Loading…

Protocol - Current Age

Add to My Toolkit
Description

The interviewer asks the respondent for his or her date of birth in MM/DD/YYYY format. If the respondent does not know his or her date of birth, the interviewer asks an alternative question to determine his or her age. NOTE: The current age of the respondent can be calculated, given the date of the interview.

Specific Instructions

The question may be asked of a proxy. Proxies are others in the household who can answer on behalf of the index person (e.g., parent, spouse) if the index person is too young, incapacitated, deceased, or cannot be located.

Protocol

What is your birthdate? MM/DD/YYYY;

9[ ]Don’t Know [ask follow-up question];

7[ ]Refused

[Follow-up question if "don’t know":] About how old are you? AGE _______;

999[ ]Don’t Know

7[ ]Refused

Availability

Available

Personnel and Training Required

The interviewer must be trained to conduct personal interviews with individuals from the general population. The interviewer must be trained and found to be competent (i.e., tested by an expert) at the completion of personal interviews.* The interviewer should be trained to prompt respondents further if a "don’t know" response is provided.

* There are multiple modes to administer this question (e.g., paper-and-pencil and computer-assisted interviews).

Equipment Needs

While the source instrument was developed to be administered by computer, the PhenX Working Group acknowledges these questions can be administered in a noncomputerized format (i.e., paper-and-pencil instrument). Computer software is necessary to develop computer-assisted instruments. The interviewer will require a laptop computer or handheld computer to administer a computer-assisted questionnaire.

Requirements
Requirement CategoryRequired
Major equipment No
Specialized training No
Specialized requirements for biospecimen collection No
Average time of greater than 15 minutes in an unaffected individual No
Mode of Administration

Interviewer-administered questionnaire

Lifestage

Infant, Toddler, Child, Adolescent, Adult, Senior, Pregnancy

Participants

Any age.

Selection Rationale

Age is a primary predictor of disease and the most important potential confounder in epidemiologic studies.

Age at date of interview or exam can be compared to age in future follow-up interviews or exams for longitudinal research purposes.

Vetted against several other current age questions, the National Health and Nutrition Examination Survey (NHANES) question was selected because it included the date of birth (DOB) and a follow-up question to collect current age if the respondent did not know his or her DOB.

Language

Chinese, English, Spanish

Standards
StandardNameIDSource
Logical Observation Identifiers Names and Codes (LOINC) Current age proto 62293-6 LOINC
caDSR Form PhenX PX010101 - Current Age 5791032 caDSR Form
Derived Variables

Age at onset, present age

Process and Review

The Expert Review Panel #2 (ERP 2) reviewed the measures in the Demographics, Environmental Exposures, and Social Environments domains.

Guidance from ERP 2 includes:

• No significant changes to measure

Back-compatible: no changes to Data Dictionary

Previous version in Toolkit archive (link)

Protocol Name from Source

National Health and Nutrition Examination Survey (NHANES), 2005-2006

Source

National Center for Health Statistics. National Health and Nutrition Examination Survey (NHANES), Screener Module 1, 2005–2006. Question numbers: SCQ.290 and SCQ.292.

General References

Public Population Project in Genomics (P3G) Data Shaper.

Protocol ID

10101

Variables
Export Variables
Variable Name Variable IDVariable DescriptiondbGaP Mapping
PX010101_Age
PX010101020000 About how old are you? Variable Mapping
PX010101_Age_Coded
PX010101020100 About how old are you? N/A
PX010101_Birthdate_Coded
PX010101010400 What is your birthdate? N/A
PX010101_Birthdate_Day
PX010101010200 What is your birthdate? Day Variable Mapping
PX010101_Birthdate_Month
PX010101010100 What is your birthdate? Month Variable Mapping
PX010101_Birthdate_Year
PX010101010300 What is your birthdate? Year Variable Mapping
Demographics
Measure Name

Current Age

Release Date

February 6, 2009

Definition

Question to determine the respondent’s current age using his or her date of birth.

Purpose

Current age is a critical component of a respondent’s demographic background. Date of birth (DOB)/current age is essential to medical research because it captures the age at the time of the clinical visit or diagnosis. Age is related to nearly all diseases and conditions and is the strongest potential confounder of other effects on these processes. Current age is often used to stratify respondents for more valid comparison (e.g., obesity of males aged 40-45 in Mississippi).

Keywords

current age, Demographics, National Center for Health Statistics, NCHS, Centers for Disease Control and Prevention, CDC, age, date of birth, DOB, National Health and Nutrition Examination Survey, NHANES, Demographics-Populations with HD

Measure Protocols
Protocol ID Protocol Name
10101 Current Age
Publications

Crusan, A., et al. (2023) Using Community-Based Participatory Research Methods to Inform the Development of Medically Tailored Food Kits for Hispanic/Latine Adults with Hypertension: A Qualitative Study. Nutrients. 2023 August; 15(16): 3600. doi: https://doi.org/10.3390/nu15163600

Olfson, M., et al. (2023) Prevalence and Correlates of Mental Disorders in Children Aged 9 and 10 Years: Results From the ABCD Study. Journal of the American Academy of Child & Adolescent Psychiatry. 2023 August; 62(8): 908-919. doi: 10.1016/j.jaac.2023.04.005

Lee, R. E., et al. (2023) Acceptability and Feasibility of Saliva-delivered PCR Coronavirus 2019 Tests for Young Children. Pediatrics. 2023 July; 152(1). doi: 10.1542/peds.2022-060352D

Olfson, M., et al. (2023) Treatment of US Children With Attention-Deficit/Hyperactivity Disorder in the Adolescent Brain Cognitive Development Study. JAMA Network Open. 2023 April; 6(4). doi: 10.1001/jamanetworkopen.2023.10999

Reed, D. M., et al. (2023) Eye Dynamics and Engineering Network Consortium: Baseline Characteristics of a Randomized Trial in Healthy Adults. Ophthalmol Glaucoma. 2023 March; 6(2): 215-223. doi: 10.1016/j.ogla.2022.09.001

Chan, N. W., et al. (2022) Social determinants of health data in solid organ transplantation: National data sources and future directions. Am J Transplant. 2022 October; 22(10): 2293-2301. doi: 10.1111/ajt.17096

Charron, E., et al. (2022) Pain Severity and Interference and Substance Use Among Community Pharmacy Patients Prescribed Opioids: A Secondary Analysis of the PHARMSCREEN Study. Journal of Pain. 2022 August; 23(8): 1448-1459. doi: 10.1016/j.jpain.2022.03.238

Aguinaldo, L. D., et al. (2022) Application of the RDoC Framework to Predict Alcohol Use and Suicidal Thoughts and Behaviors among Early Adolescents in the Adolescent Brain and Cognitive Development (ABCD) Study. Brain Sciences. 2022 July; 12(7): 15.

Brown, J. L., et al. (2022) Associations between elevated depressive symptoms and substance use, prescription opioid misuse, overdose history, pain, and general health among community pharmacy patients prescribed opioids. Substance Abuse. 2022 May; 43(1): 1110-1115. doi: 10.1080/08897077.2022.2060450

Pomeroy, A., et al. (2022) Protocol for a Longitudinal Study of the Determinants of Metabolic Syndrome Risk in Young Adults. Translational Journal of the American College of Sports Medicine. 2022 April; 7(2): 8. doi: 10.1249/tjx.0000000000000197

Brown, L. D., et al. (2022) Addressing Hispanic Obesity Disparities Using a Community Health Worker Model Grounded in Motivational Interviewing. American Journal of Health Promotion. 2022 February; 36(2): 259-268. doi: 10.1177/08901171211049679

Loring, D. W., et al. (2022) Rationale and Design of the National Neuropsychology Network. Journal of the International Neuropsychological Society. 2022 January; 28(1): 11-Jan. doi: 10.1017/S1355617721000199

Young Hye, K., et al. (2021) Predicting multilingual effects on executive function and individual connectomes in children: An ABCD study. Proceedings of the National Academy of Sciences of the United States of America. 2021 December; 118(49): 1-11. doi: 10.1073/pnas.2110811118

Schettini, E., et al. (2021) Internalizing-externalizing comorbidity and regional brain volumes in the ABCD study. Development and Psychopathology. 2021 December; 33(5): 1620-1633.

Barch, D. M., et al. (2021) Demographic and mental health assessments in the adolescent brain and cognitive development study: Updates and age-related trajectories. Developmental Cognitive Neuroscience. 2021 December; 52: 101031. doi: 10.1016/j.dcn.2021.101031

Purvis, R. S., et al. (2021) Trusted Sources of COVID-19 Vaccine Information among Hesitant Adopters in the United States. Vaccines. 2021 December; 9(12): 1418. doi: 10.3390/vaccines9121418

Braddock, A. S., et al. (2021) Assessing Racial and Ethnic Discrimination in Children: A Scoping Review of Available Measures for Child Health Disparities Research. Health Equity. 2021 October; 5(1): 727-737. doi: 10.1089/heq.2021.0008

Dawes, K., et al. (2021) Epigenetic Analyses of Alcohol Consumption in Combustible and Non-Combustible Nicotine Product Users. Epigenomes. 2021 September; 5(3): 18. doi: 10.3390/epigenomes5030018

Krebs, N. M., et al. (2021) Switching to Progressively Reduced Nicotine Content Cigarettes in Smokers With Low Socioeconomic Status: A Double-Blind Randomized Clinical Trial. Nicotine & Tobacco Research. 2021 May; 23(6): 992-1001. doi: 10.1093/ntr/ntaa247

Roth, A. R., et al. (2021) Network recall among older adults with cognitive impairments. Social Networks. 2021 January; 64: 99-108. doi: 10.1016/j.socnet.2020.08.005

Omodior, O. and W. D. Ramos (2020) Social Determinants of Health-Related Quality of Life: A Recreation Setting Analysis. Health Promotion Practice. 2020 November; 21(6): 952-961. doi: 10.1177/1524839919827572

Barbirou, M., et al. (2020) Western influenced lifestyle and Kv2.1 association as predicted biomarkers for Tunisian colorectal cancer. BMC Cancer. 2020 November; 20: Article Number: 1086. doi: 10.1186/s12885-020-07605-7

Wu, Y., et al. (2020) Short-term exposure to air pollution and its interaction effects with two ABO SNPs on blood lipid levels in northern China: A family-based study. Chemosphere. 2020 June; 249: 8. doi: 10.1016/j.chemosphere.2020.126120

Chia, A. R., et al. (2020) Maternal plasma metabolic markers of neonatal adiposity and associated maternal characteristics: The GUSTO study. Scientific Reports. 2020 June; 10(1). doi: 10.1038/s41598-020-66026-5

Kanter, J., et al. (2020) Perceptions of US Adolescents and Adults With Sickle Cell Disease on Their Quality of Care. Jama Network Open. 2020 May; 3(5). doi: 10.1001/jamanetworkopen.2020.6016

LeLaurin, J. H., et al. (2020) An Implementation Trial to Improve Tobacco Treatment for Cancer Patients: Patient Preferences, Treatment Acceptability and Effectiveness. International Journal of Environmental Research and Public Health. 2020 April; 17(7): 12. doi: 10.3390/ijerph17072280

Walsh J.J., et al. (2020) Associations between duration and type of electronic screen use and cognition in US children. Computers in Human Behavior. 2020 February; 108: 9. doi: 10.1016/j.chb.2020.106312

Harker, J. L. and J. A. Jensen. (2020) Adding insult to rivalry: Exploring the discord communicated between rivals. International Journal of Sports Marketing & Sponsorship. 2020 January; 21(4): 633-649. doi: 10.1108/IJSMS-12-2019-0141

Goodman, M., et al. (2020) Group (?Project Life Force?) versus individual suicide safety planning: A randomized clinical trial. Contemporary Clinical Trials Communications. 2020 January; 17: 100520. doi: 10.1016/j.conctc.2020.100520

Hankins, J. S., et al. (2018) Sickle Cell Clinical Research and Intervention Program (SCCRIP): A lifespan cohort study for sickle cell disease progression from the pediatric stage into adulthood. Pediatr Blood Cancer. 2018 September; 65(9): 27228. doi: 10.1002/pbc.27228

Barch, D. M., et al. (2018) Demographic, physical and mental health assessments in the adolescent brain and cognitive development study: Rationale and description. Dev Cogn Neurosci. 2018 August; 32: 55-66. doi: 10.1016/j.dcn.2017.10.010

Unger, J. B. (2018) Perceived Discrimination as a Risk Factor for Use of Emerging Tobacco Products: More Similarities Than Differences Across Demographic Groups and Attributions for Discrimination. Subst Use Misuse. 2018 August; 53(10): 1638-1644. doi: 10.1080/10826084.2017.1421226

Juan, J., et al. (2017) Joint Effects of PON1 Polymorphisms and Vegetable Intake on Ischemic Stroke: A Family-Based Case Control Study. Int J Mol Sci. 2017 December; 18(12): E2652. doi: 10.3390/ijms18122652

Kwok, R. K., et al. (2017) The GuLF STUDY: A Prospective Study of Persons Involved in the Deepwater Horizon Oil Spill Response and Clean-Up. Environ Health Perspect. 2017 April; 125(4): 570-578. doi: 10.1289/EHP715

Sanderson, S. C., et al. (2017) Public Attitudes toward Consent and Data Sharing in Biobank Research: A Large Multi-site Experimental Survey in the US. Am J Hum Genet. 2017 March; 100(3): 414-427. doi: 10.1016/j.ajhg.2017.01.021

Modibbo, F., et al. (2017) Randomized trial evaluating self-sampling for HPV DNA based tests for cervical cancer screening in Nigeria. Infect Agent Cancer. 2017 February; 12: 11. doi: 10.1186/s13027-017-0123-z

Krebs, N. M., et al. (2016) Comparison of Puff Volume With Cigarettes per Day in Predicting Nicotine Uptake Among Daily Smokers. Am J Epidemiol. 2016 July; 184(1): 48-57. doi: 10.1093/aje/kwv341

Hitz, M.M., Conway, P.G, Palcher, J.A., McCarty, C.A. (2014) Using PhenX toolkit measures and other tools to assess urban/rural differences in health behaviors: recruitment methods and outcomes. BMC Research Notes. 2014 November; 7(847). doi: 10.1186/1756-0500-7-847

McCarty, C.A., Berg, R., Rottscheit, C.M., Waudby, C.J., Kitchner, T., Brilliant, M., Ritchie, M.D. (2014) Validation of PhenX measures in the personalized medicine research project for use in gene/environment studies. BMC Med Genomics. 2014 January; 7: 3. doi: 10.1186/1755-8794-7-3