Spatial analysis of paediatric swimming pool submersions by housing type

Abstract

Objective Drowning is a major cause of unintentional childhood death. The relationship between childhood swimming pool submersions, neighbourhood sociodemographics, housing type and swimming pool location was examined in Harris County, Texas. Study design and setting Childhood pool submersion incidents were examined for spatial clustering using the Nearest Neighbor Hierarchical Cluster (Nnh) algorithm. To relate submersions to predictive factors, an Markov Chain Monte Carlo (MCMC) Poisson-Lognormal-Conditional Autoregressive (CAR) spatial regression model was tested at the census tract level. Results There were 260 submersions; 49 were fatal. Forty-two per cent occurred at single-family residences and 36% at multifamily residential buildings. The risk of a submersion was 2.7 times higher for a child at a multifamily than a single-family residence and 28 times more likely in a multifamily swimming pool than a single family pool. However, multifamily submersions were clustered because of the concentration of such buildings with pools. Spatial clustering did not occur in singlefamily residences. At the tract level, submersions in single-family and multifamily residences were best predicted by the number of pools by housing type and the number of children aged 0-17 by housing type. Conclusions Paediatric swimming pool submersions in multifamily buildings are spatially clustered. The likelihood of submersions is higher for children who live in multifamily buildings with pools than those who live in single-family homes with pools.

An overview of geospatial methods used in unintentional injury epidemiology

“…Other studies considered falls ( n =  11) (Bamzar and Ceccato 2015; Chan et al 2012; de Pina et al 2008; Dey et al 2010; Lai et al 2009a; Lai et al 2009b; Lai et al 2011; Morency et al 2012; Towne et al 2015; Turner et al 2009; Yiannakoulias et al 2003), burns ( n =  9) (Edelman et al 2010; Fouillet et al 2006; Goltsman et al 2014; Harlan et al 2013; Heng et al 2015; Mian et al 2014; Niekerk et al 2006; Stylianou et al 2015; Williams et al 2003), drowning ( n =  4) (Dai et al 2013; Maples and Tiefenbacher 2009; Sharif et al 2012; Shenoi et al 2015), occupational ( n =  2) (Breslin et al 2007; Forst et al 2015), aviation-related ( n =  2) (Grabowski et al 2002a, 2002b), poisoning ( n =  1) (Nkhoma et al 2004), natural disaster ( n =  1) (Peek-Asa et al 2000) and dog-bite ( n =  1) (Raghavan et al 2014). …”

“…Spatial autoregressive models based on CAR ( n =  1) and GWR ( n =  2) frequentist approaches were applied to investigate social and environmental factors associated with road traffic mortality (Erdogan 2009) and drownings (Dai et al 2013; Shenoi et al 2015). One of the drowning studies (Shenoi et al 2015) applied a CAR spatial regression model to estimate the influence of sociodemographic and environmental variables (e.g. ethnicity, number of pools by single family and multi-family buildings) on the number of childhood swimming pool submersions.…”

“…Statistical challenges for this type of analysis include taking into account variability and potential error in rates, due to unequal population distributions and spatial autocorrelation (Elliot et al 2000). The included studies that applied spatial regression techniques demonstrated how these methods can help to address statistical challenges associated with aggregated data by geographical regions (Dai et al 2013; Erdogan 2009; Shenoi et al 2015). These studies also analysed a diverse range of factors (e.g.…”

“…Many studies used multiple categories and methods and the boundaries between them were not always clear. For example, the most comprehensive studies began by mapping raw data, further explored the data using one or more cluster detection methods and then applied one or more spatial regression methods to understand the relationship with predictor variables (Dai et al 2013; Shenoi et al 2015). …”

A randomized controlled trial to evaluate the impact of a geo-specific poster compared to a general poster for effecting change in perceived threat and intention to avoid drowning ‘hotspots’ among children of migrant workers: evidence from Ningbo, China

“…The new programme reasoned that in a new built environment, a better understanding of where aquatic hazards are geographically could add potential value for drowning prevention among children of migrant workers. Accordingly, a new poster that mapped drowning ‘hotspots’ was developed [11], utilizing data from the Ningbo Medical Emergency Service System (see Fig. 2).…”