could not be modeled using a covariate for stopover duration alone. Rather, to improve inference drawn about long term population change using counts of unmarked migrants, analyses must include a covariate for stopover duration, as well as incorporate sampling modifications (e.g., subsampling) to reduce the probability that individuals will be detected on more than one occasion.     

Maternal Depressive Symptoms in Relation to Dimensions of Parenting in Low-Income Mothers

Relations between maternal depressive symptoms and parenting were examined in low-income, inner-city mothers and their 18- to 24-month-old toddlers. Maternal depressive symptoms were measured using the Center for Epidemiological Studies Depression Scale (CES-D) depression inventory, and 3 dimensions of parenting were assessed from maternal interviews and home visits: (a) provision of age-appropriate play materials, (b) organization of the home environment, and (c) quality of mother-child interactions. Maternal depressive symptoms related inversely to the quality of mother-child interactions, but did not relate to the provision of play materials and organization of the home environment. High scores on the CES-D were associated with less sensitivity, engagement, affection, and more rigidity in mothers; with less compliance, affection, engagement, and gentleness in children. In addition, higher CES-D scores were associated with less mutual communication, reciprocity, and enjoyment in the dyad. Neither socioeconomic status, maternal IQ, nor absence-presence of a partner related directly to parenting. These findings suggest that maternal depressive symptoms play a key role in the quality of mother-child interactions.     

Inbreeding coefficients for stochastically varying small population sizes-bias of calculation based on effective numbers

The commonly used procedure to calculate inbreeding coefficients by effective population numbers (Ne) by the harmonic mean of generation-by-generation population sizes involves a computational bias. If the individual population sizes are considered as realizations of a binomially distributed random variable with sample size N and probability p, this bias can be investigated for the two cases p = constant and p = variable (Markov chain). The bias is of practical relevance only for small probabilities p, short period of initial successive generations, and small population sizes. The largest values for this computational bias are in the range of 0.05-0.06. It is concluded that for most practical purposes the approximate procedure is appropriate.     

Evening types are prone to depression

Certain preferences for the timing of daily activities (chronotype) may predispose an individual to sleep problems and mood disorders. In this study, we have examined the link between chronotypes and depression. Participants (N?=?6071) were recruited from a random sample of the general population aged 25 to 74 yrs living in defined geographical areas, as part of the National FINRISK Study in 2007 in Finland. Chronotype assessment was based on six items from the original Horne-Östberg Morningness-Eveningness Questionnaire. Depression was assessed with four self-reported items, including two probes for a diagnosis of a major depressive episode, diagnosed or treated depression, and use of antidepressants. We also analyzed correlations between chronotype and several health indicators, such as systolic and diastolic blood pressures, resting heart rate, weight, and waist circumference. The odds ratios for a range of indicators of depression were higher for evening types (2.7- to 4.1-fold) and intermediate types (1.5- to 1.9-fold) than for morning types. Our results suggest that individuals having a preference for evening hours to carry out their daily activities are prone to depression. (Author correspondence: ilona.merikanto@helsinki.fi)     

The use and abuse of population viability analysis

A recent study by Brook et al. empirically tested the performance of population viability analysis (PVA) using data from 21 populations across a wide range of species. The study concluded that PVAs are good at predicting the future dynamics of populations. We suggest that this conclusion is a result of a bias in the studies that Brook et al. included in their analyses. We present arguments that PVAs can only be accurate at predicting extinction probabilities if data are extensive and reliable, and if the distribution of vital rates between individuals and years can be assumed stationary in the future, or if any changes can be accurately predicted. In particular, we note that although catastrophes are likely to have precipitated many extinctions, estimates of the probability of catastrophes are unreliable.     

The relationship between symptoms of depression and body weight in younger adults

A bidirectional relationship between obesity and depression may exist, though previous results are conflicting. The objectives of our study were to determine whether there is a bidirectional relationship between obesity and symptoms of depression in younger adults and whether this relationship varies with sociodemographic factors. We used data from 7,980 participants in the National Longitudinal Survey of Youth 1979 to examine whether baseline depressive symptoms (score ≥ 10 on a seven-item subscale of the CES-D) in 1992, predicted adjusted percent change in BMI between 1992 and 1994. We then examined whether obesity in 1992 predicted the development of symptoms of depression in 1994, after adjustment for confounders. We found that the presence of baseline depressive symptoms was not prospectively associated with increase in percent BMI, except in Hispanic women. Additionally, baseline obesity was not associated with higher risk of future symptoms of depression in the sample overall (adjusted risk ratio (RR) 1.20; 99% CI 0.91-1.60). However, in those of higher socioeconomic status, obesity was associated with almost double the risk of depressive symptoms compared to nonobese (highest income category: adjusted RR 1.97; 99% CI 1.14-3.40). We concluded that although obesity was not associated with risk of depression symptoms in the population overall, obesity was associated with an increased risk of developing depressive symptoms in those of higher socioeconomic status. Sociodemographic factors may be important modifiers of the relationship between obesity and depression.     

The Psychometric Properties of the Center for Epidemiologic Studies Depression Scale in Chinese Primary Care Patients: Factor Structure,Construct Validity,Reliability, Sensitivity and Responsiveness

Background

The Center for Epidemiologic Studies Depression Scale (CES-D) is a commonly used instrument to measure depressive symptomatology. Despite this, the evidence for its psychometric properties remains poorly established in Chinese populations. The aim of this study was to validate the use of the CES-D in Chinese primary care patients by examining factor structure, construct validity, reliability, sensitivity and responsiveness.

Methods and Results

The psychometric properties were assessed amongst a sample of 3686 Chinese adult primary care patients in Hong Kong. Three competing factor structure models were examined using confirmatory factor analysis. The original CES-D four-structure model had adequate fit, however the data was better fit into a bi-factor model. For the internal construct validity, corrected item-total correlations were 0.4 for most items. The convergent validity was assessed by examining the correlations between the CES-D, the Patient Health Questionnaire 9 (PHQ-9) and the Short Form-12 Health Survey (version 2) Mental Component Summary (SF-12 v2 MCS). The CES-D had a strong correlation with the PHQ-9 (coefficient: 0.78) and SF-12 v2 MCS (coefficient: -0.75). Internal consistency was assessed by McDonald’s omega hierarchical (ωH). The ωH value for the general depression factor was 0.855. The ωH values for “somatic”, “depressed affect”, “positive affect” and “interpersonal problems” were 0.434, 0.038, 0.738 and 0.730, respectively. For the two-week test-retest reliability, the intraclass correlation coefficient was 0.91. The CES-D was sensitive in detecting differences between known groups, with the AUC >0.7. Internal responsiveness of the CES-D to detect positive and negative changes was satisfactory (with p value <0.01 and all effect size statistics >0.2). The CES-D was externally responsive, with the AUC>0.7.

Conclusions

The CES-D appears to be a valid, reliable, sensitive and responsive instrument for screening and monitoring depressive symptoms in adult Chinese primary care patients. In its original four-factor and bi-factor structure, the CES-D is supported for cross-cultural comparisons of depression in multi-center studies.     

Balancing bias and precision in capture-recapture estimates of abundance

Capture‐recapture estimates of abundance using photographic identification data are sensitive to the quality of photographs used and distinctiveness of individuals in the population. Here analyses are presented for examining the effects of photographic quality and individual animal distinctiveness scores and for objectively selecting a subset of data to use for capture‐recapture analyses using humpback whale (Megaptera novaeangliae) data from a 2‐year study in the North Atlantic. Photographs were evaluated for their level of quality and whales for their level of individual distinctiveness. Photographic quality scores had a 0.21 probability of changing by a single‐quality level, and there were no changes by two or more levels. Individual distinctiveness scores were not independent of photographic quality scores. Estimates of abundance decreased as poor‐quality photographs were removed. An appropriate balance between precision and bias in abundance estimates was achieved by removing the lowest‐quality photographs and those of incompletely photographed flukes given our assumptions about the true population abundance. A simulation of the selection process implied that, if the estimates are negatively biased by heterogeneity, the increase in bias produced by decreasing the sample size is not more than 2%. Capture frequencies were independent of individual distinctiveness scores.     

Association between depressive symptoms and morningness-eveningness,sleep duration and rotating shift work in Japanese nurses

Higher depressive symptoms have been reported in rotating shift workers compared with day workers. Depressive symptoms in adults who do not engage in night work have also been shown to be associated with chronotype and sleep duration. This study examines associations between depressive symptoms, morningness-eveningness (i.e. the degree to which people prefer to be active in the morning or the evening), sleep duration and rotating shift work. Japanese nurses (1252 day workers and 1780 rotating shift workers, aged 20–59) were studied using a self-administered questionnaire. The questionnaire covered depressive symptoms, morningness-eveningness, sleep habits and demographic characteristics of the participants. The Center for Epidemiologic Studies Depression Scale (CES-D) was used to determine the levels of depressive symptoms. A Japanese version of the Morningness-Eveningness Questionnaire (MEQ) was used to measure morningness-eveningness. The CES-D score of shift workers was significantly (p < 0.05) higher than that of day workers. The MEQ score was significantly (p < 0.05) lower (i.e. greater eveningness) in shift workers than in day workers. Sleep duration on the day shift was significantly (p < 0.05) shorter in shift workers than in day workers. Simple linear regression revealed that the MEQ score, sleep duration on the day shift and current work shift (i.e. rotating shift work) were significantly (p < 0.05) associated with the CES-D score. Multivariate linear regression indicated that greater eveningness and shorter sleep duration were independently associated with higher CES-D scores, while rotating shift work was not. These associations between the MEQ score, the sleep duration and the CES-D score were also confirmed in both day workers and shift workers when the groups were analyzed separately. These results suggest that greater eveningness and shorter sleep duration on the day shift were independently associated with higher levels of depressive symptoms, which may explain associations between rotating shift workers and depressive symptoms. These findings have important implications for the development of novel strategies for preventing poor mental health in day workers and rotating shift workers.     

Sampling Considerations for Disease Surveillance in Wildlife Populations

Abstract Disease surveillance in wildlife populations involves detecting the presence of a disease, characterizing its prevalence and spread, and subsequent monitoring. A probability sample of animals selected from the population and corresponding estimators of disease prevalence and detection provide estimates with quantifiable statistical properties, but this approach is rarely used. Although wildlife scientists often assume probability sampling and random disease distributions to calculate sample sizes, convenience samples (i.e., samples of readily available animals) are typically used, and disease distributions are rarely random. We demonstrate how landscape-based simulation can be used to explore properties of estimators from convenience samples in relation to probability samples. We used simulation methods to model what is known about the habitat preferences of the wildlife population, the disease distribution, and the potential biases of the convenience-sample approach. Using chronic wasting disease in free-ranging deer (Odocoileus virginianus) as a simple illustration, we show that using probability sample designs with appropriate estimators provides unbiased surveillance parameter estimates but that the selection bias and coverage errors associated with convenience samples can lead to biased and misleading results. We also suggest practical alternatives to convenience samples that mix probability and convenience sampling. For example, a sample of land areas can be selected using a probability design that oversamples areas with larger animal populations, followed by harvesting of individual animals within sampled areas using a convenience sampling method.     

Detection, identification, and correction of a bias in an epidemiological study

The relative lack of epidemiological studies of natural populations is partly due to the difficulty of obtaining samples that are both large enough and representative of the population. Here, we present the result of an epidemiological study (December 1992-August 1995) of feline immunodeficiency virus (FIV) in a free-roaming population of domestic cats (Felis catus), with a special emphasis on sample bias. Over five trapping periods, the prevalence of FIV in sampled cats steadily declined. Across these samples we consistently achieved a very large sampling fraction (approximately 60% of the population), the sex ratio, age and weight distributions remained stable with time in the samples, and the sex ratio was similar in the samples and the population. These indices would normally indicate that our samples were representative, suggesting the decline in FIV prevalence to be real. However, a concomitant ecological study of the whole population revealed an important bias in the samples, with an initial high probability of capturing a few individuals, which appeared significantly more likely to be FIV-infected, and then a lower probability of recapturing them. Since our protocol resulted in a non-random sampling, subsequent trappings were designed to avoid this bias, by also capturing individuals who had previously learned to escape capture. This modified capture regime revealed that FIV prevalence was in fact constant in the population. This study shows how samples of large size, which are stable and appear representative of the population, can still be biased. These results may have major implications for other studies based on trapping.     

Depressive symptoms of female nursing staff working in stressful environments and their association with serum creatine kinase and lactate dehydrogenase – a preliminary study

Background

The activity of creatine kinase (CK) in serum has recently been reported to be potentially associated with several types of depression. The aim of this study is to evaluate whether serum enzymes, including CK, vary even in a healthy population with depressive symptoms caused by work-related stress. We gave questionnaires and blood examinations to 93 healthy female nursing home workers and did an enzyme-linked immunosorbent assay for the quantitative detection of CK isozyme muscle-type M chain (CK-MM) in serum.

Findings

Depressive symptoms were determined using the Center for Epidemiologic Studies Depression (CES-D) scale and compared with the results of the blood examination and serum CK-MM levels. The CES-D results showed significant negative correlations with total CK and lactate dehydrogenase (LDH) activities and CK-MM level (r?=?-0.29, p?=?0.0062; r?=?-0.29, p?=?0.0065; r?=?-0.33, p?=?0.0016, respectively).

Conclusions

Total CK and LDH activities and serum CK-MM level appear to be associated with the depressive symptoms of healthy nurses working in stressful environments, although the significance level was relatively low. The simultaneous detection of serum CK and LDH activities or serum CK-MM level and LDH activity may be useful as an indicator of depressive symptoms, at least for female nursing staff with work-related stress.

     

The Petersen–Lincoln Estimator and its Extension to Estimate the Size of a Shared Population

The Petersen–Lincoln estimator has been used to estimate the size of a population in a single mark release experiment. However, the estimator is not valid when the capture sample and recapture sample are not independent. We provide an intuitive interpretation for “independence” between samples based on 2 × 2 categorical data formed by capture/non‐capture in each of the two samples. From the interpretation, we review a general measure of “dependence” and quantify the correlation bias of the Petersen–Lincoln estimator when two types of dependences (local list dependence and heterogeneity of capture probability) exist. An important implication in the census undercount problem is that instead of using a post enumeration sample to assess the undercount of a census, one should conduct a prior enumeration sample to avoid correlation bias. We extend the Petersen–Lincoln method to the case of two populations. This new estimator of the size of the shared population is proposed and its variance is derived. We discuss a special case where the correlation bias of the proposed estimator due to dependence between samples vanishes. The proposed method is applied to a study of the relapse rate of illicit drug use in Taiwan. (© 2008 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)