Risk measures for direct real estate investments with non-normal or unknown return distributions

The volatility of returns is probably the most widely used risk measure for real estate. This is rather surprising since a number of studies have cast doubts on the view that volatility can capture the manifold risks attached to properties and corresponds to the risk attitude of investors. A central issue in this discussion is the statistical properties of real estate returns—in contrast to neoclassical capital market theory they are mostly non-normal and often unknown, which render many statistical measures useless. Based on a literature review and an analysis of data from Germany we provide evidence that volatility alone is inappropriate for measuring the risk of direct real estate. We use a unique data sample by IPD, which includes the total returns of 939 properties across different usage types (56% office, 20% retail, 8% others and 16% residential properties) from 1996 to 2009, the German IPD Index, and the German Property Index. The analysis of the distributional characteristics shows that German real estate returns in this period were not normally distributed and that a logistic distribution would have been a better fit. This is in line with most of the current literature on this subject and leads to the question which indicators are more appropriate to measure real estate risks. We suggest that a combination of quantitative and qualitative risk measures more adequately captures real estate risks and conforms better with investor attitudes to risk. Furthermore, we present criteria for the purpose of risk classification

Cost of poor adherence to anti-hypertensive therapy in five European countries

The financial burden for EU health systems associated with cardiovascular disease (CV) has been estimated to be nearly €110 billion in 2006, corresponding to 10 % of total healthcare expenditure across EU or a mean €223 annual cost per capita. The main purpose of this study is to estimate the costs related to hypertension and the economic impact of increasing adherence to anti-hypertensive therapy in five European countries (Italy, Germany, France, Spain and England). A probabilistic prevalence-based decision tree model was developed to estimate the direct costs of CV related to hypertension (CV defined as: stroke, heart attack, heart failure) in five European countries. Our model considered adherence to hypertension treatment as a main driver of blood pressure (BP) control (BP < 140/90 mmHg). Relative risk of CV, based on controlled or uncontrolled BP group, was estimated from the Framingham Heart Study and national review data. Prevalence and cost data were estimated from national literature reviews. A national payer (NP) perspective for 10 years was considered. Probabilistic sensitivity analysis was performed in order to evaluate uncertainty around the results (given as 95 % confidence intervals). The model estimated a total of 8.6 million (1.4 in Italy, 3.3 in Germany, 1.2 in Spain, 1.8 in France and 0.9 in England) CV events related to hypertension over the 10-year time horizon. Increasing the adherence rate to anti-hypertensive therapy to 70 % (baseline value is different for each country) would lead to 82,235 fewer CV events (24,058 in Italy, 7,870 in Germany, 18,870 in Spain, 24,855 in France and 6,553 in England). From the NP perspective, the direct cost associated with hypertension was estimated to be €51.3 billion (8.1 in Italy, 17.1 in Germany, 12.2 in Spain, 8.8 in France and 5.0 in England). Increasing adherence to anti-hypertensive therapy to 70 % would save a total of €332 million (CI 95 %: €319-346 million) from the NPs perspective. This study is the first attempt to estimate the economic impact of non-adherence amongst patients with diagnosed hypertension in Europe, using data from five European countries (Italy, France, Germany, Spain and England). © 2014 Springer-Verlag Berlin Heidelberg

Work and Health, a Blind Spot in Curative Healthcare? A Pilot Study

Introduction Most workers with musculoskeletal disorders on sick leave often consult with regular health care before entering a specific work rehabilitation program. However, it remains unclear to what extent regular healthcare contributes to the timely return to work (RTW). Moreover, several studies have indicated that it might postpone RTW. There is a need to establish the influence of regular healthcare on RTW as outcome; “Does visiting a regular healthcare provider influence the duration of sickness absence and recurrent sick leave due to musculoskeletal disorders?”. Methods A cohort of workers on sick leave for 2–6 weeks due to a-specific musculoskeletal disorders was followed for 12 months. The main outcomes for the present analysis were: duration of sickness absence till 100% return to work and recurrent sick leave after initial RTW. Cox regression analyses were conducted with visiting a general health practitioner, physical therapist, or medical specialist during the sick leave period as independent variables. Each regression model was adjusted for variables known to influence health care utilization like age, sex, diagnostic group, pain intensity, functional disability, general health perception, severity of complaints, job control, and physical load at work. Results Patients visiting a medical specialist reported higher pain intensity and more functional limitations and also had a worse health perception at start of the sick leave period compared with those not visiting a specialist. Visiting a medical specialist delayed return to work significantly (HR = 2.10; 95%CI 1.43–3.07). After approximately 8 weeks on sick leave workers visiting a physical therapist returned to work faster than other workers. A recurrent episode of sick leave during the follow up quick was initiated by higher pain intensity and more functional limitations at the moment of fully return to work. Visiting a primary healthcare provider during the sickness absence period did not influence the occurrence of a new sick leave period. Conclusion Despite the adjustment for severity of the musculoskeletal disorder, visiting a medical specialist was associated with a delayed full return to work. More attention to the factor ‘labor’ in the regular healthcare is warranted, especially for those patients experiencing substantial functional limitations due to musculoskeletal disorders

The Escherichia coli transcriptome mostly consists of independently regulated modules

Underlying cellular responses is a transcriptional regulatory network (TRN) that modulates gene expression. A useful description of the TRN would decompose the transcriptome into targeted effects of individual transcriptional regulators. Here, we apply unsupervised machine learning to a diverse compendium of over 250 high-quality Escherichia coli RNA-seq datasets to identify 92 statistically independent signals that modulate the expression of specific gene sets. We show that 61 of these transcriptomic signals represent the effects of currently characterized transcriptional regulators. Condition-specific activation of signals is validated by exposure of E. coli to new environmental conditions. The resulting decomposition of the transcriptome provides: a mechanistic, systems-level, network-based explanation of responses to environmental and genetic perturbations; a guide to gene and regulator function discovery; and a basis for characterizing transcriptomic differences in multiple strains. Taken together, our results show that signal summation describes the composition of a model prokaryotic transcriptome

Childbearing intentions in a low fertility context: the case of Romania

This paper applies the Theory of Planned Behaviour (TPB) to find out the predictors of fertility intentions in Romania, a low-fertility country. We analyse how attitudes, subjective norms and perceived behavioural control relate to the intention to have a child among childless individuals and one-child parents. Principal axis factor analysis confirms which items proposed by the Generation and Gender Survey (GGS 2005) act as valid and reliable measures of the suggested theoretical socio-psychological factors. Four parity-specific logistic regression models are applied to evaluate the relationship between the socio-psychological factors and childbearing intentions. Social pressure emerges as the most important aspect in fertility decision-making among childless individuals and one-child parents, and positive attitudes towards childbearing are a strong component in planning for a child. This paper also underlines the importance of the region-specific factors when studying childbearing intentions: planning for the second child significantly differs among the development regions, representing the cultural and socio-economic divisions of the Romanian territory

Oral contraceptive use and risk of melanoma in premenopausal women

Melanoma has been increasing in white populations. Incidence rates rise steeply in women until about age 50, suggesting oestrogen as a possible risk factor. Oestrogens can increase melanocyte count and melanin content and cause hyperpigmentation of the skin. We examined prospectively the association between oral contraceptive (OC) use and diagnoses of superficial spreading and nodular melanoma among 183 693 premenopausal white women in the Nurses’ Health Study (NHS) and the Nurses’ Health Study II (NHS II) cohorts. One hundred and forty six cases were confirmed in NHS during follow-up from 1976 to 1994, and 106 cases were confirmed in NHS II from 1989 to 1995. Skin reaction to sun exposure, sunburn history, mole counts, hair colour, family history of melanoma, parity, height and body mass index were also assessed and included in logistic regression models. A significant twofold increase in risk of melanoma (relative risk (RR) = 2.0, 95% confidence interval (CI) 1.2–3.4) was observed among current OC users compared to never users. Risk was further increased among current users with 10 or more years of use (RR = 3.4, 95% CI 1.7–7.0). Risk did not appear elevated among past OC users, even among those with longer durations of use, and risk did not decline linearly with time since last use. In conclusion, risk of premenopausal melanoma may be increased among women who are current OC users, particularly among those with longer durations of use. Further research is needed to determine whether low-dose oestrogen pills in particular are associated with an increase in risk and to describe possible interactions between OC use and sun exposure or other risk factors for melanoma. © 1999 Cancer Research Campaig

A genetic programming approach to development of clinical prediction models: A case study in symptomatic cardiovascular disease

BACKGROUND:Genetic programming (GP) is an evolutionary computing methodology capable of identifying complex, non-linear patterns in large data sets. Despite the potential advantages of GP over more typical, frequentist statistical approach methods, its applications to survival analyses are rare, at best. The aim of this study was to determine the utility of GP for the automatic development of clinical prediction models. METHODS:We compared GP against the commonly used Cox regression technique in terms of the development and performance of a cardiovascular risk score using data from the SMART study, a prospective cohort study of patients with symptomatic cardiovascular disease. The composite endpoint was cardiovascular death, non-fatal stroke, and myocardial infarction. A total of 3,873 patients aged 19-82 years were enrolled in the study 1996-2006. The cohort was split 70:30 into derivation and validation sets. The derivation set was used for development of both GP and Cox regression models. These models were then used to predict the discrete hazards at t = 1, 3, and 5 years. The predictive ability of both models was evaluated in terms of their risk discrimination and calibration using the validation set. RESULTS:The discrimination of both models was comparable. At time points t = 1, 3, and 5 years the C-index was 0.59, 0.69, 0.64 and 0.66, 0.70, 0.70 for the GP and Cox regression models respectively. At the same time points, the calibration of both models, which was assessed using calibration plots and the generalization of the Hosmer-Lemeshow test statistic, was also comparable, but with the Cox model being better calibrated to the validation data. CONCLUSION:Using empirical data, we demonstrated that a prediction model developed automatically by GP has predictive ability comparable to that of manually tuned Cox regression. The GP model was more complex, but it was developed in a fully automated way and comprised fewer covariates. Furthermore, it did not require the expertise normally needed for its derivation, thereby alleviating the knowledge elicitation bottleneck. Overall, GP demonstrated considerable potential as a method for the automated development of clinical prediction models for diagnostic and prognostic purposes