5 research outputs found
May measurement month 2018: a pragmatic global screening campaign to raise awareness of blood pressure by the International Society of Hypertension (vol 40, pg 2006, 2019)
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Non-standard errors
In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence generating process (EGP). We claim that EGP variation across researchers adds uncertainty: Non-standard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants
May measurement month 2018: an analysis of blood pressure screening results from Ghana.
Elevated blood pressure (BP) is one of the leading risk factors for death and disability globally. It is also an important global health challenge because of its high prevalence and resulting morbidities. Albeit, a substantial number of people who have hypertension are either oblivious of it, not treated, or being managed but remain uncontrolled. May Measurement Month (MMM) is a global initiative led by the International Society of Hypertension (ISH) with the goal of increasing awareness of high BP and serving as a spur to establish screening programmes worldwide. An opportunistic cross-sectional survey of volunteers aged ≥18 years was carried out in May 2018. Measurement of BP and collection of relevant health information were performed according to a standardized protocol for MMM. Screening sites were set up in churches, mosques, health facilities, pharmacies, recreational parks, sports facilities, shopping centres, marketplaces, universities, workplaces, and community centres across four regions of Ghana. A total of 6907 participants were screened during MMM 2018. After multiple imputation, 2354 (34.1%) had hypertension. Of individuals not taking antihypertensive medications 1526 (25.1%) were hypertensive of whom 48.4% were aware of having it. Also, of individuals taking antihypertensive medications 432 (52.2%) had uncontrolled BP. Data obtained from this project demonstrates that a significant number of people with hypertension are unaware of having it, are untreated, or are on treatment but remain uncontrolled. It also highlights the effectiveness of BP screening campaigns as a tool to identify persons with elevated BP
Recommended from our members
Non-standard errors
In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: Non-standard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants