Is the cost‐effectiveness of an early‐childhood sleep intervention to prevent obesity affected by socioeconomic position?

Abstract

Objective This study aimed to determine whether the cost‐effectiveness of an infant sleep intervention from the Prevention of Overweight in Infancy (POI) trial was influenced by socioeconomic position (SEP). Methods An SEP‐specific economic evaluation of the sleep intervention was conducted. SEP‐specific intervention costs and effects at age 5 years, derived from the trial data, were applied to a representative cohort of 4,898 4‐ to 5‐year‐old Australian children. Quality‐adjusted life years and health care costs were simulated until age 17 years using a purpose‐built SEP‐specific model. Incremental cost‐effectiveness ratios and acceptability curves were derived for each SEP group. Results The incremental cost‐effectiveness ratios, in Australian dollars per quality‐adjusted life year gained, were smaller in the low‐ ($23,010) and mid‐SEP ($18,206) groups compared with the high‐SEP group ($31,981). The probability that the intervention was cost‐effective was very high in the low‐ and mid‐SEP groups (92%‐100%) and moderately high in the high‐SEP group (79%). Conclusions An infant sleep intervention is more cost‐effective in low‐ and mid‐SEP groups compared with high‐SEP groups. Targeting this intervention to low‐SEP groups would not require trade‐offs between efficiency and equity.

A systematic review of economic evaluations of interventions targeting childhood overweight and obesity

“…192 Rates of 3% were typical in the United States and Australia, but the most recent studies in Australia applied a 5% discount rate to costs and benefits. [48][49][50]177 Across all studies, discount rates did not exceed 5%. Many studies with follow-up periods or time horizons exceeding 1 year did not report discount rates, and no justification was provided for their omission.…”

“…Some studies applied utility values obtained from meta-analyses to categorical childhood weight status (healthy weight, overweight, and obesity). 43,50,115,177 Other model-based studies with time horizons extending into adulthood applied utility decrements derived for excess weight in adult populations to childhood cohorts. 128 Alternatively, a health state utility value of 1 was assigned to childhood cohorts; that is, children were assumed to be in perfect health, regardless of their weight status.…”

“…30,31,34,35,37,38,[41][42][43][44]47 Decision analytic models typically consisted of decision trees, 51,52,68 state-transition or Markov models 41,42,47,60,61,69,73,99,106,[110][111][112]123,128,137,145,150,158,159,170 (the prevailing method in this design category), and simulation approaches (individual-level, discrete-time, and discrete-event simulations). 30,31,43,49,50,55,67,76,77,80,130,134,167,177,178 Key sensitivity analysis methods in decision-analytic modeling included determin...…”

“…81 Only one UK-based study 137 assumed a higher threshold value of GBP 60,000 per QALY, citing guidance issued by HM Treasury. 192 Studies in Australia quoted cost-effectiveness thresholds of AUD 50,000 per QALY gained, 43,44,49,50,177 per DALY averted, [99][100][101][102][103]145,150 and per HALY gained. 106,107,[109][110][111][112] Studies in the Netherlands stated EUR 19,600 per DALY averted based on the Dutch per capita income 131 and EUR 20,000 per QALY gained.…”

“…[39][40][41][45][46][47]49 Three studies assessed interventions that combined components of diet and/or physical activity with sleep modification. 43,44,50 Two studies evaluated interventions seeking to change the nutrition environment by way of protein infant formula modification. 30,42 Two studies were m-health-based: one involved deploying a multicomponent smartphone app intervention among 13-year-olds in a school setting, 47 and the other compared the use of telephone or short message service (SMS) to contact mothers of infants.…”