Conectar
by Hemant Mahajan, Poppy Alice Carson Mallinson, Judith Lieber, Santhi Bhogadi, Santosh Kumar Banjara, Anoop Shah, Vipin Gupta, Gagandeep Kaur Walia, Bharati Kulkarni, Sanjay Kinra
Background and AimCardiovascular diseases (CVDs) represent a growing public-health challenge in India, where nearly one in four deaths is CVD-related. Accurate risk stratification underpins targeted prevention, yet laboratory-dependent tools are often impractical in resource-limited settings. The World Health Organization (WHO) and GLOBORISK initiatives both offer non-laboratory-based 10-year CVD risk algorithms alongside their laboratory-based counterparts. We aimed to compare laboratory- and non-laboratory-based WHO and GLOBORISK CVD risk scores, assess their concordance, and examine relationships with sub-clinical atherosclerosis in a rural Indian cohort.
Materials and MethodsWe conducted a cross-sectional analysis of 2,465 adults (1,184 men, 1,281 women) aged 40−74 years from the third wave (2010−12) of the Andhra Pradesh Children and Parents Study (APCAPS). Participants with prior CVD were excluded. Ten-year CVD risk was calculated using sex-specific WHO (South Asia) and India-calibrated GLOBORISK models, both laboratory-based (age, sex, smoking, systolic blood pressure, diabetes, total cholesterol) and non-laboratory-based (age, sex, smoking, systolic blood pressure, BMI) algorithms. Categorical agreement was quantified via percentage agreement and quadratic weighted kappa (κ); continuous agreement by Bland-Altman analysis. We also evaluated linear associations between each risk score (categorical and continuous) and three sub-clinical atherosclerosis markers: carotid intima-media thickness (CIMT), pulse-wave velocity (PWV), and augmentation index (AIx), through sex-stratified multi-level linear regression with random intercept at the household level, adjusting for multiple testing (p Results
Median WHO-CVD-risk was 6.0% (IQR 4% − 9%) in men and 3.0% (2% − 4%) in women for both lab and non-lab models; median GLOBORISK-CVD-risk was 12.0% (9% − 16%) for lab-model vs. 15.0% (10% − 16%) for non-lab-model in men and 5.0% (3% − 9%) for lab-model vs. 5.0% (3% − 9%) for non-lab-model in women. Categorical agreement was substantial to almost perfect: WHO κ = 0.82 (overall), GLOBORISK κ = 0.72. Bland-Altman analyses demonstrated mean differences Conclusion
Non-laboratory-based WHO and GLOBORISK CVD risk scores exhibit high overall agreement with laboratory-based models and correlate strongly with subclinical atherosclerosis in rural India. However, modest underestimation in high-risk subgroups (diabetics, hypercholesterolemia) warrants cautious interpretation. These findings support the feasibility of non-lab risk assessment in resource-constrained settings, while underscoring the need for prospective validation against hard cardiovascular outcomes prior to large-scale implementation.
To assess factors associated with the adoption of the WHO Package of Essential Non-Communicable Diseases (PEN) Protocol 1 at primary healthcare (PHC) facilities in Nepal after healthcare workers received training.
Cross-sectional study.
PHC facilities across various provinces in Nepal.
A total of 180 healthcare workers trained in PEN, recruited from a random selection of 105 basic healthcare facilities.
The adoption of PEN Protocol 1 components: blood pressure measurement, blood glucose screening, 10-year cardiovascular disease (CVD) risk assessment using WHO/International Society of Hypertension risk charts and body mass index (BMI) assessment. Factors associated with protocol adoption were assessed using generalised estimating equations for ORs.
Among participants, 100% reported measuring blood pressure, while 56% measured blood sugar, 28% assessed CVD risk and 27% assessed BMI. The adoption of the CVD risk prediction chart was positively associated with the availability of amlodipine (adjusted OR (aOR) 3.00; 95% CI 1.09 to 8.27). The adoption of BMI assessment was positively associated with access to a stadiometer (aOR 3.23; 95% CI 1.26 to 8.30) and a glucometer (aOR 3.07; 95% CI 1.12 to 8.40), and negatively associated with lack of motivation/inertia of previous practice (aOR 0.60; 95% CI 0.42 to 0.87) and environmental factors such as lack of time and resources (aOR 0.57; 95% CI 0.37 to 0.89). Blood glucose level measurements were positively associated with being at a PHC centre (aOR 7.34; 95% CI 2.79 to 19.3) and the availability of metformin (OR 2.40; 95% CI 1.08 to 5.29).
Adoption of PEN Protocol 1 varied by component and was influenced by resource availability, provider motivation and system barriers. Addressing these factors is key to optimising implementation in low-resource settings.
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