Biology subjects

Schnier, C.

Publications and source records attributed to Schnier, C..

3 recordsLinked to original sources

bioRxiv2022

Patterns of mortality in domesticated ruminants in Ethiopia

BackgroundIn Ethiopia, there are limited data on the causes of ruminant mortality and reproductive losses which is hampering the development of interventions to improve livestock health. The present study aimed to establish the causes and magnitude of mortality and reproductive losses in cattle, sheep and goats across all smallholder production systems in Ethiopia. MethodsThe study, consisting of three data sources, was conducted to collect data on mortality and reproductive losses in cattle, sheep and goats. Nine regional states; Afar, Amhara, Benishangul Gumuz, Gambela, Harari, Oromia, Southern Nations, Nationalities, and Peoples Region, Somali, and Tigray were surveyed, as well as the city administrations of Dire Dawa and Addis Ababa. Questionnaire surveys were conducted with farmers and key stakeholders, as well as data collected from regional diagnostic laboratories. Data from the Living Standards Measurement Study were also included. Generalised linear mixed and hurdle models were used for data analysis, with results summarized using predicted outcomes. ResultsAnalyses indicated that most herds experienced zero mortality and reproductive losses, with rare occasions of larger losses. Diseases causing deaths varied greatly both geographically and over time. There was little agreement between the different datasets. While the models aid the understanding of patterns of mortality and reproductive losses, the degree of variation observed limited the predictive scope. ConclusionsThe models revealed some insight into why mortality rates are variable over time and are therefore less useful in measuring production or health status, and it is suggested that alternative measures of productivity, such as young stock mortality, would be more stable over time and likely more indicative.

bioinformatics↗

bioRxiv2018

Identifying Parkinson’s disease and parkinsonism cases using routinely-collected healthcare data: a systematic review

BackgroundPopulation-based, prospective studies can provide important insights into Parkinsons disease (PD) and other parkinsonian disorders. Participant follow-up in such studies is often achieved through linkage to routinely-collected healthcare datasets. We systematically reviewed the published literature on the accuracy of these datasets for this purpose.\n\nMethodsWe searched four electronic databases for published studies that compared PD and parkinsonism cases identified using routinely-collected data to a reference standard. We extracted study characteristics and two accuracy measures: positive predictive value (PPV) and/or sensitivity.\n\nResultsWe identified 18 articles, resulting in 27 measures of PPV and 14 of sensitivity. For PD, PPVs ranged from 56-90% in hospital datasets, 53-87% in prescription datasets, 81-90% in primary care datasets and was 67% in mortality datasets. Combining diagnostic and medication codes increased PPV. For parkinsonism, PPVs ranged from 36-88% in hospital datasets, 40-74% in prescription datasets, and was 94% in mortality datasets. Sensitivities ranged from 15-73% in single datasets for PD and 43-63% in single datasets for parkinsonism.\n\nConclusionsIn many settings, routinely-collected datasets generate good PPVs and reasonable sensitivities for identifying PD and parkinsonism cases. Further research is warranted to investigate primary care and medication datasets, and to develop algorithms that balance a high PPV with acceptable sensitivity.

epidemiology↗

bioRxiv2018

Can we use routinely collected hospital and GP data for epidemiological study of common hand conditions? A UK Biobank based validation project

ObjectiveRoutine health records can be of great value in epidemiological and genetic studies if they are able to reliably identify true disease cases, especially when linked to large cohort studies. Little research has been undertaken into whether coding within UK electronic health records (EHR) is able to accurately identify clinical disease cases of common hand conditions. There is therefore a relative paucity of hand surgical research using EHRs due to concerns that cases cannot be accurately identified.\n\nThe aim of this study was to investigate the accuracy of hospital and primary care coding of routine EHRs for carpal tunnel syndrome (CTS) and base of thumb osteoarthritis (BTOA). Self-reported disease state as recorded in UK Biobank, a large prospective cohort study was also investigated.\n\nMethodsCode lists for each condition were generated by a team of clinicians, clinical coders and epidemiologists. All patients recruited to UK Biobank in one geographical region (Lothian, Scotland) where linked primary and secondary care coded datasets available were included. A decision- making algorithm was designed to define an administratively-confirmed or a clinically confirmed disease case. Patient electronic medical records (EMRs) were independently interrogated by two clinicians and inter-observer reliability calculated.\n\nResultsOf the 17,201 Biobank participants in NHS Lothian, 268 had at least one code for CTS and 82 for BTOA. For CTS, 159 cases were confirmed, 100 cases had insufficient information and 9 cases were refuted. Excluding missing data, the positive predictive value (PPV) for true clinical disease cases was 96% for incident disease (90% for prevalent disease; overall 94%).\n\nFor BTOA, 27 cases were confirmed, 46 cases had insufficient information, and 9 cases were refuted. Excluding missing data, PPV for incident disease was 81% (prevalent disease 56%, overall PPV 75%). Interrogation of the disease cases with insufficient information noted a large proportion arising from primary care and self-report coding systems.\n\nAnalyzing code combinations revealed that secondary care codes had the highest PPV for CTS and BTOA, emphasizing a more robust evaluation of PPV for patients requiring hospital based care. Overall, inter-observer reliability was good, with agreement in 90% of cases (Cohens kappa of 0.79) for clinical disease cases in CTS and agreement of 98%, (kappa 0.96) for BTOA.\n\nConclusionsWe have demonstrated that coding within UK Biobank is of sufficient quality to enable use of the resource for epidemiological and genetic research into common hand conditions, and that EMRs can be used for manual validation of UK health coding systems. Further work is needed to consider potential regional and interdisciplinary differences in coding practice, in strategies for dealing with missing data in EHRs, and to validate coding of common hand conditions in primary care.

epidemiology↗