Step 1: Check analytic data
Is the data you need already on FASSE? Check out the catalog here: https://nsaph.info/analytic.html#analytic-data
If it is not, see step 2.
Step 2: Fill in the form below and add it in the comments here.
The format of the form goes like this:
* - key_name
- value
Below is the form for analytic data documentation with key_names. Fill in the value fields or choose between the options.
One dataset should correspond to one form. If your dataset is spit into multiple files of a same format (ie, admissions_2011.fst, admissions_2012.fst etc), it is fine to complete one form.
* - dataset_name
- a meaningful name (not filename)
* - dataset_author
- Name Surname
* - date_created
- Jun 15 2022
* - data_source
- MedPar (admissions), MBSF (denominator), Medicaid MAX, other (specify)
* - spatial_coverage
- US
* - spatial_resolution
- zipcode, city, county, state
* - temporal_coverage
- 1999-2016
* - temporal_resolution
- daily, monthly, annually
* - description
- Write in free text what (if any) processing was done to the data sources. Were there any selections (cuts), data quality checks and aggregations?
* - rce_location
- `~/shared_space/TEXT`
* - fasse_location
- `/n/dominici_nsaph_l3/projects/analytic/TEXT`
Optional fileds (choose as applicable):
* - publication (if this data was used in publication)
- URL
* - GitHub repository/directory on how the data was processed
- URL
* - exposures
- What were the air pollution/exposure data sources used to create this data file?
* - confounders
- What were the confounder data sources used to create this dataset?
* - meterological
- What were the meterological data sources used to create this data file?
* - other
- What other data sources were used to create this data?
* - size
- 1.2 GB
* - files
```
├── dataset_2011.fst
├── ...
└── dataset_2016.fst
```
* - header (see in R with str(dat))
```
QID : Factor
ADATE: Date
year : num
```
Embed the form here to get the JupyterBook (NSAPH handbook) entry for nsaph.info/analytic.html:
`````{dropdown} 1. Meaningful dataset name
```{list-table}
:header-rows: 0
COPY AND PASTE THE FORM HERE
````
`````
case based on the information in the patient summary file for that
individual in the year of their admission. The denominator for each
observation is calculated monthly and contains all individuals who are
eligbile for Fee for Service (FFS) hospitalization coverage and have not
died prior to that month. The CCS codes included were 2, 50, 55, 114, 157, 159, and 244.
ICD processing done using the ICD package(Wasey 2018). The author of
this package asks that it be cited in papers using data that was created
using the package.
~/shared_space/ci3_health_data/medicare/heat_related/n/dominici_nsaph_l3/projects/analytic/heat_related