Sentence examples for predictor of demand from inspiring English sources

33, 34 In the current study, poor nutritional status was an independent predictor of demand for professional nursing care, especially in the rural environment.

Using the FI/LE ratio as an indicator of healthy aging and a predictor of demands on health and social care systems, the higher ratio for the Beijing urban population compared with Hong Kong suggests that population ageing in China is projected to be accompanied by increasing frailty, and that there is a need to focus on measures to reduce the frailty burden.

Demand for treatment is not well correlated with objectively determined treatment need, and it has been recognized that objective measures of disease are not good predictors of demand.

The operational research findings suggest that if demography, task sharing and working time are included as predictors of demand on dental services, the clinical time and workforce requirements can be identified to the detail of patient group and treatment.

Previously owned homes account for 85percentt of all houses sold and are an important predictor of consumer demand.

25– 27 Nevertheless, living alone was the most powerful predictor of increased demand for nursing care, while support from family was the most significant factor protecting against it.

Obviously, care needs level of elderly per se it is not a direct factor associated with high LTC expenditures, but it is a strong predictor of the demand of LTC services and consequently an important factor to explain LTC expenditures.

Although part of the variation between GP cooperatives could not be attributed to neighbourhood characteristics, the sociodemographic composition of the neighbourhood is a fair predictor of the demand of primary OOH care.

Consequently, neighbourhood population characteristics are a fair predictor of the demand of primary OOH care in a specific area and should be taken into account to enable GP cooperatives to better match supply and demand.

On the one hand, an extensive comparison of single-hidden layer networks trained by back-propagation is required to improve our understanding of them as predictors of intermittent demand.

Besides confirming the relevance of age and sex distribution as predictors of prescribing demand, this outcome was particularly interesting, taken into account the continuous effort operated by AIFA to centrally maintain the level of pharmaceutical spending below the threshold of 13% of total healthcare spending.