Summary

The State of Early Education and Care in Boston: Supply, Demand, Affordability, and Quality, is the first in what is planned as a recurrent landscape survey of early childhood, preschool and childcare programs in every neighborhood of Boston. It focuses on potential supply, demand and gaps in child-care seats (availability, quality and affordability). This report’s estimates set a baseline understanding to help focus and track investments and policy changes for early childhood in the city.

This publication is a culmination of efforts by a diverse data committee representing providers, parents, funding agencies, policymakers, advocates, and researchers. The report includes data from several sources, such as American Community Survey, Massachusetts Department of Early Education and Care, Massachusetts Department of Elementary & Secondary Education, Boston Public Health Commission, City of Boston, among others. For detailed information on methodology, findings and recommendations, please access the full report here

The first dataset contains all Census data used in the publication. Data is presented by neighborhoods:

• Population 0 – 5 years;
• Population 0 – 2 years;
• Population 3 – 5 years;
• Race/ethnicity for children 0 – 4 years (White, non-Hispanic; Black; Asian; Hispanic/Latinx);
• Family type (married couples, female householder, male householder);
• Poverty status;
• Family median income in the past 12 months;
• Average cost of care as a percentage of median family income (infant, preschool);
• Share of families that cannot afford care (infant, preschool)

The Boston Planning & Development Agency Research Division analyzed 2013-2017 American Community Survey data to estimate numbers by ZIP-Code. The Boston Opportunity Agenda combined that data by the approximate neighborhoods and estimated cost of care and affordability.

Additional notes:

• Record Type: Each record represents a ZIP-Code defined neighborhood. See list below for detailed information on Boston ZIP-Codes used to create each one of the 15 neighborhoods.
• Data Quality: Numbers presented here came from 2013-2017 American Community Survey data. Therefore, these are ESTIMATES and have margin of errors. The smaller the geographical unit, the greater the margin of error. The Boston Planning & Development Agency analyzed the data to estimate numbers by ZIP-Code.
• Race/Ethnicity: Non-White Hispanics may be double counted due to data limitations.
• Cost of Care: The average cost of care as a percentage of median family income was computed assuming the annual average cost of infant care was $19,877 and the average cost of preschool care was $ 13,771 (Childcare Aware of America, 2019). For each neighborhood we estimated the impact of child care (infant and preschool) on its median annual family income.
• Affordability: The Department of Health and Human Services (DHHS) sets a standard regarding the affordability of child care, where the annual cost of child care should not exceed 10 percent of household annual income. Using this 10% threshold, we estimated that to afford market rate infant care, a family’s annual income would have to be at least $198,770. The census income bracket closest to this income was a family income of $150,000– 199,999. To afford preschool care, a family's annual income should be at least $137,710. Thus, the census income bracket that encompass this income is $125,000 - 149,999. For both infant and preschool care, we underestimated the number of families that can afford care.

Extreme Child Care Access Deserts (ECCADs) are Zip Code Tabulated Areas (ZCTAs) that have too few licensed early learning providers for the estimated population of children. ECCADs shows a lack of providers by eligibility category, alongside the estimated rate of children receiving Early Care and Education (ECE) services, as compared to the total population of eligible children (referred to as Uptake Estimates) by ZCTA. The Persistence map helps show which ZCTAs are frequently categorized as an ECCAD by month. DCYF’s Office of Innovation, Alignment, and Accountability (OIAA) makes use of the definition and methodology for Extreme Child Care Access Deserts developed in Massachusetts. For reference on the methodology, see Hardy et al. 2018 Research Report: Subsidized Child Care in Massachusetts: Exploring geography, access, and equity. This data set comes from the Child Care and Early Learning: Extreme Child Care Access Deserts & Uptake Estimates dashboard (https://dcyf.wa.gov/practice/oiaa/reports/early-learning-dashboards/eccad)

Young children are spending increasingly greater hours in early care and education. While research has clearly documented the importance of the quality of these experiences (National Research Council, 2000), more research is needed in several key areas. This study is an assessment of the impact of varying hours of early care and education on children's school readiness, and the specific factors in both infant and preschool classrooms that promote school readiness, using two samples: one group of 242 children attending child care centers that have been followed since infancy (Family Income, Infant Child Care, and Child Development Study); and another group of 130 children attending child care centers primarily serving low-income families. A developmental-ecological conceptual framework was employed, which considered the influence of ecological contexts on children's developmental trajectories. The following school readiness outcomes were assessed: language development and communication cognition and general knowledge, including early math social and emotional development approaches to learning health and physical development Additional information is available on the Massachusetts Early Care and Education and School Readiness Study Web site.

Massachusetts offers a variety of pathways and programs to ensure students graduate college and career ready. These include Career Technical Education (CTE) programs, Early College, and Innovation Pathways. For more information about these pathways and programs, visit the Office of College, Career, and Technical Education website.

This dataset contains student enrollment data for all public and charter schools and districts since 2022. It is a wide file with three groups of columns representing the following enrollment indicators:

• Number of students in CTE programs, Early College, and Innovation Pathways by grade, including grades 9-12 and special education beyond grade 12 (SP)
• Percent of students in CTE programs, Early College, and Innovation Pathways by race and gender
• Number and percent of students in CTE programs, Early College, and Innovation Pathways in student groups: First Language Not English, English Learners, Students with Disabilities, High Needs, Low Income, and Economically Disadvantaged

After Dark Chapter 74 enrollment is a subset of Chapter 74 Program enrollment. Also, in 2022, some districts reported enrollment in additional Innovation Pathway sectors. This report includes only the five designated sectors.

This dataset contains the same data that is also published on our DESE Profiles site: Pathways/Programs Enrollment by Grade Pathways/Programs Enrollment by Race/Gender Pathways/Programs Enrollment by Selected Population

List of Pathways and Programs

Pathways

• After Dark
• Career Connections (Non-Chapter 74 Programs)
• Career Technical Education (Chapter 74 Programs)
• Early College
• Innovation Career Pathways

• Advanced Manufacturing Technology
• Automotive Collision Repair & Refinishing
• Automotive Technology
• Carpentry
• Construction Craft Laborer
• Cosmetology
• Design & Visual Communications
• Electricity
• Exploratory
• Health Assisting
• Heating - Ventilation - Air Conditioning - Refrigeration
• Horticulture
• Information Support Services & Networking
• Metal Fabrication & Joining Technologies
• Plumbing

• Airplane Mechanics and Aircraft Maintenance Technology/Technician
• Automotive Collision Repair & Refinishing
• Automotive Technology
• Biotechnology
• Building/Property Maintenance
• Business Technology
• Cabinetmaking
• Carpentry
• Construction Craft Laborer
• Criminal Justice
• Culinary Arts
• Design & Visual Communications
• Drafting
• Early Education and Care
• Engineering Technology
• Environmental Science & Technology
• Exploratory
• Family & Consumer Studies (formerly vocational home economics)
• Fashion Technology
• Graphic Communications
• Health Assisting
• Heating - Ventilation - Air Conditioning - Refrigeration
• Hospitality Management
• Information Support Services & Networking
• Marketing/Finance
• Medical Assisting
• Programming & Web Development
• Radio & Television Broadcasting
• Robotics and Automation Technology

• Advanced Manufacturing Technology
• Agricultural Mechanics
• Animal Science
• Automotive Collision Repair & Refinishing
• Automotive Technology
• Aviation Maintenance Technology
• Biotechnology
• Building/Property Maintenance
• Business Technology
• Cabinetmaking
• Carpentry
• Construction Craft Laborer
• Cosmetology
• Criminal Justice
• Culinary Arts
• Dental Assisting
• Design & Visual Communications
• Diesel Technology
• Drafting
• Early Education and Care
• Electricity
• Electronics
• Engineering Technology
• Environmental Science & Technology
• Exploratory
• Fashion Technology
• Graphic Communications
• Health Assisting
• Heating - Ventilation - Air Conditioning - Refrigeration
• Horticulture
• Hospitality Management
• Information Support Services & Networking
• Marine Service Technology
• Marketing
• Masonry & Tile Setting
• Medical Assisting
• Metal Fabrication & Joining Technologies
• Painting & Design Technologies
• Plumbing
• Programming & Web Development
• Radio & Television Broadcasting
• Robotics and Automation Technology
• Sheet Metalworking
• Stationary Engineering

• Business and Finance
• Environmental and Life Science
• Health Care and Social Assistance
• Information
• Manufacturing

BackgroundUp to 1.45 billion people currently suffer from soil transmitted helminth infection, with the largest burden occurring in Africa and Asia. Safe and cost effective deworming treatment exists, but there is a debate about mass distribution of this treatment in high prevalence settings. While the World Health Organization recommends mass administration of anthelmintic drugs for preschool and school-aged children in high (>20%) prevalence settings, and several long run follow up studies of an influential trial have suggested large benefits that persist over time, recent systematic reviews have called this recommendation into question.Methods and findingsThis paper analyzes the long-term impact of a cluster-randomized trial in eastern Uganda that provided mass deworming treatment to preschool aged children from 2000 to 2003 on the numeracy and literacy skills of children and young adults living in those villages in 2010-2015. This study uses numeracy and literacy data collected seven to twelve years after the end of the deworming trial in a randomly selected subset of communities from the original trial, by an education-focused survey that had no relationship to the deworming study. Building on an earlier working paper which used data from 2010 and 2011 survey rounds, this paper uses an additional four years of numeracy and literacy data (2012, 2013, 2014, and 2015). Aggregating data from all survey rounds, the difference between numeracy scores in treatment versus control communities is 0.07 standard deviations (SD) (95% CI -0.10, 0.24, p = 0.40), the difference in literacy scores is 0.05 SD (95% CI -0.16, 0.27, p = 0.62), and the difference in total scores is 0.07 SD (95% CI -0.11, 0.25, p = 0.44). There are significant differences in program impact by gender, with numeracy and literacy differentially positively affected for girls, and by age, with treatment effects larger for the primary school aged subsample. There are also significant treatment interactions for those living in households with more treatment-eligible children. There is no evidence of differential treatment effects on age at program eligibility or number of years of program eligibility.ConclusionsMass deworming of preschool aged children in high prevalence communities in Uganda resulted in no statistically significant gains in numeracy or literacy 7-12 years after program completion. Point estimates were positive but imprecise; the study lacked sufficient power to rule out substantial positive effects or more modest negative effects. However, there is suggestive evidence that deworming was relatively more beneficial for girls, primary school aged children, and children living in households with other treated children.Research approvalAs this analysis was conducted on secondary data which is publicly available, no research approval was sought or received. All individual records were anonymized by the data provider prior to public release.

Metrics to quantify child growth vary across studies of the developmental origins of health and disease. We conducted a scoping review of child growth studies in which length/height, weight or body mass index (BMI) was measured at ≥ 2 time points. From a 10% random sample of eligible studies published between Jan 2010-Jun 2016, and all eligible studies from Oct 2015-June 2016, we classified growth metrics based on author-assigned labels (e.g., ‘weight gain’) and a ‘content signature’, a numeric code that summarized the metric’s conceptual and statistical properties. Heterogeneity was assessed by the number of unique content signatures, and label-to-content concordance. In 122 studies, we found 40 unique metrics of childhood growth. The most common approach to quantifying growth in length, weight or BMI was the calculation of each child’s change in z-score. Label-to-content discordance was common due to distinct content signatures carrying the same label, and because of instances in which the same content signature was assigned multiple different labels. In conclusion, the numerous distinct growth metrics and the lack of specificity in the application of metric labels challenge the integration of data and inferences from studies investigating the determinants or consequences of variations in childhood growth.

Morocco MA: Prevalence of Overweight: Weight for Height: % of Children Under 5, Modeled Estimate data was reported at 4.200 % in 2024. This records a decrease from the previous number of 4.400 % for 2023. Morocco MA: Prevalence of Overweight: Weight for Height: % of Children Under 5, Modeled Estimate data is updated yearly, averaging 10.200 % from Dec 2000 (Median) to 2024, with 25 observations. The data reached an all-time high of 14.900 % in 2004 and a record low of 4.200 % in 2024. Morocco MA: Prevalence of Overweight: Weight for Height: % of Children Under 5, Modeled Estimate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Morocco – Table MA.World Bank.WDI: Social: Health Statistics. Prevalence of overweight children is the percentage of children under age 5 whose weight for height is more than two standard deviations above the median for the international reference population of the corresponding age as established by the WHO's 2006 Child Growth Standards.;UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME).;Weighted average;Once considered only a high-income economy problem, overweight children have become a growing concern in developing countries. Research shows an association between childhood obesity and a high prevalence of diabetes, respiratory disease, high blood pressure, and psychosocial and orthopedic disorders (de Onis and Blössner 2003). Childhood obesity is associated with a higher chance of obesity, premature death, and disability in adulthood. In addition to increased future risks, obese children experience breathing difficulties and increased risk of fractures, hypertension, early markers of cardiovascular disease, insulin resistance, and psychological effects. Children in low- and middle-income countries are more vulnerable to inadequate nutrition before birth and in infancy and early childhood. Many of these children are exposed to high-fat, high-sugar, high-salt, calorie-dense, micronutrient-poor foods, which tend be lower in cost than more nutritious foods. These dietary patterns, in conjunction with low levels of physical activity, result in sharp increases in childhood obesity, while under-nutrition continues. Estimates are modeled estimates produced by the JME. Primary data sources of the anthropometric measurements are national surveys. These surveys are administered sporadically, resulting in sparse data for many countries. Furthermore, the trend of the indicators over time is usually not a straight line and varies by country. Tracking the current level and progress of indicators helps determine if countries are on track to meet certain thresholds, such as those indicated in the SDGs. Thus the JME developed statistical models and produced the modeled estimates.

Abstract

The Lao Social Indicator Survey 2011-12 (LSIS 2011-12) is a nation-wide household-based survey of social development indicators. It combines the Multiple Indicator Cluster Survey (MICS) and Lao Reproductive Health Survey (LRHS) where the LRHS applied technical platform of Demographic and Health Survey (DHS). The LSIS is based on MICS4 platform and add-on DHS modules, for example, live birth history and the maternal mortality module.

The LSIS 2011-12 was undertaken by the Ministry of Health and Ministry of Planning and Investment (Lao Statistics Bureau) in collaboration with other line ministries. UNICEF and UNFPA were the primary agencies giving financial and technical assistance to support the survey. In addition, USAID, AusAID, LuxGov, WHO, UNDP, SDC, JICA and WFP provided financial and technical input to the implementation of the LSIS.

The main purposes of LSIS are to allow continued monitoring of progress towards the Millennium Development Goals (MDGs) and to serve as a baseline for the 7th National Socio-Economic Development Plan (7th NSEDP). The survey results can also be used by the Government and development partners to prepare policies, strategies and planning to improve the social environment of people in Lao PDR, especially women and men of reproductive age (15 to 49 years) and children age under five. In addition, the survey provides key sources and references for researchers and academics to conduct further analysis and research studies in specific areas using LSIS data.

Geographic coverage

National

Analysis unit

• individuals
• households

Universe

The survey covered all de jure household members (usual residents), all women aged between 15-49 years, all children under 5 living in the household, and all men aged 15-49 years.

Kind of data

Sample survey data [ssd]

Sampling procedure

The primary objective of the sample design for the 2011-12 Lao Social Indicator Survey (LSIS) was to produce statistically reliable estimates of most indicators, at the national level, for urban and rural areas, and for each of the 17 provinces of the country. The survey was designed to provide information on fertility and early childhood mortality, family planning, reproductive and child health, nutrition, water and sanitation, child protection, child development and education, use of mass media and information technology, knowledge and behaviour regarding HIV/AIDS, and adult and maternal mortality. All women age 15-49 who were usual residents of the selected households were eligible for the survey. A male survey was also conducted in half of the households. All men aged 15-49 who were usual residents of every second household visited by the field team were eligible for the male survey.

The sizes of the provinces vary greatly, ranging from 16,000 to 140,000 households and this posed a challenge for the sample design. A multi-stage, stratified cluster sampling approach was used for the selection of the survey sample. Urban areas, rural areas with roads, and rural areas without roads in each of the 17 provinces were defined as the sampling strata.

The sampling frame for this survey consisted of all villages in the country, arranged by province, with appropriate size estimates (number of households) and other relevant information about each village. The list of villages is updated each year, with the most recent update prior to the design in December 2009.

For the calculation of the sample size, the key indicator used was the contraceptive prevalence rate (modern method).Various methods were available for allocating the sample to the different provinces. At one extreme is the method of equal allocation used in both the 2005 LRHS and the 2006 Multiple Indicator Cluster Survey (MICS), but this method is inefficient for producing national estimates. At the other extreme is the method of proportional allocation, where the share of the total sample that a province gets depends on its size. This method is good for producing national estimates, but not for producing provincial estimates as the sampling error will be large in the smaller provinces where the sample size is small. As a compromise the allocation was based on the square root of the size of each province, with a minimum of 1,000 households selected in each province and a maximum of 1,500 households.

After arriving at the sample size allocation for each province the number of villages selected per province was calculated using a fixed 'take' of 20 households per village. Once the number of villages for a province was determined, the villages (PSUs) were distributed to the urban, rural with road and rural without road domains, in proportion with the number of households in each domain.

The selection of the villages was performed by first ordering the list of villages according to the three types of locality (urban, rural with road, rural without road). Villages were selected with probability proportional to the number of households in the village, based on a fixed interval of selection and a random start chosen between 0 and the sampling interval.

Since the sampling frame was not up-to-date, a new listing of households was conducted in all the sample enumeration areas prior to the selection of households. For this purpose, teams were formed to visit each enumeration area and to list the occupied households. The listing operation took place from November 2010 to early January 2011 with 70 operators covering all 999 enumeration areas. In each province there were two teams each consisting of a lister and a mapper, except in Champasack, where three teams were assigned.

Lists of households for each enumeration area were prepared by the listing teams in the field. The households were then sequentially numbered from 1 to n (the total number of households in each enumeration area) at the Surveys Division of the Lao Statistics Bureau, where the selection of 20 households in each enumeration area was carried out using a systematic selection procedure beginning from a random start.

The sampling procedures are more fully described in "Lao PDR Social Indicator Survey 2011-2012 - Final Report" pp.292-295.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaires for the Generic MICS were structured questionnaires based on the MICS4 model questionnaire with some modifications and additions. Household questionnaires were administered in each household, which collected various information on household members including sex, age and relationship. The household questionnaire includes household listing form, education, water and sanitation, household characteristics, insecticide-treated nets, child discipline and salt iodization.

In addition to a household questionnaire, questionnaires were administered in each household for women age 15-49, children under age five and men age 15-49. For children, the questionnaire was administered to the mother or primary caretaker of the child.

The women's questionnaire includes woman's background, access to mass media and use of information/communication technology, child mortality with birth history, desire for last birth, maternal and newborn health, post-natal health checks, illness symptoms, contraception, unmet need, attitudes towards domestic violence, marriage/union, sexual behavior, HIV/AIDS, and maternal mortality.

The children's questionnaire includes child's age, birth registration, early childhood development, breastfeeding, care of illness, malaria, immunization and anthropometry.

The men's questionnaire includes man's background, access to mass media and use of information/communication technology, contraception, attitudes toward domestic violence, marriage and sexual activity, and HIV/AIDS.

The LSIS questionnaires are based on the UNICEF MICS4 model questionnaires with components added from the Demographic and Health Surveys (DHS), for example, the components the full birth history and the maternal mortality module and interviewing a subsample of men. The original questionnaires were designed in English, then translated into the Lao language and were pre-tested in three villages in Luangprabang in January 2011. Based on the results of the pre-test, modifications were made to the wording and translation of the questionnaires.

In addition to the administration of questionnaires, fieldwork teams tested the salt used for cooking in the households for inclusion of iodine, and measured the weight and height of children age under 5 years.

Cleaning operations

Data processing began simultaneously with data collection in October 2011 and was completed on 15 March 2012. Data were entered using CSPro software. The data were entered on 14 microcomputers and carried out by 14 data entry operators temporarily recruited and trained by four data entry supervisors from the Lao Statistics Bureau (LSB). In order to ensure quality control, all questionnaires were double entered and internal consistency checks were performed. Procedures and standard programmes developed under the global MICS4 programme and adapted to the LSIS questionnaire by the LSB in collaboration with a data processing expert from ICF International were used throughout. Data were analysed using the Statistical Package for the Social Sciences (SPSS), Version 19, and the model syntax and tabulation plans developed by UNICEF and ICF International were used for this purpose.

Response rate

Of the 19,960 households selected for inclusion in the LSIS, 19,018 were found to be occupied. Of these, 18,843 were successfully interviewed, yielding a household response rate of 99 percent. In the interviewed

Morocco MA: Prevalence of Overweight: Weight for Height: % of Children Under 5 data was reported at 10.700 % in 2011. This records a decrease from the previous number of 13.300 % for 2003. Morocco MA: Prevalence of Overweight: Weight for Height: % of Children Under 5 data is updated yearly, averaging 10.700 % from Dec 1987 (Median) to 2011, with 5 observations. The data reached an all-time high of 13.700 % in 1997 and a record low of 5.500 % in 1987. Morocco MA: Prevalence of Overweight: Weight for Height: % of Children Under 5 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Morocco – Table MA.World Bank: Health Statistics. Prevalence of overweight children is the percentage of children under age 5 whose weight for height is more than two standard deviations above the median for the international reference population of the corresponding age as established by the WHO's new child growth standards released in 2006.; ; UNICEF, WHO, World Bank: Joint child malnutrition estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology.; Linear mixed-effect model estimates; Estimates of overweight children are also from national survey data. Once considered only a high-income economy problem, overweight children have become a growing concern in developing countries. Research shows an association between childhood obesity and a high prevalence of diabetes, respiratory disease, high blood pressure, and psychosocial and orthopedic disorders (de Onis and Blössner 2003). Childhood obesity is associated with a higher chance of obesity, premature death, and disability in adulthood. In addition to increased future risks, obese children experience breathing difficulties and increased risk of fractures, hypertension, early markers of cardiovascular disease, insulin resistance, and psychological effects. Children in low- and middle-income countries are more vulnerable to inadequate nutrition before birth and in infancy and early childhood. Many of these children are exposed to high-fat, high-sugar, high-salt, calorie-dense, micronutrient-poor foods, which tend be lower in cost than more nutritious foods. These dietary patterns, in conjunction with low levels of physical activity, result in sharp increases in childhood obesity, while under-nutrition continues

There is a growing literature that suggests environmental exposure during key developmental periods could have harmful impacts on growth and development of humans. Understanding and estimating the relationship between early-life exposure and human growth is vital to studying the adverse health impacts of environmental exposure. We compare two statistical tools, mixed-effects models with interaction terms and growth mixture models, used to measure the association between exposure and change over time within the context of non-linear growth and non-monotonic relationships between exposure and growth. We illustrate their strengths and weaknesses through a real data example and simulation study. The data example, which focuses on the relationship between phthalates and the body mass index growth of children, indicates that the conclusions from the two models can differ. The simulation study provides a broader understanding of the robustness of these models in detecting the relationships between any exposure and growth that could be observed. Data-driven growth mixture models are more robust to non-monotonic growth and stochastic relationships but at the expense of interpretability. We offer concrete modeling strategies to estimate complex relationships with growth patterns.

Abstract

The Republic of Moldova Multiple Indicator Cluster Survey was carried out in 2012 (hereinafter the 2012 Moldova MICS) by the National Centre of Public Health of the Ministry of Health in collaboration with the National Bureau of Statistics, the Scientific Research Institute of Mother and Child Health Care, the Ministry of Labour, Social Protection and Family, the Ministry of Education, the National Centre for Health Management, and the National Centre for Reproductive Health and Medical Genetics. Financial and technical support was provided by the United Nations Children’s Fund (UNICEF), with additional contribution of the Swiss Agency for Development and Cooperation and the World Health Organization Regional Office for Europe within the EU supported project on technical assistance to the health sector.

The Multiple Indicator Cluster Survey (MICS) is an international household survey programme developed by UNICEF. The 2012 Moldova MICS was conducted as part of the fourth global round of MICS surveys (MICS4). MICS provides up-to-date information on the situation of children and women and measures key indicators that allow countries to monitor progress towards the Millennium Development Goals (MDGs) and other internationally agreed upon commitments.

The 2012 Moldova MICS was based on a nationally representative probability sample, stratified in two stages and consisting of about 12,500 households. Fieldwork was carried out between April 17 and June 30, 2012 using four Questionnaires – the Household Questionnaire, the Questionnaire for Individual Women aged 15-49 years, the Questionnaire for Children Under Five, the Questionnaire for Individual Men aged 15-49 years, as well as a Questionnaire Form for Vaccination Records at the Health Facility.

In addition to the administration of questionnaires, fieldwork teams tested the salt used for cooking in the households for its iodate content, observed the place used for handwashing, measured the weights and heights of children under the age of five, as well as the haemoglobin levels in women aged 15-49 years and children aged 6-59 months. The household response rate was 97 percent, with 89 percent, 77 percent and 96 percent response rates calculated for the women’s, men’s and under-5’s interviews respectively.

Geographic coverage

National

Analysis unit

• individuals
• households

Universe

The survey covered all de jure household members (usual residents), all women aged between 15-49 years, all children under 5 living in the household, and all men aged 15-49 years.

Kind of data

Sample survey data [ssd]

Sampling procedure

A probability-based stratified sample was selected in two stages for the 2012 Moldova MICS. Considering that the 2004 Population Census cartographic materials were discarded, it became impossible to use them as a source of data for the sampling frame. Thus, the decision was to use the 2005 Moldova DHS sample for the first stage (PSU - Primary Sampling Unit) and for the second stage a probability-based sample of the households has been selected from each PSU.

Coverage The reference population for the 2012 Moldova MICS depends on the particular indicators and is defined as follows (the size estimates are presented in Table SD.1): 1. Households; 2. Women aged 15-49 years; 3. Men aged 15-49 years; 4. Children under 5 years of age. Geographically, the reference population is placed within the administrative borders of Moldova's territorial units which are located on the western side of the Nistru (Dniester River); the population living in the eastern side (left bank of the Dniester River and the Bender municipality -Transnistrian region) are not a part of 2012 Moldova MICS.

Sample representativeness The 2012 Moldova MICS sample ensured representativeness at a national level (excluding Transnistrian region) and, like in the case of the 2005 Moldova DHS, at the level of residential areas - urban and rural. Although at the first sampling stage no stratification by zone was used, the results of the 2005 Moldova DHS survey indicate that the level of precision of the zone level estimates is acceptable.

Sample size The sample size is determined, on the one hand, by the precision expected to be achieved for the key indicators, and on the other hand, by the availability of human and financial resources. The precision of a sample survey's results is liable to be affected by two types of errors: sampling and non-sampling errors. The level of the sampling errors is inversely proportional to the square root of the sample size, whereas the nonsampling errors are affected by an increase in the sample size. Consequently, the larger the sample is, the smaller the sampling errors, and the greater the non-sampling errors are. Therefore it is important that the size of the sample is balanced so as to ensure both an acceptable precision and a minimum level of non-sampling errors.

Taking into account the limitations due to the lack of maps of census sectors, which made it impossible to select a new sample of PSUs, it was decided to use the same sample of PSUs that was used for the 2005 Moldova DHS, which included 400 census sectors. The final sample size was 12,500 households, a figure obtained by selecting respective number of households from each of the 400 PSUs drawn at the first sampling stage.

PSU (cluster) size The average number of households per PSU is around 90 in rural areas and approximately 120 in urban areas. These sizes were determined so as to ensure a reasonable workload for the enumerators involved in general 2004 Population Census conducted by the NBS. This also made the PSUs practical for updating the list of the households for the purpose of providing a sampling frame for the 2012 Moldova MICS second sampling stage in a timely and cost-effective manner.

Sampling frame The sampling frame at the first sampling stage was built on the census sectors defined for the purposes of the 2004 Population Census carried out by the NBS. This included the list of all the census sectors, put into digital form, accompanied by variables for the identification of the sectors in the 2004 PC, information on areas of residence and geographical zones, and their measure of size expressed in number of persons.

Sample Selection Procedures At the first stage of sampling, PSUs within each stratum were systematically drawn with probabilities proportional to their size (number of population based on the 2004 PC data). Prior to sampling, the census sectors in each stratum were sorted in geographical order from north to south, in order to provide an additional level of implicit stratification based on the geographic criterion. At the second sampling stage, a sample of 30 households was selected from each PSU. The selection was done in each PSU based on the lists of households registered following the update, using a simple systematic sampling procedure.

The sampling procedures are more fully described in "Moldova Multiple Indicator Cluster Survey 2012 - Final Report" pp.143-147.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaires for the Generic MICS were structured questionnaires based on the MICS4 model questionnaire with some modifications and additions. Household questionnaires were administered in each household, which collected various information on household members including sex, age and relationship. The household questionnaire includes household information panel, household listing form, education, water and sanitation, household characteristics, child discipline, hand washing and salt iodization.

In addition to a household questionnaire, questionnaires were administered in each household for women age 15-49, children under age five and men age 15-49. For children, the questionnaire was administered to the mother or primary caretaker of the child.

The women's questionnaire includes woman's information panel, woman's background, access to mass media and ICT, child mortality -birth history, desire for last birth, maternal and newborn health, post-natal health checks, illness symptoms, contraception, unmet need, attitudes toward domestic violence, marriage/union, sexual behavior, HIV/AIDS, tuberculosis, tobacco and alcohol use, life satisfaction and haemoglobin measurement.

The children's questionnaire includes child's age, birth registration, early childhood development, breastfeeding, care of illness, immunisation, anthropometry and haemoglobin measurement.

The men's questionnaire includes man's information panel, man's background, access to mass media and ICT, child mortality, attitudes toward domestic violence, marriage/union, sexual behavior, HIV/AIDS, tuberculosis, tobacco and alcohol use and life satisfaction.

MICS fourth round model questionnaires were customized based on the country’s needs so as to reflect relevant issues which are present in the Republic of Moldova in terms of children’s, women’s and men’s health, education, child protection, migration, HIV/AIDS, tuberculosis, anaemia, etc. Following content approval by the Steering Committee members, the questionnaires were translated from English and Russian into Romanian and were subsequently pre-tested (in Romanian and Russian).

Cleaning operations

Data were entered using the CSPro software on 12 computers by 12 previously trained data-entry clerks. A supervisor and an expert in data processing and analysis were responsible for the quality of data entry. Completed questionnaires were returned each week from the field to the NCPH office in Chisinau for additional editing by two office editors. In order to ensure quality control, all

This dataset is the result of the household survey conducted to gather data at endline as a part of an impact evaluation study of Alive & Thrive (A&T) interventions delivered through Building Resources Across Communities' (BRAC) Essential Health Care (EHC) Program in Bangladesh. The objective of the impact evaluation study is to evaluate the synergistic impact of A&T’s community component along with media communications and private sector activities such as the promotion and integration of micronutrient powders. A&T is a six-year initiative to facilitate change for improved infant and young child feeding (IYCF) practices at scale in Bangladesh, Ethiopia, and Viet Nam. The goal of A&T is to reduce avoidable death and disability due to suboptimal IYCF in the developing world by increasing exclusive breastfeeding (EBF) until 6 months of age and reducing stunting of children 0-24 months of age. In Bangladesh, A&T is working with the government, nongovernmental organizations, and private initiatives to support the implementation of the National IYCF Strategy and Action Plan. The BRAC organization is delivering A&T’s community interventions within its EHC Program and its Maternal, Neonatal, and Child Health (MNCH) Program. BRAC’s frontline health workers, known as Shasthya Shebika and Shasthya Kormi, delivered age-appropriate IYCF counseling and support services during home visits, antenatal and postnatal sessions, and health forums. The endline survey conducted as part of the impact evaluation of A&T interventions delivered through BRAC’s EHC platform had three components—(i) household survey, (ii) community survey, and (iii) frontline health workers survey. The household survey captured the main impact indicators for A&T (WHO-recommended IYCF indicators and child anthropometry), use and exposure to A&T’s intervention platforms, and a variety of other data related to the use of the interventions. This included data on caregiver knowledge and perceptions about IYCF practices, challenges experienced in relation to IYCF practices, caregiver resources (such as education, childcare knowledge, and experience, and physical and mental health) and household resources (such as household composition, socioeconomic status, and food security). The endline household survey was developed using the baseline evaluation questionnaires as a base, and adapted to capture key program activities, particularly with regards to the use of A&T community services and exposure to mass media. The community survey provided data on key community characteristics such as availability of infrastructure, availability, and access to education, health services, and healthcare providers. The frontline health worker survey gathered data on service provision by BRAC frontline health workers, traditional birth attendants (TBA), and village doctors. Data were also gathered on health worker time commitment, knowledge and attitude and training related to IYCF, and their job motivation, satisfaction, and supervision. The data included here are from the survey of households. The survey was conducted in the 20 upazilas across 10 districts in Bangladesh between April and June 2014 by the IFPRI team in collaboration with Data Analysis and Technical Assistance, Ltd. (DATA).

This dataset is the result of the frontline health worker (FLW) survey conducted to gather data for a process evaluation as a part of an impact evaluation study of Alive & Thrive (A&T) interventions delivered through Building Resources Across Communities' (BRAC) Essential Health Care (EHC) Program in Bangladesh. The objective of the impact evaluation study is to evaluate the synergistic impact of the A&T community component along with media communications and private sector activities such as the promotion and integration of micronutrient powders. A&T is a six-year initiative to facilitate change for improved infant and young child feeding (IYCF) practices at scale in Bangladesh, Ethiopia, and Viet Nam. The goal of A&T is to reduce avoidable death and disability due to suboptimal IYCF in the developing world by increasing exclusive breastfeeding (EBF) until 6 months of age and reducing stunting of children 0-24 months of age. In Bangladesh, A&T is working with the government, nongovernmental organizations, and private initiatives to support the implementation of the National IYCF Strategy and Action Plan. The BRAC organization is delivering A&T’s community interventions within its EHC Program and its Maternal, Neonatal, and Child Health (MNCH) Program. BRAC’s frontline health workers, known as Shasthya Shebika and Shasthya Kormi, delivered age-appropriate IYCF counseling and support services during home visits, antenatal and postnatal sessions, and health forums. The process evaluation survey conducted as part of the impact evaluation of A&T interventions delivered through BRAC’s EHC platform had two components—(i) household survey, and (ii) frontline health workers survey. The household survey captured the main impact indicators for A&T (WHO-recommended IYCF indicators and child anthropometry), use and exposure to A&T’s intervention platforms, and a variety of other data related to the use of the interventions. This included data on caregiver knowledge and perceptions about IYCF practices, challenges experienced in relation to IYCF practices, caregiver resources (such as education, childcare knowledge, and experience, and physical and mental health) and household resources (such as household composition, socioeconomic status, and food security). The frontline health worker survey gathered data on service provision by BRAC frontline health workers, traditional birth attendants (TBA), and village doctors. Data were also gathered on health worker time commitment, knowledge and attitude and training related to IYCF, complementary feeding, sanitation and hygiene practices, and their job supervision and contact with other workers; their knowledge and skills about Pustikona. Three questionnaires were developed for frontline health workers survey—(i) Shasthya Shebika (SS) questionnaire, (ii) Shasthya Kormi (SK) questionnaire, and (iii) Pushti Kormi questionnaire. The data included here are from the survey of Pushti Kormi. The survey was conducted in the 25 upazilas across 13 districts in Bangladesh between April and August 2013 by the IFPRI team in collaboration with Data Analysis and Technical Assistance, Ltd. (DATA).

Proportion of children aged 4 to 5 years classified as overweight or living with obesity. For population monitoring purposes, a child’s body mass index (BMI) is classed as overweight or obese where it is on or above the 85th centile or 95th centile, respectively, based on the British 1990 (UK90) growth reference data. The population monitoring cut offs for overweight and obesity are lower than the clinical cut offs (91st and 98th centiles for overweight and obesity) used to assess individual children; this is to capture children in the population in the clinical overweight or obesity BMI categories and those who are at high risk of moving into the clinical overweight or clinical obesity categories. This helps ensure that adequate services are planned and delivered for the whole population.

Rationale There is concern about the rise of childhood obesity and the implications of obesity persisting into adulthood. The risk of obesity in adulthood and risk of future obesity-related ill health are greater as children get older. Studies tracking child obesity into adulthood have found that the probability of children who are overweight or living with obesity becoming overweight or obese adults increases with age[1,2,3]. The health consequences of childhood obesity include: increased blood lipids, glucose intolerance, Type 2 diabetes, hypertension, increases in liver enzymes associated with fatty liver, exacerbation of conditions such as asthma and psychological problems such as social isolation, low self-esteem, teasing and bullying.

It is important to look at the prevalence of weight status across all weight/BMI categories to understand the whole picture and the movement of the population between categories over time.

The National Institute of Health and Clinical Excellence have produced guidelines to tackle obesity in adults and children - http://guidance.nice.org.uk/CG43.

1 Guo SS, Chumlea WC. Tracking of body mass index in children in relation to overweight in adulthood. The American Journal of Clinical Nutrition 1999;70(suppl): 145S-8S.

2 Serdula MK, Ivery D, Coates RJ, Freedman DS, Williamson DF, Byers T. Do obese children become obese adults? A review of the literature. Preventative Medicine 1993;22:167-77.

3 Starc G, Strel J. Tracking excess weight and obesity from childhood to young adulthood: a 12-year prospective cohort study in Slovenia. Public Health Nutrition 2011;14:49-55.

Definition of numerator Number of children in reception (aged 4 to 5 years) with a valid height and weight measured by the NCMP with a BMI classified as overweight or living with obesity, including severe obesity (BMI on or above the 85th centile of the UK90 growth reference).

Definition of denominator Number of children in reception (aged 4 to 5 years) with a valid height and weight measured by the NCMP.

Caveats Data for local authorities may not match that published by NHS England which are based on the local authority of the school attended by the child or based on the local authority that submitted the data. There is a strong correlation between deprivation and child obesity prevalence and users of these data may wish to examine the pattern in their local area. Users may wish to produce thematic maps and charts showing local child obesity prevalence. When presenting data in charts or maps it is important, where possible, to consider the confidence intervals (CIs) around the figures. This analysis supersedes previously published data for small area geographies and historically published data should not be compared to the latest publication. Estimated data published in this fingertips tool is not comparable with previously published data due to changes in methods over the different years of production. These methods changes include; moving from estimated numbers at ward level to actual numbers; revision of geographical boundaries (including ward boundary changes and conversion from 2001 MSOA boundaries to 2011 boundaries); disclosure control methodology changes. The most recently published data applies the same methods across all years of data. There is the potential for error in the collection, collation and interpretation of the data (bias may be introduced due to poor response rates and selective opt out of children with a high BMI for age/sex which it is not possible to control for). There is not a good measure of response bias and the degree of selective opt out, but participation rates (the proportion of eligible school children who were measured) may provide a reasonable proxy; the higher the participation rate, the less chance there is for selective opt out, though this is not a perfect method of assessment. Participation rates for each local authority are available in the https://fingertips.phe.org.uk/profile/national-child-measurement-programme/data#page/4/gid/8000022/ of this profile.

Proportion of children aged 10 to 11 years classified as living with obesity. For population monitoring purposes, a child’s body mass index (BMI) is classed as overweight or obese where it is on or above the 85th centile or 95th centile, respectively, based on the British 1990 (UK90) growth reference data. The population monitoring cut offs for overweight and obesity are lower than the clinical cut offs (91st and 98th centiles for overweight and obesity) used to assess individual children; this is to capture children in the population in the clinical overweight or obesity BMI categories and those who are at high risk of moving into the clinical overweight or clinical obesity categories. This helps ensure that adequate services are planned and delivered for the whole population.

Rationale There is concern about the rise of childhood obesity and the implications of obesity persisting into adulthood. The risk of obesity in adulthood and risk of future obesity-related ill health are greater as children get older. Studies tracking child obesity into adulthood have found that the probability of children who are overweight or living with obesity becoming overweight or obese adults increases with age[1,2,3]. The health consequences of childhood obesity include: increased blood lipids, glucose intolerance, Type 2 diabetes, hypertension, increases in liver enzymes associated with fatty liver, exacerbation of conditions such as asthma and psychological problems such as social isolation, low self-esteem, teasing and bullying.

It is important to look at the prevalence of weight status across all weight/BMI categories to understand the whole picture and the movement of the population between categories over time.

The National Institute of Health and Clinical Excellence have produced guidelines to tackle obesity in adults and children - http://guidance.nice.org.uk/CG43.

1 Guo SS, Chumlea WC. Tracking of body mass index in children in relation to overweight in adulthood. The American Journal of Clinical Nutrition 1999;70(suppl): 145S-8S.

2 Serdula MK, Ivery D, Coates RJ, Freedman DS, Williamson DF, Byers T. Do obese children become obese adults? A review of the literature. Preventative Medicine 1993;22:167-77.

3 Starc G, Strel J. Tracking excess weight and obesity from childhood to young adulthood: a 12-year prospective cohort study in Slovenia. Public Health Nutrition 2011;14:49-55.

Definition of numerator Number of children in year 6 (aged 10 to 11 years) with a valid height and weight measured by the NCMP with a BMI classified as living with obesity or severe obesity (BMI on or above 95th centile of the UK90 growth reference).

Definition of denominator Number of children in year 6 (aged 10 to 11 years) with a valid height and weight measured by the NCMP.

Caveats Data for local authorities may not match that published by NHS England which are based on the local authority of the school attended by the child or based on the local authority that submitted the data. There is a strong correlation between deprivation and child obesity prevalence and users of these data may wish to examine the pattern in their local area. Users may wish to produce thematic maps and charts showing local child obesity prevalence. When presenting data in charts or maps it is important, where possible, to consider the confidence intervals (CIs) around the figures. This analysis supersedes previously published data for small area geographies and historically published data should not be compared to the latest publication. Estimated data published in this fingertips tool is not comparable with previously published data due to changes in methods over the different years of production. These methods changes include; moving from estimated numbers at ward level to actual numbers; revision of geographical boundaries (including ward boundary changes and conversion from 2001 MSOA boundaries to 2011 boundaries); disclosure control methodology changes. The most recently published data applies the same methods across all years of data. There is the potential for error in the collection, collation and interpretation of the data (bias may be introduced due to poor response rates and selective opt out of children with a high BMI for age/sex which it is not possible to control for). There is not a good measure of response bias and the degree of selective opt out, but participation rates (the proportion of eligible school children who were measured) may provide a reasonable proxy; the higher the participation rate, the less chance there is for selective opt out, though this is not a perfect method of assessment. Participation rates for each local authority are available in the https://fingertips.phe.org.uk/profile/national-child-measurement-programme/data#page/4/gid/8000022/ of this profile.

Objective: Childhood disadvantage is associated with a higher risk of adult obesity, but little is known about its associations with body mass index (BMI) trajectories during adulthood. This study aimed first to identify adulthood BMI trajectories, and second to investigate how childhood disadvantage is associated with trajectory group membership. Methods: Data from the Helsinki Health Study, a longitudinal cohort study of initially 40- to 60-year-old employees of the City of Helsinki in Finland, were used. The baseline survey was conducted in 2000–2002, and similar follow-up surveys in 2007, 2012, and 2017. Based on self-reported BMI, participants’ (n =5,266; 83% women) BMI trajectories, including their retrospectively reported BMI at the age of 25 years, were examined. Data on childhood disadvantage, including parental education and 7 types of childhood adversity (their own serious illness; parental divorce, death, mental disorder, or alcohol problems; economic difficulties at home; and peer group bullying) before the age of 16 years, were obtained from the baseline survey. Group-based trajectory modeling was used to identify BMI trajectories, and multinomial logistic regression to analyze the odds for trajectory group membership for the disadvantage variables. Results: Four ascending BMI trajectories in women and men were found: persistent normal weight (trajectory 1; women 35% and men 25%), normal weight to overweight (trajectory 2; women 41% and men 48%), normal weight to class I obesity (trajectory 3; women 19% and men 23%) and overweight to class II obesity (trajectory 4; women 5% and men 4%). Compared to trajectory 1, women with multiple adversities and repetitive peer bullying in childhood had greater odds of belonging to trajectories 3 and 4, whereas men with parental alcohol problems had greater odds of belonging to trajectory 4. For women and men, a low level of parental education was associated with a higher-level BMI trajectory. Conclusions: Low parental education for both genders, multiple adversities and repetitive peer bullying in childhood among women, and parental alcohol problems among men increased the odds of developing obesity during adulthood. Further studies are needed to clarify how gender differences modify the effects of childhood disadvantage on adult BMI trajectories.

ABSTRACT BACKGROUND: Overweight and obesity have reached epidemic prevalences. Obesity control involves many factors and needs to begin early in childhood. OBJECTIVES: To ascertain the association between tracked extracurricular sports practice and weight status; and to analyze tracking of overweight and obesity among school-aged children. DESIGN AND SETTING: Prospective cohort study conducted in 13 public schools in Cianorte, Paraná, in 2012-2016. METHODS: The sample comprised 2459 schoolchildren in Cianorte, of mean age 6.3 years at baseline and 9.4 years at follow-up. Body mass index was calculated from body mass and height measurements. The children were grouped as normal weight, overweight or obese. Information on extracurricular sports practice was collected through the dichotomous question “Do you participate in any extracurricular sports?” (“yes” or “no”). RESULTS: Tracking of weight status showed that 75.5% maintained this, with kappa of 0.530. Tracking of extracurricular sports practice showed that 80.9% maintained this, with low concordance (kappa of 0.054). Weight status correlation between baseline and follow-up showed that overweight or obese individuals were 4.65 times (CI: 4.05-5.34) more likely to maintain the same classification or move from overweight to obese at follow-up. Correlation of extracurricular sports practice with overweight or obesity at follow-up was not significant. CONCLUSIONS: These results demonstrated that overweight or obese children were at higher risk of gaining weight than were normal-weight children. In addition, the proportion of these children who maintained extracurricular sports practices over the years was low. Maintenance of this variable was not associated with weight status.

BackgroundStunting, short for age, affects the overall growth and development of the children. It occurs due to chronic under nutrition. Stunting vastly occurs in impoverished regions of the world, including Ethiopia.ObjectiveThis study aimed to investigate the prevalence and correlates of stunting among under-five children in Ethiopia using marginal models.MethodsData were taken from the 2016 Ethiopian Demographic Health Survey, which is a nationally representative survey of children in the 0–59 month age group. For marginal models, generalized estimating equations and alternating logistic regression models were used for the analysis.ResultsThe prevalence of stunting among the under-five children was 34.91% in the area. The proportion was slightly higher among male (36.01%) than female (33.76%) child. The Alternating Logistic Regression model analysis revealed that the child’s age, the mother’s education level, the mother’s body mass index, the place of residence, the wealth index, and the previous birth interval were found to be significant determinants of childhood stunting, and the result shows that children born with a lower previous birth interval (less than 24 months) were more likely to be stunted than those born within a higher birth interval. Children in rural Ethiopia were more likely to be stunted than children in urban Ethiopia.ConclusionThis study found that more than one third of children were stunted in the area. The study also determined that child’s age, the mother’s education, the mother’s body mass index, the place of residence, the wealth index, and birth interval influence stunting. Therefore, it is better enhancing the nutritional intervention programs.

Overview of data collection methods and participants.

To evaluate the association between childhood parental smoking exposure and the risk of overweight/obesity from childhood to adulthood. This study leverages the data from two longitudinal population based cohort studies, the Cardiovascular Risk in Young Finns Study between years 1980–2011/2012 (YFS; N = 2,303; baseline age 3–18 years) and the Special Turku Coronary Risk Factor Intervention Project between years 1989–2009/2010 (STRIP; N = 632; baseline age 7 months). Weight, height and waist circumference were measured from childhood to adulthood. Overweight/obesity was defined as body mass index ≥25 kg/m2 in adults and using the Cole criteria in children. Central obesity was defined as waist circumference > 100/90 cm in men/women and as a waist-to-height ratio > 0.50 in children. Statistical analyses were adjusted for age, sex, socioeconomic status, smoking, birth weight, parental ages, diet and physical activity. Childhood parental smoking exposure was associated with increased risk for life-course overweight/obesity (YFS: RR1.13, 95%CI 1.02–1.24; STRIP: RR1.57, 95%CI 1.10–2.26) and central obesity (YFS: RR1.18, 95%CI 1.01–1.38; STRIP: RR1.45, 95%CI 0.98–2.15). Childhood exposure to parental smoking is associated with increased risk of overweight/obesity over the life-course.KEY MESSAGESExposure to parental smoking in childhood was associated with increased risk of overweight/obesity, central obesity and adiposity measured by skinfold thickness from childhood to adulthood. Exposure to parental smoking in childhood was associated with increased risk of overweight/obesity, central obesity and adiposity measured by skinfold thickness from childhood to adulthood.