Collaborative healthcare education programmes for continuing professional education in low and middle-income countries: A Best Evidence Medical Education (BEME) systematic review. BEME Guide No. 65

BACKGROUND

Large discrepancies exist between standards of healthcare provision in high-income (HICs) and low and middle-income countries (LMICs). The root cause is often financial, resulting in poor infrastructure and under-resourced education and healthcare systems. Continuing professional education (CPE) programmes improve staff knowledge, skills, retention, and practice, but remain costly and rare in low-resource settings. One potential solution involves healthcare education collaborations between institutions in HICs and LMICs to provide culturally appropriate CPE in LMICs. To be effective, educational partnerships must address the challenges arising from differences in cultural norms, language, available technology and organisational structures within collaborating countries.

METHODS

Seven databases and other sources were systematically searched on 7 July 2020 for relevant studies. Citations, abstracts, and studies were screened and consensus was reached on which to include within the review. 54 studies were assessed regarding the type of educational programme involved, the nature of HIC/LMIC collaboration and quality of the study design.

RESULTS

Studies varied greatly regarding the types and numbers of healthcare professionals involved, pedagogical and delivery methods, and the ways in which collaboration was undertaken. Barriers and enablers of collaboration were identified and discussed. The key findings were: 1. The methodological quality of reporting in the studies was generally poor. 2. The way in which HIC/LMIC healthcare education collaboration is undertaken varies according to many factors, including what is to be delivered, the learner group, the context, and the resources available. 3. Western bias was a major barrier. 4. The key to developing successful collaborations was the quality, nature, and duration of the relationships between those involved.

CONCLUSION

This review provides insights into factors that underpin successful HIC/LMIC healthcare CPE collaborations and outlines inequities and quality issues in reporting.

Interventions targeting hypertension and diabetes mellitus at community and primary healthcare level in low- and middle-income countries:a scoping review

BACKGROUND

Hypertension (HTN) and diabetes mellitus (DM) are highly prevalent in low- and middle-income countries (LMIC) and a leading cause of morbidity and mortality. Recent evidence on effectiveness of primary care interventions has attracted renewed calls for their implementation. This review aims to synthesize evidence pertaining to primary care interventions on these two diseases, evaluated and tested in LMICs.

METHODS

Two reviewers conducted an electronic search of three databases (Pubmed, EMBASE and Web of Science) and screened for eligible articles. Interventions covering health promotion, prevention, treatment, or rehabilitation activities at the PHC or community level were included. Studies published in English, French, Portuguese and Spanish, from January 2007 to January 2017, were included. Key extraction variables included the 12 criteria identified by the Template for Intervention Description and Replication (TIDieR) checklist and guide. The Innovative Care for Chronic Conditions Framework (ICCCF) was used to guide analysis and reporting of results.

RESULTS

198 articles were analyzed. The strategies focused on healthcare service organization (76.5%), community level (9.7 %), creating a positive policy environment (3.6%) and strategies covering multiple domains (10.2%). Studies included related to the following topics: description or testing of interventions (n=81; 41.3%), implementation or evaluation projects (n=42; 21.4%), quality improvement initiatives (n=15; 7.7%), screening and prevention efforts (n=26; 13.2%), management of HTN or DM (n=13; 6.6%), integrated health services (n=10; 5.1%), knowledge and attitude surveys (n=5; 2.5%), cost-effective lab tests (n=2; 1%) and policy making efforts (n=2; 1%). Most studies reported interventions by non-specialists (n=86; 43.4%) and multidisciplinary teams (n=49; 25.5%).

CONCLUSION

Only 198 articles were found over a 10 year period which demonstrates the limited published research on highly prevalent diseases in LMIC. This review shows the variety and complexity of approaches that have been tested to address HTN and DM in LMICs and highlights the elements of interventions needed to be addressed in order to strengthen delivery of care. Most studies reported little information regarding implementation processes to allow replication. Given the need for multi-component complex interventions, study designs and evaluation techniques will need to be adapted by including process evaluations versus simply effectiveness or outcome evaluations.

The impact of a decision support tool linked to an electronic medical record on glycemic control in people with type 2 diabetes

AIMS

We retrospectively compared glycemic control and glycemic burden in type 2 diabetes patients treated by general physicians with access to decision support with those treated by general physicians without access to decision support.

METHODS

A total of 875 patients [471 (53.8%) males] aged 54.3 [standard deviation (SD) 13.1] years followed up over 84 months. A total of 342 patients (39%) were managed with decision support, and effects on glycosylated hemoglobin (HbA1c) were assessed.

RESULTS

There was no difference between groups in starting HbA1c [7.6 (SD 1.8) versus 7.5 (SD 1.5); p = not significant] at baseline. Patients treated with decision support were more likely to have planned review of HbA1c, adjustment of medication, prescription of statins, dietetic and nurse educator inputs (71.3% versus 58.5%; Chi squared = 14.7; p = .001). The mean HbA1c in the group treated with decision support was not significantly reduced within the first year [7.5% (SD 1.8) versus 7.6% (SD 1.5); p = not significant; 95% confidence interval (CI) -0.33 to 0.17], but statistically significant differences were apparent at year 2 [7.2% (SD 2.0) versus 8% (SD 3.4); p = .0001; 95% CI -1.3 to -0.5] and sustained through year 3 [7.2% (SD 2.0) versus 8.0% (SD 2.0); p = .0001; 95% CI -1.2 to -0.6], year 4 [7.2% (SD 2.3) versus 8.2% (SD 2.5); p = .0001; 95% CI -1.2 to -0.6], year 5 [7.0% (SD 2.3) versus 8.3% (SD 2.6); p = .001; 95% CI -1.5 to -0.8], year 6 [7.0% (SD 2.0) versus 8.2% (SD 2.4); p = .001; 95% CI -1.5 to -0.9], and year 7 [6.9% (SD 1.2) versus 8% (SD 1.8); p = .001; 95% CI -1.4 to -1.0].

CONCLUSION

Use of a decision support system showed benefits in adherence to clinical care pathways and achieving significant improvements in glycemic control.