Factors explaining local privatization: A meta-regression analysis

Privatization of local public services has been implemented worldwide in the last decades. Why local governments privatize has been the subject of much discussion, and many empirical works have been devoted to analyzing the factors that explain local privatization. Such works have found a great diversity of motivations, and the variation among reported empirical results is large. To investigate this diversity we undertake a meta-regression analysis of the factors explaining the decision to privatize local services. Overall, our results indicate that significant relationships are very dependent upon the characteristics of the studies. Indeed, fiscal stress and political considerations have been found to contribute to local privatization specially in the studies of US cases published in the eighties that consider a broad range of services. Studies that focus on one service capture more accurately the influence of scale economies on privatization. Finally, governments of small towns are more affected by fiscal stress, political considerations and economic efficiency, while ideology seems to play a major role for large cities.

Evaluating sampling strategies and logistic regression methods for modelling complex land cover changes

The role of land cover change as a significant component of global change has become increasingly recognized in recent decades. Large databases measuring land cover change, and the data which can potentially be used to explain the observed changes, are also becoming more commonly available. When developing statistical models to investigate observed changes, it is important to be aware that the chosen sampling strategy and modelling techniques can influence results. We present a comparison of three sampling strategies and two forms of grouped logistic regression models (multinomial and ordinal) in the investigation of patterns of successional change after agricultural land abandonment in Switzerland. Results indicated that both ordinal and nominal transitional change occurs in the landscape and that the use of different sampling regimes and modelling techniques as investigative tools yield different results. Synthesis and applications. Our multimodel inference identified successfully a set of consistently selected indicators of land cover change, which can be used to predict further change, including annual average temperature, the number of already overgrown neighbouring areas of land and distance to historically destructive avalanche sites. This allows for more reliable decision making and planning with respect to landscape management. Although both model approaches gave similar results, ordinal regression yielded more parsimonious models that identified the important predictors of land cover change more efficiently. Thus, this approach is favourable where land cover change pattern can be interpreted as an ordinal process. Otherwise, multinomial logistic regression is a viable alternative.

Life partnerships in childhood cancer survivors, their siblings, and the general population.

BACKGROUND: Life partnerships other than marriage are rarely studied in childhood cancer survivors (CCS). We aimed (1) to describe life partnership and marriage in CCS and compare them to life partnerships in siblings and the general population; and (2) to identify socio-demographic and cancer-related factors associated with life partnership and marriage. METHODS: As part of the Swiss Childhood Cancer Survivor Study (SCCSS), a questionnaire was sent to all CCS (aged 20-40 years) registered in the Swiss Childhood Cancer Registry (SCCR), aged <16 years at diagnosis, who had survived ≥ 5 years. The proportion with life partner or married was compared between CSS and siblings and participants in the Swiss Health Survey (SHS). Multivariable logistic regression was used to identify factors associated with life partnership or marriage. RESULTS: We included 1,096 CCS of the SCCSS, 500 siblings and 5,593 participants of the SHS. Fewer CCS (47%) than siblings (61%, P < 0.001) had life partners, and fewer CCS were married (16%) than among the SHS population (26%, P > 0.001). Older (OR = 1.14, P < 0.001) and female CCS (OR = 1.85, <0.001) were more likely to have life partners. CCS who had undergone radiotherapy, bone marrow transplants (global P Treatment = 0.018) or who had a CNS diagnosis (global P Diagnosis < 0.001) were less likely to have life partners. CONCLUSION: CCS are less likely to have life partners than their peers. Most CCS with a life partner were not married. Future research should focus on the effect of these disparities on the quality of life of CCS.

Bayesian Model Averaging in the Instrumental Variable Regression Model

This paper considers the instrumental variable regression model when there is uncertainty about the set of instruments, exogeneity restrictions, the validity of identifying restrictions and the set of exogenous regressors. This uncertainty can result in a huge number of models. To avoid statistical problems associated with standard model selection procedures, we develop a reversible jump Markov chain Monte Carlo algorithm that allows us to do Bayesian model averaging. The algorithm is very exible and can be easily adapted to analyze any of the di¤erent priors that have been proposed in the Bayesian instrumental variables literature. We show how to calculate the probability of any relevant restriction (e.g. the posterior probability that over-identifying restrictions hold) and discuss diagnostic checking using the posterior distribution of discrepancy vectors. We illustrate our methods in a returns-to-schooling application.

Reversible behavioural autistic-like regression: A manifestation of a special (new?) epileptic syndrome in a 28-month-old child. A 2-year longitudinal study .

A 28-month-old boy was referred for acute onset of abnormal head movements. History revealed an insidious progressive regression in behaviour and communication over several months. Head and shoulder 'spasms' with alteration of consciousness and on one occasion ictal laughter were seen. The electroencephalograph (EEG) showed repeated bursts of brief generalized polyspikes and spike-wave during the 'spasms', followed by flattening, a special pattern which never recurred after treatment. Review of family videos showed a single 'minor' identical seizure 6 months previously. Magnetic resonance imaging was normal. Clonazepam brought immediate cessation of seizures, normalization of the EEG and a parallel spectacular improvement in communication, mood and language. Follow-up over the next 10 months showed a new regression unaccompained by recognized seizures, although numerous seizures were discovered during the videotaped neuropsychological examination, when stereotyped subtle brief paroxysmal changes in posture and behaviour could be studied in slow motion and compared with the 'prototypical' initial ones. The EEG showed predominant rare left-sided fronto-temporal discharges. Clonazepam was changed to carbamazepin with marked improvement in behaviour, language and cognition which has been sustained up to the last control at 51 months. Videotaped home observations allowed the documentation of striking qualitative and quantitative variations in social interaction and play of autistic type in relation to the epileptic activity. We conclude that this child has a special characteristic epileptic syndrome with subtle motor and vegetative symptomatology associated with an insidious catastrophic 'autistic-like' regression which could be overlooked. The methods used to document such fluctuating epileptic behavioural manifestations are discussed.

Model Switching and Model Averaging in Time- Varying Parameter Regression Models

This paper investigates the usefulness of switching Gaussian state space models as a tool for implementing dynamic model selecting (DMS) or averaging (DMA) in time-varying parameter regression models. DMS methods allow for model switching, where a different model can be chosen at each point in time. Thus, they allow for the explanatory variables in the time-varying parameter regression model to change over time. DMA will carry out model averaging in a time-varying manner. We compare our exact approach to DMA/DMS to a popular existing procedure which relies on the use of forgetting factor approximations. In an application, we use DMS to select different predictors in an in ation forecasting application. We also compare different ways of implementing DMA/DMS and investigate whether they lead to similar results.

Estimation of lean meat content in pig carcasses using X-ray Computed Tomography and PLS regression

Lean meat percentage (LMP) is an important carcass quality parameter. The aim of this work is to obtain a calibration equation for the Computed Tomography (CT) scans with the Partial Least Square Regression (PLS) technique in order to predict the LMP of the carcass and the different cuts and to study and compare two different methodologies of the selection of the variables (Variable Importance for Projection — VIP- and Stepwise) to be included in the prediction equation. The error of prediction with cross-validation (RMSEPCV) of the LMP obtained with PLS and selection based on VIP value was 0.82% and for stepwise selection it was 0.83%. The prediction of the LMP scanning only the ham had a RMSEPCV of 0.97% and if the ham and the loin were scanned the RMSEPCV was 0.90%. Results indicate that for CT data both VIP and stepwise selection are good methods. Moreover the scanning of only the ham allowed us to obtain a good prediction of the LMP of the whole carcass.

Innovation sources and productivity in Catalonian firms: a quantile regression analysis

This paper explores the effects of two main sources of innovation - intramural and external R&D— on the productivity level in a sample of 3,267 Catalonian firms. The data set used is based on the official innovation survey of Catalonia which was a part of the Spanish sample of CIS4, covering the years 2002-2004. We compare empirical results by applying usual OLS and quantile regression techniques both in manufacturing and services industries. In quantile regression, results suggest different patterns at both innovation sources as we move across conditional quantiles. The elasticity of intramural R&D activities on productivity decreased when we move up the high productivity levels both in manufacturing and services sectors, while the effects of external R&D rise in high-technology industries but are more ambiguous in low-technology and knowledge-intensive services. JEL codes: O300, C100, O140 Keywords: Innovation sources, R&D, Productivity, Quantile Regression

Prognostic importance of anaemia in patients with acute pulmonary embolism.

Although associated with adverse outcomes in other cardiopulmonary diseases, limited evidence exists on the prognostic value of anaemia in patients with acute pulmonary embolism (PE). We sought to examine the associations between anaemia and mortality and length of hospital stay in patients with PE. We evaluated 14,276 patients with a primary diagnosis of PE from 186 hospitals in Pennsylvania, USA. We used random-intercept logistic regression to assess the association between anaemia at the time of presentation and 30-day mortality and discrete-time logistic hazard models to assess the association between anaemia and time to hospital discharge, adjusting for patient (age, gender, race, insurance type, clinical and laboratory variables) and hospital (region, size, teaching status) factors. Anaemia was present in 38.7% of patients at admission. Patients with anaemia had a higher 30-day mortality (13.7% vs. 6.3%; p <0.001) and a longer length of stay (geometric mean, 6.9 vs. 6.6 days; p <0.001) compared to patients without anaemia. In multivariable analyses, anaemia remained associated with an increased odds of death (OR 1.82, 95% CI: 1.60-2.06) and a decreased odds of discharge (OR 0.85, 95% CI: 0.82-0.89). Anaemia is very common in patients presenting with PE and is independently associated with an increased short-term mortality and length of stay.

Improved virological outcome in White patients infected with HIV-1 non-B subtypes compared to subtype B.

BACKGROUND: Antiretroviral compounds have been predominantly studied in human immunodeficiency virus type 1 (HIV-1) subtype B, but only ~10% of infections worldwide are caused by this subtype. The analysis of the impact of different HIV subtypes on treatment outcome is important. METHODS: The effect of HIV-1 subtype B and non-B on the time to virological failure while taking combination antiretroviral therapy (cART) was analyzed. Other studies that have addressed this question were limited by the strong correlation between subtype and ethnicity. Our analysis was restricted to white patients from the Swiss HIV Cohort Study who started cART between 1996 and 2009. Cox regression models were performed; adjusted for age, sex, transmission category, first cART, baseline CD4 cell counts, and HIV RNA levels; and stratified for previous mono/dual nucleoside reverse-transcriptase inhibitor treatment. RESULTS: Included in our study were 4729 patients infected with subtype B and 539 with non-B subtypes. The most prevalent non-B subtypes were CRF02_AG (23.8%), A (23.4%), C (12.8%), and CRF01_AE (12.6%). The incidence of virological failure was higher in patients with subtype B (4.3 failures/100 person-years; 95% confidence interval [CI], 4.0-4.5]) compared with non-B (1.8 failures/100 person-years; 95% CI, 1.4-2.4). Cox regression models confirmed that patients infected with non-B subtypes had a lower risk of virological failure than those infected with subtype B (univariable hazard ratio [HR], 0.39 [95% CI, .30-.52; P < .001]; multivariable HR, 0.68 [95% CI, .51-.91; P = .009]). In particular, subtypes A and CRF02_AG revealed improved outcomes (multivariable HR, 0.54 [95% CI, .29-.98] and 0.39 [95% CI, .19-.79], respectively). CONCLUSIONS: Improved virological outcomes among patients infected with non-B subtypes invalidate concerns that these individuals are at a disadvantage because drugs have been designed primarily for subtype B infections.

Plasma aldosterone is independently associated with the metabolic syndrome.

The aim of this study was to analyze the associations of plasma aldosterone and plasma renin activity with the metabolic syndrome and each of its components. We analyzed data from a family based study in the Seychelles made up of 356 participants (160 men and 196 women) from 69 families of African descent. In multivariable models, plasma aldosterone was associated positively (P < 0.05) with blood pressure in older individuals (interaction with age, P < 0.05) and with waist circumference in men (interaction with sex, P < 0.05) and negatively with high-density lipoprotein cholesterol, in particular in individuals with elevated urinary potassium excretion (interaction with urinary potassium, P < 0.05); plasma renin activity was significantly associated with triglycerides and fasting blood glucose. Plasma aldosterone, but not plasma renin activity, was associated with the metabolic syndrome per se, independently of the association with its separate components. The observation that plasma renin activity was associated with some components of the metabolic syndrome, whereas plasma aldosterone was associated with other components of the metabolic syndrome, suggests different underlying mechanisms. These findings reinforce previous observations suggesting that aldosterone is associated with several cardiovascular risk factors and also suggest that aldosterone might contribute to the increased cardiovascular disease risk in individuals of African descent with the metabolic syndrome.

Morphological aspects of the myocarditis and myositis in Calomys callosus experimentally infected with Trypanosoma cruzi: fibrogenesis and spontaneous regression of fibrosis

Calomys callosus a wild rodent, is a natural host of Trypanosoma cruzi. Twelve C. callosus were infected with 10(5) trypomastigotes of the F strain (a myotropic strain) of T. cruzi. Parasitemia decreased on the 21 st day becoming negative around the 40th day of infection. All animals survived but had positive parasitological tests, until the end of the experiment. The infected animals developed severe inflammation in the myocardium and skeletal muscle. This process was pronounced from the 26 th to the 30th day and gradually subsided from the 50 th day becoming absent or residual on the 64 th day after infection. Collagen was identified by the picro Sirius red method. Fibrogenesis developed early, but regression of fibrosis occurred between the 50th and 64th day. Ultrastructural study disclosed a predominance of macrophages and fibroblasts in the inflammatory infiltrates, with small numbers of lymphocytes. Macrophages had active phagocytosis and showed points of contact with altered muscle cells. Different degrees of matrix expansion were present, with granular and fibrilar deposits and collagen bundles. These alterations subsided by the 64th days. Macrophages seem to be the main immune effector cell in the C. callosus model of infection with T. cruzi. The mechanisms involved in the rapid fibrogenesis and its regression deserve further investigation.

The use of classification and regression trees to predict the likelihood of seasonal influenza.

Background Individual signs and symptoms are of limited value for the diagnosis of influenza. Objective To develop a decision tree for the diagnosis of influenza based on a classification and regression tree (CART) analysis. Methods Data from two previous similar cohort studies were assembled into a single dataset. The data were randomly divided into a development set (70%) and a validation set (30%). We used CART analysis to develop three models that maximize the number of patients who do not require diagnostic testing prior to treatment decisions. The validation set was used to evaluate overfitting of the model to the training set. Results Model 1 has seven terminal nodes based on temperature, the onset of symptoms and the presence of chills, cough and myalgia. Model 2 was a simpler tree with only two splits based on temperature and the presence of chills. Model 3 was developed with temperature as a dichotomous variable (≥38°C) and had only two splits based on the presence of fever and myalgia. The area under the receiver operating characteristic curves (AUROCC) for the development and validation sets, respectively, were 0.82 and 0.80 for Model 1, 0.75 and 0.76 for Model 2 and 0.76 and 0.77 for Model 3. Model 2 classified 67% of patients in the validation group into a high- or low-risk group compared with only 38% for Model 1 and 54% for Model 3. Conclusions A simple decision tree (Model 2) classified two-thirds of patients as low or high risk and had an AUROCC of 0.76. After further validation in an independent population, this CART model could support clinical decision making regarding influenza, with low-risk patients requiring no further evaluation for influenza and high-risk patients being candidates for empiric symptomatic or drug therapy.