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Survey research: we can do better

Susan starr , mls, phd.

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Survey research is a commonly employed methodology in library and information science and the most frequently used research technique in papers published in the Journal of the Medical Library Association (JMLA) [ 1 ]. Unfortunately, very few of the survey reports that the JMLA receives provide sufficiently sound evidence to qualify as full-length JMLA research papers. A great deal of effort often goes into such studies, and our profession really needs the kind of evidence that the authors of these studies hoped to provide. Fortunately, the problems in these studies are not all that difficult to resolve. However, the problems do have to be addressed at the outset , before the survey is sent to potential respondents. Once the survey has been administered, it is too late.

To determine if a report qualifies for publication as a research study, the JMLA uses the definition of research given by the US Department of Health and Human Services, “a systematic investigation…designed to develop or contribute to generalizable knowledge” [ 2 ]. Problems arise when submitted surveys do not meet these criteria; either the reader cannot generalize from the findings to the population at large and/or the survey does not add to the knowledgebase of health sciences librarianship. If the results seem interesting, the JMLA may publish the paper as a brief communication in the hope that others will follow up with more in-depth investigations. However, many of these problematic surveys could have provided critically needed information, if only they had been done slightly differently. There are three common problems with the surveys that the JMLA receives, and each has a relatively straightforward solution.

Three common problems

Problem #1: The survey has not been designed to answer a question of interest to a substantial group of potential readers of the JMLA. A survey intended for publication should be designed to shed light on research questions relevant to health sciences librarianship or the delivery of biomedical information. Questions regarding user behavior, the effectiveness of interventions, barriers to using information, the utility of metadata, and so on are all potentially answerable with survey methodology. For example, a survey could be designed to reveal what influences users' decisions to use a library, whether physicians retain information retrieval techniques that are taught in medical school, what prevents clinicians from consulting published research, whether users appreciate good metadata, and so on. These are all important questions on issues of general interest, and surveys to help answer them are suitable for publication.

Problems arise because surveys can be used to provide information on local issues as well. For example, a librarian may wish to determine whether library users will tolerate increases in interlibrary loan fees, whether searchers are having trouble with a proxy server, or if local administrators approve of library services. A survey can be the best method to uncover this kind of information. However, such surveys are usually not publishable, even as a brief communication, as the questions included relate almost exclusively to local problems.

Solution #1: Before embarking on a survey intended for publication, review the current literature on the topic of interest. Design the survey to specifically address an issue of general importance that is not already answered in the literature. Survey questions should be written to provide information that can be used by others. A few questions specific to your institution or user group can also be included if necessary.

Problem #2: The results cannot be generalized beyond the group of people who answered the survey. Unfortunately, a major problem in all survey research is that respondents are almost always self-selected. Not everyone who receives a survey is likely to answer it, no matter how many times they are reminded or what incentives are offered. If those who choose to respond are different in some important way from those who do not, the results may not reflect the opinions or behaviors of the entire population under study. For example, to identify barriers to nurses' use of information, a survey should be answered by a representative sample of the nursing population. If only recent graduates of a nursing program, only pediatric nurses, or only nurses who are very annoyed with lack of access to computers in their hospital answer, the results may well be biased and so cannot be generalized to all nurses. Such a survey could be published as a brief communication, if the results were provocative and might stimulate research by others, but it would not be publishable as a research paper.

Solution #2: To address sample bias, take these three steps:

Send the survey to a representative sample of the population. Use reminders and incentives to obtain a high response rate (over 60%), thus minimizing the chances that only those with a particular perspective are answering the survey. And…

Include questions designed to identify sample bias. Questions will vary according to the topic of the survey, but typically such questions identify the demographics (age, sex, educational level, position, etc.) of the respondents or the characteristics of the organization (size, budget, location, etc.). Then…

Compare the characteristics of those answering the survey to those of known distributions of the population to identify possible bias. Samples of librarians, for example, can be compared to Medical Library Association (MLA) member surveys to determine if they reflect the general characteristics of MLA members. Samples of clinicians can be compared to statistics on the nations' medical professionals, and samples of academic libraries can be compared to the characteristics reported in the Association of Academic Health Sciences Library (AAHSL) annual survey. If the sample appears to be biased, acknowledge that as a limitation of the study's results.

Problem #3: The answers to the survey questions do not provide the information needed to address the issue at hand. Many times survey questions in studies submitted to the JMLA are ambiguous. Since it is impossible to determine what the answers represent, the paper must be rejected. A related and more subtle problem occurs when the survey did not ask about all the relevant issues. For example, a librarian might decide to survey clinicians to identify barriers to their use of mobile devices. She designs a survey that includes questions related to physical barriers, such as screen size, and questions on availability issues, such as accessibility of a particular database. The paper reports that the major barriers to use of mobile devices are physical problems with the devices. However, reviewers may note that there are many other possible barriers to using mobile technology in a clinical setting. Infrastructure issues, such as wireless connectivity in the hospital, and organizational issues, such as policies with respect to using cell phones in front of patients, can be critical factors. As a result, the conclusion of the survey is misleading, and the paper cannot be published.

Solution #3: Interview a few representative members of the intended survey population to identify all the critical aspects of the study topic before designing the survey. Then, pretest the survey on others and discuss the survey with pretest participants to identify ambiguous answers or unintelligible questions.

Benchmarking surveys

Benchmarking surveys provide data on the characteristics of a particular population of individuals, businesses, or organizations. Their intention is not to add to the knowledgebase of a discipline, but instead to provide numerical information that others can use for that purpose. The US Census is an example of a benchmarking survey; the MLA membership survey is another benchmarking tool as are the AAHSL annual survey and many of the surveys undertaken by the Pew Research Center. The data in these surveys are used by others both for practical purposes and for research. Social scientists use census data to develop economic models; academic medical libraries use AAHSL data to justify their budgets; and policy makers use the Pew data to understand social trends in the United States.

To be useful, a benchmarking study must be structured so that the data can be used either by researchers to compare different groups or by organizations, such as hospitals or libraries, to identify a peer group for comparative purposes. Using data to reliably compare groups selected according to multiple variables requires a very large scale study, an unbiased sample, and a thoroughly pretested survey instrument. Because benchmarking surveys need to be large and use a professionally constructed sample and survey instrument, most such surveys are done by organizations rather than individuals. Few, if any, benchmarking surveys submitted to the JMLA have a large enough sample to permit detailed analysis or identification of peer groups. They remain suggestive rather than conclusive and are normally only published as brief communications.

Three problems and three solutions

The solutions are not all that difficult to implement but as noted at the beginning of this editorial, they must be put in place before the survey is administered. To “develop or contribute to generalizable knowledge,” a survey needs to be created to answer a question that is important to others, gather information that will allow the researcher to identify sample bias, and use a well-designed unambiguous set of questions. The research question comes first; if the answer is already in the literature, then no further research is required. Developing a sampling methodology comes next, including identifying possible sources of bias and creating questions that will allow them to be identified. Last are interviews to refine the questions and pretesting to identify problematic language. We can do better surveys, and if we do, we will have the evidence we need to improve the delivery of biomedical information.

• 1. Gore S.A, Nordberg J.M, Palmer L.A, Piorun M.E. Trends in health sciences library and information science research: an analysis of research publications in the Bulletin of the Medical Library Association and Journal of the Medical Library Association from 1991 to 2007. J Med Lib Assoc. 2009 Jul;97(3):203–11. doi: 10.3163/1536-5050.97.3.009. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 2. US Department of Health and Human Services. Protection of human subjects. 45 CFR 46.102(d) 2005.
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Marketing survey research best practices: evidence and recommendations from a review of JAMS articles

• Methodological Paper
• Published: 10 April 2017
• Volume 46 , pages 92–108, ( 2018 )

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• John Hulland 1 ,
• Hans Baumgartner 2 &
• Keith Marion Smith 3  

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Survey research methodology is widely used in marketing, and it is important for both the field and individual researchers to follow stringent guidelines to ensure that meaningful insights are attained. To assess the extent to which marketing researchers are utilizing best practices in designing, administering, and analyzing surveys, we review the prevalence of published empirical survey work during the 2006–2015 period in three top marketing journals— Journal of the Academy of Marketing Science ( JAMS ), Journal of Marketing ( JM ), and Journal of Marketing Research ( JMR )—and then conduct an in-depth analysis of 202 survey-based studies published in JAMS . We focus on key issues in two broad areas of survey research (issues related to the choice of the object of measurement and selection of raters, and issues related to the measurement of the constructs of interest), and we describe conceptual considerations related to each specific issue, review how marketing researchers have attended to these issues in their published work, and identify appropriate best practices.

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A Retrospective on the Use of Survey Methodologies in Marketing Research

Crafting Survey Research: A Systematic Process for Conducting Survey Research

In conducting their topical review of publications in JMR , Huber et al. ( 2014 ) show evidence that the incidence of survey work has declined, particularly as new editors more skeptical of the survey method have emerged. They conclude (p. 88)—in looking at the results of their correspondence analysis—that survey research is more of a peripheral than a core topic in marketing. This perspective seems to be more prevalent in JMR than in JM and JAMS , as we note above.

A copy of the coding scheme used is available from the first author.

Several studies used more than one mode.

Traditionally, commercial researchers used phone as their primary collection mode. Today, 60% of commercial studies are conducted online (CASRO 2015 ), growing at a rate of roughly 8% per year.

Although the two categories are not necessarily mutually exclusive, the overlap was small ( n  = 4).

This is close to the number of studies in which an explicit sampling frame was employed, which makes sense (i.e., one would not expect a check for non-response bias when a convenience sample is used).

It is interesting to note that Cote and Buckley examined the extent of CMV present in papers published across a variety of disciplines, and found that CMV was lowest for marketing (16%) and highest for the field of education (> 30%). This does not mean, however, that marketers do a consistently good job of accounting for CMV.

In practice, these items need to be conceptually related yet empirically distinct from one another. Using minor variations of the same basic item just to have multiple items does not result in the advantages described here.

In general, the use of PLS (which is usually employed when the measurement model is formative or mixed) was uncommon in our review, so it appears that most studies focused on using reflective measures.

Most of the studies discussing discriminant validity used the approach proposed by Fornell and Larcker ( 1981 ). A recent paper by Voorhees et al. ( 2016 ) suggests use of two approaches to determining discriminant validity: (1) the Fornell and Larcker test and (2) a new approach proposed by Henseler et al. ( 2015 ).

This solution is not a universal panacea. For example, Kammeyer-Mueller et al. ( 2010 ) show using simulated data that under some conditions using distinct data sources can distort estimation. Their point, however, is that the researcher must think carefully about this issue and resist using easy one-size-fits-all solutions.

Podsakoff et al. ( 2003 ) also mention two other techniques—the correlated uniqueness model and the direct product model—but do not recommend their use. Only very limited use of either technique has been made in marketing, so we do not discuss them further in this paper.

These techniques are described more extensively in Podsakoff et al. ( 2003 ), and contrasted to one another. Figure 1 (p. 898) and Table 4 (p. 891) in their paper are particularly helpful in understanding the differences across approaches.

It is unclear why the procedure is called the Harman test, because Harman never proposed the test and it is unlikely that he would be pleased to have his name associated with it. Greene and Organ ( 1973 ) are sometimes cited as an early application of the Harman test (they specifically mention “Harman’s test of the single-factor model,” p. 99), but they in turn refer to an article by Brewer et al. ( 1970 ), in which Harman’s one-factor test is mentioned. Brewer et al. ( 1970 ) argued that before testing the partial correlation between two variables controlling for a third variable, researchers should test whether a single-factor model can account for the correlations between the three variables, and they mentioned that one can use “a simple algebraic solution for extraction of a single factor (Harman 1960 : 122).” If measurement error is present, three measures of the same underlying factor will not be perfectly correlated, and if a single-factor model is consistent with the data, there is no need to consider a multi-factor model (which is implied by the use of partial correlations). It is clear that the article by Brewer et al. does not say anything about systematic method variance, and although Greene and Organ talk about an “artifact due to measurement error” (p. 99), they do not specifically mention systematic measurement error. Schriesheim ( 1979 ), another early application of Harman’s test, describes a factor analysis of 14 variables, citing Harman as a general factor-analytic reference, and concludes, “no general factor was apparent, suggesting a lack of substantial method variance to confound the interpretation of results” (p. 350). It appears that Schriesheim was the first to conflate Harman and testing for common method variance, although Harman was only cited as background for deciding how many factors to extract. Several years later, Podsakoff and Organ ( 1986 ) described Harman’s one-factor test as a post-hoc method to check for the presence of common method variance (pp. 536–537), although they also mention “some problems inherent in its use” (p. 536). In sum, it appears that starting with Schriesheim, the one-factor test was interpreted as a check for the presence of common method variance, although labeling the test Harman’s one-factor test seems entirely unjustified.

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The constructive comments of the Editor-in-Chief, Area Editor, and three reviewers are gratefully acknowledged.

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John Hulland

Smeal College of Business, Penn State University, State College, PA, USA

Hans Baumgartner

D’Amore-McKim School of Business, Northeastern University, Boston, MA, USA

Keith Marion Smith

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Putting the Harman test to rest

A moment’s reflection will convince most researchers that the following two assumptions about method variance are entirely unrealistic: (1) most of the variation in ratings made in response to items meant to measure substantive constructs is due to method variance, and (2) a single source of method variance is responsible for all of the non-substantive variation in ratings. No empirical evidence exists to support these assumptions. Yet when it comes to testing for the presence of unwanted method variance in data, many researchers suspend disbelief and subscribe to these implausible assumptions. The reason, presumably, is that doing so conveniently satisfies two desiderata. First, testing for method variance has become a sine qua non in certain areas of research (e.g., managerial studies), so it is essential that the research contain some evidence that method variance was evaluated. Second, basing a test of method variance on procedures that are strongly biased against detecting method variance essentially guarantees that no evidence of method variance will ever be found in the data.

Although various procedures have been proposed to examine method variance, the most popular is the so-called Harman one-factor test, which makes both of the foregoing assumptions. Footnote 14 While the logic underlying the Harman test is convoluted, it seems to go as follows: If a single factor can account for the correlation among a set of measures, then this is prima facie evidence of common method variance. In contrast, if multiple factors are necessary to account for the correlations, then the data are free of common method variance. Why one factor indicates common method variance and not substantive variance (e.g., several substantive factors that lack discriminant validity), and why several factors indicate multiple substantive factors and not multiple sources of method variance remains unexplained. Although it is true that “if a substantial amount of common method variance is present, either (a) a single factor will emerge from the factor analysis, or (b) one ‘general’ factor will account for the majority of the covariance in the independent and criterion variables” (Podsakoff and Organ 1986 , p. 536), it is a logical fallacy (i.e., affirming the consequent) to argue that the existence of a single common factor (necessarily) implicates common method variance.

Apart from the inherent flaws of the test, several authors have pointed out various other difficulties associated with the Harman test (e.g., see Podsakoff et al. 2003 ). For example, it is not clear how much of the total variance a general factor has to account for before one can conclude that method variance is a problem. Furthermore, the likelihood that a general factor will account for a large portion of the variance decreases as the number of variables analyzed increases. Finally, the test only diagnoses potential problems with method variance but does not correct for them (e.g., Podsakoff and Organ 1986 ; Podsakoff et al. 2003 ). More sophisticated versions of the test have been proposed, which correct some of these shortcoming (e.g., if a confirmatory factor analysis is used, explicit tests of the tenability of a one-factor model are available), but the faulty logic of the test cannot be remedied.

In fact, the most misleading application of the Harman test occurs when the variance accounted for by a general factor is partialled from the observed variables. Since it is likely that the general factor contains not only method variance but also substantive variance, this means that partialling will not only remove common method variance but also substantive variance. Although researchers will most often argue that common method variance is not a problem since partialling a general factor does not materially affect the results, this conclusion is also misleading, because the test is usually conducted in such a way that the desired result is favored. For example, in most cases all loadings on the method factor are restricted to be equal, which makes the questionable assumption that the presumed method factor influences all observed variables equally, even though this assumption is not imposed for the trait loadings.

In summary, the Harman test is entirely non-diagnostic about the presence of common method variance in data. Researchers should stop going through the motions of conducting a Harman test and pretending that they are performing a meaningful investigation of systematic errors of measurement.

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Hulland, J., Baumgartner, H. & Smith, K.M. Marketing survey research best practices: evidence and recommendations from a review of JAMS articles. J. of the Acad. Mark. Sci. 46 , 92–108 (2018). https://doi.org/10.1007/s11747-017-0532-y

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Received : 19 August 2016

Accepted : 29 March 2017

Published : 10 April 2017

Issue Date : January 2018

DOI : https://doi.org/10.1007/s11747-017-0532-y

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How U.S. Public Opinion Has Changed in 20 Years of Our Surveys

When The Pew Charitable Trusts created Pew Research Center in 2004, we were surveying Americans using the established industry method at the time: calling people on their landline phones and hoping they’d answer. As the Center marks its 20th anniversary this year,  survey methods have become more diverse , and we now  conduct most of our interviews online .

Public opinion itself has also changed in major ways over the last 20 years, just as the country and world have. In this data essay, we’ll take a closer look at how Americans’ views and experiences have evolved on topics ranging from technology and politics to religion and social issues.

To mark Pew Research Center’s 20th anniversary, this data essay summarizes key shifts in public opinion and national demographics between 2004 and 2024. The essay is based on survey data from the Center and the U.S. Census Bureau. Links to these sources are available in the text. The links to the Center surveys include information about their field dates, sample sizes and other methodological details.

All references to Republicans and Democrats in this analysis include independents who lean toward each party. White, Black and Asian racial categories reflect people who identify as single-race and non-Hispanic. Hispanics are of any race.

The rise of the internet, smartphones and social media

The past two decades have witnessed the emergence of all sorts of technologies that let people interact with the world in new ways. For instance, 63% of U.S. adults used the internet in 2004, and 65% owned a cellphone (we weren’t yet asking about smartphones). Today, 95% of U.S. adults browse the internet, and 90% own a smartphone, according to our surveys.

Social media was just taking off in 2004, the year Mark Zuckerberg launched “The Facebook” (as it was known then) from his Harvard dorm room. Since then, Americans have widely adopted social media . These platforms have also become a key source of news for the U.S. public, even as concerns about misinformation and national security have grown.

Meanwhile, many traditional news organizations have struggled. In 2004, daily weekday newspaper circulation in the U.S. totaled around 55 million. By 2022, that had fallen to just under 21 million. Newspapers’ advertising dollars and employee counts  have also decreased. 

In this more fragmented news environment, Americans – particularly Republicans – have become less trusting of the information that comes from news organizations. On the whole, however, more people still say they trust information from news organizations than from social media.

Other emerging technologies

Some technological changes over the past 20 years haven’t been as widely adopted, and a few still sound like science fiction. For example, Elon Musk announced this year that his company Neuralink had implanted a computer chip in a living person’s brain. The chip is intended to allow people to use phones or computers simply by  thinking  about what they want to do on the devices – an idea that Americans are largely hesitant about .

Other innovations we’ve surveyed about that might have seemed far-fetched back in 2004 include driverless passenger vehicles , space tourism , AI chatbots like ChatGPT, and gene editing to reduce a baby’s risk of developing serious health conditions. Our research suggests that Americans are still getting introduced to and forming opinions of these technologies, so we’ll likely see public attitudes evolve on these and other innovations over the next 20 years.

Declining trust in national institutions

Around the Center’s founding in 2004, 36% of Americans said they trusted the federal government to do what is right just about always or most of the time. By April 2024, just 22% said the same.

This is part of a longer-term decline in trust. In 1964, 77% of Americans trusted the federal government to do the right thing all or most of the time. There have been a few periods of increased trust in the decades since, including shortly after the Sept. 11 terrorist attacks. But since 2008, fewer than 30% of Americans have said they trust the government to do the right thing all or most of the time.

Views of Congress and the Supreme Court have also become more negative over the past 20 years. In 2006, 53% of Americans had a favorable view of Congress , but after some ups and downs, that share fell to 26% in 2023. And the share of adults who view the nation’s highest court favorably is near its lowest mark in almost 40 years of data.

The years during and after the coronavirus pandemic have also seen a more general distrust of people who were once considered experts. Many Americans were dissatisfied with the communication they received about the pandemic from public health officials, and close to half thought officials were unprepared for the initial coronavirus outbreak in the United States. Most Americans still trust scientists to act in the public’s best interest, but fewer say this now than in 2020, especially among Republicans.

More diversity in the U.S. and its government

The U.S. has become much more diverse over the past 20 years on several measures, including immigrant status. Today, immigrants account for 13.8% of the nation’s population – near the record high from 1890 – and they have come from just about every country in the world.

Racial and ethnic diversity has also increased. Between 2004 and 2022, the U.S. population grew by 14%, according to the Census Bureau . But the Asian, Hispanic and Black populations all grew at faster rates – 74%, 55% and 22%, respectively – while the White population remained stable. As a result, the share of Americans who are White fell from 68% in 2004 to 59% in 2022. 1

As the country has become more diverse, so have its voters – and its leaders. The 118th Congress is the most racially and ethnically diverse in history, and the number of women in Congress is at an all-time high. And majorities of President Joe Biden’s judicial appointments have been women and racial or ethnic minorities, a first for any president.

Still, there’s some skepticism that women will ever achieve parity with men in political leadership. In 2023, 52% of Americans said it is only a matter of time before there are  as many women in political office as men , while 46% said men will continue to hold more high political offices.

Growing dissatisfaction with the Democratic and Republican parties

In the early 2000s, very few Americans had unfavorable views of both the Democratic and Republican parties. But over the next two decades, the share saying they dislike both major parties increased, reaching 28% in 2023.

This is just one element of Americans’ broad dissatisfaction with politics . As trust in political institutions declines, few Americans now think the political system is working even somewhat well. Majorities say that most elected officials don’t care what people like them think and that ordinary people have too little influence on Congress’ decision-making. And most see little or no common ground between Republicans and Democrats on the economy, the environment, the budget deficit, immigration, gun policy or abortion.

As a result, many Americans say they regularly feel angry or exhausted when they think about U.S. politics, and very few feel hopeful and excited. When asked for the one word or phrase they’d use to describe politics today, some of the most common answers are “divisive,” “corrupt” and “messy.”

China’s emergence as a perceived threat – and even an enemy

Americans’ views of China have become increasingly negative over the past two decades. In 2005, the first year we asked this question, 35% of U.S. adults had an unfavorable view of China. Today, about eight-in-ten view China unfavorably, and about four-in-ten say it is an enemy of the U.S. , as opposed to a competitor or a partner.

In an open-ended survey question in 2023, half of Americans named China as the country that poses the greatest threat to the U.S. – about three times the share who named Russia, the second-most common answer. In contrast, China was only the third-most popular answer in 2007, behind Iran and Iraq.

The rise of the religiously unaffiliated

Many Americans describe themselves as atheist, agnostic or “nothing in particular.” At the Center, we refer to this group as religious “nones.”

The share of Americans who identify as religious nones is significantly higher than it was when we began asking this question about religious identity in 2007. In recent years, the share of religious nones has mostly been stable, around 28%. But it’s too early to tell whether this population is leveling off or will continue to grow.

Still, religious nones are currently one of the largest religious groups in the United States. They trail Protestants, who make up 41% of U.S. adults, but make up a larger share of the population than Catholics (20%) and all other faiths (8%).

While they don’t identify with any organized religion, many religious nones do hold some religious or spiritual beliefs . For example, most say there is some higher power or spiritual force in the universe, though just 13% say they believe in “God as described in the Bible.”

A reversal in public opinion about same-sex marriage

The first same-sex marriages in the U.S. took place in 2004 in Massachusetts – to the consternation of both presidential candidates at the time, Republican George W. Bush and Democrat John Kerry. Their opinions reflected those of the general public: That year, 31% of Americans supported same-sex marriage , while 60% opposed it.

By 2023, attitudes on same-sex marriage had effectively flipped: 63% of Americans supported it and 34% opposed it.

The tide began to turn around 2010, when similar shares expressed support and opposition for same-sex marriage. Soon, larger shares began to favor than oppose it. And by 2015, the U.S. Supreme Court issued its landmark Obergefell v. Hodges ruling, which established that same-sex couples have a constitutional right to marry.

Since the world’s first same-sex marriages were legally recognized in the Netherlands in 2001, many places globally have followed a similar path as the U.S. International views of same-sex marriage vary, but in general, there has been increased support over the past decade. Same-sex marriage is currently legal in more than 30 places worldwide , mostly in Europe and the Americas.

Another reversal: Marijuana legalization

Support for legalizing marijuana has also been on the rise in the U.S. over the past two decades. Around the time the Center was established in 2004, just a third of U.S. adults said marijuana should be legalized, but that rose to 70% by 2023.

The change in attitudes is even starker when looking at the longer term. In 1969, just 12% of Americans supported legalizing marijuana. It wasn’t until 1996 that any state legalized the drug for medical purposes, and it took until 2012 for states to begin legalizing it for recreational purposes.

Today, 38 states and the District of Columbia have legalized marijuana for medical and/or recreational use.

Increasingly polarized views on climate change, guns, abortion

On several issues, the pattern is not just that Americans’ views have changed markedly over the past two decades. It’s that Democrats and Republicans have grown further apart in their views, eroding areas of common ground between the parties. (In this essay, as in most Center publications, “Democrats” and “Republicans” refer to people who identify with or lean toward that party.)

Consider climate change. In 2009, Democrats were already 36 percentage points more likely than Republicans to say climate change is a major threat to the U.S. (61% vs. 25%). But by 2022, that partisan gap had grown to 55 points: 78% of Democrats, but just 23% of Republicans, considered climate change a major threat.

Globally, people in many advanced economies tend to have similar levels of concern to U.S. Democrats. A median of 75% of adults across 19 countries we surveyed in 2022 said climate change is a major threat to their country.

The topic of guns has become increasingly partisan , too. In 2003, 56% of Republicans and 29% of Democrats said it was more important to protect Americans’ right to own guns than to control gun ownership, a 27-point gap. But by 2022, that gap had swelled to 63 points (81% vs. 18%).

These changes coincided with major court rulings, including the Supreme Court’s 2008 decision in District of Columbia v. Heller, which held that the Second Amendment guarantees an individual’s right to have a gun.

Abortion is another subject where partisan divisions have grown. In 2007, 63% of Democrats said abortion should be legal in all or most cases. That share has grown to 85% today, following the Supreme Court’s 2022 decision to overturn Roe v. Wade, which had enshrined the constitutional right to abortion in 1973.

By comparison, there has been relatively little change in opinion among Republicans: About four-in-ten continue to say abortion should be legal in all or most cases. As a result, the partisan gap has soared from 24 points in 2007 to 44 points today.

Global views vary, but support for legal abortion has generally grown over the past decade in Europe, Latin America and India. A median of 66% of adults across 27 places we surveyed now say abortion should be legal in all or most cases. In most places where we can measure political ideology on a left-right scale, people on the left are more likely than those on the right to support legal abortion. But the U.S. has by far the largest gap between the two sides.

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• White, Black and Asian populations include those who report being only one race and are not Hispanic. Hispanics are of any race. ↩

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Trauma and PTSD in the WHO World Mental Health Surveys

Ronald c kessler, sergio aguilar-gaxiola, jordi alonso, corina benjet, evelyn j bromet, graça cardoso, louisa degenhardt, giovanni de girolamo, rumyana v dinolova, finola ferry, silvia florescu, josep maria haro, yueqin huang, elie g karam, norito kawakami, jean-pierre lepine, daphna levinson, fernando navarro-mateu, beth-ellen pennell, marina piazza, josé posada-villa, kate m scott, dan j stein, margreet ten have, yolanda torres, maria carmen viana, maria v petukhova, nancy a sampson, alan m zaslavsky, karestan c koenen.

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CONTACT Ronald C. Kessler [email protected] Department of Health Care Policy , Harvard Medical School , 180 Longwood Avenue, Boston, MA02115, USA

Received 2017 Mar 23; Revised 2017 Jun 16; Accepted 2017 Jul 6; Collection date 2017.

This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/ ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Background : Although post-traumatic stress disorder (PTSD) onset-persistence is thought to vary significantly by trauma type, most epidemiological surveys are incapable of assessing this because they evaluate lifetime PTSD only for traumas nominated by respondents as their ‘worst.’

Objective : To review research on associations of trauma type with PTSD in the WHO World Mental Health (WMH) surveys, a series of epidemiological surveys that obtained representative data on trauma-specific PTSD.

Method : WMH Surveys in 24 countries (n = 68,894) assessed 29 lifetime traumas and evaluated PTSD twice for each respondent: once for the ‘worst’ lifetime trauma and separately for a randomly-selected trauma with weighting to adjust for individual differences in trauma exposures. PTSD onset-persistence was evaluated with the WHO Composite International Diagnostic Interview.

Results : In total, 70.4% of respondents experienced lifetime traumas, with exposure averaging 3.2 traumas per capita. Substantial between-trauma differences were found in PTSD onset but less in persistence. Traumas involving interpersonal violence had highest risk. Burden of PTSD, determined by multiplying trauma prevalence by trauma-specific PTSD risk and persistence, was 77.7 person-years/100 respondents. The trauma types with highest proportions of this burden were rape (13.1%), other sexual assault (15.1%), being stalked (9.8%), and unexpected death of a loved one (11.6%). The first three of these four represent relatively uncommon traumas with high PTSD risk and the last a very common trauma with low PTSD risk. The broad category of intimate partner sexual violence accounted for nearly 42.7% of all person-years with PTSD. Prior trauma history predicted both future trauma exposure and future PTSD risk.

Conclusions : Trauma exposure is common throughout the world, unequally distributed, and differential across trauma types with respect to PTSD risk. Although a substantial minority of PTSD cases remits within months after onset, mean symptom duration is considerably longer than previously recognized.

KEYWORDS: Burden of illness, disorder prevalence and persistence, epidemiology, post-traumatic stress disorder (PTSD), trauma exposure

1. Introduction

The fact that only a small minority of people in the population develops post-traumatic stress disorder (PTSD) (Atwoli, Stein, Koenen, & McLaughlin, 2015 ) even though the vast majority are exposed to traumas at some time in their life (Benjet et al., 2016 ) has raised questions about individual differences in psychological vulnerability to PTSD. These questions are the subject of considerable research (Liberzon & Abelson, 2016 ; Sayed, Iacoviello, & Charney, 2015 ; Smoller, 2016 ). One prior consideration is the possibility that PTSD risk varies significantly by trauma type. Such differences have been documented, with highest PTSD risk thought to occur after traumas involving interpersonal violence (Caramanica, Brackbill, Stellman, & Farfel, 2015 ; Fossion et al., 2015 ). A related line of research suggests that trauma history is a risk factor for subsequent PTSD, with prior traumas involving violence again possibly of special importance (Lowe, Walsh, Uddin, Galea, & Koenen, 2014 ; Smith, Summers, Dillon, & Cougle, 2016 ). Few studies estimating these differences across trauma types did so using unbiased methods, raising questions about the validity of results regarding these differences. The issue of biasedness comes up because of a common data collection convention in general population epidemiological studies of PTSD whereby respondents are asked about lifetime exposure to each of a wide range of traumas but then assessed for PTSD only for the one trauma nominated by the respondent as their worst or most upsetting lifetime trauma. This approach makes it impossible to estimate conditional risk of PTSD after trauma exposure without upward bias because the traumas for which PTSD is assessed are atypically severe.

One approach to deal with this problem is to assess PTSD twice for epidemiological survey respondents who report experiencing multiple lifetime traumas: once for the trauma nominated by the respondent as their worst lifetime trauma and a second time for a random trauma (i.e. one randomly-selected occurrence of one randomly-selected trauma type). By weighting the random trauma reports by the inverse of their probabilities of selection at the individual level and combining these weighted reports with reports about the worst lifetime trauma, a representative sample can be generated of all lifetime traumas experienced by all survey respondents. The weight would be 1 for respondents who reported lifetime exposure to only one occurrence of one trauma type.

This weighted sample of trauma occurrences can then be used to obtain estimates both of the distribution of trauma exposure in the population and the conditional probability of PTSD after exposure to traumas of different types. These estimates are unbiased if the model is correct, although they will still be subject to the biases of recall error. The current paper reports data on between-trauma differences in the distribution of trauma exposure by trauma type and the risk of PTSD associated with each trauma type in the WHO World Mental Health (WMH) surveys. The WMH Surveys are a series of community epidemiological surveys that used this weighting scheme to generate a representative sample of trauma occurrences in the general population of participating countries (Liu et al., 2017 ).

We begin by reporting results on overall lifetime prevalence and basic socio-demographic correlates of trauma exposure in the population. We break down traumas into a number of different types for this purpose. We then examine conditional risk of PTSD after trauma exposure and compare this risk across trauma types. We also examine a number of predictors of PTSD risk among people exposed to traumas controlling for trauma type, including socio-demographic predictors, information about prior trauma exposure, and information about history of mental disorder before exposure to the focal trauma. Next, we examine data on the typical course of PTSD; that is, on the duration of PTSD episodes. We examine the same range of predictors of duration as for onset. Finally, we combine all of the above information into a consolidated portrait of the population burden of PTSD broken down by trauma type. This consolidated portrait takes into consideration differences across traumas in prevalence, conditional risk of PTSD, and the duration of PTSD.

2. Methods and materials

2.1. sample.

The WMH Surveys are a coordinated series of cross-national community epidemiological surveys using consistent sampling, field procedures, and instruments designed to facilitate pooled cross-national analyses of prevalence and correlates of common mental disorders (Kessler & Ustun, 2008 ). The subset of 26 WMH Surveys considered in this paper are those that assessed lifetime PTSD after both worst and random traumas . Five of the surveys were carried out in low/lower-middle income countries (People’s Republic of China [PRC], Colombia, Nigeria, Peru, Ukraine), seven in upper-middle income countries (Brazil, Bulgaria, Colombia [administered after the previously-mentioned Colombian survey, when the country income rating had increased], Lebanon, Mexico, Romania, South Africa), and 14 in high income countries (Australia, Belgium, France, Germany, Israel, Italy, Japan, Netherlands, New Zealand, Northern Ireland, Portugal, Spain [separate national and regional surveys], USA). Each survey was based on a multi-stage clustered area probability sample of adult household residents. Three of the 26 surveys included the urbanized area of their countries (Colombia, Mexico, Peru), and five were based in specific Metropolitan areas (Beijing and Shanghai, PRC; Sao Paulo, Brazil; Medellin, Colombia; Murcia, Spain; six cities in Japan). The Nigerian survey was restricted to specific regions and the other 17 the entire country. More details about the samples are presented in Table 1 (Stein, de Jonge, Kessler, & Scott, in press ).

WMH sample characteristics by World Bank income categories. a

a World Bank (2012) Data. Retrieved from http://data.worldbank.org/country . Some of the WMH countries have moved into new income categories since the surveys were conducted. The income groupings above reflect the status of each country at the time of data collection. The current income category of each country is available at the preceding URL.

b NSMH (The Colombian National Study of Mental Health); NSMHW (The Nigerian Survey of Mental Health and Wellbeing); B-WMH (The Beijing World Mental Health Survey); S-WMH (The Shanghai World Mental Health Survey); EMSMP (La Encuesta Mundial de Salud Mental en el Peru); CMDPSD (Comorbid Mental Disorders during Periods of Social Disruption); NSHS (Bulgaria National Survey of Health and Stress); MMHHS (Medellín Mental Health Household Study); LEBANON (Lebanese Evaluation of the Burden of Ailments and Needs of the Nation); M-NCS (The Mexico National Comorbidity Survey); RMHS (Romania Mental Health Survey); SASH (South Africa Health Survey); NSMHWB (National Survey of Mental Health and Wellbeing); ESEMeD (The European Study Of The Epidemiology Of Mental Disorders); NHS (Israel National Health Survey); WMHJ 2002–2006 (World Mental Health Japan Survey); NZMHS (New Zealand Mental Health Survey); NISHS (Northern Ireland Study of Health and Stress); NMHS (Portugal National Mental Health Survey); PEGASUS-Murcia (Psychiatric Enquiry to General Population in Southeast Spain-Murcia);NCS-R (The US National Comorbidity Survey Replication).

c Most WMH Surveys are based on stratified multistage clustered area probability household samples in which samples of areas equivalent to counties or municipalities in the US were selected in the first stage followed by one or more subsequent stages of geographic sampling (e.g. towns within counties, blocks within towns, households within blocks) to arrive at a sample of households, in each of which a listing of household members was created and one or two people were selected from this listing to be interviewed. No substitution was allowed when the originally sampled household resident could not be interviewed. These household samples were selected from Census area data in all countries other than France (where telephone directories were used to select households) and the Netherlands (where postal registries were used to select households). Several WMH Surveys (Belgium, Germany, Italy, Spain-Murcia) used municipal or universal health-care registries to select respondents without listing households. The Japanese sample is the only totally un-clustered sample, with households randomly-selected in each of the 11 metropolitan areas and one random respondent selected in each sample household: 17 of the 27 surveys are based on nationally representative household samples.

d The response rate is calculated as the ratio of the number of households in which an interview was completed to the number of households originally sampled, excluding from the denominator households known not to be eligible either because of being vacant at the time of initial contact or because the residents were unable to speak the designated languages of the survey. The weighted average response rate is 70.4%

e People’s Republic of China.

f For the purposes of cross-national comparisons we limit the sample to those 18+.

g Colombia moved from the ‘lower and lower-middle income’ to the ‘upper-middle income’ category between 2003 (when the Colombian National Study of Mental Health was conducted) and 2010 (when the Medellin Mental Health Household Study was conducted), hence Colombia’s appearance in both income categories. For more information, please see footnote a .

WMH interviews were administered face-to-face in respondent homes by trained lay interviewers after obtaining informed consent using procedures approved by local Institutional Review Boards. The response rate had a weighted (by sample size) mean of 70.4% across surveys (between 45.9% in France and 97.2% in Medellin). The interviews were in two parts. Part I, administered to all respondents ( n  = 125,718), assessed core DSM-IV mental disorders. Part II, administered to all Part I respondents with core disorders and a probability subsample of other respondents ( n  = 68,894), assessed additional disorders and correlates. Traumas and PTSD were assessed in Part II. The analysis sample considered here includes the 50,855 Part II respondents who reported lifetime trauma exposure. As detailed elsewhere (Heeringa et al., 2008 ), this sample was weighted to match population geographic/socio-demographic distributions and to adjust for under-sampling of Part I non-cases.

2.2. Measures

Traumas : A total of 29 trauma types were assessed, with reports of lifetime exposure followed by questions about number of lifetime occurrences and age at first occurrence of each type. Traumas were divided into seven categories for purposes of analysis: seven related to war (e.g. combatant, civilian in war zone, relief worker, refugee); four related to physical violence (e.g. physically abused by a caregiver as a child, mugged); four related to intimate partner or sexual violence (raped, sexually assaulted, stalked, physically abused by a romantic partner); seven related to accidents (toxic chemical spill, other man-made disaster, natural disaster, life-threatening motor vehicle collision, other accident where the respondent accidentally caused serious injury to another person, other life-threatening accident, life-threatening illness); unexpected or traumatic death of a loved one; four related to traumas that happened to other people (child had life-threatening illness, other traumas that occurred to loved ones, witnessed physical fights at home as a child, witnessed any other trauma); and a residual category of ‘other’ traumas. The latter category included responses to two questions: (i) an open-ended question: Did you ever experience any other extremely traumatic or life-threatening event that I haven’t asked you about? that was backcoded into the other six categories whenever possible and the residual reports coded in an ‘other’ category; and (ii) a question about a ‘private’ trauma: Sometimes people have experiences they don’t want to talk about in interviews. I won’t ask you to describe anything like this but, without telling me what it was, did you ever have a traumatic event that you didn’t tell me about because you didn’t want to talk about it? As shown below, a surprisingly large number of respondents answered this question affirmatively.

PTSD : DSM-IV PTSD was assessed with the Composite International Diagnostic Interview (CIDI) (Kessler & Ustün, 2004 ), a fully-structured lay interview that assesses a wide range of common mental disorders. As noted in the Introduction, PTSD was assessed separately for one random occurrence of one randomly-selected trauma type reported by each respondent selected using a random numbers table for that respondent (the respondent’s random trauma ) and separately for the respondent’s self-reported worst trauma. When DSM-IV/CIDI criteria for PTSD were met, the respondent was asked how long symptoms persisted and if the symptoms were still present at the time of interview. Clinical reappraisal interviews with the SCID (Haro et al., 2006 ) blinded to CIDI diagnoses of PTSD (but instructed to focus on the same trauma as the one assessed in the CIDI in order to guarantee valid comparison of diagnoses) documented moderate CIDI–SCID concordance (Landis & Koch, 1977 ) (AUC = .69). Sensitivity and specificity were 38.3% and 99.1%, respectively, resulting in a likelihood ratio positive (Sensitivity/[1-Specificity]) of 42.0 that is well above the 10.0 typically considered definitive for a positive screen (Gardner & Altman, 2000 ). Based on these operating characteristics, a very high proportion of CIDI cases (86.1%) were confirmed by the SCID.

2.3. Analysis methods

Cross-tabulations were used to estimate the distribution of lifetime trauma exposure at the individual level and to examine the distribution of trauma exposure as well as conditional risk of PTSD associated with each trauma type in this trauma-level dataset. Means were calculated within subsamples to estimate average number of trauma occurrences given any. Discrete-time survival analysis with time-varying predictors was used to examine the socio-demographic and prior trauma predictors of each type of trauma exposure and the predictors of persistence of PTSD among cases. As noted in the Introduction, random trauma reports were weighted by the inverse of random trauma probability of selection multiplied by the respondent’s Part II weight to generate a sample representative of all traumas experienced by all respondents. Logistic regression analysis was used to examine predictors of conditional risk of PTSD among the trauma-exposed. Design-adjusted standard errors were used to assess significance of individual predictors and design-based Wald χ 2 tests to evaluate the significance of predictor sets.

3.1. Prevalence and distribution of trauma exposure

Lifetime exposure to one or more traumas was reported by a weighted 70.4% of Part II WMH respondents ( n  = 50,855, with 51,196 random and/or worst events; Table 2 ). Mean number of lifetime trauma types among those with any was 2.9, for 2.0 trauma types for capita (i.e. .704 × 2.9). The distribution of number of types among respondents with any was 32.1% one, 23.4% two, 16.6% three, 10.7% four, 6.5% five, 3.9% six, 2.5% seven, and 4.3% more than seven. By far the most common trauma types were unexpected death of a loved one (reported by 31.4% of respondents) and direct exposure to (i.e. witnessing or discovering) death or serious injury (23.7%). The next most common trauma types at the respondent level were muggings (14.5%), life-threatening automobile accidents (14.0%), and life-threatening illnesses (11.8%). ‘Private’ traumas were reported by 4.9% of respondents. When considered in terms of broader categories, the most common traumas at the respondent level were those that either occurred to a loved one or were witnessed (35.7% of respondents), those involving accidents (34.3%), and unexpected death of a loved one (31.4%) followed by physical violence (22.9%), intimate partner sexual violence (14.0%), war-related traumas (13.1%), and ‘other’ traumas (8.4%).

Prevalence and distribution of lifetime traumas in the WMH Surveys ( n  = 68,894).

a The percent of all respondents who reported ever in their lifetime experiencing the trauma type indicated in the row heading. For example, 3.1% of respondents across surveys reported a history of combat experience.

b The mean number of lifetime occurrences of the trauma type indicated in the row heading among those who reported ever experiencing that trauma type. - entries indicate that we did not assess number of occurrences for the trauma type. For example, the respondents who reported ever in their life seriously injuring or killing someone on purpose reported a mean of 2.5 such occurrences.

c The number of lifetime occurrences of the trauma type indicated in the row heading per 100 respondents, which equals the product of the two earlier row entries. For example, the 2.5 lifetime occurrences of seriously injuring or killing someone on purpose reported by 0.9% of respondents results in 2.1 (0.9 × 2.5) lifetime occurrences of such a trauma for every 100 respondents in the sample.

d The ratio of the entry in the cell of the previous column to the 321.5 total lifetime traumas for every 100 respondents. For example, the 3.1 instances of combat experience represent approximately 1.0% of the 321.5 total.

The above results do not take into consideration the fact that mean number of exposures varies significantly across trauma types for the 20 traumas that were assessed for frequency (χ 2 19  = 11,729.9, p  < .001). (The other trauma types were not assessed for frequency because they represented ongoing situations rather than discrete events.) When we multiply the proportion of respondents with any lifetime exposure to a given trauma type by mean number of exposures among those with any, we find a mean number of exposures to any trauma among people with any of 4.6. This translates into 3.2 lifetime trauma exposures per capita (i.e. .704 with any exposure × 4.6). The distribution of number of trauma occurrences among respondents with any was 25.8% one, 18.0% two, 12.9% three, 9.6% four, 7.0% five, 5.4% six, 4.2% seven, 3.2% eight, 2.4% nine, 2.2% 10, 4.7% 11–14, and 4.5% 15 or more. The most common traumas are those that happen to other people, either unexpected death of a loved one (16.5% of all traumas) or other traumas that happened to a loved one or that the respondent witnessed (25.0%), collectively accounting for over 40% of all trauma exposures. Another 24.6% are accidents and another roughly one-quarter involve either intimate partner sexual violence (9.8%) or physical violence (13.8%). The remaining categories of war-related (7.4%) and ‘other’ (2.8%) traumas are much less common.

3.2. Socio-demographic predictors of trauma exposure

Socio-demographic predictors of trauma exposure in the WMH data have been reported previously (Benjet et al., 2016 ). Using survival analysis, these analyses showed that women are much more likely than men to be exposed to intimate partner sexual violence (OR 2.3), roughly equal to men in odds of unexpected death of a loved one (OR 1.1), and significantly less likely than men to experience any of the other specific trauma types considered in our analysis (OR 0.4–0.8). Currently married respondents have significantly reduced odds of the vast majority of trauma types (OR 0.5–0.9) compared to the never married, while low education is associated with somewhat elevated risk of some (e.g. violence, accidents, natural disasters) but not all (e.g. unexpected death of a loved one) types of trauma.

Perhaps the most interesting socio-demographic correlate of trauma exposure is age. Age-of-occurrence curves in Figure 1 show that traumas associated with interpersonal violence have earliest median age-of-occurrence (age 17) followed by intimate partner sexual violence (age 18), war-related traumas (age 20), and traumas that happened to other people (age 20). Accidents, unexpected death of loved ones, and other traumas have later median ages-of-occurrence (ages 24–31).

Age-of-onset distributions of trauma exposure in the WMH Surveys.

3.3. Differential risk of PTSD depending on trauma type

As detailed in another recent WMH report (Liu et al., 2017 ), conditional risk of DSM-IV/CIDI PTSD after trauma exposure is 4.0%, but varies significantly by trauma type. ( Table 3 ) The highest conditional risk is associated with being raped (19.0%), physical abuse by a romantic partner (11.7%), being kidnapped (11.0%), and being sexually assaulted other than rape (10.5%). In terms of broader categories, the traumas associated with the highest PTSD risk are those involving intimate partner sexual violence (11.4%) and other traumas (9.2%), with aggregate conditional risk much lower in the other broad trauma categories (2.0–5.4%).

Conditional risk of DSM-IV/CIDI PTSD by trauma category in the WMH Surveys.

a The conditional risk of PTSD associated with the trauma type indicated in the row heading. For example, 3.6% of the combat experiences resulted in DSM-IV/CIDI PTSD.

b The mean number of lifetime episodes of PTSD associated with the trauma type indicated in the row heading per 100 respondents. For example, the 3.5% of lifetime war-related traumas that led to PTSD reported in the first column of Table 3 , when multiplied by the 23.9 lifetime occurrences of such traumas per 100 respondents reported in the third column of Table 2 , translates into 0.8 lifetime episodes of PTSD due to this category of traumas per 100 respondents.

c The ratio of the entry in the cell of the previous column to the total of 12.9 lifetime episodes of PTSD per 100 respondents. For example, the 0.8 cases of PTSD associated with war-related traumas represents 6.4% of the 12.9 total.

Prevalence of trauma exposure and conditional risk of PTSD both need to be considered in evaluating the trauma-specific population burden of PTSD. Given that 3.2 trauma exposures occur per capita in the population, the 4.0% aggregate conditional risk of PTSD translates into 12.9 lifetime episodes of PTSD per 100 people in the population. The trauma type associated with by far the highest number of these PTSD cases is unexpected death of a loved one (2.9 episodes of PTSD/100 population; 22.2% of all lifetime episodes of PTSD), with rape (1.1 episodes of PTSD/100 population; 8.6% of all lifetime episodes) and sexual assault other than rape (1.2 episodes of PTSD/100 population; 9.5% of all lifetime episodes) together accounting for another 18.1% of lifetime episodes. Unexpected death is a very common type of trauma (53.2 lifetime occurrences/100 population; 16.5% of all lifetime traumas) with a high-average conditional risk of PTSD (5.4%), whereas rape and other sexual assault are less common (5.8–11.7 lifetime occurrences/100 population; 1.8–3.6% of all lifetime traumas) with much higher conditional risks of PTSD (19.0–10.5%).

Four of the six trauma types associated with highest population proportions of lifetime PTSD episodes are in the category of intimate partner sexual violence. These include 4.1% of all lifetime PTSD episodes associated with physical abuse by a romantic partner, 8.6% with rape, 9.5% with other sexual assault, and 5.6% with being stalked, for a total of 27.8% of all lifetime episodes of PTSD. Intimate partner sexual traumas account for 9.8% of all lifetime trauma exposures and are associated with comparatively high conditional risk of PTSD. The only other trauma types accounting for as many cases of PTSD are the two most commonly-occurring traumas considered here: unexpected death of a loved one (22.2% of all cases of PTSD) which, as noted above, is the second most common trauma (16.5% of all traumas) associated with high-average conditional risk of PTSD, and direct exposure to death or serious injury (5.5% of all cases of PTSD), which is the most common trauma (16.8% of all traumas) and is associated with a comparatively low risk of PTSD (1.3%).

3.4. Socio-demographic predictors of PTSD conditional on trauma exposure

Controlling for trauma type, conditional PTSD risk is significantly associated with age, with risk highest during childhood–adolescence and ages 65+. Consistent with much previous research (reviewed by Olff, Langeland, Draijer, & Gersons, 2007 ; Tolin & Foa, 2006 ), women are significantly more likely to develop PTSD than are men exposed to the same traumas. We also looked at socio-economic status and marital status but found that they are not significant predictors of PTSD after controlling trauma type and respondent age–sex.

3.5. Associations of prior trauma exposure with subsequent PTSD

The literature suggests that people with a history of prior trauma exposure are more likely than others to develop PTSD after exposure to subsequent traumas (Breslau, Peterson, & Shultz, 2008 ; Caramanica et al., 2015 ). Consistent with this evidence, a previous WMH report found that the vast majority of prior trauma types are significantly and positively associated with subsequent trauma exposure (Benjet et al., 2016 ). The strongest of these associations (OR = 2.0–2.5) are for one type of physical violence (e.g. physical abuse in childhood) predicting other types of subsequent physical violence (e.g. being mugged) and intimate partner sexual violence.

As detailed in a recent WMH report (Liu et al., 2017 ), the WMH random trauma analysis replicated earlier studies in showing that history of prior trauma exposure predicts increased vulnerability to PTSD after subsequent traumas, but also went beyond previous studies in several important ways. First, this association was found to be limited to prior traumas involving physical or sexual violence (OR = 1.3–2.5). Second, the vulnerability to future PTSD associated with these prior traumas was found to be ‘generalized’ in the sense that it existed across the full range of random trauma types considered in the analysis. Third, there was evidence for two more specific types of vulnerability associated with prior lifetime exposure to the same trauma types as in the random traumas. One involved history of traumas involving physical violence, which was associated with significantly elevated odds of PTSD after subsequent re-exposure to the same trauma types (OR 3.2). This means that it is recurrent physical violence that is most strongly associated with high PTSD risk. The other involved history of traumas involving participation in sectarian violence (e.g. combat experience, purposefully injured or killed someone), which was associated with significantly reduced odds of PTSD after subsequent re-exposure to the same trauma types (OR 0.3). The last of these results might seem counter-intuitive given that military personnel and first responders working in situations of high trauma exposure are known to be at elevated risk of PTSD (Gates et al., 2012 ; Wilson, 2015 ). However, it is important to recognize that the result refers to PTSD after re-exposure , which is quite a different thing than PTSD after initial exposure. As reviewed below in the discussion section, this finding of prior experience helping to protect against the effects of sectarian violence is consistent with previous literature.

3.6. Persistence of PTSD symptoms

The results reported up to now focused on lifetime prevalence. However, population burden is more directly a function of point prevalence. And point prevalence is a joint function of lifetime prevalence and persistence. A recent comprehensive review of the literature on PTSD remission concluded that roughly half of PTSD cases remit within six months and that probability of remission does not vary dramatically across trauma types (Morina, Wicherts, Lobbrecht, & Priebe, 2014 ). We were able to investigate this issue in the WMH data by asking respondents with a history of PTSD associated with randomly-selected traumas to report on duration of symptoms and whether they still had symptoms at the time of interview. Mean duration of PTSD symptoms ( Table 4 ) averages approximately six years (72.3 months) across all traumas but varies greatly depending on trauma type from a high of over 13 years for traumas involving combat experience in war to a low of about one year for traumas involving exposure to a natural disaster. It is noteworthy that WMH respondents were asked how long they continued to have any symptoms, so these duration estimates are for symptoms rather than for meeting full PTSD criteria. Speed-of-recovery curves ( Figure 2 ) show that means in Table 4 are influenced by long right tails, with 25–40% of cases of PTSD recovering within one year, many of them within six months, the major exception being a much lower rate of rapid recovery among people with war-related PTSD. A smaller proportion of cases persists for many years. The longest median duration is five years for PTSD symptoms associated with war-related traumas followed by three years for traumas involving physical or intimate partner sexual violence. Median durations are one to two years, in comparison, for PTSD symptoms due to the other broad trauma categories.

Mean duration and years in episode of DSM-IV/CIDI PTSD by trauma type in the WMH Surveys.

a The mean duration (in months) of PTSD episodes associated with the trauma type indicated in the row heading. Recovery was defined as the number of months until the respondent stopped having any symptoms. For example, respondents with a history of PTSD due to combat experience reported that symptoms continued for a mean of 161.7 months (13.5 years).

b The number of lifetime episodes of PTSD due to the trauma type indicated in the row heading per 100 respondents from the third column in Table 3 multiplied by the mean duration (in years) from the first column of Table 4 . For example, the 0.7 lifetime episodes of PTSD due to war-related traumas per 100 respondents multiplied by the mean 6.8 years per episode results in 5.7 (0.8 × 6.8) years of PTSD due to this category of traumas per 100 respondents.

c The ratio of the entry in the cell of the previous column to the total 77.7 years of PTSD due to any trauma for every 100 respondents. For example, the 5.7 years of war-related PTSD represent 7.3% of the 77.7 total.

Speed of recovery of DSM-IV/CIDI PTSD by trauma category in the WMH Surveys. 1

1 ‘Recovery’ was defined as length of time until all symptoms remitted.

We also looked for socio-demographic predictors of PTSD symptom duration. None were significant in the total sample of cases. However, we lacked the statistical power to investigate the possibility that these predictors vary depending on trauma type. Such variation has been documented in focused studies. For example, a large prospective study of victims of Hurricane Katrina found that socio-economic status was a significant predictor of speed of PTSD recovery after that natural disaster (McLaughlin et al., 2011 ). Previous research on the predictors of recovery in trauma-specific samples have focused largely on trauma characteristics and prior psychopathology (Atwoli et al., 2017 ; Bromet et al., 2017 ; Stein et al., 2016 ), neither of which was considered in the aggregate WMH analyses due to the small numbers of cases associated with each trauma type.

Our most direct estimate of the population burden of PTSD associated with each trauma type is the number of years of PTSD at the population level associated with that trauma type. An estimate of the latter can be obtained by multiplying number of lifetime PTSD episodes/100 population by mean duration. When this is done and the trauma-specific products are summed across all trauma types we estimate that there are 77.7 lifetime person-years of PTSD in the population per 100 respondents. The four trauma types with the highest proportions of these person-years are rape (13.1%; 10.2 person-years per 100 respondents), other sexual assault (15.1%; 11.7 person-years per 100 respondents), being stalked (9.8%; 7.6 person-years per 100 respondents), and unexpected death of a loved one (11.6%; 9.0 person-years per 100 respondents). The broad category of intimate partner sexual violence accounts for nearly 42.7% of all person-years with PTSD in the population (33.2 person-years per 100 respondents).

4. Discussion

The WMH results are limited in a number of ways. For one, lifetime prevalence estimates of trauma exposure are likely to be conservative due to recall error (Belli, 2014 ). In addition, some traumas are likely to be systematically under-reported because they are embarrassing or otherwise culturally sensitive (Schaeffer, 2000 ). Both types of problems can be reduced, although not entirely overcome, with data collection enhancements. Recall failure can be reduced by using memory priming strategies and event history calendars to focus memory search (Drasch & Matthes, 2013 ). Conscious nondisclosure can be reduced by increasing anonymity; for example, by having respondents privately record sensitive information in a self-report booklet that is sealed before returning it to the interviewer or via private computerized self-administration (Gnambs & Kaspar, 2015 ). There is some concern that complete anonymity can reduce motivation to report accurately (Lelkes, Krosnick, Marx, Judd, & Park, 2012 ). These strategies were not used in the WMH Surveys, so we have to consider the WMH trauma exposure prevalence estimates as lower-bound estimates.

Another limitation of the WMH results involves the diagnoses of lifetime PTSD. These diagnoses are limited by being based on retrospective reports obtained in a cross-sectional survey using a fully-structured lay-administered diagnostic interview rather than a semi-structured clinician-administered diagnostic interview. The WMH clinical reappraisal study shows that PTSD prevalence is under-estimated in the CIDI compared to blinded semi-structured clinical interviews but that the vast majority of CIDI cases are confirmed in these clinical reappraisal interviews (Haro et al., 2006 ), suggesting that the WMH prevalence estimates are conservative. WMH results regarding PTSD persistence, in comparison, are anti-conservative because they assess persistence of any symptom rather than of the full PTSD syndrome. Results regarding predictors, finally, are limited by excluding prior psychopathology, which is known to be the strongest predictor of PTSD onset given trauma type (DiGangi et al., 2013 ; Sayed et al., 2015 ), and trauma characteristics-sequelae, which are known to be strong predictors of persistence (Morina et al., 2014 ).

Within the context of these limitations, our finding that 70.4% of respondents were exposed to one or more traumas at some time in their life is broadly consistent with previous research reviewed elsewhere (Benjet et al., 2016 ) documenting that the majority of people in the general population have experienced traumas. However, WMH went beyond previous studies in assessing frequency of exposure, documenting that trauma exposure is even more common than previously known, with a per capita mean of 2.0 trauma types and 4.6 trauma exposures. These estimates are conservative as they are based on calculations in which some ongoing traumas, such as physical abuse at the hands of a caregiver during childhood, are counted as only ‘one occurrence’ even though they often persisted over many years.

WMH results regarding the most common types of trauma are consistent with previous research in finding that unexpected death of a loved one and motor vehicle accidents are the two most common types of trauma in the general population (reviewed by Benjet et al., 2016 ). We went beyond these previous results to show that traumas occurring to other people account for over 40% of all reported qualifying (for a diagnosis of PTSD) traumas (16.5% involving unexpected death of a loved one and an additional 25.0% other traumas that either occurred to a loved one or were witnessed), that accidents are the most common type of trauma occurring to people directly (24.6%), and that traumas involving intimate partner sexual violence (9.8%) and physical violence (13.8%) account for the bulk of other traumas. It is noteworthy that the objective occurrence of traumas to loved ones is clearly under-reported by WMH respondents in that we would expect each loved one of each respondent to have as many traumas as the respondent himself or herself, but this is not the case in respondent reports, implying that respondent reports about traumas occurring to loved ones are limited to the people and traumas most psychologically salient to respondents.

We also found that trauma exposure is not distributed randomly in the population. Our results are consistent in this regard with a previous study that reviewed the literature on basic socio-demographic correlates of trauma exposure (Hatch & Dohrenwend, 2007 ) in finding that women are significantly more likely than men to experience intimate partner sexual violence and men more likely than women to experience physical violence and accidents. We found that traumas involving violence and accidents are more likely to occur in adolescence and early adulthood that other parts of the life course. We also found that being married is the most consistent socio-demographic factor associated with reduced risk of many types of trauma exposure, while traumas involving violence and accidents (including natural disasters) are inversely associated with socio-economic status. And we found that trauma exposures are correlated over time, with people exposed to earlier traumas at significantly increased risk of subsequent traumas. The latter pattern presumably reflects individual differences in predispositions, coping resources, life circumstances, and lifestyles that influence risk of trauma exposure. The WMH data were too coarse to search for modifiable risk factors that might be targeted to prevent future trauma exposure, but the strong inter-temporal patterning of exposure suggests that such an investigation might make sense. Preventive interventions with this focus already exist for recurrences of drunk driving (Miller, Curtis, Sønderlund, Day, & Droste, 2015 ), intimate partner violence (Ramsay et al., 2009 ), and sexual violence (Marques, Wiederanders, Day, Nelson, & Ommeren, 2005 ), but our results raise the possibility of also developing risk models to target broader types of secondary preventive interventions.

Our estimates of conditional PTSD risk among people exposed to traumas are for the most part lower than in previous studies due to our focus on representative samples of traumas in comparison to the worst traumas examined in most other community epidemiological studies and in samples that over-represent help-seekers focused on particular trauma types (e.g. Campbell, Dworkin, & Cabral, 2009 ; Goldmann & Galea, 2014 ).

Our finding that conditional PTSD risk is elevated after traumas involving violence is broadly consistent with previous research (see reviews in Atwoli et al., 2015 ; Ozer, Best, Lipsey, & Weiss, 2003 ). We also found that prior exposure to some traumas involving violence was associated with generalized vulnerability to subsequent PTSD. Although ongoing research is investigating pathways leading to such generalized vulnerability (Daskalakis, Bagot, Parker, Vinkers, & de Kloet, 2013 ; Levy-Gigi, Richter-Levin, Okon-Singer, Kéri, & Bonanno, 2016 ; Rutter, 2012 ), we know of no work on differential effects of trauma types in this regard. However, suggestive related evidence exists on differences in associations of childhood adversities with adult mental disorders across different childhood adversity types (Kessler et al., 2010 ; Pirkola et al., 2005 ) and profiles (McLafferty et al., 2015 ; Putnam, Harris, & Putnam, 2013 ).

Our finding that prior same-type physical violence victimizations predict elevated PTSD risk after re-victimization means that these types of victimization are especially impactful when they are recurrent. That being the case, a question can be raised about our failure to find a similar pattern for intimate partner sexual violence, as the latter seems to contradict studies showing that sexual assault re-victimization is associated with poor mental health (Classen, Palesh, & Aggarwal, 2005 ; Das & Otis, 2016 ; Miner, Flitter, & Robinson, 2006 ). However, these studies focused largely on victims of childhood sexual assault who were versus were not re-victimized as adults, whereas our analysis compares adult sexual assault victims who were versus were not previously victimized.

Our finding that prior same-type participation in sectarian violence is associated with low PTSD risk after subsequent re-exposure to the same trauma is consistent with research showing low PTSD prevalence among policemen (Levy-Gigi et al., 2016 ) and other first responders (Levy-Gigi & Richter-Levin, 2014 ) and among Israeli settlers exposed to repeated bombings (Palgi, Gelkopf, & Berger, 2015 ; Somer et al., 2009 ). These results could be due either to selection and/or to prior exposures promoting resilience (Wilson et al., 2009 ). Both experimental animal studies (Liu, 2015 ) and observational human studies (Rutter, 2012 ) support the resilience possibility, although research showing that intervening psychopathology due to prior traumas mediates the association between trauma history and subsequent PTSD (Sayed et al., 2015 ) confirms that prior traumas are more likely to create vulnerability than resilience. Research on the ‘healthy warrior effect’ supports the selection possibility (Larson, Highfill-McRoy, & Booth-Kewley, 2008 ; Wilson et al., 2009 ). As a result, we suspect that both processes are at work (i.e. both selection and environmental causation), although we have no way to estimate their relative importance with the WMH data.

Our results regarding persistence are broadly consistent with previous studies in showing that a substantial minority of PTSD cases remits within months after onset. However, we found that median duration of symptoms was longer than the six months found in a recent review of the literature (Morina et al., 2014 ). This reflects upward bias in the WMH data due to the very narrow definition of remission used in WMH, which required the respondent no longer to have any PTSD symptoms, leading to artificially high estimates of duration. This means that duration of sub-threshold PTSD is considerably longer than generally appreciated. Given that sub-threshold PTSD has been shown to be associated with considerable distress, impairment, and comorbidity (McLaughlin et al., 2015 ), the latter possibility is worthy of future investigation in prospective studies.

5. Conclusions

The WMH data document clearly that trauma exposure is common throughout the world, that this exposure is unequally distributed in the population, and that PTSD risk differs substantially across trauma types due to traumas involving interpersonal violence (especially relationship–sexual violence) carrying the highest PTSD risk. There is also high population-level burden of PTSD associated with unexpected death of a loved one, a very common trauma type that is associated with low individual-level PTSD risk. Although a substantial minority of PTSD cases remits within months after onset, mean symptom duration is considerably longer than previously recognized.

The WMH Surveys were designed as needs assessment surveys to help governments gain insights into the population burden of mental disorders. Because of this, the most important implications of the results are for policy planners in recognizing that PTSD is a very commonly-occurring condition. Although we did not present any results about severity of illness, other WMH results document clearly that PTSD is a seriously impairing disorder (Kessler et al., 2009 ). PTSD causes substantial loss of human capital from a societal perspective both in the form of days out of role (Alonso et al., 2011 ) and in the form of decreased productivity on days in role (Ormel et al., 2008 ). Roughly half of people with PTSD in high income countries and about half that number in low or middle income countries seek some type of treatment (Koenen et al., 2017 ), but the type and duration of treatment seldom meet even minimal standards for treatment adequacy (Wang et al., 2007 ). These results suggest that outreach efforts are needed to increase the proportion of people with PTSD who obtain treatment and that treatment quality improvement efforts are needed for patients in treatment.

Supplementary Material

Acknowledgments.

We thank the staff of the WMH Data Collection and Data Analysis Coordination Centres for assistance with instrumentation, fieldwork, and consultation on data analysis. None of the funders had any role in the design, analysis, interpretation of results, or preparation of this paper. The views and opinions expressed in this paper are those of the authors and do not necessarily represent the views or policies of the World Health Organization, other sponsoring organizations, agencies, or governments.

All authors have read and approved the final manuscript. Study design: Kessler. Data collection: All authors. Data interpretation: All authors. Manuscript writing: Kessler. Data analysis: Kessler, Petukhova. Manuscript editing: All authors.

Funding Statement

The World Health Organization World Mental Health (WMH) Survey Initiative is supported by the National Institute of Mental Health (NIMH; R01 MH070884 and R01 MH093612-01), the John D. and Catherine T. MacArthur Foundation, the Pfizer Foundation, the US Public Health Service (R13-MH066849, R01-MH069864, and R01 DA016558), the Fogarty International Center (FIRCA R03-TW006481), the Pan American Health Organization, Eli Lilly and Company, Ortho-McNeil Pharmaceutical, Inc., GlaxoSmithKline, and Bristol-Myers Squibb. A complete list of all within-country and cross-national WMH publications can be found at http://www.hcp.med.harvard.edu/wmh/ . The 2007 Australian National Survey of Mental Health and Wellbeing was funded by the Australian Government Department of Health and Ageing. The São Paulo Megacity Mental Health Survey is supported by the State of São Paulo Research Foundation (FAPESP) Thematic Project Grant 03/00204-3. The Bulgarian Epidemiological Study of common mental disorders EPIBUL is supported by the Ministry of Health and the National Center for Public Health Protection. The Chinese World Mental Health Survey Initiative is supported by the Pfizer Foundation. The Colombian National Study of Mental Health (NSMH) is supported by the Ministry of Social Protection. The Mental Health Study Medellín – Colombia was carried out and supported jointly by the Center for Excellence on Research in Mental Health (CES University) and the Secretary of Health of Medellín. The ESEMeD project is funded by the European Commission (Contracts QLG5-1999-01042; SANCO 2004123, and EAHC 20081308), the Piedmont Region (Italy), Fondo de Investigación Sanitaria, Instituto de Salud Carlos III, Spain (FIS 00/0028), Ministerio de Ciencia y Tecnología, Spain (SAF 2000-158-CE), Departament de Salut, Generalitat de Catalunya, Spain, Instituto de Salud Carlos III (CIBER CB06/02/0046, RETICS RD06/0011 REM-TAP), and other local agencies and by an unrestricted educational grant from GlaxoSmithKline. The Israel National Health Survey is funded by the Ministry of Health with support from the Israel National Institute for Health Policy and Health Services Research and the National Insurance Institute of Israel. The World Mental Health Japan (WMHJ) Survey is supported by the Grant for Research on Psychiatric and Neurological Diseases and Mental Health (H13-SHOGAI-023, H14-TOKUBETSU-026, H16-KOKORO-013) from the Japan Ministry of Health, Labour and Welfare. The Lebanese Evaluation of the Burden of Ailments and Needs Of the Nation (L.E.B.A.N.O.N.) is supported by the Lebanese Ministry of Public Health, the WHO (Lebanon), National Institute of Health/Fogarty International Center (R03 TW006481-01), anonymous private donations to IDRAAC, Lebanon, and unrestricted grants from Algorithm, AstraZeneca, Benta, Bella Pharma, Eli Lilly, GlaxoSmithKline, Lundbeck, Novartis, Servier, OmniPharma, Phenicia, Pfizer, UPO. The Mexican National Comorbidity Survey (MNCS) is supported by The National Institute of Psychiatry Ramon de la Fuente (INPRFMDIES 4280) and by the National Council on Science and Technology (CONACyT-G30544- H), with supplemental support from the PanAmerican Health Organization (PAHO). Te Rau Hinengaro: The New Zealand Mental Health Survey (NZMHS) is supported by the New Zealand Ministry of Health, Alcohol Advisory Council, and the Health Research Council. The Northern Ireland Study of Mental Health was funded by the Health & Social Care Research & Development Division of the Public Health Agency. The Peruvian World Mental Health Study was funded by the National Institute of Health of the Ministry of Health of Peru. The Portuguese Mental Health Study was carried out by the Department of Mental Health, Faculty of Medical Sciences, NOVA University of Lisbon, with collaboration of the Portuguese Catholic University, and was funded by Champalimaud Foundation, Gulbenkian Foundation, Foundation for Science and Technology (FCT) and Ministry of Health. The Romania WMH study projects ‘Policies in Mental Health Area’ and ‘National Study regarding Mental Health and Services Use’ were carried out by the National School of Public Health & Health Services Management (former National Institute for Research & Development in Health), with technical support of Metro Media Transilvania, the National Institute of Statistics-National Centre for Training in Statistics, S.C. Cheyenne Services SRL, Statistics Netherlands and were funded by the Ministry of Public Health (former Ministry of Health) with supplemental support of Eli Lilly Romania SRL. The South Africa Stress and Health Study (SASH) is supported by the US National Institute of Mental Health (R01-MH059575) and National Institute of Drug Abuse with supplemental funding from the South African Department of Health and the University of Michigan. The Psychiatric Enquiry to General Population in Southeast Spain – Murcia (PEGASUS-Murcia) Project has been financed by the Regional Health Authorities of Murcia (Servicio Murciano de Salud and Consejería de Sanidad y Política Social) and Fundación para la Formación e Investigación Sanitarias (FFIS) of Murcia. The Ukraine Comorbid Mental Disorders during Periods of Social Disruption (CMDPSD) study is funded by the US National Institute of Mental Health (RO1-MH61905). The US National Comorbidity Survey Replication (NCS-R) is supported by the National Institute of Mental Health (NIMH; U01-MH60220) with supplemental support from the National Institute of Drug Abuse (NIDA), the Substance Abuse and Mental Health Services Administration (SAMHSA), the Robert Wood Johnson Foundation (RWJF; Grant 044708), and the John W. Alden Trust. Dr Dan Stein is supported by the Medical Research Council of South Africa (MRC). Corina Benjet has received funding from the (Mexican) National Council of Science and Technology (grant CB-2010-01-155221).

Highlights of article

Lifetime trauma exposure is the norm in most countries.

Interpersonal violence traumas carry highest PTSD risk.

Lifetime population burden of PTSD is 77.7 person-years/100 respondents across surveys.

Trauma types with highest PTSD burden include those involving intimate partner sexual violence (relatively uncommon traumas associated with high PTSD risk) and unexpected death of a loved one (a very common trauma associated with low PTSD risk).

Although many cases remit within months, PTSD symptoms typically are quite persistent.

Disclosure statement

In the past three years, Dr Haro received personal fees from Lundbeck. In the past three years, Dr Kawakami served as a consultant for Junpukai Foundation, SB At Work, Sekisui Co., Ltd., and received grant funding from Infosoft Technologies, Ministry of Health, Labour, and Welfare, and Japan Society for Promotion of Science. In the past three years, Dr Kessler received support for his epidemiological studies from Sanofi Aventis; was a consultant for Johnson & Johnson Wellness and Prevention, Shire, Takeda; and served on an advisory board for the Johnson & Johnson Services Inc. Lake Nona Life Project. Kessler is a co-owner of DataStat, Inc., a market research firm that carries out healthcare research. In the past three years, Dr Stein has received research grants and/or consultancy honoraria from Biocodex, Lundbeck, Servier, and Sun.

Group Information

The WHO World Mental Health Survey collaborators are Sergio Aguilar-Gaxiola, MD, PhD, Ali Al-Hamzawi, MD, Mohammed Salih Al-Kaisy, MD, Jordi Alonso, MD, PhD, Laura Helena Andrade, MD, PhD, Corina Benjet, PhD, Guilherme Borges, ScD, Evelyn J. Bromet, PhD, Ronny Bruffaerts, PhD, Brendan Bunting, PhD, Jose Miguel Caldas de Almeida, MD, PhD, Graça Cardoso, MD, PhD, Somnath Chatterji, MD, Alfredo H. Cia, MD, Louisa Degenhardt, PhD, Koen Demyttenaere, MD, PhD, John Fayyad, MD, Silvia Florescu, MD, PhD, Giovanni de Girolamo, MD, Oye Gureje, MD, DSc, FRCPsych, Josep Maria Haro, MD, PhD, Yanling He, MD, Hristo Hinkov, MD, PhD, Chi-yi Hu, MD, PhD, Yueqin Huang, MD, MPH, PhD, Peter de Jonge, PhD, Aimee Nasser Karam, PhD, Elie G. Karam, MD, Norito Kawakami, MD, DMSc, Ronald C. Kessler, PhD, Andrzej Kiejna, MD, PhD, Viviane Kovess-Masfety, MD, PhD, Sing Lee, MB, BS, Jean-Pierre Lepine, MD, Daphna Levinson, PhD, John McGrath, MD, PhD, Maria Elena Medina-Mora, PhD, Jacek Moskalewicz, PhD, Fernando Navarro-Mateu, MD, PhD, Beth-Ellen Pennell, MA, Marina Piazza, MPH, ScD, Jose Posada-Villa, MD, Kate M. Scott, PhD, Tim Slade, PhD, Juan Carlos Stagnaro, MD, PhD, Dan J. Stein, FRCPC, PhD, Margreet ten Have, PhD, Yolanda Torres, MPH, Dra.HC, Maria Carmen Viana, MD, PhD, Harvey Whiteford, MBBS, PhD, David R. Williams, MPH, PhD, Bogdan Wojtyniak, ScD.

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