000			00595nab|a22002177a|4500
999			_c63527 _d63519
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003			MX-TxCIM
005			20210326204028.0
008			202101s2020||||xxk|||p|op||||00||0|eng|d
022			_a1476-072X
024	8		_ahttps://doi.org/10.1186/s12942-020-00240-2
040			_aMX-TxCIM
041			_aeng
100	1		_aOostendorp, R. _919269
245	1	0	_aWho lacks and who benefits from diet diversity : _bevidence from (impact) profiling for children in Zimbabwe
260			_aLondon (United Kingdom) : _bBioMed Central, _c2020.
500			_aPeer review
500			_aOpen Access
520			_aBackground: The impact of diet diversity—defined as the number of different foods or food groups consumed over a given reference period—on child nutrition outcomes strongly interacts with agro-ecological, institutional, and socio-economic drivers of child food and nutrition security. Yet, the literature on the impact of diet diversity typically estimates average treatment effects, largely ignoring impact heterogeneity among different groups. Methods: In this paper, we introduce a new method of profiling to identify groups of treatment units that stand to gain the most from a given intervention. We start from the ‘polling approach’ which provides a fully flexible (non-parametric) method to profile vulnerability patterns (patterns in ‘needs’) across highly heterogeneous environments [35]. Here we combine this polling methodology with matching techniques to identify ‘impact profiles’ showing how impact varies across non-parametric profiles. We use this method to explore the potential for improving child nutrition outcomes, in particular stunting, through targeted improvements in dietary diversity in a physically and socio-economically diverse country, namely Zimbabwe. Complex interaction effects with agro-ecological, institutional and socio-economic conditions are accounted for. Finally, we analyze whether targeting interventions at the neediest (as identified by the polling approach) will also create the largest benefits. Results: The dominant profile for stunted children is that they are young (6–12 months), live in poorer/poorest households, in rural areas characterized by significant sloping of the terrain and with one-sided emphasis on maize cultivation and medium dry conditions. When moving from “need” to “maximal impact”, we calculate both the coverage in “need” as well as the impact coverage, and find that targeting on need does not always provide the largest impact. Conclusions: Policy-makers need to remain alert that targeting on need is not always the same as targeting on impact. Estimation of heterogeneous treatment effects allows for more efficient targeting. It also enhances the external validity of the estimated impact findings, as the impact of child diet diversity on stunting depends on various agro-ecological variables, and policy-makers can relate these findings to areas outside our study area with similar agro-ecological conditions.
546			_aText in English
650		7	_aMalnutrition _2AGROVOC _96463
650		7	_aChildren _2AGROVOC _92809
650		7	_aImpact assessment _2AGROVOC _98668
650		7	_aDiet _2AGROVOC _95374
651		7	_2AGROVOC _94496 _aZimbabwe
700	1		_919270 _aWesenbeeck, L. van
700	1		_919271 _aSonneveld, B.
700	1		_911695 _aZikhali, P.
773	0		_tInternational Journal of Health Geographics _dLondon (United Kingdom) : BioMed Central, 2020. _x1476-072X _gv. 19, art. 45
856	4		_yClick here to access online _uhttps://doi.org/10.1186/s12942-020-00240-2
942			_cJA _n0 _2ddc