Childhood transitions between weight status categories: evidence from the UK Millennium Cohort Study

Background: Assessing the cost-effectiveness of interventions targeting childhood excess weight requires estimates of the hazards of transitioning between weight status categories. Current estimates are based on studies characterized by insufficient sample sizes, a lack of national representativeness, and untested assumptions. Objectives: We sought to (1) estimate transition probabilities and hazard ratios for transitioning between childhood weight status categories, (2) test the validity of the underlying assumption in the literature that transitions between childhood bodyweight categories are time-homogeneous, (3) account for complex sampling procedures when deriving nationally representative transition estimates, and (4) explore the impact of child, maternal, and sociodemographic characteristics. Methods: We applied a multistate transition modeling approach accounting for complex survey design to UK Millennium Cohort Study (MCS) data to predict transition probabilities and hazard ratios for weight status movements for children aged 3–17. Surveys were conducted at ages 3 (wave 2 in 2004), 5 (wave 3 in 2006), 7 (wave 4 in 2008), 11 (wave 5 in 2012), 14 (wave 6 in 2015), and 17 (wave 7 in 2018) years. We derived datasets that included repeated body mass index measurements across waves after excluding multiple births and children with missing or implausible bodyweight records. To account for the stratified cluster sample design of the MCS, we incorporated survey weights and jackknife replicates of survey weights. Using a validation dataset from the MCS, we tested the validity of our models. Finally, we estimated the relationships between state transitions and child, maternal, and sociodemographic factors. Results: The datasets for our primary analysis consisted of 10,399 children for waves 2–3, 10,729 for waves 3–4, 9685 for waves 4–5, 8593 for waves 5–6, and 7085 for waves 6–7. All datasets consisted of roughly equal splits of boys and girls. Under the assumption of time-heterogeneous transition rates (our base-case model), younger children (ages 3–5 and 5–7 years) had significantly higher annual transition probabilities of moving from healthy weight to overweight (0.033, 95% confidence interval [CI] 0.026–0.041, and 0.027, 95% CI 0.021–0.033, respectively) compared to older children (0.015, 95% CI 0.012–0.018, at ages 7–11; 0.018, 95% CI 0.013–0.023, at ages 11–14; and 0.018, 95% CI 0.013–0.025 at ages 14–17 years). However, the resolution of unhealthy weight was more strongly age-dependent than transitions from healthy weight to non-healthy weight states. Transition hazards differed by child, maternal, and sociodemographic factors. Conclusions: Our models generated estimates of bodyweight status transitions in a representative UK childhood population. Compared to our scenario models (i.e., time-homogeneous transition rates), our base-case model fits the observed data best, indicating a non-time-homogeneous pattern in transitions between bodyweight categories during childhood. Transition hazards varied significantly by age and across subpopulations, suggesting that conducting subgroup-specific cost-effectiveness analyses of childhood weight management interventions will optimize decision-making

Psychometric Performance of Generic Childhood Multi-Attribute Utility Instruments in Preterm and Low Birthweight Populations: A Systematic Review

Background: Individuals born preterm (gestational age < 37 weeks) and/or at low birthweight (<2500 g) are at increased risk of health impairments from birth to adulthood. This review aimed to evaluate the psychometric performance of generic childhood-specific or childhood-compatible multi-attribute utility instruments (MAUIs) in preterm and/or low birthweight (PLB) populations. Methods: Searches covered seven databases, including studies that targeted childhood (aged < 18 years) and/or adult (≥18 years) PLB populations; provided psychometric evidence for generic childhood-specific or compatible MAUI(s) (any language version); and published in English. Eighteen psychometric properties were evaluated using a four-part criteria rating system. Data syntheses identified psychometric evidence gaps and summarised the psychometric assessment methods/results. Results: A total of 42 studies were included, generating 178 criteria rating outputs across four MAUIs: 17D, CHSCS-PS, HUI2, and HUI3. Moreover, 64.0% of outputs concerned the HUI3 MAUI, and 38.2% related to known-group validity. There was no evidence for five psychometric properties. Only 6.7% of outputs concerned reliability and proxy–child agreement. No MAUI outperformed others across all properties. The frequently applied HUI2 and HUI3 lacked content validity evidence. Conclusions: This psychometric evidence catalogue should inform the selection of MAUI(s) suited to the specific aims of applications targeting PLB populations. Further psychometric research is warranted to address the gaps in psychometric evidence

Health economic aspects of childhood excess weight: A structured review

An economic perspective is crucial to understand the broad consequences of childhood excess weight (CEW). These can manifest in the form of elevated health care and societal costs, impaired health status, or inefficiencies in the allocation of resources targeted at its prevention, management, or treatment. Although existing systematic reviews provide summaries of distinct economic research strands covering CEW, they have a restricted focus that overlooks relevant evidence. The overarching aim of this structured review was to update and enhance recent key reviews of four strands of economic evidence in this area, namely, (1) economic costs associated with CEW, (2) health utilities associated with CEW, (3) economic evaluations of interventions targeting CEW, and (4) economic determinants and broader consequences of CEW. Our de novo searches identified six additional studies for the first research strand, five studies for the second, thirty-one for the third, and two for the fourth. Most studies were conducted in a small number of high-income countries. Our review highlights knowledge gaps across all the research strands. Evidence from this structured review can act as data input into future economic evaluations in this area and highlights areas where future economic research should be targeted

The Lancet Nigeria Commission: investing in health and the future of the nation.

Funder: Wellcome TrustHealth is central to the development of any country. Nigeria’s gross domestic product is the largest in Africa, but its per capita income of about ₦770 000 (US$2000) is low with a highly inequitable distribution of income, wealth, and therefore, health. It is a picture of poverty amidst plenty. Nigeria is both a wealthy country and a very poor one. About 40% of Nigerians live in poverty, in social conditions that create ill health, and with the ever-present risk of catastrophic expenditures from high out-of-pocket spending for health. Even compared with countries of similar income levels in Africa, Nigeria’s population health outcomes are poor, with national statistics masking drastic differences between rich and poor, urban and rural populations, and different regions