Dataset: CDC Maternal Health Survey

About this Dataset

CDC Maternal Health Survey

Attitudes and Experiences Before, During, and After Pregnancy

About this dataset

The Centers for Disease Control and Prevention (CDC) is proud to present PRAMS, the Pregnancy Risk Assessment Monitoring System. This survey provides valuable insights and analysis on maternal health, mindset, and experiences pre-pregnancy through postpartum phase. Statistically representative data is gathered from mothers all over the United States concerning issues such as abuse, alcohol use, contraception, breastfeeding, mental health, obesity and many more.

This survey provides an invaluable source of information which is key in targeting areas that need improvement when it comes to maternal wellbeing. Armed with PRAMS data state health officials are able to work towards promoting a healthy environment for mothers and their babies during this important period of life. Rich in data points ranging from smoking exposure to infant sleep behavior trends can be identified across states as well as nationally with this unique system supported by CDC's partnership with state health departments.

Here you will find a-mazing datasets containing columns such like Year or LocationAbbr or Response allowing you analyze some really meaningful stuff like: Are women in certain parts of the US more likely compared to others to breastfeed? What about rates at which pregnant mothers take prenatal care? Dive into the 2019 CDC PRAMStat dataset today!

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How to use the dataset

In order to make full use of this dataset it’s important that you understand what each column contains so that you can extract the most relevant data for your purposes. Here are some tips for understanding how to maximize this dataset:

Look through each column carefully – take note of which columns contain numerical information (Data_Value_Unit), categorical responses (Response) or location descriptions (Location Desc).

Make sure that you are aware of any standard errors that may be associated with data values (Data_Value_Std_Err).

It’s useful to know the source(DataSource)of your data so if possible check out who has collected it.

Check what classifications have been used in BreakOut columns – this can give additional insight into how subjects were divided up within datasets.

Understand how pregnancies were grouped together geographically by taking a look at LocationAbbr and Geolocation columns - understanding where surveys have been done can help break down regional differences in responses.
With these steps will help you navigate through your dataset so that you can accurately interpret questions posed by pregnant women from different locations across the U.S.

Research Ideas

Using this dataset, public health officials could analyze maternal attitudes and experiences over a period of time to develop targeted strategies to improve maternal health.

This dataset can be used to create predictive models of maternal behavior based on the amount of prenatal care received and other factors such as alcohol use, sleep behavior and tobacco use.

Analyzing this dataset would also allow researchers to identify trends in infant wellbeing outcomes across various states/municipalities with different policies/interventions in place which can then be replicated in other areas with similar characteristics

Acknowledgements

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

License

License: Open Database License (ODbL) v1.0

You are free to:

Share - copy and redistribute the material in any medium or format.

Adapt - remix, transform, and build upon the material for any purpose, even commercially.

You must:

Give appropriate credit - Provide a link to the license, and indicate if changes were made.

ShareAlike - You must distribute your contributions under the same license as the original.

Keep intact - all notices that refer to this license, including copyright notices.

No Derivatives - If you remix, transform, or build upon the material, you may not distribute the modified material.

No additional restrictions - You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.

Columns

File: rows.csv

Column name	Description
Year	Year of the survey. (Integer)
LocationAbbr	Abbreviation of the state or location surveyed. (String)
LocationDesc	Description of the state or location surveyed. (String)
Class	Type of survey question. (String)
Topic	Topic of the survey question. (String)
Question	The survey question. (String)
DataSource	Source of the survey data. (String)
Response	Response to the survey question. (String)
Data_Value_Unit	Unit of the data value. (String)
Data_Value_Type	Type of data value. (String)
Data_Value_Footnote_Symbol	Symbol for the footnote of the data value. (String)
Data_Value_Std_Err	Standard error of the data value. (Float)
Sample_Size	Sample size of the survey. (Integer)
Break_Out	Break out of the survey. (String)
Break_Out_Category	Category of the break out. (String)
Geolocation	Geolocation of the survey. (String)

Acknowledgements

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

Tables

Rows

@kaggle.thedevastator_cdc_maternal_health_survey.rows

6.69 MB
520381 rows
28 columns


CREATE TABLE rows (
  "index" BIGINT,
  "year" BIGINT,
  "locationabbr" VARCHAR,
  "locationdesc" VARCHAR,
  "class" VARCHAR,
  "topic" VARCHAR,
  "question" VARCHAR,
  "datasource" VARCHAR,
  "response" VARCHAR,
  "data_value_unit" VARCHAR,
  "data_value_type" VARCHAR,
  "data_value" DOUBLE,
  "data_value_footnote_symbol" VARCHAR,
  "data_value_footnote" VARCHAR,
  "data_value_std_err" VARCHAR,
  "low_confidence_limit" DOUBLE,
  "high_confidence_limit" DOUBLE,
  "sample_size" DOUBLE,
  "break_out" VARCHAR,
  "break_out_category" VARCHAR,
  "geolocation" VARCHAR,
  "classid" VARCHAR,
  "topicid" VARCHAR,
  "questionid" VARCHAR,
  "locationid" DOUBLE,
  "breakoutid" VARCHAR,
  "breakoutcategoryid" VARCHAR,
  "responseid" VARCHAR
);