scholarly journals Social protection and the level and inequality of child mortality in 101 low- and middle-income countries: A statistical modelling analysis

2021 ◽  
Vol 11 ◽  
Author(s):  
Zhihui Li ◽  
Xinyan Zhou ◽  
Shuyao Ran ◽  
Fernando C Wehrmeister
Author(s):  
Suman Verma

Effective social protection policies are crucial to realizing adolescents’ rights, ensuring their well-being, breaking the cycle of poverty and vulnerability, and helping them realize their full developmental potential. Low- and middle-income countries (LMICs) have extended social security coverage to ensure basic protections—while continuing to develop social protection systems. Social protection for LMIC adolescents in the context of gross violations of their basic rights is examined. Prevalence, consequences of protection rights violations, and the role and impact of social protection programs in ensuring enhanced opportunities for development and well-being among young people are discussed. Results demonstrate direct impacts (e.g., increased income, consumption, goods and services access; greater social inclusion; reduced household stress). LMICs need integrated social protection policy and program expansion if the 2030 Agenda for Sustainable Development is to be realized. With adolescent-centered policies and investments, governments can help adolescents realize their rights to a fulfilling and productive life.


Author(s):  
Andrea Bizzego ◽  
Giulio Gabrieli ◽  
Marc H. Bornstein ◽  
Kirby Deater-Deckard ◽  
Jennifer E. Lansford ◽  
...  

Child Mortality (CM) is a worldwide concern, annually affecting as many as 6.81% children in low- and middle-income countries (LMIC). We used data of the Multiple Indicators Cluster Survey (MICS) (N = 275,160) from 27 LMIC and a machine-learning approach to rank 37 distal causes of CM and identify the top 10 causes in terms of predictive potency. Based on the top 10 causes, we identified households with improved conditions. We retrospectively validated the results by investigating the association between variations of CM and variations of the percentage of households with improved conditions at country-level, between the 2005–2007 and the 2013–2017 administrations of the MICS. A unique contribution of our approach is to identify lesser-known distal causes which likely account for better-known proximal causes: notably, the identified distal causes and preventable and treatable through social, educational, and physical interventions. We demonstrate how machine learning can be used to obtain operational information from big dataset to guide interventions and policy makers.


PLoS ONE ◽  
2016 ◽  
Vol 11 (1) ◽  
pp. e0144908 ◽  
Author(s):  
David M. Bishai ◽  
Robert Cohen ◽  
Y. Natalia Alfonso ◽  
Taghreed Adam ◽  
Shyama Kuruvilla ◽  
...  

2020 ◽  
Author(s):  
Andrea Bizzego ◽  
Giulio Gabrieli ◽  
Marc H. Bornstein ◽  
Kirby Deater-Deckard ◽  
Jennifer E. Lansford ◽  
...  

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