United States US: Prevalence of Wasting: Weight for Height: Female: % of Children Under 5 data was reported at 0.700 % in 2012. This records an increase from the previous number of 0.500 % for 2009. United States US: Prevalence of Wasting: Weight for Height: Female: % of Children Under 5 data is updated yearly, averaging 0.550 % from Dec 1991 (Median) to 2012, with 6 observations. The data reached an all-time high of 0.800 % in 2005 and a record low of 0.100 % in 2001. United States US: Prevalence of Wasting: Weight for Height: Female: % of Children Under 5 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Health Statistics. Prevalence of wasting, female, is the proportion of girls under age 5 whose weight for height is more than two standard deviations below the median for the international reference population ages 0-59.; ; World Health Organization, Global Database on Child Growth and Malnutrition. Country-level data are unadjusted data from national surveys, and thus may not be comparable across countries.; Linear mixed-effect model estimates; Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF, www.childinfo.org). Estimates of child malnutrition, based on prevalence of underweight and stunting, are from national survey data. The proportion of underweight children is the most common malnutrition indicator. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition.

🔗 Check out my notebook here: Link

This dataset includes malnutrition indicators and some of the features that might impact malnutrition. The detailed description of the dataset is given below:

• Percentage-of-underweight-children-data: Percentage of children aged 5 years or below who are underweight by country.

• Prevalence of Underweight among Female Adults (Age Standardized Estimate): Percentage of female adults whos BMI is less than 18.

• GDP per capita (constant 2015 US$): GDP per capita is gross domestic product divided by midyear population. GDP is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. Data are in constant 2015 U.S. dollars.

• Domestic general government health expenditure (% of GDP): Public expenditure on health from domestic sources as a share of the economy as measured by GDP.

• Maternal mortality ratio (modeled estimate, per 100,000 live births): Maternal mortality ratio is the number of women who die from pregnancy-related causes while pregnant or within 42 days of pregnancy termination per 100,000 live births. The data are estimated with a regression model using information on the proportion of maternal deaths among non-AIDS deaths in women ages 15-49, fertility, birth attendants, and GDP measured using purchasing power parities (PPPs).

• Mean-age-at-first-birth-of-women-aged-20-50-data: Average age at which women of age 20-50 years have their first child.

• School enrollment, secondary, female (% gross): Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown. Secondary education completes the provision of basic education that began at the primary level, and aims at laying the foundations for lifelong learning and human development, by offering more subject- or skill-oriented instruction using more specialized teachers.

About this dataset

Context

The dataset tabulates the data for the New York, NY population pyramid, which represents the New York population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey 5-Year estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for New York, NY, is 26.1.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for New York, NY, is 22.4.
• Total dependency ratio for New York, NY is 48.5.
• Potential support ratio, which is the number of youth (working age population) per elderly, for New York, NY is 4.5.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group for the New York population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the New York for the selected age group is shown in the following column.
• Population (Female): The female population in the New York for the selected age group is shown in the following column.
• Total Population: The total population of the New York for the selected age group is shown in the following column.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for New York Population by Age. You can refer the same here

United States US: Prevalence of Underweight: Weight for Age: Female: % of Children Under 5 data was reported at 0.400 % in 2012. This records a decrease from the previous number of 0.700 % for 2009. United States US: Prevalence of Underweight: Weight for Age: Female: % of Children Under 5 data is updated yearly, averaging 0.800 % from Dec 1991 (Median) to 2012, with 6 observations. The data reached an all-time high of 1.200 % in 1991 and a record low of 0.400 % in 2012. United States US: Prevalence of Underweight: Weight for Age: Female: % of Children Under 5 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Health Statistics. Prevalence of underweight, female, is the percentage of girls under age 5 whose weight for age is more than two standard deviations below the median for the international reference population ages 0-59 months. The data are based on the WHO's new child growth standards released in 2006.; ; World Health Organization, Global Database on Child Growth and Malnutrition. Country-level data are unadjusted data from national surveys, and thus may not be comparable across countries.; Linear mixed-effect model estimates; Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF, www.childinfo.org). Estimates of child malnutrition, based on prevalence of underweight and stunting, are from national survey data. The proportion of underweight children is the most common malnutrition indicator. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition.

About this dataset

Context

The dataset tabulates the data for the Anna Maria, FL population pyramid, which represents the Anna Maria population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Anna Maria, FL, is 9.6.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Anna Maria, FL, is 90.4.
• Total dependency ratio for Anna Maria, FL is 100.0.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Anna Maria, FL is 1.1.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group for the Anna Maria population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Anna Maria for the selected age group is shown in the following column.
• Population (Female): The female population in the Anna Maria for the selected age group is shown in the following column.
• Total Population: The total population of the Anna Maria for the selected age group is shown in the following column.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Anna Maria Population by Age. You can refer the same here

This survey of prosecutors was undertaken to describe current practice and identify "promising practices" with respect to cases involving domestic violence and child victims or witnesses. It sought to answer the following questions: (1) What are the challenges facing prosecutors when children are exposed to domestic violence? (2) How are new laws regarding domestic violence committed in the presence of children, now operating in a small number of states, affecting practice? (3) What can prosecutors do to help battered women and their children? To gather data on these topics, the researchers conducted a national telephone survey of prosecutors. Questions asked include case assignment, jurisdiction of the prosecutor's office, caseload, protocol for coordinating cases, asking about domestic violence when investigating child abuse cases, asking about children when investigating domestic violence cases, and how the respondent found out when a child abuse case involved domestic violence or when a domestic violence case involved children. Other variables cover whether police routinely checked for prior Child Protective Services (CPS) reports, if these cases were heard by the same judge, in the same court, and were handled by the same prosecutor, if there were laws identifying exposure to domestic violence as child abuse, if there were laws applying or enhancing criminal penalties when children were exposed to domestic violence, if the state legislature was considering any such action, if prosecutors were using other avenues to enhance penalties, if there was pertinent caselaw, and if the respondent's office had a no-drop policy for domestic violence cases. Additional items focus on whether the presence of children influenced decisions to prosecute, if the office would report or prosecute a battered woman who abused her children, or failed to protect her children from abuse or from exposure to domestic violence, how often the office prosecuted such women, if there was a batterers' treatment program in the community, how often batterers were sentenced to attend the treatment program, if there were programs to which the respondent could refer battered mothers and children, what types of programs were operating, and if prosecutors had received training on domestic violence issues.

In 1986, the Congress enacted Public Laws 99-500 and 99-591, requiring a biennial report on the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC). In response to these requirements, FNS developed a prototype system that allowed for the routine acquisition of information on WIC participants from WIC State Agencies. Since 1992, State Agencies have provided electronic copies of these data to FNS on a biennial basis. FNS and the National WIC Association (formerly National Association of WIC Directors) agreed on a set of data elements for the transfer of information. In addition, FNS established a minimum standard dataset for reporting participation data. For each biennial reporting cycle, each State Agency is required to submit a participant-level dataset containing standardized information on persons enrolled at local agencies for the reference month of April. The 2018 Participant and Program Characteristics (PC2018) is the fourteenth data submission to be completed using the WIC PC reporting system. In April 2018, there were 90 State agencies: the 50 States, American Samoa, the District of Columbia, Guam, the Northern Mariana Islands, Puerto Rico, the American Virgin Islands, and 34 Indian tribal organizations. Processing methods and equipment used Specifications on formats (“Guidance for States Providing Participant Data”) were provided to all State agencies in January 2018. This guide specified 20 minimum dataset (MDS) elements and 11 supplemental dataset (SDS) elements to be reported on each WIC participant. Each State Agency was required to submit all 20 MDS items and any SDS items collected by the State agency.   Study date(s) and duration The information for each participant was from the participants’ most current WIC certification as of April 2018. Study spatial scale (size of replicates and spatial scale of study area) In April 2018, there were 90 State agencies: the 50 States, American Samoa, the District of Columbia, Guam, the Northern Mariana Islands, Puerto Rico, the American Virgin Islands, and 34 Indian tribal organizations. Level of true replication Unknown Sampling precision (within-replicate sampling or pseudoreplication) State Agency Data Submissions. PC2018 is a participant dataset consisting of 7,837,672 active records. The records, submitted to USDA by the State Agencies, comprise a census of all WIC enrollees, so there is no sampling involved in the collection of this data. PII Analytic Datasets. State agency files were combined to create a national census participant file of approximately 7.8 million records. The census dataset contains potentially personally identifiable information (PII) and is therefore not made available to the public. National Sample Dataset. The public use SAS analytic dataset made available to the public has been constructed from a nationally representative sample drawn from the census of WIC participants, selected by participant category. The national sample consists of 1 percent of the total number of participants, or 78,365 records. The distribution by category is 6,825 pregnant women, 6,189 breastfeeding women, 5,134 postpartum women, 18,552 infants, and 41,665 children. Level of subsampling (number and repeat or within-replicate sampling) The proportionate (or self-weighting) sample was drawn by WIC participant category: pregnant women, breastfeeding women, postpartum women, infants, and children. In this type of sample design, each WIC participant has the same probability of selection across all strata. Sampling weights are not needed when the data are analyzed. In a proportionate stratified sample, the largest stratum accounts for the highest percentage of the analytic sample. Study design (before–after, control–impacts, time series, before–after-control–impacts) None – Non-experimental Description of any data manipulation, modeling, or statistical analysis undertaken Each entry in the dataset contains all MDS and SDS information submitted by the State agency on the sampled WIC participant. In addition, the file contains constructed variables used for analytic purposes. To protect individual privacy, the public use file does not include State agency, local agency, or case identification numbers. Description of any gaps in the data or other limiting factors All State agencies except New Mexico provided data on a census of their WIC participants. Resources in this dataset:Resource Title: WIC Participant and Program Characteristics 2018 Data. File Name: wicpc.wicpc2018_public_use.csvResource Title: WIC Participant and Program Characteristics 2018 Dataset Codebook. File Name: PC2018 National Sample File Public Use Codebook updated.docxResource Description: The 2018 Participant and Program Characteristics (PC2018) is the fourteenth data submission to be completed using the WIC PC reporting system. In April 2018, there were 90 State agencies: the 50 States, American Samoa, the District of Columbia, Guam, the Northern Mariana Islands, Puerto Rico, the American Virgin Islands, and 34 Indian tribal organizations.Resource Title: WIC Participant and Program Characteristics 2018 Datasets SAS STATA SPSS. File Name: wicpc2018_agdatacoomonsupload.zipResource Description: The 2018 Participant and Program Characteristics (PC2018) is the fourteenth data submission to be completed using the WIC PC reporting system. In April 2018, there were 90 State agencies: the 50 States, American Samoa, the District of Columbia, Guam, the Northern Mariana Islands, Puerto Rico, the American Virgin Islands, and 34 Indian tribal organizations.

United States US: Fertility Rate: Total: Births per Woman data was reported at 1.800 Ratio in 2016. This records a decrease from the previous number of 1.843 Ratio for 2015. United States US: Fertility Rate: Total: Births per Woman data is updated yearly, averaging 2.002 Ratio from Dec 1960 (Median) to 2016, with 57 observations. The data reached an all-time high of 3.654 Ratio in 1960 and a record low of 1.738 Ratio in 1976. United States US: Fertility Rate: Total: Births per Woman data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Health Statistics. Total fertility rate represents the number of children that would be born to a woman if she were to live to the end of her childbearing years and bear children in accordance with age-specific fertility rates of the specified year.; ; (1) United Nations Population Division. World Population Prospects: 2017 Revision. (2) Census reports and other statistical publications from national statistical offices, (3) Eurostat: Demographic Statistics, (4) United Nations Statistical Division. Population and Vital Statistics Reprot (various years), (5) U.S. Census Bureau: International Database, and (6) Secretariat of the Pacific Community: Statistics and Demography Programme.; Weighted average; Relevance to gender indicator: it can indicate the status of women within households and a woman’s decision about the number and spacing of children.

United States US: Prevalence of Overweight: Weight for Height: Female: % of Children Under 5 data was reported at 6.900 % in 2012. This records an increase from the previous number of 6.400 % for 2009. United States US: Prevalence of Overweight: Weight for Height: Female: % of Children Under 5 data is updated yearly, averaging 6.900 % from Dec 1991 (Median) to 2012, with 6 observations. The data reached an all-time high of 8.700 % in 2005 and a record low of 5.100 % in 1991. United States US: Prevalence of Overweight: Weight for Height: Female: % of Children Under 5 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Health Statistics. Prevalence of overweight, female, is the percentage of girls 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.; ; World Health Organization, Global Database on Child Growth and Malnutrition. Country-level data are unadjusted data from national surveys, and thus may not be comparable across countries.; 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

The past five decades have seen a remarkable convergence in the economic roles of men and women in society. Yet, persistently large gender gaps in terms of labor supply, earnings, and representation in top jobs remain. Moreover, in countries like the U.S., convergence in labor market outcomes appears to have slowed in recent decades. In this article, we focus on the role of children and show that many potential explanations for the remaining gender disparities in labor market outcomes are related to the fact that children impose significantly larger penalties on the career trajectories of women relative to men. In the U.S., we document that more than two-thirds of the overall gender earnings gap can be accounted for by the differential impacts of children on women and men. We propose a simple model of household decision-making to motivate the link between children and gender gaps in the labor market, and to help rationalize how various factors potentially interact with parenthood to produce differential outcomes by gender. We discuss several forces that might make the road to gender equity even more challenging for modern cohorts of parents, and offer a critical discussion of public policies that seek to address the remaining gaps.

Description of the experiment setting: location, influential climatic conditions, controlled conditions (e.g. temperature, light cycle) In 1986, the Congress enacted Public Laws 99-500 and 99-591, requiring a biennial report on the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC). In response to these requirements, FNS developed a prototype system that allowed for the routine acquisition of information on WIC participants from WIC State Agencies. Since 1992, State Agencies have provided electronic copies of these data to FNS on a biennial basis. FNS and the National WIC Association (formerly National Association of WIC Directors) agreed on a set of data elements for the transfer of information. In addition, FNS established a minimum standard dataset for reporting participation data. For each biennial reporting cycle, each State Agency is required to submit a participant-level dataset containing standardized information on persons enrolled at local agencies for the reference month of April. The 2016 Participant and Program Characteristics (PC2016) is the thirteenth data submission to be completed using the WIC PC reporting system. In April 2016, there were 90 State agencies: the 50 States, American Samoa, the District of Columbia, Guam, the Northern Mariana Islands, Puerto Rico, the American Virgin Islands, and 34 Indian tribal organizations. Processing methods and equipment used Specifications on formats (“Guidance for States Providing Participant Data”) were provided to all State agencies in January 2016. This guide specified 20 minimum dataset (MDS) elements and 11 supplemental dataset (SDS) elements to be reported on each WIC participant. Each State Agency was required to submit all 20 MDS items and any SDS items collected by the State agency.   Study date(s) and duration The information for each participant was from the participants’ most current WIC certification as of April 2016. Due to management information constraints, Connecticut provided data for a month other than April 2016, specifically August 16 – September 15, 2016. Study spatial scale (size of replicates and spatial scale of study area) In April 2016, there were 90 State agencies: the 50 States, American Samoa, the District of Columbia, Guam, the Northern Mariana Islands, Puerto Rico, the American Virgin Islands, and 34 Indian tribal organizations. Level of true replication Unknown Sampling precision (within-replicate sampling or pseudoreplication) State Agency Data Submissions. PC2016 is a participant dataset consisting of 8,815,472 active records. The records, submitted to USDA by the State Agencies, comprise a census of all WIC enrollees, so there is no sampling involved in the collection of this data. PII Analytic Datasets. State agency files were combined to create a national census participant file of approximately 8.8 million records. The census dataset contains potentially personally identifiable information (PII) and is therefore not made available to the public. National Sample Dataset. The public use SAS analytic dataset made available to the public has been constructed from a nationally representative sample drawn from the census of WIC participants, selected by participant category. The nationally representative sample is composed of 60,003 records. The distribution by category is 5,449 pregnant women, 4,661 breastfeeding women, 3,904 postpartum women, 13,999 infants, and 31,990 children. Level of subsampling (number and repeat or within-replicate sampling) The proportionate (or self-weighting) sample was drawn by WIC participant category: pregnant women, breastfeeding women, postpartum women, infants, and children. In this type of sample design, each WIC participant has the same probability of selection across all strata. Sampling weights are not needed when the data are analyzed. In a proportionate stratified sample, the largest stratum accounts for the highest percentage of the analytic sample. Study design (before–after, control–impacts, time series, before–after-control–impacts) None – Non-experimental Description of any data manipulation, modeling, or statistical analysis undertaken Each entry in the dataset contains all MDS and SDS information submitted by the State agency on the sampled WIC participant. In addition, the file contains constructed variables used for analytic purposes. To protect individual privacy, the public use file does not include State agency, local agency, or case identification numbers. Description of any gaps in the data or other limiting factors Due to management information constraints, Connecticut provided data for a month other than April 2016, specifically August 16 – September 15, 2016.   Outcome measurement methods and equipment used None Resources in this dataset:Resource Title: WIC Participant and Program Characteristics 2016. File Name: wicpc_2016_public.csvResource Description: The 2016 Participant and Program Characteristics (PC2016) is the thirteenth data submission to be completed using the WIC PC reporting system. In April 2016, there were 90 State agencies: the 50 States, American Samoa, the District of Columbia, Guam, the Northern Mariana Islands, Puerto Rico, the American Virgin Islands, and 34 Indian tribal organizations.Resource Software Recommended: SAS, version 9.4,url: https://www.sas.com/en_us/software/sas9.html Resource Title: WIC Participant and Program Characteristics 2016 Codebook. File Name: WICPC2016_PUBLIC_CODEBOOK.xlsxResource Software Recommended: SAS, version 9.4,url: https://www.sas.com/en_us/software/sas9.html Resource Title: WIC Participant and Program Characteristics 2016 - Zip File with SAS, SPSS and STATA data. File Name: WIC_PC_2016_SAS_SPSS_STATA_Files.zipResource Description: WIC Participant and Program Characteristics 2016 - Zip File with SAS, SPSS and STATA data

This dataset includes birth rates for unmarried women by age group, race, and Hispanic origin in the United States since 1970. Methods for collecting information on marital status changed over the reporting period and have been documented in: • Ventura SJ, Bachrach CA. Nonmarital childbearing in the United States, 1940–99. National vital statistics reports; vol 48 no 16. Hyattsville, Maryland: National Center for Health Statistics. 2000. Available from: http://www.cdc.gov/nchs/data/nvsr/nvsr48/nvs48_16.pdf. • National Center for Health Statistics. User guide to the 2013 natality public use file. Hyattsville, Maryland: National Center for Health Statistics. 2014. Available from: http://www.cdc.gov/nchs/data_access/VitalStatsOnline.htm. National data on births by Hispanics origin exclude data for Louisiana, New Hampshire, and Oklahoma in 1989; for New Hampshire and Oklahoma in 1990; for New Hampshire in 1991 and 1992. Information on reporting Hispanic origin is detailed in the Technical Appendix for the 1999 public-use natality data file (see (ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/Dataset_Documentation/DVS/natality/Nat1999doc.pdf.) All birth data by race before 1980 are based on race of the child. Starting in 1980, birth data by race are based on race of the mother. SOURCES CDC/NCHS, National Vital Statistics System, birth data (see http://www.cdc.gov/nchs/births.htm); public-use data files (see http://www.cdc.gov/nchs/data_access/Vitalstatsonline.htm); and CDC WONDER (see http://wonder.cdc.gov/). REFERENCES Curtin SC, Ventura SJ, Martinez GM. Recent declines in nonmarital childbearing in the United States. NCHS data brief, no 162. Hyattsville, MD: National Center for Health Statistics. 2014. Available from: http://www.cdc.gov/nchs/data/databriefs/db162.pdf. Martin JA, Hamilton BE, Osterman MJK, et al. Births: Final data for 2015. National vital statistics reports; vol 66 no 1. Hyattsville, MD: National Center for Health Statistics. 2017. Available from: https://www.cdc.gov/nchs/data/nvsr/nvsr66/nvsr66_01.pdf.

In 2023 the poverty rate in the United States was highest among people between 18 and 24, with a rate of 16 percent for male Americans and a rate of 21 percent for female Americans. The lowest poverty rate for both men and women was for those aged between 45 and 54. What is the poverty line? The poverty line is a metric used by the U.S. Census Bureau to define poverty in the United States. It is a specific income level that is considered to be the bare minimum a person or family needs to meet their basic needs. If a family’s annual pre-tax income is below this income level, then they are considered impoverished. The poverty guideline for a family of four in 2021 was 26,500 U.S. dollars. Living below the poverty line According to the most recent data, almost one-fifth of African Americans in the United States live below the poverty line; the most out of any ethnic group. Additionally, over 7.42 million families in the U.S. live in poverty – a figure that has held mostly steady since 1990, outside the 2008 financial crisis which threw 9.52 million families into poverty by 2012. The poverty gender gap Wage inequality has been an ongoing discussion in U.S. discourse for many years now. The poverty gap for women is most pronounced during their child-bearing years, shrinks, and then grows again in old age. While progress has been made on the gender pay gap over the last 30 years, there are still significant disparities, even in occupations that predominantly employ men. Additionally, women are often having to spend more time attending to child and household duties than men.

This dataset contains monthly data for the current fiscal year for each WIC State agency. There are currently 90 WIC State agencies: the 50 geographic states, the District of Columbia, Puerto Rico, Guam, the Virgin Islands, American Samoa, Northern Marianas, and 34 Indian tribal organizations (ITO's). The dataset contains number of Pregnant Women, Breastfeeding Women, Postpartum Women, Total Women, Infants and children participating in the WIC program and the associated food and administrative cost.

In 2021, survey data on book readers in the United States revealed that ** percent of female respondents stated they had read or listened to at least one book in the previous 12 months. The share of men who had read a book in that time frame was slightly lower at ** percent, but marked an increase from the ** percent recorded two years earlier. Book readers in the U.S. Whilst digital formats have overtaken print in terms of popularity and consumption, many U.S. media consumers still enjoy a good book – though women tend to read more books than men. A 2018 survey showed that 11 percent of U.S. women read ** or more books that year, compared to **** percent of male respondents. Women were also more likely to prefer reading books in paperback format, and much less likely to be fans of comic books. Regardless of adults’ preferences, something which many U.S. readers agree on is the importance of reading to children. According to a report on family reading habits, 58 percent of parents with a child aged between three and five years old said that they read aloud to their child **** to ***** times per week. The frequency of read-aloud time diminished as children grew older, usually because the child could then read independently, though some children eventually swap reading for other activities such as playing video games, watching television, or using social media.

Families of tax filers; Census families with children by age of children and children by age groups (final T1 Family File; T1FF).

This dataset includes teen birth rates for females by age group, race, and Hispanic origin in the United States since 1960. Data availability varies by race and ethnicity groups. All birth data by race before 1980 are based on race of the child. Since 1980, birth data by race are based on race of the mother. For race, data are available for Black and White births since 1960, and for American Indians/Alaska Native and Asian/Pacific Islander births since 1980. Data on Hispanic origin are available since 1989. Teen birth rates for specific racial and ethnic categories are also available since 1989. From 2003 through 2015, the birth data by race were based on the “bridged” race categories (5). Starting in 2016, the race categories for reporting birth data changed; the new race and Hispanic origin categories are: Non-Hispanic, Single Race White; Non-Hispanic, Single Race Black; Non-Hispanic, Single Race American Indian/Alaska Native; Non-Hispanic, Single Race Asian; and, Non-Hispanic, Single Race Native Hawaiian/Pacific Islander (5,6). Birth data by the prior, “bridged” race (and Hispanic origin) categories are included through 2018 for comparison. National data on births by Hispanic origin exclude data for Louisiana, New Hampshire, and Oklahoma in 1989; New Hampshire and Oklahoma in 1990; and New Hampshire in 1991 and 1992. Birth and fertility rates for the Central and South American population includes other and unknown Hispanic. Information on reporting Hispanic origin is detailed in the Technical Appendix for the 1999 public-use natality data file (see ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/Dataset_Documentation/DVS/natality/Nat1999doc.pdf). SOURCES NCHS, National Vital Statistics System, birth data (see https://www.cdc.gov/nchs/births.htm); public-use data files (see https://www.cdc.gov/nchs/data_access/VitalStatsOnline.htm); and CDC WONDER (see http://wonder.cdc.gov/). REFERENCES National Office of Vital Statistics. Vital Statistics of the United States, 1950, Volume I. 1954. Available from: https://www.cdc.gov/nchs/data/vsus/vsus_1950_1.pdf. Hetzel AM. U.S. vital statistics system: major activities and developments, 1950-95. National Center for Health Statistics. 1997. Available from: https://www.cdc.gov/nchs/data/misc/usvss.pdf. National Center for Health Statistics. Vital Statistics of the United States, 1967, Volume I–Natality. 1969. Available from: https://www.cdc.gov/nchs/data/vsus/nat67_1.pdf. Martin JA, Hamilton BE, Osterman MJK, et al. Births: Final data for 2015. National vital statistics reports; vol 66 no 1. Hyattsville, MD: National Center for Health Statistics. 2017. Available from: https://www.cdc.gov/nchs/data/nvsr/nvsr66/nvsr66_01.pdf. Martin JA, Hamilton BE, Osterman MJK, Driscoll AK, Drake P. Births: Final data for 2016. National Vital Statistics Reports; vol 67 no 1. Hyattsville, MD: National Center for Health Statistics. 2018. Available from: https://www.cdc.gov/nvsr/nvsr67/nvsr67_01.pdf. Martin JA, Hamilton BE, Osterman MJK, Driscoll AK, Births: Final data for 2018. National vital statistics reports; vol 68 no 13. Hyattsville, MD: National Center for Health Statistics. 2019. Available from: https://www.cdc.gov/nchs/data/nvsr/nvsr68/nvsr68_13.pdf.

United States US: Prevalence of Stunting: Height for Age: Male: % of Children Under 5 data was reported at 3.000 % in 2012. This records a decrease from the previous number of 3.200 % for 2009. United States US: Prevalence of Stunting: Height for Age: Male: % of Children Under 5 data is updated yearly, averaging 3.600 % from Dec 1991 (Median) to 2012, with 6 observations. The data reached an all-time high of 4.500 % in 2002 and a record low of 3.000 % in 2012. United States US: Prevalence of Stunting: Height for Age: Male: % of Children Under 5 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Health Statistics. Prevalence of stunting, male, is the percentage of boys under age 5 whose height for age is more than two standard deviations below the median for the international reference population ages 0-59 months. For children up to two years old height is measured by recumbent length. For older children height is measured by stature while standing. The data are based on the WHO's new child growth standards released in 2006.; ; World Health Organization, Global Database on Child Growth and Malnutrition. Country-level data are unadjusted data from national surveys, and thus may not be comparable across countries.; Linear mixed-effect model estimates; Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF, www.childinfo.org). Estimates of child malnutrition, based on prevalence of underweight and stunting, are from national survey data. The proportion of underweight children is the most common malnutrition indicator. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition.

Introduction

California Health and Human Services (CalHHS) single and multi-program participation counts with demographics by year and geography, both the Annual (cumulative) and July (point in time) files. The data dictionary for each file is loaded as a resource within the CSV preview page.

Background

The CalHHS Program Data and Dashboard contain participation data related to seven California Health & Human Services agency programs: CalWORKs, CalFresh, In-Home Supportive Services, Foster Care, Medi-Cal (California Medicaid), Women, Infants, & Children, and Developmental Services. Users are able to view these data at the county level or by legislative district level (U.S. Congress, State Assembly, State Senate). Statistics on persons served, persons per case, average grant amount, and basic demographics are presented in both tabular spreadsheets and customizable visualizations by program. In addition to presenting statistics for each program separately, the dashboard also provides data on the number of individuals participating in more than one program at the same time in a given district or county.

Methodology

The Children's Data Network has prepared a methodology document for the process involved in creating this data. If you would like to review the methodology used, please click the link below to visit the CalHHS Records Reconciliation Methodology. https://data.chhs.ca.gov/pages/calhhs-program-counts-methodology

Cervical Cancer Risk Factors for Biopsy: This Dataset is Obtained from UCI Repository and kindly acknowledged! This file contains a List of Risk Factors for Cervical Cancer leading to a Biopsy Examination! About 11,000 new cases of invasive cervical cancer are diagnosed each year in the U.S. However, the number of new cervical cancer cases has been declining steadily over the past decades. Although it is the most preventable type of cancer, each year cervical cancer kills about 4,000 women in the U.S. and about 300,000 women worldwide. In the United States, cervical cancer mortality rates plunged by 74% from 1955 - 1992 thanks to increased screening and early detection with the Pap test. AGE Fifty percent of cervical cancer diagnoses occur in women ages 35 - 54, and about 20% occur in women over 65 years of age. The median age of diagnosis is 48 years. About 15% of women develop cervical cancer between the ages of 20 - 30. Cervical cancer is extremely rare in women younger than age 20. However, many young women become infected with multiple types of human papilloma virus, which then can increase their risk of getting cervical cancer in the future. Young women with early abnormal changes who do not have regular examinations are at high risk for localized cancer by the time they are age 40, and for invasive cancer by age 50. SOCIOECONOMIC AND ETHNIC FACTORS Although the rate of cervical cancer has declined among both Caucasian and African-American women over the past decades, it remains much more prevalent in African-Americans -- whose death rates are twice as high as Caucasian women. Hispanic American women have more than twice the risk of invasive cervical cancer as Caucasian women, also due to a lower rate of screening. These differences, however, are almost certainly due to social and economic differences. Numerous studies report that high poverty levels are linked with low screening rates. In addition, lack of health insurance, limited transportation, and language difficulties hinder a poor woman’s access to screening services. HIGH SEXUAL ACTIVITY Human papilloma virus (HPV) is the main risk factor for cervical cancer. In adults, the most important risk factor for HPV is sexual activity with an infected person. Women most at risk for cervical cancer are those with a history of multiple sexual partners, sexual intercourse at age 17 years or younger, or both. A woman who has never been sexually active has a very low risk for developing cervical cancer. Sexual activity with multiple partners increases the likelihood of many other sexually transmitted infections (chlamydia, gonorrhea, syphilis).Studies have found an association between chlamydia and cervical cancer risk, including the possibility that chlamydia may prolong HPV infection. FAMILY HISTORY Women have a higher risk of cervical cancer if they have a first-degree relative (mother, sister) who has had cervical cancer. USE OF ORAL CONTRACEPTIVES Studies have reported a strong association between cervical cancer and long-term use of oral contraception (OC). Women who take birth control pills for more than 5 - 10 years appear to have a much higher risk HPV infection (up to four times higher) than those who do not use OCs. (Women taking OCs for fewer than 5 years do not have a significantly higher risk.) The reasons for this risk from OC use are not entirely clear. Women who use OCs may be less likely to use a diaphragm, condoms, or other methods that offer some protection against sexual transmitted diseases, including HPV. Some research also suggests that the hormones in OCs might help the virus enter the genetic material of cervical cells. HAVING MANY CHILDREN Studies indicate that having many children increases the risk for developing cervical cancer, particularly in women infected with HPV. SMOKING Smoking is associated with a higher risk for precancerous changes (dysplasia) in the cervix and for progression to invasive cervical cancer, especially for women infected with HPV. IMMUNOSUPPRESSION Women with weak immune systems, (such as those with HIV / AIDS), are more susceptible to acquiring HPV. Immunocompromised patients are also at higher risk for having cervical precancer develop rapidly into invasive cancer. DIETHYLSTILBESTROL (DES) From 1938 - 1971, diethylstilbestrol (DES), an estrogen-related drug, was widely prescribed to pregnant women to help prevent miscarriages. The daughters of these women face a higher risk for cervical cancer. DES is no longer prsecribed.