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P09 Associations between measures of child poverty, health-care use, and health outcomes in english national datasets
  1. DS Hargreaves1,
  2. JM Pitchforth1,
  3. J de Sa2,
  4. CR Cheung3
  1. 1Population, Policy and Practice Programme, University College London Institute of Child Health, London, UK
  2. 2London School of Hygiene and Tropical Medicine, London,UK
  3. 3Evelina London Children’s Hospital, St Thomas’ Hospital, London, UK


Aims The UK Government has recently proposed the abolition of income-based measures of child poverty in favour of environmental, educational, and employment measures of deprivation. We aimed to study how strongly these proposed measures are associated with health outcomes among children and young people compared with a relative income measure.

Methods With data from Hospital Episode Statistics (2013/14) (n=16.4 million), we compared inequalities in inpatient admissions of children and young people aged 0–24 years per thousand (total and chronic conditions) using deciles of five area-based deprivation measures: index of multiple deprivation (IMD), income, income deprivation affecting children (IDACI), education, living environment. With data from Health Survey for England (2014)(n=3085), we compared inequalities in self/ parent-reported general health and long-standing illness using equivalised household income quintiles, IMD quintiles, and whether the household reference person was employed (n=2417, 780.3%); not working (397, 12.9%); or retired, long-term sick, or other (274, 8.9%). The ratios of hospital admission rates and weighted prevalence of poor self-reported health were compared between the most and least deprived groups within each measure.

Results Total admission rates were higher among the most versus least deprived IMD deciles (ratio 1.60, 95% CI 1.59–1.61). The ratio was greater when income deciles were compared (1.69 (1.68–1.71)) and smaller when analysis was by education (1.59 (1.58–1.60)), IDACI (1.52(1.51–1.53)), and living environment (1.01(1.00–1.02)). Admission inequalities for chronic conditions were narrower (IMD 1.18(1.16–1.20); income 1.25(1.22–1.27)). In the household-level analyses, inequalities in fair/poor, self/ parent-reported health were seen when comparing lowest versus highest income quintiles (12.2% vs 3.9%; ratio 3.12(1.99–5.87)), not working versus being employed (14.1% vs 6.1%; 2.30(1.68– 3.03)), and most versus least deprived IMD quintiles (9.8% vs 6.5%; 1.49(1.05–2.21)). For long-standing illness, the equivalent data were: income (22.0% vs 11.0%; 1.99(1.50–2.77)); employment (23.6% vs 15.8%; 1.50(1.21–1.81)); IMD (18.9% vs 17.2%; 1.10(0.88–1.39)).

Conclusions Child poverty measures differ significantly in their association with key indicators of population health and healthcare use. Of the measures studied here, hospital admissions were most strongly associated with income inequality. Self-reported health outcomes were also more strongly linked to household income than area-level IMD.

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