Article Text
Abstract
Background Despite recent improvements, low height-for-age, a key indicator of inadequate child nutrition, is an ongoing public health issue in low-income and middle-income countries. Paid maternity leave has the potential to improve child nutrition, but few studies have estimated its impact.
Methods We used data from 583 227 children younger than 5 years in 37 countries surveyed as part of the Demographic and Health Surveys (2000–2014) to compare the change in children’s height-for-age z score in five countries that increased their legislated duration of paid maternity leave (Uganda, Zambia, Zimbabwe, Bangladesh and Lesotho) relative to 32 other countries that did not. A quasiexperimental difference-in-difference design involving a linear regression of height-for-age z score on the number of weeks of legislated paid maternity leave was used. We included fixed effects for country and birth year to control for, respectively, fixed country characteristics and shared trends in height-for-age, and adjusted for time-varying covariates such as gross domestic product per capita and the female labour force participation rate.
Results The mean height-for-age z scores in the pretreatment period were -1.91 (SD=1.44) and –1.47 (SD=1.57) in countries that did and did not change their policies, respectively. The scores increased in treated and control countries over time. A 1-month increase in legislated paid maternity leave was associated with a decrease of 0.08(95% CI −0.20 to 0.04) in child height-for-age z score. Sensitivity analyses did not support a robust association between paid maternity leave policies and height-for-age z score.
Conclusion We found little evidence that recent changes in legislated paid maternity leave have been sufficient to affect child height-for-age z scores. The relatively short durations of leave, the potential for low coverage and the strong increasing trend in children’s growth may explain our findings. Future studies considering longer durations or combined interventions may reveal further insight to support policy.
- Epidemiology
- Nutrition
- Prevention strategies
- Other study design
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Footnotes
Contributors JH led the initial development and framing of the global quantitative laws and policy databases on which this analysis is based. AN, JH and DJ conceptualised the idea. DJ performed the literature search, conducted the analyses and drafted the manuscript. SH and AN helped with the statistical analyses. All authors reviewed the manuscript for important intellectual content, contributed to the interpretation of results and revision of the manuscript, and approved the final draft.
Funding All authors acknowledge funding from the Canadian Institutes of Health Research Operating Grant, Examining the impact of social policies on health equity (ROH-115209). AN is supported by the Canada Research Chairs Program. SH was supported by a Chercheur-boursier Junior 2 from le Fonds de recherche du Québec – Santé (FRQS).
Competing interests None declared.
Ethics approval IRB at McGill University.
Provenance and peer review Not commissioned; externally peer reviewed.
Data sharing statement Analyses utilised two sources of data, specifically: (1) height-for-age z score information collected from respondents surveyed as part of the Demographic and Health Surveys (DHS) (Table 1) and (2) measures of maternity leave policies. The DHS data are publicly available, but users must first register with the DHS program. Registration, which requires a summary of the proposed study and selection of country datasets, can be completed at: http://www.dhsprogram.com/data. Data on current maternity leave policies for each sampled country were provided by UCLA’s World Legal Rights Data Centre (WoRLD) and then collected retrospectively to 1995 by McGill University’s Maternal and Child Health Equity (MACHEquity) research program. The policy data are freely available through the website, www.machequity.com, without any restriction. The policy data, as well as the statistical code for producing the analytic dataset and replicating our results (given DHS data that must be downloaded from the DHS program), are also available with unrestricted access from the corresponding author’s Dataverse: https://dataverse.harvard.edu/.