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How Do You Know Which Health Care Effectiveness Research You Can Trust? A Guide to Study Design for the Perplexed.

Soumerai SB, Starr D, Majumdar SR - Prev Chronic Dis (2015)

View Article: PubMed Central - PubMed

Affiliation: Harvard Medical School and Harvard Pilgrim Health Care Institute, 133 Brookline Ave, 6th Floor, Boston, MA 02215. Email: ssoumerai@hms.harvard.edu. Dr Soumerai is also co-chair of the Evaluative Sciences and Statistics Concentration of Harvard University's PhD Program in Health Policy.

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Medscape, LLC is accredited by the ACCME to provide continuing medical education for physicians... Medscape, LLC designates this Journal-based CME activity for a maximum of 1... Upon completion of this activity, participants will be able to: Define healthy user bias in health care research and means to reduce it Assess means to reduce selection bias in health care research Assess how to overcome confounding factors by indication in health care research Evaluate social desirability bias and history bias in health care research Another pattern in the evolution of science is that early studies of new treatments tend to show the most dramatic, positive health effects, and these effects diminish or disappear as more rigorous and larger studies are conducted... As these positive effects decrease, harmful side effects emerge... Sometimes researchers may publish overly definitive conclusions using unreliable study designs, reasoning that it is better to have unreliable data than no data at all and that the natural progression of science will eventually sort things out... We do not agree... For example, one of many weak cohort studies purported to show that flu vaccines reduce mortality in the elderly (Figure 2)... This study, which was widely reported in the news media and influenced policy, found significant differences in the rate of flu-related deaths and hospitalizations among the vaccinated elderly compared with that of their unvaccinated peers... One of the oldest and most accepted “truths” in the history of medication safety research is that benzodiazepines (popular medications such as Valium and Xanax that are prescribed for sleep and anxiety) may cause hip fractures among the elderly... This intervention took place during an explosion of research and news media reporting on treatments for acute myocardial infarction that could have influenced the prescribing behavior of physicians... These data demonstrate that inpatient mortality in the United States was declining before, during, and after the 100,000 Lives Campaign... The program itself probably had no effect on the trend, yet the widespread policy and media reports led to several European countries adopting this “successful” model of patient safety at considerable costs... Subsequently, several large RCTs demonstrated that many components of the 100,000 Lives Campaign were not particularly effective, especially when compared with the benefits reported in the IHI’s press releases.

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Example of weak post-only cross-sectional study that did not control for selection bias: the study observed differences between practices with EHRs and practices with paper records after the introduction of EHRs but did not control for types of providers adopting EHRs. Note the unlikely outcome for nonsmoker. Figure is based on data extracted from Cebul et al (26). Abbreviations: BMI, body mass index; EHR, electronic health record.Health OutcomePercentage of Patients Achieving OutcomeElectronic Health Record–Based PracticePaper-Based PracticeBlood pressure control (<140/80 mm Hg)5639Weight control (body mass index <30)3334Nonsmoker8252
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Figure 5: Example of weak post-only cross-sectional study that did not control for selection bias: the study observed differences between practices with EHRs and practices with paper records after the introduction of EHRs but did not control for types of providers adopting EHRs. Note the unlikely outcome for nonsmoker. Figure is based on data extracted from Cebul et al (26). Abbreviations: BMI, body mass index; EHR, electronic health record.Health OutcomePercentage of Patients Achieving OutcomeElectronic Health Record–Based PracticePaper-Based PracticeBlood pressure control (<140/80 mm Hg)5639Weight control (body mass index <30)3334Nonsmoker8252

Mentions: The following example illustrates how a weak cross-sectional study (a simple correlation between a health IT program and supposed health effects at one point in time) did not account for selection biases and led to exaggerated conclusions about the benefits of health IT (25,26). The researchers set out to compare health care sites using EHRs with health care sites using paper records to determine whether patients with diabetes in health care settings with health IT had better health outcomes than patients with diabetes in settings with only paper records (Figure 5).


How Do You Know Which Health Care Effectiveness Research You Can Trust? A Guide to Study Design for the Perplexed.

Soumerai SB, Starr D, Majumdar SR - Prev Chronic Dis (2015)

Example of weak post-only cross-sectional study that did not control for selection bias: the study observed differences between practices with EHRs and practices with paper records after the introduction of EHRs but did not control for types of providers adopting EHRs. Note the unlikely outcome for nonsmoker. Figure is based on data extracted from Cebul et al (26). Abbreviations: BMI, body mass index; EHR, electronic health record.Health OutcomePercentage of Patients Achieving OutcomeElectronic Health Record–Based PracticePaper-Based PracticeBlood pressure control (<140/80 mm Hg)5639Weight control (body mass index <30)3334Nonsmoker8252
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4492215&req=5

Figure 5: Example of weak post-only cross-sectional study that did not control for selection bias: the study observed differences between practices with EHRs and practices with paper records after the introduction of EHRs but did not control for types of providers adopting EHRs. Note the unlikely outcome for nonsmoker. Figure is based on data extracted from Cebul et al (26). Abbreviations: BMI, body mass index; EHR, electronic health record.Health OutcomePercentage of Patients Achieving OutcomeElectronic Health Record–Based PracticePaper-Based PracticeBlood pressure control (<140/80 mm Hg)5639Weight control (body mass index <30)3334Nonsmoker8252
Mentions: The following example illustrates how a weak cross-sectional study (a simple correlation between a health IT program and supposed health effects at one point in time) did not account for selection biases and led to exaggerated conclusions about the benefits of health IT (25,26). The researchers set out to compare health care sites using EHRs with health care sites using paper records to determine whether patients with diabetes in health care settings with health IT had better health outcomes than patients with diabetes in settings with only paper records (Figure 5).

View Article: PubMed Central - PubMed

Affiliation: Harvard Medical School and Harvard Pilgrim Health Care Institute, 133 Brookline Ave, 6th Floor, Boston, MA 02215. Email: ssoumerai@hms.harvard.edu. Dr Soumerai is also co-chair of the Evaluative Sciences and Statistics Concentration of Harvard University's PhD Program in Health Policy.

AUTOMATICALLY GENERATED EXCERPT
Please rate it.

Medscape, LLC is accredited by the ACCME to provide continuing medical education for physicians... Medscape, LLC designates this Journal-based CME activity for a maximum of 1... Upon completion of this activity, participants will be able to: Define healthy user bias in health care research and means to reduce it Assess means to reduce selection bias in health care research Assess how to overcome confounding factors by indication in health care research Evaluate social desirability bias and history bias in health care research Another pattern in the evolution of science is that early studies of new treatments tend to show the most dramatic, positive health effects, and these effects diminish or disappear as more rigorous and larger studies are conducted... As these positive effects decrease, harmful side effects emerge... Sometimes researchers may publish overly definitive conclusions using unreliable study designs, reasoning that it is better to have unreliable data than no data at all and that the natural progression of science will eventually sort things out... We do not agree... For example, one of many weak cohort studies purported to show that flu vaccines reduce mortality in the elderly (Figure 2)... This study, which was widely reported in the news media and influenced policy, found significant differences in the rate of flu-related deaths and hospitalizations among the vaccinated elderly compared with that of their unvaccinated peers... One of the oldest and most accepted “truths” in the history of medication safety research is that benzodiazepines (popular medications such as Valium and Xanax that are prescribed for sleep and anxiety) may cause hip fractures among the elderly... This intervention took place during an explosion of research and news media reporting on treatments for acute myocardial infarction that could have influenced the prescribing behavior of physicians... These data demonstrate that inpatient mortality in the United States was declining before, during, and after the 100,000 Lives Campaign... The program itself probably had no effect on the trend, yet the widespread policy and media reports led to several European countries adopting this “successful” model of patient safety at considerable costs... Subsequently, several large RCTs demonstrated that many components of the 100,000 Lives Campaign were not particularly effective, especially when compared with the benefits reported in the IHI’s press releases.

Show MeSH