Linking Time-Use Data to Explore Health Outcomes: Choosing to Vaccinate Against Influenza


To inform public health and medical decision makers concerning vaccination interventions, a methodology for merging and analyzing detailed activity data and health outcomes is presented. The objective is to investigate relationships between individual’s activity choices and their decision to receive an influenza vaccination. Data from the Behavioral Risk Factor Surveillance System (BRFSS) are used to predict vaccination rates in the American Time Use Survey (ATUS) data between 2003 and 2013 by using combined socioeconomic and demographic characteristics. The correlations between the extensive (do or not do) and intensive (how much) decisions to perform activities and influenza vaccination are further explored. Significant positive and negative correlations were found between several activities and vaccination. For some activities, the sign of the correlation flips when considering either the intensive or the extensive decision. This flip occurs with highly studied activities, like smoking. Correlations between activities and vaccination can provide an additional metric for targeting those least likely to vaccinate. The methodology outlined in this paper can be replicated to explore correlation among actions and other health outcomes.

This is a preview of subscription content, log in to check access.

Figure 1
Figure 2


  1. 1.

    Charts and tables of time spent on various activities by demographic characteristics are available online at the ATUS website, The website also includes coding lexicons, data dictionaries, and examples of the questionnaires used. Average time spent on various activities is available on the ATUS Tables subpage. A full categorized activity codebook is available in Table S2 in the online appendix.

  2. 2.

    BRFSS data are available at This site includes survey data, documentation, and examples of the questionnaires used.

  3. 3.

    Omitting variables from the regression may bias specific parameter estimates. However, our goal is to predict the likelihood of vaccination and not study the determinants of vaccination. Vaccination determinants such as health care/insurance are correlated with income and employment status, which bias the specific coefficient estimates on income and employment status when omitted but should not affect the out-of-sample prediction for the ATUS respondents of interest.

  4. 4.

    This is analogous to the problem of underestimating the standard error of an imputed variable.

  5. 5.

    We chose 100 estimates due to computing time constraints, currently the intensive decision model takes 4.37 h to estimate, and the extensive decision takes 22.75 h to estimate.


  1. Arcavi, L., & Benowitz, N. L. (2004). Cigarette smoking and infection. Archives of Internal Medicine, 164(20), 2206–2216.

    Article  PubMed  Google Scholar 

  2. Avery, E. J., & Lariscy, R. W. (2014). Preventable disease practices among a lower SES, multicultural, nonurban, US Community: the roles of vaccination efficacy and personal constraints. Health Communication, 29(8), 826–836.

    Article  PubMed  Google Scholar 

  3. Bayham, J., Kuminoff, N. V, Gunn, Q., & Fenichel, E. P. (2015). Measured voluntary avoidance behaviour during the 2009 A/H1N1 epidemic. In Proc. R. Soc. B (Vol. 282, p. 20150814). The Royal Society.

  4. Bearden, D. T., & Holt, T. (2005). Statewide impact of pharmacist-delivered adult influenza vaccinations. American Journal of Preventive Medicine, 29(5), 450–452.

    Article  PubMed  Google Scholar 

  5. Berry, K., Bayham, J., Meyer, S. R., & Fenichel, E. P. (2017). The Allocation of Time and Risk of Lyme: A Case of Ecosystem Service Income and Substitution Effects. Environmental and Resource Economics, 1–20.

  6. Bish, A., Yardley, L., Nicoll, A., & Michie, S. (2011). Factors associated with uptake of vaccination against pandemic influenza: a systematic review. Vaccine, 29(38), 6472–6484.

    Article  PubMed  Google Scholar 

  7. BLS. (2012). American Time Use Survey. Washington, DC.

  8. Burns, V. E., Ring, C., & Carroll, D. (2005). Factors influencing influenza vaccination uptake in an elderly, community-based sample. Vaccine, 23(27), 3604–3608.

    Article  PubMed  Google Scholar 

  9. CDC. (2013). Behavioral Risk Factor Surveillance System Survey Data. Atlanta, Georgia. Retrieved from

  10. CDC. (2016a). ACIP Recommendations. Retrieved from

  11. CDC. (2016b). Flu Vaccination Coverage, United States, 2014-15 Influenza Season. Retrieved from

  12. Culotta, A., Kumar, N. R., & Cutler, J. (2015). Predicting the Demographics of Twitter Users from Website Traffic Data. In Proceedings of the 29th AAAI Conference on Artificial Intelligence (pp. 72–78).

  13. DHHS. (2012). Healthy People 2020. Retrieved from

  14. Dredze, M., Broniatowski, D. A., Smith, M. C., & Hilyard, K. M. (2016). Understanding Vaccine Refusal: Why We Need Social Media Now. American Journal of Preventive Medicine (Vol. 50).

  15. Frew, P. M., Saint-Victor, D. S., Owens, L. E., & Omer, S. B. (2014). Socioecological and message framing factors influencing maternal influenza immunization among minority women. Vaccine, 32(15), 1736–1744.

    Article  PubMed  Google Scholar 

  16. Jacob, V., Chattopadhyay, S. K., Hopkins, D. P., Morgan, J. M., Pitan, A. A., Clymer, J. M., & Force, C. P. S. T. (2016). Increasing Coverage of Appropriate Vaccinations: A Community Guide Systematic Economic Review. American Journal of Preventive Medicine, 50(6), 797–808.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Kosinski, M., Stillwell, D., & Graepel, T. (2013). Private traits and attributes are predictable from digital records of human behavior. Proceedings of the National Academy of Sciences, 110(15), 5802–5805.

    Article  CAS  Google Scholar 

  18. Lee, B. Y., Mehrotra, A., Burns, R. M., & Harris, K. M. (2009). Alternative vaccination locations: who uses them and can they increase flu vaccination rates? Vaccine, 27(32), 4252–4256.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Merrill, R. M., & Beard, J. D. (2009). Influenza vaccination in the United States, 2005–2007. Medical Science Monitor Basic Research, 15(7), PH92-PH100.

  20. Mostow, S. R. (2001). Use of alternative sites to administer influenza vaccine improves acceptance by both physicians and patients. In International Congress Series (Vol. 1219, pp. 703–706). Elsevier.

  21. Mullahy, J. (1998). It’ll only hurt a second? Microeconomic determinants of who gets flu shots. National Bureau of Economic Research.

    Google Scholar 

  22. Nichol, K. L., Mac Donald, R., & Hauge, M. (1996). Factors associated with influenza and pneumococcal vaccination behavior among high-risk adults. Journal of General Internal Medicine, 11(11), 673–677.

    Article  PubMed  CAS  Google Scholar 

  23. Plans-Rubió, P., & Plans-Rubio, P. (2012). The vaccination coverage required to establish herd immunity against influenza viruses. Preventive Medicine, 55(1), 72–77.

    Article  PubMed  Google Scholar 

  24. Postema, A. S., & Breiman, R. F. (2000). Adult Immunization Programs in Nontraditional Settings: Quality Standards and Guidance for Program Evaluation: A Report of the National Vaccine Advisory Committee. Morbidity and Mortality Weekly Report: Recommendations and Reports, vii-13.

  25. R Core Team. (2015). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. Retrieved from

  26. Reed, C., Kim, I. K., Singleton, J. A., Chaves, S. S., Flannery, B., Finelli, L., … Jernigan, D. (2014). Estimated influenza illnesses and hospitalizations averted by vaccination–United States, 2013-14 influenza season. MMWR. Morbidity and Mortality Weekly Report, 63(49), 1151–1154.

    PubMed  PubMed Central  Google Scholar 

  27. Scheminske, M., Henninger, M., Irving, S. A., Thompson, M., Williams, J., Shifflett, P., … Naleway, A. L. (2015). The Association Between Influenza Vaccination and Other Preventative Health Behaviors in a Cohort of Pregnant Women. Health Education & Behavior, 42(3), 402–408.

    Article  Google Scholar 

  28. Singleton, J. A., Poel, A. J., Lu, P.-J., Nichol, K. L., & Iwane, M. K. (2005). Where adults reported receiving influenza vaccination in the United States. American Journal of Infection Control, 33(10), 563–570.

    Article  PubMed  Google Scholar 

  29. Stehr-Green, P. A., Sprauer, M. A., Williams, W. W., & Sullivan, K. M. (1990). Predictors of vaccination behavior among persons ages 65 years and older. American Journal of Public Health, 80(9), 1127–1129.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  30. Stockwell, M. S., Kharbanda, E. O., Martinez, R. A., Vargas, C. Y., Vawdrey, D. K., & Camargo, S. (2012). Effect of a text messaging intervention on influenza vaccination in an urban, low-income pediatric and adolescent population: a randomized controlled trial. JAMA, 307(16), 1702–1708.

    Article  PubMed  CAS  Google Scholar 

  31. Uscher-Pines, L., Harris, K. M., Burns, R. M., & Mehrotra, A. (2012). The growth of retail clinics in vaccination delivery in the US. American Journal of Preventive Medicine, 43(1), 63–66.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Ward, C. J. (2014). Influenza vaccination campaigns: is an ounce of prevention worth a pound of cure? American Economic Journal: Applied Economics, 6(1), 38–72.

    Google Scholar 

  33. Weitzel, K. W., & Goode, J. V. (1999). Implementation of a pharmacy-based immunization program in a supermarket chain. Journal of the American Pharmaceutical Association (1996), 40(2), 252–256.

  34. Zagheni, E., Billari, F. C., Manfredi, P., Melegaro, A., Mossong, J., & Edmunds, W. J. (2008). Using time-use data to parameterize models for the spread of close-contact infectious diseases. American Journal of Epidemiology, 168(9), 1082–1090.

    Article  PubMed  Google Scholar 

Download references


This publication was made possible by Grant Number 1R01GM100471-01 from the National Institute of General Medical Sciences (NIGMS) at the National Institutes of Health and NSF. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of NIGMS. This work was also funded by NSF Grant No. 1414374 as part of the joint NSF-NIH-USDA Ecology and Evolution of Infectious Diseases program.

Author information



Corresponding author

Correspondence to Kevin Berry.

Electronic Supplementary Material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOCX 102 kb)



Vaccination and Activity Choice

Two regression models are used to examine the relationship between vaccination and activities at the extensive (logistic regression) and intensive (Poisson regression) margins.

Estimating the Do–Don’t Do Decision (Extensive Choices)

The logistic regression of whether or not individuals perform an activity consists of a binary indicator equal to one if respondent \( i = 1, \ldots ,n \) does activity \( j = 1, \ldots ,J \) as a function of the probability of vaccination, \( \hat{v}_{i} \), and the vector of controls, \( \varvec{x} \).

$$\Pr \left( {Bin\_a_{{ij}} = 1} \right) = \frac{1}{{1 + e^{{ - \left( {\beta _{0} + \beta _{1} \hat{v}_{i} + \varvec{\beta ^{\prime}x}_{i} } \right)}} }} $$

When \( \Pr \left( {Bin\_a_{ij} = 1} \right) \), the individual chooses to participate in an activity, otherwise it equals zero. The estimated vaccination probabilities, \( \hat{v}_{i} \), have associated prediction error. Therefore, Monte Carlo simulation is used to estimate the vaccination coefficient and associated standard errors. The Monte Carlo procedure is as follows:

  1. 1.

    Draw a vaccination probability from a normal distribution with mean equal to the coefficient estimated from the preliminary logistic regression in Eq. (1), \( \eta_{v} \), and its standard error.

  2. 2.

    Estimate the logistic activity regression in Eq. (2) and store coefficient \( \beta_{1} \).

  3. 3.

    Repeat steps 1 and 2 100 times.

  4. 4.

    Calculate the mean and standard error based on the 100Footnote 5 different coefficient estimates from each run.

Odds ratios are calculated for the impact of a 1% increase in vaccination probability on the likelihood of performing an activity. All odds ratios and risk ratios are calculated for a 1% increase in relative risk of vaccination. Odds ratios from Eq. (2) are interpreted as the odds that an individual participates in activity \( j \), conditional on \( \hat{v}_{i} \) being 1% higher, relative to the odds that an individual participates in \( j \) conditional on their vaccination probability being unchanged.

Estimating Time Spent (Duration Decision)

The intensive activity decision, conditional on doing the activity, is modeled as a Poisson regression. The minutes spent doing an activity, \( a_{ij} \), is regressed on the continuous probability of being vaccinated \( \hat{v}_{i} \) and a vector of controls \( \varvec{x}_{i} \),

$$ \Pr \left( {a = a_{ij} |x_{i} } \right) = \frac{{e^{{ - \mu_{ij} }} \mu_{ij}^{{\hat{a}_{ij} }} }}{{\hat{a}_{ij} !}}, \quad a_{ij} = 0,1,2 \ldots $$
$${\text{ln}}\left( {\mu _{{{\text{ij}}}} } \right) = \delta _{0} + \delta _{1} \hat{v}_{i} + \varvec{\delta }^{\prime} \varvec{x}_{i} + \epsilon _{i} $$

which is estimated with quasi-Poisson errors, which assume that variance is a linear function of the mean to control for possible over dispersion. The coefficient, \( \delta_{1} \), and its standard error are estimated by the Monte Carlo simulation methods described in the previous section.

Risk ratios are calculated for the second equation in (3) and are interpreted as the ratio of the probability an individual spends another minute participating in an activity conditional on their vaccination probability being 1% higher, relative to the probability of spending another moment participating in that activity conditional on their vaccination probability being unchanged.

Grocery Shopping

We also estimate a model of activity choice and grocery store attendance. Because it is not necessary to statistically match the datasets, we use the full ATUS dataset and do not perform Monte Carlo simulations. We observe the duration of each activity done by respondents on their interview day as well as time spent at the grocery store location.

Estimating time spent (duration decision) consists of a logit model of the decision of how much time to spend at the grocery store on the extensive decision to participate in an activity. Let \( g_{i} \) be the total minutes respondent \( i \) spends at a grocery store and the binary variable, \( d_{i}^{g} \), be equal to one if respondent \( i \) spends at least 1 min at a grocery store.

$$ Pr\left( {{\text{Bin}}_{{a_{{ij}} }} = 1} \right) = \frac{1}{{1 + e^{{ - \left( {\gamma _{0} + \gamma _{1} d_{i}^{g} + \gamma _{2} g_{i} *d_{i}^{g} + \varvec{\alpha ^{\prime}x}_{i} } \right)}} }}\quad \forall ~i,j $$

Variable \( a_{ij} \) remains the time spent on activity \( j \) by person \( i \) and \( d_{i}^{g} \) is time spent at a grocery store. We include the vector \( x_{i} \) of controls that consist of age, sex, income, education, the day of the week, month and year as well as the state. We again estimate this equation for each activity \( j \) in the ATUS. The model contains both the binary grocery store variable and the interaction between the binary and continuous variables. Including both variables provides flexibility to the model to accommodate the difference between the effect of zero and 1 min versus 10 and 11 min on activity.

We calculate odds ratios for all activities in Eq. (3) for both the first minute spend in a grocery store, and every additional minute. The interpretation of the odds ratio for \( \gamma_{1} \) is the ratio of the odds that an individual participates in activity \( j \) subject to spending any time at all in a grocery store (binary variable, \( d_{i}^{g} \)) relative to the odds that an individual participates in activity \( j \) subject to not spending time in a grocery store. The odds ratio for \( \gamma_{2} \) is the ratio of the odds an individual participates in an activity conditional on spending an additional minute at the grocery store, versus not having spent more time at the grocery store (but still having gone to a grocery store for at least 1 min).

We use a count model to study the association between intensive activity decisions and the extensive decision to spend time in grocery store, \( d_{i}^{g} , \) and the amount of time in grocery store, \( g_{i} \),

$$ \Pr \left( {a = a_{ij} |x_{i} } \right) = \frac{{e^{{ - \lambda_{ij} }} \lambda_{ij}^{{\hat{a}_{ij} }} }}{{\hat{a}_{ij} !}} $$
$$\ln \left( {\lambda _{{ij}} } \right) = \gamma _{0} + \gamma _{1} d_{i}^{g} + \gamma _{2} g_{i} *d_{i}^{g} + \varvec{\alpha }^{\prime} \varvec{x}_{i} + \epsilon _{i} $$

The model uses quasi-Poisson methods as an ad hoc control for over dispersion. The risk ratio of \( \gamma_{1} \) and \( \gamma_{2} \) is analogous to the odds ratios for \( \gamma_{1} \) and \( \gamma_{2} \) in the first stage, except they are not ratios of probabilities instead of odds.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Berry, K., Anderson, J.E., Bayham, J. et al. Linking Time-Use Data to Explore Health Outcomes: Choosing to Vaccinate Against Influenza. EcoHealth 15, 290–301 (2018).

Download citation


  • Nontraditional vaccination campaigns
  • American Time Use Survey
  • Influenza
  • Behavioral Risk Factor Surveillance System Survey
  • Public health
  • Vaccination