Search results
Found 23235 matches for
A blog by Dr Gurpreet Singh Kalra and Shawn D. Mathis, members of cohort 1 of the MSc in Global Healthcare Leadership
Prostate specific antigen retesting intervals and trends in England: population based cohort study
OBJECTIVE: To characterise the use of the prostate specific antigen (PSA) test in primary care in England. DESIGN: Population based open cohort study.England. PARTICIPANTS: 10 235 805 male patients older than 18 years and registered at 1442 general practices that contributed to the Clinical Practice Research Datalink between 2000 and 2018. Data were linked to the National Cancer Registry, Hospital Episode Statistics, and Office for National Statistics. MAIN OUTCOME MEASURES: Population based temporal trends and annual percentage changes were analysed using age standardised PSA testing rates. Mixed effects negative binomial regression models investigated individual patient rate ratios of PSA testing. Linear mixed effects models examined factors associated with an individual patient's length of PSA retesting intervals. All results were analysed by region, deprivation, age, ethnicity, family history of prostate cancer, symptom presentation, and PSA value. RESULTS: 1 521 116 patients had at least one PSA test, resulting in 3 835 440 PSA tests overall. 48.4% (735 750) of these patients had multiple tests and 72.8% (535 990) of them never presented with a PSA value above the age specific referral threshold. The median retesting interval overall was 12.6 months (interquartile range 6.2-27.5). Testing rates varied by region, deprivation, ethnicity, family history, age, PSA value, and symptoms. Once tested, patients had shorter retesting intervals if they were older, were of an ethnicity other than white, had a family history of prostate cancer, or had previously raised PSA levels. Despite considerable variation in testing rates by region and deprivation, the length of retesting intervals was similar across these groups. CONCLUSIONS: PSA testing before a diagnosis of prostate cancer in primary care in England varied. Among patients who underwent multiple tests, many were tested more frequently than recommended, raising concerns about overtesting. PSA retesting is occurring in patients without recorded symptoms and in those with low PSA values. To ensure maximum benefit to patients while reducing the risk of overtesting, research is urgently needed to determine appropriate evidence based PSA retesting intervals.
Adequacy of clinical guideline recommendations for patients with low-risk cancer managed with monitoring: systematic review
Objectives: The aim of this systematic review was to summarize national and international guidelines that made recommendations for monitoring patients diagnosed with low-risk cancer. It appraised the quality of guidelines and determined whether the guidelines adequately identified patients for monitoring, specified which tests to use, defined monitoring intervals, and stated triggers for further intervention. It then assessed the evidence to support each recommendation. Study Design and Setting: Following the Preferred Reporting Items for Systematic Reviews and Meta-analyses, we searched PubMed and Turning Research into Practice databases for national and international guidelines' that were written in English and developed or updated between 2012 and 2023. Quality of individual guidelines was assessed using the AGREE II tool. Results: Across the 41 published guidelines, 48 different recommendations were identified: 15 (31%) for prostate cancer, 11 (23%) for renal cancer, 6 (12.5%) for thyroid cancer, and 10 (21%) for blood cancer. The remaining 6 (12.5%) were for brain, gastrointestinal, oral cavity, bone and pheochromocytoma and paraganglioma cancer. When combining all guidelines, 48 (100%) stated which patients qualify for monitoring, 31 (65%) specified which tests to use, 25 (52%) provided recommendations for surveillance intervals, and 23 (48%) outlined triggers to initiate intervention. Across all cancer sites, there was a strong positive trend with higher levels of evidence being associated with an increased likelihood of a recommendation being specific (P = 0.001) and the evidence for intervals was based on expert opinion or other guidance. Conclusion: With the exception of prostate cancer, the evidence base for monitoring low-risk cancer is weak and consequently recommendations in clinical guidelines are inconsistent. There is a lack of direct evidence to support monitoring recommendations in the literature making guideline developers reliant on expert opinion, alternative guidelines, or indirect or nonspecific evidence.
Neuropeptide Y and Derivates Are Not Ready for Prime Time in Prostate Cancer Early Detection
Neuropeptide Y (NPY) and related peptides have been proposed as promising biomarkers for the diagnosis of prostate cancer by previous immunoassays and immunohistochemical studies. In this study, we evaluated the additional value of NPY and related peptides compared with prostate-specific antigen (PSA). We performed a comprehensive analysis of NPY, its precursors, and metabolite concentrations in both plasma and tissue samples from 181 patients using a highly specific liquid chromatography tandem mass spectrometry method. Compared with PSA, NPY and related peptides (NPYs) were less accurate at diagnosing significant prostate cancer. Combinations of NPYs in a stepwise approach did not improve a model that would be beneficial for patients. NPY may be beneficial for patients presenting with a PSA concentration in the gray area between 4 and 9 ng/ml, but the strength of this conclusion is limited. Thus, the use of NPYs as standalone or in combination with other variables, such as PSA, prostate volume, or age, to improve the diagnosis is not supported by our study. Patient summary: This study evaluated neuropeptide Y (NPY) of the family of endogenous peptides as a new biomarker to diagnose prostate cancer. We found that NPY in a patient's blood was not more helpful at diagnosing prostate cancer than the standard prostate-specific antigen blood test. Further research is needed to explore the potential of NPY and related peptides in specific subgroups of patients.
BLOod Test Trend for cancEr Detection (BLOTTED): protocol for an observational and prediction model development study using English primary care electronic health record data.
BACKGROUND: Simple blood tests can play an important role in identifying patients for cancer investigation. The current evidence base is limited almost entirely to tests used in isolation. However, recent evidence suggests combining multiple types of blood tests and investigating trends in blood test results over time could be more useful to select patients for further cancer investigation. Such trends could increase cancer yield and reduce unnecessary referrals. We aim to explore whether trends in blood test results are more useful than symptoms or single blood test results in selecting primary care patients for cancer investigation. We aim to develop clinical prediction models that incorporate trends in blood tests to identify the risk of cancer. METHODS: Primary care electronic health record data from the English Clinical Practice Research Datalink Aurum primary care database will be accessed and linked to cancer registrations and secondary care datasets. Using a cohort study design, we will describe patterns in blood testing (aim 1) and explore associations between covariates and trends in blood tests with cancer using mixed-effects, Cox, and dynamic models (aim 2). To build the predictive models for the risk of cancer, we will use dynamic risk modelling (such as multivariate joint modelling) and machine learning, incorporating simultaneous trends in multiple blood tests, together with other covariates (aim 3). Model performance will be assessed using various performance measures, including c-statistic and calibration plots. DISCUSSION: These models will form decision rules to help general practitioners find patients who need a referral for further investigation of cancer. This could increase cancer yield, reduce unnecessary referrals, and give more patients the opportunity for treatment and improved outcomes.
The Association between Blood Test Trends and Undiagnosed Cancer: A Systematic Review and Critical Appraisal
Clinical guidelines include monitoring blood test abnormalities to identify patients at increased risk of undiagnosed cancer. Noting blood test changes over time may improve cancer risk stratification by considering a patient’s individual baseline and important changes within the normal range. We aimed to review the published literature to understand the association between blood test trends and undiagnosed cancer. MEDLINE and EMBASE were searched until 15 May 2023 for studies assessing the association between blood test trends and undiagnosed cancer. We used descriptive summaries and narratively synthesised studies. We included 29 articles. Common blood tests were haemoglobin (24%, n = 7), C-reactive protein (17%, n = 5), and fasting blood glucose (17%, n = 5), and common cancers were pancreatic (29%, n = 8) and colorectal (17%, n = 5). Of the 30 blood tests studied, an increasing trend in eight (27%) was associated with eight cancer types, and a decreasing trend in 17 (57%) with 10 cancer types. No association was reported between trends in 11 (37%) tests and breast, bile duct, glioma, haematological combined, liver, prostate, or thyroid cancers. Our review highlights trends in blood tests that could facilitate the identification of individuals at increased risk of undiagnosed cancer. For most possible combinations of tests and cancers, there was limited or no evidence.
Clinical Prediction Models Incorporating Blood Test Trend for Cancer Detection: Systematic Review, Meta-Analysis, and Critical Appraisal
Background: Blood tests used to identify patients at increased risk of undiagnosed cancer are commonly used in isolation, primarily by monitoring whether results fall outside the normal range. Some prediction models incorporate changes over repeated blood tests (or trends) to improve individualized cancer risk identification, as relevant trends may be confined within the normal range. Objective: Our aim was to critically appraise existing diagnostic prediction models incorporating blood test trends for the risk of cancer. Methods: MEDLINE and EMBASE were searched until April 3, 2025 for diagnostic prediction model studies using blood test trends for cancer risk. Screening was performed by 4 reviewers. Data extraction for each article was performed by 2 reviewers independently. To critically appraise models, we narratively synthesized studies, including model building and validation strategies, model reporting, and the added value of blood test trends. We also reviewed the performance measures of each model, including discrimination and calibration. We performed a random-effects meta-analysis of the c-statistic for a trends-based prediction model if there were at least 3 studies validating the model. The risk of bias was assessed using the PROBAST (prediction model risk of bias assessment tool). Results: We included 16 articles, with a total of 7 models developed and 14 external validation studies. In the 7 models derived, full blood count (FBC) trends were most commonly used (86%, n=7 models). Cancers modeled were colorectal (43%, n=3), gastro-intestinal (29%, n=2), nonsmall cell lung (14%, n=1), and pancreatic (14%, n=1). In total, 2 models used statistical logistic regression, 2 used joint modeling, and 1 each used XGBoost, decision trees, and random forests. The number of blood test trends included in the models ranged from 1 to 26. A total of 2 of 4 models were reported with the full set of coefficients needed to predict risk, with the remaining excluding at least one coefficient from their article or were not publicly accessible. The c-statistic ranged 0.69‐0.87 among validation studies. The ColonFlag model using trends in the FBC was commonly externally validated, with a pooled c-statistic=0.81 (95% CI 0.77-0.85; n=4 studies) for 6-month colorectal cancer risk. Models were often inadequately tested, with only one external validation study assessing model calibration. All 16 studies scored a low risk of bias regarding predictor and outcome details. All but one study scored a high risk of bias in the analysis domain, with most studies often removing patients with missing data from analysis or not adjusting the derived model for overfitting. Conclusions: Our review highlights that blood test trends may inform further investigation for cancer. However, models were not available for most cancer sites, were rarely externally validated, and rarely assessed calibration when they were externally validated.
Guideline of guidelines: a critical appraisal of the evidence for PSA retesting intervals
Objectives: To summarise the recommendations for prostate-specific antigen (PSA) retesting intervals and to evaluate the evidence cited by each guideline by conducting a systematic review of clinical practice guidelines. Methods: We searched PubMed and the Turning Research into Practice (TRIP) database for guidelines written in English and developed or updated in 2013–2024. Guideline quality assessment was performed using the AGREE II tool. We narratively synthesised results. Results: Eleven guidelines were included. Ten (91%) recommended PSA retesting intervals of approximately 2 to 4 years. A total of 37 studies were referenced as evidence for the recommended intervals across the 11 guidelines. Five of these studies (14%) had the objective of determining PSA retesting intervals. Fourteen studies (38%) analysed single PSA test results. Five guideline recommendations partially aligned with the evidence referenced and five did not align. Conclusions: Generally, for asymptomatic patients aged ≥50 years with PSA levels between 1 and 3 ng/mL, most guidance recommended a retesting interval of 2–4 years, with the possibility to extend the interval to 4–10 years for patients with a PSA value <1 ng/mL. Until research generates direct evidence for PSA retesting intervals for both asymptomatic and symptomatic patients, clinicians and patients engaging in shared decision-making should be aware that current guidelines lack direct evidence for recommended PSA retesting intervals.
The experiences and needs of supporting individuals of young people who self-harm: A systematic review and thematic synthesis
Self-harm in young people is a serious international health concern that impacts on those providing informal support: the supporting individuals of young people. We aimed to highlight the experiences, views, and needs of these supporting individuals of young people. We conducted a systematic review and thematic synthesis: PROSPERO CRD42020168527. MEDLINE, PsycINFO, EMBASE, AMED, CINAHL, ASSIA, and Web of Science were searched from inception to 6 May 2020 with citation tracking of eligible studies done on 1 Oct 2021. Primary outcomes were experiences, perspectives, and needs of parents, carers, or other family members of young people aged 12–25. Searches found 6167 citations, of which 22 papers were included in synthesis. Supporting individuals seek an explanation for and were personally affected by self-harm in young people. It is important that these individuals are themselves supported, especially as they negotiate new identities when handling self-harm in young people, as they attempt to offer support. The GRADE-CERQual confidence in findings is moderate. Recommendations informed by the synthesis findings are made for the future development of interventions. Clinicians and health service providers who manage self-harm in young people should incorporate these identified unmet needs of supporting individuals in a holistic approach to self-harm care. Future research must co-produce and evaluate interventions for supporting individuals. Funding: FM was supported by a NIHR School for Primary Care Research GP Career Progression Fellowship (SCPR-157 2020–20) to undertake this review and is now funded by a NIHR Doctoral Fellowship (NIHR300957). CCG is part-funded by the NIHR Applied Research Collaboration West Midlands.
Assessment and management of medical emergencies in eating disorders: Guidance for GPs
Eating disorders are common, affect people of all ages, and can present as medical emergencies in community, primary care, or hospital settings. In 2017, in response to the death of a 19-year-old female with anorexia nervosa, the Parliamentary and Health Service Ombudsman produced a report entitled Ignoring the Alarms: How NHS Eating Disorder Services are Failing Patients.1 In 2019, the Royal College of Psychiatrists began work to update existing eating disorder guidance (MARSIPAN and Junior MARSIPAN) alongside expert reference groups guided by the National Collaborating Centre for Mental Health; this resulted in the new Medical Emergencies in Eating Disorders [MEED]: Guidance on Recognition and Management report.2 This new guidance is intended for people of all ages covering all eating disorders. This practice piece consolidates the key recommendations for GPs and primary care teams.
Assessment and management of allergic rhinitis: A review and evidence-informed approach for family medicine
Allergic rhinitis is an inflammatory disorder affecting nasal mucosa in response to allergen exposure and is commonly assessed and managed in family medicine. In this article, we review new international guidelines on the diagnosis and management of allergic rhinitis and generate evidence-informed recommendations for family medicine doctors.
Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021
Background: Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. Methods: The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model—a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates—with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality—which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. Findings: The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2–100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1–290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1–211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4–48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3–37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7–9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. Interpretation: Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. Funding: Bill & Melinda Gates Foundation.
Global age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950–2021, and the impact of the COVID-19 pandemic: a comprehensive demographic analysis for the Global Burden of Disease Study 2021
Background: Estimates of demographic metrics are crucial to assess levels and trends of population health outcomes. The profound impact of the COVID-19 pandemic on populations worldwide has underscored the need for timely estimates to understand this unprecedented event within the context of long-term population health trends. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 provides new demographic estimates for 204 countries and territories and 811 additional subnational locations from 1950 to 2021, with a particular emphasis on changes in mortality and life expectancy that occurred during the 2020–21 COVID-19 pandemic period. Methods: 22 223 data sources from vital registration, sample registration, surveys, censuses, and other sources were used to estimate mortality, with a subset of these sources used exclusively to estimate excess mortality due to the COVID-19 pandemic. 2026 data sources were used for population estimation. Additional sources were used to estimate migration; the effects of the HIV epidemic; and demographic discontinuities due to conflicts, famines, natural disasters, and pandemics, which are used as inputs for estimating mortality and population. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate under-5 mortality rates, which synthesised 30 763 location-years of vital registration and sample registration data, 1365 surveys and censuses, and 80 other sources. ST-GPR was also used to estimate adult mortality (between ages 15 and 59 years) based on information from 31 642 location-years of vital registration and sample registration data, 355 surveys and censuses, and 24 other sources. Estimates of child and adult mortality rates were then used to generate life tables with a relational model life table system. For countries with large HIV epidemics, life tables were adjusted using independent estimates of HIV-specific mortality generated via an epidemiological analysis of HIV prevalence surveys, antenatal clinic serosurveillance, and other data sources. Excess mortality due to the COVID-19 pandemic in 2020 and 2021 was determined by subtracting observed all-cause mortality (adjusted for late registration and mortality anomalies) from the mortality expected in the absence of the pandemic. Expected mortality was calculated based on historical trends using an ensemble of models. In location-years where all-cause mortality data were unavailable, we estimated excess mortality rates using a regression model with covariates pertaining to the pandemic. Population size was computed using a Bayesian hierarchical cohort component model. Life expectancy was calculated using age-specific mortality rates and standard demographic methods. Uncertainty intervals (UIs) were calculated for every metric using the 25th and 975th ordered values from a 1000-draw posterior distribution. Findings: Global all-cause mortality followed two distinct patterns over the study period: age-standardised mortality rates declined between 1950 and 2019 (a 62·8% [95% UI 60·5–65·1] decline), and increased during the COVID-19 pandemic period (2020–21; 5·1% [0·9–9·6] increase). In contrast with the overall reverse in mortality trends during the pandemic period, child mortality continued to decline, with 4·66 million (3·98–5·50) global deaths in children younger than 5 years in 2021 compared with 5·21 million (4·50–6·01) in 2019. An estimated 131 million (126–137) people died globally from all causes in 2020 and 2021 combined, of which 15·9 million (14·7–17·2) were due to the COVID-19 pandemic (measured by excess mortality, which includes deaths directly due to SARS-CoV-2 infection and those indirectly due to other social, economic, or behavioural changes associated with the pandemic). Excess mortality rates exceeded 150 deaths per 100 000 population during at least one year of the pandemic in 80 countries and territories, whereas 20 nations had a negative excess mortality rate in 2020 or 2021, indicating that all-cause mortality in these countries was lower during the pandemic than expected based on historical trends. Between 1950 and 2021, global life expectancy at birth increased by 22·7 years (20·8–24·8), from 49·0 years (46·7–51·3) to 71·7 years (70·9–72·5). Global life expectancy at birth declined by 1·6 years (1·0–2·2) between 2019 and 2021, reversing historical trends. An increase in life expectancy was only observed in 32 (15·7%) of 204 countries and territories between 2019 and 2021. The global population reached 7·89 billion (7·67–8·13) people in 2021, by which time 56 of 204 countries and territories had peaked and subsequently populations have declined. The largest proportion of population growth between 2020 and 2021 was in sub-Saharan Africa (39·5% [28·4–52·7]) and south Asia (26·3% [9·0–44·7]). From 2000 to 2021, the ratio of the population aged 65 years and older to the population aged younger than 15 years increased in 188 (92·2%) of 204 nations. Interpretation: Global adult mortality rates markedly increased during the COVID-19 pandemic in 2020 and 2021, reversing past decreasing trends, while child mortality rates continued to decline, albeit more slowly than in earlier years. Although COVID-19 had a substantial impact on many demographic indicators during the first 2 years of the pandemic, overall global health progress over the 72 years evaluated has been profound, with considerable improvements in mortality and life expectancy. Additionally, we observed a deceleration of global population growth since 2017, despite steady or increasing growth in lower-income countries, combined with a continued global shift of population age structures towards older ages. These demographic changes will likely present future challenges to health systems, economies, and societies. The comprehensive demographic estimates reported here will enable researchers, policy makers, health practitioners, and other key stakeholders to better understand and address the profound changes that have occurred in the global health landscape following the first 2 years of the COVID-19 pandemic, and longer-term trends beyond the pandemic. Funding: Bill & Melinda Gates Foundation.
Characteristics of individuals from ethnic minority backgrounds who die by suicide: A systematic review and meta-analysis
We aimed to estimate the prevalence of clinical and modifiable sociodemographic characteristics of individuals from ethnic minority backgrounds who died by suicide and, where possible, compare them to the majority population. Databases were searched for studies published between 01/01/2000–19/12/2023. Absolute and relative prevalence estimates of characteristics were reported by minority group (Indigenous; migrant; other ethnic minority) then stratified by continent and, where applicable, migrant sub-type and ethnicity. A narrative synthesis was conducted with moderate-high quality studies. We identified fifty-seven studies across 16 countries; the majority from North America, Europe and Oceania. When examining moderate-high quality evidence, there were generally limited numbers of studies reporting the prevalence of each characteristic by ethnic minority status, especially for social factors. Based on the available data, we found a high prevalence mental health problems among people who died by suicide from Indigenous (20.8–60.7 %), migrant (37.2–42.9 %) and other ethnic minority (29.9–37.3 %) backgrounds. However, people from Asian/Pacific Islander, Black and Hispanic backgrounds were less likely to have mental health problems reported compared to majority populations. Indigenous people and migrants generally had lower contact with mental health services compared to majority groups. We also found evidence of a lower prevalence of depression and higher prevalence of alcohol and substance use problems among Indigenous compared to non-Indigenous individuals, and greater levels of economic disadvantage among migrants compared to non-migrants. Our findings highlight differences in the characteristics of people who die by suicide based on ethnicity and migration status and identify potential targets for research and suicide prevention strategies.