Feasibility of using routinely collected inpatient data to monitor quality and inform choice: a case study using the UK inflammatory bowel disease audit.
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ABSTRACT:
SUBMITTER: Roberts SE
PROVIDER: S-EPMC5517215 | biostudies-other | 2011 Jul
REPOSITORIES: biostudies-other
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Project description:BackgroundInfluenza remains a significant burden on health systems. Effective responses rely on the timely understanding of the magnitude and the evolution of an outbreak. For monitoring purposes, data on severe cases of influenza in England are reported weekly to Public Health England. These data are both readily available and have the potential to provide valuable information to estimate and predict the key transmission features of seasonal and pandemic influenza.MethodsWe propose an epidemic model that links the underlying unobserved influenza transmission process to data on severe influenza cases. Within a Bayesian framework, we infer retrospectively the parameters of the epidemic model for each seasonal outbreak from 2012 to 2015, including: the effective reproduction number; the initial susceptibility; the probability of admission to intensive care given infection; and the effect of school closure on transmission. The model is also implemented in real time to assess whether early forecasting of the number of admissions to intensive care is possible.ResultsOur model of admissions data allows reconstruction of the underlying transmission dynamics revealing: increased transmission during the season 2013/14 and a noticeable effect of the Christmas school holiday on disease spread during seasons 2012/13 and 2014/15. When information on the initial immunity of the population is available, forecasts of the number of admissions to intensive care can be substantially improved.ConclusionReadily available severe case data can be effectively used to estimate epidemiological characteristics and to predict the evolution of an epidemic, crucially allowing real-time monitoring of the transmission and severity of the outbreak.
| S-EPMC6020250 | biostudies-literature
Project description:Importance:Inpatient violence remains a significant problem despite existing risk assessment methods. The lack of robustness and the high degree of effort needed to use current methods might be mitigated by using routinely registered clinical notes. Objective:To develop and validate a multivariable prediction model for assessing inpatient violence risk based on machine learning techniques applied to clinical notes written in patients' electronic health records. Design, Setting, and Participants:This prognostic study used retrospective clinical notes registered in electronic health records during admission at 2 independent psychiatric health care institutions in the Netherlands. No exclusion criteria for individual patients were defined. At site 1, all adults admitted between January 2013 and August 2018 were included, and at site 2 all adults admitted to general psychiatric wards between June 2016 and August 2018 were included. Data were analyzed between September 2018 and February 2019. Main Outcomes and Measures:Predictive validity and generalizability of prognostic models measured using area under the curve (AUC). Results:Clinical notes recorded during a total of 3189 admissions of 2209 unique individuals at site 1 (mean [SD] age, 34.0 [16.6] years; 1536 [48.2%] male) and 3253 admissions of 1919 unique individuals at site 2 (mean [SD] age, 45.9 [16.6] years; 2097 [64.5%] male) were analyzed. Violent outcome was determined using the Staff Observation Aggression Scale-Revised. Nested cross-validation was used to train and evaluate models that assess violence risk during the first 4 weeks of admission based on clinical notes available after 24 hours. The predictive validity of models was measured at site 1 (AUC = 0.797; 95% CI, 0.771-0.822) and site 2 (AUC = 0.764; 95% CI, 0.732-0.797). The validation of pretrained models in the other site resulted in AUCs of 0.722 (95% CI, 0.690-0.753) at site 1 and 0.643 (95% CI, 0.610-0.675) at site 2; the difference in AUCs between the internally trained model and the model trained on other-site data was significant at site 1 (AUC difference = 0.075; 95% CI, 0.045-0.105; P < .001) and site 2 (AUC difference = 0.121; 95% CI, 0.085-0.156; P < .001). Conclusions and Relevance:Internally validated predictions resulted in AUC values with good predictive validity, suggesting that automatic violence risk assessment using routinely registered clinical notes is possible. The validation of trained models using data from other sites corroborates previous findings that violence risk assessment generalizes modestly to different populations.
| S-EPMC6613290 | biostudies-literature
Project description:OBJECTIVE: To identify where in England there are likely to be most constraints on choice of hospital for patients waiting longer than six months for elective care. DESIGN: Cross sectional study using routinely collected data. SETTING: Population of England and NHS trusts and private sector hospitals in England. PARTICIPANTS: All residents in England. MAIN OUTCOME MEASURES: Availability of beds (available and unoccupied hospital beds), demand (number of people waiting longer than six months), and access (travel time to facilities) to hospital care in England. RESULTS: Most people in England already have an extensive potential choice of hospital. The number of available and unoccupied beds within 60 minutes' travel time was lowest in the Scottish borders, North Yorkshire, and parts of East Anglia, Lincolnshire, Devon, and Cornwall. This pattern was not altered by adding in private facilities. Putting demand with this supply, the number of people in a geographical area waiting longer than six months per bed within 60 minutes' travel time was highest in the south east (except London), parts of the south west (Cornwall, Bristol), East Anglia, and the Welsh border. CONCLUSION: People in the south east (outside London), East Anglia, and parts of the south west are likely to have to travel further to exercise meaningful choice of hospital for elective care.
| S-EPMC548177 | biostudies-other
Project description:BackgroundDelirium is a brain condition associated with poor outcomes in rehabilitation. It is therefore important to assess delirium incidence in rehabilitation.PurposeTo develop and validate a chart-based method to identify incident delirium episodes within the electronic database of a Swiss rehabilitation clinic, and to identify a study population of validated incident delirium episodes for further research purposes.DesignRetrospective validation study.SettingsRoutinely collected inpatient clinical data from ZURZACH Care.ParticipantsAll patients undergoing rehabilitation at ZURZACH Care, Rehaklinik Bad Zurzach between 2015 and 2018 were included.MethodsWithin the study population, we identified all rehabilitation stays for which ≥2 delirium-predictive key words (common terms used to describe delirious patients) were recorded in the medical charts. We excluded all prevalent delirium episodes and defined the remaining episodes to be potentially incident. At least two physicians independently confirmed or refuted each potential incident delirium episode by reviewing the patient charts. We calculated the positive predictive value (PPV) with 95% confidence interval (95% CI) for all potential incident delirium episodes and for specific subgroups.ResultsWithin 10,515 rehabilitation stays we identified 554 potential incident delirium episodes. Overall, 125 potential incident delirium episodes were confirmed by expert review. The PPV of the chart-based method varied from 0.23 (95% CI 0.19-0.26) overall to 0.69 (95% CI 0.56-0.79) in specific subgroups.ConclusionsOur chart-based method was able to capture incident delirium episodes with low to moderate accuracy. By conducting an additional expert review of the medical charts, we identified a study population of validated incident delirium episodes. Our chart-based method contributes towards an automated detection of potential incident delirium episodes that, supplemented with expert review, efficiently yields a validated population of incident delirium episodes for research purposes.
| S-EPMC8705493 | biostudies-literature
Project description:IntroductionIt is estimated that peripheral arterial disease occurs in one in five people aged over 60 years in the UK. Major lower limb amputation is a debilitating and life-changing potential outcome of peripheral arterial disease. A number of risk factors are involved in the development of the disease including smoking and diabetes. There is debate over the prevalence of major lower limb amputation in the UK with regional variations unexplained. The choice of data source can affect the epidemiological calculations and sources can also differ in the ability to explain variation. This study will aim to estimate the prevalence/incidence/number of major lower limb amputation in the UK. It will also identify sources of routinely collected electronic health data which report the epidemiology of major lower limb amputation in the UK.Methods and analysisA systematic search of peer-reviewed journals will be conducted in Medline, Excerpta Medica database, Cumulative Index of Nursing and Allied Health Literature, Allied and Complementary Medicine Database, The Cochrane Library and Scopus. A grey literature search for government and parliament publications, conference abstracts, theses and unpublished articles will be performed. Articles will be screened against the inclusion/exclusion criteria and data extracted using a pretested extraction form by two independent reviewers. Prevalence, incidence or number of cases (depending on data reported) will be extracted. Disagreements will be resolved by discussion. Data synthesis will be performed either as a narrative summary or by meta-analysis. Heterogeneity will be assessed using the I2 statistic. If heterogeneity is low-moderate, pooled estimates will be calculated using random-effects models. If possible, meta-regression for time trends in the incidence of major lower limb amputation will be performed along with subgroup analysis, primarily in regional variation.Ethics and disseminationEthics approval is not required for this study as study data are anonymised and available in the public domain. Dissemination will be by publication in a peer reviewed journal and by appropriate conference presentation.PROSPERO registration numberCRD42020165592.
| S-EPMC7295407 | biostudies-literature
Project description:ObjectivesTo evaluate the characteristics of individuals recorded as having a dementia diagnosis in different routinely collected records and to examine the extent of overlap of dementia coding across data sources. Also, to present comparisons of secondary and primary care records providing value for researchers using routinely collected records for dementia outcome capture.Study designA prospective cohort study.Setting and participantsA cohort of 25 639 men and women in Norfolk, aged 40-79 years at recruitment (1993-1997) followed until 2018 linked to routinely collected to identify dementia cases. Data sources include mortality from death certification and National Health Service (NHS) hospital or secondary care records. Primary care records for a subset of the cohort were also reviewed.Primary outcome measureDiagnosis of dementia (any-cause).ResultsOver 2000 participants (n=2635 individuals) were found to have a dementia diagnosis recorded in one or more of the data sources examined. Limited concordance was observed across the secondary care data sources. We also observed discrepancies with primary care records for the subset and report on potential linkage-related selection bias.ConclusionsUse of different types of record linkage from varying parts of the UK's health system reveals differences in recorded dementia diagnosis, indicating that dementia can be identified to varying extents in different parts of the NHS system. However, there is considerable variation, and limited overlap in those identified. We present potential selection biases that might occur depending on whether cause of death, or primary and secondary care data sources are used. With the expansion of using routinely collected health data, researchers must be aware of these potential biases and inaccuracies, reporting carefully on the likely extent of limitations and challenges of the data sources they use.
| S-EPMC9204445 | biostudies-literature
Project description:ObjectiveTo examine the potential for using routinely collected administrative data to compare the quality and safety of stroke care at a hospital level, including evaluating any bias due to variations in coding practice.DesignA retrospective cohort study of English hospitals' performance against six process and outcome indicators covering the acute care pathway. We used logistic regression to adjust the outcome measures for case mix.SettingHospitals in England.ParticipantsStroke patients (ICD-10 I60-I64) admitted to English National Health Service public acute hospitals between April 2009 and March 2010, accounting for 91 936 admissions.Main outcome measureThe quality and safety were measured using six indicators spanning the hospital care pathway, from timely access to brain scans to emergency readmissions following discharge after stroke.ResultsThere were 182 occurrences of hospitals performing statistically differently from the national average at the 99.8% significance level across the six indicators. Differences in coding practice appeared to only partially explain the variation.ConclusionsHospital administrative data provide a practical and achievable method for evaluating aspects of stroke care across the acute pathway. However, without improvements in coding and further validation, it is unclear whether the cause of the variation is the quality of care or the result of different local care pathways and data coding accuracy.
| S-EPMC3723302 | biostudies-literature
Project description:BackgroundAdherence to antiretroviral treatment (ART) among postpartum women with HIV is essential for optimal health and prevention of perinatal transmission. However, suboptimal adherence with subsequent viremia is common, and adherence challenges are often underreported. We aimed to predict viremia to facilitate targeted adherence support in sub-Saharan Africa during this critical period.MethodsData are from PROMISE 1077BF/FF, which enrolled perinatal women between 2011 and 2014. This analysis includes postpartum women receiving ART per study randomization or country-specific criteria to continue from pregnancy. We aimed to predict viremia (single and confirmed events) after 3 months on ART at >50, >400, and >1000 copies/mL within 6-month intervals through 24 months. We built models with routine clinical and demographic data using the least absolute shrinkage and selection operator and SuperLearner (which incorporates multiple algorithms).ResultsAmong 1321 women included, the median age was 26 years and 96% were in WHO stage 1. Between 0 and 24 months postpartum, 42%, 31%, and 28% of women experienced viremia >50, >400, and >1000 copies/mL, respectively, at least once. Across models, the cross-validated area under the receiver operating curve ranged from 0.74 [95% confidence interval (CI): 0.72 to 0.76] to 0.78 (95% CI: 0.76 to 0.80). To achieve 90% sensitivity predicting confirmed viremia >50 copies/mL, 64% of women would be classified as high risk.ConclusionsUsing routinely collected data to predict viremia in >1300 postpartum women with HIV, we achieved moderate model discrimination, but insufficient to inform targeted adherence support. Psychosocial characteristics or objective adherence metrics may be required for improved prediction of viremia in this population.
| S-EPMC8585692 | biostudies-literature
Project description:In most countries with large drug resistant tuberculosis epidemics, only those cases that are at highest risk of having MDRTB receive a drug sensitivity test (DST) at the time of diagnosis. Because of this prioritized testing, identification of MDRTB transmission hotspots in communities where TB cases do not receive DST is challenging, as any observed aggregation of MDRTB may reflect systematic differences in how testing is distributed in communities. We introduce a new disease mapping method, which estimates this missing information through probability-weighted locations, to identify geographic areas of increased risk of MDRTB transmission. We apply this method to routinely collected data from two districts in Lima, Peru over three consecutive years. This method identifies an area in the eastern part of Lima where previously untreated cases have increased risk of MDRTB. This may indicate an area of increased transmission of drug resistant disease, a finding that may otherwise have been missed by routine analysis of programmatic data. The risk of MDR among retreatment cases is also highest in these probable transmission hotspots, though a high level of MDR among retreatment cases is present throughout the study area. Identifying potential multidrug resistant tuberculosis (MDRTB) transmission hotspots may allow for targeted investigation and deployment of resources.
| S-EPMC3323731 | biostudies-literature
Project description:ObjectiveTo examine whether gender differences in primary care consultation rates (1) vary by age and deprivation status and (2) diminish when consultation for reproductive reasons or common underlying morbidities are accounted for.DesignCross-sectional study of a cohort of patients registered with general practice.SettingUK primary care.SubjectsPatients (1 869 149 men and 1 916 898 women) registered with 446 eligible practices in 2010.Primary outcome measuresPrimary care consultation rate.ResultsThis study analyses routinely collected primary care consultation data. The crude consultation rate was 32% lower in men than women. The magnitude of gender difference varied across the life course, and there was no 'excess' female consulting in early and later life. The greatest gender gap in primary care consultations was seen among those aged between 16 and 60 years. Gender differences in consulting were higher in people from more deprived areas than among those from more affluent areas. Accounting for reproductive-related consultations diminished but did not eradicate the gender gap. However, consultation rates in men and women who had comparable underlying morbidities (as assessed by receipt of medication) were similar; men in receipt of antidepressant medication were only 8% less likely to consult than women in receipt of antidepressant medication (relative risk (RR) 0.916, 95% CI 0.913 to 0.918), and men in receipt of medication to treat cardiovascular disease were just 5% less likely to consult (RR=0.950, 95% CI 0.948 to 0.952) than women receiving similar medication. These small gender differences diminished further, particularly for depression (RR=0.950, 95% CI 0.947 to 0.953), after also taking account of reproductive consultations.ConclusionsOverall gender differences in consulting are most marked between the ages of 16 and 60 years; these differences are only partially accounted for by consultations for reproductive reasons. Differences in consultation rates between men and women were largely eradicated when comparing men and women in receipt of medication for similar underlying morbidities.
| S-EPMC3753483 | biostudies-literature