Record linkage to correct under-ascertainment of cancers in HIV cohorts: The Sinikithemba HIV clinic linkage project.
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ABSTRACT:
SUBMITTER: Sengayi M
PROVIDER: S-EPMC5084785 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Little is known on the risk of cancer in HIV-positive children in sub-Saharan Africa. We examined incidence and risk factors of AIDS-defining and other cancers in pediatric antiretroviral therapy (ART) programs in South Africa.We linked the records of 5 ART programs in Johannesburg and Cape Town to those of pediatric oncology units, based on name and surname, date of birth, folder and civil identification numbers. We calculated incidence rates and obtained hazard ratios (HR) with 95% confidence intervals (CI) from Cox regression models including ART, sex, age and degree of immunodeficiency. Missing CD4 counts and CD4% were multiply imputed. Immunodeficiency was defined according to World Health Organization 2005 criteria.Data of 11,707 HIV-positive children were included in the analysis. During 29,348 person-years of follow-up 24 cancers were diagnosed, for an incidence rate of 82 per 100,000 person-years (95% CI: 55-122). The most frequent cancers were Kaposi sarcoma (34 per 100,000 person-years) and non-Hodgkin Lymphoma (31 per 100,000 person-years). The incidence of non AIDS-defining malignancies was 17 per 100,000. The risk of developing cancer was lower on ART (HR: 0.29; 95% CI: 0.09-0.86), and increased with age at enrollment (>10 vs. <3 years: HR: 7.3; 95% CI: 2.2-24.6) and immunodeficiency at enrollment (advanced/severe versus no/mild: HR: 3.5; 95% CI: 1.1-12.0). The HR for the effect of ART from complete case analysis was similar but ceased to be statistically significant (P = 0.078).Early HIV diagnosis and linkage to care, with start of ART before advanced immunodeficiency develops, may substantially reduce the burden of cancer in HIV-positive children in South Africa and elsewhere.
| S-EPMC4865449 | biostudies-literature
Project description:BackgroundHistorically, counting influenza recorded in administrative health outcome databases has been considered insufficient to estimate influenza attributable morbidity and mortality in populations. We used database record linkage to evaluate whether modern databases have similar limitations.MethodsPerson-level records were linked across databases of laboratory notified influenza, emergency department (ED) presentations, hospital admissions and death registrations, from the population (∼6.9 million) of New South Wales (NSW), Australia, 2005 to 2008.ResultsThere were 2568 virologically diagnosed influenza infections notified. Among those, 25% of 40 who died, 49% of 1451 with a hospital admission and 7% of 1742 with an ED presentation had influenza recorded on the respective database record. Compared with persons aged ≥65 years and residents of regional and remote areas, respectively, children and residents of major cities were more likely to have influenza coded on their admission record. Compared with older persons and admitted patients, respectively, working age persons and non-admitted persons were more likely to have influenza coded on their ED record. On both ED and admission records, persons with influenza type A infection were more likely than those with type B infection to have influenza coded. Among death registrations, hospital admissions and ED presentations with influenza recorded as a cause of illness, 15%, 28% and 1.4%, respectively, also had laboratory notified influenza. Time trends in counts of influenza recorded on the ED, admission and death databases reflected the trend in counts of virologically diagnosed influenza.ConclusionsA minority of the death, hospital admission and ED records for persons with a virologically diagnosed influenza infection identified influenza as a cause of illness. Few database records with influenza recorded as a cause had laboratory confirmation. The databases have limited value for estimating incidence of influenza outcomes, but can be used for monitoring variation in incidence over time.
| S-EPMC4038604 | biostudies-literature
Project description:BackgroundAdvancements in linking publicly available census records with vital and administrative records have enabled novel investigations in epidemiology and social history. However, in the absence of unique identifiers, the linkage of the records may be uncertain or only be successful for a subset of the census cohort, resulting in missing data. For survival analysis, differential ascertainment of event times can impact inference on risk associations and median survival.MethodsWe modify some existing approaches that are commonly used to handle missing survival times to accommodate this imperfect linkage situation including complete case analysis, censoring, weighting, and several multiple imputation methods. We then conduct simulation studies to compare the performance of the proposed approaches in estimating the associations of a risk factor or exposure in terms of hazard ratio (HR) and median survival times in the presence of missing survival times. The effects of different missing data mechanisms and exposure-survival associations on their performance are also explored. The approaches are applied to a historic cohort of residents in Ambler, PA, established using the 1930 US census, from which only 2,440 out of 4,514 individuals (54%) had death records retrievable from publicly available data sources and death certificates. Using this cohort, we examine the effects of occupational and paraoccupational asbestos exposure on survival and disparities in mortality by race and gender.ResultsWe show that imputation based on conditional survival results in less bias and greater efficiency relative to a complete case analysis when estimating log-hazard ratios and median survival times. When the approaches are applied to the Ambler cohort, we find a significant association between occupational exposure and mortality, particularly among black individuals and males, but not between paraoccupational exposure and mortality.DiscussionThis investigation illustrates the strengths and weaknesses of different imputation methods for missing survival times due to imperfect linkage of the administrative or registry data. The performance of the methods may depend on the missingness process as well as the parameter being estimated and models of interest, and such factors should be considered when choosing the methods to address the missing event times.
| S-EPMC10935812 | biostudies-literature
Project description:Large prospective cohort studies may offer an opportunity to study the etiology and natural history of rare cancers. Cancer diagnoses in observational cohort studies are often self-reported. Little information exists on the validity of self-reported cancer diagnosis, especially rare cancers, in Canada. This study evaluated the validity of self-reported cancer diagnosis in Alberta's Tomorrow Project (ATP), a provincial cohort in Canada. ATP data were linked to the Alberta Cancer Registry (ACR). The first instance of self-reported cancer in a follow-up survey was compared to the first cancer diagnosis in the ACR after enrollment. The sensitivity and positive predictive value (PPV) were estimated for the reporting of cancer status, reporting of common or rare cancer, and reporting of site-specific cancer. Logistic regression analysis explored factors associated with false positive, false negative, and incorrect cancer site reporting. In the 30,843 ATP participants who consented to registry linkage, there were 810 primary cancer diagnoses in the ACR and 959 self-reports of first cancer post-enrollment, for a cancer status sensitivity of 92.1% (95% CI: 90.0-93.9) and PPV of 77.8% (95% CI: 75.0-80.4). Compared to common cancers, rare cancers had a lower sensitivity (62.8% vs. 89.6%) and PPV (35.8% vs. 84.5%). Participants with a rare cancer were more likely to report an incorrect site than those with a common cancer. Rare cancers were less likely to be captured by active follow-up than common cancers. While rare cancer research may be feasible in large cohort studies, registry linkage is necessary to capture rare cancer diagnoses completely and accurately.
| S-EPMC7718062 | biostudies-literature
Project description:PurposeTo investigate health, mortality and healthcare inequalities experienced by people with intellectual disabilities, and autistic people, and their determinants; an important step towards identifying and implementing solutions to reduce inequalities. This paper describes the cohorts, record-linkages and variables that will be used.ParticipantsScotland's Census, 2011 was used to identify Scotland's citizens with intellectual disabilities, and autistic citizens, and representative general population samples with neither. Using Scotland's community health index, the Census data (demography, household, employment, long-term conditions) were linked with routinely collected health, death and healthcare data: Scotland's register of deaths, Scottish morbidity data 06 (SMR06: cancer incidence, mortality, treatments), Prescribing Information System (identifying asthma/chronic obstructive pulmonary disease; angina/congestive heart failure/hypertension; peptic ulcer/reflux; constipation; diabetes; thyroid disorder; depression; bipolar disorders; anxiety/sleep; psychosis; attention deficit hyperactivity disorder; epilepsy; glaucoma), SMR01 (general/acute hospital admissions and causes, ambulatory care sensitive admissions), SMR04 (mental health admissions and causes), Scottish Care Information-Diabetes Collaboration (diabetic care quality, diabetic outcomes), national bowel screening programme and cervical screening.Findings to dateOf the whole population, 0.5% had intellectual disabilities, and 0.6% were autistic. Linkage was successful for >92%. The resultant e-cohorts include: (1) 22 538 people with intellectual disabilities (12 837 men and 9701 women), 4509 of whom are children <16 years, (2) 27 741 autistic people (21 390 men and 6351 women), 15 387 of whom are children <16 years and (3) representative general population samples with neither condition. Very good general health was reported for only 3389 (15.0%) people with intellectual disabilities, 10 510 (38.0%) autistic people, compared with 52.4% general population. Mental health conditions were reported for 4755 (21.1%) people with intellectual disabilities, 3998 (14.4%) autistic people, compared with 4.2% general population.Future plansAnalyses will determine the extent of premature mortality, causes of death, and avoidable deaths, profile of health conditions and cancers, healthcare quality and screening and determinants of mortality and healthcare.
| S-EPMC9109103 | biostudies-literature
Project description:Studies involving the use of probabilistic record linkage are becoming increasingly common. However, the methods underpinning probabilistic record linkage are not widely taught or understood, and therefore these studies can appear to be a 'black box' research tool. In this article, we aim to describe the process of probabilistic record linkage through a simple exemplar. We first introduce the concept of deterministic linkage and contrast this with probabilistic linkage. We illustrate each step of the process using a simple exemplar and describe the data structure required to perform a probabilistic linkage. We describe the process of calculating and interpreting matched weights and how to convert matched weights into posterior probabilities of a match using Bayes theorem. We conclude this article with a brief discussion of some of the computational demands of record linkage, how you might assess the quality of your linkage algorithm, and how epidemiologists can maximize the value of their record-linked research using robust record linkage methods.
| S-EPMC5005943 | biostudies-literature
Project description:For the past 80 years, social scientists have been linking historical censuses across time to study economic and geographic mobility. In recent decades, the quantity of historical census record linkage has exploded, owing largely to the advent of new machine-readable data created by genealogical organizations. Investigators are examining economic and geographic mobility across multiple generations, but also engaging many new topics. Several analysts are exploring the effects of early-life socioeconomic conditions, environmental exposures, or natural disasters on family, health and economic outcomes in later life. Other studies exploit natural experiments to gauge the impact of policy interventions such as social welfare programs and educational reforms. The new data sources have led to a proliferation of record linkage methodology, and some widespread approaches inadvertently introduce errors that can lead to false inferences. A new generation of large-scale shared data infrastructure now in preparation will ameliorate weaknesses of current linkage methods.
| S-EPMC6200350 | biostudies-literature
Project description:ContextPalliative care can improve the lives of people with serious illness, yet clear operational definitions of this population do not exist. Prior efforts to identify this population have not focused on Medicare Advantage (MA) and commercial health plan enrollees.ObjectivesWe aimed to operationalize our conceptual definition of serious illness to identify those with serious medical conditions (SMC) among commercial insurance and MA enrollees, and to compare the populations identified through electronic health record (EHR) or claims data sources.MethodsWe used de-identified claims and EHR data from the OptumLabs Data Warehouse (2016-2017), to identify adults age ≥18 with SMC and examine their utilization and mortality. Within the subset found in both data sources, we compared the performance of claims and EHR data.ResultsWithin claims, SMC was identified among 10% of those aged ≥18 (5.4% ages 18-64, 27% age ≥65). Within EHR, SMC was identified among 9% of those aged ≥18 (5.6% ages 18-64, 21% ages ≥65). Hospital, emergency department and mortality rates were similar between the EHR and claims-based groups. Only 50% of people identified as having SMC were recognized by both data sources.ConclusionThese results demonstrate the feasibility of identifying adults with SMC in a commercially insured population, including MA enrollees; yet separate use of EHR or claims result in populations that differ. Future research should examine methods to combine these data sources to optimize identification and support population management, quality measurement, and research to improve the care of those living with serious illness.
| S-EPMC8419009 | biostudies-literature
Project description:Data derived from electronic health records (EHR) are heterogeneous with availability of specific measures dependent on the type and timing of patients' healthcare interactions. This creates a challenge for research using EHR-derived exposures because gold-standard exposure data, determined by a definitive assessment, may only be available for a subset of the population. Alternative approaches to exposure ascertainment in this case include restricting the analytic sample to only those patients with gold-standard exposure data available (exclusion); using gold-standard data, when available, and using a proxy exposure measure when the gold standard is unavailable (best available); or using a proxy exposure measure for everyone (common data). Exclusion may induce selection bias in outcome/exposure association estimates, while incorporating information from a proxy exposure via either the best available or common data approaches may result in information bias due to measurement error. The objective of this paper was to explore the bias and efficiency of these three analytic approaches across a broad range of scenarios motivated by a study of the association between chronic hyperglycemia and five-year mortality in an EHR-derived cohort of colon cancer survivors. We found that the best available approach tended to mitigate inefficiency and selection bias resulting from exclusion while suffering from less information bias than the common data approach. However, bias in all three approaches can be severe, particularly when both selection bias and information bias are present. When risk of either of these biases is judged to be more than moderate, EHR-based analyses may lead to erroneous conclusions.
| S-EPMC8336686 | biostudies-literature
Project description:This paper presents methods to estimate the number of persons with HIV in North Carolina jails by applying finite population inferential approaches to data collected using web scraping and record linkage techniques. Administrative data are linked with web-scraped rosters of incarcerated persons in a nonrandom subset of counties. Outcome regression and calibration weighting are adapted for state-level estimation. Methods are compared in simulations and are applied to data from the US state of North Carolina. Outcome regression yielded more precise inference and allowed for county-level estimates, an important study objective, while calibration weighting exhibited double robustness under misspecification of the outcome or weight model.
| S-EPMC9970294 | biostudies-literature