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Many trait measurements are size-dependent, and while we often divide these traits by size before fitting statistical models to control for the effect of size, this approach does not account for allometry and the intermediate outcome problem. We describe these problems and outline potential solutions.

An experimental treatment is intended to change a focal variable, but it often affects other unintended variables, referred to as mediators or intermediate outcomes [14]. For example, maternal dietary conditions, such as dietary restriction or over-nutrition, may influence offspring size (x), as well as offspring food intake (the focal trait; y) [2, 11]. If we know that offspring body mass and offspring food intake are correlated, we may want to account for the effect of offspring size when assessing experimental effects on offspring food intake. However, as both offspring body mass and food intake are measured after the treatment has been applied (offspring body mass cannot be measured before maternal diet is manipulated), we do not know the chain of causation. Imagine two scenarios. In scenario A, offspring body mass and food intake are mechanistically linked, and the maternal diet treatment subsequently affects both traits (direct effects; Fig. 2a). Alternatively, in scenario B, the treatment affects offspring body size (direct effect), which then influences offspring food intake (indirect effect), as well as a potential direct effect of the treatment on offspring food intake (Fig. 2b).

Two scenarios of the relationship among an experimental treatment, a trait of interest (focal variable, y) and an intermediate outcome (x). a The treatment affects both x and y, and therefore x and y are correlated (dotted line with a double-headed arrow) but x does not affect y. b The treatment affects both x and y, and then x also affects y

The treatment of active infective endocarditis (IE) presents a clinical dilemma with uncertain outcomes. This study sets out to determine the early and intermediate outcomes of patients treated surgically for active IE at an academic medical center.

One mechanism to improve outcomes of patients with endocarditis is early detection and treatment. The recommendations for management of these patients was recently published by Baddour et al. [5]. All patients with suspected endocarditis should have [1] 3 sets of blood cultures drawn from separate sites and an echocardiogram performed expeditiously. Once the organism is identified then antibiotic specific therapy to sterilize vegetations in IE are started. Furthermore, the duration of therapy must be sufficient to ensure complete eradication of microorganisms within vegetations [15].

Measures of the distribution of health in and among populations are as relevant as measures of the level of health in and among populations (15). Understanding the distribution of health can focus attention and action on specific health determinants and population groups to reduce inequalities in health and improve the overall level of health. Although the distribution of health outcomes could be assessed on any measurable geographic, demographic, social, or economic characteristic, someresearchers argue that health inequalities should be assessed by using specific social and economic characteristics that have historically determined social status (for example, wealth, ethnicity, sex, educational attainment) (19). Others suggest that this viewpoint excludes potentially relevant determinants of health (20). Metrics to assess the distribution of outcomes include measures of inequality (Gini index), measures of association (rate ratio), measures of impact (population-attributableproportion), and measures based on ranking (concentration index) (21,22).

In 2008, Wold reviewed 35 sets of health indicators in use (26). Although not an exhaustive list, these 35 sets provide a representative view of health indicators and their intended uses, which include presenting a picture of the health of a place, stimulating action to improve health, and tracking progress toward meeting objectives (Table 2). No set of indicators is explicitly used as a guide to financially reward improvement in health outcomes.

The principal sources of data available for US population health outcomes are mortality data derived from death certificates and data on subjective health status, functional status, and experiential state derived from population health surveys. The National Vital Statistics System (NVSS) collects and compiles data on births and deaths from all registration districts (most commonly states) in the United States. The most commonly used surveys are NHIS, BRFSS, NHANES, and the National Survey onDrug Use and Health (NSDUH). Several states conduct city- or county-level risk factor surveys by using BRFSS methods and questions, and an increasing number of cities and counties now conduct their own surveys based on or derived from BRFSS. A few states and local areas (Wisconsin and New York City, for example) conduct surveys based on NHIS or NHANES methods to provide state or local estimates of health outcomes and determinants.

This metric has the advantages of the overall mortality metric, as above, and allows public health programs to monitor the effect of specific interventions on more specific outcomes. An example might be monitoring increases in life expectancy or reductions in motor vehicle injury-related mortality resulting from efforts to modify driver behavior and to make roads and vehicles safer.

Summary measures of population health, which combine information on death and nonfatal health outcomes, have the advantage of simplicity and parsimony and may be easier to communicate to the public and track over time than the series of basic measures previously recommended. If a summary measure is desirable, the health-adjusted life expectancy and healthy life years are good choices because they are based on life expectancy and use a population-based measure of HRQL, rather than anexpert judgment-based measure.

We undertook multivariate analysis of data collected as part of the National Evaluation of Intermediate Care Services. Data were analysed on between 337 and 403 older people admitted to 14 different intermediate care teams. Independent variables were the numbers of different types of staff within a team and the ratio of support staff to professionally qualified staff within teams. Outcome measures include the Barthel index, EQ-5D, length of service provision and costs of care.

There has been growing international interest in 'workforce engineering and redesign' over recent years, which has resulted in an increase in research exploring the impact of different approaches to staffing on patient and service outcomes, particularly in the areas of medicine and nursing. There are several drivers for workforce change including skills shortages; productivity improvements; cost containment; quality improvement; technological innovation; and health sector reform. The modernisation of the National Health Service has led to substantial changes to the numbers and types of staff, and their ways of working. For instance, workforce shortages and restructuring in the UK have created opportunities for staff to perform roles that are outside their traditional scope of practice[1].

Only one experimental study specifically examined the impact of different models of staffing on costs and outcomes [8] by comparing hospital at home with care on a hospital ward. Staffing models were not attributed to outcomes, however the research showed that cost efficiency of services was negatively influenced by employing high grade nurses in roles with little direct clinical input. In contrast, the costs of the other members of the multidisciplinary team (eg therapists) constituted a relatively small component of the total cost. The authors suggested that increasing the proportion of nurses involved in more direct nursing care could reduce the costs of the service.

There is evidence from a number of qualitative studies that intermediate care requires staff to work across professional boundaries, and that initially, this can create tensions, however generally this improves with time, and is perceived by staff to enhance patient and service outcomes[9, 15, 16].

The literature demonstrates that patient satisfaction is positively associated with well trained workers and respectful staff, however is negatively associated with poor recruitment and retention and delayed or absent workers [17]. It is also evident that service user perceptions of service quality are likely to be positively influenced by patient characteristics, such as age, and organisational characteristics such as the intensity of care received, staffing organisation, employment conditions for staff, good recruitment and retention rates and greater levels of staff experience and training [18]. Many of the same factors have been found to significantly influence patient functional gain [19]. Staff experience and training such as competency of support workers in delivering rehabilitation and the presence of advanced practice nurses in teams can improve patient functional gains. Similarly patient functional outcomes can also be enhanced by greater intensity of care, greater therapy and general staffing levels and the use of agency staff have also been found to improve functional gains and outcomes.

Teamwork, team order and organisation have also been found to improve functional outcomes [20]. Several studies however have indicated that there are other factors that contribute to functional gain outside of these workforce variables. Patient characteristics such as higher cognitive ability of patients [21], the patient mix [19] and a longer stay in a post-acute care facility [21] were all found to positively impact on functional gain. 041b061a72


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