US Births by County and State

1985-2015 Aggregated Data

By data.world's Admin [source]

About this dataset

This dataset contains an aggregation of birth data from the United Statesbetween 1985 and 2015. It consists of information on mothers' locations by state (including District of Columbia) and county, as well as information such as the month they gave birth, and aggregates giving the sum of births during that month. This data has been provided by both the National Bureau for Economic Research and National Center for Health Statistics, whose shared mission is to understand how life works in order to aid individuals in making decisions about their health and wellbeing. This dataset provides valuable insight into population trends across time and location - for example, which states have higher or lower birthrates than others? Which counties experience dramatic fluctuations over time? Given its scope, this dataset could be used in a number of contexts--from epidemiology research to population forecasting. Be sure to check out our other datasets related to births while you're here!

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How to use the dataset

This dataset could be used to examine local trends in birth rates over time or analyze births at different geographical locations. In order to maximize your use of this dataset, it is important that you understand what information the various columns contain.

The main columns are: State (including District of Columbia), County (coded using the FIPS county code number), Month (numbering from 1 for January through 12 for December), Year (4-digit year) countyBirths (calculated sum of births that occurred to mothers living in a county for a given month) and stateBirths (calculated sum of births that occurred to mothers living in a state for a given month). These fields should provide enough information for you analyze trends across geographic locations both at monthly and yearly levels. You could also consider combining variables such as Year with State or Year with Month or any other grouping combinations depending on your analysis goal.

In addition, while all data were downloaded on April 5th 2017, it is worth noting that all sources used followed privacy guidelines as laid out by NCHC so individual births occurring after 2005 are not included due to geolocation concerns.
We hope you find this dataset useful and can benefit from its content! With proper understanding of what each field contains, we are confident you will gain valuable insights on birth rates across counties within the United States during this period

Research Ideas

• Establishing county-level trends in birth rates for the US over time.
• Analyzing the relationship between month of birth and health outcomes for US babies after they are born (e.g., infant mortality, neurological development, etc.).
• Comparing state/county-level differences in average numbers of twins born each year

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: allBirthData.csv | Column name | Description | |:-----------------|:-----------------------------------------------------------------------------------------------------------------| | State | The numerical order of the state where the mother lives. (Integer) | | Month | The month in which the birth took place. (Integer) | | Year | The year of the birth. (Integer) | | countyBirths | The calculated sum of births that occurred to mothers living in that county for that particular month. (Integer) | | stateBirths | The aggregate number at the level of entire states for any given month-year combination. (Integer) | | County | The county where the mother lives, coded using FIPS County Code. (Integer) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit data.world's Admin.

About this dataset

This dataset contains US baby names from the Social Security Administration dating back to 1879. With over 150 years of data, this is one of the most comprehensive datasets on baby names in the US. The data includes the name, year of birth, sex, and number of babies with that name for each year. This dataset is a great resource for anyone interested in studying baby naming trends over time

How to use the dataset

How to use the US Baby Names by Year of Birth dataset:

This dataset is a compilation of over 140 years of data from the Social Security Administration. It includes data on baby names, year of birth, and sex. There are also columns for the number of babies with that name born in that year.

This dataset can be used to track changes in baby naming trends over time, or to study how popular names have changed in popularity. It can also be used to study how naming trends differ between sexes, or between different years

Research Ideas

This dataset could be used for a number of things, including: 1. Determining baby name trends over time 2. Finding out what the most popular baby names are in the US 3. Analyzing how baby name popularity has changed over the years

Columns

• index: the index of the dataframe
• YearOfBirth: the year in which the baby was born
• Name: the name of the baby
• Sex: the sex of the baby
• Number: the number of babies with that name and sex

Acknowledgements

If you use this dataset in your research, please credit @nickgott, @rflprr and the Social Security Administration via Data.gov

Data Source

US Social Security applications are a great way to track trends in how babies born in the US are named.

Data.gov releases two datasets that are helplful for this: one at the national level and another at the state level. Note that only names with at least 5 babies born in the same year (/ state) are included in this dataset for privacy.

I've taken the raw files here and combined/normalized them into two CSV files (one for each dataset) as well as a SQLite database with two equivalently-defined tables. The code that did these transformations is available here.

New to data exploration in R? Take the free, interactive DataCamp course, "Data Exploration With Kaggle Scripts," to learn the basics of visualizing data with ggplot. You'll also create your first Kaggle Scripts along the way.

Number and percentage of live births, by month of birth, 1991 to most recent year.

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.

Popular Baby Names by Sex and Ethnic Group Data were collected through civil birth registration. Each record represents the ranking of a baby name in the order of frequency. Data can be used to represent the popularity of a name. Caution should be used when assessing the rank of a baby name if the frequency count is close to 10; the ranking may vary year to year.

Baby names from social security card applications in the United States spanning three decades, including state, gender, year of birth, name, and the number of babies given each name.

Recommended Analysis

1. What were the most popular baby names of each decade? How does this change over time?
2. Which baby names had the biggest jumps and drops in popularity?
3. Are there differences in which names are given to boys vs girls vs both over time?
4. Are there differences in baby name popularity based on the region in the United States?

The data (name, year of birth, sex, and number) are from a 100 percent sample of Social Security card applications for 1880 on.

Baltimore City Child Health

An Exploration of 2010 Birth, Prenatal Visit, Lead Exposure and Teen Birth Rates

By City of Baltimore [source]

About this dataset

This Baltimore City Child and Family Health Indicators dataset provides us with crucial information that can support the health and well-being of Baltimore City residents. It contains 13 indicators such as low birth weight, prenatal visits, teen births, and more. This data is sourced from the Maryland Department of Health & Mental Hygiene (DHMH), Baltimore Substance Abuse Systems (BSAS), theBaltimore City Health Department, and the US Census Bureau. Through this data set we can gain a better understanding of how Baltimore City citizens’ health compares to other areas and how it has changed over time. By investigating this dataset we are given an opportunity to create potential strategies for providing better care for our community. With discoveries from these indicators, together as a city we can bring about lasting change in protecting public health within Baltimore

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How to use the dataset

This dataset provides valuable information about the health and wellbeing of children and families in Baltimore City in 2010. The data is organized by CSA (Census Statistical Area) and includes stats on term births, low birth weight births, prenatal visits, teen births, and lead testing. This dataset can be used to analyze trends in children's health over time as well as identify potential areas that need more attention or resources.

To use this dataset: - Read through the data dictionary to understand what each column represents.
- Choose which columns you would like to explore further.
- Filter or subset the data as you see fit then visualize it with graphs or maps to better understand how conditions vary across neighborhoods in Baltimore City.
- Consider comparing the data from this year with prior years if available for deeper analysis of changes over time.
- Look for correlations among columns that could help explain disparities between neighborhoods and create strategies for improving outcomes through policy interventions or other programs designed specifically for those areas needs

Research Ideas

• Mapping health disparities in high-risk areas to target public health interventions.
• Identifying neighborhoods in need of additional resources for prenatal care, infant care, and lead testing and create specific programs to address these needs.
• Creating an online dashboard that displays real time data on Baltimore City’s population health indicators such as birth weight, teenage pregnancies, and lead poisoning for the public to access easily

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: BNIA_Child_Fam_Health_2010.csv | Column name | Description | |:---------------|:----------------------------------------------------------| | the_geom | Geometry of the Census Statistical Area (CSA) (Geometry) | | CSA2010 | Census Statistical Area (CSA) (String) | | termbir10 | Total number of term births in 2010 (Integer) | | birthwt10 | Total number of low birth weight births in 2010 (Integer) | | prenatal10 | Total number of prenatal visits in 2010 (Integer) | | teenbir10 | Total number of teen births in 2010 (Integer) | | leadtest10 | Total number of lead tests conducted in 2010 (Integer) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit City of Baltimore.

U.S. names organized by year and gender assigned at birth. Contains data from 1983 to 2022, and all names with 5 or more uses.

Contains the following columns:

Ranking (1 being the most-used name)

Name

Gender assigned at birth

Number of times the name was given

Percentage of births per gender given that name

Explore gender statistics related to families and households, including data on both sexes, percent of total for both sexes, total live births, population, and residential information. Access valuable insights and trends for countries such as Portugal, Belgium, Spain, France, Italy, United Kingdom, United States, and many more.

Both sexes, Percent of Total for Both Sexes, Total Live Births, Population, Residential, birth

Portugal, Belgium, Spain, Bosnia and Herzegovina, France, Denmark, Italy, Uzbekistan, Bulgaria, United Kingdom, Slovenia, Czechia, Poland, Ukraine, Latvia, Sweden, Iceland, Armenia, Georgia, Canada, Montenegro, Hungary, United States, Andorra, Republic of Moldova, Croatia, Malta, San Marino, Turkmenistan, Azerbaijan, Kyrgyzstan, North Macedonia, Russian Federation, Greece, Luxembourg, Monaco, Slovakia, Norway, Tajikistan, Albania, Liechtenstein, Serbia, Switzerland, Lithuania, Estonia, Turkiye, Cyprus, Germany, Finland, Ireland, Israel, Kazakhstan, Austria, Belarus, Netherlands, RomaniaFollow data.kapsarc.org for timely data to advance energy economics research.Source: UNECE Statistical Database, compiled from national and international (Eurostat, UN Statistics Division Demographic Yearbook, WHO European health for all database and UNICEF TransMONEE) official sources.Definition: A live birth is the complete expulsion or extraction from its mother of a product of conception, irrespective of the duration of pregnancy, which after such separation breathes or shows any other evidence of life such as beating of the heart, pulsation of the umbilical cord or definite movement of voluntary muscles, whether or not the umbilical cord has been cut or the placenta is attached.General note: Data come from registers, unless otherwise specified. In years 2003 and before, the number of live births for girl child and boy child may not add up to the number for both sexes (Total) due to the rounding up of numbers.

Background: Standard pediatric growth curves cannot be used to impute missing height or weight measurements in individual children. The Michaelis-Menten equation, used for characterizing substrate-enzyme saturation curves, has been shown to model growth in many organisms including nonhuman vertebrates. We investigated whether this equation could be used to interpolate missing growth data in children in the first three years of life and compared this interpolation to several common interpolation methods and pediatric growth models. Methods: We developed a modified Michaelis-Menten equation and compared expected to actual growth, first in a local birth cohort (N=97) and then in a large, outpatient, pediatric sample (N=14,695). Results: The modified Michaelis-Menten equation showed excellent fit for both infant weight (median RMSE: boys: 0.22kg [IQR:0.19; 90%<0.43]; girls: 0.20kg [IQR:0.17; 90%<0.39]) and height (median RMSE: boys: 0.93cm [IQR:0.53; 90%<1.0]; girls: 0.91cm [IQR:0.50;90%<1.0]). Growth data were modeled accurately with as few as four values from routine well-baby visits in year 1 and seven values in years 1-3; birth weight or length was essential for best fit. Interpolation with this equation had comparable (for weight) or lower (for height) mean RMSE compared to the best-performing alternative models. Conclusions: A modified Michaelis-Menten equation accurately describes growth in healthy babies aged 0–36 months, allowing interpolation of missing weight and height values in individual longitudinal measurement series. The growth pattern in healthy babies in resource-rich environments mirrors an enzymatic saturation curve. Methods Sources of data: Information on infants was ascertained from two sources: the STORK birth cohort and the STARR research registry. (1) Detailed methods for the STORK birth cohort have been described previously. In brief, a multiethnic cohort of mothers and babies was followed from the second trimester of pregnancy to the babies’ third birthday. Healthy women aged 18–42 years with a single-fetus pregnancy were enrolled. Households were visited every four months until the baby’s third birthday (nine baby visits), with the weight of the baby at each visit recorded in pounds. Medical charts were abstracted for birth weight and length. (2) STARR (starr.stanford.edu) contains electronic medical record information from all pediatric and adult patients seen at Stanford Health Care (Stanford, CA). STARR staff provided anonymized information (weight, height and age in days for each visit through age three years; sex; race/ethnicity) for all babies during the period 03/2013–01/2022 followed from birth to at least 36 months of age with at least five well-baby care visits over the first year of life.
Inclusion of data for modeling: All observed weight and height values were evaluated in kilograms (kg) and centimeters (cm), respectively. Any values assessed beyond 1,125 days (roughly 36 months) and values for height and weight deemed implausible by at least two reviewers (e.g., significant losses in height, or marked outliers for weight and height) were excluded from the analysis. Additionally, weights assessed between birth and 19 days were excluded. At least five observations across the 36-month period were required: babies with fewer than five weight or height values after the previous criteria were excluded from analyses. Model: We developed our weight model using values from STORK babies and then replicated it with values from the STARR babies. Height models were evaluated in STARR babies only because STORK data on height were scant. The Michaelis-Menten equation is described as follows: v = Vmax ([S]/(Km + [S]) , where v is the rate of product formation, Vmax is the maximum rate of the system, [S] is the substrate concentration, and Km is a constant based upon the enzyme’s affinity for the particular substrate. For this study the equation became: P = a1 (Age/(b1+ Age)) + c1, where P was the predicted value of weight (kg) or height (cm), Age was the age of the infant in days, and c1 was an additional constant over the original Michaelis-Menten equation that accounted for the infant’s non-zero weight or length at birth. Each of the parameters a1, b1 and c1 was unique to each child and was calculated using the nonlinear least squares (nls) method. In our case, weight data were fitted to a model using the statistical language R, by calling the formula nls() with the following parameters: fitted_model <-nls(weights~(c1+(a1*ages)/(b1+ages)), start = list(a1 = 5, b1 = 20, c1=2.5)), where weights and ages were vectors of each subject’s weight in kg and age in days. The default Gauss-Newton algorithm was used. The optimization objective is not convex in the parameters and can suffer from local optima and boundary conditions. In such cases good starting values are essential: the starting parameter values (a1=5, b1=20, c1=2.5) were adjusted manually using the STORK dataset to minimize model failures; these tended to occur when the parameter values, particularly a1 and b1, increased without bound during the iterative steps required to optimize the model. These same parameter values were used for the larger STARR dataset. The starting height parameter values for height modeling were higher than those for weight modeling, due to the different units involved (cm vs. kg) (a1=60, b1=530, c1=50). Because this was a non-linear model, goodness of fit was assessed primarily via root mean squared error (RMSE) for both weight and height. Imputation tests: To test for the influence of specific time points on the models, we limited our analysis to STARR babies with all recommended well-baby visits (12 over three years). Each scheduled visit except day 1 occurred in a time window around the expected well-baby visit (Visit1: Day 1, Visit2: days 20–44, Visit3: 46–90, Visit4: 95–148, Visit5: 158–225, Visit6: 250–298, Visit7: 310–399, Visit8: 410–490, Visit9: 500–600, Visit10: 640–800, Visit11: 842–982, Visit12: 1024–1125). We considered two different sets: infants with all scheduled visits in the first year of life (seven total visits) and those with all scheduled visits over the full three-year timeframe (12 total visits). We fit these two sets to the model, identifying baseline RMSE. Then, every visit, and every combination of two to five visits were dropped, so that the RMSE or model failures for a combination of visits could be compared to baseline. Prediction: We sought to predict weight or height at 36 months (Y3) from growth measures assessed only up to 12 months (Y1) or to 24 months (Y1+Y2), utilizing the “last value” approach. In brief, the last observation for each child (here, growth measures at 36 months) is used to assess overall model fit, by focusing on how accurately the model can extrapolate the measure at this time point. We identified all STARR infants with at least five time points in Y1 and at least two time points in both Y2 and Y3, with the selection of these time points based on maximizing the number of later time points within the constraints of the well-baby visit schedule for Y2 and Y3. The per-subject set of time points (Y1-Y3) was fitted using the modified Michaelis-Menten equation and the mean squared error was calculated, acting as the “baseline” error. The model was then run on the subset of Y1 only and of Y1+Y2 only. To test predictive accuracy of these subsets, the RMSE was calculated using the actual weights or heights versus the predicted weights or heights of the three time series. Comparison with other models: We examined how well the modified Michaelis-Menten equation performed interpolation in STARR babies compared to ten other commonly used interpolation methods and pediatric growth models including: (1) the ‘last observation carried forward’ model; (2) the linear model; (3) the robust linear model (RLM method, base R MASS package); (4) the Laird and Ware linear model (LWMOD method); (5) the generalized additive model (GAM method); (6) locally estimated scatterplot smoothing (LOESS method, base R stats package); (7) the smooth spline model (smooth.spline method, base R stats package); (8) the multilevel spline model (Wand method); (9) the SITAR (superimposition by translation and rotation) model and (10) fast covariance estimation (FACE method). Model fit used the holdout approach: a single datapoint (other than birth weight or birth length) was randomly removed from each subject, and the RMSE of the removed datapoint was calculated as the model fitted to the remaining data. The hbgd package was used to fit all models except the ‘last observation carried forward’ model, the linear model and the SITAR model. For the ‘last observation carried forward’ model, the holdout data point was interpolated by the last observation by converting the random holdout value to NA and then using the function na.locf() from the zoo R package. For the simple linear model, the holdout-filtered data were used to determine the slope and intercept via R’s lm() function, which were then used to calculate the holdout value. For the SITAR model, each subject was fitted by calling the sitar() function with df=2 to minimize failures, and the RMSE of the random holdout point was subsequently calculated with the predict() function. For this analysis, set.seed(1234) was used to initialize the pseudorandom generator.

The goal is to predict the rate of heart disease (per 100,000 individuals) across the United States at the county-level from other socioeconomic indicators. The data is compiled from a wide range of sources and made publicly available by the United States Department of Agriculture Economic Research Service (USDA ERS).

There are 33 variables in this dataset. Each row in the dataset represents a United States county, and the dataset we are working with covers two particular years, denoted a, and b We don't provide a unique identifier for an individual county, just a row_id for each row.

The variables in the dataset have names that of the form category_variable, where category is the high level category of the variable (e.g. econ or health). variable is what the specific column contains.

We're trying to predict the variable heart_disease_mortality_per_100k (a positive integer) for each row of the test data set.

Columns

area — information about the county

area_rucc — Rural-Urban Continuum Codes "form a classification scheme that distinguishes metropolitan counties by the population size of their metro area, and nonmetropolitan counties by degree of urbanization and adjacency to a metro area. The official Office of Management and Budget (OMB) metro and nonmetro categories have been subdivided into three metro and six nonmetro categories. Each county in the U.S. is assigned one of the 9 codes." (USDA Economic Research Service, https://www.ers.usda.gov/data-products/rural-urban-continuum-codes/)

area_urban_influence — Urban Influence Codes "form a classification scheme that distinguishes metropolitan counties by population size of their metro area, and nonmetropolitan counties by size of the largest city or town and proximity to metro and micropolitan areas." (USDA Economic Research Service, https://www.ers.usda.gov/data-products/urban-influence-codes/)

econ — economic indicators

econ_economic_typology — County Typology Codes "classify all U.S. counties according to six mutually exclusive categories of economic dependence and six overlapping categories of policy-relevant themes. The economic dependence types include farming, mining, manufacturing, Federal/State government, recreation, and nonspecialized counties. The policy-relevant types include low education, low employment, persistent poverty, persistent child poverty, population loss, and retirement destination." (USDA Economic Research Service, https://www.ers.usda.gov/data-products/county-typology-codes.aspx)

econ_pct_civilian_labor — Civilian labor force, annual average, as percent of population (Bureau of Labor Statistics, http://www.bls.gov/lau/)

econ_pct_unemployment — Unemployment, annual average, as percent of population (Bureau of Labor Statistics, http://www.bls.gov/lau/)

econ_pct_uninsured_adults — Percent of adults without health insurance (Bureau of Labor Statistics, http://www.bls.gov/lau/) econ_pct_uninsured_children — Percent of children without health insurance (Bureau of Labor Statistics, http://www.bls.gov/lau/)

health — health indicators

health_pct_adult_obesity — Percent of adults who meet clinical definition of obese (National Center for Chronic Disease Prevention and Health Promotion)

health_pct_adult_smoking — Percent of adults who smoke (Behavioral Risk Factor Surveillance System)

health_pct_diabetes — Percent of population with diabetes (National Center for Chronic Disease Prevention and Health Promotion, Division of Diabetes Translation)

health_pct_low_birthweight — Percent of babies born with low birth weight (National Center for Health Statistics)

health_pct_excessive_drinking — Percent of adult population that engages in excessive consumption of alcohol (Behavioral Risk Factor Surveillance System, )

health_pct_physical_inacticity — Percent of adult population that is physically inactive (National Center for Chronic Disease Prevention and Health Promotion)

health_air_pollution_particulate_matter — Fine particulate matter in µg/m³ (CDC WONDER, https://wonder.cdc.gov/wonder/help/pm.html)

health_homicides_per_100k — Deaths by homicide per 100,000 population (National Center for Health Statistics)

health_motor_vehicle_crash_deaths_per_100k — Deaths by motor vehicle crash per 100,000 population (National Center for Health Statistics)

health_pop_per_dentist — Population per dentist (HRSA Area Resource File)

health_pop_per_primary_care_physician — Population per Primary Care Physician (HRSA Area Resource File)

demo — demographics information

demo_pct_female — Percent of population that is female (US Census Population Estimates)

demo_pct_below_18_years_of_age — Percent of population that is below 18 years of age (US Census Population Estimates)

demo_pct_aged_65_years_and_older — Percent of population that is aged 65 years or older (US Census Population Estimates)

dem...

Teen mothers often have a higher proportion of low birthweight babies than do mothers in the 20 to 39 year age group. There is a significant concentration of high low birthweight rates for teen mothers in Atlantic Canada. Areas with very high 1996 low birthweight rates (8.0% and grater) are most commonly found in Quebec and Ontario. Low birthweight (LBW) is a health status indicator, and is defined as babies born with weight under 2500 grams. The proportion of low birthweight babies born to mothers 15 years of age and older indicates the health and well-being of a population. Health status refers to the state of health of a person or group, and measures causes of sickness and death. It can also include people’s assessment of their own health.

This database is comprised of 951 participants who provided self-report data online in their school classrooms. The data was collected in 2016 and 2017. The dataset is comprised of 509 males (54%) and 442 females (46%). Their ages ranged from 12 to 16 years (M = 13.69, SD = 0.72). Seven participants did not report their age. The majority were born in Australia (N = 849, 89%). The next most common countries of birth were China (N = 24, 2.5%), the UK (N = 23, 2.4%), and the USA (N = 9, 0.9%). Data were drawn from students at five Australian independent secondary schools.

The data contains item responses for the Spence Children’s Anxiety Scale (SCAS; Spence, 1998) which is comprised of 44 items. The Social media question asked about frequency of use with the question “How often do you use social media?”. The response options ranged from constantly to once a week or less. Items measuring Fear of Missing Out were included and incorporated the following five questions based on the APS Stress and Wellbeing in Australia Survey (APS, 2015). These were “When I have a good time it is important for me to share the details online; I am afraid that I will miss out on something if I don’t stay connected to my online social networks; I feel worried and uncomfortable when I can’t access my social media accounts; I find it difficult to relax or sleep after spending time on social networking sites; I feel my brain burnout with the constant connectivity of social media. Internal consistency for this measure was α = .81. Self compassion was measured using the 12-item short-form of the Self-Compassion Scale (SCS-SF; Raes et al., 2011).

The data set has the option of downloading an excel file (composed of two worksheet tabs) or CSV files 1) Data and 2) Variable labels.

References:

Australian Psychological Society. (2015). Stress and wellbeing in Australia survey. _{https://www.headsup.org.au/docs/default-source/default-document-library/stress-and-wellbeing-in-australia-report.pdf?sfvrsn=7f08274d_4}

Raes, F., Pommier, E., Neff, K. D., & Van Gucht, D. (2011). Construction and factorial validation of a short form of the self-compassion scale. Clinical Psychology and Psychotherapy, 18(3), 250-255. _{https://doi.org/10.1002/cpp.702}

Spence, S. H. (1998). A measure of anxiety symptoms among children. Behaviour Research and Therapy, 36(5), 545-566. _{https://doi.org/10.1016/S0005-7967(98)00034-5}

United States US: Prevalence of Underweight: Weight for Age: % of Children Under 5 data was reported at 0.500 % in 2012. This records a decrease from the previous number of 0.800 % for 2009. United States US: Prevalence of Underweight: Weight for Age: % of Children Under 5 data is updated yearly, averaging 0.900 % from Dec 1991 (Median) to 2012, with 5 observations. The data reached an all-time high of 1.100 % in 2005 and a record low of 0.500 % in 2012. United States US: Prevalence of Underweight: Weight for Age: % 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 children is the percentage of children 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 child growth standards released in 2006.; ; UNICEF, WHO, World Bank: Joint child malnutrition estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology.; Linear mixed-effect model estimates; 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.

This dataset represents a group of paper records (a "series") within the Marie C. McCormick papers, 1956-2016 (inclusive), 1968-2009 (bulk), which can be accessed on-site at the Center for the History of Medicine at the Francis A. Countway Library of Medicine in Boston, Massachusetts. The series consists of administrative and regulatory records generated and compiled by Marie C. McCormick as a product of her service as Principal Investigator of phase IV of the Infant Health and Development Program. The Infant Health and Development Program (IHDP) consisted of four phases, and was concerned with the short- and long-term outcomes of low birthweight and high-risk pregnancies. For records from the first three phases, please see the “Infant Health and Development Program, Phases I-III Records, 1984-2002” dataset. Regulatory records include: survey instruments; protocols and methodologies; and codebooks. Administrative records include: institutional review board certification records and safety plan activation records for each site; grant applications; budgets; reports; subject lists; meeting agendas; and administrative correspondence. Frequent topics include: engagement and motivation in school; behavior and mental health; cognitive and linguistic ability; health status; mothers’ supervisory attitudes and strategies; mothers’ aspirations for their children; mothers’ coping and mental health; differences between lighter and heavier low-birthweight children; and differences between more and less affluent families. Series also includes: occasional summarized, analyzed, and assessment data tables and charts; manuscript drafts and collected publications; and five CDs and one DVD, containing SAS and SPSS dataset files and administrative, regulatory, and publishing records. More IHDP records may be found in the “Infant Health and Development Program, Phases I-III Records, 1984-2002” and “Infant Health and Development Program, Phase IV Electronic Records, 2000-2016” datasets. Data and associated records are accessible onsite at the Center for the History of Medicine per the conditions governing access described below. Conditions Governing Access to Original Collection Materials: The series represented by this dataset includes longitudinal patient information that is restricted for 80 years from the most recently dated records in the series, personnel information that is restricted for 80 years from the date of record creation, and Harvard University records that are restricted for 50 years from the date of record creation. Access to electronic records is also premised on the availability of a computer station, requisite software, and/or the ability of Public Services staff to review and/or print out records of interest in advance of an on-site visit. Researchers should contact Public Services for more information. The Marie C. McCormick papers were processed with grant funding from the Andrew W. Mellon Foundation, as awarded and administered by the Council on Library and Information Resources (CLIR) in 2016. View the Marie C. McCormick Papers finding aid for a full collection inventory of both paper and digital records, and for more information about accessing and using the collection.

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.

This data collection supplies standard monthly labor force information 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. Supplemental items pertain to immigrant women. Information provided includes date of birth, country of birth, citizenship status, year entered the United States, number of children born, date of birth of the most recent child, total number of children born in countries outside American jurisdiction, and number of children born in countries outside American jurisdiction currently living in the household. Information on demographic characteristics such as, age, sex, race, marital 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/ICPSR08265.v1. We highly recommend using the ICPSR version as they may make this dataset available in multiple data formats in the future.

The 1994 Zimbabwe Demographic and Health Survey (ZDHS) is a nationally representative survey of 6,128 women age 15-49 and 2,141 men age 15-54. The ZDHS was implemented by the Central Statistical Office (CSO), with significant technical guidance provided by the Ministry of Health and Child Welfare (MOH&CW) and the Zimbabwe National Family Planning Council (ZNFPC). Macro International Inc. (U.S.A.) provided technical assistance throughout the course of the project in the context of the Demographic and Health Surveys (DHS) programme, while financial assistance was provided by the U.S, Agency for International Development (USAID/Harare). Data collection for the ZDHS was conducted from July to November 1994. As in the 1988 ZDHS, the 1994 ZDHS was designed to provide information on levels and trends in fertility, family planning knowledge and use, infant and child mortality, and maternal and child health. How- ever, the 1994 ZDHS went further, collecting data on: compliance with contraceptive pill use, knowledge and behaviours related to AIDS and other sexually transmitted diseases, and mortality related to pregnancy and childbearing (i.e., maternal mortality). The ZDHS data are intended for use by programme managers and policymakers to evaluate and improve family planning and health programmes in Zimbabwe. The primary objectives of the 1994 ZDHS were to provide up-to-date information on: fertility levels; nuptiality; sexual activity; fertility preferences; awareness and use of family planning methods; breastfeeding practices; nutritional status of mothers and young children; early childhood mortality and maternal mortality; maternal and child health, and awareness and behaviour regarding AIDS and other sexually transmitted diseases. The 1994 ZDHS is a follow-up of the 1988 ZDHS, also implemented by CSO. While significantly expanded in scope, the 1994 ZDHS provides updated estimates of basic demographic and health indicators covered in the earlier survey. MAIN RESULTS FERTILITY Survey results show that Zimbabwe has experienced a fairly rapid decline in fertility over the past decade. Despite the decline in fertility, childbearing still begins early for many women. One in five women age 15-19 has begun childbearing (i.e., has already given birth or is pregnant with her first child). More than half of women have had a child before age 20. Births that occur too soon after a previous birth face higher risks of undemutrition, illness, and death. The 1994 ZDHS indicates that 12 percent of births in Zimbabwe take place less than two years after a prior birth. Marriage. The age at which women and men marry has risen slowly over the past 20 years. Nineteen percent of currently married women are in a polygynous union (i.e., their husband has at least one other wife). This represents a small rise in polygyny since the 1988 ZDHS when 17 percent of married women were in polygynous unions. Fertility Preferences. Around one-third of both women and men in Zimbabwe want no more children. The survey results show that, of births in the last three years, 1 in 10 was unwanted and in 1 in three was mistimed. If all unwanted births were avoided, the fertility rate in Zimbabwe would fall from 4.3 to 3.5 children per woman. FAMILY PLANNING Knowledge and use of family planning in Zimbabwe has continued to rise over the last several years. The 1994 ZDHS shows that virtually all married women (99 percent) and men (100 percent) were able to cite at least one modem method of contraception. Contraceptive use varies widely among geographic and socioeconomic subgroups. Fifty-eight per- cent of married women in Harare are using a modem method versus 28 percent in Manicaland. Government-sponsored providers remain the chief source of contraceptive methods in Zimbabwe. Survey results show that 15 percent of married women have an unmet need for family planning (either for spacing or limiting births). CHILDHOOD MORTALITY One of the main objectives of the ZDHS was to document the levels and trends in mortality among children under age five. The 1994 ZDHS results show that child survival prospects have not improved since the late 1980s. The ZDHS results show that childhood mortality is especially high when associated with two factors: short preceding birth interval and low level of maternal education. MATERNAL AND CHILD HEALTH Utilisation of antenatal services is high in Zimbabwe; in the three years before the survey, mothers received antenatal care for 93 percent of births. About 70 percent of births take place in health facilities; however, this figure varies from around 53 percent in Manicaland and Mashonaland Central to 94 percent in Bulawayo. It is important for the health of both the mother and child that trained medical personnel are available in cases of prolonged or obstructed delivery, which are major causes of maternal morbidity and mortality. Twenty-four percent of children under age three were reported to have had diarrhoea in the two weeks preceding the survey. Nutrition. Almost all children (99 percent) are breastfed for some period of time; When food supplementation begins, wide disparity exists in the types of food received by children in different geographic and socioecoaomic groups. Generally, children living in urban areas (Harare and Bulawayo, in particular) and children of more educated women receive protein-rich foods (e.g., meat, eggs, etc.) on a more regular basis than other children. AIDS AIDS-related Knowledge and Behaviour. All but a fraction of Zimbabwean women and men have heard of AIDS, but the quality of that knowledge is sometimes poor. Condom use and limiting the number of sexual partners were cited most frequently by both women and men as ways to avoid the AIDS Virus. While general knowledge of condoms is nearly universal among both women and men, when asked where they could get a condom, 30 Percent of women and 20 percent of men could not cite a single source.