Falls Risk Prediction for Older Inpatients in Acute Care Medical Wards: Is There an Interest to Combine an Early Nurse Assessment and the Artificial Neural Network Analysis?

Background

Identification of the risk of falls is important among older inpatients. This study aims to examine performance criteria (i.e.; sensitivity, specificity, positive predictive value, negative predictive value and accuracy) for fall prediction resulting from a nurse assessment and an artificial neural networks (ANNs) analysis in older inpatients hospitalized in acute care medical wards.

Methods

A total of 848 older inpatients (mean age, 83.0±7.2 years; 41.8% female) admitted to acute care medical wards in Angers University hospital (France) were included in this study using an observational prospective cohort design. Within 24 hours after admission of older inpatients, nurses performed a bedside clinical assessment. Participants were separated into non-fallers and fallers (i.e.; ≥1 fall during hospitalization stay). The analysis was conducted using three feed forward ANNs (multilayer perceptron [MLP], averaged neural network, and neuroevolution of augmenting topologies [NEAT]).

Results

Seventy-three (8.6%) participants fell at least once during their hospital stay. ANNs showed a high specificity, regardless of which ANN was used, and the highest value reported was with MLP (99.8%). In contrast, sensitivity was lower, with values ranging between 98.4 to 14.8%. MLP had the highest accuracy (99.7).

Conclusions

Performance criteria for fall prediction resulting from a bedside nursing assessment and an ANNs analysis was associated with a high specificity but a low sensitivity, suggesting that this combined approach should be used more as a diagnostic test than a screening test when considering older inpatients in acute care medical ward.

     

The Jefferson Scale of Empathy: a nationwide study of measurement properties,underlying components,latent variable structure,and national norms in medical students

The Jefferson Scale of Empathy (JSE) is a broadly used instrument developed to measure empathy in the context of health professions education and patient care. Evidence in support of psychometrics of the JSE has been reported in health professions students and practitioners with the exception of osteopathic medical students. This study was designed to examine measurement properties, underlying components, and latent variable structure of the JSE in a nationwide sample of first-year matriculants at U.S. colleges of osteopathic medicine, and to develop a national norm table for the assessment of JSE scores. A web-based survey was administered at the beginning of the 2017–2018 academic year which included the JSE, a scale to detect “good impression” responses, and demographic/background information. Usable surveys were received from 6009 students enrolled in 41 college campuses (median response rate?=?92%). The JSE mean score and standard deviation for the sample were 116.54 and 10.85, respectively. Item-total score correlations were positive and statistically significant (p?<?0.01), and Cronbach α?=?0.82. Significant gender differences were observed on the JSE scores in favor of women. Also, significant differences were found on item scores between top and bottom third scorers on the JSE. Three factors of Perspective Taking, Compassionate Care, and Walking in Patient’s Shoes emerged in an exploratory factor analysis by using half of the sample. Results of confirmatory factor analysis with another half of the sample confirmed the 3-factor model. We also developed a national norm table which is the first to assess students’ JSE scores against national data.     

Small area estimation of under-5 mortality in Bangladesh,Cameroon, Chad,Mozambique, Uganda,and Zambia using spatially misaligned data

Background

The under-5 mortality rate (U5MR) is an important metric of child health and survival. Country-level estimates of U5MR are readily available, but efforts to estimate U5MR subnationally have been limited, in part, due to spatial misalignment of available data sources (e.g., use of different administrative levels, or as a result of historical boundary changes).

Methods

We analyzed all available complete and summary birth history data in surveys and censuses in six countries (Bangladesh, Cameroon, Chad, Mozambique, Uganda, and Zambia) at the finest geographic level available in each data source. We then developed small area estimation models capable of incorporating spatially misaligned data. These small area estimation models were applied to the birth history data in order to estimate trends in U5MR from 1980 to 2015 at the second administrative level in Cameroon, Chad, Mozambique, Uganda, and Zambia and at the third administrative level in Bangladesh.

Results

We found substantial variation in U5MR in all six countries: there was more than a two-fold difference in U5MR between the area with the highest rate and the area with the lowest rate in every country. All areas in all countries experienced declines in U5MR between 1980 and 2015, but the degree varied both within and between countries. In Cameroon, Chad, Mozambique, and Zambia we found areas with U5MRs in 2015 that were higher than in other parts of the same country in 1980. Comparing subnational U5MR to country-level targets for the Millennium Development Goals (MDG), we find that 12.8% of areas in Bangladesh did not meet the country-level target, although the country as whole did. A minority of areas in Chad, Mozambique, Uganda, and Zambia met the country-level MDG targets while these countries as a whole did not.

Conclusions

Subnational estimates of U5MR reveal significant within-country variation. These estimates could be used for identifying high-need areas and positive deviants, tracking trends in geographic inequalities, and evaluating progress towards international development targets such as the Sustainable Development Goals.

     

Comparison of Health and Risk Factors of Older,Working-age Australians,Italians and Italian-born Migrants to Australia,with Data from an Italian (PASSI), and an Australian (SAMSS) Risk Factor Surveillance System

Italian-born migrants (post-WWII) are the largest non-English-speaking background migrant group in South Australia. A cross-sectional, inter-country comparison using independent samples (40–69 years of age) from two (one in Australia, one in Italy) similar risk factor and chronic disease surveillance systems. None of the three groups (Italians, Australian-born and Italian-born Australians) had definitively worse health although the Italians had high rates for four of the seven risk factors reported (current high blood pressure, current high cholesterol, current smoking, eating less than five fruit and/or vegetables per day) than Australian-born and Italian-born Australians. Italian-born Australians had higher rates for insufficient physical activity, overweight/obese, poor self-reported health and diabetes. Australian respondents were more likely to report having two or more drinks of alcohol per day. Issues facing an ageing population require appropriate health care needs and an assessment of structural or cultural barriers to health services.