Trends in child growth in the population covered by Plan Nacer and Programa Sumar between 2005 and 2013, in Argentina

Similar documents
Performance-Based Intergovernmental Transfers

APPENDIX 2: SUMMARY OF EVIDENCE

Data Profile of Sagar District

Human Development Indices and Indicators: 2018 Statistical Update. Argentina

Do Conditional Cash Transfers (CCT) Really Improve Education and Health and Fight Poverty? The Evidence

Alice Nabalamba, Ph.D. Statistics Department African Development Bank Group

Investing on Human Development: More than Conditioning Cash Transfers. Woodrow Wilson Center September 25, 2008

Sustaining Development: Results from a Study of Sustainability and Exit Strategies among Development Food Assistance Projects India Country Study

Intervention costing in OneHealth: Concepts related to Population in Need, Target Population and Coverage

Human Development Indices and Indicators: 2018 Statistical Update. Russian Federation

Human Development Indices and Indicators: 2018 Statistical Update. Brazil

Human Development Indices and Indicators: 2018 Statistical Update. Costa Rica

Human Development Indices and Indicators: 2018 Statistical Update. Switzerland

Human Development Indices and Indicators: 2018 Statistical Update. Congo

Hüsnü M. Özyeğin Foundation Rural Development Program

Human Development Indices and Indicators: 2018 Statistical Update. Turkey

Human Development Indices and Indicators: 2018 Statistical Update. Belgium

Human Development Indices and Indicators: 2018 Statistical Update. Peru

Calibrating the 2018 Social Progress Index to the Sustainable Development Goals

Human Development Indices and Indicators: 2018 Statistical Update. Uzbekistan

ECONOMIC ANALYSIS. A. Short-Term Effects on Income Poverty and Vulnerability

Eswatini (Kingdom of)

Human Development Indices and Indicators: 2018 Statistical Update. Paraguay

JLN/GIZ Case Studies on Payment Innovation for Primary Health Care

Experience in Setting National Nutrition Targets and Commitments to Actions: The Case for Zambia

Human Development Indices and Indicators: 2018 Statistical Update. Dominica

The ways to reach universal coverage in Argentina

Special Evaluation Study. MICROFINANCE DEVELOPMENT STRATEGY 2000 Sector Performance and Client Welfare September 2012

DIFFERENCE DIFFERENCES

Human Development Indices and Indicators: 2018 Statistical Update. Nigeria

GFF Monitoring strategy

Government Quality Matter?

Obesity, Disability, and Movement onto the DI Rolls

Conditional Cash Transfers for Improving Utilization of Health Services. Health Systems Innovation Workshop Abuja, January 25 th -29 th, 2010

Impacts of Conditional Cash Transfers on Health Status: The Bolsa Familia Program in Brazil

School Attendance, Child Labour and Cash

24,700 cr GoI allocations for Ministry of Women and Child Development (MWCD) in FY

Antipoverty transfers and growth

Appendix 2 Basic Check List

Universal Social Protection

Explanatory note on the 2014 Human Development Report composite indices. Argentina. HDI values and rank changes in the 2014 Human Development Report

BROAD DEMOGRAPHIC TRENDS IN LDCs

PROJECT INFORMATION DOCUMENT (PID) CONCEPT STAGE Report No.: AB3313 Project Name. BO-Enhancing Human Capital of Children and Youth Region

Tracking Tool glossary and technical notes 20/11/2016

22,095 cr GoI allocations for Ministry of Women and Child Development (MWCD) in FY

Oman. Country coverage and the methodology of the Statistical Annex of the 2015 HDR

Substantive insights from an income-based intervention to reduce poverty

Session III Differences in Differences (Dif- and Panel Data

E Distribution: GENERAL. Executive Board First Regular Session. Rome, 9 11 February January 2009 ORIGINAL: ENGLISH

Bargaining with Grandma: The Impact of the South African Pension on Household Decision Making

MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question.

Working Paper No. 247

Impact of Global Financial Crisis and Assessment of Policy Responses. Suzanne Duryea November 18, 2010

Serbia. Country coverage and the methodology of the Statistical Annex of the 2015 HDR

Two-Sample Cross Tabulation: Application to Poverty and Child. Malnutrition in Tanzania

Planning Sample Size for Randomized Evaluations Esther Duflo J-PAL

Challenges and Opportunities with NCHS Linked Data Files

Explanatory note on the 2014 Human Development Report composite indices. Ireland. HDI values and rank changes in the 2014 Human Development Report

Explanatory note on the 2014 Human Development Report composite indices. Switzerland. HDI values and rank changes in the 2014 Human Development Report

Data needs for analyses of inequalities: WHAT WE LEARNED FROM THE COUNTDOWN TO 2015 By Cesar G Victora

Health Insurance for Poor People in the Province Of Santa Fe, Argentina: The Power of the Clear Model for All

Argentina Increasing Utilization of Health Care Services among the Uninsured Population: The Plan Nacer Program

PROJECT INFORMATION DOCUMENT (PID) CONCEPT STAGE Report No.: AB2560 Project Name. Bahia Integrated Water Management Region

Findings Brief. NC Rural Health Research Program

Family Policies and low Fertility: How does the social network influence the Impact of Policies

Measuring Impact. Impact Evaluation Methods for Policymakers. Sebastian Martinez. The World Bank

MED 146 Deliverable 1.24 Five Year Florida Medicaid Maternal and Child Health Status Indicators Report:

Impact of Transfer Income on Cognitive Impairment in the Elderly

Cash versus Kind: Understanding the Preferences of the Bicycle- Programme Beneficiaries in Bihar

Sustainable Development Goals and Early Childhood in Argentina: Gaps and priority actions to leave no one behind

NORMAL RANDOM VARIABLES (Normal or gaussian distribution)

What is Global Hunger Index & Position of India in the Index?

Chapter 7 INTERNATIONAL GENDER PERSPECTIVE

TABLES Table 1. GDSW Budget - Staff by Years (as of June) Staff Number

Montenegro. Country coverage and the methodology of the Statistical Annex of the 2015 HDR

Explanatory note on the 2014 Human Development Report composite indices. Brazil. HDI values and rank changes in the 2014 Human Development Report

Final. Spring 2009 Economics of Development

Explanatory note on the 2014 Human Development Report composite indices. Ukraine. HDI values and rank changes in the 2014 Human Development Report

How House Price Dynamics and Credit Constraints affect the Equity Extraction of Senior Homeowners

Slovenia. HDI values and rank changes in the 2013 Human Development Report

NATURE OF THE INCREASE

The Effect of Cash Transfer Programs on Poverty Reduction

Briefing note for countries on the 2015 Human Development Report. Lesotho

Explanatory note on the 2014 Human Development Report composite indices. Colombia. HDI values and rank changes in the 2014 Human Development Report

Basudeb Guha-Khasnobis 1 and Gautam Hazarika 2

Poverty, Inequality, and Development

Beneficiary View. Cameroon - Total Net ODA as a Percentage of GNI 12. Cameroon - Total Net ODA Disbursements Per Capita 120

Chapter 3 - Lecture 3 Expected Values of Discrete Random Va

Labor supply responses to health shocks in Senegal

Daniel Fernández Kranz IE Business School Núria Rodríguez-Planas Universitat Autònoma de Barcelona

Children's Health Coverage in Mississippi, CPS /27/2010. Center for Mississippi Health Policy

Small Area Health Insurance Estimates from the Census Bureau: 2008 and 2009

Universal Health Coverage in LAC: A review of Argentina and Uruguay cases.

CHAPTER 4 DATA ANALYSIS Data Hypothesis

International Workshop on Sustainable Development Goals (SDG) Indicators Beijing, China June 2018

Chapter 8. Variables. Copyright 2004 Brooks/Cole, a division of Thomson Learning, Inc.

Motivation. Conditional cash transfer (CCT) programs have become very popular: first in Latin America and now across the world

Rwanda. UNICEF/Till Muellenmeister. Health Budget Brief

Moral hazard in a voluntary deposit insurance system: Revisited

EICT Microsimulations for New Public Policy Initiatives for Mexico to tackle poverty

Transcription:

Trends in child growth in the population covered by Plan Nacer and Programa Sumar between 2005 and 2013, in Argentina María Eugenia Szretter Instituto de Cálculo y Departamento de Matemática Facultad de Ciencias Exactas y Naturales Universidad de Buenos Aires

Published article Based on: Nuñez, P. A.; Fernández-Slezak, D.; Farall, A.; Szretter, M.E.; Salomón, O.D. Valeggia, C.R. (2016). Impact of Universal Health Coverage on Child Growth and Nutrition in Argentina. American Journal of Public Health. 106 (4), 720-726

Published article Based on: Nuñez, P. A.; Fernández-Slezak, D.; Farall, A.; Szretter, M.E.; Salomón, O.D. Valeggia, C.R. (2016). Impact of Universal Health Coverage on Child Growth and Nutrition in Argentina. American Journal of Public Health. 106 (4), 720-726 Objective.

Published article Based on: Nuñez, P. A.; Fernández-Slezak, D.; Farall, A.; Szretter, M.E.; Salomón, O.D. Valeggia, C.R. (2016). Impact of Universal Health Coverage on Child Growth and Nutrition in Argentina. American Journal of Public Health. 106 (4), 720-726 Objective. To estimate trends of undernutrition (stunting and underweight) among children younger than 5 years covered by the universal health coverage programs.

Published article Based on: Nuñez, P. A.; Fernández-Slezak, D.; Farall, A.; Szretter, M.E.; Salomón, O.D. Valeggia, C.R. (2016). Impact of Universal Health Coverage on Child Growth and Nutrition in Argentina. American Journal of Public Health. 106 (4), 720-726 Objective. To estimate trends of undernutrition (stunting and underweight) among children younger than 5 years covered by the universal health coverage programs. Method. Through a statistical model.

Plan Nacer Plan Nacer aimed to improve health status of uninsured children and pregnant women in situations of vulnerability directed resources to the public health care system to incentivize the provision of health services to beneficiaries covered pregnant women and up to 45 days after birth, and children up to age 6 years Implemented in two phases. (in 2005) in 9 provinces in the northern regions of Argentina (in 2007) expanded to cover the rest of the country

Programa Sumar follow-up program launched in 2012 and extended health-care coverage to 5.7 million children and adolescents (0-19 years) and 3.8 million women up to 64 years Both programs focus on 14 specific indicators of pregnancy (detection and controls) neonatal care immunization anthropometric checkups for children

Data available Data for each record: anonymous identifier for each individual, health center (geographical source), the rural versus urban area of the health center,

Data available Data for each record: anonymous identifier for each individual, health center (geographical source), the rural versus urban area of the health center, birth date visit date age (in days) gender weight (in kg) height (in cm)

Data Processing During the 9-year period, Plan Nacer and Programa Sumar collected more than 13 million records 6386 health centers Data clean-up we removed approximately 13 % of the records with missing or biologically implausible data computed z-scores according to World Health Organization 2007 standards tables, at individual-level

Data Processing During the 9-year period, Plan Nacer and Programa Sumar collected more than 13 million records 6386 health centers Data clean-up we removed approximately 13 % of the records with missing or biologically implausible data computed z-scores according to World Health Organization 2007 standards tables, at individual-level

Z-scores: HAZ and WAZ Computed height for age z-score (HAZ) and weight for age z-score (WAZ) for children younger than 5 years covered by the programs, at each health control. Definition (stunting and severe stunting) A child is said to be stunted or severe stunted if his/her HAZ falls below 2 standard deviations or 3 standard deviations of zero, respectively. stunting: retraso en el crecimiento

Prevalence of Stunting Definition (prevalence of stunting and severe stunting) We define the prevalence of stunting and prevalence of severe stunting as the proportion of stunted (or severe stunted) children in a population, respectively. Likewise for underweight and severe underweight (for WAZ).

Flowchart of the data source

Do we need a statistical model if we have the population? Summarize the global behavior of the prevalence

Do we need a statistical model if we have the population? Summarize the global behavior of the prevalence Avoid potential bias effects in the analysis That results from imbalanced interactions of the variables (observational study) Example (1) The distribution of ages of the children included in the study is not homogeneous: during the first years of the programs [2005-2006] younger than average children were included Warning! Stunting and underweight are related to age

Do we need a statistical model if we have the population? Example (2) Different health centers (within departments and provinces) were enrolled in the study at different times, having heterogeneous exposure during the overall period.

Do we need a statistical model if we have the population? Example (2) Different health centers (within departments and provinces) were enrolled in the study at different times, having heterogeneous exposure during the overall period. Warning! If health centers with higher prevalence had joined the study in earlier stages than had centers with lower prevalence, the temporal trend would show an artificial decrease in prevalence at the national level even if the true prevalence was constant.

Do we need a statistical model if we have the population? Example (2) Different health centers (within departments and provinces) were enrolled in the study at different times, having heterogeneous exposure during the overall period. Warning! If health centers with higher prevalence had joined the study in earlier stages than had centers with lower prevalence, the temporal trend would show an artificial decrease in prevalence at the national level even if the true prevalence was constant. Example (3) Different number of repeated measurement of the same child.

Do we need a statistical model if we have the population? Example (2) Different health centers (within departments and provinces) were enrolled in the study at different times, having heterogeneous exposure during the overall period. Warning! If health centers with higher prevalence had joined the study in earlier stages than had centers with lower prevalence, the temporal trend would show an artificial decrease in prevalence at the national level even if the true prevalence was constant. Example (3) Different number of repeated measurement of the same child. Warning! More measurements could be associated with stunting and underweight.

Modelling stunting (HAZ< 2) prevalence P stunted at certain time, age, rural, sex = β 0 + β 1 time + β 2 (time) 2 + γ 1 age + γ 2 (age) 2 + γ 3 (age) 3 + γ 4 (age) 4 + γ 5 (age) 5 + γ 6 (age) 6 + β 3 rural + β 4 sex

Modelling stunting (HAZ< 2) prevalence P stunted at certain time, age, rural, sex = β 0 + β 1 time + β 2 (time) 2 + γ 1 age + γ 2 (age) 2 + γ 3 (age) 3 + γ 4 (age) 4 + γ 5 (age) 5 + γ 6 (age) 6 + β 3 rural + β 4 sex where, all β s and γ s are constants to be determined (estimated) by the data,

Modelling stunting (HAZ< 2) prevalence P stunted at certain time, age, rural, sex = β 0 + β 1 time + β 2 (time) 2 + γ 1 age + γ 2 (age) 2 + γ 3 (age) 3 + γ 4 (age) 4 + γ 5 (age) 5 + γ 6 (age) 6 + β 3 rural + β 4 sex where, all β s and γ s are constants to be determined (estimated) by the data, rural = 1 if is computed for a health center in a rural zone, 0 if not sex = 1 for a girl, and zero otherwise

Modelling stunting (HAZ< 2) prevalence P stunted at certain time, age, rural, sex and child = i = β 0 + β 1 time + β 2 (time) 2 + γ 1 age + γ 2 (age) 2 + γ 3 (age) 3 + γ 4 (age) 4 + γ 5 (age) 5 + γ 6 (age) 6 + β 3 rural + β 4 sex + b i b i is a random variable representing the deviation from the population prevalence for the i-th child

Modelling stunting (HAZ< 2) prevalence P stunted at certain time, age, rural, sex and child = i = β 0 + β 1 time + β 2 (time) 2 + γ 1 age + γ 2 (age) 2 + γ 3 (age) 3 + γ 4 (age) 4 + γ 5 (age) 5 + γ 6 (age) 6 + β 3 rural + β 4 sex + b i b i is a random variable representing the deviation from the population prevalence for the i-th child Mixed effect model: the random effect b i takes into account the correlation among observations (along time) for the same child.

Results Curves depict estimated prevalence of stunting and severe stunting with the model for the whole population (A) and then conditioning to the mean (observed) values of gender, urban vs rural residence, and age. Circles represent empirical proportions, with the total area proportional to the number of records in the year.

Results Curves depict estimated prevalence of stunting for age with the model for the whole population (left) and the observed ones (to the right), for different years. On the bottom, the same for underweight.

Results The prevalence of stunting decreased from 20.6 % to 11.3 %, between 2005 and 2013, nationwide Comparable results for each region When we compare two childs with all other characteristics being equal. Prevalence of stunting for girls is 2.8 lower than for boys Rural inhabitants have a 2.6 higher probability of being stunted than urban ones