DESIGN: Using data from the 2011-2012 US National Health and Nutrition Examination Surveys, we examined the relationship between HbA1c and a single fasting measure of blood glucose in a non-clinical population of people with known diabetes (n=333). A linear equation for estimating HbA1c from blood glucose was developed. Appropriate blood glucose cut-off values were set for poor glycaemic control (HbA1c≥69.4 mmol/mol).
RESULTS: The HbA1c and blood glucose measures were well correlated (r=0.7). Three blood glucose cut-off values were considered for classifying poor glycaemic control: 8.0, 8.9, and 11.4 mmol/L. A blood glucose of 11.4 had a specificity of 1, but poor sensitivity (0.37); 8.9 had high specificity (0.94) and moderate sensitivity (0.7); 8.0 was associated with good specificity (0.81) and sensitivity (0.75).
CONCLUSIONS: Where HbA1c measurement is too expensive for community surveillance, a single blood glucose measure may be a reasonable alternative. Generalising the specific results from these US data to low resource settings may not be appropriate, but the general approach is worthy of further investigation.
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.
OBJECTIVE: We sought to synthesise existing literature on the predictors and processes informing attitudes and beliefs of smoking health professionals' own cessation.
METHODS: A five-step methodological framework for scoping reviews was followed. We conducted a systematic search of EMBASE, PubMed, Web of Science, and PsycINFO databases, as well as Google Scholar for relevant articles. Titles, abstracts, and full texts were screened against predefined criteria: research published between 1990 and 2021, in English-language peer-reviewed journals; participants included doctors, nurses, medical, and student nurses who smoke.
RESULTS: The initial search yielded 120, 883 articles, with 27 selected for synthesis. Prevalence estimates and predictors of smoking behaviour have remained the primary focus of smoking health professional research. Few studies explicitly examined the relevant predictors of quit attempts and quit attempt success. There is evidence that age and work environment factors predict quit attempt success in some health professional groups. There is also some evidence of tobacco smoking stigma experiences among nurses and nursing students who smoke.
CONCLUSION: Although cessation support is desperately needed for health professionals who smoke, the evidence for factors predicting quit success remains limited. To better guide future research, first, more theoretical work is required to identify the relevant predictors. Second, these should be tested using prospective research designs that take a multi-focal perspective to clarify the targets for change.
CONCLUDING REMARKS: DengueTools was able to make significant advances in methods for understanding and controlling dengue transmission in a range of settings. These will have implications for public health agendas to counteract dengue, including vaccination programmes.
OUTLOOK: Towards the end of the DengueTools project, Zika virus emerged as an unexpected epidemic in the central and southern America. Given the similarities between the dengue and Zika viruses, with vectors in common, some of the DengueTools thinking translated readily into the Zika situation.