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Methodology

• Population vs. Sample | Definitions, Differences & Examples

Population vs. Sample | Definitions, Differences & Examples

Published on May 14, 2020 by Pritha Bhandari . Revised on June 21, 2023.

A population is the entire group that you want to draw conclusions about.

A sample is the specific group that you will collect data from. The size of the sample is always less than the total size of the population.

In research, a population doesn’t always refer to people. It can mean a group containing elements of anything you want to study, such as objects, events, organizations, countries, species, organisms, etc.

Table of contents

Collecting data from a population, collecting data from a sample, population parameter vs. sample statistic, practice questions : populations vs. samples, other interesting articles, frequently asked questions about samples and populations.

Populations are used when your research question requires, or when you have access to, data from every member of the population.

Usually, it is only straightforward to collect data from a whole population when it is small, accessible and cooperative.

For larger and more dispersed populations, it is often difficult or impossible to collect data from every individual. For example, every 10 years, the federal US government aims to count every person living in the country using the US Census. This data is used to distribute funding across the nation.

However, historically, marginalized and low-income groups have been difficult to contact, locate and encourage participation from. Because of non-responses, the population count is incomplete and biased towards some groups, which results in disproportionate funding across the country.

In cases like this, sampling can be used to make more precise inferences about the population.

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When your population is large in size, geographically dispersed, or difficult to contact, it’s necessary to use a sample. With statistical analysis , you can use sample data to make estimates or test hypotheses about population data.

Ideally, a sample should be randomly selected and representative of the population. Using probability sampling methods (such as simple random sampling or stratified sampling ) reduces the risk of sampling bias and enhances both internal and external validity .

For practical reasons, researchers often use non-probability sampling methods. Non-probability samples are chosen for specific criteria; they may be more convenient or cheaper to access. Because of non-random selection methods, any statistical inferences about the broader population will be weaker than with a probability sample.

Reasons for sampling

• Necessity : Sometimes it’s simply not possible to study the whole population due to its size or inaccessibility.
• Practicality : It’s easier and more efficient to collect data from a sample.
• Cost-effectiveness : There are fewer participant, laboratory, equipment, and researcher costs involved.
• Manageability : Storing and running statistical analyses on smaller datasets is easier and reliable.

When you collect data from a population or a sample, there are various measurements and numbers you can calculate from the data. A parameter is a measure that describes the whole population. A statistic is a measure that describes the sample.

You can use estimation or hypothesis testing to estimate how likely it is that a sample statistic differs from the population parameter.

Sampling error

A sampling error is the difference between a population parameter and a sample statistic. In your study, the sampling error is the difference between the mean political attitude rating of your sample and the true mean political attitude rating of all undergraduate students in the Netherlands.

Sampling errors happen even when you use a randomly selected sample. This is because random samples are not identical to the population in terms of numerical measures like means and standard deviations .

Because the aim of scientific research is to generalize findings from the sample to the population, you want the sampling error to be low. You can reduce sampling error by increasing the sample size.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Student’s  t -distribution
• Normal distribution
• Null and Alternative Hypotheses
• Chi square tests
• Confidence interval
• Cluster sampling
• Stratified sampling
• Data cleansing
• Reproducibility vs Replicability
• Peer review
• Likert scale

Research bias

• Implicit bias
• Framing effect
• Cognitive bias
• Placebo effect
• Hawthorne effect
• Hindsight bias
• Affect heuristic

Samples are used to make inferences about populations . Samples are easier to collect data from because they are practical, cost-effective, convenient, and manageable.

Populations are used when a research question requires data from every member of the population. This is usually only feasible when the population is small and easily accessible.

A statistic refers to measures about the sample , while a parameter refers to measures about the population .

A sampling error is the difference between a population parameter and a sample statistic .

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Module 1 - Population Health

Part 1 - asking questions and generating evidence.

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Defining Populations

Categories of eligibility criteria for study populations, dynamic and stationary (fixed) populations, sampling from a population.

We can begin by defining a population as a collection of individuals who share at least one common or organizing characteristi c. While this definition is broad, it retains the flexibility to define populations in several ways depending upon the public health question of interest. How we define populations for study affects analysis , interpretation , and generalizability of results.

When studying population health, it is useful to define study populations based on eligibility criteria, i.e., the characteristics of individuals that make them appropriate for an epidemiologic study. 

There are three main categories that are useful for defining eligibility for a study population:

Public health questions often focus on specific geographic areas of varying size (village, city, county, state, country) over a specific period of time. People living in a specific location may have many common characteristics that might influence health, including climate, environmental exposures, culture, socioeconomic factors, nutrition, etc. Individuals born during the same period of time (birth cohorts) are often found to have a similar course with respect to health outcomes, and different birth cohorts may have dissimilar health outcomes. Since people frequently move from one place to another, geographically defined cohorts can be dynamic, with people moving in or moving out. Obviously, living within a given geographic area is the primary criterion for membership in the population. Given the dynamic nature of these studies, it is sometimes useful to think of the population as being comprised not of people, but as individual lengths of "person-time" during which each individual met the eligibility criteria. For example, consider a study population focusing on health issues in Woburn, MA from 1970-1980. An individual who moved from Los Angeles to Woburn in 1975 and then moved back to LA two years later would only have contributed 2 person-years of information to the overall study.

If one were interested in studying the health outcomes of newborn infants based on their birth weight, the study population would logically be comprised of neonates and would not necessarily focus narrowly on geography or year of birth. Similarly, the study population might be defined by an event such as the attacks on the World Trade Center and the health consequences among responders to that event. These two examples illustrate relatively stationary populations, but populations defined in this way can be dynamic, such as a study of 70-80 year-olds. During a longitudinal study, new subjects would continually become eligible, will others would become ineligible by virtue of exceeding the age limit or by dying.

The study population might also be defined based on the likelihood of achieving a successful study. For example, in 1981 the Physicians' Health Study invited all 261,248 male physicians between 40 and 84 years of age who lived in the United States and who were registered with the American Medical Association to participate in a randomized clinical trial to test the efficacy of low-dose aspirin and beta carotene in the primary prevention of cardiovascular disease and cancer. Almost half responded to the invitation, but there were also a number of other eligibility criteria and 26,062 were told they could not participate because of a prior history of myocardial infarction, stroke, cancer, or other excluding criteria.

The 33,223 who were eligible and willing were enrolled in a "run-in" phase during which all received active aspirin and placebo beta-carotene. After 18 weeks, participants were asked about their health status, side effects, compliance, and willingness to continue in the trial, and over 11,000 decided not to participate.

The remaining 22,071 physicians were then randomized to one of the four treatment arms of the study. Physicians were chosen because they could provide reliable information on questionnaires, and they would be easier to follow, particularly since they were all registered physicians. Restriction to those between 40-84 years old ensured a population at higher risk of having one of the outcomes of interest, and women were excluded because there were so few female physicians in that age group in 1981.

Finally, the run-in phase narrowed the population even further to the subset of physicians who were most likely to be able and willing to comply with the regimen over time. So, there were multiple eligibility criteria that enhanced the likelihood of a study that would successfully answer the questions being addressed.

An individual may meet the eligibility criteria to be included in a population at one point in time, but not at another. Populations with individuals moving in and out of eligibility are termed dynamic in contrast to stationary or fixed populations.

 A population of homeless people would be considered very dynamic, and it would be difficult to conduct a longitudinal follow up study in them. In contrast, workers who dealt with the aftermath of the attacks on the World Trade Center (a population defined by event) would be considered a stationary or fixed population, because they had experienced the defining event and would be considered members of that cohort until they died, even if they moved elsewhere. The distinction between dynamic and stationary populations is not strict, but it is something that should be considered when designing a study. When studying relatively dynamic populations, consideration should be given to considering data collection based on the "person-time" contributed by individuals when they were eligible. This will be discussed in greater detail in the module on measuring the frequency of health events.

When studying a population, it would be ideal to have all of the information we wanted from all members of the population. However, this is rarely possible because of the time and resources that would be required to collect the information needed. Because of this we commonly take samples that are representative of the population of interest and study them in a way that enables us to make valid inferences about the population from which they were drawn. In order to obtain accurate answers to the questions being addressed and achieve the research goals it is essential to:

• Define the population of interest and
• Define the question to be answered

These requirements go hand-in-hand, because selection of an appropriate study population is dependent upon the question being addressed. Sometimes the study population seems obvious given the research question, but the study populations may be broader than that which at first seems obvious. For example, we saw previously that a study of the causes of hypertension could be conducted among male civil servants in London by comparing the characteristics of people with hypertension to those without it. However, a more complete understanding might be achieved by broadening the study population to include additional populations. When suspicions of an unusually high frequency of leukemia and other diseases arose in Woburn, MA in the late 1970s, one avenue of study would be to designate Woburn as the population of interest and compare the characteristics of diseased residents to those of non-diseased residents. However, this by itself would omit other important comparisons. For example, how did the frequency of leukemia and other diseases in Woburn compare to that observed in Massachusetts in general? Or to the frequency observed across the United States? And how did environmental conditions in Woburn differ from those in other locations?

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Content ©2019. All Rights Reserved. Date last modified: May 16, 2019. Wayne W. LaMorte, MD, PhD, MPH

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3 Study Population

• Published: March 2018
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Chapter 3 discusses the decision-making process of choosing the study population. This is critical given that any study’s main goal is to make inferences that go beyond the individuals under study and can be used to explain the phenomenon in the broader population with shared characteristics or conditions. In this chapter, the definition of the target population is discussed—i.e. the portion of the general population from which a researcher wants to draw robust conclusions or inferences. The sampling process according to the study phase is also summarized, focusing on phase II and III clinical trials, as phase I trials are especially designed to assess safety, while phase IV trials are open-label studies, usually assessing post-marketing safety. The internal and external validity of a study is also discussed, as well as sampling methods, both probabilistic (simple random, systematic, stratified, cluster, or multistage sampling) and non-probabilistic (convenience, consecutive, and snowball sampling).

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Study Population

• First Online: 01 January 2010

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• Lawrence M. Friedman 4 ,
• Curt D. Furberg 5 &
• David L. DeMets 6  

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Defining the study population is an integral part of posing the primary question. It is not enough to claim that an intervention is or is not effective without describing the type of participant on which the intervention was tested. The description requires specification of criteria for eligibility. This chapter focuses on how to define the study population. In addition, it considers two questions. First, to what extent will the results of the trial be generalizable to a broader population? Second, what impact does selection of eligibility criteria have on participant recruitment, or, more generally, study feasibility? This issue is also discussed in Chap. 10.

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CONSORT. http://www.consort-statement.org

Van Spall HGC, Toren A, Kiss A, Fowler RA. Eligibility criteria of randomized controlled trials published in high-impact general medical journals: a systematic sampling review. JAMA 2007;297:1233–1240.

Article   Google Scholar  

Diabetic Retinopathy Study Research Group. Preliminary report on effects of photocoagulation therapy. Am J Ophthalmol 1976;81:383–396.

Google Scholar  

Diabetic Retinopathy Study Research Group. Photocoagulation treatment of proliferative diabetic retinopathy: the second report of diabetic retinopathy study findings. Ophthalmology 1978;85:82–106.

Fraser DW, Tsai TR, Orenstein W, et al. Legionnaires’ Disease: description of an epidemic of pneumonia. N Engl J Med 1977;297:1189–1197.

Veterans Administration Cooperative Study Group on Antihypertensive Agents. Effects of treatment on morbidity in hypertension: results in patients with diastolic blood pressures averaging 115 through 129 mm Hg. JAMA 1967;202:1028–1034.

Veterans Administration Cooperative Study Group on Antihypertensive Agents. Effects of treatment on morbidity in hypertension: II. Results in patients with diastolic blood pressure averaging 90 through 114 mm Hg. JAMA 1970;213:1143–1152.

Hypertension Detection and Follow-up Program Cooperative Group. Five-year findings of the hypertension detection and follow-up program. 1. Reduction in mortality of persons with high blood pressure, including mild hypertension. JAMA 1979;242:2562–2571.

Ridker PM, Danielson E, Fonseca FAH, et al. Rosuvastatin to prevent vascular events in men and women with elevated C-reactive protein. N Engl J Med 2008;359:2195–2207.

Sondik EJ, Brown BW, Jr., Silvers A. High risk subjects and the cost of large field trials. J Chronic Dis 1974;27:177–187.

Tunis SR, Stryer DB, Clancy CM. Practical clinical trials: increasing the value of clinical research for decision making in clinical and health policy. JAMA 2003;290:1624–1632.

Douglas PS. Gender, cardiology, and optimal medical care. Circulation 1986;74:917–919.

Bennett JC, for the Board on Health Sciences Policy of the Institute of Medicine. Inclusion of women in clinical trials – policies for population subgroups. N Engl J Med 1993;329:288–292.

Freedman LS, Simon R, Foulkes MA, et al. Inclusion of women and minorities in clinical trials and the NIH Revitalization Act of 1993 – the perspective of NIH clinical trialists. Control Clin Trials 1995;16:277–285.

NIH Policy and Guidelines on the Inclusion of Women and Minorities as Subjects in Clinical Research – Amended, October, 2001. http://grants.nih.gov/grants/funding/women_min/guidelines_amended_10_2001.htm

Horwitz O, Wilbek E. Effect of tuberculosis infection on mortality risk. Am Rev Respir Dis 1971;104:643–655.

Wilhelmsen L, Ljungberg S, Wedel H, Werko L. A comparison between participants and non-participants in a primary preventive trial. J Chronic Dis 1976;29:331–339.

Smith P, Arnesen H. Mortality in non-consenters in a post-myocardial infarction trial. J Intern Med 1990;228:253–256.

Pedersen TR. The Norwegian Multicenter Study of timolol after myocardial infarction. Circulation 1983;67(suppl 1):I-49–I-53.

CASS Principal Investigators and Their Associates. Coronary Artery Surgery Study (CASS): a randomized trial of coronary artery bypass surgery. Comparability of entry characteristics and survival in randomized patients and nonrandomized patients meeting randomization criteria. J Am Coll Cardiol 1984;3:114–128.

Antithrombotic Trialists’ Collaboration. Collaborative meta-analysis of randomised clinical trials of antiplatelet therapy for prevention of death, myocardial infarction, and stroke in high risk patients. BMJ 2002;324:71–86; correction BMJ 2002;324:141.

Steering Committee of the Physicians’ Health Study Research Group. Final report on the aspirin component of the ongoing Physicians’ Health Study. N Engl J Med 1989;321:129–135.

Peto R, Gray R, Collins R, et al. Randomized trial of prophylactic daily aspirin in British male doctors. Br Med J 1988;296:313–316.

Ridker PM, Cook NR, Lee I-M, et al. A randomized trial of low-dose aspirin in the primary prevention of cardiovascular disease in women. N Engl J Med 2005;352:1293–1304.

Berger JS, Roncaglioni MC, Avanzini F, et al. Aspirin for the primary prevention of cardiovascular events in women and men: a sex-specific meta-analysis of randomized controlled trials. JAMA 2006;295:306–313; correction JAMA 2006;295:2002.

Benedict GW. LRC Coronary Prevention Trial: Baltimore. Clin Pharmacol Ther 1979;25:685–687.

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Lawrence M. Friedman

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Friedman, L.M., Furberg, C.D., DeMets, D.L. (2010). Study Population. In: Fundamentals of Clinical Trials. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-1586-3_4

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Home Market Research

Target Population: What It Is + Strategies for Targeting

Understanding your target population is key to any successful campaign, whether it’s marketing, public health, or social initiatives. But what exactly is a target population, and how can you effectively reach the right people? 

In this blog, we will explore the concept of a target population and outline the best strategies for targeting them to ensure your efforts are both effective and impactful.

What is the Target Population?

A target population is a specific group of individuals that a particular study, program, campaign, or intervention is designed to reach, influence, or study. This group is characterized by certain common attributes or criteria that make it the focus of the effort. 

Understanding and defining a target audience is crucial for the effectiveness and relevance of various activities in fields like research, marketing, and public health.

Importance of Identifying a Target Population

Identifying a target population is crucial across various fields— research , marketing, public health, and more. Identifying and understanding your target audience is crucial for several reasons:

Resource Efficiency

Focusing on a specific group can help you allocate your resources more effectively, ensuring that your efforts aren’t wasted on people who are unlikely to engage with your message.

Personalized Messaging: 

Knowing your target population allows you to tailor your messaging to resonate with their specific needs, challenges, and interests, increasing the chances of your message being well-received.

Higher Conversion Rates: 

When your efforts are directed toward those most likely to respond, you’re more likely to see higher conversion rates, whether that means more sales, higher customer engagement , or greater participation in your initiative.

Strategies for Effective Population Targeting

Once you’ve identified your target population, it’s important to employ strategies that effectively reach and engage them. Here’s how to do it:

1. Define Your Objectives

Start by clearly defining what you want to achieve. From boosting sales, raising awareness, to improving customer satisfaction , having clear goals helps you focus your efforts and measure success. Setting specific, measurable objectives of the research and data types ensures that you have a clear direction and criteria for evaluating your strategies.

2. Segment Your Audience

Segmentation involves dividing your target population into smaller, more manageable groups based on shared characteristics. This can be done through:

• Demographic Segmentation : Age, gender, income, education level.
• Geographic Segmentation : City, region, country.
• Psychographic Segmentation : Interests, values, lifestyle.
• Behavioral Segmentation : Buying habits, brand loyalty, usage patterns.

By understanding these segments, you can tailor your messaging to address the unique needs and preferences of each group. For example, within the entire population of “ millennials ,” you might identify sub-groups like “ tech-savvy young professionals ” or “ health-conscious parents .”

3. Utilize Data and Analytics

Leverage data to guide your targeting efforts. Collect data through surveys, social media sentiment analysis , and website analytics to better understand your audience. This data can reveal trends and patterns that help predict future behaviors. The more data-driven your approach, the more accurate your targeting will be.

• Surveys : Collect detailed information about your audience’s preferences and behaviors.
• Social Media Analytics: Track engagement, interests, and demographics.
• Website Analytics: Monitor user behavior and interaction patterns.

4. Create Detailed Audience Personas

Develop personas that represent your ideal customers or target groups. These personas should include demographic information, interests, challenges, and motivations. 

For example, a persona might be “Jessica, a 30-year-old marketing manager who loves fitness and is always on the lookout for new wellness apps.” Creating these profiles helps you understand and empathize with your audience, making it easier to craft messages that resonate with them.

5. Leverage Digital Marketing Tools

Take advantage of digital marketing tools to reach your target population effectively in your market research study. These tools include:

• Social Media Advertising: Target specific groups based on their interests and behaviors.
• Search Engine Optimization (SEO): Optimize your website and content to attract relevant traffic.
• Programmatic Advertising: Use automated systems to buy and place ads that reach your audience in real time.

6. Test and Optimize Your Strategies

Continuously test different strategies to see what works best for your target population. Employ A/B testing to compare different messages, formats, or channels. Gather feedback from your audience and be prepared to adjust your approach based on the results. This iterative process helps you refine your targeting strategies and improve overall effectiveness.

7. Maintain Ethical Standards

Ensure that your targeting practices respect privacy and are free from bias. Follow data protection regulations like GDPR, and avoid stereotypes or unfair assumptions. Ethical targeting fosters trust and helps build long-term relationships with your audience.

8. Collaborate with Influencers and Partners

Partner with influencers or brands that share your target audience. Influencers can amplify your message to their followers, while partnerships can provide access to new segments of your population. For example, a fitness app might collaborate with a well-known fitness influencer to reach a larger, relevant audience.

9. Monitor and Adapt Your Approach

Regularly monitor the performance of your targeting efforts. Use analytics to track key performance indicators (KPIs) such as engagement rates, conversion rates, and return on investment (ROI). Be ready to adapt your strategy based on new data, trends, or feedback. This flexibility ensures you stay relevant and effective in your target market.

10. Employ a Multichannel Strategy

Don’t rely on a single method to reach your audience. Use a mix of online and offline channels, such as social media, email marketing, events, and traditional advertising, to ensure you reach your target population wherever they are. Consistent messaging across all channels reinforces your brand and helps achieve your objectives.

Examples of Target Population

Here are some examples of target audiences across different contexts:

1. Marketing a Product

• Target audience: Young adults aged 18-24 who are tech-savvy and live in urban areas.
• Example: A smartphone company might target this group for a new feature-rich phone by advertising on social media platforms like Instagram and TikTok, where this demographic spends a lot of time.

2. Public Health Campaign

• Target audience: Middle-aged adults (45-65) with a history of smoking.
• Example: A public health organization might target this group for a smoking cessation program, using outreach methods like community health seminars and informational brochures in clinics.

3. Educational Program

• Target audience: High school students in underprivileged areas.
• Example: A non-profit organization might target this group for a scholarship and mentoring program, focusing on schools in low-income neighborhoods and promoting the program through school counselors and community centers.

How QuestionPro Helps in Target Population?

QuestionPro offers a range of tools and features that can significantly help identify, understand, and effectively reach a target audience. Here’s how:

1. Survey Creation and Distribution

QuestionPro allows you to create detailed and customizable surveys that can be tailored to your specific target population. This helps in gathering relevant data directly from the group you want to study. You can distribute surveys through various channels like: 

• Social media

It ensures you reach your target population wherever they are.

2. Advanced Segmentation

With the data collected through surveys, you can segment your audience based on demographics (age, gender, income) or psychographics (lifestyle, values, interests). This helps you understand different sub-groups within your target population. QuestionPro also enables you to gather data on behaviors, such as purchase history or usage patterns, helping you refine your target population further.

3. Audience Targeting

QuestionPro’s audience targeting tools allow you to select specific criteria for your survey respondents. This ensures that you’re gathering insights from the exact population segment you’re interested in.

4. Data Analysis and Reporting

QuestionPro provides real-time data analysis, allowing you to quickly see trends and insights as responses come in. This helps you make timely decisions about your target population.

5. Persona Development

The insights gathered through QuestionPro can be used to create detailed personas that represent your target population. These personas can include demographic details, behavioral patterns, and preferences, making it easier to tailor your strategies.

6. Feedback Loops

By regularly using QuestionPro to collect feedback from your target population, you can continuously refine your understanding of their needs and preferences. This helps you adapt your strategies over time to better meet their expectations.

7. Global Reach

If your target population is spread across different regions or speaks multiple languages, QuestionPro supports multilingual surveys, making it easier to reach a global audience.

Targeting the right population is a crucial step in any successful campaign. By understanding who your target population is and employing these strategies to reach and engage them, you can maximize the effectiveness of your efforts, leading to better results and a higher return on investment. 

Effective population targeting is the key to success, whether you’re marketing a product, promoting a cause, or launching a new initiative.

QuestionPro helps organizations precisely identify and engage with their target population, gather meaningful insights, and make data-driven decisions to achieve their goals. Contact QuestionPro for more information!

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USGS is helping with the design of a population survey and developing mathematical models to assess potential activities to manage the population of invasive blue catfish ( Ictalurus furcatus ) in the Chesapeake Bay. This research will help managers determine the cost and feasibility of approaches to control this invasive species.  

What is the issue?

Following introductions to the eastern U.S. for recreational fishing in the 1970s, populations of blue catfish (Ictalurus furcatus) have grown dramatically in many tidal tributaries in recent years and blue catfish is now considered invasive to the Chesapeake Bay. Uncertainty about blue catfish population size and growth hinder management decisions to address the invasion.

What’s at stake?

The characteristics that have allowed blue catfish to so dramatically expand their range including large size, long lifespan and varied diet make them a threat to important regional fisheries such as striped bass, American shad, and blue crab, among others. However, without a better understanding of current abundance and population growth rate, any steps taken to control blue catfish in Chesapeake Bay may be inefficient or ineffective. Managers also need to determine how to monitor populations before and after control activities to assess the impact of management actions. 

What is our approach?

To better understand and work towards controlling population growth of invasive blue catfish in Chesapeake Bay, USGS is using quantitative methods to model population trends of blue catfish and to evaluate alternative management actions. USGS will also assist Maryland Department of Natural Resources to design a monitoring program for monitoring trends in relative abundance of blue catfish to help evaluate the effectiveness of management actions and further inform the mathematical models.

What are the benefits?

Mathematical modeling can assist in evaluating and quantifying the cost of alternative management actions before committing resources to a specific approach. Results from this study will help Maryland Department of Natural Resources, Atlantic States Marine Fisheries Commission, Chesapeake Bay Program and others determine how changes in relative abundance may affect future population size and age structure of blue catfish in Chesapeake Bay and ultimately inform what actions managers take to address this invasive species.   

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New Research Reveals The Most Entrepreneurial States in America

I’m Ben Aston, a digital project manager and founder of thedpm.com. I've been in the industry for more than 20 years working in the UK at London’s top digital agencies including Dare, Wunderman, Lowe and DDB. I’ve delivered everything from film to CMS', games to advertising and eCRM to eCommerce sites. I’ve been fortunate enough to work across a wide range of great clients; automotive brands including Land Rover, Volkswagen and Honda; Utility brands including BT, British Gas and Exxon, FMCG brands such as Unilever, and consumer electronics brands including Sony. I'm a Certified Scrum Master, PRINCE2 Practitioner and productivity nut!

Florida was found to be the most entrepreneurial state in the U.S., followed by Georgia and Michigan.

Whether you're contemplating a side hustle, bootstrapping the development of the next killer app, or already have a pocketful of funding to launch your dream business, your location matters.

Recent research by The Digital Project Manager analyzed eight indicators for how entrepreneurial a state is, including:

• Percentage of the population that starts a new business
• Percentage of start-ups still active after one year
• Number of small businesses per 100,000 people
• Growth rate of business applications

Here's what the data showed.

Entrepreneurial Index Score: 65.12 

Floridians clearly have an entrepreneurial mindset that not only benefits themselves, but also their communities through the jobs that they create.   Florida has the highest percentage of the population that has started a business (0.61%). Of those, 86% started their venture out of choice rather than necessity, i.e., because they were unemployed or required another stream of income.

These start-ups have the highest number of jobs created in the first year with 6.53 new jobs per 1,000 people. With 13,238 small businesses per 100,000 people, this is the most in any state.

Entrepreneurial Index Score: 59.31 

Georgia is in second place, with 10,871 small businesses per 100,000 people, demonstrating a clear entrepreneurial drive.

This is supported by the second-highest percentage of the population that has started a new business (0.47%). Georgians also have a high search interest on Google for "how to start a business." 

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3. Michigan

Entrepreneurial index score: 58.39 .

Michigan is the third most entrepreneurial state. In 2022, there was 1772% growth in business applications compared to 2019. 78% of start-ups are still active after one year and there are 9,091 small businesses per 100,000 people.

The national average for business failure after five years is 49%, but Michigan is slightly below that at 47%.

4. Oklahoma

Entrepreneurial index score: 57.58 .

In Oklahoma, 0.44% of the population has started a new business. Of those, 82% are still active after one year, with 9,075 small businesses per 100,000 people.

84% of those in Oklahoma who start businesses do so by choice rather than a need to.  

Entrepreneurial Index Score: 57.36 

In Montana there are 11,336 small businesses per 100,000 people. Within the first year, start-ups will create 6.14 new jobs per 1,000 people, and 81% of those start-ups will still be active after the year mark. After five years, only 45% of businesses will fail, which is one of the lowest failure rates across the US. 

Entrepreneurial Index Score: 57.31 

Wyoming has 12,357 small businesses per 100,000 people. From 2019 to 2022 there was a 120% increase in applications for businesses.

This is also reflected in the Google searches in the state. "How to start a business" has one of the highest levels of search interest in Wyoming compared to the rest of America.  

7. Colorado

Entrepreneurial index score: 57.21 .

In Colorado 0.42% of the population have started up a new business. 81% of these start-up businesses are still active after one year, and within that year create 6.09 new jobs per 1,000 people..  

8. California

Entrepreneurial index score: 57.04 .

California is the most populous state in the U.S., which provides many opportunities for people to start businesses. There are 10,792 small businesses per 100,000 people in California.

82% of start-ups will still be active after one year, creating 5.7 new jobs per 1,000 people. 0.43% of Californians have started businesses and only 44% will fail after five years, which is one of the lowest failure rates. 

Entrepreneurial Index Score: 56.63 

In Idaho, start-ups will create 6.11 new jobs per 1,000 people in their first year. 89% of these start-ups are created by choice and not a necessity.

Although there was a much lower business application growth rate of only 0.20% from 2019-2022, there are still 9,320 small businesses per 100,000 people.  

Entrepreneurial Index Score: 56.38 

Texas has the second highest population, and per 100,000 people there are 10,163 small businesses. There was a growth rate of business applications between 2019 and 2022 of 52%.

81% of new businesses will still be active after a year and will create 5.18 new jobs per 1,000 people, opening op further opportunity for residents of the state.  

Nuala Turner, longtime editor of TheDigitalProjectManager.com, with deep experience covering business topics including project management software and project scheduling software , commented: “Entrepreneurship and new businesses are a driving force in economic growth and create opportunities for communities, allowing them and their people to thrive. Entrepreneurs should be encouraged to make the steps to start businesses, and this data shows the dedication and motivation that residents in the U.S. have to take the leap into being businesses owners.  

This research should serve as encouragement to the budding entrepreneurs of the states mentioned above: it's worth taking the leap to turn your passion into an income.” 

Method: Index created by ranking each factor out of 10 and adding the overall scores. The factors included were: 

• Percent of population that starts a new business
• Entrepreneurs who started a business by choice and not a necessity
• Number of jobs created by start-ups in their first year
• Percent of startups that are still active after one year
• Search interest on Google for "how to start a business" 
• Number of small businesses in each state
• Small businesses per 100,000 of the population
• Growth rate of business applications 2019-2022
• Business failure rates after five years

Full Index of every US State:

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Prostate cancer incidence and mortality in Europe and implications for screening activities: population based study

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• Peer review
• Freddie Bray , scientist 1 ,
• Rune Kvale , senior researcher 3 4 ,
• Valentina Lorenzoni , visiting scientist 6 ,
• 1 Cancer Surveillance Branch, International Agency for Research on Cancer, Lyon, France
• 2 State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China
• 3 The Cancer Registry of Norway, Oslo, Norway
• 4 Department of Oncology and Medical Physics, Haukeland University Hospital, Bergen, Norway
• 5 Cancer Epidemiology Unit, Centro di Riferimento Oncologico di Aviano (CRO), IRCCS, Aviano, Italy
• 6 Institute of Management, Scuola Superiore Sant’Anna, Pisa, Italy
• 7 Tampere University, Faculty of Social Sciences, Unit of Health Sciences, Tampere, Finland
• 8 Tampere University Hospital and FiCan-Mid Regional Cancer Centre, Tampere, Finland
• Correspondence to: S Vaccarella vaccarellas{at}iarc.who.int
• Accepted 4 July 2024

Objective To provide a baseline comparative assessment of the main epidemiological features of prostate cancer in European populations as background for the proposed EU screening initiatives.

Design Population based study.

Setting 26 European countries, 19 in the EU, 1980-2017. National or subnational incidence data were extracted from population based cancer registries from the International Agency for Research on Cancer’s Global Cancer Observatory, and mortality data from the World Health Organization.

Population Men aged 35-84 years from 26 eligible countries.

Results Over the past decades, incidence rates for prostate cancer varied markedly in both magnitude and rate of change, in parallel with temporal variations in prostate specific antigen testing. The variation in incidence across countries was largest around the mid-2000s, with rates spanning from 46 (Ukraine) to 336 (France) per 100 000 men. Thereafter, incidence started to decline in several countries, but with the latest rates nevertheless remaining raised and increasing again in the most recent quinquennium in several countries. Mortality rates during 1980-2020 were much lower and less variable than incidence rates, with steady declines in most countries and lesser temporal differences between countries. Overall, the up to 20-fold variation in prostate cancer incidence contrasts with a corresponding fivefold variation in mortality. Also, the inverse U-shape of the age specific curves for incidence contrasted with the mortality pattern, which increased progressively with age. The difference between the highest and lowest incidence rates across countries ranged from 89.6 per 100 000 men in 1985 to 385.8 per 100 000 men in 2007, while mortality rates across countries ranged from 23.7 per 100 000 men in 1983 to 35.6 per 100 000 men in 2006.

Conclusions The epidemiological features of prostate cancer presented here are indicative of overdiagnosis varying over time and across populations. Although the results are ecological in nature and must be interpreted with caution, they do support previous recommendations that any future implementation of prostate cancer screening must be carefully designed with an emphasis on minimising the harms of overdiagnosis.

Introduction

Prostate cancer is currently the most diagnosed malignancy among men and the third most common cause of death from male specific cancers in EU member states. 1 In the European Economic Area, which includes the 26 EU member states, Iceland, Lichtenstein, and Norway, and comprises 219 million men, around 341 000 men were diagnosed as having prostate cancer in 2020 (equivalent to 23% of all cancers in men) and about 71 000 men died from the disease (10% of all deaths from male specific cancers) in the same year. 1

Screening men to check their prostate specific antigen (PSA) levels aims to reduce mortality from prostate cancer. 2 The European Randomized Study of Screening for Prostate Cancer found a reduction in deaths from prostate cancer (after around 10 years), 3 4 whereas the other large randomised trials—the Prostate, Lung, Colorectal and Ovarian trial, 4 5 which reported no reduction in mortality (although likely the results were affected by contamination), 6 and the CAP (cluster randomised trial of PSA testing for prostate cancer) in the UK—found similarly negative results based on a single screen. In addition, PSA based screening may lead to overdiagnosis through the detection of low risk tumours that are unlikely to progress, with the risk of overtreatment and adverse effects that could lower men’s quality of life. 7 8 The potential for overdiagnosis and overtreatment is higher when screening for prostate cancer than when screening for breast, cervix, and colorectal cancers, with autopsy studies reporting that up to one third of men of screening age harbour an indolent prostate cancer. 9

Because of the delicate risk-benefit balance, almost all European countries, except Lithuania, have thus far opted against establishing prostate cancer screening programmes in favour of shared decision making about PSA testing between men and their doctors. 10 Differing individual attitudes and local practices towards PSA testing against a backdrop of on-demand and opportunistic screening unguided by clear protocols (in particular, the testing of older men) are likely to have a less than optimal effect on the population, with a possibly different net balance between the benefits and harms at population level than that observed in randomised clinical trials. 11 12

The EU Beating Cancer Plan recently released the European Commission’s council recommendations proposing a gradual and well planned implementation of screening programmes for prostate cancer in men younger than 70. 13 14 The suggested approach involves PSA testing initially, followed by magnetic resonance imaging (MRI) or other diagnostic tests for men with raised PSA levels before considering biopsy. The aim of the proposed approach is to maintain the benefit of mortality reduction while reducing overdiagnosis. 15 Modelling studies have suggested that this could be a cost effective procedure. 16

Given that opportunistic PSA testing has largely been carried out in Europe, it is important to assess the effect on prostate cancer incidence and mortality at population level. In addition, baseline data on national levels and trends in prostate cancer outcomes before the possible initiation of screening with new approaches are needed. We therefore carried out a comparative assessment of the main epidemiological features of prostate cancer in 26 European countries, quantifying the range of variability in incidence rates against temporal variations in PSA testing and relative to mortality rates as a contribution to the evaluation of the population level impact of the EU initiative.

Data sources

We obtained long term data on the annual incidence of prostate cancer (international classification of diseases, 10th revision, ICD-10 code C61) from the International Agency for Research on Cancer’s CI5plus (Cancer Incidence in Five Continents Plus) database and the Global Cancer Observatory. 1 17 18 From population based cancer registries we retrieved national or subnational recorded incidence data for 26 European countries during 1980-2017. Countries with populations less than 1 000 000 (Iceland and Malta) were not analysed. Coverage and availability of data within this period varied by country, but for most of the countries, the last year with incidence data was 2017 (see supplementary table S1). We obtained mortality data for the 26 European countries for 1980-2020 based on national vital registration from the World Health Organization. 19 Population coverage of the mortality database was nearly 100% in all selected countries, except Cyprus (86%). 19 Supplementary table S2 shows data availability and missing data points within the study period. We also extracted the most recent (2020) national incidence and mortality estimates from GLOBOCAN 2020 (with UK countries combined). 1

Review on PSA testing

We carried out a review of the literature on PSA testing across European countries. PubMed was searched using keywords (time trend OR trend) AND (prostate-specific antigen) AND (testing OR screening OR testing rate). The reference lists of relevant articles were also checked to identify additional eligible studies. We selected only studies that provided information on trends in PSA testing in European countries for at least three years. When several studies reported the time trends of PSA testing for one country, we selected the study with the longest periods of data. Overall, information on trends in PSA testing was available for 12 countries (see supplementary table S3), although quality and type of information varied. Therefore we were unable to derive precise characterisation of prevalence, patterns, and trends in PSA testing from the literature, and the available estimates were not directly comparable across countries because they referred to different indicators, age groups, and data sources across populations. Consequently, it was not possible to carry out a quantitative analysis linking levels of PSA testing with incidence of prostate cancer across countries but only to provide a visual assessment of the temporal trajectory of PSA testing against that of incidence by country.

Statistical analysis

We restricted all analyses to the age group 35-84 years, with missing mortality data points removed. Annual age standardised rates of prostate cancer incidence and mortality per 100 000 men were calculated using the world standard population as a reference. 20 To assess the temporal trends of prostate cancer incidence and mortality by country, we plotted the line chart of annual age standardised rates against calendar years based on all available data points. We assessed trends by country continuously by single year, whereas when emphasis was put on the range of variability in incidence and mortality across the continent, we smoothed trends using Loess regression. The average annual percentage change was calculated as 100×( e β −1), where β is the regression coefficient in the generalised linear regression models between natural logarithm of annual age standardised rate and year, with a gaussian distribution and identity link function. 21

Information on trends in PSA testing was retrieved from the literature for the 12 studied countries and is displayed against the corresponding trend in incidence. To assess the discrepancy between incidence and mortality, we grouped calendar years into four periods of five years each (1998-2002, 2003-07, 2008-12, and 2013-17). We calculated the standardised rate ratios of annual age standardised rates between incidence and mortality and then compared the standardised rate ratio across periods. 22 Age curves were also plotted for both incidence and mortality over the four periods.

All analyses were performed using R software (version 4.0.3).

Patient and public involvement

This study used deidentified and aggregated registration data provided by patients and collected by staff from local registries in the countries studied. No patients were involved in the development of the research question, outcome measures, study design, or implementation of the study, as it is not possible nor permitted to attempt to identify and engage them. Although no patients were directly involved in this paper, one impetus for this research was the clinical context of the proposed prostate cancer screening programmes in the EU. Results will be disseminated to the public through media and a press release written using layman’s terms.

Time trends of prostate cancer incidence and mortality rates

Figure 1 , figure 2 , figure 3 , and supplementary figure S1 show trends in prostate cancer incidence and mortality by country on an arithmetic scale (more suitable to assess and compare absolute values) and semi-log scale (more suitable to assess and compare relative changes over time), respectively. Supplementary figure S2 shows the trajectory of incidence and mortality over time by country. Increases in incidence were seen in almost every country, although the pace of increase varied greatly across countries. Increases in incidence were highest in northern Europe, France, and the Baltic countries—notably in Lithuania where the rates peaked at 435 per 100 000 men in 2007. In several countries (France, Switzerland, Italy, and Lithuania) the rates showed a parabolic increase, culminating after the mid-2000s and followed by subsequent declines, whereas in other countries the rates stabilised (Denmark, Sweden, Norway, Ireland, Spain, and Slovenia). Increases in incidence were, however, observed in the most recent quinquennium (2013-17) in several countries. In contrast, mortality rates decreased in most countries after the early 2000s, except in the Baltic countries and eastern Europe (eg, Estonia, Latvia, Belarus, Bulgaria, Poland, and Ukraine), where marked increasing trends, from previously low rates, were observed.

Time trends of age standardised incidence and mortality rates for prostate cancer per 100 000 men aged 35-84 years on an arithmetic scale in northern Europe

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Time trends of age standardised incidence and mortality rates for prostate cancer per 100 000 men aged 35-84 years on an arithmetic scale in central and southern Europe

Time trends of age standardised incidence and mortality rates for prostate cancer per 100 000 men aged 35-84 years on an arithmetic scale in the Baltic countries and eastern Europe

Three patterns can be distinguished in trends for prostate cancer incidence and mortality. Among the European countries included, nearly half exhibited upward trends in incidence (generally from the early 1990s to the late 2000s), followed by stable or downward trends, with corresponding mortality rates in uniform decline (such as the Nordic countries, France, Switzerland, and Italy). A second pattern involved increasing incidence rates throughout the study period, accompanied by downward mortality trends, as was observed in Britain (England, Wales, and Scotland) and the Czech Republic. The incidence of prostate cancer increased with stable or increasing mortality in the remaining countries, particularly in eastern and Baltic Europe (including Croatia, Estonia, Latvia, Belarus, Ukraine, Poland, Slovakia, and Bulgaria). Supplementary table S4 shows the average annual percentage changes of prostate cancer incidence and mortality.

Range of geographical and temporal variations in incidence and mortality rates

Figure 4 shows the range of variability in the annual age standardised rates for prostate cancer incidence and mortality across European countries and over time, highlighting the contrasting levels and magnitudes of differences in incidence versus mortality. Overall, prostate cancer incidence rates tended to rise during the study period, but with a variable pace and peak incidence in different countries and calendar periods. Consequently, the range varied considerably over time, the lowest rates being at the beginning of the study period in 1980 (from 17.6 in Belarus to 109.4 in Sweden per 100 000), then increasing substantially up until around 2005 (from 46.0 in Ukraine to 335.6 in France) and thereafter somewhat narrowing (from 62.7 in Ukraine to 299.3 in Lithuania) until around 2012, although rising trends were observed thereafter in several countries. The difference between the highest and lowest incidence rates across countries ranged from 89.6 per 100 000 men in 1985 to 385.8 per 100 000 men in 2007.

Range of age standardised incidence and mortality rates of prostate cancer per 100 000 men aged 35-84 years over time among the included European countries. Lines are smoothed by the Loess regression algorithm (bandwidth: 0.4)

Compared with incidence, mortality rates were much lower in absolute terms and, despite the declines observed in most countries, presented a smaller range of values, spanning from 12 (Ukraine and Belarus) in 1981 to 53 (Latvia) deaths per 100 000 men in 2006. Considering all countries and periods, the 20-fold maximum variation in prostate cancer incidence contrasts with the fivefold variation in mortality. The difference between the highest and lowest mortality rates across countries ranged from 23.7 per 100 000 men in 1983 to 35.6 per 100 000 men in 2006.

Trends in incidence against trends in PSA testing

Supplementary figure S3 shows the trends in incidence of prostate cancer against trends in PSA testing for the 12 countries where information on both indicators was available. A correlation was evident between the direction and rate of change in incidence relative to PSA testing across all countries assessed, although data on PSA trends are subject to major limitations. Supplementary table S3 provides detailed information on the review of PSA testing in Europe.

Divergence between incidence and mortality

The divergence between incidence and mortality increased in all countries over two decades ( fig 5 ). The standardised rate ratios between incidence and mortality were around 2~4 in most countries during 1998-2002, but higher values (5~7) were observed for several central European countries (Germany, Austria, France, Switzerland, Italy, and Spain). The standardised rate ratios almost doubled by 2013-17 compared with 1998-2012 and reached over 5 for almost all included countries other than Croatia, Latvia, and several countries in eastern Europe. High standardised rate ratios (>10) were found in Ireland, France, Italy, and Spain in 2013-17.

Standardised rate ratios between incidence and mortality for prostate cancer across different periods among men aged 35-84 years

Supplementary figure S4 also shows age standardised incidence rates for incidence and mortality in Europe in 2020, ranked by increasing order of incidence. Higher incidence rates were not consistently associated with the level of mortality rates.

Changes in age curves of prostate cancer incidence and mortality over time

Figure 6 , figure 7 , figure 8 (all on arithmetic scale), and supplementary figure S5 (on logarithmic scale) show the temporal change in age specific incidence and mortality. The age specific profiles changed markedly for incidence, but not for mortality. The incidence curves resembled an inverse U-shape peaking at around 70 years of age during the period 1998-2017, as seen in France, Sweden, Denmark, Norway, Ireland, Estonia, Lithuania, Slovenia, and the Czech Republic. The corresponding age specific curves in the central European countries decreased in 2008-12 after an earlier peak around 2003-07, although increases were observed in the recent quinquennium 2013-17 in some countries in the region. In contrast, the mortality curves remained relatively stable over time, showing a consistent increase with age in all European countries.

Age specific incidence and mortality rates of prostate cancer per 100 000 men during 1998-2002, 2003-07, 2008-12, and 2013-17 in northern Europe

Age specific incidence and mortality rates of prostate cancer per 100 000 men during 1998-2002, 2003-07, 2008-12, and 2013-17 in central and southern Europe

Age specific incidence and mortality rates of prostate cancer per 100 000 men during 1998-2002, 2003-07, 2008-12, and 2013-17 in the Baltic countries and eastern Europe

Our study found noticeable differences in both the magnitude of prostate cancer incidence rates across Europe and the rate of change in the generally upward trends over the past decades. The divergence between countries reached its maximum around the period 2000-10. Thereafter the rates declined in several countries, with somewhat reduced variability in rates, even though they remained high, and even increased in several countries in the most recent years. Such temporal variations in prostate cancer incidence correlated with the national variations in PSA testing. In contrast, mortality rates were substantially lower and showed less variability than incidence, with a more homogeneous pattern over time. Uniform declines in mortality were generally seen across the European continent, although less marked than the increases in incidence. In the Baltic countries and eastern Europe, however, mortality trends remained relatively flat.

The delivery and uptake of PSA testing have been shown to have a rapid effect on the number of new diagnoses of prostate cancer and corresponding incidence rates at the population level. It is widely acknowledged that in the US the large increase (starting in the 1970s and peaking around 2000) and subsequent decline in incidence resulted from the initial increasing use of transurethral resections of the prostate (from the 1970s) and subsequent use of PSA testing (from the mid-1980s) 23 and was followed by a decline, partly as a result of the US Preventive Services Task Force’s recommendation aimed to discourage the practice. 24 Our study confirmed this pattern for incidence in Europe yet also found large heterogeneity across countries. Conversely, the extent of the effect of PSA testing on mortality at the population level is less clear. In the US, the decline in mortality from the mid-1990s followed by a period of stability could be attributed to the use of PSA testing as well as to advances in effective treatment for late stage prostate cancer (whereas the cancers are localised at diagnosis). Yet, disentangling the contribution of the two components is challenging. The patterns of prostate cancer in Europe appear to replicate the earlier observations in the US. This suggests the same mechanism and implicates the potential contributions of both PSA testing and improved treatment outcomes.

In this respect, this comparative assessment should help to improve the understanding of the effect of PSA testing on incidence and mortality in Europe by highlighting consistent patterns across countries. Specifically, our results suggest that the intensity and coverage of PSA testing has been a critical driver for the increasing trends in prostate cancer incidence in Europe. Nevertheless, the possible benefits in terms of reduced mortality appeared to be relatively consistent everywhere, regardless of the extent of the increase in incidence as an indicator of PSA testing. 25 In addition, the magnitude of prostate cancer incidence showed little interdependence with mortality at the national level.

The changes in the age specific incidence curves showed a progressively younger age at peak incidence and increasing resemblance to an inverted U-shape. Older data from the 1960s and 1970s suggest that before the initiation of PSA testing, the incidence of prostate cancer increased strongly with age. 1 In contrast, in our study the age specific mortality curves did not substantially change over time and increased steadily with age. The decline in mortality rates affected all age groups proportionally, and the trend towards earlier diagnosis in younger men seemed to have only a negligible impact on subsequent mortality in older age groups.

In most Baltic and eastern European countries, mortality rates were relatively stable, in contrast with the declines elsewhere. Explanations may include the limited extent of PSA testing, as well as a slower adoption of therapeutic advances (compared with more affluent areas on the continent). Lithuania was an exception, with minor declines in mortality in the most recent period, possibly because it is the only country in Europe offering population screening using the PSA test (since 2006). 26 As the national programme has been accompanied by a substantial amount of opportunistic testing, prostate cancer incidence in Lithuania has increased rapidly, reaching the highest levels ever recorded in Europe.

Overall, our findings imply that unregulated and opportunistic PSA testing has had a differential effect at the population level in Europe compared with the results of the randomised screening trials and appear consistent with overdiagnosis. The PSA based screening trials reported a 1.4-fold or smaller increase in incidence, 3 5 27 whereas national incidence rates in most European countries more than doubled from 1990 to 2017 (and in some countries increased up to eightfold, as in Lithuania). The epidemiological features observed in our study, specifically the rapid inconsistent increase in incidence but not mortality and the progressive change in age specific incidence curves, are difficult to explain for factors other than PSA testing. Our findings have commonalities with what has been previously reported for thyroid cancer, where overdiagnosis is an established driver of rapid increases in incidence. 28 Opportunistic examinations of the thyroid (often with ultrasound) have spread rapidly in many countries, 29 30 despite the lack of evidence for a mortality benefit from thyroid screening (in contrast with prostate cancer screening) and of current guidelines, which recommend against population screening for thyroid cancer. 31

The value of early detection of prostate cancer has been debated extensively, and most of the recent guidelines recommend that asymptomatic men should be offered an informed decision making process about the potential benefits and harms of prostate cancer screening. However, it is still not clear how shared decision making should be implemented, or its possible effect on patient outcomes. 32 33 34 In countries where such policies have been introduced, the prevalence of PSA testing is disproportionally higher among older men 11 and among men with a higher socioeconomic position. 35 This limits the benefits, increases the risk of overdiagnosis, and increases social inequalities.

Overtreatment may be a consequence of overdiagnosis, with harms of overdiagnosis exacerbated by aggressive management. Recent improvements in the de-escalation of treatment for low risk men with prostate cancer are observed in some countries. In Norway, for instance, the proportion of low risk men managed primarily by active surveillance instead of immediate treatment increased from 20% to 80% during 2008-21, with only 7% of such men treated radically in 2021 (eg, with surgery or radiotherapy). 36 In England, treatment of low risk men was estimated to be 4% in 2018. 37 Heterogeneity in the management and treatment of low risk and high risk localised prostate cancer is substantial, however, even across high income countries. 37 International society based treatment guidelines should be enforced to minimise overtreatment. In addition, many men initially treated with active surveillance decided to switch to active treatment within a few years. The process of reducing unnecessary treatment for prostate cancer is multifaceted and involves various aspects of health systems and the attitude of decision makers, medical practitioners, and patients and their families. 38 It is important to monitor whether opportunistic use of PSA testing, with the consequent cascade of biopsies, aggressive management, and treatment, will continue in the future, especially in settings where the provision of healthcare services is particularly unregulated.

The European Commission has recently recommended that “countries should take a stepwise approach, including piloting and further research to evaluate the feasibility of implementation of organised programmes aimed at assuring appropriate management and quality on the basis of prostate specific antigen (PSA) testing for men up to 70, in combination with additional magnetic resonance imaging (MRI) scanning as a follow-up test.” 39 The use of pre-biopsy MRI and of targeted prostate biopsies compared with systematic biopsies alone, should reduce the number of men who will receive an unnecessary diagnosis of prostate cancer, and although changes in clinical practice are already occurring in some settings, they are too recent for any potential effect to be observable in our study. To this extent, some proposals have been advanced, including the implementation of systematically designed, risk based national prostate cancer detection programmes aimed at reducing overdiagnosis and overtreatment and increasing equity. 11 40

Limitations of this study

The present analysis may refer to different age groups, periods in time, and indicators of PSA testing, and therefore the results should be interpreted with caution. The limitations of this study include the lack of data on cancer stage (due to problems with comparability across cancer registries) and on treatment modalities. This is of importance as increases in prostate cancer incidence and mortality at more advanced stages have been observed in the US following the USPSTF recommendations against PSA based screening in 2008 and 2012. 41 42 However, the data used in this analysis include IARC’s GLOBOCAN and CI5, for which the underlying sources are commonly robust and internationally comparable. Although data for Cyprus and Slovakia have been available only since the early 2000s and early 1990s, respectively, for all other countries under study, cancer incidence trends could be analysed up until the relatively recent period of 2017. In some countries, mortality data are missing for a few years, but those are generally scattered throughout the study period, their impact on the trends and on the general conclusions of the study are negligible. Ecological studies in multiple settings, such as the present one, are an appropriate approach for quantifying and monitoring overdiagnosis. 43 The current review on PSA testing, as noted, does have several limitations. In addition, although we could retrieve data from the literature on the frequency of PSA testing for 12 out of the 26 countries, the information available could not be synthesised or enable quantitative assessments given the lack of comparability of the measures used. A visual inspection of the trend variations in PSA testing showed a strong parallelism with prostate cancer incidence in countries where both indicators were available, but these findings should also be interpreted with caution. In addition to PSA testing, other factors may have affected incidence and mortality rates. The descriptive nature of the data used in the present study, the incompleteness of the data both geographically and temporally, and the lack of information on confounding factors, mean that causality cannot be assumed. The established risk factors for prostate cancer include age (adjusted for in our analysis) as well as family history and genetic predisposition, but these cannot change rapidly within a population. Putative factors include diet, specific drugs, and occupational factors, 44 45 but overall, the cause remains poorly understood. It is, however, unlikely that changes in the prevalence of one or more risk factors could have caused such a surge in incidence, given the variability internationally, and the contrasting mortality trend.

Conclusions

Overall, our results suggest that several of the epidemiological characteristics of prostate cancer in the Europe countries included, particularly the contrast between large heterogeneity in trends for incidence with the more uniform reduction in mortality, are compatible with the highly variable patterns of PSA testing across Europe. The current high incidence of prostate cancer in many countries may be inflated by unregulated and opportunistic PSA testing that serves to mask any variations due to causal factors and may be indicative of overdiagnosis. The importance of these results is further emphasised by the proposed EU guidelines endorsing prostate cancer screening, assuming that resources are available, and that prostate cancer is a public health priority. Careful monitoring and assessment of the benefits and harms, including overdiagnosis, will be essential for the potential implementation of the guidelines and the prospective introduction of population-wide prostate cancer screening.

What is already known on this topic

Unregulated and opportunistic testing of prostate specific antigen has been, and still is, common in Europe

The EU Beating Cancer Plan recently released the European Commission’s council recommendations proposing a new strategy for prostate cancer screening programmes

A baseline assessment of the main epidemiological features of prostate cancer outcomes in Europe is needed before the possible initiation of screening with new approaches

What this study adds

This study found that the magnitude of prostate cancer incidence rates varied markedly across European countries and over time, in parallel with national trends of prostate specific antigen testing. Conversely, the mild and steady declines in mortality rates were at much lower levels and showed a more homogeneous and less variable pattern

The epidemiological features analysed in this study suggest that unregulated and opportunistic screening with prostate specific antigen likely leads to a population effect on prostate cancer outcomes that is less than optimal compared to that observed in randomised clinical trials

The present results are ecological in nature and should be interpreted with caution, but they reinforce the need for prudently planned prostate cancer screening programmes, especially to mitigate harms from overdiagnosis

Ethics statements

Ethical approval.

Not required as the study used publicly available aggregated data.

Data availability statement

All data used for analyses are available from the International Agency for Research on Cancer at http://ci5.iarc.fr and the World Health Organization at https://www.who.int/data/data-collection-tools/who-mortality-database .

Acknowledgments

We thank Frédéric Lam and Murielle Colombet for technical and data support and the cancer registries and staff for sharing data needed for this study. Where authors are identified as staff of the International Agency for Research on Cancer or the World Health Organization, the authors alone are responsible for the views expressed in this article, and they do not necessarily represent the decisions, policy, or views of the International Agency for Research on Cancer or WHO.

Contributors: SV and ML are joint first authors. SV, ML, and LDM conceived and designed the study. ML contributed to data collection, analyses, and interpretation of the results. SV wrote the first draft of the manuscript. FB, RV, DS, VL, and AA critically discussed and interpreted the results and contributed to the final version of the paper. SV is the guarantor of the study and had final responsibility for the decision to submit the manuscript. All authors read and approved the final version of the paper. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted.

Funding: The Italian Association for Cancer Research supported the work of DS and LDM (grant No 28893).

Competing interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/disclosure-of-interest/ and declare: DS and LDM were supported by the Italian Association for Cancer Research; no financial relationships with any organisations that might have an interest in the submitted work in the previous three years; no other relationships or activities that could appear to have influenced the submitted work.

Transparency: The lead author (SV) affirms that the manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned (and, if relevant, registered) have been explained.

Dissemination to participants and related patient and public communities: Study results will be disseminated to the public, health professionals, and policy makers through a press release written using layman’s terms on the International Agency for Research on Cancer’s website. Findings will be shared through mass media communications and social media postings. We will also present findings at national and international conferences oriented towards researchers and clinicians in the specialty of cancer prevention and control. Since the study is based on deidentified and aggregated registration data, we have no plans to disseminate results to individual study participants beyond the usual channels of publication.

Provenance and peer review: Not commissioned; externally peer reviewed.

This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ .

• ↵ Ferlay JEM, Lam F, Colombet M, et al. Global Cancer Observatory: Cancer Today. International Agency for Research on Cancer, 2020. https://gco.iarc.fr/en .
• Draisma G ,
• Etzioni R ,
• Tsodikov A ,
• Schröder FH ,
• Hugosson J ,
• Roobol MJ ,
• ERSPC Investigators
• Carlsson S ,
• Andriole GL ,
• Crawford ED ,
• Grubb RL 3rd . ,
• PLCO Project Team
• Djulbegovic M ,
• Paschen U ,
• Lampert U ,
• Del Mar C ,
• Dickinson J ,
• ↵ European Association of Urology. Archive of the Prostate Cancer guideline. EAU https://uroweb.org/ guidelines/archive/prostate-cancer . 2020.
• Vickers A ,
• O’Brien F ,
• Montorsi F ,
• Arnsrud Godtman R ,
• Holmberg E ,
• Stranne J ,
• ↵ European Union. Council Recommendation of 9 December 2022 on strengthening prevention through early detection: A new EU approach on cancer screening replacing Council Recommendation. https://eur-lex.europa.eu/legal-content/EN/TXT/HTML/?uri=OJ:C:2022:473:FULL&from=EN .
• ↵ European Commission. Proposal for a Council Recommendation on strengthening prevention through early detection: A new EU approach on cancer screening. 2022 https://health.ec.europa.eu/system/files/2022-09/com_2022-474_act_en.pdf .
• Van Poppel H ,
• Albreht T ,
• Hogenhout R ,
• Callender T ,
• Emberton M ,
• Pharoah PDP ,
• ↵ Ferlay J Colombet M, Bray F. Cancer Incidence in Five Continents, CI5plus: IARC CancerBase No 9. International Agency for Research on Cancer, 2018. http://ci5.iarc.fr
• ↵ Bray FCM, Aitken JF, Bardot A, et al, eds. IARC CancerBase No 19. Vol XII. Cancer Incidence in Five Continent. Lyon, International Agency for Research on Cancer, 2023. https://ci5.iarc.fr/
• ↵ World Health Organization. World Organization Mortality Database. https://www.who.int/data/data-collection-tools/who-mortality-database .
• Kurihara M ,
• Legler JM ,
• U.S. Preventive Services Task Force
• Taborelli M ,
• Toffolutti F ,
• Krilaviciute A ,
• Smailyte G ,
• Martin RM ,
• Donovan JL ,
• Turner EL ,
• CAP Trial Group
• Vaccarella S ,
• Franceschi S ,
• Plummer M ,
• Dal Maso L ,
• Vaccarella S
• Pizzato M ,
• Bibbins-Domingo K ,
• Grossman DC ,
• US Preventive Services Task Force
• van den Bergh RCN ,
• Martínez-González NA ,
• Rosemann T ,
• Neuner-Jehle S
• Riikonen JM ,
• Guyatt GH ,
• Kilpeläinen TP ,
• ↵ Kreft Registeret. National Quality Register for Prostate Cancer. https://www.kreftregisteret.no/globalassets/publikasjoner-og-rapporter/arsrapporter/publisert-2023/arsrapport-2022-nasjonalt-kvalitetsregister-for-prostatakreft.pdf .
• Nossiter J ,
• Skolarus TA ,
• Chapman CH ,
• ↵ https://eur-lex.europa.eu/legal-content/EN/TXT/HTML/?uri=CELEX:52022DC0474 .
• Alterbeck M ,
• Thimansson E ,
• Bengtsson J ,
• Siegel RL ,
• Miller KD ,
• Carter JL ,
• Coletti RJ ,
• Bergengren O ,
• Pekala KR ,
• Matsoukas K ,
• Markozannes G ,
• Tzoulaki I ,

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Exploring the Inherent Heterogeneity of Vaccine Hesitancy: A Study of a Childhood-Vaccine-Hesitant Population

Monika lamot.

1 Department of Sociology, Faculty of Arts, University of Maribor, 2000 Maribor, Slovenia

Andrej Kirbiš

Mitja vrdelja.

2 National Institute of Public Health, 1000 Ljubljana, Slovenia; [email protected]

Associated Data

The data are available upon reasonable request to the authors.

Vaccine hesitancy and its determinants have been previously widely researched. Vaccine hesitancy has been defined as a continuum of attitudes, ranging from accepting vaccines with doubts to rejecting them. The present study aims to explore the heterogeneity of a childhood-vaccine-hesitant group by using a person-oriented approach–latent profile analysis. A non-representative cross-sectional sample of vaccine-hesitant Slovenians (N = 421, M age = 35.21, 82.9% women) was used to identify differences based on their reliance on personal research (“self” researching instead of relying on science), overconfidence in knowledge, endorsement of conspiracy theories, complementary and alternative medicine, and trust in the healthcare system. The analysis revealed three profiles of vaccine-hesitant individuals. The most hesitant profile—vaccine rejecting—expressed the greatest reliance on personal research, expressed the highest endorsement of conspiracy theories and complementary and alternative medicine, showed moderate overconfidence in their knowledge, and expressed the highest levels of distrust in the healthcare system. We further found differences in sociodemographic structure and that the identified profiles differed in their attitudes regarding MMR, HPV, and Seasonal Influenza vaccinations. The present study demonstrates the heterogeneity of the vaccine-hesitant community and offers insights into some of the traits, which are crucial for designing pro-vaccine campaigns.

1. Introduction

Vaccine hesitancy has been defined by prior research as a continuum of attitudes [ 1 ], yet the individuals exhibiting vaccine-hesitant attitudes are often treated as a homogenous group [ 2 , 3 ]. However, recent studies challenge this perspective, showing that treating vaccine-hesitant people as a homogenous group, i.e., merely comparing vaccine hesitant groups to non-vaccine hesitant groups, may limit the full understanding of the complexities within vaccine-hesitant individuals [ 4 , 5 , 6 ]. Recent work by Howard [ 4 ] and Zhou et al. [ 5 ] highlights the importance of a nuanced understanding of members of groups which express reservations or fully oppose vaccinations. Specifically, Howard [ 4 ] studied a diverse sample from several countries, including the United States, Portugal, Poland, United Kingdom, Mexico, and Italy. The sample consisted of both vaccine-hesitant and non-hesitant individuals, and eight distinct profiles were identified. Amongst vaccine-hesitant individuals, a profile named ‘Distrusting’ emerged, characterized by concerns about vaccine safety and efficacy, as well as politically conservative views, which demonstrated the least willingness to vaccinate. Zhou et al. [ 5 ] classified vaccine-hesitant individuals from the U.S. into five profiles based on their psychological characteristics, such as fear and disgust, further underscoring the group’s complexity.

The present study aims to build on these insightful works by examining the within-group heterogeneity of vaccine-hesitant individuals and investigating whether they differ based on the depth (i.e., intensity) of their hesitancy, specific attitudes about vaccines and vaccination, attitudes towards specific vaccines (Measles, Mumps, and Rubella (MMR), human papillomavirus (HPV), and Seasonal Influenza), and sociodemographic characteristics. In addition, there is a lack of studies on vaccine-hesitant groups from East–Central European countries, as most studies focus on Western countries. Examining this populace is particularly critical, as several studies show that East and Central Europeans express some of the highest levels of vaccine hesitancy worldwide [ 7 , 8 ].

2. Predictors of Vaccine Hesitancy

The previous literature has explored various predictors of vaccine hesitancy, including positive attitudes towards complementary and alternative medicine [ 9 , 10 ], distrust in the healthcare system [ 11 , 12 ], endorsement of conspiracy theories [ 13 , 14 , 15 ], engagement in personal research (information-seeking behavior on the Internet) [ 16 , 17 ], and overconfidence in one’s knowledge [ 18 ]. While these studies offer valuable insights into the phenomenon of vaccine hesitancy, much less is known about the different intensities of these beliefs within the vaccine-hesitant population.

In the present study, we examined the predictors of vaccine hesitancy as latent indicators upon which vaccine-hesitant individuals were classified into subgroups. The following research question was posited:

Research Question 1 : Do distinct profiles of vaccine hesitant individuals exist that vary qualitatively and quantitatively in their endorsement of complementary and alternative medicine, conspiracy theories, trust in the healthcare system, engagement in “self” researching information, and overconfidence in their knowledge?

2.1. Sociodemographic Characteristics of Vaccine Hesitant Individuals

Research has identified several sociodemographic determinants of vaccine hesitancy, including gender [ 19 , 20 ], age [ 21 , 22 ], lower income [ 3 , 23 , 24 ], lower education [ 25 , 26 ], and political orientation [ 27 , 28 , 29 ]. It has also been established that determinants of vaccine hesitancy vary across countries [ 7 , 30 ], further confirming the complexities of the vaccine-hesitant population.

The importance of the need for a fine-grained investigation of vaccine-hesitant groups and their traits was demonstrated by recent studies employing latent profile analysis (LPA), which gave insights into the underlying sociodemographic characteristics among vaccine-hesitant individuals. For instance, work by Hornsey et al. [ 31 ], Gravelle et al. [ 32 ], Lamot et al. [ 6 ], and Howard [ 4 ] revealed differences in sociodemographic structures within the vaccine-hesitant population, demonstrating the potential value of a more detailed, subgroup-level analysis. Gravelle et al. [ 32 ], for instance, found that the youngest people were the most polarized about vaccines, with strong feelings for or against vaccination. In addition, individuals who were extremely conservative were more likely to be vaccine hesitant or anti-vaccine.

Research Question 2 : Do sociodemographic determinants (gender, age, education, income, and political orientation) predict the profile membership of vaccine-hesitant individuals?

2.2. Do Vaccine Hesitant Individuals Differ on Their Stance on Vaccination and Specific Vaccines?

Research on general populations demonstrates a clear association between vaccine hesitancy and concerns about vaccine safety [ 33 , 34 , 35 ], the perceived efficacy of vaccines for preventing infectious diseases [ 36 , 37 , 38 ], and the perceived role of vaccination for one’s personal and public health [ 39 ].

However, less attention has been paid to such concerns within vaccine-hesitant groups. For example, Howard [ 4 ] demonstrates that within-group differences do exist; specifically, he found that the most vaccine-hesitant profile differed in terms of their mean score regarding vaccine safety in comparison to the other identified profiles. This underscores the complexity of vaccine attitudes and concerns within vaccine-hesitant individuals.

While recent research has primarily focused on attitudes towards the COVID-19 vaccine due to the most recent pandemic [ 40 , 41 , 42 ], the pre-pandemic literature has also examined vaccine hesitancy related to the MMR, HPV, and Seasonal Influenza vaccines [ 43 , 44 ]. Weiss et al. [ 45 ] conducted a Latent Class Analysis of parents of young children and examined their attitudes towards MMR vaccination. The analysis identified three classes, two of which expressed vaccine-hesitant and anti-vaccine sentiments. The “hesitant” group doubted that vaccination is necessary (i.e., did not perceive the three diseases as threatening). On the other hand, the third identified class (the vaccine-rejecting one) more likely agreed that immunization is an artificial intrusion into the natural immune system and therefore wanted to vaccinate their children only if necessary.

Research Question 3 : Do identified profiles differ in vaccine-related concerns (vaccine safety, efficacy, and importance)?

Research Question 4 : Do identified profiles differ in attitudes towards specific vaccines (against MMR, HPV, and Seasonal Influenza)?

2.3. The Role of Information Sources in Understanding Vaccine Hesitancy

Finally, the present study explored the importance of information sources in shaping vaccine attitudes among the vaccine-hesitant individuals. The sources where individuals seek information about vaccines can greatly influence their perceptions and decisions, ranging from mainstream media and healthcare providers to social networks and Internet searches [ 46 , 47 , 48 ]. Charron et al. [ 46 ] found that greater vaccination intention was found among individuals who obtained information about vaccines from healthcare professionals, while vaccine hesitancy was associated with obtaining information from the Internet or from relatives. Furthermore, Reno et al. [ 48 ] found that vaccine-hesitant individuals were less likely to obtain information from the mainstream media and more likely to obtain information from social media. However, there remains a gap in understanding how obtaining information about vaccines from different sources differs among the subgroups of the vaccine-hesitant population.

Research Question 5 : Do identified profiles differ in the importance they give to different information sources about vaccines?

3.1. Data Collection

The data were collected in November 2019 using the web-based survey platform 1ka ( https://www.1ka.si/ (accessed on 24 July 2024). A snowball sample strategy was used that involved the social media networks Facebook and Twitter. According to Thornton et al. [ 49 ] and King et al. [ 50 ], recruitment via social media is especially beneficial for addressing difficult-to-reach communities. We thus used this sampling strategy to increase the number of vaccine-hesitant people in our sample. However, because of the nature of this sampling technique, the sample does not represent the entire Slovenian vaccine-hesitant population.

The data gathering procedure adhered strictly to the standards of the Helsinki Declaration. Before they began filling out the questionnaire, respondents were given a brief overview of the study and its aims. They were also informed that their participation was anonymous and voluntary and that they could opt out at any time. After reading this information, participants proceeded to the questionnaire, confirming their informed consent.

3.2. Participants

A total of 661 individuals from Slovenia completed the survey. Because we focused on vaccine-hesitant individuals, we excluded participants who expressed positive attitudes toward vaccines. This exclusion was determined based on their response to a question about their attitudes toward vaccines. Individuals who responded with “All recommended childhood vaccines should be given to all children who have no contraindications; I have no doubts about them” were removed from the sample, as their response did not indicate any hesitancy towards childhood vaccines. Thus, our final sample included 421 vaccine-hesitant individuals, all of whom were over 18 years old. The median age of the sample was 35.21 years. The sample was predominantly composed of women (82.9%).

3.3. Measures

Latent indicators.

Several attitudes were measured to identify characteristics of vaccine-hesitant groups. Attitudes towards complementary and alternative medicine were assessed through five items from the Holistic Complementary and Alternative Health Questionnaire (HCAMQ), for example, “It’s always worth trying complementary and alternative medicine before going to the doctor” [ 51 ]. The respondents gave their answers on a 5-point Likert scale, with options ranging from strongly disagree (1) to strongly agree (5). The scale demonstrated great internal consistency (Cronbach’s alpha = 0.82).

Endorsement of conspiracy theories was measured with several items. We used two items from the Generic Conspiracist Beliefs questionnaire, “Some important societal events were the result of the actions of a small group secretly manipulating world events” and “The spread of some viruses and diseases is the result of deliberate, covert efforts by certain organizations” [ 52 ]. Next, the statement “The physical traces that airplanes leave in the sky (i.e., chemtrails) are chemical traces or chemical weapons” was self-developed. We also added the item “Vaccination programs are profit-motivated by the pharmaceutical industry” due to some studies indicating a link between anti-vaccination attitudes and beliefs about the pharmaceutical lobbying of vaccination programs [ 53 ]. Responses were measured on a 5-point Likert scale (1 = strongly disagree, 5 = strongly agree). The four-item construct demonstrated high internal reliability (Cronbach’s alpha = 0.82).

The reliance on personal research was tapped with the statement “Instead of relying on science and scientists, it is better if an individual informs themselves before making important decisions.” Responses were collected on a 4-point Likert scale (1 = strongly disagree, 4 = strongly agree). Overconfidence was evaluated through the following two statements: “I think my knowledge in general is comparable to the knowledge of doctors”, and “I think my knowledge in general is comparable to the knowledge of scientists”. Participants responded on a 5-point Likert scale (1 = strongly disagree, 5 = strongly agree). The two variables were combined into a single item based on the median score (Spearman–Brown’s coefficient = 0.86, p < 0.001).

3.4. Outcomes

The intensity of vaccine hesitancy was measured with the question “Which of the following statements best describes your attitudes towards vaccination?” Possible answers were (1) all recommended children’s vaccines should be given to all children who have no contraindications; however, I have some reservations about them, (2) most recommended children’s vaccines need to be given to children who have no contraindications, but not in all cases and/or not for all vaccines that are in the vaccination program, (3) some recommended children’s vaccines need to be given to children who have no contraindications, but in most cases and/or not for the majority of vaccines that are in the vaccination program, and (4) children should not be given any of the recommended vaccines [ 54 ].

We measured general attitudes toward vaccination with three items, “In general, I think vaccines are effective”, “In general, I think vaccines are safe”, and “Vaccines are important for the health of children”. The items were measured on a 5-point Likert scale (1 = strongly disagree, 5 = strongly agree).

Attitudes towards specific vaccines were also examined. For the MMR vaccine, we used the following statement “Scientific evidence shows that there is no link between the MMR vaccine and autism” [ 55 ]. Attitudes towards there HPV vaccine were tapped with “The current HPV vaccine can prevent the onset of cervical cancer” [ 56 ]. Attitudes towards Seasonal Influenza vaccines were measured with the following statement: “I find the seasonal flu vaccine to be very safe” [ 25 ]. The respondents gave their answers on a 5-point Likert scale, ranging from strongly disagree (1) to strongly agree (5).

Finally, the importance of information sources about vaccines was measured on a 5-point scale (1 = not important at all; 5 = very important). Respondents rated the importance of the following information sources: doctor, family, friends, the National Institute of Public Health, forums and websites, TV, newspapers, alternative healers (such as homeopath and chiropractic), and social media.

3.5. Sociodemographic Predictors

The sociodemographic variables included in the analysis were gender (1 = male, 2 = female), age (in years), income (respondents wrote their income in EUR), education (1 = uncompleted elementary education; 11 = PhD), and political orientation (0 = left; 10 = right).

3.6. Statistical Analyses

Latent profile analysis (LPA) was employed in our analyses. LPA is a statistical method employed for identifying “latent” subgroups within a larger population based on their characteristics. It enhances better understanding of the underlying structure of a population and how different determinants may be related to group membership [ 57 ]. The present paper used Mplus 8.3 for conducting LPA. Deciding the profile solution (i.e., the number of profiles) included several model-fit statistics. Standard indicators used in LPA include the Bayesian Information Criterion (BIC), Sample-adjusted Bayesian Information Criterion (SABIC), Akaike’s Information Criterion (AIC), Bootstrap Likelihood Ratio test (BLRT), Lo–Mendell–Rubin test (LMR), entropy, and posterior classification [ 58 ]. In this study, special consideration was given to LMR and BLRT, which compare the k 0 model with the k −1 model. A significant value of the BLRT and LMR tests implies that the k 0 solution is superior to the k −1 solution. BIC and SABIC were also of particular interest—the lower values of these criteria suggest a better model fit, as AIC and entropy have often shown poor performance in selecting the correct number of classes [ 59 ]. Other criteria, such as smallest profile size and interpretability, were also considered when determining the number of profiles. As Ferguson et al. [ 57 ] emphasize, profiles comprising less than 5% of the sample might be misleading. Moreover, considerations were also made in terms of interpretability, especially in evaluating if an additional profile offers new and significant perspectives.

After deciding on the number of profiles, the analysis was carried out in SPSS 28. Using class probabilities, we examined if there were quantitative differences among the profiles in relation to the five latent variables. Predictors of profile membership were analyzed using multinomial regression, while differences in vaccine attitudes between profiles were examined with analysis of variance (ANOVA).

Model fit statistics are presented in Table 1 . The Bootstrap Likelihood Ratio test (BLRT) indicated a five-profile solution, while the Lo–Mendell–Rubin (LMR) test suggested a four-profile solution. When comparing the three- and four-profile solutions, we found that the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and Sample-Size Adjusted Bayesian Information Criterion (SA-BIC) did not decrease substantially. Moreover, the three-profile solution yielded both higher entropy and a larger proportion of the smallest class. Furthermore, we examined the interpretability of the profiles and found that the additional profile in the four-profile solution did not differ substantially. Therefore, considering all model fit indices, the principles of parsimony, and interpretability, we opted for a three-profile solution.

Model fit statistics.

Note. LL = Log-Likelihood. AIC = Akaike Information Criterion. BIC = Bayesian Information Criterion. SA-BIC = Sample-Size Adjusted Bayesian Information Criterion. LMR- p = Lo–Mendell–Rubin Adjusted Likelihood Ratio Test. BLRT- p = Bootstrap Likelihood Ratio Test. Bolded is the model solutions that shows the best model fit.

ANOVA ( Table 2 ) revealed statistically significant differences between profiles in endorsement of complementary and alternative medicine (CAM) and conspiracy theories, preference for self-research, trust in the healthcare system, and tendency toward overconfidence in own knowledge. Post hoc comparisons using Tukey’s test, required because of the unequal profile sizes, showed that all pairwise comparisons were significant at the p < 0.001 level for both CAM and endorsement of conspiracy theories. Specifically, profile 1 showed a statistically significantly higher tendency to endorse CAM compared to profile 2, although it was lower than profile 3. Regarding conspiracy theories, profile 3 was more likely to uphold such theories than either profile 1 or profile 2, while the latter showed less positive attitudes toward conspiracies compared to profile 1.

Profile Means on latent indicators.

Note. a ANOVA. b Welch’s ANOVA. Letters in superscripts indicate significant pairwise comparisons. CAM = complementary and alternative medicine. HCS = healthcare system. *** p < 0.001.

Further pairwise comparisons were examined using the Games–Howell post hoc test to account for unequal group sizes and violation of the assumption of homogeneity of variances when examining differences in personal research, trust in the healthcare system, and overconfidence in knowledge. It was found that profile 2 was less inclined toward personal research compared to profiles 1 and 3, with profile 3 showing the greatest tendency among the three. In addition, profile 3 demonstrated the most negative attitudes toward the healthcare system compared to profiles 1 and 2, with profile 1 showing a significantly more positive attitudes than profile 2; all differences reached the p < 0.001 level. Finally, regarding overconfidence in knowledge, profile 3 showed significantly greater overconfidence in own knowledge than profiles 1 or 2 ( p < 0.001), while no significant difference was found between profiles 1 and 2.

The three extracted profiles thus demonstrated unique patterns across CAM and conspiracy theories endorsement, personal research, trust in the healthcare system, and overconfidence in knowledge. The standardized values of the indicator variables for each extracted profile are shown in Figure 1 . A value of zero represents the sample mean, whereas negative values represent scores below the sample mean and positive values represent scores above the sample mean. The magnitude of these numbers shows the distance from the sample mean in standard deviation units.

Z-standardized values.

Profile 1, labeled Skeptics, consisted of 184 individuals who demonstrated moderate trust in the healthcare system, low endorsement of both CAM and conspiracy theories, lower reliance on personal research, and low overconfidence in knowledge. The second profile (Conventionalists, n = 41) revealed a similar pattern to Skeptics, However, the two notably differ in the magnitude of their attitudes. Specifically, profile 2 exhibited high trust in the healthcare system and the lowest endorse=ment of CAM and conspiracies, personal research, and overconfidence. Despite being the smallest profile, it is noteworthy that it displayed the highest trust in the healthcare system among the three vaccine-hesitant groups. Lastly, profile 3 (Self-directed researchers, n = 196) was the largest profile and presented a very different pattern from the previous two. Profile 3 revealed high endorsement of CAM and conspiracy theories, a tendency towards personal research, distrust in the healthcare system, and moderate overconfidence in knowledge.

Next, we were interested in whether gender, age, income, education, and political orientation predict profile membership ( Table 3 ). The multinomial logistic regression model was statistically significant, outperforming the null model (χ 2 (10) = 47,321, Nagelkerke R 2 = 0.14, p < 0.001). For profile 1 (Skeptics) membership, age, income, education, and gender were significant predictors. Those who were younger ( b = −0.03, p < 0.05), more educated ( b = 0.12, p < 0.05) and being of male gender ( b = 1.27, p < 0.001) were more likely to belong to profile 1 compared to profile 3 (Self-directed researchers), while income did not significantly predict membership of profile 1.

Logistic regression of sociodemographic factors predicting profile membership.

Note. Profile 3 (Self-directed researchers) is the reference category. B = unstandardized beta; SE = standard error; Wald = Wald statistical test.

For Profile 2 (Conventionalists) membership, education, political orientation, and gender were significant predictors. More educated individuals ( b = 0.20, p < 0.05), individuals with more leftist political orientations ( b = 0.35, p < 0.001), and men ( b = 1.81, p < 0.001) were more likely to belong to this profile compared to profile 3. Age and income were not significant predictors of this profile.

Figure 2 represents the results of the Chi-square test, which was used to examine the differences among profiles in their intensity of vaccine hesitancy (Cramer’s V = 0.45, p < 0.001). Profile 2 exhibited a weaker level of vaccine hesitancy, while profile 1 demonstrated a moderate level of hesitancy. Profile 3, on the other hand, revealed the greatest level of vaccine hesitation.

Differences in the intensity of vaccine hesitancy among profiles.

We furthermore examined if the profiles differ in their attitudes towards vaccination and specific vaccines ( Table 4 ). The former was investigated through attitudes towards efficacy, safety, and the importance of vaccines for one’s health. It was found that profile 1 consistently displayed more neutral attitudes towards vaccination, profile 2 showed the most positive attitudes, while profile 3 exhibited the most negative attitudes. Profiles 1 and 3 were the most skeptical about the safety of vaccines, ( M = 2.56, SD = 1.28 and M = 1.24, SD = 0.63, respectively). Games–Howell post hoc comparisons indicated significant differences across all groups on all measured variables, as denoted by the superscript in the table.

Differences between profiles in vaccine attitudes.

Note. Presented is Welch’s ANOVA and Games–Howell post hoc test, due to non-homogeneity of variances and unequal sizes of groups. Letters in superscripts indicate significant pairwise comparisons. *** p < 0.001.

Table 4 also shows differences in attitudes toward specific vaccines. Regarding the MMR vaccine, profile 2 showed the most positive attitudes, profile 1 displayed more neutral attitudes, and profile 3 expressed considerably negative attitudes towards this vaccine. The results also suggest that, while profiles 1 and 2 show differences in their degree of positive attitudes towards the MMR vaccine, both are more favorable when compared to profile 3. This pattern stayed consistent for the HPV and Seasonal Influenza vaccines; however, we observed a slight variation in the degree of difference in attitudes between the profiles across the different vaccines. This suggests that, while these groups can be characterized by their overall attitudes towards vaccination, the specific vaccine in question may influence the intensity of their attitudes. For instance, profile 1 scored 2.82 on the MMR vaccine and 2.31 on the Seasonal Influenza vaccine. Although both scores indicate a neutral stance, there is a slight difference between them. Similarly, for profile 3, the mean score for the HPV vaccine was 1.33, while, for the Seasonal Influenza vaccine, it was 1.13.

Lastly, levels of importance of different sources of information about vaccines were examined across the three profiles ( Figure 3 ). Profile 1 gave the highest level of importance to information obtained by doctors ( M = 3.74, SD = 0.93), family ( M = 3.74, SD = 0.90), friends ( M = 3.68, SD = 0.81), and the National Institute of Public Health ( M = 3.34, SD = 0.98). This profile also exhibited a tendency towards confidence in alternative healers ( M = 3.28, SD = 0.95) but only gave a moderate level of importance to information in forums and websites ( M = 3.09, SD = 0.92), social media ( M = 2.68, SD = 0.98), and mainstream media, namely TV ( M = 2.60, SD = 0.99) and newspapers ( M = 2.68, SD = 0.96).

Importance of different information sources about vaccination across profiles.

Profile 2 gave the highest level of importance to information obtained from doctors ( M = 4.27, SD = 0.87), followed by the National Institute of Public Health ( M = 4.17, SD = 0.77). Surprisingly, the importance of family and friends as a source of information was slightly lower in this profile than in profile 1, with mean scores of 3.61 ( SD = 0.74) and 3.46 ( SD = 0.81), respectively. Online forums and websites, television, and newspapers were less important, with all mean scores falling below 3.00. Notably, of the three profiles, this group gave the lowest level of importance to information on social media ( M = 2.17, SD = 0.95) and alternative healers ( M = 2.34, SD = 0.99).

Profile 3, on the other hand, gave the highest level of importance to alternative healers ( M = 3.93, SD = 0.91), as well as a moderately high level of importance to forums and websites ( M = 3.41, SD = 0.99) and social media ( M = 3.22, SD = 1.08). With mean scores of 2.61 ( SD = 1.25), 3.55 ( SD = 1.10), and 3.50 ( SD = 0.97), the importance of information about vaccines from doctors, family, and friends was lower. Among the three profiles, the importance of the National Institute of Public Health was the lowest ( M = 2.18, SD = 1.22) and the importance of the mainstream media was likewise low ( M = 2.08, SD = 1.11 for TV, and M = 2.16, SD = 1.05 for newspaper).

5. Discussion

The present study aimed to explore the complexity of vaccine hesitancy using latent profile analysis, which identified three distinct vaccine-hesitant profiles, namely Skeptics, Conventionalists, and Self-directed researchers. These profiles were examined through trust in the healthcare system, endorsement of complementary and alternative medicine, beliefs in conspiracy theories, reliance on personal research, and sociodemographic variables.

We identified three distinct profiles in our study. Skeptics demonstrated moderate trust in the healthcare system but had low endorsement of CAM and conspiracy theories and relied less on personal research. They were typically older, higher-educated men, with low overconfidence in their knowledge. Conventionalists showed the highest trust in the healthcare system, minimal reliance on personal research, and low overconfidence. This group included more educated individuals, men, and those with a leftist political orientation. Self-directed researchers, in contrast, had the highest distrust in the healthcare system, high endorsement of CAM and conspiracy theories, and high reliance on personal research. They also exhibited moderate overconfidence and the highest level of vaccine hesitancy. Furthermore, attitudes toward vaccines varied among the profiles. Conventionalists held positive views on vaccine efficacy, safety, and importance, Skeptics were neutral, and Self-directed researchers had the most negative attitudes. Trust in information sources also differed. Skeptics trusted doctors, family, friends, and the National Institute of Public Health but had moderate trust in alternative healers and lower trust in social media and mainstream media. Conventionalists prioritized doctors and the National Institute of Public Health, with less emphasis on family and friends, and had the lowest reliance on social media and alternative healers. Self-directed researchers, however, valued information from alternative healers the most, followed by online forums and social media. Information from doctors, family, and friends was less important, as was information about vaccines from the National Institute of Public Health and mainstream media.

Vaccine hesitancy, especially more radical and opposing views of vaccines, were previously associated with trusting and obtaining information from social media [ 48 ], the Internet, and relatives [ 46 ]. In our study, these information sources were common amongst the “Self-directed researcher” category, which opposed vaccination the most. Interestingly, Conventionalists and, to some extent, Skeptics both expressed the importance of information from healthcare professionals, which was previously confirmed as an important predictor for positive attitudes [ 46 ]. This raises the interesting question of what role the intensity of beliefs and trust plays in vaccine hesitancy.

Furthermore, our study, similar to previous LPA research, underscores the importance of sociodemographic characteristics in understanding vaccine hesitancy. Consistent with findings from Howard [ 4 ] and Hornsey et al. [ 31 ], the profiles highlight significant sociodemographic distinctions, such as education level and age. For instance, Conventionalists were generally higher-educated and older, aligning with Hornsey et al.’s [ 31 ] identification, who also found that the profile expressing the most positive attitudes also consisted of higher-educated individuals. In addition, our study confirms findings from Howard [ 4 ], who noted that conservative political orientation was most common among the most vaccine-hesitant profiles, and with Zhou et al. [ 5 ], who found that more liberal-leaning individuals were less hesitant in comparison to conservatives, who were in the profile which expressed the lowest vaccination intention.

Drawing similarities or differences based on other constructs is more challenging, as the studies used different latent variables to identify profiles. However, we can partially compare our results with those of Lamot et al. [ 6 ], who used satisfaction with the healthcare system and endorsement of conspiracy theories as latent indicators. They found that individuals who were the most conspiratorial and distrustful were also more opposed to vaccination; this is consistent with our findings, where Self-directed researchers were the most conspiratorial and had the highest levels of vaccine-rejection. Similarly, Park et al. [ 60 ] reported the highest childhood vaccination intent within the profile of US mothers who were the least distrustful of medical institutions. In a study of Hong Kong nurses, the “skeptics” were the least likely to receive a vaccination and expressed the lowest levels of institutional trust [ 61 ]. Among Italian healthcare workers, Portoghese et al. [ 62 ] found that the highest levels of conspiracy beliefs were among the vaccine “rejector” and “hesitant” profiles. Among UK adults, the profile least likely to receive a vaccination was “social media users”, who, similarly to our findings, were most likely to obtain their health information from social media compared to other profiles [ 63 ].

Even though the present study contributes to theoretical and practical understanding of vaccine hesitancy, it has several limitations. First, the sample only included individuals from Slovenia, which may limit the generalization of the results to other countries. Future studies should use representative samples, including people from different regions, ages, genders, educational levels, and socioeconomic backgrounds. Moreover, cultural factors specific to Slovenia might have influenced these profiles. The Conventionalist profile consisted of a very small sample ( n = 41), which may affect the robustness and reliability of findings related to this group. Additionally, the study relied on self-reported data, which are subject to biases such as social desirability and recall bias. Next, causality cannot be established due to the cross-sectional design of the study. Finally, the study focused on a limited number of variables, potentially overlooking other relevant determinants and traits. Future studies should include individuals’ characteristics, such as media use habits, the impact of social media use, religious affiliation, and past vaccination experiences.

Future research studies should delve more into the observed gradient in belief intensity between the profiles we detected, which suggests a promising avenue for further research. It could also further explore which profiles emerge in different cultural and societal contexts (i.e., replication of the numbers and characteristics of profiles in different cultural contexts). In addition, future research endeavors should consider long-term follow-up surveys to observe trends in vaccine hesitators, assess the effectiveness of interventions, and adjust strategies in time.

Our results also provide potential avenues for targeted interventions that are tailored to specific subgroups. Understanding the characteristics of vaccine-hesitant subgroups can help public health communication and campaigns develop more effective and targeted interventions. Skeptics, for example, who already express a certain level of trust in healthcare, may be most receptive to messages from medical professionals. Given that Skeptics are typically older, higher-educated men, interventions could focus on leveraging their existing trust in healthcare professionals and providing detailed, evidence-based information to address their specific concerns. Self-directed researchers may need a different approach that addresses their specific concerns and distrust of traditional healthcare systems. This group, characterized by high endorsement of CAM and conspiracy theories and moderate overconfidence, could benefit from interventions involving trusted community figures or alternative medicine experts who can bridge the gap between traditional and alternative medicine perspectives. In addition, reaching out to younger women in this group through online platforms and providing credible information that counteracts misinformation could be effective.

Conventionalists, who have the highest trust in the healthcare system and tend to include more educated individuals with a left-leaning political orientation, may respond well to public health messages that emphasize the collective benefits of vaccination. Campaigns could focus on reinforcing their positive views on the efficacy, safety, and importance of vaccines while leveraging their trust in physicians and public health institutions. Second, the effectiveness of targeted interventions for each specific subgroup could then also be examined, employing insights from our study. For instance, tailored communication strategies could be tested to determine which messages and messengers are the most effective in regard to changing attitudes and behaviors within each profile.

In conclusion, our findings not only further confirm the heterogeneous nature of vaccine-hesitant groups but also offer critical insights for public health interventions. By acknowledging the existence of distinct profiles within the vaccine-hesitant population, strategies can be tailored to address the nuanced beliefs and attitudes of these subgroups more effectively.

Funding Statement

This research was partly funded by The Slovenian Research and Innovation Agency (ARIS), grant numbers V5-2242 and J5-4579, and the Slovenian Ministry of Health (V5-2242).

Author Contributions

Conceptualization, M.L. and A.K.; methodology, M.L.; formal analysis, M.L.; resources, A.K.; data curation, M.V.; writing—original draft preparation, M.L.; writing—review and editing, M.L., A.K. and M.V.; supervision, A.K.; funding acquisition, A.K. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

The study was conducted according to the guidelines of the Declaration of Helsinki. All study participants gave their written informed consent to use and share their data for scientific purposes. No personally identifiable information of respondents was obtained in the survey, ensuring anonymity. Subjects were informed that participation was on a fully voluntary basis, that completion of the questionnaire indicates their consent for study participation, and that all gathered data would be collectively elaborated on, having no other purpose than evaluation of determinants of vaccine attitudes. In addition, they were informed that they may withdraw from the survey at any point without any penalty.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Conflicts of interest.

The authors declare that they have no financial, professional or personal conflicting interest related to this study. The funders had no role in the design of the study; the collection, analyses or interpretation of data; the writing of the manuscript; or the decision to publish the results.

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New Study Reports High Rates of Anxiety and Depression in 11- to 13-Year-Olds During the COVID-19 Pandemic

Rates of depression in 11- to 13-year-olds increased significantly between the early and middle stages of the COVID-19 pandemic, and rates of anxiety and suicidal ideation stayed consistently high during the same period, according to a new study conducted in three U.S. states. The study, supported by the National Center for Complementary and Integrative Health with co-funding from the National Institute on Drug Abuse, the Office of Disease Prevention, and the Office of Behavioral and Social Sciences Research, and published in the Journal of Adolescence, also showed differences among population subgroups, with the greatest concerns about girls and Hispanic/Latinx youth in the early pandemic and among girls and Medicaid-insured youth at mid-pandemic.

Research conducted before the COVID-19 pandemic showed rates of anxiety, depression, and suicidal ideation among U.S. adolescents ranging from about 4 to 12 percent. Studies conducted during the early stage of the COVID-19 pandemic showed similar or slightly higher rates. This study extended the findings of previous research by analyzing data collected from a group of early adolescents, aged 11 to 13 years, during two time periods within the pandemic: March to September 2020 (early pandemic) and September 2020 to May 2021 (mid-pandemic). 

The 623 participants were recruited from pediatric primary care practices in California, Colorado, and Michigan for a pragmatic trial study testing the feasibility and effectiveness of implementing Guiding Good Choices, a family-focused substance use prevention program for caregivers of younger adolescents in health care systems. They completed a baseline behavioral health survey between March and September 2020, and then, because of a pandemic-related delay in the start of the study, they completed the survey again between September 2020 and May 2021. The survey included measures of anxiety (generalized anxiety disorder scale-7 [GAD-7]) and depression (patient health questionnaire-9 [PHQ-9]). The PHQ-9 item on “thoughts that you would be better off dead or of hurting yourself in some way” was used to assess suicidal ideation. 

During the early stage of the pandemic, 10.5 percent of the youth reported moderate-to-severe depression, with the lowest rate in boys (3.6 percent) and the highest rates in Hispanic/Latinx youth (16.7 percent) and girls (16.0 percent). In the overall sample, the rate of moderate-to-severe depression increased significantly from early to mid-pandemic, from 10.5 to 15.1 percent. The largest increases were seen in boys, black youth, and Medicaid-insured youth. Hispanic/Latinx youth showed a nonsignificant decrease in depression (from 16.7 to 13.9 percent). 

In the early stage of the pandemic, 12.0 percent of the youth reported moderate-to-severe anxiety, with the lowest rates in boys (4.6 percent) and Black youth (7.1 percent), and the highest rates in girls (17.7 percent) and Hispanic/Latinx youth (15.3 percent). The overall sample showed a nonsignificant increase in the prevalence of anxiety between early and mid-pandemic. In contrast, a decrease in the prevalence of anxiety was seen among Hispanic/Latinx youth at mid-pandemic.  

Early in the pandemic, 9.3 percent of the youth reported suicidal ideation, with the lowest rate among boys (4.8 percent) and the highest rates among girls (13.1 percent) and Hispanic/Latinx youth (12.2 percent). Nonsignificant increases were observed in the overall sample and some subgroups from early to mid-pandemic, and a nonsignificant decrease was seen among Hispanic/Latinx youth.

The researchers said that the COVID-19 pandemic may have had a persistent negative impact on mental health in early adolescents, as symptoms did not improve despite reductions in restrictions and closures in response to COVID-19 between early and mid-pandemic. The findings underscore the need for continued support for youth who experienced pandemic-related stressors. The researchers suggested that the possible improvements seen among Hispanic/Latinx youth from early to mid-pandemic might reflect the larger prevalence of multigenerational households in this group, which could contribute to a greater sense of community, perceived support, and resiliency. Additionally, most of the Hispanic/Latinx youth lived in Northern California or Colorado, where cities created outdoor space to promote outdoor activities during the pandemic, and these efforts were shown to significantly lower anxiety and depression. 

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• Danzo S, Kuklinski MR, Sterling SA, et al. Anxiety, depression, and suicidal ideation among early adolescents during the COVID-19 pandemic . Journal of Adolescence. 2024;96(6):1379-1387.

Publication Date: April 28, 2024

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