Dataset Information

Use of multiple data sources to estimate hepatitis C seroprevalence among prisoners: A retrospective cohort study.

ABSTRACT: Hepatitis C is a major cause of preventable morbidity and mortality. Prisoners are a key population for hepatitis C control programs, and with the advent of highly effective therapies, prisons are increasingly important sites for hepatitis C diagnosis and treatment. Accurate estimates of hepatitis C prevalence among prisoners are needed in order to plan and resource service provision, however many prevalence estimates are based on surveys compromised by limited and potentially biased participation. We aimed to compare estimates derived from three different data sources, and to assess whether the use of self-report as a supplementary data source may help researchers assess the risk of selection bias. We used three data sources to estimate the prevalence of hepatitis C antibodies in a large cohort of Australian prisoners-prison medical records, self-reported status during a face-to-face interview prior to release from prison, and data from a statewide notifiable conditions surveillance system. Of 1,315 participants, 33.8% had at least one indicator of hepatitis C seropositivity, however less than one third of these (9.5% of the entire cohort) were identified by all three data sources. Among participants of known status, self-report had a sensitivity of 80.1% and a positive predictive value of 97.8%. Any one data source used in isolation would have under-estimated the prevalence of hepatitis C in this cohort. Using multiple data sources in studies of hepatitis C seroprevalence among prisoners may improve case detection and help researchers assess the risk of selection bias due to non-participation in serological testing.

SUBMITTER: Snow KJ

PROVIDER: S-EPMC5501572 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Use of multiple data sources to estimate hepatitis C seroprevalence among prisoners: A retrospective cohort study.

Snow Kathryn J KJ Richards Alun H AH Kinner Stuart A SA

PloS one 20170707 7

Hepatitis C is a major cause of preventable morbidity and mortality. Prisoners are a key population for hepatitis C control programs, and with the advent of highly effective therapies, prisons are increasingly important sites for hepatitis C diagnosis and treatment. Accurate estimates of hepatitis C prevalence among prisoners are needed in order to plan and resource service provision, however many prevalence estimates are based on surveys compromised by limited and potentially biased participati ...[more]

PMID: 28686715

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Project description:ImportanceSuicide is a leading cause of death in the US. However, official national statistics on suicide rates are delayed by 1 to 2 years, hampering evidence-based public health planning and decision-making.ObjectiveTo estimate weekly suicide fatalities in the US in near real time.Design, setting, and participantsThis cross-sectional national study used a machine learning pipeline to combine signals from several streams of real-time information to estimate weekly suicide fatalities in the US in near real time. This 2-phase approach first fits optimal machine learning models to each individual data stream and subsequently combines predictions made from each data stream via an artificial neural network. National-level US administrative data on suicide deaths, health services, and economic, meteorological, and online data were variously obtained from 2014 to 2017. Data were analyzed from January 1, 2014, to December 31, 2017.ExposuresLongitudinal data on suicide-related exposures were obtained from multiple, heterogeneous streams: emergency department visits for suicide ideation and attempts collected via the National Syndromic Surveillance Program (2015-2017); calls to the National Suicide Prevention Lifeline (2014-2017); calls to US poison control centers for intentional self-harm (2014-2017); consumer price index and seasonality-adjusted unemployment rate, hourly earnings, home price index, and 3-month and 10-year yield curves from the Federal Reserve Economic Data (2014-2017); weekly daylight hours (2014-2017); Google and YouTube search trends related to suicide (2014-2017); and public posts on suicide on Reddit (2 314 533 posts), Twitter (9 327 472 tweets; 2015-2017), and Tumblr (1 670 378 posts; 2014-2017).Main outcomes and measuresWeekly estimates of suicide fatalities in the US were obtained through a machine learning pipeline that integrated the above data sources. Estimates were compared statistically with actual fatalities recorded by the National Vital Statistics System.ResultsCombining information from multiple data streams, the machine learning method yielded estimates of weekly suicide deaths with high correlation to actual counts and trends (Pearson correlation, 0.811; P < .001), while estimating annual suicide rates with low error (0.55%).Conclusions and relevanceThe proposed ensemble machine learning framework reduces the error for annual suicide rate estimation to less than one-tenth of that of current forecasting approaches that use only historical information on suicide deaths. These findings establish a novel approach for tracking suicide fatalities in near real time and provide the potential for an effective public health response such as supporting budgetary decisions or deploying interventions.

Project description:Background and objectivesStudies that use electronic health data typically identify heart failure (HF) events from hospitalizations with a principal diagnosis of HF. This approach may underestimate the total burden of HF among persons with CKD. We assessed the accuracy of algorithms for identifying validated HF events from hospitalizations and outpatient encounters, and we used this validation information to estimate the rate of HF events in a large CKD population.Design, setting, participants, & measurementsWe identified a cohort of 15,141 adults age 18-89 years with an eGFR<60 ml/min per 1.73 m2 from 2008 to 2011. Potential HF events during follow-up were randomly sampled for validation with medical record review. Positive predictive values from the validation study were used to estimate the rate of validated HF events in the full cohort.ResultsA total of 1864 participants had at least one health care encounter that qualified as a potential HF event during 2.7 years of mean follow-up. Among 313 potential events that were randomly sampled for validation, positive predictive values were 92% for hospitalizations with a principal diagnosis of HF, 32% for hospitalizations with a secondary diagnosis of HF, and 70% for qualifying outpatient HF encounters. Through use of this validation information in the full cohort, the rate of validated HF events estimated from the most comprehensive algorithm that included principal and secondary diagnosis hospitalizations and outpatient encounters was 35.2 events/1000 person-years (95% confidence interval, 33.1 to 37.4), compared with 9.5 events/1000 person-years (95% confidence interval, 8.7 to 10.5) from the algorithm that included only principal diagnosis hospitalizations. Outpatient encounters accounted for 20% of the total number of validated HF events.ConclusionsIn studies that rely on electronic health data, algorithms that include hospitalizations with a secondary diagnosis of HF and outpatient HF encounters more fully capture the burden of HF, although validation of HF events may be necessary with this approach.

Dataset Information

Use of multiple data sources to estimate hepatitis C seroprevalence among prisoners: A retrospective cohort study.

Publications

Use of multiple data sources to estimate hepatitis C seroprevalence among prisoners: A retrospective cohort study.

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