About this dataset

Context

The dataset tabulates the population of Fertile by race. It includes the population of Fertile across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of Fertile across relevant racial categories.

Key observations

The percent distribution of Fertile population by race (across all racial categories recognized by the U.S. Census Bureau): 99.26% are white and 0.74% are American Indian and Alaska Native.

Content

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

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the Fertile
• Population: The population of the racial category (excluding ethnicity) in the Fertile is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Fertile total population. Please note that the sum of all percentages may not equal one due to rounding of values.

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 Fertile Population by Race & Ethnicity. You can refer the same here

About this dataset

Context

The dataset tabulates the population of Fertile by race. It includes the population of Fertile across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of Fertile across relevant racial categories.

Key observations

The percent distribution of Fertile population by race (across all racial categories recognized by the U.S. Census Bureau): 95.91% are white, 0.38% are American Indian and Alaska Native and 3.71% are multiracial.

Content

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

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the Fertile
• Population: The population of the racial category (excluding ethnicity) in the Fertile is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Fertile total population. Please note that the sum of all percentages may not equal one due to rounding of values.

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 Fertile Population by Race & Ethnicity. You can refer the same here

Abstract copyright UK Data Service and data collection copyright owner.Understanding Society (the UK Household Longitudinal Study), which began in 2009, is conducted by the Institute for Social and Economic Research (ISER) at the University of Essex, and the survey research organisations Kantar Public and NatCen. It builds on and incorporates, the British Household Panel Survey (BHPS), which began in 1991. The Harmonized Histories is an international comparative dataset, created through harmonising data from existing surveys into one common format. The aim of Harmonized Histories is to facilitate cross-national research on topics related to transition to adulthood, family formation, and childbearing. The dataset focuses on fertility and partnership histories but also captures information on socio-economic status, place of residence and information on the childhood family. You can find more information about Harmonized Histories and access to the datasets from other countries via the Generations & Gender Programme (GGP) website. Two datasets are provided. The first includes all people aged 16 or over who participated in the full interview of Wave 1 of the Understanding Society project and the data as is collected at Wave 1. The second dataset follows the people who are in the first dataset prospectively. Thus, it includes all the retrospective information from the first dataset and has been updated when things changed, for instance the partners got married or had children. For more information please refer to the User Guide. Harmonized Histories uses Understanding Society for data on the UK. As Harmonized Histories is a cross-national project, please note that the variable naming conventions and terminology used in this dataset are different to the standard Understanding Society naming and terms. Further information may also be found on the Understanding Society mainstage webpage and links to publications based on the study can be found on the Understanding Society Latest Research webpage. Understanding Society acknowledges Professor Brienna Perelli-Harris, Dr Niels Blom and Karolin Kubisch for making this dataset available to Understanding Society. Suitable data analysis software These data are provided by the depositor in Stata format. Users are strongly advised to analyse them in Stata, although SPSS and tab-delimited text versions are also available if needed. Users should note that transfer to other software formats may result in unforeseen issues.

Background:
The Millennium Cohort Study (MCS) is a large-scale, multi-purpose longitudinal dataset providing information about babies born at the beginning of the 21st century, their progress through life, and the families who are bringing them up, for the four countries of the United Kingdom. The original objectives of the first MCS survey, as laid down in the proposal to the Economic and Social Research Council (ESRC) in March 2000, were:

• to chart the initial conditions of social, economic and health advantages and disadvantages facing children born at the start of the 21st century, capturing information that the research community of the future will require
• to provide a basis for comparing patterns of development with the preceding cohorts (the National Child Development Study, held at the UK Data Archive under GN 33004, and the 1970 Birth Cohort Study, held under GN 33229)
• to collect information on previously neglected topics, such as fathers' involvement in children's care and development
• to focus on parents as the most immediate elements of the children's 'background', charting their experience as mothers and fathers of newborn babies in the year 2000, recording how they (and any other children in the family) adapted to the newcomer, and what their aspirations for her/his future may be
• to emphasise intergenerational links including those back to the parents' own childhood
• to investigate the wider social ecology of the family, including social networks, civic engagement and community facilities and services, splicing in geo-coded data when available

Additional objectives subsequently included for MCS were:

• to provide control cases for the national evaluation of Sure Start (a government programme intended to alleviate child poverty and social exclusion)
• to provide samples of adequate size to analyse and compare the smaller countries of the United Kingdom, and include disadvantaged areas of England

Further information about the MCS can be found on the Centre for Longitudinal Studies web pages.

The content of MCS studies, including questions, topics and variables can be explored via the CLOSER Discovery website.

The first sweep (MCS1) interviewed both mothers and (where resident) fathers (or father-figures) of infants included in the sample when the babies were nine months old, and the second sweep (MCS2) was carried out with the same respondents when the children were three years of age. The third sweep (MCS3) was conducted in 2006, when the children were aged five years old, the fourth sweep (MCS4) in 2008, when they were seven years old, the fifth sweep (MCS5) in 2012-2013, when they were eleven years old, the sixth sweep (MCS6) in 2015, when they were fourteen years old, and the seventh sweep (MCS7) in 2018, when they were seventeen years old.

Safeguarded versions of MCS studies:
The Safeguarded versions of MCS1, MCS2, MCS3, MCS4, MCS5, MCS6 and MCS7 are held under UK Data Archive SNs 4683, 5350, 5795, 6411, 7464, 8156 and 8682 respectively. The longitudinal family file is held under SN 8172.

Polygenic Indices
Polygenic indices are available under Special Licence SN 9437. Derived summary scores have been created that combine the estimated effects of many different genes on a specific trait or characteristic, such as a person's risk of Alzheimer's disease, asthma, substance abuse, or mental health disorders, for example. These polygenic scores can be combined with existing survey data to offer a more nuanced understanding of how cohort members' outcomes may be shaped.

Sub-sample studies:
Some studies based on sub-samples of MCS have also been conducted, including a study of MCS respondent mothers who had received assisted fertility treatment, conducted in 2003 (see EUL SN 5559). Also, birth registration and maternity hospital episodes for the MCS respondents are held as a separate dataset (see EUL SN 5614).

Release of Sweeps 1 to 4 to Long Format (Summer 2020)
To support longitudinal research and make it easier to compare data from different time points, all data from across all sweeps is now in a consistent format. The update affects the data from sweeps 1 to 4 (from 9 months to 7 years), which are updated from the old/wide to a new/long format to match the format of data of sweeps 5 and 6 (age 11 and 14 sweeps). The old/wide formatted datasets contained one row per family with multiple variables for different respondents. The new/long formatted datasets contain one row per respondent (per parent or per cohort member) for each MCS family. Additional updates have been made to all sweeps to harmonise variable labels and enhance anonymisation.

How to access genetic and/or bio-medical sample data from a range of longitudinal surveys:
For information on how to access biomedical data from MCS that are not held at the UKDS, see the CLS Genetic data and biological samples webpage.

Secure Access datasets:
Secure Access versions of the MCS have more restrictive access conditions than versions available under the standard Safeguarded Licence or Special Licence (see 'Access data' tab above).

Secure Access versions of the MCS include:

• detailed sensitive variables not available under EUL. These have been grouped thematically and are held under SN 8753 (socio-economic, accommodation and occupational data), SN 8754 (self-reported health, behaviour and fertility), SN 8755 (demographics, language and religion) and SN 8756 (exact participation dates). These files replace previously available studies held under SNs 8456 and 8622-8627
  
• detailed geographical identifier files which are grouped by sweep held under SN 7758 (MCS1), SN 7759 (MCS2), SN 7760 (MCS3), SN 7761 (MCS4), SN 7762 (MCS5 2001 Census Boundaries), SN 7763 (MCS5 2011 Census Boundaries), SN 8231 (MCS6 2001 Census Boundaries), SN 8232 (MCS6 2011 Census Boundaries), SN 8757 (MCS7), SN 8758 (MCS7 2001 Census Boundaries) and SN 8759 (MCS7 2011 Census Boundaries). These files replace previously available files grouped by geography SN 7049 (Ward level), SN 7050 (Lower Super Output Area level), and SN 7051 (Output Area level)
• linked education administrative datasets for Key Stages 1, 2, 4 and 5 held under SN 8481 (England). This replaces previously available datasets for Key Stage 1 (SN 6862) and Key Stage 2 (SN 7712)
  
• linked education administrative datasets for Key Stage 1 held under SN 7414 (Scotland)
• linked education administrative dataset for Key Stages 1, 2, 3 and 4 under SN 9085 (Wales)
  
• linked NHS Patient Episode Database for Wales (PEDW) for MCS1 – MCS5 held under SN 8302
• linked Scottish Medical Records data held under SNs 8709, 8710, 8711, 8712, 8713 and 8714;
• Banded Distances to English Grammar Schools for MCS5 held under SN 8394
• linked Health Administrative Datasets (Hospital Episode Statistics) for England for years 2000-2019 held under SN 9030
• linked Hospital of Birth data held under SN 5724.

The linked education administrative datasets held under SNs 8481,7414 and 9085 may be ordered alongside the MCS detailed geographical identifier files only if sufficient justification is provided in the application.

Researchers applying for access to the Secure Access MCS datasets should indicate on their ESRC Accredited Researcher application form the EUL dataset(s) that they also wish to access (selected from the MCS Series Access web page).

Participants for the Millennium Cohort Study: Age 9 months, Sweep 1, 2003: Survey of Mothers who Received Assisted Fertility Treatment were drawn from existing MCS respondents. Consenting parents who had reported having any medical infertility treatment for the pregnancy covered by MCS were sent a postal questionnaire which asked more detail about their experiences of infertility treatment. Much of the resulting statistical analysis involved comparing the women who had received different types of infertility treatment, either with each other or with women who had received no such treatment. All analyses allowed for the clustered, stratified sample design, with re-weighting included where necessary to allow for the different sampling proportions.

For the second edition (March 2008), a new version of the data file was deposited, with the family serial number variable (famsrno) replaced by a new serial number variable, mcsid (MCS Research Serial Number). The documentation remains unchanged.

This dataset tracks annual two or more races student percentage from 2013 to 2023 for Fertile-Beltrami School District vs. Minnesota

About this dataset

Context

The dataset tabulates the Non-Hispanic population of Fertile by race. It includes the distribution of the Non-Hispanic population of Fertile across various race categories as identified by the Census Bureau. The dataset can be utilized to understand the Non-Hispanic population distribution of Fertile across relevant racial categories.

Key observations

Of the Non-Hispanic population in Fertile, the largest racial group is White alone with a population of 268 (100% of the total Non-Hispanic population).

Content

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

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (for Non-Hispanic) for the Fertile
• Population: The population of the racial category (for Non-Hispanic) in the Fertile is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Fertile total Non-Hispanic population. Please note that the sum of all percentages may not equal one due to rounding of values.

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 Fertile Population by Race & Ethnicity. You can refer the same here

The 1993 Turkish Demographic and Health Survey (TDHS) is a nationally representative survey of ever-married women less than 50 years old. The survey was designed to provide information on fertility levels and trends, infant and child mortality, family planning, and maternal and child health. The TDHS was conducted by the Hacettepe University Institute of Population Studies under a subcontract through an agreement between the General Directorate of Mother and Child Health and Family Planning, Ministry of Health and Macro International Inc. of Calverton, Maryland. Fieldwork was conducted from August to October 1993. Interviews were carried out in 8,619 households and with 6,519 women. The Turkish Demographic and Health Survey (TDHS) is a national sample survey of ever-married women of reproductive ages, designed to collect data on fertility, marriage patterns, family planning, early age mortality, socioeconomic characteristics, breastfeeding, immunisation of children, treatment of children during episodes of illness, and nutritional status of women and children. The TDHS, as part of the international DHS project, is also the latest survey in a series of national-level population and health surveys in Turkey, which have been conducted by the Institute of Population Studies, Haeettepe University (HIPS). More specifically, the objectives of the TDHS are to: Collect data at the national level that will allow the calculation of demographic rates, particularly fertility and childhood mortality rates; Analyse the direct and indirect factors that determine levels and trends in fertility and childhood mortality; Measure the level of contraceptive knowledge and practice by method, region, and urban- rural residence; Collect data on mother and child health, including immunisations, prevalence and treatment of diarrhoea, acute respiratory infections among children under five, antenatal care, assistance at delivery, and breastfeeding; Measure the nutritional status of children under five and of their mothers using anthropometric measurements. The TDHS information is intended to assist policy makers and administrators in evaluating existing programs and in designing new strategies for improving family planning and health services in Turkey. MAIN RESULTS Fertility in Turkey is continuing to decline. If Turkish women maintain current fertility rates during their reproductive years, they can expect to have all average of 2.7 children by the end of their reproductive years. The highest fertility rate is observed for the age group 20-24. There are marked regional differences in fertility rates, ranging from 4.4 children per woman in the East to 2.0 children per woman in the West. Fertility also varies widely by urban-rural residence and by education level. A woman living in rural areas will have almost one child more than a woman living in an urban area. Women who have no education have almost one child more than women who have a primary-level education and 2.5 children more than women with secondary-level education. The first requirement of success ill family planning is the knowledge of family planning methods. Knowledge of any method is almost universal among Turkish women and almost all those who know a method also know the source of the method. Eighty percent of currently married women have used a method sometime in their life. One third of currently married women report ever using the IUD. Overall, 63 percent of currently married women are currently using a method. The majority of these women are modern method users (35 percent), but a very substantial proportion use traditional methods (28 percent). the IUD is the most commonly used modern method (I 9 percent), allowed by the condom (7 percent) and the pill (5 percent). Regional differences are substantial. The level of current use is 42 percent in tile East, 72 percent in tile West and more than 60 percent in tile other three regions. "File common complaints about tile methods are side effects and health concerns; these are especially prevalent for the pill and IUD. One of the major child health indicators is immunisation coverage. Among children age 12-23 months, the coverage rates for BCG and the first two doses of DPT and polio were about 90 percent, with most of the children receiving those vaccines before age one. The results indicate that 65 percent of the children had received all vaccinations at some time before the survey. On a regional basis, coverage is significantly lower in the Eastern region (41 percent), followed by the Northern and Central regions (61 percent and 65 percent, respectively). Acute respiratory infections (ARI) and diarrhea are the two most prevalent diseases of children under age five in Turkey. In the two weeks preceding the survey, the prevalence of ARI was 12 percent and the prevalence of diarrhea was 25 percent for children under age five. Among children with diarrhea 56 percent were given more fluids than usual. Breastfeeding in Turkey is widespread. Almost all Turkish children (95 percent) are breastfed for some period of time. The median duration of breastfeeding is 12 months, but supplementary foods and liquids are introduced at an early age. One-third of children are being given supplementary food as early as one month of age and by the age of 2-3 months, half of the children are already being given supplementary foods or liquids. By age five, almost one-filth of children arc stunted (short for their age), compared to an international reference population. Stunting is more prevalent in rural areas, in the East, among children of mothers with little or no education, among children who are of higher birth order, and among those born less than 24 months after a prior birth. Overall, wasting is not a problem. Two percent of children are wasted (thin for their height), and I I percent of children under five are underweight for their age. The survey results show that obesity is d problem among mothers. According to Body Mass Index (BMI) calculations, 51 percent of mothers are overweight, of which 19 percent are obese.

ART data are made available as part of the National ART Surveillance System (NASS) that collects success rates, services, profiles and annual summary data from fertility clinics across the U.S. There are four datasets available: ART Services and Profiles, ART Patient and Cycle Characteristics, ART Success Rates, and ART Summary. All four datasets may be linked by “ClinicID.” ClinicID is a unique identifier for each clinic that reported cycles. The Patient and Cycle Characteristics dataset summarizes the types of ART services performed and the kinds of patients who received ART procedures in a specific clinic. Please note patient characteristics are presented per cycle rather than per patient. As a result, patients who had more than one ART cycle within the reporting year are represented more than once.

The 1992 Malawi Demographic and Health Survey (MDHS) was a nationally representative sample survey designed to provide information on levels and trends in fertility, early childhood mortality and morbidity, family planning knowledge and use, and maternal and child health. The survey was implemented by the National Statistical Office during September to November 1992. In 5323 households, 4849 women age 15-49 years and 1151 men age 20-54 years were interviewed. The Malawi Demographic and Health Survey (MDHS) was a national sample survey of women and men of reproductive age designed to provide, among other things, information on fertility, family planning, child survival, and health of mothers and children. Specifically, the main objectives of the survey were to: Collect up-to-date information on fertility, infant and child mortality, and family planning Collect information on health-related matters, including breastleeding, antenatal and maternity services, vaccinations, and childhood diseases and treatment Assess the nutritional status of mothers and children Collect information on knowledge and attitudes regarding AIDS Collect information suitable for the estimation of mortality related to pregnancy and childbearing Assess the availability of health and family planning services. MAIN FINDINGS The findings indicate that fertility in Malawi has been declining over the last decade; at current levels a woman will give birth to an average of 6.7 children during her lifetime. Fertility in rural areas is 6.9 children per woman compared to 5.5 children in urban areas. Fertility is higher in the Central Region (7.4 children per woman) than in the Northem Region (6.7) or Southern Region (6.2). Over the last decade, the average age at which a woman first gives birth has risen slightly over the last decade from 18.3 to 18.9 years. Still, over one third of women currently under 20 years of age have either already given birlh to at least one child or are currently pregnant. Although 58 percent of currently married women would like to have another child, only 19 percent want one within the next two years. Thirty-seven percent would prefer to walt two or more years. Nearly one quarter of married women want no more children than they already have. Thus, a majority of women (61 percent) want either to delay their next birth or end childbearing altogether. This represents the proportion of women who are potentially in need of family planning. Women reported an average ideal family size of 5.7 children (i.e., wanted fertility), one child less than the actual fertility level measured in the surveyfurther evidence of the need for family planning methods. Knowledge of contraceptive methods is high among all age groups and socioeconomic strata of women and men. Most women and men also know of a source to obtain a contraceptive method, although this varies by the type of method. The contraceptive pill is the most commonly cited method known by women; men are most familiar with condoms. Despite widespread knowledge of family planning, current use of contraception remains quite low. Only 7 percent of currently married women were using a modem method and another 6 percent were using a traditional method of family planning at the time of the survey. This does, however, represent an increase in the contraceptive prevalence rate (modem methods) from about 1 percent estimated from data collected in the 1984 Family Formation Survey. The modem methods most commonly used by women are the pill (2.2 percent), female sterilisation (1.7 percent), condoms (1.7 percent), and injections (1.5 percent). Men reported higher rates of contraceptive use (13 percent use of modem methods) than women. However, when comparing method-specific use rates, nearly all of the difference in use between men and women is explained by much higher condom use among men. Early childhood mortality remains high in Malawi; the under-five mortality rate currently stands at 234 deaths per 1000 live births. The infant mortality rate was estimated at 134 per 10130 live births. This means that nearly one in seven children dies before his first birthday, and nearly one in four children does not reach his fifth birthday. The probability of child death is linked to several factors, most strikingly, low levels of maternal education and short intervals between births. Children of uneducated women are twice as likely to die in the first five years of life as children of women with a secondary education. Similarly, the probablity of under-five mortality for children with a previous birth interval of less than 2 years is two times greater than for children with a birth interval of 4 or more years. Children living in rural areas have a higher rate ofunder-fwe mortality than urban children, and children in the Central Region have higher mortality than their counterparts in the Northem and Southem Regions. Data were collected that allow estimation ofmatemalmortality. It is estimated that for every 100,000 live births, 620 women die due to causes related to pregnancy and childbearing. The height and weight of children under five years old and their mothers were collected in the survey. The results show that nearly one half of children under age five are stunted, i.e., too short for their age; about half of these are severely stunted. By age 3, two-thirds of children are stunted. As with childhood mortality, chronic undernutrition is more common in rural areas and among children of uneducated women. The duration of breastfeeding is relatively long in Malawi (median length, 21 months), but supplemental liquids and foods are introduced at an early age. By age 2-3 months, 76 percent of children are already receiving supplements. Mothers were asked to report on recent episodes of illness among their young children. The results indicate that children age 6-23 months are the most vulnerable to fever, acute respiratory infection (ARI), and diarrhea. Over half of the children in this age group were reported to have had a fever, about 40 percent had a bout with diarrhea, and 20 percent had symptoms indicating ARI in the two-week period before the survey. Less than half of recently sick children had been taken to a health facility for treatment. Sixty-three percent of children with diarrhea were given rehydration therapy, using either prepackaged rehydration salts or a home-based preparation. However, one quarter of children with diarrhea received less fluid than normal during the illness, and for 17 percent of children still being breastfed, breastfeeding of the sick child was reduced. Use of basic, preventive maternal and child health services is generally high. For 90 percent of recent births, mothers had received antenatal care from a trained medical person, most commonly a nurse or trained midwife. For 86 percent of births, mothers had received at least one dose of tetanus toxoid during pregnancy. Over half of recent births were delivered in a health facility. Child vaccination coverage is high; 82 percent of children age 12-23 months had received the full complement of recommended vaccines, 67 percent by exact age 12 months. BCG coverage and first dose coverage for DPT and polio vaccine were 97 percent. However, 9 percent of children age 12-23 months who received the first doses of DPT and polio vaccine failed to eventually receive the recommended third doses. Information was collected on knowledge and attitudes regarding AIDS. General knowledge of AIDS is nearly universal in Malawi; 98 percent of men and 95 percent of women said they had heard of AIDS. Further, the vast majority of men and women know that the disease is transmitted through sexual intercourse. Men tended to know more different ways of disease transmission than women, and were more likely to mention condom use as a means to prevent spread of AIDS. Women, especially those living in rural areas, are more likely to hold misconceptions about modes of disease transmission. Thirty percent of rural women believe that AIDS can not be prevented.

This deposit contains three do files which were constructed as part of the project “Intergenerational income mobility: Gender, Partnerships and Poverty in the UK”, UKRI grant number ES/P007899/1. The aim of the do files is to convert partnership, fertility, and labour market activity information provided with the age 55 wave of the National Child Development Study into monthly panel format. There are separate do files to do this for each of the three aspects.This important new work looks to fill an 'evidence deficit' within the literature on intergenerational economic mobility by investigating intergenerational income mobility for two groups who are often overlooked in existing research: women and the poorest in society. To do this, the research will make two methodological advancements to previous work: First, moving to focus on the family unit in the second generation and total family resources rather than individual labour market earnings and second, looking across adulthood to observe partnership, fertility and poverty dynamics rather than a point-in-time static view of these important factors. Specifically it will ask four research questions: 1) What is the relationship between family incomes of parents in childhood and family incomes of daughters throughout adulthood? The majority of previous studies of intergenerational income mobility have focused on the relationship between parents' income in childhood and sons' prime-age labour market earnings. Women have therefore been consistently disregarded due to difficulties observing prime-age labour market earnings for women. This is because women often exit the labour market for fertility reasons, and the timing of this exit and the duration of the spell out of the labour market are related to both parental childhood income and current labour market earnings. This means that previous studies that have focused on employed women only are not representative of the entire population of women. By combining our two advancements, considering total family income and looking across adulthood for women, we can minimise these issues. The life course approach enables us to observe average resources across a long window of time, dealing with issues of temporary labour market withdrawal, while the use of total family income gives the most complete picture of resources available to the family unit including partner's earnings and income from other sources, including benefits. 2) What role do partnerships and assortative mating play in this process across the life course? The shift to focusing on the whole family unit emphasises the importance of partnerships including when they occur and breakdown and who people partner with in terms of education and current labour market earnings. Previous research on intergenerational income mobility in the UK has suggested an important role for who people partner with but has been limited to only focusing on those in partnerships. This work will advance our understanding of partnership dynamics by looking across adulthood at both those in partnerships and at the importance of family breakdown and lone parenthood in this relationship. 3) What is the extent of intergenerational poverty in the UK, and does this persist through adulthood? The previous focus on individuals' labour market earnings has often neglected to consider intergenerational income mobility for the poorest in society: those without labour market earnings for lengthy periods of time who rely on other income from transfers and benefits. The shift in focus to total family resources and the life course approach will allow us to assess whether those who grew up in poor households are more likely to experience persistent poverty themselves in adulthood. 4) What is the role of early skills, education and labour market experiences, including job tenure and progression, in driving these newly estimated relationships? Finally our proposed work will consider the potential mechanisms for these new estimates of intergenerational income mobility for women and the poorest in society for the first time and expand our understanding of potential mechanisms for men. While our previous work showed the importance of early skills and education in transmitting inequality across generations for males, this new work will also consider the role of labour market experiences including job tenure and promotions as part of the process. The NCDS covers all children in England, Scotland and Wales born in one week in 1958. The archived materials are do files that alter the format of existing NCDS datasets to create derived datasets. Original data can be accessed via Related Resources.

This data collection supplies standard monthly labor force data for the week prior to the survey. Comprehensive information is given on the employment status, occupation, and industry of persons 14 years old and older. Additional data are available concerning weeks worked and hours per week worked, reason not working full-time, total income and income components, and residence. For this supplement, a battery of questions was asked of women 15-59 years old to obtain information on their childbearing history. Data include the total number of children born, date of birth of the first and most recent child(ren), and date of first marriage. Women 18-44 years old were also asked about the number of additional children they expected to have. Information on demographic characteristics, such as age, sex, race, martial status, veteran status, household relationship, educational background, and Hispanic origin, is available for each person in the household enumerated. (Source: downloaded from ICPSR 7/13/10)

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR at https://doi.org/10.3886/ICPSR08321.v1. We highly recommend using the ICPSR version as they may make this dataset available in multiple data formats in the future.

Existing research indicates that pregnant women who conceived through fertility treatment might experience more stress and anxiety compared to women who conceived spontaneously. Therefore, these women might have additional antenatal care needs. A search for both quantitative and qualitative studies was performed in PubMed, PsycINFO, CINAHL and MEDLINE through May 2021, guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist. 21 articles met the inclusion criteria. After methodological quality appraisal using the Mixed Methods Appraising Tool, 15 studies were included in the review. Analysis of the studies identified behavioral, relational/social, emotional, and cognitive needs and women’s preference about maternity care. Women who conceived through fertility treatment reported lower social and physical functioning scores and elevated levels of anxiety and depression compared to women who conceived spontaneously. They reported difficulties adjusting to pregnancy and experienced a care gap between discharge from the fertility clinic and going to local maternity care services for their first consultation, and a care gap postpartum. Women who conceived through fertility treatment have additional antenatal care needs. We recommend to offer these women more frequent check-ins, and to pay attention to the impact of their infertility and treatment on their pregnancy.

Abstract copyright UK Data Service and data collection copyright owner.Background:The Millennium Cohort Study (MCS) is a large-scale, multi-purpose longitudinal dataset providing information about babies born at the beginning of the 21st century, their progress through life, and the families who are bringing them up, for the four countries of the United Kingdom. The original objectives of the first MCS survey, as laid down in the proposal to the Economic and Social Research Council (ESRC) in March 2000, were:to chart the initial conditions of social, economic and health advantages and disadvantages facing children born at the start of the 21st century, capturing information that the research community of the future will requireto provide a basis for comparing patterns of development with the preceding cohorts (the National Child Development Study, held at the UK Data Archive under GN 33004, and the 1970 Birth Cohort Study, held under GN 33229)to collect information on previously neglected topics, such as fathers' involvement in children's care and developmentto focus on parents as the most immediate elements of the children's 'background', charting their experience as mothers and fathers of newborn babies in the year 2000, recording how they (and any other children in the family) adapted to the newcomer, and what their aspirations for her/his future may beto emphasise intergenerational links including those back to the parents' own childhoodto investigate the wider social ecology of the family, including social networks, civic engagement and community facilities and services, splicing in geo-coded data when availableAdditional objectives subsequently included for MCS were:to provide control cases for the national evaluation of Sure Start (a government programme intended to alleviate child poverty and social exclusion)to provide samples of adequate size to analyse and compare the smaller countries of the United Kingdom, and include disadvantaged areas of EnglandFurther information about the MCS can be found on the Centre for Longitudinal Studies web pages.The content of MCS studies, including questions, topics and variables can be explored via the CLOSER Discovery website. The first sweep (MCS1) interviewed both mothers and (where resident) fathers (or father-figures) of infants included in the sample when the babies were nine months old, and the second sweep (MCS2) was carried out with the same respondents when the children were three years of age. The third sweep (MCS3) was conducted in 2006, when the children were aged five years old, the fourth sweep (MCS4) in 2008, when they were seven years old, the fifth sweep (MCS5) in 2012-2013, when they were eleven years old, the sixth sweep (MCS6) in 2015, when they were fourteen years old, and the seventh sweep (MCS7) in 2018, when they were seventeen years old.End User Licence versions of MCS studies:The End User Licence (EUL) versions of MCS1, MCS2, MCS3, MCS4, MCS5, MCS6 and MCS7 are held under UK Data Archive SNs 4683, 5350, 5795, 6411, 7464, 8156 and 8682 respectively. The longitudinal family file is held under SN 8172.Sub-sample studies:Some studies based on sub-samples of MCS have also been conducted, including a study of MCS respondent mothers who had received assisted fertility treatment, conducted in 2003 (see EUL SN 5559). Also, birth registration and maternity hospital episodes for the MCS respondents are held as a separate dataset (see EUL SN 5614).Release of Sweeps 1 to 4 to Long Format (Summer 2020)To support longitudinal research and make it easier to compare data from different time points, all data from across all sweeps is now in a consistent format. The update affects the data from sweeps 1 to 4 (from 9 months to 7 years), which are updated from the old/wide to a new/long format to match the format of data of sweeps 5 and 6 (age 11 and 14 sweeps). The old/wide formatted datasets contained one row per family with multiple variables for different respondents. The new/long formatted datasets contain one row per respondent (per parent or per cohort member) for each MCS family. Additional updates have been made to all sweeps to harmonise variable labels and enhance anonymisation. How to access genetic and/or bio-medical sample data from a range of longitudinal surveys:For information on how to access biomedical data from MCS that are not held at the UKDS, see the CLS Genetic data and biological samples webpage.Secure Access datasets:Secure Access versions of the MCS have more restrictive access conditions than versions available under the standard End User Licence or Special Licence (see 'Access data' tab above).Secure Access versions of the MCS include:detailed sensitive variables not available under EUL. These have been grouped thematically and are held under SN 8753 (socio-economic, accommodation and occupational data), SN 8754 (self-reported health, behaviour and fertility), SN 8755 (demographics, language and religion) and SN 8756 (exact participation dates). These files replace previously available studies held under SNs 8456 and 8622-8627detailed geographical identifier files which are grouped by sweep held under SN 7758 (MCS1), SN 7759 (MCS2), SN 7760 (MCS3), SN 7761 (MCS4), SN 7762 (MCS5 2001 Census Boundaries), SN 7763 (MCS5 2011 Census Boundaries), SN 8231 (MCS6 2001 Census Boundaries), SN 8232 (MCS6 2011 Census Boundaries), SN 8757 (MCS7), SN 8758 (MCS7 2001 Census Boundaries) and SN 8759 (MCS7 2011 Census Boundaries). These files replace previously available files grouped by geography SN 7049 (Ward level), SN 7050 (Lower Super Output Area level), and SN 7051 (Output Area level)linked education administrative datasets for Key Stages 1, 2 and 4 held under SN 8481 (England). This replaces previously available datasets for Key Stage 1 (SN 6862) and Key Stage 2 (SN 7712)linked education administrative datasets for Key Stage 1 held under SN 7414 (Scotland)linked education administrative dataset for Key Stages 1, 2, 3 and 4 under SN 9085 (Wales)linked NHS Patient Episode Database for Wales (PEDW) for MCS1 – MCS5 held under SN 8302linked Scottish Medical Records data held under SNs 8709, 8710, 8711, 8712, 8713 and 8714;Banded Distances to English Grammar Schools for MCS5 held under SN 8394linked Health Administrative Datasets (Hospital Episode Statistics) for England for years 2000-2019 held under SN 9030linked Hospital of Birth data held under SN 5724.The linked education administrative datasets held under SNs 8481,7414 and 9085 may be ordered alongside the MCS detailed geographical identifier files only if sufficient justification is provided in the application. Users are also only allowed access to either 2001 or 2011 of Geographical Identifiers Census Boundaries studies. So for MCS5 either SN 7762 (2001 Census Boundaries) or SN 7763 (2011 Census Boundaries), for the MCS6 users are only allowed either SN 8231 (2001 Census Boundaries) or SN 8232 (2011 Census Boundaries); and the same applies for MCS7 so either SN 8758 (2001 Census Boundaries) or SN 8759 (2011 Census Boundaries).Researchers applying for access to the Secure Access MCS datasets should indicate on their ESRC Accredited Researcher application form the EUL dataset(s) that they also wish to access (selected from the MCS Series Access web page). International Data Access Network (IDAN)These data are now available to researchers based outside the UK. Selected UKDS SecureLab/controlled datasets from the Institute for Social and Economic Research (ISER) and the Centre for Longitudinal Studies (CLS) have been made available under the International Data Access Network (IDAN) scheme, via a Safe Room access point at one of the UKDS IDAN partners. Prospective users should read the UKDS SecureLab application guide for non-ONS data for researchers outside of the UK via Safe Room Remote Desktop Access. Further details about the IDAN scheme can be found on the UKDS International Data Access Network webpage and on the IDAN website. Main Topics: SN 8754 - Millennium Cohort Study, Sweeps 1-7, 2001-2019: Self-Reported Health, Behaviour and Fertility: Secure Access contains sensitive variables relating to respondents' health for sweeps 1-5 and sweep 7. Variables cover birth time, fertility treatment, detailed health problems and worries, parent and partner limiting longstanding illness, BNF codes for regular medication, eyesight and hearing problems, ICD codes, self-harm and drug use. A MCS Research ID variable is also provided in each data file to facilitate matching to other MCS data files. Multi-stage stratified random sample Face-to-face interview

The 1997 the Kyrgyz Republic Demographic and Health Survey (KRDHS) is a nationally representative survey of 3,848 women age 15-49. Fieldwork was conducted from August to November 1997. The KRDHS was sponsored by the Ministry of Health (MOH), and was funded by the United States Agency for International Development. The Research Institute of Obstetrics and Pediatrics implemented the survey with technical assistance from the Demographic and Health Surveys (DHS) program. The purpose of the KRDHS was to provide data to the MOH on factors which determine the health status of women and children such as fertility, contraception, induced abortion, maternal care, infant mortality, nutritional status, and anemia. Some statistics presented in this report are currently available to the MOH from other sources. For example, the MOH collects and regularly publishes information on fertility, contraception, induced abortion and infant mortality. However, the survey presents information on these indices in a manner which is not currently available, i.e., by population subgroups such as those defined by age, marital duration, education, and ethnicity. Additionally, the survey provides statistics on some issues not previously available in the Kyrgyz Republic: for example, breastfeeding practices and anemia status of women and children. When considered together, existing MOH data and the KRDHS data provide a more complete picture of the health conditions in the Kyrgyz Republic than was previously available. A secondary objective of the survey was to enhance the capabilities of institutions in the Kyrgyz Republic to collect, process, and analyze population and health data. MAIN FINDINGS FERTILITY Fertility Rates. Survey results indicate a total fertility rate (TFR) for all of the Kyrgyz Republic of 3.4 children per woman. Fertility levels differ for different population groups. The TFR for women living in urban areas (2.3 children per woman) is substantially lower than for women living in rural areas (3.9). The TFR for Kyrgyz women (3.6 children per woman) is higher than for women of Russian ethnicity (1.5) but lower than Uzbek women (4.2). Among the regions of the Kyrgyz Republic, the TFR is lowest in Bishkek City (1.7 children per woman), and the highest in the East Region (4.3), and intermediate in the North and South Regions (3.1 and3.9, respectively). Time Trends. The KRDHS data show that fertility has declined in the Kyrgyz Republic in recent years. The decline in fertility from 5-9 to 0-4 years prior to the survey increases with age, from an 8 percent decline among 20-24 year olds to a 38 percent decline among 35-39 year olds. The declining trend in fertility can be seen by comparing the completed family size of women near the end of their childbearing years with the current TFR. Completed family size among women 40-49 is 4.6 children which is more than one child greater than the current TFR (3.4). Birth Intervals. Overall, 30 percent of births in the Kyrgyz Republic take place within 24 months of the previous birth. The median birth interval is 31.9 months. Age at Onset of Childbearing. The median age at which women in the Kyrgyz Republic begin childbearing has been holding steady over the past two decades at approximately 21.6 years. Most women have their first birth while in their early twenties, although about 20 percent of women give birth before age 20. Nearly half of married women in the Kyrgyz Republic (45 percent) do not want to have more children. Additional one-quarter of women (26 percent) want to delay their next birth by at least two years. These are the women who are potentially in need of some method of family planning. FAMILY PLANNING Ever Use. Among currently married women, 83 percent report having used a method of contraception at some time. The women most likely to have ever used a method of contraception are those age 30-44 (among both currently married and all women). Current Use. Overall, among currently married women, 60 percent report that they are currently using a contraceptive method. About half (49 percent) are using a modern method of contraception and another 11 percent are using a traditional method. The IUD is by far the most commonly used method; 38 percent of currently married women are using the IUD. Other modern methods of contraception account for only a small amount of use among currently married women: pills (2 percent), condoms (6 percent), and injectables and female sterilization (1 and 2 percent, respectively). Thus, the practice of family planning in the Kyrgyz Republic places high reliance on a single method, the IUD. Source of Methods. The vast majority of women obtain their contraceptives through the public sector (97 percent): 35 percent from a government hospital, and 36 percent from a women counseling center. The source of supply of the method depends on the method being used. For example, most women using IUDs obtain them at women counseling centers (42 percent) or hospitals (39 percent). Government pharmacies supply 46 percent of pill users and 75 percent of condom users. Pill users also obtain supplies from women counseling centers or (33 percent). Fertility Preferences. A majority of women in the Kyrgyz Republic (45 percent) indicated that they desire no more children. By age 25-29, 20 percent want no more children, and by age 30-34, nearly half (46 percent) want no more children. Thus, many women come to the preference to stop childbearing at relatively young ages-when they have 20 or more potential years of childbearing ahead of them. For some of these women, the most appropriate method of contraception may be a long-acting method such as female sterilization. However, there is a deficiency of use of this method in the Kyrgyz Republic. In the interests of providing a broad range of safe and effective methods, information about and access to sterilization should be increased so that individual women can make informed decisions about using this method. INDUCED ABORTION Abortion Rates. From the KRDHS data, the total abortion rate (TAR)-the number of abortions a woman will have in her lifetime based on the currently prevailing abortion rates-was calculated. For the Kyrgyz Republic, the TAR for the period from mid-1994 to mid-1997 is 1.6 abortions per woman. The TAR for the Kyrgyz Republic is lower than recent estimates of the TAR for other areas of the former Soviet Union such as Kazakhstan (1.8), and Yekaterinburg and Perm in Russia (2.3 and 2.8, respectively), but higher than for Uzbekistan (0.7). The TAR is higher in urban areas (2.1 abortions per woman) than in rural areas (1.3). The TAR in Bishkek City is 2.0 which is two times higher than in other regions of the Kyrgyz Republic. Additionally the TAR is substantially lower among ethnic Kyrgyz women (1.3) than among women of Uzbek and Russian ethnicities (1.9 and 2.2 percent, respectively). INFANT MORTALITY In the KRDHS, infant mortality data were collected based on the international definition of a live birth which, irrespective of the duration of pregnancy, is a birth that breathes or shows any sign of life (United Nations, 1992). Mortality Rates. For the five-year period before the survey (i.e., approximately mid-1992 to mid1997), infant mortality in the Kyrgyz Republic is estimated at 61 infant deaths per 1,000 births. The estimates of neonatal and postneonatal mortality are 32 and 30 per 1,000. The MOH publishes infant mortality rates annually but the definition of a live birth used by the MOH differs from that used in the survey. As is the case in most of the republics of the former Soviet Union, a pregnancy that terminates at less than 28 weeks of gestation is considered premature and is classified as a late miscarriage even if signs of life are present at the time of delivery. Thus, some events classified as late miscarriages in the MOH system would be classified as live births and infant deaths according to the definitions used in the KRDHS. Infant mortality rates based on the MOH data for the years 1983 through 1996 show a persistent declining trend throughout the period, starting at about 40 per 1,000 in the early 1980s and declining to 26 per 1,000 in 1996. This time trend is similar to that displayed by the rates estimated from the KRDHS. Thus, the estimates from both the KRDHS and the Ministry document a substantial decline in infant mortality; 25 percent over the period from 1982-87 to 1992-97 according to the KRDHS and 28 percent over the period from 1983-87 to 1993-96 according to the MOH estimates. This is strong evidence of improvements in infant survivorship in recent years in the Kyrgyz Republic. It should be noted that the rates from the survey are much higher than the MOH rates. For example, the KRDHS estimate of 61 per 1,000 for the period 1992-97 is twice the MOH estimate of 29 per 1,000 for 1993-96. Certainly, one factor leading to this difference are the differences in the definitions of a live birth and infant death in the KRDHS survey and in the MOH protocols. A thorough assessment of the difference between the two estimates would need to take into consideration the sampling variability of the survey's estimate. However, given the magnitude of the difference, it is likely that it arises from a combination of definitional and methodological differences between the survey and MOH registration system. MATERNAL AND CHILD HEALTH The Kyrgyz Republic has a well-developed health system with an extensive infrastructure of facilities that provide maternal care services. This system includes special delivery hospitals, the obstetrics and gynecology departments of general hospitals, women counseling centers, and doctor's assistant/midwife posts (FAPs). There is an extensive network of FAPs throughout the rural areas. Delivery. Virtually all births in the Kyrgyz Republic (96 percent) are delivered at health facilities: 95 percent in delivery hospitals and another 1 percent in either general hospitals

Abstract (en): The Integrated Fertility Survey Series (IFSS) integrates data from ten underlying component studies of family and fertility: the Growth of American Families studies of 1955 and 1960; the National Survey of Fertility of 1965 and 1970; and the National Surveys of Family Growth of 1973, 1976, 1982, 1988, 1995, and 2002. The first release contains harmonized sociodemographic variables for all respondents from all ten component studies, including those related to marital status, race and ethnicity, education, income, migration, religion, and region of origin, among others. The second release adds harmonized husband/partner sociodemographic variables as well as harmonized union history variables. The third release adds harmonized pregnancy, adoption, non-biological children, and menstruation variables. The fourth release adds harmonized fertility variables. The fifth release includes the addition of the pregnancy interval file. This file contains 217,128 pregnancy records with information pertaining to the pregnancies of all respondents. The sixth release adds comparative sample variables to the respondent and pregnancy interval files, and includes the addition of the contraceptive calendar file. This file contains 53,317 records with information pertaining to type and frequency of contraceptive use. The seventh release includes additional variables related to contraceptive knowledge, contraceptive use, birth control and family planning services, sexual history, infertility, and sterilizing operations. It also adds sociodemographic and union history variables. Imputed data through the third release are also included. Additional information about the Integrated Fertility Survey Series can be found on the IFSS Web site. The purpose of the Integrated Fertility Survey Series is to create a harmonized data set of ten component surveys of fertility and family growth. Integration of these data sets will allow for easier and more efficient analysis of family and fertility data over time. Data were harmonized from ten component studies of family and fertility, including the 1955 and 1960 Growth of American Families studies, 1965 and 1970 National Fertility Surveys, and the 1973, 1976, 1982, 1988, 1995, and 2002 National Surveys of Family Growth. IFSS staff harmonized all concepts that appeared in at least five of the component studies. In special cases, concepts that appears in as few as two component studies were also harmonized. Comparability notes, located on the IFSS Web site, outline the processes by which data were harmonized. Variables include sociodemographic, union history, pregnancy, fertility and pregnancy interval variables. These include variables related to: birth and date of interview; education; family structure in the respondent's childhood; life on farms; geography; household roster; income; respondent's mother; nativity; geographical origin; race and ethnicity; religion; marital status; urbanicity; employment; dates of marriage, divorce, and death of husbands; dates of cohabitation; age at marriage; husband characteristics; subsample filter variables; weights and standard error codes; menstruation; adoption; non-biological children; fertility assistance; fertility intentions; and pregnancy including outcomes, dates, contraception, nursing and additional variables. A weight variable with two implied decimal places has been included and must be used in any analysis. Methodology for the computation of the weight variable is available on the IFSS Web site. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Performed consistency checks.; Created variable labels and/or value labels.; Standardized missing values.; Performed recodes and/or calculated derived variables.; Checked for undocumented or out-of-range codes.. The universe includes all respondents in the following studies: the Growth of American Families studies of 1955 and 1960; the National Fertility Surveys of 1965 and 1970; and the National Surveys of Family Growth of 1973, 1976, 1982, 1988, 1995, and 2002. No primary data collection or sampling was performed. 2015-06-18 ICPSR added files that provide the basis for the onl...

Abstract

The Armenia Demographic and Health Survey (ADHS) was a nationally representative sample survey designed to provide information on population and health issues in Armenia. The primary goal of the survey was to develop a single integrated set of demographic and health data, the first such data set pertaining to the population of the Republic of Armenia. In addition to integrating measures of reproductive, child, and adult health, another feature of the DHS survey is that the majority of data are presented at the marz level.

The ADHS was conducted by the National Statistical Service and the Ministry of Health of the Republic of Armenia during October through December 2000. ORC Macro provided technical support for the survey through the MEASURE DHS+ project. MEASURE DHS+ is a worldwide project, sponsored by the USAID, with a mandate to assist countries in obtaining information on key population and health indicators. USAID/Armenia provided funding for the survey. The United Nations Children’s Fund (UNICEF)/Armenia provided support through the donation of equipment.

The ADHS collected national- and regional-level data on fertility and contraceptive use, maternal and child health, adult health, and AIDS and other sexually transmitted diseases. The survey obtained detailed information on these issues from women of reproductive age and, on certain topics, from men as well. Data are presented by marz wherever sample size permits.

The ADHS results are intended to provide the information needed to evaluate existing social programs and to design new strategies for improving the health of and health services for the people of Armenia. The ADHS also contributes to the growing international database on demographic and health-related variables.

Geographic coverage

National

Analysis unit

• Household
• Children under five years
• Women age 15-49
• Men age 15-54

Kind of data

Sample survey data

Sampling procedure

The sample was designed to provide estimates of most survey indicators (including fertility, abortion, and contraceptive prevalence) for Yerevan and each of the other ten administrative regions (marzes). The design also called for estimates of infant and child mortality at the national level for Yerevan and other urban areas and rural areas.

The target sample size of 6,500 completed interviews with women age 15-49 was allocated as follows: 1,500 to Yerevan and 500 to each of the ten marzes. Within each marz, the sample was allocated between urban and rural areas in proportion to the population size. This gave a target sample of approximately 2,300 completed interviews for urban areas exclusive of Yerevan and 2,700 completed interviews for the rural sector. Interviews were completed with 6,430 women. Men age 15-54 were interviewed in every third household; this yielded 1,719 completed interviews.

A two-stage sample was used. In the first stage, 260 areas or primary sampling units (PSUs) were selected with probability proportional to population size (PPS) by systematic selection from a list of areas. The list of areas was the 1996 Data Base of Addresses and Households constructed by the National Statistical Service. Because most selected areas were too large to be directly listed, a separate segmentation operation was conducted prior to household listing. Large selected areas were divided into segments of which two segments were included in the sample. A complete listing of households was then carried out in selected segments as well as selected areas that were not segmented.

The listing of households served as the sampling frame for the selection of households in the second stage of sampling. Within each area, households were selected systematically so as to yield an average of 25 completed interviews with eligible women per area. All women 15-49 who stayed in the sampled households on the night before the interview were eligible for the survey. In each segment, a subsample of one-third of all households was selected for the men's component of the survey. In these households, all men 15-54 who stayed in the household on the previous night were eligible for the survey.

Note: See detailed description of sample design in APPENDIX A of the survey report.

Mode of data collection

Face-to-face [f2f]

Research instrument

Three questionnaires were used in the ADHS: a Household Questionnaire, a Women’s Questionnaire, and a Men’s Questionnaire. The questionnaires were based on the model survey instruments developed for the MEASURE DHS+ program. The model questionnaires were adapted for use during a series of expert meetings hosted by the Center of Perinatology, Obstetrics, and Gynecology. The questionnaires were developed in English and translated into Armenian and Russian. The questionnaires were pretested in July 2000.

The Household Questionnaire was used to list all usual members of and visitors to a household and to collect information on the physical characteristics of the dwelling unit. The first part of the household questionnaire collected information on the age, sex, residence, educational attainment, and relationship to the household head of each household member or visitor. This information provided basic demographic data for Armenian households. It also was used to identify the women and men who were eligible for the individual interview (i.e., women 15-49 and men 15-54). The second part of the Household Questionnaire consisted of questions on housing characteristics (e.g., the flooring material, the source of water, and the type of toilet facilities) and on ownership of a variety of consumer goods.

The Women’s Questionnaire obtained information on the following topics: - Background characteristics - Pregnancy history - Antenatal, delivery, and postnatal care - Knowledge and use of contraception - Attitudes toward contraception and abortion - Reproductive and adult health - Vaccinations, birth registration, and health of children under age five - Episodes of diarrhea and respiratory illness of children under age five - Breastfeeding and weaning practices - Height and weight of women and children under age five - Hemoglobin measurement of women and children under age five - Marriage and recent sexual activity - Fertility preferences - Knowledge of and attitude toward AIDS and other sexually transmitted infections.

The Men’s Questionnaire focused on the following topics: - Background characteristics - Health - Marriage and recent sexual activity - Attitudes toward and use of condoms - Knowledge of and attitude toward AIDS and other sexually transmitted infections.

Cleaning operations

After a team had completed interviewing in a cluster, questionnaires were returned promptly to the National Statistical Service in Yerevan for data processing. The office editing staff first checked that questionnaires for all selected households and eligible respondents had been received from the field staff. In addition, a few questions that had not been precoded (e.g., occupation) were coded at this time. Using the ISSA (Integrated System for Survey Analysis) software, a specially trained team of data processing staff entered the questionnaires and edited the resulting data set on microcomputers. The process of office editing and data processing was initiated soon after the beginning of fieldwork and was completed by the end of January 2001.

Response rate

A total of 6,524 households were selected for the sample, of which 6,150 were occupied at the time of fieldwork. The main reason for the difference is that some of the dwelling units that were occupied during the household listing operation were either vacant or the household was away for an extended period at the time of interviewing. Of the occupied households, 97 percent were successfully interviewed.

In these households, 6,685 women were identified as eligible for the individual interview (i.e., age 15-49). Interviews were completed with 96 percent of them. Of the 1,913 eligible men identified, 90 percent were successfully interviewed. The principal reason for non-response among eligible women and men was the failure to find them at home despite repeated visits to the household. The refusal rate was low.

The overall response rates, the product of the household and the individual response rates, were 94 percent for women and 87 percent for men.

Note: See summarized response rates by residence (urban/rural) in Table 1.1 of the survey report.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: (1) nonsampling errors, and (2) sampling errors. Nonsampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the 2000 Armenia Demographic and Health Survey (ADHS) to minimize this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the ADHS is only one of many samples that could have been selected from the same population, using the same design and expected size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability between all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey

This record contains data related to article “Why are they not coming back? A single-center follow-up study on oncological women oocyte's storing for fertility preservation"

Abstract

Introduction: Oocyte cryopreservation is a valid option for female cancer patients to preserve fertility. The number of patients undergoing fertility preservation (FP) cycles has increased over the past years. Nevertheless, the rates of patients returning to use their cryopreserved material have shown to be considerably low, ranging from 5-8%, but significant data regarding the reasons of such low return rates are scarce.

Methods: This study is a single-center follow-up retrospective study evaluating the return rate of oncological women who underwent FP at a tertiary care Fertility Center and assessing the reasons influencing the patients who did not return. Data about patients who returned to attempt pregnancy were retrieved from internal registries. Non-returned patients were assessed with a standardized phone survey investigating health condition, marital status and family projects, spontaneous conceptions, and the reasons why they had not returned to use their gametes. A univariate analysis between returned and non-returned patients was performed.

Results: Of the 397 patients who received counseling about FP, 171 (43.1%) underwent oocyte cryopreservation between 2001 and 2017. Nine (5%) died, and 17 (10%) were lost at follow-up. A total of 20 patients (11.7%) returned and 125 did not. In the non-returned group, 37 (29.6%) did not have a partner, 10 (8%) had a previous spontaneous conception, and 15 (12%) had recurrent malignancy at the time of follow-up. In the univariate analysis, younger age at freezing (31.8±6.2 vs. 35.2±4.7; p 0.018), lack of a partner (p 0.002), type of cancer (other than breast cancer; p 0.024) were the significant factors in the non-returned group. As for the personal reason for not coming back, patients mainly answered as follows: lack of a partner (29, 23.2%), the desire for spontaneous motherhood (24, 19.2%), previous spontaneous pregnancies after FP procedures (16, 12.8%), and still ongoing hormonal therapy for breast cancer (13, 10.4%). All patients confirmed their will to keep the storage of their oocytes.

Discussion: The impact of a cancer diagnosis on a woman's maternal desire, sentimental status and life priorities should be studied more thoroughly. Studies investigating hormonal therapy suppression in breast cancer patients seeking pregnancy should be encouraged.

Clinical trial registration: https://clinicaltrials.gov, identifier NCT05223764.

This data set consists of engagement related multi-modal team behaviors & learning outcomes collected in the context of a robot mediated collaborative and constructivist learning activity called JUSThink [1,2]. The data set can be useful for those looking to explore/validate theoretical models of engagement. The dataset is inspired by our efforts of critically assessing engagement modelling in educational HRI contexts which eventually lead us to proposing the concept of 'Productive Engagement'. More on this can be found in [3,4,5].

The JUSThink platform consists of two screens and a QTrobot acting as a guide and a mediator. The platform aims to (1) improve the computational skills of children by imparting intuitive knowledge about minimum-spanning-tree problems and (2) promote collaboration among the team via its design. As an experimental setup for HRI studies, it also serves as a platform for designing and evaluating robot behaviors that are effective for the pedagogical goals or in general HRI problems such as trust, robot perception, engagement, collaboration. The minimum-spanning-tree problem is introduced through a gold mining scenario based on a map of Switzerland, where mountains represent gold mines labelled with Swiss cities names.

The features in the dataset are grounded and motivated by the engagement literature in HRI and Intelligent Tutoring Systems. The dataset consists of team level data collected from 34 teams of two (68 children) where the children are aged between 9 and 12. More specifically, it contains:

• PE-HRI:behavioral.cvs: This file consists of team level multi-modal behavioral data namely log data that captures interaction with the setup, speech behavior, affective states, and gaze patterns. The definition for the each feature is given below:

  • T_add: The number of times a team added an edge on the map.

  • T_remove: The number of times a team removed an edge from the map.

  • T_ratio_add_del: The ratio of addition of edges over deletion of edges by a team.

  • T_action: The total number of actions taken by a team (add, delete, submit, presses on the screen).

  • T_hist: The number of times a team opened the sub-window with history of their previous solutions.

  • T_help: The number of times a team opened the instructions manual. Please note that the robot initially gives all the instructions before the game-play while a video is played for demonstration of the functionality of the game.

  • T1_T1_rem: The number of times a team, either member, followed the pattern consecutively: I add an edge, I then delete it.

  • T1_T1_add: The number of times a team, either member, followed the pattern consecutively: I delete an edge, I add it back.

  • T1_T2_rem: The number of times a team, either member, followed the pattern consecutively: I add an edge, you then delete it.

  • T1_T2_add: The number of times a team, either member, followed the pattern consecutively: I delete an edge, you add it back.

  • redundant_exist: The number of times the team had redundant edges in their map.

  • positive_valence: The average value of positive valence for the team.

  • negative_valence: The average value of negative valence for the team.

  • mean_pos_minus_neg_valence: The difference of the average value of positive and negative valence for the team.

  • arousal: The average value of arousal for the team.

  • smile_count: The average percentage of time of a team smiling.

  • at_partner: The average percentage of time a team has a team member looking at their partner.

  • at_robot: The average percentage of time a team is looking at the robot.

  • other: The average percentage of time a team is looking in the direction opposite to the robot.

  • screen_left: The average percentage of time a team is looking at the left side of the screen.

  • screen_right: The average percentage of time a team is looking at the right side of the screen.

  • screen_right_left_ratio: The ratio of looking at the right side of the screen over the left side.

  • voice_activity: The average percentage of time a team is speaking over the entire duration of the task.

  • silence: The average percentage of time a team is silent over the entire duration of the task.

  • short_pauses: The average percentage of time a team pauses briefly (0.15 sec) over their speech activity.

  • long_pauses: The average percentage of time a team makes long pauses (1.5 sec) over their speech activity.

  • overlap: The average percentage of time the speech of the team members overlaps over the entire duration of the task.

  • overlap_to_speech_ratio: The ratio of the speech overlap over the speech activity (voice_activity) of the team.

• PE-HRI:learning_and_performance.csv: This file consists of the team level performance and learning metrics which are defined below:

  • last_error: This is the error of the last submitted solution. Note that if a team has found an optimal solution (error = 0) the game stops, therefore making last error = 0. This is a metric for performance in the task.

  • T_LG_absolute: It is a team-level learning outcome that we calculate by taking the average of the two individual absolute learning gains of the team members. The individual absolute gain is the difference between a participant’s post-test and pre-test score, divided by the maximum score that can be achieved (10), which grasps how much the participant learned of all the knowledge available.

  • T_LG_relative: It is a team-level learning outcome that we calculate by taking the average of the two individual relative learning gains of the team members. The individual relative gain is the difference between a participant’s post-test and pre-test score, divided by the difference between the maximum score that can be achieved and the pre-test score. This grasps how much the participant learned of the knowledge that he/she didn’t possess before the activity.

  • T_LG_joint_abs: It is a team-level learning outcome defined as the difference between the number of questions that both of the team members answer correctly in the post-test and in the pre-test, which grasps the amount of knowledge acquired together by the team members during the activity.

More details on the JUSThink learning activity can be found in the linked identifiers (present in a tab on the right side of the page). Lastly, a temporal version of this data is also available [6].

The High Resolution Digital Elevation Model (HRDEM) product is derived from airborne LiDAR data (mainly in the south) and satellite images in the north. The complete coverage of the Canadian territory is gradually being established. It includes a Digital Terrain Model (DTM), a Digital Surface Model (DSM) and other derived data. For DTM datasets, derived data available are slope, aspect, shaded relief, color relief and color shaded relief maps and for DSM datasets, derived data available are shaded relief, color relief and color shaded relief maps. The productive forest line is used to separate the northern and the southern parts of the country. This line is approximate and may change based on requirements. In the southern part of the country (south of the productive forest line), DTM and DSM datasets are generated from airborne LiDAR data. They are offered at a 1 m or 2 m resolution and projected to the UTM NAD83 (CSRS) coordinate system and the corresponding zones. The datasets at a 1 m resolution cover an area of 10 km x 10 km while datasets at a 2 m resolution cover an area of 20 km by 20 km. In the northern part of the country (north of the productive forest line), due to the low density of vegetation and infrastructure, only DSM datasets are generally generated. Most of these datasets have optical digital images as their source data. They are generated at a 2 m resolution using the Polar Stereographic North coordinate system referenced to WGS84 horizontal datum or UTM NAD83 (CSRS) coordinate system. Each dataset covers an area of 50 km by 50 km. For some locations in the north, DSM and DTM datasets can also be generated from airborne LiDAR data. In this case, these products will be generated with the same specifications as those generated from airborne LiDAR in the southern part of the country. The HRDEM product is referenced to the Canadian Geodetic Vertical Datum of 2013 (CGVD2013), which is now the reference standard for heights across Canada. Source data for HRDEM datasets is acquired through multiple projects with different partners. Since data is being acquired by project, there is no integration or edgematching done between projects. The tiles are aligned within each project. The product High Resolution Digital Elevation Model (HRDEM) is part of the CanElevation Series created in support to the National Elevation Data Strategy implemented by NRCan. Collaboration is a key factor to the success of the National Elevation Data Strategy. Refer to the “Supporting Document” section to access the list of the different partners including links to their respective data.

This layer shows fertility in past 12 months by age of mother. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. The calculated percentages are slightly different from traditional age-specific fertility rates in that the total number of live births (due to twins or higher-order multiple births) is not available in this table. This layer is symbolized to show the percent of women age 15 to 50 who had a birth in the past 12 months. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B13016 Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.