<p>To understand mental disorders, we need to describe the patterns of different mental disorders across time and place. There are many different ways to count the frequency of mental disorders (e.g. incidence, 12 months and lifetime prevalence, lifetime morbid risk etc). From a practical perspective, we rely on population-based registers and surveys to enumerate the frequency of mental disorders in the community. Registers can provide a wealth of information, especially if datasets can be linked. However, these registers can be biased. Most registers are designed for administrative needs, and thus often oversample individuals who seek help from health settings or who have more severe disabling disorders. These registers ignore individuals who do not seek help for their mental disorders. To address these biases, community-based surveys provide an important perspective on the mental health of societies (Wang et al., <span>2011</span>). While surveys also have biases (related to participation rates and recall of past events), they allow health planners and researchers to drill down into important topics related to the causes and consequence of mental disorders. From a planning perspective, it is valuable to have data on duration of untreated disorder, the adequacy of treatment and the participants' perspective of the impact of the disorder on their daily life. From a research perspective, it is important to explore potential risk factors that may have caused the disorder. This includes a range of questions related to exposure to childhood adversity, natural disasters, war and civilian conflict, pandemics and other stressors.</p><p>The empirical foundation of mental health epidemiology has been enriched over the last few decades, as more sites have conducted large, well-planned community-based surveys. In particular, the field of psychiatric epidemiology has greatly benefited from the international collaboration under the banner of the WHO World Mental Health (WMH) Surveys (Scott et al., <span>2018</span>). Design features, survey instruments and analytic strategies have been shared, enhancing workforce skills and enabling, cross-national studies (Kessler et al., <span>2018</span>). For example, in 2007 data were available from a total of 16 countries on key mental health estimates related to age of onset, lifetime prevalence, and cumulative lifetime risk (Kessler et al., <span>2007</span>). Sixteen years later, data from 13 additional countries were available (McGrath et al., <span>2023</span>). The updated study included data from 32 WMH surveys conducted in 29 countries (including 12 low- and middle-income).</p><p>With all these new surveys, it would be fair to ask if we still need additional community-based surveys. The answer is simple—yes. Put bluntly, ‘if you don't count it, it doesn't count’ (McGrath et al., <span>2018</span>). There are still many gaps in the global landscape of mental health epidemiology (Kestel et al., <span>2022</span>;
{"title":"Closing the gaps in mental health epidemiology—New survey data from Qatar","authors":"John J. McGrath","doi":"10.1002/mpr.2014","DOIUrl":"10.1002/mpr.2014","url":null,"abstract":"<p>To understand mental disorders, we need to describe the patterns of different mental disorders across time and place. There are many different ways to count the frequency of mental disorders (e.g. incidence, 12 months and lifetime prevalence, lifetime morbid risk etc). From a practical perspective, we rely on population-based registers and surveys to enumerate the frequency of mental disorders in the community. Registers can provide a wealth of information, especially if datasets can be linked. However, these registers can be biased. Most registers are designed for administrative needs, and thus often oversample individuals who seek help from health settings or who have more severe disabling disorders. These registers ignore individuals who do not seek help for their mental disorders. To address these biases, community-based surveys provide an important perspective on the mental health of societies (Wang et al., <span>2011</span>). While surveys also have biases (related to participation rates and recall of past events), they allow health planners and researchers to drill down into important topics related to the causes and consequence of mental disorders. From a planning perspective, it is valuable to have data on duration of untreated disorder, the adequacy of treatment and the participants' perspective of the impact of the disorder on their daily life. From a research perspective, it is important to explore potential risk factors that may have caused the disorder. This includes a range of questions related to exposure to childhood adversity, natural disasters, war and civilian conflict, pandemics and other stressors.</p><p>The empirical foundation of mental health epidemiology has been enriched over the last few decades, as more sites have conducted large, well-planned community-based surveys. In particular, the field of psychiatric epidemiology has greatly benefited from the international collaboration under the banner of the WHO World Mental Health (WMH) Surveys (Scott et al., <span>2018</span>). Design features, survey instruments and analytic strategies have been shared, enhancing workforce skills and enabling, cross-national studies (Kessler et al., <span>2018</span>). For example, in 2007 data were available from a total of 16 countries on key mental health estimates related to age of onset, lifetime prevalence, and cumulative lifetime risk (Kessler et al., <span>2007</span>). Sixteen years later, data from 13 additional countries were available (McGrath et al., <span>2023</span>). The updated study included data from 32 WMH surveys conducted in 29 countries (including 12 low- and middle-income).</p><p>With all these new surveys, it would be fair to ask if we still need additional community-based surveys. The answer is simple—yes. Put bluntly, ‘if you don't count it, it doesn't count’ (McGrath et al., <span>2018</span>). There are still many gaps in the global landscape of mental health epidemiology (Kestel et al., <span>2022</span>; ","PeriodicalId":50310,"journal":{"name":"International Journal of Methods in Psychiatric Research","volume":"33 S1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mpr.2014","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140898276","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}