Cross sectional survey of socioeconomic variations in severity and mechanism of childhood injuries in Trent 1992-7

Abstract

Objective To determine the relation between morbidity from injury and deprivation for different levels of injury severity and for different injury mechanisms for children aged 0-14 years. Design Cross sectional survey of routinely collected hospital admission data for injury 1992-7. Setting 862 electoral wards in Trent Region. Subjects 21 587 injury related hospital admissions for children aged 0-4 years and 35 042 admissions for children aged 5-14. Main outcome measures Rate ratios for hospital admission for all injuries, all injuries involving long bone fracture, and all injuries involving long bone fracture requiring an operation; rate ratios for hospital admission for six types of injury mechanism divided by quintiles of the electoral wards' Townsend scores for deprivation. Rate ratios calculated by Poisson regression, with adjustment for distance from nearest hospital admitting patients with injuries, rurality, ethnicity, and percentage of males in each electoral ward. Results Both total number of admissions for injury and admissions for injuries of higher severity increased with increasing socioeconomic deprivation. These gradients were more marked for 0-4 year old children than 5-14 year olds. In terms of injury mechanisms, the steepest socioeconomic gradients (where the rate for the fifth of electoral wards with the highest deprivation scores was >3 times that of the fifth with the lowest scores) were for pedestrian injuries (adjusted rate ratio 3.65 (95% confidence interval 2.94 to 4.54)), burns and scalds (adjusted rate ratio 3.49 (2.81 to 4.34)), and poisoning (adjusted rate ratio 2.98 (2.65 to 3.34)). Conclusion There are steep socioeconomic gradients for injury morbidity including the most common mechanisms of injury. This has implications for targeting injury prevention interventions and resources.

Mediators and Adverse Effects of Child Poverty in the United States

“…85 Children in poor neighborhoods are at increased risk of cycling accidents, pedestrian injuries, falls, burns, poisonings, and chemical burns. 86 If children in the least healthy communities experienced mortality rates from unintentional injuries and homicide similar to those of the most resource-rich communities, overall US child mortality would decrease by one-third. 87 Injuries from child maltreatment and developmental consequences of neglect also are risks for poor children.…”

Children at risk of medicinal and non-medicinal poisoning: a population-based case-control study in general practice

“…5 But how can the primary care team establish which children are at greatest risk? The data collected routinely by primary care do include information on known risk factors for childhood poisoning injury, including child age, 6-10 sex, 6,7,11 birth order, 12 maternal age, 8,13 depression, 13 socioeconomic disadvantage, 12,[14][15][16] and single parenthood, 13 but these data are not currently used to identify families with the greatest potential to benefit from poisoning-prevention interventions. Given that primary care clinicians have specific responsibilities for poison prevention when they are prescribing or advising parents to purchase over-the-counter medications that are potential poisoning agents, this study has been undertaken to determine if children at risk of poisoning, and more specifically poisoning by medicines and non-medicines, can be identified using primary care data.…”

Road crash involvement and professional status: A prospective study using the French Gazel cohort

“…Whereas our available data is not completely adequate for a thorough investigation of the effect of social position on susceptibility to injury in crash (in some of these crashes it could be possible that the injury casualty is not the studied subject), these results support the hypothesis that low social position subjects have a greater fragility in road crashes which can be partly explained by health deprivation or illness (Chandola et al 2003;Hippisley-Cox et al 2002;Kennedy et al 1998;Power 1994;Roberts 1997;Walsh and Jarvis 1992). It could also be explained by the fact that the lowest social status subjects do not have the safest vehicles (Choo and L. 2003), that they have the least maintained vehicles (Cubbin and Smith 2002;MacDonald and Romberg 1978) and that they wear seat belts less often than other groups of road users (Kweon and Kockelman 2003;Lerner et al 2001;Shinar et al 2001).…”