Dataset Information

Point-of-contact Interactive Record Linkage (PIRL): A software tool to prospectively link demographic surveillance and health facility data.

ABSTRACT: Linking a health and demographic surveillance system (HDSS) to data from a health facility that serves the HDSS population generates a research infrastructure for directly observed data on access to and utilization of health facility services. Many HDSS sites, however, are in areas that lack unique national identifiers or suffer from data quality issues, such as incomplete records, spelling errors, and name and residence changes, all of which complicate record linkage approaches when applied retrospectively. We developed Point-of-contact Interactive Record Linkage (PIRL) software that is used to prospectively link health records from a local health facility to an HDSS in rural Tanzania. This prospective approach to record linkage is carried out in the presence of the individual whose records are being linked, which has the advantage that any uncertainty surrounding their identity can be resolved during a brief interaction, whereby extraneous information (e.g., household membership) can be referred to as an additional criterion to adjudicate between multiple potential matches. Our software uses a probabilistic record linkage algorithm based on the Fellegi-Sunter model to search and rank potential matches in the HDSS data source. Key advantages of this software are its ability to perform multiple searches for the same individual and save patient-specific notes that are retrieved during subsequent clinic visits. A search on the HDSS database (n=110,000) takes less than 15 seconds to complete. Excluding time spent obtaining written consent, the median duration of time we spend with each patient is six minutes. In this setting, a purely automated retrospective approach to record linkage would have only correctly identified about half of the true matches and resulted in high linkage errors; therefore highlighting immediate benefit of conducting interactive record linkage using the PIRL software.

SUBMITTER: Rentsch CT

PROVIDER: S-EPMC5841575 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Point-of-contact Interactive Record Linkage (PIRL): A software tool to prospectively link demographic surveillance and health facility data.

Rentsch Christopher T CT Kabudula Chodziwadziwa Whiteson CW Catlett Jason J Beckles David D Machemba Richard R Mtenga Baltazar B Masilela Nkosinathi N Michael Denna D Natalis Redempta R Urassa Mark M Todd Jim J Zaba Basia B Reniers Georges G

Gates open research 20170101

Linking a health and demographic surveillance system (HDSS) to data from a health facility that serves the HDSS population generates a research infrastructure for directly observed data on access to and utilization of health facility services. Many HDSS sites, however, are in areas that lack unique national identifiers or suffer from data quality issues, such as incomplete records, spelling errors, and name and residence changes, all of which complicate record linkage approaches when applied ret ...[more]

PMID: 29528050

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Project description:Introduction:Health and demographic surveillance systems (HDSS) have been an invaluable resource for monitoring the health status of populations, but often contain self-reported health service utilisation, which are subject to reporting bias. Objective:To implement point-of-contact interactive record linkage (PIRL) between demographic and health facility systems data, characterise attributes associated with (un)successful record linkage, and compare findings with a fully automated retrospective linkage approach. Methods:Individuals visiting the Kisesa Health Centre were matched to their HDSS records during a short up-take interview in the waiting area of the health facility. The search algorithm was used to rank potential matches, from which the true match(es) were selected after consultation with the patient. Multivariable logistic regression models were used to identify characteristics associated with being matched to an HDSS record. Records matched based on respondent's clarifications were subsequently used as the gold-standard to evaluate fully automated retrospective record linkage by calculating sensitivity and positive predictive value (PPV). Results:Among 2,624 individuals who reportedly lived in the HDSS coverage area, we matched 2,206 (84.1%) to their HDSS records. Characteristics associated with a higher odds of being matched were increased age (OR 1.07, 95% CI 1.02, 1.12; per 5-year increment), a later consent into the study (OR 2.07, 95% CI 1.37, 3.12; in the most recent six-month period), and fieldworker level of experience. The main drivers of the linkage algorithm were name, sex, year of birth, village, sub-village, and household member name. At the lowest match score threshold, automated retrospective linkage would have only correctly identified and linked 55% (1440/2612) of the records with a PPV of 55% (1440/2612). Conclusion:Where resources are available, PIRL is a viable approach to link HDSS and other administrative data sources that outperforms purely retrospective approaches.

Project description:Linkage of medical databases, including insurer claims and electronic health records (EHRs), is increasingly common. However, few studies have investigated the behavior and output of linkage software. To determine how linkage quality is affected by different algorithms, blocking variables, methods for string matching and weight determination, and decision rules, we compared the performance of 4 nonproprietary linkage software packages linking patient identifiers from noninteroperable inpatient and outpatient EHRs. We linked datasets using first and last name, gender, and date of birth (DOB). We evaluated DOB and year of birth (YOB) as blocking variables and used exact and inexact matching methods. We compared the weights assigned to record pairs and evaluated how matching weights corresponded to a gold standard, medical record number. Deduplicated datasets contained 69,523 inpatient and 176,154 outpatient records, respectively. Linkage runs blocking on DOB produced weights ranging in number from 8 for exact matching to 64,273 for inexact matching. Linkage runs blocking on YOB produced 8 to 916,806 weights. Exact matching matched record pairs with identical test characteristics (sensitivity 90.48%, specificity 99.78%) for the highest ranked group, but algorithms differentially prioritized certain variables. Inexact matching behaved more variably, leading to dramatic differences in sensitivity (range 0.04-93.36%) and positive predictive value (PPV) (range 86.67-97.35%), even for the most highly ranked record pairs. Blocking on DOB led to higher PPV of highly ranked record pairs. An ensemble approach based on averaging scaled matching weights led to modestly improved accuracy. In summary, we found few differences in the rankings of record pairs with the highest matching weights across 4 linkage packages. Performance was more consistent for exact string matching than for inexact string matching. Most methods and software packages performed similarly when comparing matching accuracy with the gold standard. In some settings, an ensemble matching approach may outperform individual linkage algorithms.

Project description:IntroductionHealth and Demographic Surveillance Systems (HDSS) are important sources of population health data in sub-Saharan Africa, but the recording of pregnancies, pregnancy outcomes, and early mortality is often incomplete.ObjectiveThis study assessed HDSS pregnancy reporting completeness and identified predictors of unreported pregnancies that likely ended in adverse outcomes.MethodsThe analysis utilized individually-linked HDSS and antenatal care (ANC) data from Siaya, Kenya for pregnancies in 2018-2020. We cross-checked ANC records with HDSS pregnancy registrations and outcomes. Pregnancies observed in the ANC that were missing reports in the HDSS despite a data collection round following the expected delivery date were identified as likely adverse outcomes, and we investigated the characteristics of such individuals. Clinical data were used to investigate the timing of HDSS pregnancy registration relative to care seeking and gestational age, and examine misclassification of miscarriages and stillbirths.ResultsFrom an analytical sample of 2,475 pregnancies observed in the ANC registers, 46% had pregnancy registrations in the HDSS, and 89% had retrospectively reported pregnancy outcomes. 1% of registered pregnancies were missing outcomes, compared to 10% of those lacking registration. Registered pregnancies had higher rates of stillbirth and perinatal mortality than those lacking registration. In 77% of cases, women accessed ANC prior to registering the pregnancy in the HDSS. Half of reported miscarriages were misclassified stillbirths. We identified 141 unreported pregnancies that likely ended in adverse outcomes. Such cases were more common among those who visited ANC clinics during the first trimester, made fewer overall visits, were HIV-positive, and outside of formal union.ConclusionsRecord linkage with ANC clinics revealed pregnancy underreporting in HDSS, resulting in biased measurement of perinatal mortality. Integrating records of ANC usage into routine data collection can augment HDSS pregnancy surveillance and improve monitoring of adverse pregnancy outcomes and early mortality.

Project description:BackgroundThere is substantial prior research on the perspectives of patients on the use of health information for research. Numerous communication barriers challenge transparency between researchers and data participants in secondary database research (eg, waiver of informed consent and knowledge gaps). Individual concerns and misconceptions challenge the trust in researchers among patients despite efforts to protect data. Technical software used to protect research data can further complicate the public's understanding of research. For example, MiNDFIRL (Minimum Necessary Disclosure For Interactive Record Linkage) is a prototype software that can be used to enhance the confidentiality of data sets by restricting disclosures of identifying information during the record linkage process. However, software, such as MiNDFIRL, which is used to protect data, must overcome the aforementioned communication barriers. One proposed solution is the creation of an interactive web-based frequently asked question (FAQ) template that can be adapted and used to communicate research issues to data subjects.ObjectiveThis study aims to improve communication with patients and transparency about how complex software, such as MiNDFIRL, is used to enhance privacy in secondary database studies to maintain the public's trust in researchers.MethodsA Delphi technique with 3 rounds of the survey was used to develop the FAQ document to communicate privacy issues related to a generic secondary database study using the MiNDFIRL software. The Delphi panel consisted of 38 patients with chronic health conditions. We revised the FAQ between Delphi rounds and provided participants with a summary of the feedback. We adopted a conservative consensus threshold of less than 10% negative feedback per FAQ section.ResultsWe developed a consensus language for 21 of the 24 FAQ sections. Participant feedback demonstrated preference differences (eg, brevity vs comprehensiveness). We adapted the final FAQ into an interactive web-based format that 94% (31/33) of the participants found helpful or very helpful. The template FAQ and MiNDFIRL source code are available on GitHub. The results indicate the following patient communication considerations: patients have diverse and varied preferences; the tone is important but challenging; and patients want information on security, identifiers, and final disposition of information.ConclusionsThe findings of this study provide insights into what research-related information is useful to patients and how researchers can communicate such information. These findings align with the current understanding of health literacy and its challenges. Communication is essential to transparency and ethical data use, yet it is exceedingly challenging. Developing FAQ template language to accompany a complex software may enable researchers to provide greater transparency when informed consent is not possible.

Dataset Information

Point-of-contact Interactive Record Linkage (PIRL): A software tool to prospectively link demographic surveillance and health facility data.

Publications

Point-of-contact Interactive Record Linkage (PIRL): A software tool to prospectively link demographic surveillance and health facility data.

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