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The purpose of this paper is to explore the whole system cost of the care pathway for older people (aged 65-years old and over) admitted to hospitals as a result of falls in Torbay, a community of 131,000 in the southwest of England with a high proportion of older residents, over a two-year period.

The paper analysed patient-level linked acute hospital, community care and local authority-funded social care data to track patients’ care costs – for those patients admitted to an acute hospital due to their fall – in the 12 months before and after their fall.

On average, the cost of hospital, community and social care services for each admitted for a fall were almost four times as much in the 12 months after admission, than the cost of the admission itself. Over the 12 months that followed admission for falls, costs were 70 per cent higher than in the 12 months before the fall. The most dramatic increase was in community health care costs (160 per cent), compared to a 37 per cent increase in social care costs and a 35 per cent increase in acute hospital care costs. For patients who had a minor fall and those who survived 12 months after the fall, the costs of care home services increased significantly; for patients with hip fracture, the costs of community care services increased significantly; for patients who did not survive 12 months after the fall, the cost of acute inpatient and community health visits increased significantly.

This is the only study that has assessed the costs across the acute hospital, community care and social care pathway for this group of patients, in an English population. This will help commissioners and providers understand and develop better-integrated responses to frail elderly patients needs.

The fear of falling in older people can affect health, well‐being, care needs and quality of life. Fall‐monitoring equipment that can automatically raise a call for assistance is increasingly being developed, and may reduce the fear of falling. This study sought to discover the views of users and providers on technology of this type of and to quantify the impact fall detectors have on the fear of falling. The results of the study suggest that fall‐monitoring equipment is not considered routinely as part of a care package, and knowledge of devices is poor. A trial of automatic fall detectors provided indicated that they may reduce fear of falling for certain users and so should be considered in falls management, but more evidence and understanding are required to appreciate both the benefits and shortfalls of providing equipment of this sort.

Falls are the most frequently reported adverse event in hospitalised patients and carry a risk of great harm for the frail elderly. This intervention aimed to prevent high‐risk in‐patients on an acute aged care ward from falling.

Patients assessed at high falls risk were accommodated in a room staffed by volunteer companion‐observers. The volunteers engaged them in conversation, played cards, opened meals and used the call bell to summon nurses if patients attempted to move from the bed or chair without assistance. Because of occupational health and safety considerations, the volunteers did not assist patients to ambulate.

The falls rate in the acute aged care ward decreased by 44 percent (p<0.000). No patients fell in the observation room when volunteers were present. Relatives of participating in‐patients expressed appreciation of the volunteer role, in terms of increased safety and also companionship. Volunteers exercised initiative in determining their pattern of work and developing resources to support their role.

Because volunteers are not present around the clock, other strategies are needed to prevent wandering, frequently confused older in‐patients from falling during the night.

In a context where frail elderly patients need constant supervision, using volunteers is a reasonable strategy.

This intervention used an inexpensive, human resources‐based approach to significantly reduce the incidence of falls in the population at highest risk of falling. The additional benefits to patients in terms of cognitive improvement bear further investigation.

As part of the Trust’s clinical governance arrangements and to facilitate a systematic approach to clinical governance a risk assessment was conducted. This assessment identified that falls were a significant risk of patients, both during episodes of in‐patient care and in their own homes. There is little evidence nationally to guide good practice; therefore a multidispiplinary steering group was set up to develop a comprehensive approach to falls prevention. This resulted in the development of evidence based falls prediction tools, Trust policy and guidelines and extensive staff training programmes. Information leaflets have been provided to patients and additional services such as falls groups have been developed.

The Falls Exercise Prevention Programme for older people was introduced as a new project for North Bradford Primary Care Trust in 2002. The introduction of this new service was in response to an identified need to help reduce the incidence of falls, reduce serious injuries as a result of falling, and help improve and maintain the quality of life for those older people who had been identified as having had a fall or being at high risk of falling. The service was not established to be a front line service but to be incorporated into the falls referral pathway. The exercises undertaken are all evidence based and proven to be effective with this population. However, the Group promotes other aspects of quality of life for older people and promotes a holistic approach including health promotion and education. The group has now expanded to incorporate a ‘Walking for Health’ project; a ‘Walk From Home’ project and a ‘Buddy‐Up’ system. The Group is continually growing and future plans are to incorporate ‘Walks for Fallers’. Evaluation of the service provides information to continually improve and extend the services on offer.

Falls at home among the elderly are frequent and costly occurrences. Timely responses to falls are possible when initiated by the use of Personal Emergency Response Systems (PERS). More work is needed, however, in preventing these falls from occurring. Means to prevent falls among the elderly include using higher technology such as telehealth systems and lower technology such as home modifications that better ensure safety of elderly residents. Fall prevention programmes that are developed specifically for the elderly living in their own homes need to comprise a combination of both low‐ and high‐tech preventative strategies.

This systematic review paper aims to investigate seasonal ambient change’s impact on the incidence of falls among older adults.

The population, exposure, outcome (PEO) structured framework was used to frame the research question prior to using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis framework. Three databases were searched, and a total of 12 studies were found for inclusion, and quality appraisal was carried out. Data extraction was performed, and narrative analysis was carried out.

Of the 12 studies, 2 found no link between seasonality and fall incidence. One study found fall rates increased during warmer months, and 9 of the 12 studies found that winter months and their associated seasonal changes led to an increase in the incidence in falls. The overall result was that cooler temperatures typically seen during winter months carried an increased risk of falling for older adults.

Additional research is needed, most likely examining the climate one lives in. However, the findings are relevant and can be used to inform health-care providers and older adults of the increased risk of falling during the winter.

Fall accidents can cause casualties and economic losses in the construction industry. Fall portents, such as loss of balance (LOB) and sudden sways, can result in fatal, nonfatal or attempted fall accidents. All of them are worthy of studying to take measures to prevent future accidents. Detecting fall portents can proactively and comprehensively help managers assess the risk to workers as well as in the construction environment and further prevent fall accidents.

This study focused on the postures of workers and aimed to directly detect fall portents using a computer vision (CV)-based noncontact approach. Firstly, a joint coordinate matrix generated from a three-dimensional pose estimation model is employed, and then the matrix is preprocessed by principal component analysis, K-means and pre-experiments. Finally, a modified fusion K-nearest neighbor-based machine learning model is built to fuse information from the x, y and z axes and output the worker's pose status into three stages.

The proposed model can output the worker's pose status into three stages (steady–unsteady–fallen) and provide corresponding confidence probabilities for each category. Experiments conducted to evaluate the approach show that the model accuracy reaches 85.02% with threshold-based postprocessing. The proposed fall-portent detection approach can extract the fall risk of workers in the both pre- and post-event phases based on noncontact approach.

First, three-dimensional (3D) pose estimation needs sufficient information, which means it may not perform well when applied in complicated environments or when the shooting distance is extremely large. Second, solely focusing on fall-related factors may not be comprehensive enough. Future studies can incorporate the results of this research as an indicator into the risk assessment system to achieve a more comprehensive and accurate evaluation of worker and site risk.

The proposed machine learning model determines whether the worker is in a status of steady, unsteady or fallen using a CV-based approach. From the perspective of construction management, when detecting fall-related actions on construction sites, the noncontact approach based on CV has irreplaceable advantages of no interruption to workers and low cost. It can make use of the surveillance cameras on construction sites to recognize both preceding events and happened accidents. The detection of fall portents can help worker risk assessment and safety management.

Existing studies using sensor-based approaches are high-cost and invasive for construction workers, and others using CV-based approaches either oversimplify by binary classification of the non-entire fall process or indirectly achieve fall-portent detection. Instead, this study aims to detect fall portents directly by worker's posture and divide the entire fall process into three stages using a CV-based noncontact approach. It can help managers carry out more comprehensive risk assessment and develop preventive measures.