Availability Of Robust Data Research Articles

Contrasting ecological information content in whaling archives with modern cetacean surveys for conservation planning and identification of historical distribution changes.

Many species are restricted to a marginal or suboptimal fraction of their historical range due to anthropogenic impacts, making it hard to interpret their ecological preferences from modern-day data alone. However, inferring past ecological states is limited by the availability of robust data and biases in historical archives, posing a challenge for policy makers . To highlight how historical records can be used to understand the ecological requirements of threatened species and inform conservation, we investigated sperm whale (Physeter macrocephalus) distribution in the Western Indian Ocean. We assessed differences in information content and habitat suitability predictions based on whale occurrence data from Yankee whaling logs (1792-1912) and from modern cetacean surveys (1995-2020). We built maximum entropy habitat suitability models containing static (bathymetry-derived) variables to compare models comprising historical-only and modern-only data. Using both historical and modern habitat suitability predictions we assessed marine protected area (MPA) placement by contrasting suitability in- and outside MPAs. The historical model predicted high habitat suitability in shelf and coastal regions near continents and islands, whereas the modern model predicted a less coastal distribution with high habitat suitability more restricted to areas of steep topography. The proportion of high habitat suitability inside versus outside MPAs was higher when applying the historical predictions than the modern predictions, suggesting that different marine spatial planning optimums can be reached from either data sources. Moreover, differences in relative habitat suitability predictions between eras were consistent with the historical depletion of sperm whales from coastal regions, which were easily accessed and targeted by whalers, resulting in a modern distribution limited more to steep continental margins and remote oceanic ridges. The use of historical data can provide important new insights and, through cautious interpretation, inform conservation planning and policy, for example, by identifying refugee species and regions of anticipated population recovery.

Factor V Leiden G1691A and Prothrombin Gene G20210A Mutations on Pregnancy Outcome.

Factor V Leiden (FVL) G1619A mutation and prothrombin gene (PTG) G20210A are the most common inherited thrombophilias. They have been associated with various obstetric complications such as venous thromboembolism, recurrent pregnancy loss, preeclampsia, abruptio placentae, and small for gestational age fetus. The prevalence of these two mutations is 3-15% in Caucasians and is assumed to be far less common in other ethnic populations. However, there have been several controversies regarding advising routine screening of these thrombophilias because of a widely variable strength of association between different ethnic groups, as well as contradictory conclusions by different studies in regards to the association. In this study, the literature was analyzed thoroughly for the effect of FVL G1619A and PTG G20210A mutations on various obstetric outcomes. A review of multiple case-control and prospective studies suggests that despite the availability of robust data on this subject the results remain inconclusive and insubstantial. Further superior quality research, preferably prospective studies, is warranted to conclusively establish this relationship and to enable practitioners to follow a definitive protocol in the screening of various populations for these mutations to achieve an improved pregnancy outcome.

The Use of Scientific Theory to Guide Indian Mate Selection Research

The topic of mate selection in contemporary India provides an opportunity to illustrate the value of using scientific theory to guide family research. This modified transcript from a keynote address first describes the benefit to theory guided family science research and then provides a few select examples of the way theory informs a better understanding of patterns and trends in contemporary Indian mate selection. The availability of robust data, powerful computing and advanced methodologies has made data mining, or the unguided exploration of the data, more attractive to researchers. When data analysis is not guided by theoretical principals, generalizable advances in research is compromised. This paper focuses on a quantitative, deductive approach to knowledge building yet understands that qualitative and inductive research is also important in the scientific process and theory building. Contemporary Indian mate selection continues to adapt to a 21stcentury, globally influenced, socio-cultural landscape. The Indian population is large and diverse. The author recognizes that heterogeneity while also connecting current Indian mate selection patterns to select well established research in the field.

Shaping innovations in long-term care for stroke survivors with multimorbidity through stakeholder engagement.

BackgroundStroke, like many long-term conditions, tends to be managed in isolation of its associated risk factors and multimorbidity. With increasing access to clinical and research data there is the potential to combine data from a variety of sources to inform interventions to improve healthcare. A ‘Learning Health System’ (LHS) is an innovative model of care which transforms integrated data into knowledge to improve healthcare. The objective of this study is to develop a process of engaging stakeholders in the use of clinical and research data to co-produce potential solutions, informed by a LHS, to improve long-term care for stroke survivors with multimorbidity.MethodsWe used a stakeholder engagement study design informed by co-production principles to engage stakeholders, including service users, carers, general practitioners and other health and social care professionals, service managers, commissioners of services, policy makers, third sector representatives and researchers. Over a 10 month period we used a range of methods including stakeholder group meetings, focus groups, nominal group techniques (priority setting and consensus building) and interviews. Qualitative data were recorded, transcribed and analysed thematically.Results37 participants took part in the study. The concept of how data might drive intervention development was difficult to convey and understand. The engagement process led to four priority areas for needs for data and information being identified by stakeholders: 1) improving continuity of care; 2) improving management of mental health consequences; 3) better access to health and social care; and 4) targeting multiple risk factors. These priorities informed preliminary design interventions. The final choice of intervention was agreed by consensus, informed by consideration of the gap in evidence and local service provision, and availability of robust data. This shaped a co-produced decision support tool to improve secondary prevention after stroke for further development.ConclusionsStakeholder engagement to identify data-driven solutions is feasible but requires resources. While a number of potential interventions were identified, the final choice rested not just on stakeholder priorities but also on data availability. Further work is required to evaluate the impact and implementation of data-driven interventions for long-term stroke survivors.

A benchmarking study of two trauma centres highlighting limitations when standardising mortality for comorbidity

IntroductionA continuous process of trauma centre evaluation is essential to ensure the development and progression of trauma care at regional, national and international levels. Evaluation may be by comparison between pooled datasets or by direct benchmarking between centres. This study attempts to benchmark mortality at two trauma centres standardising this for multiple case-mix factors, which includes the prevalence of individual background pre-existing diseases within the study population.MethodsTrauma patients with an Injury Severity Score (ISS) >15 admitted to the two centres in 2001 and 2002 were included in the study with the exception of those who died in the emergency department. Patient characteristics were analysed in terms of 18 case-mix factors including Glasgow Coma Scale on arrival, Injury Severity Score and the presence or absence of 9 co-morbidity types, and patient outcome was compared based on in-hospital mortality before and after standardisation.ResultsCrude mortality was greater at UHNS (18.2 vs 14.5%) with a non-significant odds ratio of 1.31 prior to adjusting for case-mix (P = 0.171). Adjustment for case mix using logistic regression analysis altered the odds ratio to 1.64, which was not significant (P = 0.069).DiscussionThis study did not demonstrate any significant difference in the outcome of patients treated at either hospital during the study period. More importantly it has raised several important methodological issues pertinent to researchers undertaking registry based benchmarking studies. Data at the two registries was collected by personnel with differing backgrounds, in formats that were not completely compatible and was collected for patients that met different admissions criteria. The inclusion of a meaningful analysis of pre-existing disease was limited by the availability of robust data and sample size. We suggest greater communication between trauma research coordinators to ensure equivalent data collection and facilitate future benchmarking studies.