Background The aim of this study was first, to investigate whether women starting oral contraceptive (OC) use at a young age and before first birth have an increased risk for breast cancer and second, to report difficulties encountered in studying long-term health impacts of medical technologies. Methods Breast cancers occurring up until 1997 among 37153 Helsinki students born between 1946 and 1960 were identified by record linkage from the Finnish Cancer Registry; for each cancer case, five age-matched random controls were picked from the same student population. Those who had used the Helsinki Student Health Service (HSHS) at least three times (150 cases and 316 controls) form the final study subjects. Data on OC use and background characteristics were collected from patient records, and data on live births were derived from the population register. Odds ratios (OR) were adjusted for number of births, smoking and sports activity. Results Compared to the few non-users, OC users had a higher risk of breast cancer: the adjusted OR was 2.1 (95% confidence interval 1.1–4.2). Among OC users, no statistically significant differences in risk of breast cancer were found in regard to starting age or first birth, but small numbers made confidence intervals wide. Even though we had chosen students to be our study group, the population turned out to be unsuitable to answer our research question: most women had started their OC use old (at the age of 20 or later) and there were very few unexposed (almost all had used OC and before their first birth). Conclusions Because adoption of the modern pattern of OC use was not common among students, it is unlikely that the impact of early and extended OC use can be studied before 2010, when women born in the 1960s are 40 to 50 years old.

I’d like to request data on the number of prescriptions for the contraceptive pill in the UK over the past 5 years 2019 – 2023. I would like this data broken down year by year. Response Under Section 21 of the Act, we are not required to provide information in response to a request if it is already reasonably accessible to you. There is Prescription Cost Analysis (PCA) monthly administrative data published in the Open Data Portal that includes contraceptive prescriptions items dispensed in the community in England on a monthly basis and submitted to the NHSBSA for reimbursement. These files can be filtered by product name to locate the information you require: Prescription cost analysis (PCA) contains information on all prescription items dispensed in England and submitted to the NHSBSA for reimbursement: https://opendata.nhsbsa.net/dataset/prescription-cost-analysis-pca-annual-statistics The English prescribing dataset (EPD) contains information on prescriptions issued in England that have been dispensed in England, Wales, Scotland, Guernsey, Alderney, Jersey, and the Isle of Man. https://opendata.nhsbsa.net/dataset/english-prescribing-data-epd On the Prescription Cost Analysis (PCA) Monthly Administrative Data page scroll down to Data and Resources and find the month required. Click the explore button. Then click Preview (to download only contraceptive data) rather than the entire (all BNF Sections) dataset. Use the Add Filter button and choose BNF_SECTIONs Contraceptive Devices and Contraceptives. The data includes items, total quantity, and NIC. There is a Data Dictionary describing each column of data (once you select a time period at the bottom of the screen). Go to the Data Explorer options and select Download filtered records. You will need this for each month required. Please note, that to obtain the figures for contraceptive prescriptions, you will need to add a filter for the BNF_SECTIONs Contraceptive Devices and Contraceptives. The data includes items, total quantity and NIC. There is a Data Dictionary describing each column of data once you select a time period (at the bottom of the screen). To view a video on how to use the Open Data Portal: https://www.youtube.com/watch?v=JNC1EQJAPNY

The use of Most or Moderately effective contraceptive (M/M) or Long-Acting Reversible Contraceptive (LARC) types by disability status, contraceptive type, age group, and year of interest, for 2014-2016. This data was compiled for the Measure CCW: Contraceptive Care – All Women Ages 15-44, as part of the Maternal and Infant Health Initiative, Contraceptive Care Quality grant.

The tables provide data on contraceptive activity taking place at dedicated Sexual and Reproductive Health (SRH) services in England, as recorded in the Sexual and Reproductive Health Activity Dataset (SRHAD), a mandated collection for all providers of NHS SRH services. A limited amount of data is presented from other sources; sterilisations and vasectomies in NHS hospitals and contraceptives dispensed in the community.

The number of women eligible to receive contraceptive services, by program and by age group, for 2014-2016. This data was compiled for the Measure CCW: Contraceptive Care – All Women Ages 15-44, as part of the Maternal and Infant Health Initiative, Contraceptive Care Quality grant.

The DHS is intended to serve as a primary source for international population and health information for policymakers and for the research community. In general, DHS has four objectives: To provide participating countries with a database and analysis useful for informed choices, To expand the international population and health database, To advance survey methodology, and To help develop in participating countries technical skills and resources necessary to conduct demographic and health surveys. Apart from estimating fertility and contraceptive prevalence rates, DHS also covers the topic of child health, which has become the focus of many development programs aimed at improving the quality of life in general. The Indonesian DHS survey did not include health-related questions because this information was collected in the 1987 SUSENAS in more detail and with wider geographic coverage. Hence, the Indonesian DHS was named the "National Indonesian Contraceptive Prevalence Survey" (NICPS). The National Indonesia Contraceptive Prevalence Survey (NICPS) was a collaborative effort between the Indonesian National Family Planning Coordinating Board (NFPCB), the Institute for Resource Development of Westinghouse and the Central Bureau of Statistics (CBS). The survey was part of an international program in which similar surveys are being implemented in developing countries in Asia, Africa, and Latin America. The 1987 NICPS was specifically designed to meet the following objectives: To provide data on the family planning and fertility behavior of the Indonesian population necessary for program organizers and policymakers in evaluating and enhancing the national family planning program, and To measure changes in fertility and contraceptive prevalence rates and at the same time study factors which affect the change, such as marriage patterns, urban/rural residence, education, breastfeeding habits, and availability of contraception.

Dataset of birth control use in women with cerebral palsy at a children's hospital and rehabilitation hospital in New England

Analysis of ‘🛟 Contraceptive Method Choice’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/contraceptive-method-choicee on 13 February 2022.

--- Dataset description provided by original source is as follows ---

About this dataset

Dataset is a subset of the 1987 National Indonesia Contraceptive Prevalence Survey.# Source:
Origin:
This dataset is a subset of the 1987 National Indonesia Contraceptive Prevalence Survey
Creator:
Tjen-Sien Lim (limt '@' stat.wisc.edu)
Donor:
Tjen-Sien Lim (limt '@' stat.wisc.edu)

Data Set Information:

This dataset is a subset of the 1987 National Indonesia Contraceptive Prevalence Survey. The samples are married women who were either not pregnant or do not know if they were at the time of interview. The problem is to predict the current contraceptive method choice (no use, long-term methods, or short-term methods) of a woman based on her demographic and socio-economic characteristics.

Attribute Information:

1. Wife's age (numerical) 2. Wife's education (categorical) 1=low, 2, 3, 4=high 3. Husband's education (categorical) 1=low, 2, 3, 4=high 4. Number of children ever born (numerical) 5. Wife's religion (binary) 0=Non-Islam, 1=Islam 6. Wife's now working? (binary) 0=Yes, 1=No 7. Husband's occupation (categorical) 1, 2, 3, 4 8. Standard-of-living index (categorical) 1=low, 2, 3, 4=high 9. Media exposure (binary) 0=Good, 1=Not good 10. Contraceptive method used (class attribute) 1=No-use, 2=Long-term, 3=Short-term

Relevant Papers:

Lim, T.-S., Loh, W.-Y. & Shih, Y.-S. (1999). A Comparison of Prediction Accuracy, Complexity, and Training Time of Thirty-three Old and New Classification Algorithms. Machine Learning. ( or )

Papers That Cite This Data Set1:

Earl Harris Jr. Information Gain Versus Gain Ratio: A Study of Split Method Biases. The MITRE Corporation/Washington C. 2001.

• Soumya Ray and David Page. Generalized Skewing for Functions with Continuous and Nominal Attributes. Department of Computer Sciences and Department of Biostatistics and Medical Informatics, University of Wis.
• Jos'e L. Balc'azar. Rules with Bounded Negations and the Coverage Inference Scheme. Dept. LSI, UPC.

Citation Request:

Please refer to the Machine Learning Repository's citation policy.
[1] Papers were automatically harvested and associated with this data set, in collaborationwith Rexa.info

Source: http://archive.ics.uci.edu/ml/datasets/Contraceptive+Method+Choice

This dataset was created by UCI and contains around 1000 samples along with 1.1, 2.1, technical information and other features such as: - 2 - 3.1 - and more.

How to use this dataset

• Analyze 3 in relation to 1.2
• Study the influence of 24 on 1
• More datasets

Acknowledgements

If you use this dataset in your research, please credit UCI

Start A New Notebook!

--- Original source retains full ownership of the source dataset ---

Central African Republic CF: Contraceptive Prevalence: Any Methods: % of Women Aged 15-49 data was reported at 17.800 % in 2019. This records an increase from the previous number of 15.180 % for 2010. Central African Republic CF: Contraceptive Prevalence: Any Methods: % of Women Aged 15-49 data is updated yearly, averaging 17.800 % from Dec 1995 (Median) to 2019, with 5 observations. The data reached an all-time high of 27.943 % in 2000 and a record low of 14.800 % in 1995. Central African Republic CF: Contraceptive Prevalence: Any Methods: % of Women Aged 15-49 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Central African Republic – Table CF.World Bank.WDI: Social: Health Statistics. Contraceptive prevalence, any method is the percentage of married women ages 15-49 who are practicing, or whose sexual partners are practicing, any method of contraception (modern or traditional). Modern methods of contraception include female and male sterilization, oral hormonal pills, the intra-uterine device (IUD), the male condom, injectables, the implant (including Norplant), vaginal barrier methods, the female condom and emergency contraception. Traditional methods of contraception include rhythm (e.g., fertility awareness based methods, periodic abstinence), withdrawal and other traditional methods.;Household surveys, including Demographic and Health Surveys and Multiple Indicator Cluster Surveys. Largely compiled by United Nations Population Division (World Contraceptive Use 2024).;Weighted average;

This dataset includes the following variables: number of contraceptive implants; intrauterine contraceptives (IUCs); Essure procedures and tubal ligation services delivered by providers and the number of female clients receiving these services; number of vasectomies delivered by providers and number received by male clients.

The use of Most or Moderately effective contraceptive (M/M) or Long-Acting Reversible Contraceptive (LARC) types by program, contraceptive type, age group, and year of interest, for 2014-2016. This data was compiled for the Measure CCW: Contraceptive Care – All Women Ages 15-44, as part of the Maternal and Infant Health Initiative, Contraceptive Care Quality grant.

The use of Most or Moderately effective contraceptive (M/M) or Long-Acting Reversible Contraceptive (LARC) types by age group and year of interest, for 2014-2016. This data was compiled for the Measure CCW: Contraceptive Care – All Women Ages 15-44, as part of the Maternal and Infant Health Initiative, Contraceptive Care Quality grant.

This dataset tracks the updates made on the dataset "Contraceptive Care Use for Women by Race/Ethnicity, Contraceptive Type, and Age Group" as a repository for previous versions of the data and metadata.

This dataset tracks the updates made on the dataset "Statewide Contraceptive Care Use for Women by Contraceptive Type and Age Group" as a repository for previous versions of the data and metadata.

The National Survey of Family Growth (NSFG) gathers information on family life, marriage and divorce, pregnancy, infertility, use of contraception, and men's and women's health. The survey results are used by the U.S. Department of Health and Human Services and others to plan health services and health education programs, and to do statistical studies of families, fertility, and health. Years included: 1973, 1976, 1982, 1988, 1995, 2002, 2006-2010; Data use agreement at time of file download:

The use of Most or Moderately effective contraceptive (M/M) or Long-Acting Reversible Contraceptive (LARC) types by primary language used, contraceptive type, age group, and year of interest, for 2014-2016. This data was compiled for the Measure CCW: Contraceptive Care – All Women Ages 15-44, as part of the Maternal and Infant Health Initiative, Contraceptive Care Quality grant.

A computerized data set of demographic, economic and social data for 227 countries of the world. Information presented includes population, health, nutrition, mortality, fertility, family planning and contraceptive use, literacy, housing, and economic activity data. Tabular data are broken down by such variables as age, sex, and urban/rural residence. Data are organized as a series of statistical tables identified by country and table number. Each record consists of the data values associated with a single row of a given table. There are 105 tables with data for 208 countries. The second file is a note file, containing text of notes associated with various tables. These notes provide information such as definitions of categories (i.e. urban/rural) and how various values were calculated. The IDB was created in the U.S. Census Bureau''s International Programs Center (IPC) to help IPC staff meet the needs of organizations that sponsor IPC research. The IDB provides quick access to specialized information, with emphasis on demographic measures, for individual countries or groups of countries. The IDB combines data from country sources (typically censuses and surveys) with IPC estimates and projections to provide information dating back as far as 1950 and as far ahead as 2050. Because the IDB is maintained as a research tool for IPC sponsor requirements, the amount of information available may vary by country. As funding and research activity permit, the IPC updates and expands the data base content. Types of data include: * Population by age and sex * Vital rates, infant mortality, and life tables * Fertility and child survivorship * Migration * Marital status * Family planning Data characteristics: * Temporal: Selected years, 1950present, projected demographic data to 2050. * Spatial: 227 countries and areas. * Resolution: National population, selected data by urban/rural * residence, selected data by age and sex. Sources of data include: * U.S. Census Bureau * International projects (e.g., the Demographic and Health Survey) * United Nations agencies Links: * ICPSR: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/08490

This dataset presents the percentage of women aged 15–49 who are using modern methods of contraception, based on the 2024 revision of the UN Population Division’s World Population Prospects. Modern methods include hormonal contraception, intrauterine devices, implants, condoms, and sterilisation. This indicator reflects access to and use of voluntary, rights-based family planning services and is critical for monitoring progress toward universal access to reproductive healthcare.Data Dictionary: The data is collated with the following columns:Column headingContent of this columnPossible valuesRefNumerical counter for each row of data, for ease of identification1+CountryShort name for the country195 countries in total – all 194 WHO member states plus PalestineISO3Three-digit alphabetical codes International Standard ISO 3166-1 assigned by the International Organization for Standardization (ISO). e.g. AFG (Afghanistan)ISO22 letter identifier code for the countrye.g. AF (Afghanistan)ICM_regionICM Region for countryAFR (Africa), AMR (Americas), EMR (Eastern Mediterranean), EUR (Europe), SEAR (South east Asia) or WPR (Western Pacific)CodeUnique project code for each indicator:GGTXXnnnGG=data group e.g. OU for outcomeT = N for novice or E for ExpertXX = identifier number 00 to 30nnn = identifier name eg mmre.g. OUN01sbafor Outcome Novice Indicator 01 skilled birth attendance Short_nameIndicator namee.g. maternal mortality ratioDescriptionText description of the indicator to be used on websitee.g. Maternal mortality ratio (maternal deaths per 100,000 live births)Value_typeDescribes the indicator typeNumeric: decimal numberPercentage: value between 0 & 100Text: value from list of text optionsY/N: yes or noValue_categoryExpect this to be ‘total’ for all indicators for Phase 1, but this could allow future disaggregation, e.g. male/female; urban/ruraltotalYearThe year that the indicator value was reported. For most indicators, we will only report if 2014 or more recente.g. 2020Latest_Value‘LATEST’ if this is the most recent reported value for the indicator since 2014, otherwise ‘No’. Useful for indicators with time trend data.LATEST or NOValueIndicator valuee.g. 99.8. NB Some indicators are calculated to several decimal places. We present the value to the number of decimal places that should be displayed on the Hub.SourceFor Caesarean birth rate [OUN13cbr] ONLY, this column indicates the source of the data, either OECD when reported, or UNICEF otherwise.OECD or UNICEFTargetHow does the latest value compare with Global guidelines / targets?meets targetdoes not meet targetmeets global standarddoes not meet global standardRankGlobal rank for indicator, i.e. the country with the best global score for this indicator will have rank = 1, next = 2, etc. This ranking is only appropriate for a few indicators, others will show ‘na’1-195Rank out ofThe total number of countries who have reported a value for this indicator. Ranking scores will only go as high as this number.Up to 195TrendIf historic data is available, an indication of the change over time. If there is a global target, then the trend is either getting better, static or getting worse. For mmr [OUN04mmr] and nmr [OUN05nmr] the average annual rate of reduction (arr) between 2016 and latest value is used to determine the trend:arr <-1.0 = getting worsearr >=-1.0 AND <=1.0 = staticarr >1.0 = getting betterFor other indicators, the trend is estimated by comparing the average of the last three years with the average ten years ago:decreasing if now < 95% 10 yrs agoincreasing if now > 105% 10 yrs agostatic otherwiseincreasingdecreasing Or, if there is a global target: getting better,static,getting worseNotesClarification comments, when necessary LongitudeFor use with mapping LatitudeFor use with mapping DateDate data uploaded to the Hubthe following codes are also possible values:not reported does not apply don’t knowThis is one of many datasets featured on the Midwives’ Data Hub, a digital platform designed to strengthen midwifery and advocate for better maternal and newborn health services.

Comprehensive dataset of 785 Birth control centers in United States as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

We include a description of the data sets in the meta-data as well as sample code and results from a simulated data set. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: The R code is available on line here: https://github.com/warrenjl/SpGPCW. Format: Abstract The data used in the application section of the manuscript consist of geocoded birth records from the North Carolina State Center for Health Statistics, 2005-2008. In the simulation study section of the manuscript, we simulate synthetic data that closely match some of the key features of the birth certificate data while maintaining confidentiality of any actual pregnant women. Availability Due to the highly sensitive and identifying information contained in the birth certificate data (including latitude/longitude and address of residence at delivery), we are unable to make the data from the application section publicly available. However, we will make one of the simulated datasets available for any reader interested in applying the method to realistic simulated birth records data. This will also allow the user to become familiar with the required inputs of the model, how the data should be structured, and what type of output is obtained. While we cannot provide the application data here, access to the North Carolina birth records can be requested through the North Carolina State Center for Health Statistics and requires an appropriate data use agreement. Description Permissions: These are simulated data without any identifying information or informative birth-level covariates. We also standardize the pollution exposures on each week by subtracting off the median exposure amount on a given week and dividing by the interquartile range (IQR) (as in the actual application to the true NC birth records data). The dataset that we provide includes weekly average pregnancy exposures that have already been standardized in this way while the medians and IQRs are not given. This further protects identifiability of the spatial locations used in the analysis. File format: R workspace file. Metadata (including data dictionary) • y: Vector of binary responses (1: preterm birth, 0: control) • x: Matrix of covariates; one row for each simulated individual • z: Matrix of standardized pollution exposures • n: Number of simulated individuals • m: Number of exposure time periods (e.g., weeks of pregnancy) • p: Number of columns in the covariate design matrix • alpha_true: Vector of “true” critical window locations/magnitudes (i.e., the ground truth that we want to estimate). This dataset is associated with the following publication: Warren, J., W. Kong, T. Luben, and H. Chang. Critical Window Variable Selection: Estimating the Impact of Air Pollution on Very Preterm Birth. Biostatistics. Oxford University Press, OXFORD, UK, 1-30, (2019).