Objective To compare the medicines included in national essential medicines lists with the World Health Organization’s (WHO’s) Model list of essential medicines , and assess the extent to which countries’ characteristics, such as WHO region, size and health care expenditure, account for the differences. Methods We searched the WHO’s Essential Medicines and Health Products Information Portal for national essential medicines lists. We compared each national list of essential medicines with both the 2017 WHO model list and other national lists. We used linear regression to determine whether differences were dependent on WHO Region, population size, life expectancy, infant mortality, gross domestic product and health-care expenditure. Findings We identified 137 national lists of essential medicines that collectively included 2068 unique medicines. Each national list contained between 44 and 983 medicines (median 310: interquartile range, IQR: 269 to 422). The number of differences between each country’s essential medicines list and WHO’s model list ranged from 93 to 815 (median: 296; IQR: 265 to 381). Linear regression showed that only WHO region and health-care expenditure were significantly associated with the number of differences (adjusted R 2 : 0.33; P < 0.05). Most medicines (1248; 60%) were listed by no more than 10% (14) of countries. Conclusion The substantial differences between national lists of essential medicines are only partly explained by differences in country characteristics and thus may not be related to different priority needs. This information helps to identify opportunities to improve essential medicines lists.

BackgroundStarting in the late 1990s, the pharmaceutical industry sought to increase prescribing of opioids for chronic non-cancer pain. Influencing the content of clinical practice guidelines may have been one strategy industry employed. In this study we assessed potential risk of bias from financial conflicts of interest with the pharmaceutical industry in guidelines for opioid prescribing for chronic non-cancer pain published between 2007 and 2013, the peak of opioid prescribing. MethodsWe used the Guideline Panel Review (GPR) to appraise the guidelines included in the 2014 systematic review and critical appraisal by Nuckols et al. These were English language opioid prescribing guidelines for adults with chronic non-cancer pain published between July 2007 and July 2013, the peak of opioid prescribing. The GPR assigns red flags to items known to introduce potential bias from financial conflicts of interest. We operationalized the GPR by creating specific definitions for each red flag. Two reviewers independently evaluated each guideline. Disagreements were resolved with discussion. We also compared our score to the critical appraisal scores for overall quality from the study by Nuckols et al. ResultsWe appraised 13 guidelines, which received 43 red flags in total. Guidelines had 3.3 red flags on average (out of a possible seven) with range from one to six. Four guidelines had missing information, so red flags may be higher than reported. The guidelines with the

BackgroundHigh quality primary care is fundamental to achieving health for all. Research priority setting is a key facilitator of improving how research activity responds to concrete needs. There has never before been an attempt to identify international primary care research priorities, in order to guide resource allocation and to enhance global primary care. This study aimed to identify a list of top 10 primary care research priorities, as identified by members of the public, health professionals working in primary care, researchers, and policymakers.MethodsWe adapted the James Lind Alliance Priority Setting Partnership process, to conduct multiple rounds of stakeholder recruitment and prioritization. The study included an online survey conducted in three languages, followed by an in-person priority setting exercise involving primary care stakeholders from 13 countries.FindingsParticipants identified a list of top 10 international primary care research priorities. These were focused on diverse topics such as enhancing use of information and communication technology, and improving integration of indigenous communities’ knowledge in the design of primary care services. The main limitations of the study related to challenges in engaging an adequate diversity and number of appropriate stakeholders, particularly members of the public, in aggregating the diverse set of responses into coherent categories representative of the participants’ perspectives and in adequately representing the diversity of submitted responses while ensuring research priorities on the final list are sufficiently actionable to guide resource allocation.ConclusionsThe top 10 identified research priorities have the potential to guide research resource allocation, supporting funding agencies and initiatives to promote global primary care research and practice.

No abstract

No abstract

No abstract

Background Measuring body mass index (BMI) has been proposed as a method of screening for preventive primary care and population surveillance of childhood obesity. However, the accuracy of routinely collected measurements has been questioned. The purpose of this study was to assess the reliability of height, length and weight measurements collected during well-child visits in primary care relative to trained research personnel. Methods A cross-sectional study of measurement reliability was conducted in community pediatric and family medicine primary care practices. Each participating child, ages 0 to 18 years, was measured four consecutive times; twice by a primary care team member (e.g. nurses, practice personnel) and twice by a trained research assistant. Inter- and intra-observer reliability was calculated using the technical error of measurement (TEM), relative TEM (%TEM), and a coefficient of reliability (R). Results Six trained research assistants and 16 primary care team members performed measurements in three practices. All %TEM values for intra-observer reliability of length, height, and weight were classified as ‘acceptable’ (< 2%; range 0.19% to 0.70%). Inter-observer reliability was also classified as ‘acceptable’ (< 2%; range 0.36% to 1.03%) for all measurements. Coefficients of reliability (R) were all > 99% for both intra- and inter-observer reliability. Length measurements in children < 2 years had the highest measurement error. There were some significant differences in length intra-observer reliability between observers. Conclusion There was agreement between routine measurements and research measurements although there were some differences in length measurement reliability between practice staff and research assistants. These results provide justification for using routinely collected data from selected primary care practices for secondary purposes such as BMI population surveillance and research. Electronic supplementary material The online version of this article (10.1186/s12874-019-0726-8) contains supplementary material, which is available to authorized users.

No abstract