Bommer, Christian

[["dc.bibliographiccitation.firstpage","e001175"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","BMJ Global Health"],["dc.bibliographiccitation.volume","4"],["dc.contributor.author","Bommer, Christian"],["dc.contributor.author","Vollmer, Sebastian"],["dc.contributor.author","Subramanian, S V"],["dc.date.accessioned","2019-07-09T11:49:58Z"],["dc.date.available","2019-07-09T11:49:58Z"],["dc.date.issued","2019"],["dc.description.abstract","Introduction Reducing stunting is an important part of the global health agenda. Despite likely changes in risk factors as children age, determinants of stunting are typically analysed without taking into account age-related heterogeneity. We aim to fill this gap by providing an in-depth analysis of the role of socioeconomic status (SES) as a moderator for the stunting-age pattern. Methods Epidemiological and socioeconomic data from 72 Demographic and Health Surveys (DHS) were used to calculate stunting-age patterns by SES quartiles, derived from an index of household assets. We further investigated how differences in age-specific stunting rates between children from rich and poor households are explained by determinants that could be modified by nutrition-specific versus nutrition-sensitive interventions. Results While stunting prevalence in the pooled sample of 72 DHS is low in children up to the age of 5 months (maximum prevalence of 17.8% (95% CI 16.4;19.3)), stunting rates in older children tend to exceed those of younger ones in the age bracket of 6–20 months. This pattern is more pronounced in the poorest than in the richest quartile, with large differences in stunting prevalence at 20 months (stunting rates: 40.7% (95% CI 39.5 to 41.8) in the full sample, 50.3% (95% CI 48.2 to 52.4) in the poorest quartile and 29.2% (95% CI 26.8 to 31.5) in the richest quartile). When adjusting for determinants related to nutrition-specific interventions only, SES-related differences decrease by up to 30.1%. Much stronger effects (up to 59.2%) occur when determinants related to nutrition-sensitive interventions are additionally included. Conclusion While differences between children from rich and poor households are small during the first 5 months of life, SES is an important moderator for age-specific stunting rates in older children. Determinants related to nutrition-specific interventions are not sufficient to explain these SES-related differences, which could imply that a multifactorial approach is needed to reduce age-specific stunting rates in the poorest children."],["dc.identifier.doi","10.1136/bmjgh-2018-001175"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/15820"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/59665"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.rights","CC BY-NC 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by-nc/4.0"],["dc.subject.ddc","300"],["dc.subject.ddc","320"],["dc.title","How socioeconomic status moderates the stunting-age relationship in low-income and middle-income countries"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","53"],["dc.bibliographiccitation.journal","Journal of Clinical Epidemiology"],["dc.bibliographiccitation.lastpage","66"],["dc.bibliographiccitation.volume","89"],["dc.contributor.author","Bärnighausen, Till"],["dc.contributor.author","Oldenburg, Catherine"],["dc.contributor.author","Tugwell, Peter"],["dc.contributor.author","Bommer, Christian"],["dc.contributor.author","Ebert, Cara"],["dc.contributor.author","Barreto, Mauricio"],["dc.contributor.author","Djimeu, Eric"],["dc.contributor.author","Haber, Noah"],["dc.contributor.author","Waddington, Hugh"],["dc.contributor.author","Rockers, Peter"],["dc.contributor.author","Sianesi, Barbara"],["dc.contributor.author","Bor, Jacob"],["dc.contributor.author","Fink, Günther"],["dc.contributor.author","Valentine, Jeffrey"],["dc.contributor.author","Tanner, Jeffrey"],["dc.contributor.author","Stanley, Tom"],["dc.contributor.author","Sierra, Eduardo"],["dc.contributor.author","Tchetgen, Eric Tchetgen"],["dc.contributor.author","Atun, Rifat"],["dc.contributor.author","Vollmer, Sebastian"],["dc.date.accessioned","2020-12-10T14:25:00Z"],["dc.date.available","2020-12-10T14:25:00Z"],["dc.date.issued","2017"],["dc.description.abstract","Quasi-experimental designs are gaining popularity in epidemiology and health systems research—in particular for the evaluation of health care practice, programs, and policy—because they allow strong causal inferences without randomized controlled experiments. We describe the concepts underlying five important quasi-experimental designs: Instrumental Variables, Regression Discontinuity, Interrupted Time Series, Fixed Effects, and Difference-in-Differences designs. We illustrate each of the designs with an example from health research. We then describe the assumptions required for each of the designs to ensure valid causal inference and discuss the tests available to examine the assumptions."],["dc.identifier.doi","10.1016/j.jclinepi.2017.02.017"],["dc.identifier.issn","0895-4356"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/72403"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-354"],["dc.relation.orgunit","Department für Volkswirtschaftslehre (VWL)"],["dc.title","Quasi-experimental study designs series—paper 7: assessing the assumptions"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dc.type.version","submitted_version"],["dspace.entity.type","Publication"]]