AREAS COVERED: This review covers the epidemiology and burden of COPD in LMICs, and challenges and recommendations related to health-care systems, prevention, diagnosis, and treatment. Main challenges are related to under-resourced health-care systems (such as limited availability of spirometry, rehabilitation, and medicines). Lack of policy and practical local guidelines on COPD diagnosis and management further contribute to the low diagnostic and treatment rates. In the absence of, or limited number of respiratory specialists, primary care practitioners (general practitioners, nurses, pharmacists, physiotherapists, and community health workers) play an even more pivotal role in COPD management in LMICs.
EXPERT OPINION: Raising awareness on COPD, educating health-care workers, patients, and communities on cost-effective preventive measures as well as improving availability, affordability and proper use of diagnostic and pharmacological and non-pharmacologic treatment in primary care are the key interventions needed to improve COPD prevention, diagnosis, and care in LMICs.
OBJECTIVES: This study reviewed the scope of diabetes datasets, health information ecosystems, and human resource capacity in four countries to assess whether a diabetes phenotyping algorithm (developed under a companion study) could be successfully applied.
METHODS: The capacity assessment was undertaken with four countries: Trinidad, Malaysia, Kenya, and Rwanda. Diabetes programme staff completed a checklist of available diabetes data variables and then participated in semi-structured interviews about Health Information System (HIS) ecosystem conditions, diabetes programme context, and human resource needs. Descriptive analysis was undertaken.
RESULTS: Only Malaysia collected the full set of the required diabetes data for the diabetes algorithm, although all countries did collect the required diabetes complication data. An HIS ecosystem existed in all settings, with variations in data hosting and sharing. All countries had access to HIS or ICT support, and epidemiologists or biostatisticians to support dataset preparation and algorithm application.
CONCLUSIONS: Malaysia was found to be most ready to apply the phenotyping algorithm. A fundamental impediment in the other settings was the absence of several core diabetes data variables. Additionally, if countries digitise diabetes data collection and centralise diabetes data hosting, this will simplify dataset preparation for algorithm application. These issues reflect common LMIC health systems' weaknesses in relation to diabetes care, and specifically highlight the importance of investment in improving diabetes data, which can guide population-tailored prevention and management approaches.