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Meta-epidemiologic consideration of confounding for health care decision making
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As patients, we all want to believe that there is the right medical solution for every ailment and that our doctor knows best. What we usually don’t know is that our doctor’s knowledge is based on experience and on evidence. However, the evidence can be flawed, exaggerated, or may not actually apply to us. While there are many things that can go wrong in clinical studies, the main focus of this dissertation is on the concept of confounding. Confounding occurs when a specific exposure and outcome have a common cause. For example, more breast cancer patients receiving surgery as the observed “exposure” survive than those receiving chemotherapy. Concluding that surgery is better for survival may, however, be confounded by cancer stage because those who were operated on had a less advanced cancer stage and thus were more likely to survive to begin with. Minimizing the impact of such confounding in research studies on treatment effects is important because it can alter the estimates of a treatment effect and thus may lead to wrong conclusions and ultimately to wrong treatment decisions.
For many health topics, there are myriads of studies available and whether or not their results can give us reliable answers to what we want to know depends on a variety of factors. The most important factor is the study design. Randomized controlled trials (RCTs) are the current gold standard to produce evidence for treatment decisions. They measure the causal effect of a treatment versus a control on a specific outcome. The key element is that study participants are randomly assigned to treatment or control (which could be a placebo or another treatment). The randomization tries to balance all known (such as age) and unknown characteristics (such as undiagnosed diseases) of the participants which means that they also balance all known and unknown confounding factors. The only difference between the participant groups will then be the allocation to a treatment or control. This would be the perfect study design if the circumstances were ideal, i.e. if every participant adhered to the assigned treatment and stayed on the study until the end. In reality, the participants often do not adhere (e.g. because the exercise program of a weight-loss study is too demanding) or they become lost to follow-up (e.g. because they moved away or did not want to be on the study anymore). However, not every clinical question can be answered in an RCT. Another important research design are observational studies, where the exposure of patients to an intervention or a control is not decided by the study investigators (thus observational) and may thus depend on a number of other known and unknown factors, e.g. doctors’ decisions or patient’s preferences. This study design is very prone to confounding and requires careful statistical analyses. Statistical methods can then be used to retrospectively address issues like confounding or confounding that changes over time. One such statistical method is marginal structural models (MSM). MSM allow a causal interpretation of results under the assumptions that all confounding factors are known, correctly measured and properly implemented in the statistical models. However, even with the latest statistical methods, RCTs and observational studies may not give the same answer when trying to solve the same question. Hence, the aims of the doctoral projects were 1) to evaluate the extent to which confounding is actively considered in the conclusions from observational studies; 2) to evaluate the agreement of treatment effects from non-randomized studies using MSM with reported effects from RCTs on the same topic; 3) to evaluate when MSM is used in RCTs and how these results differ from the main (non-MSM) results of the same trial.
First, we assessed the scope of the issue within the health professionals’ literature. Are authors of scientific papers aware of the problem of confounding for the interpretation of their results and do they present their results in light of its possible impact? Second, if observational studies use MSM to reduce the impact of confounding and allow a causal interpretation, the results should be similar to those from RCTs on the same clinical question. To assess how well they agree, we used established approaches to compare the effects, for example we determined how often the effects from both designs indicated concordantly that a treatment is beneficial or not. Third, we conducted an empirical analysis of where and why MSM is used to analyze randomized comparisons, a rather new and emerging approach to address confounding within randomized trials, and how these results compare to non-MSM results from the same trial.
We found that observational studies in general tend to have unsatisfactory or no discussion of confounding at all. If confounding was mentioned, it was either deemed irrelevant for the respective research or results are not brought in context of necessary cautious interpretation. Studies that did, however, report possible limitations due to confounding were actually cited more by other researchers than studies that deemed an influence due to confounding unlikely. This means research that is carefully reported may have more impact on science than other research.
When MSM was applied to observational study data, the effects often had opposite directions (i.e. one showed harm and the other benefit of the intervention) and were more favorable for the experimental treatment than in randomized studies on the same research question. This was even more so when the studies focused on informing health care decision making rather than statistical methodology.
MSM was applied to RCTs to minimize the influence of confounding that arises when study participants do not adhere to the protocol. Within the main publication and the publication reporting MSM-based results (sometimes the same), authors reported on average 6 analyses for one outcome in the same population and at the same point in time. Most of these results, however, pointed in the same direction and had more or less similar effect sizes, which means that the clinical interpretation is often similar.
We can never be certain that we know all confounding factors, measured them correctly and implemented them correctly in the statistical models. Even research that used causal modelling techniques may still come to different answers than RCTs evaluating the same clinical question would. Hence, confounding should be more carefully acknowledged in non-randomized research, doing so is not associated with lower citation impact. Results from causal modelling can be useful sensitivity analyses that can help researchers to get a bigger picture of the impact of other influencing factors. Health care decision makers should remain cautious when using non-randomized evidence to guide their health care decisions
Skilled Attendants at Every Birth: Essential Requirements for the Delivery of Basic Emergency Obstetric Care, 2014
No full textGlobally, from 1990 to 2014, a 12% increase in the proportion of births attended by skilled birth attendants was achieved. But while some low- and middle- countries managed to improve access to skilled care at birth, several did not manage to reduce their high maternal mortality rates. The aim of this project was to gain an understanding of macro and micro level factors that influence the provision of emergency obstetric care by skilled birth attendants in low income country settings. Project Objectives: 1. To identify the broad range of factors which promote or prevent the delivery of obstetric care by skilled birth attendants in low- and middle- income countries through a qualitative review of the literature. 2. To explore the implementation of the policy to scale up skilled birth attendance and identify lessons learned from implementation of selected strategies to recruit, deploy and retain skilled birth attendants in Uganda. 3. To explore the barriers and facilitators at the micro level that influence provision of obstetric care by skilled birth attendants at the peripheral level of the health system in Uganda. 4. To compare the findings from the Uganda case study with other regions and global initiatives. For further information about "Skilled Attendants at Every Birth: Essential Requirements for the Delivery of Basic Emergency Obstetric Care, 2014", please contact the principal investigator
Bruk av åpen kildekode-lisensiering til utforskning og utvikling av vaksine og medisin, 2013
No full textThis proposal will focus on the application of open source licensing to vaccine and drug discovery and development. Open source licensing, widely used within the computer software industry, allows an individual to share intellectual property rather than copyrighting or patenting it. The main goal of this research project is to explore, develop and evaluate an Open Source Drug Discovery (OSDD) business model in regards to vaccine and drug development that has the potential to substantially reduce the costs of vaccines/drugs. This research project will focus on five diseases: HIV/AIDS, malaria, tuberculosis, schistosomiasis and toxoplasmosis. These diseases were chosen because they include both Type II and Type III diseases which will allow for comparisons in the data gathering objectives. Additionally, they are typically regarded as neglected diseases. This is a multidisciplinary project, which will principally focus on the area of health economics, but will also take into account research performed in the fields of law (pertaining to intellectual property), bioinformatics, public health, business administration, philosophy and ethics. This proposal is in line with GLOBVAC's thematic guidelines since this research is aimed at providing improved access to vaccines for marginalized populations in low- and middle income countries. Research findings should stimulate technology transfer to low- and middle-income countries. With an open source license, any vaccine producer can manufacture the vaccine. Therefore, low-income countries could pool their resources and tender out the manufacture of a vaccine based upon price and quality. Developing nations' researchers can collaborate on vaccines to make the most of their research budgets. Open source could lead to cheaper, more accessible vaccines for the developing world
Centre for Epidemic Interventions Research (CEIR), Annual Report 2022
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During the COVID-19 pandemic it became apparent that the scientific basis for many infection control interventions was weak, and that more research on the effects of public health and social measures was needed. To address this gap, the Norwegian Ministry of Health asked the Norwegian Institute of Public Health to establish a research centre with a specific focus on this topic area. The Centre for Epidemic Interventions Research (CEIR) was formally established in the autumn of 2021, hosted by the Norwegian Institute of
Public Health (NIPH).
CEIR has been tasked with three main activities, as defined by the following aims
1. Prepare for the conduct of studies of effects of non-pharmaceutical infection
control measures
2. Carry out effect studies
3. Develop and evaluate tools to support the use of research in decision-making in
health crises, and improve critical health literacy in the population.publishedVersio
Screening for iron deficiencey anemia among young children
No full textNORSK: Gjeldende retningslinjer for å avdekke jernmangelanemi hos barn anbefaler målrettede undersøkelser blant ettåringer som ikke har fått ekstra jerntilskudd, er påfallende bleke eller mer enn vanlig syke. Videre anbefales at innvandrerbarn fra utenomvestlige land screenes ved ettårs alder og ved andre aldre hos nyankomne ved førstegangsundersøkelse.
Disse anbefalingene er omdiskuterte. Helsedirektoratet har derfor bedt om en gjennomgang av hvordan andre land håndterer denne problemstillingen, og hva slags kunnskapsgrunnlag de baserer sine anbefalinger på.
· Vi fant åtte retningslinjer der det gis anbefalinger om screening av småbarn for jernmangelanemi.
· Anbefalingene er tildels sprikende.
· Det er ikke utført kontrollerte studier der effekten av screening for jernmangelanemi har vært evaluert.
· Det er ingen bred konsensus om hvorvidt testegenskapene til analysene som kan benyttes ved screening er tilstrekkelig gode
· Det er heller ingen enighet om det å iverksette behandling (jerntilskudd) ved positivt testresultat er et hensiktsmessig tiltak.
· Det mangelfulle kunnskapsgrunnlaget gjør at skjønnsmessige vurderinger nødvendigvis blir avgjørende for hvordan forskningen blir fortolket og omsatt til praktiske anbefalinge
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