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Quantitative methods for health research
A practical introduction to epidemiology, biostatistics, and research methodology for the whole health care community This comprehensive text, which has been extensively revised with new material and additional topics, utilizes a practical slant to introduce health professionals and students to epidemiology, biostatistics, and research methodology. It draws examples from a wide range of topics, covering all of the main contemporary health research methods, including survival analysis, Cox regression, and systematic reviews and meta-analysis—the explanation of which go beyond introductory concepts. This second edition of Quantitative Methods for Health Research: A Practical Interactive Guide to Epidemiology and Statistics also helps develop critical skills that will prepare students to move on to more advanced and specialized methods. A clear distinction is made between knowledge and concepts that all students should ensure they understand, and those that can be pursued further by those who wish to do so. Self-assessment exercises throughout the text help students explore and reflect on their understanding. A program of practical exercises in SPSS (using a prepared data set) helps to consolidate the theory and develop skills and confidence in data handling, analysis, and interpretation. Highlights of the book include: * Combining epidemiology and bio-statistics to demonstrate the relevance and strength of statistical methods * Emphasis on the interpretation of statistics using examples from a variety of public health and health care situations to stress relevance and application * Use of concepts related to examples of published research to show the application of methods and balance between ideals and the realities of research in practice * Integration of practical data analysis exercises to develop skills and confidence * Supplementation by a student companion website which provides guidance on data handling in SPSS and study data sets as referred to in the text Quantitative Methods for Health Research, Second Edition is a practical learning resource for students, practitioners and researchers in public health, health care and related disciplines, providing both a course book and a useful introductory reference.
eBook
Understanding the Assumptions Underlying Instrumental Variable Analyses: a Brief Review of Falsification Strategies and Related Tools
Purpose of ReviewInstrumental variable (IV) methods continue to be applied to questions ranging from genetic to social epidemiology. In the epidemiologic literature, discussion of whether the assumptions underlying IV analyses hold is often limited to only certain assumptions and even then, arguments are mostly made using subject matter knowledge. To complement subject matter knowledge, there exist a variety of falsification strategies and other tools for weighing the plausibility of the assumptions underlying IV analyses.Recent FindingsThere are many tools that can refute the IV assumptions or help estimate the magnitude or direction of possible bias if the conditions do not hold perfectly. Many of these tools, including both recently developed strategies and strategies described decades ago, are underused or only used in specific applications of IV methods in epidemiology.SummaryAlthough estimating causal effects with IV analyses relies on unverifiable assumptions, the assumptions can sometimes be refuted. We suggest that the epidemiologists using IV analyses employ all the falsification strategies that apply to their research question in order to avoid settings that demonstrably violate a core condition for valid inference.
Journal Article
Respondent-Driven Sampling: a Sampling Method for Hard-to-Reach Populations and Beyond
Purpose of ReviewWe provided an overview of sampling methods for hard-to-reach populations and guidance on implementing one of the most popular approaches: respondent-driven sampling (RDS).Recent FindingsLimitations related to generating a sampling frame for marginalized populations can make them “hard-to-reach” when conducting population health research. Data analyzed from non-probability-based or convenience samples may produce estimates that are biased or not generalizable to the target population. In RDS and time-location sampling (TLS), factors that influence inclusion can be estimated and accounted for in an effort to generate representative samples. RDS is particularly equipped to reach the most hidden members of hard-to-reach populations.SummaryTLS, RDS, or a combination can provide a rigorous method to identify and recruit samples from hard-to-reach populations and more generalizable estimates of population characteristics. Researchers interested in sampling hard-to-reach populations should expand their toolkits to include these methods.
Journal Article
Monitoring the health of populations by tracking disease outbreaks : saving humanity from the next plague
\"Today the citizens of developed counties have never experienced a large-scale disease outbreak. One reason is the success of the public health community, including epidemiologists and biostatisticians, in tracking and identifying disease outbreaks. Monitoring the Health of Populations by Tracking Disease Outbreaks: Saving Humanity from the Next Plague is the story of the application of statistics for disease detection and tracking. The work of public health officials often critically depends on the use of statistical methods to help discern whether an outbreak may be occurring and, if there is sufficient evidence of an outbreak, then to locate and track it\"-- Provided by publisher.
Book
A Selective Review of Negative Control Methods in Epidemiology
Purpose of ReviewNegative controls are a powerful tool to detect and adjust for bias in epidemiological research. This paper introduces negative controls to a broader audience and provides guidance on principled design and causal analysis based on a formal negative control framework.Recent FindingsWe review and summarize causal and statistical assumptions, practical strategies, and validation criteria that can be combined with subject-matter knowledge to perform negative control analyses. We also review existing statistical methodologies for the detection, reduction, and correction of confounding bias, and briefly discuss recent advances towards nonparametric identification of causal effects in a double-negative control design.SummaryThere is great potential for valid and accurate causal inference leveraging contemporary healthcare data in which negative controls are routinely available. Design and analysis of observational data leveraging negative controls is an area of growing interest in health and social sciences. Despite these developments, further effort is needed to disseminate these novel methods to ensure they are adopted by practicing epidemiologists.
Journal Article
Crisis averted : the hidden science of fighting outbreaks
\"A fascinating window into the secret life of epidemiology, weaving together stories of triumph and tragedy, with a boots-on-the-ground perspective on how we can avert the next public health crisis There are few visible markers of the accomplishments of public health. If epidemiologists do their jobs, nothing happens. An outbreak does not grow into an epidemic. A child does not go hungry. A would-be smoker never lights up. These achievements are rarely noticed or celebrated, but Caitlin Rivers lives for such victories. By making sure that things don't happen, she and legions of scientists, practitioners, and policymakers change the course of history. We have many of the tools and experiences needed to prevent the next crisis, but countless challenges remain, including constantly emerging pathogens, the rapid growth of biotechnology, and the inconsistent cycles of funding for government organizations like the CDC. Progress can be slow, even with the next pandemic potentially right around the corner, but the unsung heroes in public health remain focused on their missions. Crisis Averted is their story-from the eradication of smallpox in the 20th century to today's safeguards against extraterrestrial germs. By taking a candid look at how we solve problems in public health, Caitlin Rivers illuminates the role of epidemiology in all our lives and lays out the case for what can be accomplished, given sufficient vision, leadership, and resources. Crisis Averted is an inspiring and galvanizing clarion call for us to work together towards a healthier, more resilient future\"-- Provided by publisher.
BOOK
The Dose Response Multicentre Investigation on Fluid Assessment (DoReMIFA) in critically ill patients
Background
The previously published “Dose Response Multicentre International Collaborative Initiative (DoReMi)” study concluded that the high mortality of critically ill patients with acute kidney injury (AKI) was unlikely to be related to an inadequate dose of renal replacement therapy (RRT) and other factors were contributing. This follow-up study aimed to investigate the impact of daily fluid balance and fluid accumulation on mortality of critically ill patients without AKI (N-AKI), with AKI (AKI) and with AKI on RRT (AKI-RRT) receiving an adequate dose of RRT.
Methods
We prospectively enrolled all consecutive patients admitted to 21 intensive care units (ICUs) from nine countries and collected baseline characteristics, comorbidities, severity of illness, presence of sepsis, daily physiologic parameters and fluid intake-output, AKI stage, need for RRT and survival status. Daily fluid balance was computed and fluid overload (FO) was defined as percentage of admission body weight (BW). Maximum fluid overload (MFO) was the peak value of FO.
Results
We analysed 1734 patients. A total of 991 (57 %) had N-AKI, 560 (32 %) had AKI but did not have RRT and 183 (11 %) had AKI-RRT. ICU mortality was 22.3 % in AKI patients and 5.6 % in those without AKI (
p
< 0.0001). Progressive fluid accumulation was seen in all three groups. Maximum fluid accumulation occurred on day 2 in N-AKI patients (2.8 % of BW), on day 3 in AKI patients not receiving RRT (4.3 % of BW) and on day 5 in AKI-RRT patients (7.9 % of BW). The main findings were: (1) the odds ratio (OR) for hospital mortality increased by 1.075 (95 % confidence interval 1.055–1.095) with every 1 % increase of MFO. When adjusting for severity of illness and AKI status, the OR changed to 1.044. This phenomenon was a continuum and independent of thresholds as previously reported. (2) Multivariate analysis confirmed that the speed of fluid accumulation was independently associated with ICU mortality. (3) Fluid accumulation increased significantly in the 3-day period prior to the diagnosis of AKI and peaked 3 days later.
Conclusions
In critically ill patients, the severity and speed of fluid accumulation are independent risk factors for ICU mortality. Fluid balance abnormality precedes and follows the diagnosis of AKI.
Journal Article