This paper analyzes the impact of urbanization on CO2 emissions in developing countries from 1975 to 2003. It contributes to the existing literature by examining the effect of urbanization, taking ...into account dynamics and the presence of heterogeneity in the sample of countries. The results show an inverted-U shaped relationship between urbanization and CO2 emissions. Indeed, the elasticity emission-urbanization is positive for low urbanization levels, which is in accordance with the higher environmental impact observed in less developed regions. Among our contributions is the estimation of a semi-parametric mixture model that allows for unknown distributional shapes and endogenously classifies countries into homogeneous groups. Three groups of countries are identified for which urbanization's impact differs considerably. For two of the groups, a threshold level is identified beyond which the emission-urbanization elasticity is negative and further increases in the urbanization rate do not contribute to higher emissions. However, for the third group only population and affluence, but not urbanization, contribute to explain emissions. The differential impact of urbanization on CO2 emissions should therefore be taken into account in future discussions of climate change policies.
► We model the relationship between urbanization and CO2 emissions. ► The STIRPAT model, the EKC hypothesis and the modernization theory are considered. ► We used several dynamic panel-data estimation methods. ► The results show different patterns for three groups of countries. ► We obtained some evidence confirming the ecological modernization theory.
Aims
To identify nursing care most frequently missed in acute adult inpatient wards and to determine evidence for the association of missed care with nurse staffing.
Background
Research has ...established associations between nurse staffing levels and adverse patient outcomes including in‐hospital mortality. However, the causal nature of this relationship is uncertain and omissions of nursing care (referred as missed care, care left undone or rationed care) have been proposed as a factor which may provide a more direct indicator of nurse staffing adequacy.
Design
Systematic review.
Data Sources
We searched the Cochrane Library, CINAHL, Embase and Medline for quantitative studies of associations between staffing and missed care. We searched key journals, personal libraries and reference lists of articles.
Review Methods
Two reviewers independently selected studies. Quality appraisal was based on the National Institute for Health and Care Excellence quality appraisal checklist for studies reporting correlations and associations. Data were ed on study design, missed care prevalence and measures of association. Synthesis was narrative.
Results
Eighteen studies gave subjective reports of missed care. Seventy‐five per cent or more nurses reported omitting some care. Fourteen studies found low nurse staffing levels were significantly associated with higher reports of missed care. There was little evidence that adding support workers to the team reduced missed care.
Conclusions
Low Registered Nurse staffing is associated with reports of missed nursing care in hospitals. Missed care is a promising indicator of nurse staffing adequacy. The extent to which the relationships observed represent actual failures, is yet to be investigated.
The analysis of circular data has been recently the focus of a wide range of literature, with the general objective of providing reliable parameter estimates in the presence of heterogeneity and/or ...dependence among observations under a longitudinal setting. In this paper, we extend the variance component model approach to the analysis of longitudinal circular data, defining a mixed effects model for radial projections onto the circle and introducing dependence between projections through a set of correlated random coefficients. Estimation is carried out by numerical integration through an expectation-maximization algorithm without parametric assumptions upon the random coefficients distribution. The resulting model is a finite mixture of projected normal distributions. A simulation study has been carried out to investigate the behavior of the proposed model in a series of empirical situations. The proposed model is computationally parsimonious and, when applied to a real dataset on animal orientation, produces novel results.
•We provide an efficient, easy to compute and robust lower bound estimator for the number of undetected cases of Covid-19.•Capture–recapture methods are employed, based on the cumulative time-series ...distribution of cases and deaths.•Heterogeneity has been accounted for by assuming a geometrical distribution underlying the data generation process.•Results from several European countries are mentioned.•The estimated ratio of the total estimated cases to the observed cases is around the value of 2.3 for all the analyzed countries.
A major open question, affecting the decisions of policy makers, is the estimation of the true number of Covid-19 infections. Most of them are undetected, because of a large number of asymptomatic cases. We provide an efficient, easy to compute and robust lower bound estimator for the number of undetected cases.
A modified version of the Chao estimator is proposed, based on the cumulative time-series distributions of cases and deaths. Heterogeneity has been addressed by assuming a geometrical distribution underlying the data generation process. An (approximated) analytical variance of the estimator has been derived to compute reliable confidence intervals at 95% level.
A motivating application to the Austrian situation is provided and compared with an independent and representative study on prevalence of Covid-19 infection. Our estimates match well with the results from the independent prevalence study, but the capture–recapture estimate has less uncertainty involved as it is based on a larger sample size. Results from other European countries are mentioned in the discussion. The estimated ratio of the total estimated cases to the observed cases is around the value of 2.3 for all the analyzed countries.
The proposed method answers to a fundamental open question: “How many undetected cases are going around?”. CR methods provide a straightforward solution to shed light on undetected cases, incorporating heterogeneity that may arise in the probability of being detected.
To determine the association between daily levels of registered nurse (RN) and nursing assistant staffing and hospital mortality.
This is a retrospective longitudinal observational study using ...routinely collected data. We used multilevel/hierarchical mixed-effects regression models to explore the association between patient outcomes and daily variation in RN and nursing assistant staffing, measured as hours per patient per day relative to ward mean. Analyses were controlled for ward and patient risk.
138 133 adult patients spending >1 days on general wards between 1 April 2012 and 31 March 2015.
In-hospital deaths.
Hospital mortality was 4.1%. The hazard of death was increased by 3% for every day a patient experienced RN staffing below ward mean (adjusted HR (aHR) 1.03, 95% CI 1.01 to 1.05). Relative to ward mean, each additional hour of RN care available over the first 5 days of a patient's stay was associated with 3% reduction in the hazard of death (aHR 0.97, 95% CI 0.94 to 1.0). Days where admissions per RN exceeded 125% of the ward mean were associated with an increased hazard of death (aHR 1.05, 95% CI 1.01 1.09). Although low nursing assistant staffing was associated with increases in mortality, high nursing assistant staffing was also associated with increased mortality.
Lower RN staffing and higher levels of admissions per RN are associated with increased risk of death during an admission to hospital. These findings highlight the possible consequences of reduced nurse staffing and do not give support to policies that encourage the use of nursing assistants to compensate for shortages of RNs.
Aims and objectives
Systematic review of the impact of missed nursing care on outcomes in adults, on acute hospital wards and in nursing homes.
Background
A considerable body of evidence supports the ...hypothesis that lower levels of registered nurses on duty increase the likelihood of patients dying on hospital wards, and the risk of many aspects of care being either delayed or left undone (missed). However, the direct consequence of missed care remains unclear.
Design
Systematic review.
Methods
We searched Medline (via Ovid), CINAHL (EBSCOhost) and Scopus for studies examining the association of missed nursing care and at least one patient outcome. Studies regarding registered nurses, healthcare assistants/support workers/nurses’ aides were retained. Only adult settings were included. Because of the nature of the review, qualitative studies, editorials, letters and commentaries were excluded. PRISMA guidelines were followed in reporting the review.
Results
Fourteen studies reported associations between missed care and patient outcomes. Some studies were secondary analyses of a large parent study. Most of the studies used nurse or patient reports to capture outcomes, with some using administrative data. Four studies found significantly decreased patient satisfaction associated with missed care. Seven studies reported associations with one or more patient outcomes including medication errors, urinary tract infections, patient falls, pressure ulcers, critical incidents, quality of care and patient readmissions. Three studies investigated whether there was a link between missed care and mortality and from these results no clear associations emerged.
Conclusions
The review shows the modest evidence base of studies exploring missed care and patient outcomes generated mostly from nurse and patient self‐reported data. To support the assertion that nurse staffing levels and skill mix are associated with adverse outcomes as a result of missed care, more research that uses objective staffing and outcome measures is required.
Relevance to clinical practice
Although nurses may exercise judgements in rationing care in the face of pressure, there are nonetheless adverse consequences for patients (ranging from poor experience of care to increased risk of infection, readmissions and complications due to critical incidents from undetected physiological deterioration). Hospitals should pay attention to nurses’ reports of missed care and consider routine monitoring as a quality and safety indicator.
A large and increasing number of studies have reported a relationship between low nurse staffing levels and adverse outcomes, including higher mortality rates. Despite the evidence being extensive in ...size, and having been sometimes described as “compelling” and “overwhelming”, there are limitations that existing studies have not yet been able to address. One result of these weaknesses can be observed in the guidelines on safe staffing in acute hospital wards issued by the influential body that sets standards for the National Health Service in England, the National Institute for Health and Care Excellence, which concluded there is insufficient good quality evidence available to fully inform practice.
In this paper we explore this apparent contradiction. After summarising the evidence review that informed the National Institute for Health and Care Excellence guideline on safe staffing and related evidence, we move on to discussing the complex challenges that arise when attempting to apply this evidence to practice. Among these, we introduce the concept of endogeneity, a form of bias in the estimation of causal effects. Although current evidence is broadly consistent with a cause and effect relationship, endogeneity means that estimates of the size of effect, essential for building an economic case, may be biased and in some cases qualitatively wrong. We expand on three limitations that are likely to lead to endogeneity in many previous studies: omitted variables, which refers to the absence of control for variables such as medical staffing and patient case mix; simultaneity, which occurs when the outcome can influence the level of staffing just as staffing influences outcome; and common-method variance, which may be present when both outcomes and staffing levels variables are derived from the same survey.
Thus while current evidence is important and has influenced policy because it illustrates the potential risks and benefits associated with changes in nurse staffing, it may not provide operational solutions. We conclude by posing a series of questions about design and methods for future researchers who intend to further explore this complex relationship between nurse staffing levels and outcomes. These questions are intended to reflect on the potential added value of new research given what is already known, and to encourage those conducting research to take opportunities to produce research that fills gaps in the existing knowledge for practice. By doing this we hope that future studies can better quantify both the benefits and costs of changes in nurse staffing levels and, therefore, serve as a more useful tool for those delivering services.
Despite recent methodological advances in hidden Markov regression models and a rapid increase in their application in a wide range of empirical settings, complex clustering-based research questions ...that include the contribution of the covariates set to the classification and the presence of atypical observations are often addressed ignoring the possible effects of wrong model assumptions. Hidden Markov regression models with random covariates (HMRMRCs) have been recently proposed as an improvement over the classical fixed covariates approach, allowing the covariates to contribute to the underlying clustering structure. To make the approach more flexible, when all the considered random variables are continuous, HMRMRCs are here defined focusing on three multivariate elliptical distributions: the normal (reference distribution), the
t
, and the contaminated normal. The latter two, heavy-tailed generalizations of the normal distribution, are introduced to protect the reference model for the occurrence of mildly atypical points and also allow us their automatic detection. Identifiability conditions are provided, EM-based algorithms are outlined for parameter estimation, and various implementation and operational issues are discussed. Properties of the estimators of the regression coefficients, as well as of the hidden path parameters, are evaluated through Monte Carlo experiments with the aim of showing the consequences of wrong model assumptions on paramaters estimates and inferred clustering. Artificial and real data analyses are provided to investigate models behavior in presence of heterogeneity and atypical observations.
This paper develops a quantile hidden semi-Markov regression to jointly estimate multiple quantiles for the analysis of multivariate time series. The approach is based upon the Multivariate ...Asymmetric Laplace (MAL) distribution, which allows to model the quantiles of all univariate conditional distributions of a multivariate response simultaneously, incorporating the correlation structure among the outcomes. Unobserved serial heterogeneity across observations is modeled by introducing regime-dependent parameters that evolve according to a latent finite-state semi-Markov chain. Exploiting the hierarchical representation of the MAL, inference is carried out using an efficient Expectation-Maximization algorithm based on closed form updates for all model parameters, without parametric assumptions about the states’ sojourn distributions. The validity of the proposed methodology is analyzed both by a simulation study and through the empirical analysis of air pollutant concentrations in a small Italian city.
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