208 research outputs found
Dedicated outreach service for hard to reach patients with tuberculosis in London: observational study and economic evaluation.
OBJECTIVE: To assess the cost effectiveness of the Find and Treat service for diagnosing and managing hard to reach individuals with active tuberculosis. DESIGN: Economic evaluation using a discrete, multiple age cohort, compartmental model of treated and untreated cases of active tuberculosis. SETTING: London, United Kingdom. Population Hard to reach individuals with active pulmonary tuberculosis screened or managed by the Find and Treat service (48 mobile screening unit cases, 188 cases referred for case management support, and 180 cases referred for loss to follow-up), and 252 passively presenting controls from London's enhanced tuberculosis surveillance system. MAIN OUTCOME MEASURES: Incremental costs, quality adjusted life years (QALYs), and cost effectiveness ratios for the Find and Treat service. RESULTS: The model estimated that, on average, the Find and Treat service identifies 16 and manages 123 active cases of tuberculosis each year in hard to reach groups in London. The service has a net cost of £1.4 million/year and, under conservative assumptions, gains 220 QALYs. The incremental cost effectiveness ratio was £6400-£10,000/QALY gained (about €7300-€11,000 or 16 000 in September 2011). The two Find and Treat components were also cost effective, even in unfavourable scenarios (mobile screening unit (for undiagnosed cases), £18,000-£26,000/QALY gained; case management support team, £4100-£6800/QALY gained). CONCLUSIONS: Both the screening and case management components of the Find and Treat service are likely to be cost effective in London. The cost effectiveness of the mobile screening unit in particular could be even greater than estimated, in view of the secondary effects of infection transmission and development of antibiotic resistance
Causes of hospital admission and mortality among 6683 people who use heroin: a cohort study comparing relative and absolute risks
Background:
Mortality in high-risk groups such as people who use illicit drugs is often expressed in relative terms such as standardised ratios. These measures are highest for diseases that are rare in the general population, such as hepatitis C, and may understate the importance of common long-term conditions.
Population:
6683 people in community-based treatment for heroin dependence between 2006 and 2017 in London, England, linked to national hospital and mortality databases with 55,683 years of follow-up.
Method:
Age- and sex-specific mortality and hospital admission rates in the general population of London were used to calculate the number of expected events. We compared standardised ratios (relative risk) to excess deaths and admissions (absolute risk) across ICD-10 chapters and subcategories.
Results:
Drug-related diseases had the highest relative risks, with a standardised mortality ratio (SMR) of 48 (95% CI 42–54) and standardised admission ratio (SAR) of 293 (95% CI 282–304). By contrast, other diseases had an SMR of 4.4 (95% CI 4.0–4.9) and an SAR of 3.15 (95% CI 3.11–3.19). However, the majority of the 621 excess deaths (95% CI 569–676) were not drug-related (361; 58%). The largest groups were liver disease (75 excess deaths) and COPD (45). Similarly, 80% (11,790) of the 14,668 excess admissions (95% CI 14,382–14,957) were not drug-related. The largest groups were skin infections (1073 excess admissions), alcohol (1060), COPD (812) and head injury (612).
Conclusions:
Although relative risks of drug-related diseases are very high, most excess morbidity and mortality in this cohort was caused by common long-term conditions
Data-driven discovery of changes in clinical code usage over time: a case-study on changes in cardiovascular disease recording in two English electronic health records databases (2001-2015)
[EN] Objectives To demonstrate how data-driven variability methods can be used to identify changes in disease recording in two English electronic health records databases between 2001 and 2015.
Design Repeated cross-sectional analysis that applied data-driven temporal variability methods to assess month-by-month changes in routinely collected medical data. A measure of difference between months was calculated based on joint distributions of age, gender, socioeconomic status and recorded cardiovascular diseases. Distances between months were used to identify temporal trends in data recording.
Setting 400 English primary care practices from the Clinical Practice Research Datalink (CPRD GOLD) and 451 hospital providers from the Hospital Episode Statistics (HES).
Main outcomes The proportion of patients (CPRD GOLD) and hospital admissions (HES) with a recorded cardiovascular disease (CPRD GOLD: coronary heart disease, heart failure, peripheral arterial disease, stroke; HES: International Classification of Disease codes I20-I69/G45).
Results Both databases showed gradual changes in cardiovascular disease recording between 2001 and 2008. The recorded prevalence of included cardiovascular diseases in CPRD GOLD increased by 47%-62%, which partially reversed after 2008. For hospital records in HES, there was a relative decrease in angina pectoris (-34.4%) and unspecified stroke (-42.3%) over the same time period, with a concomitant increase in chronic coronary heart disease (+14.3%). Multiple abrupt changes in the use of myocardial infarction codes in hospital were found in March/April 2010, 2012 and 2014, possibly linked to updates of clinical coding guidelines.
Conclusions Identified temporal variability could be related to potentially non-medical causes such as updated coding guidelines. These artificial changes may introduce temporal correlation among diagnoses inferred from routine data, violating the assumptions of frequently used statistical methods. Temporal variability measures provide an objective and robust technique to identify, and subsequently account for, those changes in electronic health records studies without any prior knowledge of the data collection process.VN is funded by a Public Health England PhD Studentship. RWA is supported by a Wellcome Trust Clinical Research Career Development Fellowship (206602/Z/17/Z). JMGG and CS contributions to this work were partially supported by the MTS4up Spanish project (National Plan for Scientific and Technical Research and Innovation 2013-2016, No. DPI2016-80054-R), the CrowdHealth H2020-SC1-2016-CNECT project (No. 727560) (JMGG) and the Inadvance H2020-SC1-BHC-2018-2020 project (No. 825750). PR and DA did not receive any direct funding for this project. Access to the Clinical Practice Research Datalink was supported by the UK Economic and Social Research Council (ES/P008321/1). Access to aggregated Hospital Episode Statistics was provided by Public Health England. This work was further supported by Health Data Research UK, which is funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation and the Wellcome Trust.Rockenschaub, P.; Nguyen, V.; Aldridge, RW.; Acosta, D.; Garcia-Gomez, JM.; Sáez Silvestre, C. (2020). Data-driven discovery of changes in clinical code usage over time: a case-study on changes in cardiovascular disease recording in two English electronic health records databases (2001-2015). BMJ Open. 10(2):1-9. https://doi.org/10.1136/bmjopen-2019-034396S19102Hripcsak, G., & Albers, D. J. (2013). Next-generation phenotyping of electronic health records. Journal of the American Medical Informatics Association, 20(1), 117-121. doi:10.1136/amiajnl-2012-001145Burton, P. R., Murtagh, M. J., Boyd, A., Williams, J. B., Dove, E. S., Wallace, S. E., … Knoppers, B. M. (2015). Data Safe Havens in health research and healthcare. Bioinformatics, 31(20), 3241-3248. doi:10.1093/bioinformatics/btv279Cruz-Correia R , Rodrigues P , Freitas A . Chapter: 4, Data quality and integration issues in electronic health records. In: Information discovery on electronic health records. CRC Press, 2009: 55–95.Massoudi, B. L., Goodman, K. W., Gotham, I. J., Holmes, J. H., Lang, L., Miner, K., … Fu, P. C. (2012). An informatics agenda for public health: summarized recommendations from the 2011 AMIA PHI Conference. Journal of the American Medical Informatics Association, 19(5), 688-695. doi:10.1136/amiajnl-2011-000507Schlegel, D. R., & Ficheur, G. (2017). Secondary Use of Patient Data: Review of the Literature Published in 2016. Yearbook of Medical Informatics, 26(01), 68-71. doi:10.15265/iy-2017-032Weiskopf, N. G., & Weng, C. (2013). Methods and dimensions of electronic health record data quality assessment: enabling reuse for clinical research. Journal of the American Medical Informatics Association, 20(1), 144-151. doi:10.1136/amiajnl-2011-000681Herrett, E., Thomas, S. L., Schoonen, W. M., Smeeth, L., & Hall, A. J. (2010). Validation and validity of diagnoses in the General Practice Research Database: a systematic review. British Journal of Clinical Pharmacology, 69(1), 4-14. doi:10.1111/j.1365-2125.2009.03537.xSáez, C., Zurriaga, O., Pérez-Panadés, J., Melchor, I., Robles, M., & GarcÃa-Gómez, J. M. (2016). Applying probabilistic temporal and multisite data quality control methods to a public health mortality registry in Spain: a systematic approach to quality control of repositories. Journal of the American Medical Informatics Association, 23(6), 1085-1095. doi:10.1093/jamia/ocw010Tate AR , Dungey S , Glew S , et al . Quality of recording of diabetes in the UK: how does the GP's method of coding clinical data affect incidence estimates? cross-sectional study using the CPRD database. BMJ Open 2017;7:e012905.doi:10.1136/bmjopen-2016-012905Calvert M , Shankar A , McManus RJ , et al . Effect of the quality and outcomes framework on diabetes care in the United Kingdom: retrospective cohort study. BMJ 2009;338:b1870.doi:10.1136/bmj.b1870Sáez, C., Robles, M., & GarcÃa-Gómez, J. M. (2016). Stability metrics for multi-source biomedical data based on simplicial projections from probability distribution distances. Statistical Methods in Medical Research, 26(1), 312-336. doi:10.1177/0962280214545122Herrett, E., Gallagher, A. M., Bhaskaran, K., Forbes, H., Mathur, R., van Staa, T., & Smeeth, L. (2015). Data Resource Profile: Clinical Practice Research Datalink (CPRD). International Journal of Epidemiology, 44(3), 827-836. doi:10.1093/ije/dyv098Herbert, A., Wijlaars, L., Zylbersztejn, A., Cromwell, D., & Hardelid, P. (2017). Data Resource Profile: Hospital Episode Statistics Admitted Patient Care (HES APC). International Journal of Epidemiology, 46(4), 1093-1093i. doi:10.1093/ije/dyx015Chisholm J . The read clinical classification. BMJ 1990;300:1092.doi:10.1136/bmj.300.6732.1092Denaxas, S., Gonzalez-Izquierdo, A., Direk, K., Fitzpatrick, N. K., Fatemifar, G., Banerjee, A., … Hemingway, H. (2019). UK phenomics platform for developing and validating electronic health record phenotypes: CALIBER. Journal of the American Medical Informatics Association, 26(12), 1545-1559. doi:10.1093/jamia/ocz105Department for Communities and Local Government . The English Index of Multiple Deprivation (IMD) 2015 - Guidance. Available: https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/464430/English_Index_of_Multiple_Deprivation_2015_-_Guidance.pdf [Accessed 8 Dec 2019].Sáez, C., Rodrigues, P. P., Gama, J., Robles, M., & GarcÃa-Gómez, J. M. (2014). Probabilistic change detection and visualization methods for the assessment of temporal stability in biomedical data quality. Data Mining and Knowledge Discovery, 29(4), 950-975. doi:10.1007/s10618-014-0378-6Borg, I., & Groenen, P. (2003). Modern Multidimensional Scaling: Theory and Applications. Journal of Educational Measurement, 40(3), 277-280. doi:10.1111/j.1745-3984.2003.tb01108.xSáez, C., & GarcÃa-Gómez, J. M. (2018). Kinematics of Big Biomedical Data to characterize temporal variability and seasonality of data repositories: Functional Data Analysis of data temporal evolution over non-parametric statistical manifolds. International Journal of Medical Informatics, 119, 109-124. doi:10.1016/j.ijmedinf.2018.09.015Conrad, N., Judge, A., Tran, J., Mohseni, H., Hedgecott, D., Crespillo, A. P., … Rahimi, K. (2018). Temporal trends and patterns in heart failure incidence: a population-based study of 4 million individuals. The Lancet, 391(10120), 572-580. doi:10.1016/s0140-6736(17)32520-5Herrett E , Shah AD , Boggon R , et al . Completeness and diagnostic validity of recording acute myocardial infarction events in primary care, hospital care, disease registry, and national mortality records: cohort study. BMJ 2013;346:f2350.doi:10.1136/bmj.f2350Pujades-Rodriguez M , Timmis A , Stogiannis D , et al . Socioeconomic deprivation and the incidence of 12 cardiovascular diseases in 1.9 million women and men: implications for risk prediction and prevention. PLoS One 2014;9:e104671.doi:10.1371/journal.pone.0104671Lee S , Shafe ACE , Cowie MR . Uk stroke incidence, mortality and cardiovascular risk management 1999-2008: time-trend analysis from the general practice research database. BMJ Open 2011;1:e000269.doi:10.1136/bmjopen-2011-000269Bhatnagar, P., Wickramasinghe, K., Williams, J., Rayner, M., & Townsend, N. (2015). The epidemiology of cardiovascular disease in the UK 2014. Heart, 101(15), 1182-1189. doi:10.1136/heartjnl-2015-307516Taylor, C. J., Ordóñez-Mena, J. M., Roalfe, A. K., Lay-Flurrie, S., Jones, N. R., Marshall, T., & Hobbs, F. D. R. (2019). Trends in survival after a diagnosis of heart failure in the United Kingdom 2000-2017: population based cohort study. BMJ, l223. doi:10.1136/bmj.l223Gho JMIH , Schmidt AF , Pasea L , et al . An electronic health records cohort study on heart failure following myocardial infarction in England: incidence and predictors. BMJ Open 2018;8:e018331.doi:10.1136/bmjopen-2017-018331Quint JK , Müllerova H , DiSantostefano RL , et al . Validation of chronic obstructive pulmonary disease recording in the clinical practice research Datalink (CPRD-GOLD). BMJ Open 2014;4:e005540.doi:10.1136/bmjopen-2014-005540Bhaskaran K , Forbes HJ , Douglas I , et al . Representativeness and optimal use of body mass index (BMI) in the UK clinical practice research Datalink (CPRD). BMJ Open 2013;3:e003389.doi:10.1136/bmjopen-2013-003389Booth, H. P., Prevost, A. T., & Gulliford, M. C. (2013). Validity of smoking prevalence estimates from primary care electronic health records compared with national population survey data for England, 2007 to 2011. Pharmacoepidemiology and Drug Safety, 22(12), 1357-1361. doi:10.1002/pds.3537Booth H , Dedman D , Wolf A . CPRD aurum frequently asked questions (FAQs). CPRD 2019.Burns, E. M., Rigby, E., Mamidanna, R., Bottle, A., Aylin, P., Ziprin, P., & Faiz, O. D. (2011). Systematic review of discharge coding accuracy. Journal of Public Health, 34(1), 138-148. doi:10.1093/pubmed/fdr054Marmot, M. G., Stansfeld, S., Patel, C., North, F., Head, J., White, I., … Smith, G. D. (1991). Health inequalities among British civil servants: the Whitehall II study. The Lancet, 337(8754), 1387-1393. doi:10.1016/0140-6736(91)93068-kKivimäki, M., Batty, G. D., Singh-Manoux, A., Britton, A., Brunner, E. J., & Shipley, M. J. (2017). Validity of Cardiovascular Disease Event Ascertainment Using Linkage to UK Hospital Records. Epidemiology, 28(5), 735-739. doi:10.1097/ede.0000000000000688Crosignani, P. G. (2003). Breast cancer and hormone-replacement therapy in the Million Women Study. Maturitas, 46(2), 91-92. doi:10.1016/j.maturitas.2003.09.002Wright FL , Green J , Canoy D , et al . Vascular disease in women: comparison of diagnoses in hospital episode statistics and general practice records in England. BMC Med Res Methodol 2012;12:161.doi:10.1186/1471-2288-12-161Herrett, E., Smeeth, L., Walker, L., & Weston, C. (2010). The Myocardial Ischaemia National Audit Project (MINAP). Heart, 96(16), 1264-1267. doi:10.1136/hrt.2009.192328Silver LE , Heneghan C , Mehta Z , et al . Substantial underestimation of incidence of acute myocardial infarction by hospital discharge diagnostic coding data: a prospective population-based study. Heart 2009;95.Health and Social Care Information Centre . Coding clinic guidance. 5th edn, 2010.Health & Social Care Information Centre . National Clinical Coding Standards - ICD-10. 4th edn, 2013. https://hscic.kahootz.com/connect.ti/t_c_home/view?objectId=31445829Wang, R. Y., & Strong, D. M. (1996). Beyond Accuracy: What Data Quality Means to Data Consumers. Journal of Management Information Systems, 12(4), 5-33. doi:10.1080/07421222.1996.1151809
Human mobility variations in response to restriction policies during the COVID-19 pandemic: An analysis from the Virus Watch community cohort in England, UK
Objective: Since the outbreak of COVID-19, public health and social measures to contain its transmission (e.g., social distancing and lockdowns) have dramatically changed people's lives in rural and urban areas globally. To facilitate future management of the pandemic, it is important to understand how different socio-demographic groups adhere to such demands. This study aims to evaluate the influences of restriction policies on human mobility variations associated with socio-demographic groups in England, UK. Methods: Using mobile phone global positioning system (GPS) trajectory data, we measured variations in human mobility across socio-demographic groups during different restriction periods from Oct 14, 2020 to Sep 15, 2021. The six restriction periods which varied in degree of mobility restriction policies, denoted as "Three-tier Restriction," "Second National Lockdown," "Four-tier Restriction," "Third National Lockdown," "Steps out of Lockdown," and "Post-restriction," respectively. Individual human mobility was measured with respect to the time period people stayed at home, visited places outside the home, and traveled long distances. We compared these indicators across the six restriction periods and across socio-demographic groups. Results: All human mobility indicators significantly differed across the six restriction periods, and the influences of restriction policies on individual mobility behaviors are correlated with socio-demographic groups. In particular, influences relating to mobility behaviors are stronger in younger and low-income groups in the second and third national lockdowns. Conclusions: This study enhances our understanding of the influences of COVID-19 pandemic restriction policies on human mobility behaviors within different social groups in England. The findings can be usefully extended to support policy-making by investigating human mobility and differences in policy effects across not only age and income groups, but also across geographical regions
Black, Asian and Minority Ethnic groups in England are at increased risk of death from COVID-19: indirect standardisation of NHS mortality data
Background: International and UK data suggest that Black, Asian and Minority Ethnic (BAME) groups are at increased risk of infection and death from COVID-19. We aimed to explore the risk of death in minority ethnic groups in England using data reported by NHS England.
Methods: We used NHS data on patients with a positive COVID-19 test who died in hospitals in England published on 28th April, with deaths by ethnicity available from 1st March 2020 up to 5pm on 21 April 2020. We undertook indirect standardisation of these data (using the whole population of England as the reference) to produce ethnic specific standardised mortality ratios (SMRs) adjusted for age and geographical region.
Results: The largest total number of deaths in minority ethnic groups were Indian (492 deaths) and Black Caribbean (460 deaths) groups. Adjusting for region we found a lower risk of death for White Irish (SMR 0.52; 95%CIs 0.45-0.60) and White British ethnic groups (0.88; 95%CIs 0.86-0.0.89), but increased risk of death for Black African (3.24; 95%CIs 2.90-3.62), Black Caribbean (2.21; 95%CIs 2.02-2.41), Pakistani (3.29; 95%CIs 2.96-3.64), Bangladeshi (2.41; 95%CIs 1.98-2.91) and Indian (1.70; 95%CIs 1.56-1.85) minority ethnic groups.
Conclusion: Our analysis adds to the evidence that BAME people are at increased risk of death from COVID-19 even after adjusting for geographical region, but was limited by the lack of data on deaths outside of NHS settings and ethnicity denominator data being based on the 2011 census. Despite these limitations, we believe there is an urgent need to take action to reduce the risk of death for BAME groups and better understand why some ethnic groups experience greater risk. Actions that are likely to reduce these inequities include ensuring adequate income protection, reducing occupational risks, reducing barriers in accessing healthcare and providing culturally and linguistically appropriate public health communications
Increasing healthy life expectancy equitably in England by 5 years by 2035: could it be achieved?
In 2018, the UK Government’s Secretary of State for Health and Social Care articulated an ambition to increase healthy life expectancy by five years by 2035 for England, while also reducing the gap in this between the rich and the poor1. While we doubt that England – or indeed any high-income country – could achieve this ambition, we describe a set of policies with the potential to make a significant contribution
Seasonality and immunity to laboratory-confirmed seasonal coronaviruses (HCoV-NL63, HCoV-OC43, and HCoV-229E): results from the Flu Watch cohort study
Background: There is currently a pandemic caused by the novel coronavirus SARS-CoV-2. The intensity and duration of this first wave in the UK may be dependent on whether SARS-CoV-2 transmits more effectively in the winter than the summer and the UK Government response is partially built upon the assumption that those infected will develop immunity to reinfection in the short term. In this paper we examine evidence for seasonality and immunity to laboratory-confirmed seasonal coronavirus (HCoV) from a prospective cohort study in England. Methods: In this analysis of the Flu Watch cohort, we examine seasonal trends for PCR-confirmed coronavirus infections (HCoV-NL63, HCoV-OC43, and HCoV-229E) in all participants during winter seasons (2006-2007, 2007-2008, 2008-2009) and during the first wave of the 2009 H1N1 influenza pandemic (May-Sep 2009). We also included data from the pandemic and �post-pandemic� winter seasons (2009-2010 and 2010-2011) to identify individuals with two confirmed HCoV infections and examine evidence for immunity against homologous reinfection. Results: We tested 1,104 swabs taken during respiratory illness and detected HCoV in 199 during the first four seasons. The rate of confirmed HCoV infection across all seasons was 390 (95% CI 338-448) per 100,000 person-weeks; highest in the Nov-Mar 2008/9 season at 674 (95%CI 537-835). The highest rate was in February at 759 (95% CI 580-975). Data collected during May-Sep 2009 showed there was small amounts of ongoing transmission, with four cases detected during this period. Eight participants had two confirmed infections, of which none had the same strain twice. Conclusion: Our results provide evidence that HCoV infection in England is most intense in winter, but that there is a small amount of ongoing transmission during summer periods. We found some evidence of immunity against homologous reinfection.</ns3:p
Effects of non-health-targeted policies on migrant health: a systematic review and meta-analysis
Background:
Government policies can strongly influence migrants' health. Using a Health in All Policies approach, we systematically reviewed evidence on the impact of public policies outside of the health-care system on migrant health.
Methods:
We searched the PubMed, Embase, and Web of Science databases from Jan 1, 2000, to Sept 1, 2017, for quantitative studies comparing the health effects of non-health-targeted public policies on migrants with those on a relevant comparison population. We searched for articles written in English, Swedish, Danish, Norwegian, Finnish, French, Spanish, or Portuguese. Qualitative studies and grey literature were excluded. We evaluated policy effects by migration stage (entry, integration, and exit) and by health outcome using narrative synthesis (all included studies) and random-effects meta-analysis (all studies whose results were amenable to statistical pooling). We summarised meta-analysis outcomes as standardised mean difference (SMD, 95% CI) or odds ratio (OR, 95% CI). To assess certainty, we created tables containing a summary of the findings according to the Grading of Recommendations Assessment, Development, and Evaluation. Our study was registered with PROSPERO, number CRD42017076104.
Findings:
We identified 43 243 potentially eligible records. 46 articles were narratively synthesised and 19 contributed to the meta-analysis. All studies were published in high-income countries and examined policies of entry (nine articles) and integration (37 articles). Restrictive entry policies (eg, temporary visa status, detention) were associated with poor mental health (SMD 0·44, 95% CI 0·13–0·75; I2=92·1%). In the integration phase, restrictive policies in general, and specifically regarding welfare eligibility and documentation requirements, were found to increase odds of poor self-rated health (OR 1·67, 95% CI 1·35–1·98; I2=82·0%) and mortality (1·38, 1·10–1·65; I2=98·9%). Restricted eligibility for welfare support decreased the odds of general health-care service use (0·92, 0·85–0·98; I2=0·0%), but did not reduce public health insurance coverage (0·89, 0·71–1·07; I2=99·4%), nor markedly affect proportions of people without health insurance (1·06, 0·90–1·21; I2=54·9%).
Interpretation:
Restrictive entry and integration policies are linked to poor migrant health outcomes in high-income countries. Efforts to improve the health of migrants would benefit from adopting a Health in All Policies perspective
A rapid review and meta-analysis of the asymptomatic proportion of PCR-confirmed SARS-CoV-2 infections in community settings
Background: Cross-sectional studies indicate that up to 80% of active SARS-CoV-2 infections may be asymptomatic. However, accurate estimates of the asymptomatic proportion require systematic detection and follow-up to differentiate between truly asymptomatic and pre-symptomatic cases. We conducted a rapid review and meta-analysis of the asymptomatic proportion of PCR-confirmed SARS-CoV-2 infections based on methodologically appropriate studies in community settings. Methods: We searched Medline and EMBASE for peer-reviewed articles, and BioRxiv and MedRxiv for pre-prints published before 25/08/2020. We included studies based in community settings that involved systematic PCR testing on participants and follow-up symptom monitoring regardless of symptom status. We extracted data on study characteristics, frequencies of PCR-confirmed infections by symptom status, and (if available) cycle threshold/genome copy number values and/or duration of viral shedding by symptom status, and age of asymptomatic versus (pre)symptomatic cases. We computed estimates of the asymptomatic proportion and 95% confidence intervals for each study and overall using random effect meta-analysis. Results: We screened 1138 studies and included 21. The pooled asymptomatic proportion of SARS-CoV-2 infections was 23% (95% CI 16%-30%). When stratified by testing context, the asymptomatic proportion ranged from 6% (95% CI 0-17%) for household contacts to 47% (95% CI 21-75%) for non-outbreak point prevalence surveys with follow-up symptom monitoring. Estimates of viral load and duration of viral shedding appeared to be similar for asymptomatic and symptomatic cases based on available data, though detailed reporting of viral load and natural history of viral shedding by symptom status were limited. Evidence into the relationship between age and symptom status was inconclusive. Conclusion: Asymptomatic viral shedding comprises a substantial minority of SARS-CoV-2 infections when estimated using methodologically appropriate studies. Further investigation into variation in the asymptomatic proportion by testing context, the degree and duration of infectiousness for asymptomatic infections, and demographic predictors of symptom status are warranted.</ns4:p
Morbidity and mortality in homeless individuals, prisoners, sex workers, and individuals with substance use disorders in high-income countries: a systematic review and meta-analysis.
BACKGROUND: Inclusion health focuses on people in extremely poor health due to poverty, marginalisation, and multimorbidity. We aimed to review morbidity and mortality data on four overlapping populations who experience considerable social exclusion: homeless populations, individuals with substance use disorders, sex workers, and imprisoned individuals. METHODS: For this systematic review and meta-analysis, we searched MEDLINE, Embase, and the Cochrane Library for studies published between Jan 1, 2005, and Oct 1, 2015. We included only systematic reviews, meta-analyses, interventional studies, and observational studies that had morbidity and mortality outcomes, were published in English, from high-income countries, and were done in populations with a history of homelessness, imprisonment, sex work, or substance use disorder (excluding cannabis and alcohol use). Studies with only perinatal outcomes and studies of individuals with a specific health condition or those recruited from intensive care or high dependency hospital units were excluded. We screened studies using systematic review software and extracted data from published reports. Primary outcomes were measures of morbidity (prevalence or incidence) and mortality (standardised mortality ratios [SMRs] and mortality rates). Summary estimates were calculated using a random effects model. FINDINGS: Our search identified 7946 articles, of which 337 studies were included for analysis. All-cause standardised mortality ratios were significantly increased in 91 (99%) of 92 extracted datapoints and were 11·86 (95% CI 10·42-13·30; I2=94·1%) in female individuals and 7·88 (7·03-8·74; I2=99·1%) in men. Summary SMR estimates for the International Classification of Diseases disease categories with two or more included datapoints were highest for deaths due to injury, poisoning, and other external causes, in both men (7·89; 95% CI 6·40-9·37; I2=98·1%) and women (18·72; 13·73-23·71; I2=91·5%). Disease prevalence was consistently raised across the following categories: infections (eg, highest reported was 90% for hepatitis C, 67 [65%] of 103 individuals for hepatitis B, and 133 [51%] of 263 individuals for latent tuberculosis infection), mental health (eg, highest reported was 9 [4%] of 227 individuals for schizophrenia), cardiovascular conditions (eg, highest reported was 32 [13%] of 247 individuals for coronary heart disease), and respiratory conditions (eg, highest reported was 9 [26%] of 35 individuals for asthma). INTERPRETATION: Our study shows that homeless populations, individuals with substance use disorders, sex workers, and imprisoned individuals experience extreme health inequities across a wide range of health conditions, with the relative effect of exclusion being greater in female individuals than male individuals. The high heterogeneity between studies should be explored further using improved data collection in population subgroups. The extreme health inequity identified demands intensive cross-sectoral policy and service action to prevent exclusion and improve health outcomes in individuals who are already marginalised. FUNDING: Wellcome Trust, National Institute for Health Research, NHS England, NHS Research Scotland Scottish Senior Clinical Fellowship, Medical Research Council, Chief Scientist Office, and the Central and North West London NHS Trust
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