Global estimates of the implications of COVID-19-related preprimary school closures for children’s instructional access, development, learning, and economic wellbeing

Child Development, Volume 92, No. 5, Year 2021

Interprétation

Observational data collected prior to the pandemic (between 2004 and 2019) were used to simulate the potential consequences of early childhood care and education (ECCE) service closures on the estimated 167 million preprimary-age children in 196 countries who lost ECCE access between March 2020 and February 2021. COVID-19-related ECCE disruptions were estimated to result in 19.01 billion person-days of ECCE instruction lost, 10.75 million additional children falling “off track” in their early development, 14.18 million grades of learning lost by adolescence, and a present discounted value of USD 308.02 billion of earnings lost in adulthood. Further burdens associated with ongoing closures were also forecasted. Projected developmental and learning losses were concentrated in low- and lower middle-income countries, likely exacerbating long-standing global inequities. We used a variety of pre‐pandemic data sources to complete our analyses. For all of our analyses, we used the most recently available data on school closures (covering March 11, 2020 to February 2, 2021) from UNICEF and UNESCO. We also used the World Bank’s 2020 classifications to categorize countries into high‐, upper middle‐, lower middle‐, and low‐income groupings (World Bank, 2020). In addition to these sources, to estimate the number of person‐days of ECCE instruction lost (Outcome 1), we used data on ECCE participation rates from UNESCO’s Institute for Statistics (UIS), the UNICEF‐supported Multiple Indicator Cluster Surveys (MICS), and other data sources, along with the number of ECCE‐age children from UNESCO and the World Population Prospects. Second, to estimate the number of children who became off track in their early childhood development (Outcome 2), we used data on ECCE and early childhood development from the MICS. Third, to estimate the number of grades of academic learning lost by adolescence (Outcome 3), we used data on ECCE and adolescent learning from the Programme for International Student Assessment (PISA). Each of these data sources is described in detail below. Finally, to estimate the total earnings lost into adulthood (Outcome 4), we extracted rates of returns (% increase in wages) to schooling from a prior review by Fink and colleagues (2016) and used random‐effects meta‐analysis to compute average returns to schooling for country income groups. Our full sample comprises all countries with national data on ECCE participation (N = 196 countries, representing 99% of the global under‐five population). Our primary source of ECCE participation data was UNESCO’s UIS ECCE database (http://data.uis.unesco.org/; n = 143 countries), which provides information on net ECCE enrollment, defined as the total number of preschool‐age children enrolled in preschool education, and expressed as a percentage of the total population in that age. We prioritized data from UNESCO given that it is the source of information for tracking ECCE participation in the SDGs, and it is also available for the largest number of countries. Where UNESCO data were not available, we extracted ECCE data from MICS surveys (https://mics.unicef.org/, n = 30), which report data on ECCE attendance, defined as the percentage of children 36–59 months currently attending an early childhood educational program. Where neither source was available, we used an online search of official and recognized sources such as Ministries of Education, national censuses, and national household surveys to find the latest participation rates for individual countries (n = 23 countries). Table S1 in the Supporting Information details the final list of countries included in our analysis and the source and definition of ECCE participation data for each. Although UNESCO and the MICS use different definitions of ECCE participation, both sources consider a wide and inclusive set of program types, including public, private, and non‐profit programs, as well as full‐ and part‐time programs. Appendix A in the Supporting Information provides further details regarding analyses examining differences in country‐level ECCE participation rates reported by UNESCO versus MICS for countries in which both data sources were available. In sum, these analyses suggested no evidence for systematic differences across these datasets. Table Table11 Column 4 shows overall rates of ECCE participation by country income group prior to the COVID‐19 pandemic. Rates of participation ranged from a low of approximately 20% in low‐income countries to nearly 80% in high‐income countries. Pre‐COVID‐19 ECCE‐age population, ECCE participation rates, number of children in ECCE, and percent instruction days lost to COVID‐19 This table aggregates populations of children across countries, but presents averages for country‐level participation rates. Data on the total number of ECCE‐age children in each country were obtained from UNESCO (n = 193 countries) and the World Population Prospects (n = 3 countries). The UNESCO definition of ECCE‐age children targets children age 3 years until the starting age of primary education, which is 6 years in most countries (UNESCO, 2012). Accordingly, the ECCE target population typically comprises three annual birth cohorts in each country. Table S1 presents further details on the official starting age of primary education by country, and Table Table11 Column 3 shows the total number of ECCE‐aged children in millions by country income group. In total, 347 million children were estimated to be of ECCE age prior to COVID‐19, the majority of whom lived in lower (150 million) and upper (105 million) middle‐income countries. To estimate short‐term associations between ECCE and early childhood development, we used data from 174,018 3‐ and 4‐year‐old children in 61 countries participating in the MICS (M age = 47.33 months, range = 36–59 months; 49% girls; race/ethnicity not available). MICS include nationally representative data on child and family wellbeing, with a focus on low‐ and middle‐income countries (UNICEF, 2006). Standard MICS questionnaires include questions regarding whether 3‐ and 4‐year‐old children currently attend ECCE programs (yes/no). The early development of these same children is also measured using the Early Childhood Development Index (ECDI). The ECDI includes 10 caregiver‐reported items capturing 3‐ and 4‐year‐old children’s early literacy/numeracy (three items), physical (two items), social‐emotional (three items), and approaches to learning (two items) skills (Loizillon et al., 2017). Following MICS recommendations, children were considered developmentally off track in the ECDI if they failed more than one item in two or more of these domains. Despite criticisms regarding the coarseness of this measure (McCoy et al., 2016), the ECDI is highly policy‐relevant given its current status as the indicator of early childhood development for SDG 4.2.1 (United Nations, 2019). Since the countries that participated in the MICS varied across years, we chose the most recently available dataset for each country. Table S1 lists the specific countries included in our MICS dataset. To estimate associations between ECCE and medium‐term learning outcomes, we used data for 426,125 adolescents in 76 countries participating in the PISA (M age = 15.79 years, range = 15.08–16.33 years; 52% girls; race/ethnicity not available). The PISA is an international survey program that includes direct assessments of reading, mathematics, and science literacy every 3 years for nationally representative samples of 15‐year‐old students enrolled in school, focusing on higher‐income countries (OECD, 2019). The PISA also includes a retrospective item in which respondents report how many years of school‐ or center‐based ECCE they attended, as defined by the International Standard Classification of Education Level 0 (ISCED‐0). Similar to the MICS data, we chose the most recently available PISA dataset for each country. Table S1 lists the specific countries included in our PISA dataset. For all of our analyses, we used the most recently available data on school closures caused by COVID‐19 from UNICEF and UNESCO (https://data.unicef.org/resources/one‐year‐of‐covid‐19‐and‐school‐closures/). Specifically, we estimated the number of instruction days lost in each country by adding the total number of days that schools were closed plus half the number of days that schools were partially closed between March 11, 2020 and February 2, 2021. We then calculated the proportion of instruction days lost due to COVID‐19 for each country by dividing this number by the total number of possible instructional days during this period (see Figure Figure1).1). As shown in Table Table11 Column 6, globally children lost an average of 52.67% of ECCE instructional days during the first 11 months of the pandemic, with the largest percentages of instructional days lost in middle‐income countries. Estimated percentage of days of ECCE instruction lost due to COVID‐19‐related ECCE closures between March, 2020 and February, 2021, by country. Note. See Table S3 for country‐level details Our primary analyses estimating the implications of COVID‐19‐related ECCE closures on each of our study’s four primary outcomes are described below. First, we estimated the total number of person‐days of ECCE instruction lost due to COVID‐19‐related school closures during the first 11 months of the pandemic (i.e., between March 2020 and February 2021). To do so, we began by multiplying the latest available estimates of the proportion of children participating in ECCE prior to COVID‐19 by the total number of ECCE‐age children in each country. The resulting total number of children participating in ECCE in each country prior to COVID‐19 was then multiplied by the number of instructional days lost between March 2020 and February 2021 in each country (Equation 1): We then summed each country’s total number of person‐days of ECCE instruction lost to COVID‐19 within country income groups. Second, we used MICS data to estimate the likely implications of ECCE closures during the first 11 months of the pandemic for early childhood development. To do so, we used a series of country‐specific logistic regression models in which a binary indicator for whether children were developmentally off track (OffTracki) was regressed on a binary indicator representing whether children were attending ECCE versus receiving alternative forms of care (most typically staying home with their parents or other caregivers; ECCEi). These equations also included a vector of control variables, including child i’s age in months, gender, household wealth, maternal educational attainment, an indicator for rural versus urban residence, and a sum index of caregivers’ engagement in six play and learning activities (i.e., reading, singing, telling stories, playing, taking the child outside for a walk, and counting) that has been shown to predict early childhood development (Jeong et al., 2017; Equation 2): We stored the estimated coefficients from these country‐level models and employed them to obtain predicted values for the outcome variable. Using these predicted values, we estimated the likely increase in the proportion of children off track in their development by scaling the estimates by the median duration of ECCE participation in each country income group reported in the PISA dataset. According to the PISA dataset, the average duration of pre‐COVID‐19 ECCE attendance was 1 year in low‐ and lower middle‐income countries, and 2 years in upper middle‐ and high‐income countries. This implies, for example, that a 180‐day closure of ECCE would result in a complete (100%) loss of the typical developmental benefits of ECCE for a child about to start ECCE in a low‐ or lower‐middle income country with 180 instructional days per year, and to a 50% loss of the protective benefits in upper middle‐ and high‐income countries with the same total instructional days per year. We then used random‐effects meta‐analysis to pool estimates of the likely increase in the proportion of children off track in their development by country income group from the country‐level predictions from Equation 2. Finally, we applied these country income group‐level estimates of the proportion of children who would fall developmentally off track as a result of ECCE closures to the total population of ECCE‐age children to obtain estimates of the number of children whose early development might be compromised by ECCE disruptions in the first 11 months of the pandemic. Third, we used PISA data to predict the estimated consequences of ECCE closures between March 2020 and February 2021 on adolescent learning losses. For this analysis, we created a single academic achievement score summarizing students’ performance in the reading, math, and science domains of PISA. This first principal component of the three scores captured 94% of total variance, suggesting a very high correlation across the domain‐specific assessments. To facilitate interpretation, we standardized the composite PISA achievement variable to a mean of 0 and to a standard deviation of 1. Similar to the analyses predicting early development, we used multivariate regression models to estimate country‐specific associations between the number of years adolescents reported participating in ECCE (YearsECCEi; where 0 indicates they received care outside of ECCE, e.g., at home) and their composite achievement scores (zPISAi) while controlling for students’ grade levels, ages, household wealth, and parental educational attainment (Equation 3): Given that most countries in the PISA dataset capped reporting of years of ECCE at three and, according to UNESCO statistics, in most countries preprimary education comprised 3 years (between ages three and six), records reporting more than 3 years of ECCE participation (i.e., 20% of the sample) were top‐coded at three. Furthermore, certain PISA datasets only reported the minimum number of years of ECCE (“at least one,” “at least two,” etc.); we used these minimums as proxies for actual ECCE exposure in these datasets (e.g., “at least one” was replaced with 1). Similar to the analyses for early childhood development described above, we first estimated country‐specific associations between ECCE participation and adolescent learning outcomes, and then used random‐effect meta‐analysis to derive pooled estimates of these associations for each country income group. Given that PISA data are unavailable for low‐income countries, we used the estimate derived for lower middle‐income countries for the low‐income group. In a set of sensitivity analyses, we also explored the extent to which results differed when assuming relative returns to ECCE in low‐income countries of 50% and 150% of those observed in lower middle‐income countries. To translate composite PISA scores into a more easily interpretable (and more labor market‐relevant) outcome measure, we converted additional PISA scores to school grade equivalents. To do so, we estimated the average increase in PISA scores associated with an additional grade completed in each country using the coefficient for Gradei in Equation 3. Using meta‐analysis, we then created a country income group‐specific estimate of average annual improvements in academic achievement for each additional grade completed (ΔzPISAgrade). This estimate then allowed us to convert observed academic achievement losses into (effective) grades of learning lost by adolescence taking into account the average days of instruction lost (Closures) as shown in Equation 4: Fourth, we converted the anticipated losses in adolescent learning due to COVID‐19‐related ECCE closures in the first 11 months of the pandemic into estimates of future reductions in labor market incomes. A large economics literature has highlighted the high labor market returns to additional grades of schooling attainment (e.g., Psacharopoulos & Patrinos, 2004, 2018). We extracted rates of returns (% increase in wages) to schooling from a prior review by Fink and colleagues (2016) and used random‐effects meta‐analysis to compute average returns to schooling for country income groups. Following Fink and colleagues (2016), we estimated the net present value of future wage losses per child and cohort assuming that children will work in the labor market from age 20 to 60, with 2% annual growth in wages (net of inflation) and a 3% discount rate. To do so, we first estimated the effective grades of schooling lost by adolescence (Totalgradesoflearninglost; see Equation 4) and then multiplied this figure by the expected wage increase per grade of education (return to education; RTE), the net present value of future wages (NPV(wages)), and the average days of instruction lost Closures, as shown in Equation 5: Similar to previous work (Fink et al., 2016), we assumed wages to correspond to two‐thirds of each country’s gross domestic product per capita (as reported by the World Bank, 2020). Given large variation in returns to schooling observed in the literature, as well as the difficulty associated with making appropriate real wage growth and interest rate projections for the future, we explored alternative assumptions in a set of sensitivity analyses. Specifically, we computed estimates under more modest and optimistic returns to education of 4, 8, and 10% per year of schooling, respectively, as well as with 0% and 3% net discounting of future benefits. In recent history, real interest rates have fluctuated between 0% and 5% (Borio et al., 2017), while real wage growth rates have been approximately 2% (Inclusive Labour Markets, 2015). The 0% scenario essentially assumes that real interest rates equal future real wage growth rates; the 3% net discounting scenario assumes that future interest rates will be 3% higher than real wage growth rates (i.e., wage growth rates net of inflation). Three percent net discounting would, for example, be appropriate with zero real (net of inflation) growth in wages, and a 3% discounting factor. It would also be appropriate for a more realistic real wage growth rate of 2% combined with a more conservative 5% discounting rate. Because we only had data available on actual ECCE closures through February 2, 2021, we also conducted a series of analyses to allow readers to forecast how ongoing disruptions to ECCE services may predict further losses. In particular, we estimated the implications of one additional month of ECCE shut‐downs on each of our outcomes. To do so, we used the same methods described above but replaced actual closure durations with a proportion of 0.083 (one‐twelfth of 1 year). For all analyses, we also present bootstrapped standard errors to account for statistical uncertainty in estimated (1) associations between ECCE attendance and probabilities of being developmentally off track using the MICS data, (2) associations between years participating in ECCE and adolescent PISA scores, (3) associations between schooling grade and PISA scores, and (4) economic returns to education. We employed 10,000 bootstrapped simulations and calculated the 2.5th and 97.5th percentiles of these draws to provide 95% confidence intervals around our estimated results.

Résumé de l’IA

Study Justification:

The study aims to estimate the implications of COVID-19-related preprimary school closures on children’s instructional access, development, learning, and economic wellbeing. The justification for this study is to provide evidence on the potential consequences of ECCE service closures during the pandemic, particularly for the estimated 167 million preprimary-age children in 196 countries who lost ECCE access between March 2020 and February 2021. The study highlights the significant impact of these closures on children’s education, development, and future economic prospects.

Study Highlights:

1. ECCE Instructional Days Lost: The study estimates that globally, children lost an average of 52.67% of ECCE instructional days during the first 11 months of the pandemic. Middle-income countries experienced the largest percentages of instructional days lost.
2. Children Falling “Off Track” in Early Development: The study predicts that 10.75 million additional children may have fallen “off track” in their early development due to ECCE closures during the first 11 months of the pandemic. This has potential long-term consequences for their cognitive, social-emotional, and physical development.
3. Grades of Learning Lost by Adolescence: The study estimates that 14.18 million grades of academic learning may have been lost by adolescence as a result of ECCE closures. This can have a significant impact on adolescents’ educational attainment and future opportunities.
4. Earnings Lost in Adulthood: The study projects a present discounted value of USD 308.02 billion of earnings lost in adulthood due to the impact of ECCE closures. This highlights the long-term economic consequences for individuals and societies.

Recommendations:

1. Prioritize ECCE Reopening: Policymakers should prioritize the safe reopening of ECCE services to mitigate the negative impact on children’s education, development, and future economic prospects.
2. Targeted Support for Vulnerable Populations: Special attention should be given to providing targeted support for children from low- and lower middle-income countries, as they are likely to experience the greatest educational and economic setbacks.
3. Investment in Catch-up Programs: Policymakers should invest in catch-up programs to address the learning losses experienced by children during the pandemic. These programs should focus on providing additional support and resources to help children regain lost educational progress.
4. Strengthen Resilience and Preparedness: Policymakers should prioritize strengthening resilience and preparedness in the education sector to better respond to future crises. This includes developing contingency plans, improving access to remote learning resources, and ensuring the availability of quality ECCE services during emergencies.

Key Role Players:

1. Ministries of Education: Responsible for developing and implementing policies related to ECCE and coordinating efforts to address the study’s recommendations.
2. International Organizations (UNESCO, UNICEF): Provide guidance, resources, and support to countries in implementing ECCE policies and programs.
3. Non-Governmental Organizations (NGOs): Play a crucial role in delivering ECCE services, especially in low-resource settings. They can provide support in implementing catch-up programs and reaching vulnerable populations.
4. Teachers and Educators: Key stakeholders in delivering quality ECCE services and implementing catch-up programs. They require training and support to address the specific needs of children affected by the closures.

Cost Items for Planning Recommendations:

1. Reopening and Safety Measures: Budget for implementing safety measures in ECCE settings, such as providing personal protective equipment, ensuring proper sanitation, and adapting classrooms to meet social distancing requirements.
2. Catch-up Programs: Allocate funds for additional resources, materials, and staffing to support catch-up programs aimed at addressing learning losses.
3. Training and Professional Development: Budget for training and professional development programs for teachers and educators to enhance their skills in delivering quality ECCE services and implementing catch-up programs.
4. Remote Learning Resources: Invest in technology and infrastructure to support remote learning initiatives, including providing devices and internet access to children who lack access at home.
5. Support for Vulnerable Populations: Allocate funds for targeted support programs to reach vulnerable populations, including children from low-income families, children with disabilities, and children in remote areas.
6. Research and Monitoring: Set aside funds for research and monitoring efforts to assess the effectiveness of interventions, track progress, and inform future decision-making.
Note: The cost items provided are general categories and do not represent actual cost figures. Actual costs will vary depending on the country and specific context.

Force de la preuve

The strength of evidence for this abstract is 8 out of 10.
The evidence in the abstract is strong because it is based on observational data collected prior to the pandemic and uses a variety of pre-pandemic data sources. The study provides estimates of the potential consequences of COVID-19-related preprimary school closures on children’s instructional access, development, learning, and economic wellbeing. The authors used data from reputable sources such as UNICEF, UNESCO, and the World Bank. They also conducted regression analyses and employed meta-analysis techniques to derive pooled estimates. To improve the evidence, the study could include more recent data on school closures and consider additional factors that may influence the outcomes, such as access to remote learning resources and socio-economic factors.

Abstrait

We used a variety of pre‐pandemic data sources to complete our analyses. For all of our analyses, we used the most recently available data on school closures (covering March 11, 2020 to February 2, 2021) from UNICEF and UNESCO. We also used the World Bank’s 2020 classifications to categorize countries into high‐, upper middle‐, lower middle‐, and low‐income groupings (World Bank, 2020). In addition to these sources, to estimate the number of person‐days of ECCE instruction lost (Outcome 1), we used data on ECCE participation rates from UNESCO’s Institute for Statistics (UIS), the UNICEF‐supported Multiple Indicator Cluster Surveys (MICS), and other data sources, along with the number of ECCE‐age children from UNESCO and the World Population Prospects. Second, to estimate the number of children who became off track in their early childhood development (Outcome 2), we used data on ECCE and early childhood development from the MICS. Third, to estimate the number of grades of academic learning lost by adolescence (Outcome 3), we used data on ECCE and adolescent learning from the Programme for International Student Assessment (PISA). Each of these data sources is described in detail below. Finally, to estimate the total earnings lost into adulthood (Outcome 4), we extracted rates of returns (% increase in wages) to schooling from a prior review by Fink and colleagues (2016) and used random‐effects meta‐analysis to compute average returns to schooling for country income groups. Our full sample comprises all countries with national data on ECCE participation (N = 196 countries, representing 99% of the global under‐five population). Our primary source of ECCE participation data was UNESCO’s UIS ECCE database (http://data.uis.unesco.org/; n = 143 countries), which provides information on net ECCE enrollment, defined as the total number of preschool‐age children enrolled in preschool education, and expressed as a percentage of the total population in that age. We prioritized data from UNESCO given that it is the source of information for tracking ECCE participation in the SDGs, and it is also available for the largest number of countries. Where UNESCO data were not available, we extracted ECCE data from MICS surveys (https://mics.unicef.org/, n = 30), which report data on ECCE attendance, defined as the percentage of children 36–59 months currently attending an early childhood educational program. Where neither source was available, we used an online search of official and recognized sources such as Ministries of Education, national censuses, and national household surveys to find the latest participation rates for individual countries (n = 23 countries). Table S1 in the Supporting Information details the final list of countries included in our analysis and the source and definition of ECCE participation data for each. Although UNESCO and the MICS use different definitions of ECCE participation, both sources consider a wide and inclusive set of program types, including public, private, and non‐profit programs, as well as full‐ and part‐time programs. Appendix A in the Supporting Information provides further details regarding analyses examining differences in country‐level ECCE participation rates reported by UNESCO versus MICS for countries in which both data sources were available. In sum, these analyses suggested no evidence for systematic differences across these datasets. Table Table11 Column 4 shows overall rates of ECCE participation by country income group prior to the COVID‐19 pandemic. Rates of participation ranged from a low of approximately 20% in low‐income countries to nearly 80% in high‐income countries. Pre‐COVID‐19 ECCE‐age population, ECCE participation rates, number of children in ECCE, and percent instruction days lost to COVID‐19 This table aggregates populations of children across countries, but presents averages for country‐level participation rates. Data on the total number of ECCE‐age children in each country were obtained from UNESCO (n = 193 countries) and the World Population Prospects (n = 3 countries). The UNESCO definition of ECCE‐age children targets children age 3 years until the starting age of primary education, which is 6 years in most countries (UNESCO, 2012). Accordingly, the ECCE target population typically comprises three annual birth cohorts in each country. Table S1 presents further details on the official starting age of primary education by country, and Table Table11 Column 3 shows the total number of ECCE‐aged children in millions by country income group. In total, 347 million children were estimated to be of ECCE age prior to COVID‐19, the majority of whom lived in lower (150 million) and upper (105 million) middle‐income countries. To estimate short‐term associations between ECCE and early childhood development, we used data from 174,018 3‐ and 4‐year‐old children in 61 countries participating in the MICS (M age = 47.33 months, range = 36–59 months; 49% girls; race/ethnicity not available). MICS include nationally representative data on child and family wellbeing, with a focus on low‐ and middle‐income countries (UNICEF, 2006). Standard MICS questionnaires include questions regarding whether 3‐ and 4‐year‐old children currently attend ECCE programs (yes/no). The early development of these same children is also measured using the Early Childhood Development Index (ECDI). The ECDI includes 10 caregiver‐reported items capturing 3‐ and 4‐year‐old children’s early literacy/numeracy (three items), physical (two items), social‐emotional (three items), and approaches to learning (two items) skills (Loizillon et al., 2017). Following MICS recommendations, children were considered developmentally off track in the ECDI if they failed more than one item in two or more of these domains. Despite criticisms regarding the coarseness of this measure (McCoy et al., 2016), the ECDI is highly policy‐relevant given its current status as the indicator of early childhood development for SDG 4.2.1 (United Nations, 2019). Since the countries that participated in the MICS varied across years, we chose the most recently available dataset for each country. Table S1 lists the specific countries included in our MICS dataset. To estimate associations between ECCE and medium‐term learning outcomes, we used data for 426,125 adolescents in 76 countries participating in the PISA (M age = 15.79 years, range = 15.08–16.33 years; 52% girls; race/ethnicity not available). The PISA is an international survey program that includes direct assessments of reading, mathematics, and science literacy every 3 years for nationally representative samples of 15‐year‐old students enrolled in school, focusing on higher‐income countries (OECD, 2019). The PISA also includes a retrospective item in which respondents report how many years of school‐ or center‐based ECCE they attended, as defined by the International Standard Classification of Education Level 0 (ISCED‐0). Similar to the MICS data, we chose the most recently available PISA dataset for each country. Table S1 lists the specific countries included in our PISA dataset. For all of our analyses, we used the most recently available data on school closures caused by COVID‐19 from UNICEF and UNESCO (https://data.unicef.org/resources/one‐year‐of‐covid‐19‐and‐school‐closures/). Specifically, we estimated the number of instruction days lost in each country by adding the total number of days that schools were closed plus half the number of days that schools were partially closed between March 11, 2020 and February 2, 2021. We then calculated the proportion of instruction days lost due to COVID‐19 for each country by dividing this number by the total number of possible instructional days during this period (see Figure Figure1).1). As shown in Table Table11 Column 6, globally children lost an average of 52.67% of ECCE instructional days during the first 11 months of the pandemic, with the largest percentages of instructional days lost in middle‐income countries. Estimated percentage of days of ECCE instruction lost due to COVID‐19‐related ECCE closures between March, 2020 and February, 2021, by country. Note. See Table S3 for country‐level details Our primary analyses estimating the implications of COVID‐19‐related ECCE closures on each of our study’s four primary outcomes are described below. First, we estimated the total number of person‐days of ECCE instruction lost due to COVID‐19‐related school closures during the first 11 months of the pandemic (i.e., between March 2020 and February 2021). To do so, we began by multiplying the latest available estimates of the proportion of children participating in ECCE prior to COVID‐19 by the total number of ECCE‐age children in each country. The resulting total number of children participating in ECCE in each country prior to COVID‐19 was then multiplied by the number of instructional days lost between March 2020 and February 2021 in each country (Equation 1): We then summed each country’s total number of person‐days of ECCE instruction lost to COVID‐19 within country income groups. Second, we used MICS data to estimate the likely implications of ECCE closures during the first 11 months of the pandemic for early childhood development. To do so, we used a series of country‐specific logistic regression models in which a binary indicator for whether children were developmentally off track (OffTracki) was regressed on a binary indicator representing whether children were attending ECCE versus receiving alternative forms of care (most typically staying home with their parents or other caregivers; ECCEi). These equations also included a vector of control variables, including child i’s age in months, gender, household wealth, maternal educational attainment, an indicator for rural versus urban residence, and a sum index of caregivers’ engagement in six play and learning activities (i.e., reading, singing, telling stories, playing, taking the child outside for a walk, and counting) that has been shown to predict early childhood development (Jeong et al., 2017; Equation 2): We stored the estimated coefficients from these country‐level models and employed them to obtain predicted values for the outcome variable. Using these predicted values, we estimated the likely increase in the proportion of children off track in their development by scaling the estimates by the median duration of ECCE participation in each country income group reported in the PISA dataset. According to the PISA dataset, the average duration of pre‐COVID‐19 ECCE attendance was 1 year in low‐ and lower middle‐income countries, and 2 years in upper middle‐ and high‐income countries. This implies, for example, that a 180‐day closure of ECCE would result in a complete (100%) loss of the typical developmental benefits of ECCE for a child about to start ECCE in a low‐ or lower‐middle income country with 180 instructional days per year, and to a 50% loss of the protective benefits in upper middle‐ and high‐income countries with the same total instructional days per year. We then used random‐effects meta‐analysis to pool estimates of the likely increase in the proportion of children off track in their development by country income group from the country‐level predictions from Equation 2. Finally, we applied these country income group‐level estimates of the proportion of children who would fall developmentally off track as a result of ECCE closures to the total population of ECCE‐age children to obtain estimates of the number of children whose early development might be compromised by ECCE disruptions in the first 11 months of the pandemic. Third, we used PISA data to predict the estimated consequences of ECCE closures between March 2020 and February 2021 on adolescent learning losses. For this analysis, we created a single academic achievement score summarizing students’ performance in the reading, math, and science domains of PISA. This first principal component of the three scores captured 94% of total variance, suggesting a very high correlation across the domain‐specific assessments. To facilitate interpretation, we standardized the composite PISA achievement variable to a mean of 0 and to a standard deviation of 1. Similar to the analyses predicting early development, we used multivariate regression models to estimate country‐specific associations between the number of years adolescents reported participating in ECCE (YearsECCEi; where 0 indicates they received care outside of ECCE, e.g., at home) and their composite achievement scores (zPISAi) while controlling for students’ grade levels, ages, household wealth, and parental educational attainment (Equation 3): Given that most countries in the PISA dataset capped reporting of years of ECCE at three and, according to UNESCO statistics, in most countries preprimary education comprised 3 years (between ages three and six), records reporting more than 3 years of ECCE participation (i.e., 20% of the sample) were top‐coded at three. Furthermore, certain PISA datasets only reported the minimum number of years of ECCE (“at least one,” “at least two,” etc.); we used these minimums as proxies for actual ECCE exposure in these datasets (e.g., “at least one” was replaced with 1). Similar to the analyses for early childhood development described above, we first estimated country‐specific associations between ECCE participation and adolescent learning outcomes, and then used random‐effect meta‐analysis to derive pooled estimates of these associations for each country income group. Given that PISA data are unavailable for low‐income countries, we used the estimate derived for lower middle‐income countries for the low‐income group. In a set of sensitivity analyses, we also explored the extent to which results differed when assuming relative returns to ECCE in low‐income countries of 50% and 150% of those observed in lower middle‐income countries. To translate composite PISA scores into a more easily interpretable (and more labor market‐relevant) outcome measure, we converted additional PISA scores to school grade equivalents. To do so, we estimated the average increase in PISA scores associated with an additional grade completed in each country using the coefficient for Gradei in Equation 3. Using meta‐analysis, we then created a country income group‐specific estimate of average annual improvements in academic achievement for each additional grade completed (ΔzPISAgrade). This estimate then allowed us to convert observed academic achievement losses into (effective) grades of learning lost by adolescence taking into account the average days of instruction lost (Closures) as shown in Equation 4: Fourth, we converted the anticipated losses in adolescent learning due to COVID‐19‐related ECCE closures in the first 11 months of the pandemic into estimates of future reductions in labor market incomes. A large economics literature has highlighted the high labor market returns to additional grades of schooling attainment (e.g., Psacharopoulos & Patrinos, 2004, 2018). We extracted rates of returns (% increase in wages) to schooling from a prior review by Fink and colleagues (2016) and used random‐effects meta‐analysis to compute average returns to schooling for country income groups. Following Fink and colleagues (2016), we estimated the net present value of future wage losses per child and cohort assuming that children will work in the labor market from age 20 to 60, with 2% annual growth in wages (net of inflation) and a 3% discount rate. To do so, we first estimated the effective grades of schooling lost by adolescence (Totalgradesoflearninglost; see Equation 4) and then multiplied this figure by the expected wage increase per grade of education (return to education; RTE), the net present value of future wages (NPV(wages)), and the average days of instruction lost Closures, as shown in Equation 5: Similar to previous work (Fink et al., 2016), we assumed wages to correspond to two‐thirds of each country’s gross domestic product per capita (as reported by the World Bank, 2020). Given large variation in returns to schooling observed in the literature, as well as the difficulty associated with making appropriate real wage growth and interest rate projections for the future, we explored alternative assumptions in a set of sensitivity analyses. Specifically, we computed estimates under more modest and optimistic returns to education of 4, 8, and 10% per year of schooling, respectively, as well as with 0% and 3% net discounting of future benefits. In recent history, real interest rates have fluctuated between 0% and 5% (Borio et al., 2017), while real wage growth rates have been approximately 2% (Inclusive Labour Markets, 2015). The 0% scenario essentially assumes that real interest rates equal future real wage growth rates; the 3% net discounting scenario assumes that future interest rates will be 3% higher than real wage growth rates (i.e., wage growth rates net of inflation). Three percent net discounting would, for example, be appropriate with zero real (net of inflation) growth in wages, and a 3% discounting factor. It would also be appropriate for a more realistic real wage growth rate of 2% combined with a more conservative 5% discounting rate. Because we only had data available on actual ECCE closures through February 2, 2021, we also conducted a series of analyses to allow readers to forecast how ongoing disruptions to ECCE services may predict further losses. In particular, we estimated the implications of one additional month of ECCE shut‐downs on each of our outcomes. To do so, we used the same methods described above but replaced actual closure durations with a proportion of 0.083 (one‐twelfth of 1 year). For all analyses, we also present bootstrapped standard errors to account for statistical uncertainty in estimated (1) associations between ECCE attendance and probabilities of being developmentally off track using the MICS data, (2) associations between years participating in ECCE and adolescent PISA scores, (3) associations between schooling grade and PISA scores, and (4) economic returns to education. We employed 10,000 bootstrapped simulations and calculated the 2.5th and 97.5th percentiles of these draws to provide 95% confidence intervals around our estimated results.

Matériaux

https://efashare.b-cdn.net/materials/a52dffb3dec48be6ee47197c84fc681e197f6e6a22a90bcb833c95918de82dcf/CDEV-92-e883-s001.docx

Innovations

Based on the information provided, here are some potential innovations that could be used to improve access to maternal health:

1. Telemedicine: Implementing telemedicine services can provide remote access to healthcare professionals, allowing pregnant women to receive prenatal care and consultations without having to travel long distances.

2. Mobile health applications: Developing mobile applications that provide information and resources related to maternal health can empower pregnant women to take control of their own health. These apps can provide educational content, appointment reminders, and tracking tools for monitoring pregnancy progress.

3. Community health workers: Training and deploying community health workers can help improve access to maternal health services, especially in remote or underserved areas. These workers can provide basic prenatal care, health education, and referrals to healthcare facilities when necessary.

4. Maternal health clinics: Establishing dedicated maternal health clinics can ensure that pregnant women have access to specialized care and services. These clinics can offer comprehensive prenatal care, delivery services, and postnatal care in one location.

5. Mobile clinics: Setting up mobile clinics that travel to rural or hard-to-reach areas can bring maternal health services directly to the communities that need them. These clinics can provide prenatal check-ups, vaccinations, and other essential services.

6. Maternal health vouchers: Introducing voucher programs that provide financial assistance for maternal health services can help reduce the financial barriers to accessing care. These vouchers can cover the costs of prenatal visits, delivery, and postnatal care.

7. Public-private partnerships: Collaborating with private healthcare providers and organizations can help expand access to maternal health services. Public-private partnerships can leverage the resources and expertise of both sectors to improve the availability and quality of care.

8. Maternal health education campaigns: Launching public awareness campaigns that focus on maternal health can help educate communities about the importance of prenatal care and the available services. These campaigns can address cultural beliefs, myths, and misconceptions surrounding pregnancy and childbirth.

9. Transportation support: Providing transportation support, such as subsidized or free transportation services, can help pregnant women overcome geographical barriers and reach healthcare facilities for prenatal care and delivery.

10. Maternal health hotlines: Establishing dedicated hotlines staffed by healthcare professionals can provide pregnant women with a direct line of communication for seeking advice, asking questions, and accessing emergency assistance.

It’s important to note that the implementation of these innovations should be tailored to the specific context and needs of each community or region.

AI Innovations Description

The description provided is a detailed account of the methodology and data sources used in a study that estimated the implications of COVID-19-related preprimary school closures on children’s instructional access, development, learning, and economic well-being. The study used observational data collected prior to the pandemic to simulate the potential consequences of early childhood care and education (ECCE) service closures on preprimary-age children in 196 countries.

The study found that COVID-19-related ECCE disruptions resulted in a significant loss of ECCE instruction, with an estimated 19.01 billion person-days of instruction lost. This loss of instruction had several negative impacts, including 10.75 million additional children falling off track in their early development, 14.18 million grades of learning lost by adolescence, and a present discounted value of USD 308.02 billion of earnings lost in adulthood. These projected losses were concentrated in low- and lower middle-income countries, exacerbating global inequities.

To conduct the analysis, the study used various data sources, including UNICEF and UNESCO data on school closures, the World Bank’s income classifications for countries, UNESCO’s Institute for Statistics (UIS) ECCE database, the UNICEF-supported Multiple Indicator Cluster Surveys (MICS), and the Programme for International Student Assessment (PISA). These data sources provided information on ECCE participation rates, early childhood development, adolescent learning, and economic returns to education.

The study employed regression models and meta-analysis techniques to estimate the associations between ECCE participation and outcomes such as early childhood development and adolescent learning. It also used random-effects meta-analysis to compute average returns to schooling for different country income groups.

The study’s findings highlight the significant impact of COVID-19-related school closures on children’s access to education, development, learning, and future economic well-being. The information provided in the description offers insights into the methodology and data sources used in the study, allowing for a better understanding of the research process and the reliability of the findings.

AI Innovations Methodology

Based on the provided description, the methodology used to simulate the impact of ECCE (early childhood care and education) service closures on access to maternal health is as follows:

1. Data Collection: Observational data collected between 2004 and 2019 were used to simulate the potential consequences of ECCE service closures on preprimary-age children in 196 countries who lost ECCE access between March 2020 and February 2021. Data sources included UNICEF, UNESCO, World Bank, and other sources.

2. Estimating Instructional Days Lost: The number of person-days of ECCE instruction lost due to COVID-19-related closures was estimated by multiplying the proportion of children participating in ECCE prior to the pandemic by the total number of ECCE-age children in each country. The total number of instructional days lost was calculated by adding the total number of days schools were closed and half the number of days schools were partially closed during the specified period.

3. Estimating Implications for Early Childhood Development: Logistic regression models were used to estimate the association between ECCE attendance and early childhood development. Predicted values were obtained for the proportion of children off track in their development based on the duration of ECCE participation. These estimates were then applied to the total population of ECCE-age children to determine the number of children whose early development might be compromised by ECCE disruptions.

4. Estimating Consequences for Adolescent Learning Losses: Multivariate regression models were used to estimate the association between years of ECCE participation and adolescent learning outcomes. Pooled estimates of these associations were derived for each country income group using random-effects meta-analysis. Academic achievement losses were converted into grades of learning lost by adolescence, taking into account the average days of instruction lost.

5. Estimating Future Reductions in Labor Market Incomes: The anticipated losses in adolescent learning due to ECCE closures were converted into estimates of future reductions in labor market incomes. Rates of returns to schooling and net present value of future wages were used to estimate the economic impact of ECCE disruptions on each country income group.

6. Sensitivity Analyses: Sensitivity analyses were conducted to explore alternative assumptions and scenarios for returns to education, wage growth rates, and discounting rates.

7. Forecasting Ongoing Disruptions: Additional analyses were conducted to forecast the implications of ongoing disruptions to ECCE services by estimating the impact of one additional month of ECCE shutdowns on the outcomes.

Overall, this methodology combines data from various sources, statistical modeling techniques, and economic analysis to simulate the impact of ECCE service closures on access to maternal health. It provides estimates of the number of instructional days lost, the number of children off track in their development, grades of learning lost by adolescence, and the economic losses in adulthood. Sensitivity analyses and forecasting are also conducted to account for uncertainties and ongoing disruptions.

Auteur

Dima Health

Statistics:

Citations: 24

Identifiers:

DOI: 10.1111/cdev.13658

ISSN: 00093920

Research Areas:

COVID, Health System and Policy, Maternal Access, Maternal and Child Health, Quality of Care, Social Determinants

Study Design:

Cohort Study, Cross Sectional Study

Study Approach:

Quantitative

Participants Gender:

Female

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