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Filter Cart

The Filter Cart provides a powerful way to query and access data for which you may be interested.  

A few points related to the filter cart are important to understand with the NDA Query/Filter implementation: 

First, the filter cart is populated asyncronously.  So, when you run a query, it may take a moment to populate but this will happen in the background so you can define other queries during this time.  

When you are adding your first filter, all data associated with your query will be added to the filter cart (whether it be a collection, a concept, a study, a data structure/elment or subjects). Not all data structures or collections will necessarily be displayed.  For example, if you select the NDA imaging structure image03, and further restrict that query to scan_type fMRI, only fMRI images will appear and only the image03 structure will be shown.  To see other data structures, select "Find All Subject Data" which will query all data for those subjects. When a secord or third filter is applied, an AND condition is used.  A subject must exist in all filters.  If the subject does not appear in any one filter, that subjects data will not be included in your filter cart. If that happens, clear your filter cart, and start over.  

It is best to package more data than you need and access those data using other tools, independent of the NDA (e.g. miNDAR snapshot), to limit the data selected.  If you have any questions on data access, are interested in using avaialble web services, or need help accessing data, please contact us for assistance.  

Frequently Asked Questions



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Health Productivity Questionnaire



World Health Organization - Health and Work Performance Questionnaire

Download Definition as
Download Submission Template as
Element NameData TypeSizeRequiredDescriptionValue RangeNotesAliases
subjectkeyGUIDRequiredThe NDAR Global Unique Identifier (GUID) for research subjectNDAR*pseduo_guids, pseudo_guids
src_subject_idString20RequiredSubject ID how it's defined in lab/projectid
interview_dateDateRequiredDate on which the interview/genetic test/sampling/imaging/biospecimen was completed. MM/DD/YYYYRequired fieldinterview_dt_t1, interview_dt_t2, interview_dt_t3, interview_dt_t4
interview_ageIntegerRequiredAge in months at the time of the interview/test/sampling/imaging.0 :: 1260Age is rounded to chronological month. If the research participant is 15-days-old at time of interview, the appropriate value would be 0 months. If the participant is 16-days-old, the value would be 1 month.age_t1_mos, age_t2_mos, age_t3_mos, age_t4_mos
sexString20RequiredSex of the subject
M = Male; F = Femalegender, gender_t1, gender_t2, gender_t3, gender_t4
hpq_1FloatRecommendedAbout how many hours altogether did you work in the past 7 days?If more than 97, enter 97hpq1_t2, hpq1_t3, hpq1_t4
hpq_2FloatRecommendedHow many hours does your employer expect you to work in a typical 7-day week?If it varies, estimate the average. If more than 97, enter 97hpq2_t2, hpq2_t3, hpq2_t4
hpq_3FloatRecommendedHow many days did you miss an entire work day because of problems with your physical or mental health? (Please include only days missed for your own health, not someone else's health.)hpq3a_t1, hpq3a_t2, hpq3a_t3, hpq3a_t4
hpq_4FloatRecommendedHow many days did you miss an entire work day for any other reason (including vacation)?hpq3b_t1, hpq3b_t2, hpq3b_t3, hpq3b_t4
hpq_5FloatRecommendedHow many days did you miss part of a work day because of problems with your physical or mental health? (Please include only days missed for your own health, not someone else's health.)hpq3c_t2, hpq3c_t3, hpq3c_t4
hpq_6FloatRecommendedHow many days did you miss part of a work day for any other reason (including vacation)?hpq3d_t1, hpq3d_t2, hpq3d_t3, hpq3d_t4
hpq_7FloatRecommendedHow many days did you come in early, go home late, or work on your day off?hpq3e_t1, hpq3e_t2, hpq3e_t3, hpq3e_t4
hpq_8FloatRecommendedAbout how many hours altogether did you work in the past 4 weeks (28 days)?hpq4_t2, hpq4_t3, hpq4_t4
hpq_9FloatRecommendedHow would you rate the usual performance of most workers in a job similar to yours?0::100=Worst Performance; 10=Top Performancehpq5_t2, hpq5_t3, hpq5_t4
hpq_10FloatRecommendedHow would you rate your usual job performance over the past year or two?0::100=Worst Performance; 10=Top Performancehpq6_t2, hpq6_t3, hpq6_t4
hpq_11FloatRecommendedHow would you rate your overall job performance on the days you worked during the past 4 weeks (28 days)?0::100=Worst Performance; 10=Top Performancehpq7_t2, hpq7_t3, hpq7_t4
hpq_aaFloatRecommendedAbsolute absenteeism- scoreAbsenteeism: the practice of regularly staying away from work or school without good reason.hpqtot_1a_t2, hpqtot_1a_t3, hpqtot_1a_t4
hpq_raFloatRecommendedRelative absenteeism- scoreAbsenteeism: the practice of regularly staying away from work or school without good reason.hpqtot_1b_t2, hpqtot_1b_t3, hpqtot_1b_t4
hpq_rhrsFloatRecommendedRelative hours of work- scorehpqtot_1c_t2, hpqtot_1c_t3, hpqtot_1c_t4
commentsString4,000RecommendedComments about assessment
version_formString100RecommendedForm used/assessment name
hpqtot_2a_t2FloatRecommendedAbsolute 7-day absenteeism, how many hours someone is expected to work in a 7-day period minus number of hours workedhpqtot_2a_t3, hpqtot_2a_t4
hpqtot_2b_t2FloatRecommendedRelative 7-day absenteeism, percentage of expected hours worked, ranges from negative # (worked more than expected) to 1 (always absent)hpqtot_2b_t3, hpqtot_2b_t4
hpqtot_2c_t2FloatRecommendedRelative hours of work in past 7 days, ratio of number of hours worked : hours expectedhpqtot_2c_t3, hpqtot_2c_t4
hpqtot_3a_t2FloatRecommendedAbsolute presenteeism, ranges from complete lack of performance (0) to no lack of performance (100)hpqtot_3a_t3, hpqtot_3a_t4
hpqtot_3b_t2FloatRecommendedRelative presenteeism, ratio of perfomance : performance of most workershpqtot_3b_t3, hpqtot_3b_t4
Data Structure

This page displays the data structure defined for the measure identified in the title and structure short name. The table below displays a list of data elements in this structure (also called variables) and the following information:

  • Element Name: This is the standard element name
  • Data Type: Which type of data this element is, e.g. String, Float, File location.
  • Size: If applicable, the character limit of this element
  • Required: This column displays whether the element is Required for valid submissions, Recommended for valid submissions, Conditional on other elements, or Optional
  • Description: A basic description
  • Value Range: Which values can appear validly in this element (case sensitive for strings)
  • Notes: Expanded description or notes on coding of values
  • Aliases: A list of currently supported Aliases (alternate element names)
  • For valid elements with shared data, on the far left is a Filter button you can use to view a summary of shared data for that element and apply a query filter to your Cart based on selected value ranges

At the top of this page you can also:

  • Use the search bar to filter the elements displayed. This will not filter on the Size of Required columns
  • Download a copy of this definition in CSV format
  • Download a blank CSV submission template prepopulated with the correct structure header rows ready to fill with subject records and upload

Please email the The NDA Help Desk with any questions.

Distribution for DataStructure: hpq01 and Element:
Chart Help

Filters enable researchers to view the data shared in NDA before applying for access or for selecting specific data for download or NDA Study assignment. For those with access to NDA shared data, you may select specific values to be included by selecting an individual bar chart item or by selecting a range of values (e.g. interview_age) using the "Add Range" button. Note that not all elements have appropriately distinct values like comments and subjectkey and are not available for filtering. Additionally, item level detail is not always provided by the research community as indicated by the number of null values given.

Filters for multiple data elements within a structure are supported. Selections across multiple data structures will be supported in a future version of NDA.