Co-Morbidity Chronic Condition

**Overview**
This data package shows the information on chronic condition dyads and triads for the year 2013 to 2015 by the Centers for Medicare and Medicaid Services (CMS).

**Description**
This data package contains information regarding chronic conditions. Dyads represent the combinations of two chronic conditions among Medicare beneficiaries with at least two of the conditions. There are 171 dyads. Chronic condition triads represent the combinations of three chronic conditions among Medicare beneficiaries who have at least three of the conditions. The data used in the chronic condition reports are based upon CMS administrative enrollment and claims data for Medicare beneficiaries enrolled in the fee-for-service program.

**Benefits**
- Useful data package for the health care provider to serve as guidance about the different chronic conditions in patients.

**License Information**
The use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes.

**Included Datasets**
- [Chronic Condition Dyads 2013](https://www.johnsnowlabs.com/marketplace/chronic-condition-dyads-2013)
- Chronic conditions are an increasing concern in the United States, where they affect nearly half of the adult population and their prevalence has increased in recent years. These conditions result in numerous adverse health outcomes, increased health care needs, and subsequently higher medical costs. This dataset provides co-morbidity dyads illustrated by the combinations of the 19 chronic conditions for the year 2013.
- [Chronic Condition Dyads 2014](https://www.johnsnowlabs.com/marketplace/chronic-condition-dyads-2014)
- Chronic conditions are an increasing concern in the United States, where they affect nearly half of the adult population and their prevalence has increased in recent years. These conditions result in numerous adverse health outcomes, increased health care needs, and subsequently higher medical costs. This dataset provides co-morbidity dyads illustrated by the combinations of the 19 chronic conditions for the year 2014.
- [Chronic Condition Dyads 2015](https://www.johnsnowlabs.com/marketplace/chronic-condition-dyads-2015)
- Chronic conditions are an increasing concern in the United States, where they affect nearly half of the adult population and their prevalence has increased in recent years. These conditions result in numerous adverse health outcomes, increased health care needs, and subsequently higher medical costs. This dataset provides co-morbidity dyads illustrated by the combinations of the 19 chronic conditions for the year 2015.
- [Chronic Condition Dyads 2016](https://www.johnsnowlabs.com/marketplace/chronic-condition-dyads-2016)
- Chronic conditions are an increasing concern in the United States, where they affect nearly half of the adult population and their prevalence has increased in recent years. These conditions result in numerous adverse health outcomes, increased healthcare needs and subsequently higher medical costs. This dataset provides co-morbidity dyads illustrated by the combinations of the 19 chronic conditions for the year 2016.
- [Chronic Condition Dyads 2017](https://www.johnsnowlabs.com/marketplace/chronic-condition-dyads-2017)
- Chronic conditions are an increasing concern in the United States, where they affect nearly half of the adult population and their prevalence has increased in recent years. These conditions result in numerous adverse health outcomes, increased healthcare needs and subsequently higher medical costs. This dataset provides co-morbidity dyads illustrated by the combinations of the 19 chronic conditions for the year 2017.
- [Chronic Condition Triads 2013](https://www.johnsnowlabs.com/marketplace/chronic-condition-triads-2013)
- This dataset shows the chronic condition reports are based upon CMS administrative enrollment and claims data for Medicare beneficiaries enrolled in the fee-for-service program. Exclude Medicare beneficiaries with any Medicare Advantage enrollment during the year since claims data are not available for these beneficiaries.
- [Chronic Condition Triads 2014](https://www.johnsnowlabs.com/marketplace/chronic-condition-triads-2014)
- This dataset shows the chronic condition reports are based upon CMS administrative enrollment and claims data for Medicare beneficiaries enrolled in the fee-for-service program. Exclude Medicare beneficiaries with any Medicare Advantage enrollment during the year since claims data are not available for these beneficiaries.
- [Chronic Condition Triads 2015](https://www.johnsnowlabs.com/marketplace/chronic-condition-triads-2015)
- This dataset shows the chronic condition reports are based upon Centers for Medicare and Medicaid Services (CMS) administrative enrollment and claims data for Medicare beneficiaries enrolled in the fee-for-service program. Exclude Medicare beneficiaries with any Medicare Advantage enrollment during the year since claims data are not available for these beneficiaries.
- [Chronic Condition Triads 2016](https://www.johnsnowlabs.com/marketplace/chronic-condition-triads-2016)
- This dataset shows the chronic condition reports that are based upon Centers for Medicare and Medicaid Services (CMS) administrative enrollment and claims data for Medicare beneficiaries enrolled in the fee-for-service program. Exclude Medicare beneficiaries with any Medicare Advantage enrollment during the year since claims data are not available for these beneficiaries.
- [Chronic Condition Triads 2017](https://www.johnsnowlabs.com/marketplace/chronic-condition-triads-2017)
- This dataset shows the chronic condition reports are based upon CMS administrative enrollment and claims data for Medicare beneficiaries enrolled in the fee-for-service program. Exclude Medicare beneficiaries with any Medicare Advantage enrollment during the year since claims data are not available for these beneficiaries.

**Data Engineering Overview**
**We deliver high-quality data**
- Each dataset goes through 3 levels of quality review
- 2 Manual reviews are done by domain experts
- Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints
- Data is normalized into one unified type system
- All dates, unites, codes, currencies look the same
- All null values are normalized to the same value
- All dataset and field names are SQL and Hive compliant
- Data and Metadata
- Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters
- Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated
- Data Updates
- Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted

**Our data is curated and enriched by domain experts**

Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:
- Field names, descriptions, and normalized values are chosen by people who actually understand their meaning
- Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset
- Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations
- The data is always kept up to date – even when the source requires manual effort to get updates
- Support for data subscribers is provided directly by the domain experts who curated the data sets
- Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.

**Need Help?**
If you have questions about our products, contact us at [[e-mail]](mailto:[e-mail]).

搜集汇总

背景与挑战

背景概述

该数据集包含美国医疗保险和医疗补助服务中心(CMS)2013-2015年间的慢性病共病数据，涵盖171种二元组合及多种三元组合情况，适用于医疗保健提供者研究患者慢性病模式。数据经过专业团队严格质量控制，包含多个年份的独立子数据集。