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

ABSTRACT: 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.

SUBMITTER: Berry K

PROVIDER: S-EPMC5976524 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

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

Berry Kevin K Anderson Julia E JE Bayham Jude J Fenichel Eli P EP

EcoHealth 20171130 2

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 u ...[more]

PMID: 29192341

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Project description:BackgroundBy mid 2023, European countries reached 75% of vaccine coverage for COVID-19 and although vaccination rates are quite high, many people are still hesitant. A plethora of studies have investigated factors associated with COVID-19 vaccine hesitancy, however, insufficient attention has been paid to the reasons why people get vaccinated against COVID-19. Our work aims to investigate the role of reasons in the decision to get vaccinated against COVID-19 in a representative sample of 1,689 adult Italians (March-April 2021) balanced in terms of age, gender, educational level and area of residence.MethodsThrough an online questionnaire, we asked participants to freely report up to three reasons for and against COVID-19 vaccination, and the weight each had in the decision to get vaccinated. We first investigated the role of emotional competence and COVID-19 risk perception in the generation of both reasons using regression models. Next, we studied the role that the different reasons had in the vaccination decision, considering both the intention to vaccinate (using a beta regression model) and the decision made by the participants who already had the opportunity to get vaccinated (using a logistic regression model). Finally, two different classification tree analyses were carried out to characterize profiles with a low or high willingness to get vaccinated or with a low or high probability to accept/book the vaccine.ResultsHigh emotional competence positively influences the generation of both reasons (ORs > 1.5), whereas high risk perception increases the generation of positive reasons (ORs > 1.4) while decreasing reasons against vaccination (OR = 0.64). As pro-reasons increase, vaccination acceptance increases, while the opposite happens as against-reasons increase (all p < 0.001). One strong reason in favor of vaccines is enough to unbalance the decision toward acceptance of vaccination, even when reasons against it are also present (p < 0.001). Protection and absence of distrust are the reasons that mostly drive willingness to be vaccinated and acceptance of an offered vaccine.ConclusionsKnowing the reasons that drive people's decision about such an important choice can suggest new communication insights to reduce possible negative reactions toward vaccination and people's hesitancy. Results are discussed considering results of other national and international studies.

Project description:BackgroundThe timing of the biannual WHO influenza vaccine composition selection and production cycle has been historically directed to the influenza seasonality patterns in the temperate regions of the northern and southern hemispheres. Influenza activity, however, is poorly understood in the tropics with multiple peaks and identifiable year-round activity. The evidence-base needed to take informed decisions on vaccination timing and vaccine formulation is often lacking for the tropics and subtropics. This paper aims to assess influenza seasonality in the tropics and subtropics. It explores geographical grouping of countries into vaccination zones based on optimal timing of influenza vaccination.MethodsInfluenza seasonality was assessed by different analytic approaches (weekly proportion of positive cases, time series analysis, etc.) using FluNet and national surveillance data. In case of discordance in the seasonality assessment, consensus was built through discussions with in-country experts. Countries with similar onset periods of their primary influenza season were grouped into geographical zones.ResultsThe number and period of peak activity was ascertained for 70 of the 138 countries in the tropics and subtropics. Thirty-seven countries had one and seventeen countries had two distinct peaks. Countries near the equator had secondary peaks or even identifiable year-round activity. The main influenza season in most of South America and Asia started between April and June. The start of the main season varied widely in Africa (October and December in northern Africa, April and June in Southern Africa and a mixed pattern in tropical Africa). Eight "influenza vaccination zones" (two each in America and Asia, and four in Africa and Middle East) were defined with recommendations for vaccination timing and vaccine formulation. The main limitation of our study is that FluNet and national surveillance data may lack the granularity to detect sub-national variability in seasonality patterns.ConclusionDistinct influenza seasonality patterns, though complex, could be ascertained for most countries in the tropics and subtropics using national surveillance data. It may be possible to group countries into zones based on similar recommendations for vaccine timing and formulation.

Dataset Information

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

Publications

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

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