Ev\u27rybody Calls Me Honey

[Verse 1]Met a little pickaninny,Wand\u27ring down the street one day,Looking so forlorn and lonely,Seemed that he had lost his way.Someone asked him what is name was,This little tot with kinky head,Looking up with his big brown eyes,This is what he said: [Chorus]Ev\u27rybody calls me honey,Don\u27t know why they do,Maybe it\u27s because my Mammy calls me honey too;Ain\u27t been around any honey bees,\u27Fraid that they might sting me,Still ev\u27rybody calls me honey,You can call me honey too. [Verse 2]When a little bot o\u27 fellowFolks said he was like a rose,Wondered what they ought to call him,even Mammy didn\u27t know;From his eyes the sun beams peepin\u27Told of a disposition sweet;That\u27s the reason he\u27ll tell you this,Should you ever meet.[Chorus

Don\u27t Be Anybody\u27s Soldier Boy But Mine

https://digitalcommons.library.umaine.edu/mmb-vp/1318/thumbnail.jp

Ev\u27rybody Calls Me Honey

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Development and external validation of the eFalls tool: a multivariable prediction model for the risk of ED attendance or hospitalisation with a fall or fracture in older adults

BackgroundFalls are common in older adults and can devastate personal independence through injury such as fracture and fear of future falls. Methods to identify people for falls prevention interventions are currently limited, with high risks of bias in published prediction models. We have developed and externally validated the eFalls prediction model using routinely collected primary care electronic health records (EHR) to predict risk of emergency department attendance/hospitalisation with fall or fracture within 1 year.MethodsData comprised two independent, retrospective cohorts of adults aged ≥65 years: the population of Wales, from the Secure Anonymised Information Linkage Databank (model development); the population of Bradford and Airedale, England, from Connected Bradford (external validation). Predictors included electronic frailty index components, supplemented with variables informed by literature reviews and clinical expertise. Fall/fracture risk was modelled using multivariable logistic regression with a Least Absolute Shrinkage and Selection Operator penalty. Predictive performance was assessed through calibration, discrimination and clinical utility. Apparent, internal–external cross-validation and external validation performance were assessed across general practices and in clinically relevant subgroups.ResultsThe model’s discrimination performance (c-statistic) was 0.72 (95% confidence interval, CI: 0.68 to 0.76) on internal–external cross-validation and 0.82 (95% CI: 0.80 to 0.83) on external validation. Calibration was variable across practices, with some over-prediction in the validation population (calibration-in-the-large, −0.87; 95% CI: −0.96 to −0.78). Clinical utility on external validation was improved after recalibration.ConclusionThe eFalls prediction model shows good performance and could support proactive stratification for falls prevention services if appropriately embedded into primary care EHR systems

Determinants of Water Connection Type and Ownership of Water-Using Appliances in Ireland. ESRI WP216. October 2007

Domestic water demand is influenced both by the number of households and their characteristics, in particular the extent to which they employ water-using appliances. This paper focuses on domestic ownership of water-using appliances in Ireland, a country where rapid economic and demographic change are putting pressure on water and sewerage infrastructure. Using a large household micro-dataset, we use discrete response logistic models to examine the determinants of the water and sewage mains connection status of Irish homes, identify the characteristics of households that are associated with having larger or smaller numbers of appliances, and investigate what types of households own particular combinations of appliances

Exploring The Responsibilities Of Single-Inhabitant Smart Homes With Use Cases

DOI: 10.3233/AIS-2010-0076This paper makes a number of contributions to the field of requirements analysis for Smart Homes. It introduces Use Cases as a tool for exploring the responsibilities of Smart Homes and it proposes a modification of the conventional Use Case structure to suit the particular requirements of Smart Homes. It presents a taxonomy of Smart-Home-related Use Cases with seven categories. It draws on those Use Cases as raw material for developing questions and conclusions about the design of Smart Homes for single elderly inhabitants, and it introduces the SHMUC repository, a web-based repository of Use Cases related to Smart Homes that anyone can exploit and to which anyone may contribute

Energy-using appliances and energy-saving features: Determinants of ownership in Ireland

Energy usage and energy efficiency are of increasing concern in Ireland. Regression analyses on a large household micro-dataset reveal that those homes that have more energy-saving features are also likely to have a high 'potential energy use'. Statistically significant dwelling features include location, value and dwelling type, while household features such as income, age, period of residency, social status and tenure type are also important

Validating the QCOVID risk prediction algorithm for risk of mortality from COVID-19 in the adult population in Wales, UK.

IntroductionCOVID-19 risk prediction algorithms can be used to identify at-risk individuals from short-term serious adverse COVID-19 outcomes such as hospitalisation and death. It is important to validate these algorithms in different and diverse populations to help guide risk management decisions and target vaccination and treatment programs to the most vulnerable individuals in society.ObjectivesTo validate externally the QCOVID risk prediction algorithm that predicts mortality outcomes from COVID-19 in the adult population of Wales, UK.MethodsWe conducted a retrospective cohort study using routinely collected individual-level data held in the Secure Anonymised Information Linkage (SAIL) Databank. The cohort included individuals aged between 19 and 100 years, living in Wales on 24th January 2020, registered with a SAIL-providing general practice, and followed-up to death or study end (28th July 2020). Demographic, primary and secondary healthcare, and dispensing data were used to derive all the predictor variables used to develop the published QCOVID algorithm. Mortality data were used to define time to confirmed or suspected COVID-19 death. Performance metrics, including R2 values (explained variation), Brier scores, and measures of discrimination and calibration were calculated for two periods (24th January–30th April 2020 and 1st May–28th July 2020) to assess algorithm performance.Results1,956,760 individuals were included. 1,192 (0.06%) and 610 (0.03%) COVID-19 deaths occurred in the first and second time periods, respectively. The algorithms fitted the Welsh data and population well, explaining 68.8% (95% CI: 66.9-70.4) of the variation in time to death, Harrell’s C statistic: 0.929 (95% CI: 0.921-0.937) and D statistic: 3.036 (95% CI: 2.913-3.159) for males in the first period. Similar results were found for females and in the second time period for both sexes.ConclusionsThe QCOVID algorithm developed in England can be used for public health risk management for the adult Welsh population

The impact of dementia, frailty and care home characteristics on SARS-CoV-2 incidence in a national cohort of Welsh care home residents during a period of high community prevalence

Backgrounddementia may increase care home residents’ risk of COVID-19, but there is a lack of evidence on this effect and on interactions with individual and care home-level factors.Methodswe created a national cross-sectional retrospective cohort of care home residents in Wales for 1 September to 31 December 2020. Risk factors were analysed using multi-level logistic regression to model the likelihood of SARS-CoV-2 infection and mortality.Resultsthe cohort included 9,571 individuals in 673 homes. Dementia was diagnosed in 5,647 individuals (59%); 1,488 (15.5%) individuals tested positive for SARS-CoV-2. We estimated the effects of age, dementia, frailty, care home size, proportion of residents with dementia, nursing and dementia services, communal space and region. The final model included the proportion of residents with dementia (OR for positive test 4.54 (95% CIs 1.55–13.27) where 75% of residents had dementia compared to no residents with dementia) and frailty (OR 1.29 (95% CIs 1.05–1.59) for severe frailty compared with no frailty). Analysis suggested 76% of the variation was due to setting rather than individual factors. Additional analysis suggested severe frailty and proportion of residents with dementia was associated with all-cause mortality, as was dementia diagnosis. Mortality analyses were challenging to interpret.Discussionwhilst individual frailty increased the risk of COVID-19 infection, dementia was a risk factor at care home but not individual level. These findings suggest whole-setting interventions, particularly in homes with high proportions of residents with dementia and including those with low/no individual risk factors may reduce the impact of COVID-19