Spatial temporal modeling of hospitalizations for fall-related hip fractures in older people

An overview of geospatial methods used in unintentional injury epidemiology

Background

Injuries are a leading cause of death and disability around the world. Injury incidence is often associated with socio-economic and physical environmental factors. The application of geospatial methods has been recognised as important to gain greater understanding of the complex nature of injury and the associated diverse range of geographically-diverse risk factors. Therefore, the aim of this paper is to provide an overview of geospatial methods applied in unintentional injury epidemiological studies.

Methods

Nine electronic databases were searched for papers published in 2000–2015, inclusive. Included were papers reporting unintentional injuries using geospatial methods for one or more categories of spatial epidemiological methods (mapping; clustering/cluster detection; and ecological analysis). Results describe the included injury cause categories, types of data and details relating to the applied geospatial methods.

Results

From over 6,000 articles, 67 studies met all inclusion criteria. The major categories of injury data reported with geospatial methods were road traffic (n = 36), falls (n = 11), burns (n = 9), drowning (n = 4), and others (n = 7). Grouped by categories, mapping was the most frequently used method, with 62 (93%) studies applying this approach independently or in conjunction with other geospatial methods. Clustering/cluster detection methods were less common, applied in 27 (40%) studies. Three studies (4%) applied spatial regression methods (one study using a conditional autoregressive model and two studies using geographically weighted regression) to examine the relationship between injury incidence (drowning, road deaths) with aggregated data in relation to explanatory factors (socio-economic and environmental).

Conclusion

The number of studies using geospatial methods to investigate unintentional injuries has increased over recent years. While the majority of studies have focused on road traffic injuries, other injury cause categories, particularly falls and burns, have also demonstrated the application of these methods. Geospatial investigations of injury have largely been limited to mapping of data to visualise spatial structures. Use of more sophisticated approaches will help to understand a broader range of spatial risk factors, which remain under-explored when using traditional epidemiological approaches.

Electronic supplementary material

The online version of this article (doi:10.1186/s40621-016-0097-0) contains supplementary material, which is available to authorized users.

Modeling seasonal variation of hip fracture in Montreal, Canada

The investigation of the association of the climate variables with hip fracture incidences is important in social health issues. This study examined and modeled the seasonal variation of monthly population based hip fracture rate (HFr) time series. The seasonal ARIMA time series modeling approach is used to model monthly HFr incidences time series of female and male patients of the ages 40-74 and 75+ of Montreal, Québec province, Canada, in the period of 1993-2004. The correlation coefficients between meteorological variables such as temperature, snow depth, rainfall depth and day length and HFr are significant. The nonparametric Mann-Kendall test for trend assessment and the nonparametric Levene's test and Wilcoxon's test for checking the difference of HFr before and after change point are also used. The seasonality in HFr indicated sharp difference between winter and summer time. The trend assessment showed decreasing trends in HFr of female and male groups. The nonparametric test also indicated a significant change of the mean HFr. A seasonal ARIMA model was applied for HFr time series without trend and a time trend ARIMA model (TT-ARIMA) was developed and fitted to HFr time series with a significant trend. The multi criteria evaluation showed the adequacy of SARIMA and TT-ARIMA models for modeling seasonal hip fracture time series with and without significant trend. In the time series analysis of HFr of the Montreal region, the effects of the seasonal variation of climate variables on hip fracture are clear. The Seasonal ARIMA model is useful for modeling HFr time series without trend. However, for time series with significant trend, the TT-ARIMA model should be applied for modeling HFr time series.

A protocol for evidence-based targeting and evaluation of statewide strategies for preventing falls among community-dwelling older people in Victoria, Australia

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.