International Journal of Population Data Science最新文献

Background: Approximately thirty thousand people in Scotland are diagnosed with cancer annually, of whom a third live less than one year. The timing, nature and value of hospital-based healthcare for patients with advanced cancer are not well understood. The study's aim was to describe the timing and nature of hospital-based healthcare use and associated costs in the last year of life for patients with a cancer diagnosis.

Methods: We undertook a Scottish population-wide administrative data linkage study of hospital-based healthcare use for individuals with a cancer diagnosis, who died aged 60 and over between 2012 and 2017. Hospital admissions and length of stay (LOS), as well as the number and nature of outpatient and day case appointments were analysed. Generalised linear models were used to adjust costs for age, gender, socioeconomic deprivation status, rural-urban (RU) status and comorbidity.

Results: The study included 85,732 decedents with a cancer diagnosis. For 64,553 (75.3%) of them, cancer was the primary cause of death. Mean age at death was 80.01 (SD 8.15) years. The mean number of inpatient stays in the last year of life was 5.88 (SD 5.68), with a mean LOS of 7 days. Admission rates rose sharply in the last month of life. One year adjusted and unadjusted costs decreased with increasing age. A higher comorbidity burden was associated with higher costs. Major cost differences were present between cancer types.

Conclusions: People in Scotland in their last year of life with cancer are high users of secondary care. Hospitalisation accounts for a high proportion of costs, particularly in the last month of life. Further research is needed to examine triggers for hospitalisations and to identify influenceable reasons for unwarranted variation in hospital use among different cancer cohorts.

Introduction: Digitalisation of Electronic Health Record (EHR) data has created unique opportunities for research. However, these data are routinely collected for operational purposes and so are not curated to the standard required for research. Harnessing such routine data at large scale allows efficient and long-term epidemiological and health services research.

Objectives: To describe the establishment a linked EHR derived data platform in the National Centre for Healthy Ageing, Melbourne, Australia, aimed at enabling research targeting national health priority areas in ageing.

Methods: Our approach incorporated: data validation, curation and warehousing to ensure quality and completeness; end-user engagement and consensus on the platform content; implementation of an artificial intelligence (AI) pipeline for extraction of text-based data items; early consumer involvement; and implementation of routine collection of patient reported outcome measures, in a multisite public health service.

Results: Data for a cohort of >800,000 patients collected over a 10-year period have been curated within the platform's research data warehouse. So far 117 items have been identified as suitable for inclusion, from 11 research relevant datasets held within the health service EHR systems. Data access, extraction and release processes, guided by the Five Safes Framework, are being tested through project use-cases. A natural language processing (NLP) pipeline has been implemented and a framework for the routine collection and incorporation of patient reported outcome measures developed.

Conclusions: We highlight the importance of establishing comprehensive processes for the foundations of a data platform utilising routine data not collected for research purposes. These robust foundations will facilitate future expansion through linkages to other datasets for the efficient and cost-effective study of health related to ageing at a large scale.

Introduction: The purpose of VIKING II is to create an observational cohort of volunteers with ancestry from the Northern Isles of Scotland, primarily for identifying genetic variants influencing disease. The new online protocol is separate to, but follows on from, earlier genetic epidemiological clinic-based studies in the isolated populations of Orkney and Shetland. These populations are favourable for the study of rarer genetic variants due to genetic drift, the large number of relatives, and availability of pedigree information. They are known to be genetically distinct from mainland British populations.

Methods and analysis: Online methods are being used to recruit ~4,000 people who have Northern Isles ancestry, living anywhere in the world. The option for participants to have actionable genetic results returned is offered. Consent will be taken electronically. Data will be collected at baseline through an online questionnaire and longitudinally through linkage to NHS data in the electronic health record. The questionnaire collects a variety of phenotypes including personal and family health. DNA will be extracted from saliva samples then genome-wide genotyped and exome sequenced. VIKING II aims to capitalise on the special features of the Northern Isles populations to create a research cohort that will facilitate the analysis of genetic variants associated with a broad range of traits and disease endpoints, including otherwise rare variants that have drifted to high frequency in these populations.

Ethics and dissemination: The South East Scotland Research Ethics Committee gave the study a favourable opinion. VIKING II is sponsored by the University of Edinburgh and NHS Lothian. Summary research findings will be disseminated to participants and funding bodies, presented at conferences and reported in peer-reviewed publications.

Article summary: Strengths and limitations of this studyDetailed data and biological sample collection of research volunteers with unique ancestry.Consent for access to routinely collected clinical EHR data and for future re-contact, providing a longitudinal component.Optional consent for return of actionable genetic results.~4,000 participants is a relatively small number for certain types of genetic analyses, so the cohort is underpowered on its own, in some study designs.Resources to maintain the cohort, and to store data and DNA samples, are significant, with sustainability dependent on infrastructure support and funding.

Introduction: Newborn babies who require admission for specialist care can experience immediate and sometimes lasting impacts. For babies admitted to special care nurseries (SCN), there is no dataset comparable to that of the Australian and New Zealand Neonatal Network (ANZNN), which has helped improve the quality and consistency of neonatal intensive care through standardised data collection.

Objectives: We aim to establish a proof-of-concept, Victoria-wide registry of babies admitted to SCN, embedded within the whole-of-Victoria Generation Victoria (GenV) cohort.

Methods: This prototype registry is a depth sub-cohort nested within GenV, targeting all babies born in Victoria from Oct-2021 to Oct-2023. Infants admitted to SCN are eligible. The minimum dataset will be harmonised with ANZNN for common constructs but also include SCN-only items, and will cover maternal, antenatal, newborn, respiratory/respiratory support, cardiac, infection, nutrition, feeding, cerebral and other items. As well as the dataset, this protocol outlines the anticipated cohort, timeline for this registry, and how this will serve as a resource for longitudinal research through its integration with the GenV longitudinal cohort and linked datasets.

Conclusion: The registry will provide the opportunity to better understand the health and future outcomes of the large and growing cohort of children that require specialist care after birth. The data would generate translatable evidence and could lay the groundwork for a stand-alone ongoing clinical quality registry post-GenV.

Introduction: Health 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.

Objective: This study assessed HDSS pregnancy reporting completeness and identified predictors of unreported pregnancies that likely ended in adverse outcomes.

Methods: The 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.

Results: From 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.

Conclusions: Record 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.