A protocol for evidence-based targeting and evaluation of statewide strategies for preventing falls among community-dwelling older people in Victoria, Australia
- Day, Lesley, Finch, Caroline, Hill, Keith, Haines, Terry, Clemson, Lindy, Thomas, Margaret, Thompson, Catherine
- Authors: Day, Lesley , Finch, Caroline , Hill, Keith , Haines, Terry , Clemson, Lindy , Thomas, Margaret , Thompson, Catherine
- Date: 2011
- Type: Text , Journal article
- Relation: Injury Prevention Vol. 17, no. 2 (2011), p. 1-8
- Relation: http://purl.org/au-research/grants/nhmrc/565900
- Relation: http://purl.org/au-research/grants/nhmrc/546282
- Full Text:
- Reviewed:
- Description: Background: Falls are a significant threat to the safety, health and independence of older citizens. Despite the now substantial evidence about effective falls prevention interventions, translation into falls reductions has not yet been fully realised. While the hip fracture rate is decreasing, the number and rate of fall-related hospital admissions among older people is increasing. The challenge now is to deliver the most effective interventions efficiently at a population level, and for these interventions to be taken up by older people. Objective: To support the development, and evaluation of, effective falls prevention policy and practice in the state of Victoria, Australia. Methods: The RE-AIM model (Reach, Efficacy, Adoption, Implementation, Maintenance) was used to identify strategies for an effective programme. Research objectives were developed to support the strategies. These include: (1) identification of subgroups of older people most frequently admitted to hospital for falls; (2) examining the acceptability of established falls interventions; (3) identification of factors that encourage and support relevant lifestyle changes; (4) identifying opportunities to incorporate confirmed interventions in existing programmes and services; (5) developing guidelines for sustainability. The research results will subsequently guide strategy details for the falls prevention plan. RE-AIM will provide the framework for the evaluation structure. Outcome measures: Measures to monitor the implementation of the selected interventions will be determined for each intervention, based on the five key factors of the RE-AIM model. The overall effect of the falls prevention plan will be monitored by time series analysis of fall-related hospital admission rates for community-dwelling older people.
- Authors: Day, Lesley , Finch, Caroline , Hill, Keith , Haines, Terry , Clemson, Lindy , Thomas, Margaret , Thompson, Catherine
- Date: 2011
- Type: Text , Journal article
- Relation: Injury Prevention Vol. 17, no. 2 (2011), p. 1-8
- Relation: http://purl.org/au-research/grants/nhmrc/565900
- Relation: http://purl.org/au-research/grants/nhmrc/546282
- Full Text:
- Reviewed:
- Description: Background: Falls are a significant threat to the safety, health and independence of older citizens. Despite the now substantial evidence about effective falls prevention interventions, translation into falls reductions has not yet been fully realised. While the hip fracture rate is decreasing, the number and rate of fall-related hospital admissions among older people is increasing. The challenge now is to deliver the most effective interventions efficiently at a population level, and for these interventions to be taken up by older people. Objective: To support the development, and evaluation of, effective falls prevention policy and practice in the state of Victoria, Australia. Methods: The RE-AIM model (Reach, Efficacy, Adoption, Implementation, Maintenance) was used to identify strategies for an effective programme. Research objectives were developed to support the strategies. These include: (1) identification of subgroups of older people most frequently admitted to hospital for falls; (2) examining the acceptability of established falls interventions; (3) identification of factors that encourage and support relevant lifestyle changes; (4) identifying opportunities to incorporate confirmed interventions in existing programmes and services; (5) developing guidelines for sustainability. The research results will subsequently guide strategy details for the falls prevention plan. RE-AIM will provide the framework for the evaluation structure. Outcome measures: Measures to monitor the implementation of the selected interventions will be determined for each intervention, based on the five key factors of the RE-AIM model. The overall effect of the falls prevention plan will be monitored by time series analysis of fall-related hospital admission rates for community-dwelling older people.
Statistical modelling for falls count data
- Ullah, Shahid, Finch, Caroline, Day, Lesley
- Authors: Ullah, Shahid , Finch, Caroline , Day, Lesley
- Date: 2010
- Type: Text , Journal article
- Relation: Accident Analysis and Prevention Vol. 42, no. 2 (2010), p. 384-392
- Relation: http://purl.org/au-research/grants/nhmrc/565900
- Full Text:
- Reviewed:
- Description: Falls and their injury outcomes have count distributions that are highly skewed toward the right with clumping at zero, posing analytical challenges. Different modelling approaches have been used in the published literature to describe falls count distributions, often without consideration of the underlying statistical and modelling assumptions. This paper compares the use of modified Poisson and negative binomial (NB) models as alternatives to Poisson (P) regression, for the analysis of fall outcome counts. Four different count-based regression models (P, NB, zero-inflated Poisson (ZIP), zero-inflated negative binomial (ZINB)) were each individually fitted to four separate fall count datasets from Australia, New Zealand and United States. The finite mixtures of P and NB regression models were also compared to the standard NB model. Both analytical (F, Vuong and bootstrap tests) and graphical approaches were used to select and compare models. Simulation studies assessed the size and power of each model fit. This study confirms that falls count distributions are over-dispersed, but not dispersed due to excess zero counts or heterogeneous population. Accordingly, the P model generally provided the poorest fit to all datasets. The fit improved significantly with NB and both zero-inflated models. The fit was also improved with the NB model, compared to finite mixtures of both P and NB regression models. Although there was little difference in fit between NB and ZINB models, in the interests of parsimony it is recommended that future studies involving modelling of falls count data routinely use the NB models in preference to the P or ZINB or finite mixture distribution. The fact that these conclusions apply across four separate datasets from four different samples of older people participating in studies of different methodology, adds strength to this general guiding principle. © 2009 Elsevier Ltd. All rights reserved.
- Authors: Ullah, Shahid , Finch, Caroline , Day, Lesley
- Date: 2010
- Type: Text , Journal article
- Relation: Accident Analysis and Prevention Vol. 42, no. 2 (2010), p. 384-392
- Relation: http://purl.org/au-research/grants/nhmrc/565900
- Full Text:
- Reviewed:
- Description: Falls and their injury outcomes have count distributions that are highly skewed toward the right with clumping at zero, posing analytical challenges. Different modelling approaches have been used in the published literature to describe falls count distributions, often without consideration of the underlying statistical and modelling assumptions. This paper compares the use of modified Poisson and negative binomial (NB) models as alternatives to Poisson (P) regression, for the analysis of fall outcome counts. Four different count-based regression models (P, NB, zero-inflated Poisson (ZIP), zero-inflated negative binomial (ZINB)) were each individually fitted to four separate fall count datasets from Australia, New Zealand and United States. The finite mixtures of P and NB regression models were also compared to the standard NB model. Both analytical (F, Vuong and bootstrap tests) and graphical approaches were used to select and compare models. Simulation studies assessed the size and power of each model fit. This study confirms that falls count distributions are over-dispersed, but not dispersed due to excess zero counts or heterogeneous population. Accordingly, the P model generally provided the poorest fit to all datasets. The fit improved significantly with NB and both zero-inflated models. The fit was also improved with the NB model, compared to finite mixtures of both P and NB regression models. Although there was little difference in fit between NB and ZINB models, in the interests of parsimony it is recommended that future studies involving modelling of falls count data routinely use the NB models in preference to the P or ZINB or finite mixture distribution. The fact that these conclusions apply across four separate datasets from four different samples of older people participating in studies of different methodology, adds strength to this general guiding principle. © 2009 Elsevier Ltd. All rights reserved.
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