Effectiveness of community-wide and individual high-risk strategies to prevent diabetes: a modelling study

Evaluation of the effectiveness of the Iran-package of essential non-communicable disease (IraPEN) program in reducing cardiovascular disease risk in pilot areas

BACKGROUND

To assess the effectiveness of the IraPEN program in decreasing the risk of cardiovascular disease (CVD) and associated risk factors in selected areas of Iran.

METHODS

A secondary data analysis of longitudinal data collected between 2016 and 2017 from health centers in four pilot areas. The target population consisted of people aged 40 years and above, as well as individuals aged 30-40 years who had at least one CVD risk factor. We compared mean CVD risk and risk factor levels before and one year after the intervention and utilized Generalized Estimating Equations to analyze the trends during subsequent visits.

RESULTS

A total of 160,223 eligible individuals (93,081 female) were screened at baseline, of which 25,764 individuals (17,386 female) were followed up for at least one year. The proportion of men with a CVD risk score above 10%, according to the WHO/ISH risk charts, decreased from 7.5 to 5.3%, while the proportion of women decreased from 6.1 to 4.7%. Based on the revised WHO risk score, the mean CVD risk for those with a risk score above 10% at baseline and one year later was 0.198 and 0.177 in men and 0.119 and 0.109 in women, respectively. There was a significant decrease in all risk factors, except for waist circumference in both sexes and BMI in women. The trend analysis of risk factors over time confirmed these findings.

CONCLUSION

The program was modestly effective in reducing CVD risk in the pilot areas. However, further modifications may be needed to enhance its effectiveness.

The Predictive Potential of C-Peptide in Differentiating Type 1 Diabetes From Type 2 Diabetes in an Outpatient Population in Abu Dhabi

PURPOSE

We aimed to investigate the predictive potential of plasma connecting peptide (C-peptide) in differentiating type 1 diabetes (T1D) from type 2 diabetes (T2D) and to inform evidence-based diabetes classification criteria.

METHODS

A retrospective review was performed of all the patients with diabetes visiting an outpatient diabetology, endocrinology, general practice and family medicine tertiary health care center between January 2016 and December 2021.

FINDINGS

Two hundred twelve individuals with diabetes were included, 85 (44.8%) with T1D and 127 (55.2%) with T2D. Mean (SD) age at diagnosis was 35.9 (15.1) years, and 112 (52.8%) men. Median (interquartile range [IQR]) duration of diabetes was 3.8 (3.0-4.5) years (T1D, 3.9 [3.5-4.6]; T2D, 3.4 [2.4-4.4]; P = 0.001). Body mass index was <18.5 kg/m2 in 5 (2.5%) individuals (T1D, 5; T2D, none), 18.5 to <25 kg/m2 in 57 (28.5%) (T1D, 32; T2D, 25), 25 to <30 kg/m2 in 58 (29%) (T1D, 28; T2D, 30), and >30 kg/m2 in 80 (40.0%) (T1D, 20; T2D, 60). Median (IQR) glycosylated hemoglobin was 7.4% (6.7%-8.5%) (T1D, 8.3% [7.2%-9.9%]; T2D, 7% [6.3%-7.6%]; P = 0.0001). Median (IQR) C-peptide concentration was 0.59 nmol/L (0.01-1.14 nmol/L) (T1D, 0.01 nmol/L [0.003-0.05 nmol/L]; T2D, 1.03 nmol/L [0.70-1.44 nmol/L]; P = 0.0001). C-peptide concentration of ≤0.16 nmol/L showed 92.9% sensitivity, 1-specificity of 2.4%, and AUC of 97.2% (CI, 94.7%-99.6%; P = 0.0001) in differentiating T1D from T2D.

IMPLICATIONS

To our knowledge, this is the first study in the Middle East and North Africa region highlighting the role of C-peptide in diabetes classification. The estimated cutoff point for C-peptide concentration (≤0.16 nmol/L) will certainly help in accurately classifying the T1D and will rule out the routine clinical judgmental approaches in the region, especially in those scenarios and periods where it is always difficult to diagnose the diabetes type. Quantifying the cutoff for C-peptide is among the vital strengths of this study that will provide a better treatment plan in diabetes care management. Also, we evaluated concomitant glucose levels to rule out the phenomenon of falsely low C-peptide values in the setting of hypoglycemia or severe glucose toxicity. Based on our findings, C-peptide testing could be included in postulating an evidence-based guideline that differentiates T1D from T2D. Despite this, our study has some limitations, including the selection bias due to the retrospective design and low C-peptide levels could be indicative of low pancreatic reserves due to other causes or long-standing T2D, and quantifying these reasons requires additional resources and time.

Predictive Modelling of Diabetes Risk in Population Groups Defined by Socioeconomic and Lifestyle Factors in Canada: A Cross-Sectional Study

Objectives

This study modelled diabetes risk for population groups in Canada defined by socioeconomic and lifestyle characteristics and investigated inequities in diabetes risk using a validated population risk prediction algorithm.

Methods

We defined population groups, informed by determinants of health frameworks. We applied the Diabetes Population Risk Tool (DPoRT) to 2017/2018 Canadian Community Health Survey data to predict 10-year diabetes risk and cases across population groups. We modelled a preventive intervention scenario to estimate reductions in diabetes for population groups and impacts on the inequity in diabetes risk across income and education.

Results

The population group with at least one lifestyle and at least one socioeconomic/structural risk factor had the highest estimated 10-year diabetes risk and number of new cases. When an intervention with a 5% relative risk reduction was modelled for this population group, diabetes risk decreased by 0.5% (females) and 0.7% (males) and the inequity in diabetes risk across income and education levels was reduced.

Conclusion

Preventative interventions that address socioeconomic and structural risk factors have potential to reduce inequities in diabetes risk and overall diabetes burden.

Modeling chronic disease risk across equity factors using a population-based prediction model: the Chronic Disease Population Risk Tool (CDPoRT)

BACKGROUND

Predicting chronic disease incidence at a population level can help inform overall future chronic disease burden and opportunities for prevention. This study aimed to estimate the future burden of chronic disease in Ontario, Canada, using a population-level risk prediction algorithm and model interventions for equity-deserving groups who experience barriers to services and resources due to disadvantages and discrimination.

METHODS

The validated Chronic Disease Population Risk Tool (CDPoRT) estimates the 10-year risk and incidence of major chronic diseases. CDPoRT was applied to data from the 2017/2018 Canadian Community Health Survey to predict baseline 10-year chronic disease estimates to 2027/2028 in the adult population of Ontario, Canada, and among equity-deserving groups. CDPoRT was used to model prevention scenarios of 2% and 5% risk reductions over 10 years targeting high-risk equity-deserving groups.

RESULTS

Baseline chronic disease risk was highest among those with less than secondary school education (37.5%), severe food insecurity (19.5%), low income (21.2%) and extreme workplace stress (15.0%). CDPoRT predicted 1.42 million new chronic disease cases in Ontario from 2017/2018 to 2027/2028. Reducing chronic disease risk by 5% prevented 1500 cases among those with less than secondary school education, prevented 14 900 cases among those with low household income and prevented 2800 cases among food-insecure populations. Large reductions of 57 100 cases were found by applying a 5% risk reduction in individuals with quite a bit workplace stress.

CONCLUSION

Considerable reduction in chronic disease cases was predicted across equity-defined scenarios, suggesting the need for prevention strategies that consider upstream determinants affecting chronic disease risk.

External validation and application of the Diabetes Population Risk Tool (DPoRT) for prediction of type 2 diabetes onset in the US population

INTRODUCTION

Characterizing diabetes risk in the population is important for population health assessment and diabetes prevention planning. We aimed to externally validate an existing 10-year population risk model for type 2 diabetes in the USA and model the population benefit of diabetes prevention approaches using population survey data.

RESEARCH DESIGN AND METHODS

The Diabetes Population Risk Tool (DPoRT), originally derived and validated in Canada, was applied to an external validation cohort of 23 477 adults from the 2009 National Health Interview Survey (NHIS). We assessed predictive performance for discrimination (C-statistic) and calibration plots against observed incident diabetes cases identified from the NHIS 2009-2018 cycles. We applied DPoRT to the 2018 NHIS cohort (n=21 187) to generate 10-year risk prediction estimates and characterize the preventive benefit of three diabetes prevention scenarios: (1) community-wide strategy; (2) high-risk strategy and (3) combined approach.

RESULTS

DPoRT demonstrated good discrimination (C-statistic=0.778 (males); 0.787 (females)) and good calibration across the range of risk. We predicted a baseline risk of 10.2% and 21 076 000 new cases of diabetes in the USA from 2018 to 2028. The community-wide strategy and high-risk strategy estimated diabetes risk reductions of 0.2% and 0.3%, respectively. The combined approach estimated a 0.4% risk reduction and 843 000 diabetes cases averted in 10 years.

CONCLUSIONS

DPoRT has transportability for predicting population-level diabetes risk in the USA using routinely collected survey data. We demonstrate the model's applicability for population health assessment and diabetes prevention planning. Our modeling predicted that the combination of community-wide and targeted prevention approaches for those at highest risk are needed to reduce diabetes burden in the USA.

Bridging ethics and epidemiology: Modelling ethical standards of health equity

Health inequities are differences in health that are 'unjust'. Yet, despite competing ethical views about what counts as an 'unjust difference in health', theoretical insights from ethics have not been systematically integrated into epidemiological research. Using diabetes as an example, we explore the impact of adopting different ethical standards of health equity on population health outcomes. Specifically, we explore how the implementation of population-level weight-loss interventions using different ethical standards of equity impacts the intervention's implementation and resultant population health outcomes. We conducted a risk prediction modelling study using the nationally representative 2015-16 Canadian Community Health Survey (n = 75,044, 54% women). We used the Diabetes Population Risk Tool (DPoRT) to calculate individual-level 10-year diabetes risk. Hypothetical weight-loss interventions were modelled in individuals with overweight or obesity based on each ethical standard: 1) health sufficiency (reduce DPoRT risk below a high-risk threshold (16.5%); 2) health equality (equalize DPoRT risk to the low risk group (5%)); 3) social-health sufficiency (reduce DPoRT risk <16.5 in individuals with lower education); 4) social-health equality (equalize DPoRT risk to the level of individuals with high education). For each scenario, we calculated intervention impacts, diabetes cases prevented or delayed, and relative and absolute educational inequities in diabetes. Overall, we estimated that achieving health sufficiency (i.e., all individuals below the diabetes risk threshold) was more feasible than achieving health equality (i.e., diabetes risk equalized for all individuals), requiring smaller initial investments and fewer interventions; however, fewer diabetes cases were prevented or delayed. Further, targeting only diabetes inequalities related to education reduced the target population size and number of interventions required, but consequently resulted in even fewer diabetes cases prevented or delayed. Using diabetes as an example, we found that an explicit, ethically-informed definition of health equity is essential to guide population-level interventions that aim to reduce health inequities.

The Predictive Ability of C-Peptide in Distinguishing Type 1 Diabetes From Type 2 Diabetes: A Systematic Review and Meta-Analysis

OBJECTIVE

This systematic review and meta-analysis aimed to investigate the predictive ability of plasma connecting peptide (C-peptide) levels in discriminating type 1 diabetes (T1D) from type 2 diabetes (T2D) and to inform evidence-based guidelines in diabetes classification.

METHODS

We conducted a holistic review and meta-analysis using PubMed, MEDLINE, EMBASE, and Scopus. The citations were screened from 1942 to 2021. The quality criteria and the preferred reporting items for systematic reviews and meta-analysis checklist were applied. The protocol was registered with the International Prospective Register of Systematic Reviews (PROSPERO) (CRD42022355088).

RESULTS

A total of 23,658 abstracts were screened and 46 full texts reviewed. Of the 46 articles screened, 12 articles were included for the meta-analysis. Included studies varied by race, age, time, and proportion of individuals. The main outcome measure in all studies was C-peptide levels. A significant association was reported between C-peptide levels and the classification and diagnosis of diabetes. Furthermore, lower concentrations and the cutoff of <0.20 nmol/L for fasting or random plasma C-peptide was indicative of T1D. In addition, this meta-analysis revealed the predictive ability of C-peptide levels in discriminating T1D from T2D. Results were consistent using both fixed- and random-effect models. The I2 value (98.8%) affirmed the variability in effect estimates was due to heterogeneity rather than sampling error among all selected studies.

CONCLUSION

Plasma C-peptide levels are highly associated and predictive of the accurate classification and diagnosis of diabetes types. A plasma C-peptide cutoff of ≤0.20 mmol/L is indicative of T1D and of ≥0.30 mmol/L in the fasting or random state is indicative of T2D.

The idea of uniform change: is it time to revisit a central tenet of Rose's "Strategy of Preventive Medicine"?

A mean-centric view of populations, whereby a change in the mean of a health variable at the population level is assumed to result in uniform change across the distribution, is a core component of Geoffrey Rose's concept of the "population strategy" to disease prevention. This idea also has a critical role in Rose's observation that individuals who are considered abnormal or sick (the rightward tail of the distribution) and those who are considered normal (the center) are very closely related, and that true preventive medicine must focus on shifting the normal or average. In this Perspective, we revisit these core tenets of Rose's concept of preventive medicine after providing an overview of the key concepts that he developed. We examine whether these assumptions apply to population changes in body mass index (BMI) and show that there is considerable evidence of a widening of the BMI distribution in populations over time. We argue that, with respect to BMI, the idea of using statistical measures of a population solely on the basis of means and the assumption that populations are coherent entities that change uniformly over time may not fully capture the true nature of changes in the population. These issues have important implications for how we assess and interpret the health of populations over time with implications for the balance between universal and targeted strategies aimed at improving health.

Risk distribution and its influence on the population targets for diabetes prevention

OBJECTIVE

To quantify the influence of type 2 diabetes risk distribution on prevention benefit and apply a method to optimally identify population targets.

METHODS

We used data from the 2011 Canadian Community Health Survey (N=45,040) and the validated Diabetes Population Risk Tool to calculate 10-year diabetes risk. We calculated the Gini coefficient as a measure of risk dispersion. Intervention benefit was estimated using absolute risk reduction (ARR), number-needed-to-treat (NNT), and number of cases prevented.

RESULTS

There is a wide variation of diabetes risk in Canada (Gini=0.48) and with an inverse relation to risk (r=-0.99). Risk dispersion is lower among individuals meeting an empirically derived risk cut-off (Gini=0.18). Targeting prevention based on a risk cut-off (10-year risk ≥ 16.5%) resulted in a greater number of cases prevented (340 thousand), higher ARR (7.7%) and lower NNT (13) compared to targeting individuals based on risk factor targets.

CONCLUSIONS

This study provides empirical evidence to demonstrate that risk variability is an important consideration for estimating the prevention benefit. Prioritizing target populations using an empirically derived cut-off based on a multivariate risk score will result in greater benefit and efficiency compared to risk factor targets.

Learning lessons from operational research in infectious diseases: can the same model be used for noncommunicable diseases in developing countries?

About three-quarters of global deaths from noncommunicable diseases (NCDs) occur in developing countries. Nearly a third of these deaths occur before the age of 60 years. These deaths are projected to increase, fueled by such factors as urbanization, nutrition transition, lifestyle changes, and aging. Despite this burden, there is a paucity of research on NCDs, due to the higher priority given to infectious disease research. Less than 10% of research on cardiovascular diseases comes from developing countries. This paper assesses what lessons from operational research on infectious diseases could be applied to NCDs. The lessons are drawn from the priority setting for research, integration of research into programs and routine service delivery, the use of routine data, rapid-assessment survey methods, modeling, chemoprophylaxis, and the translational process of findings into policy and practice. With the lines between infectious diseases and NCDs becoming blurred, it is justifiable to integrate the programs for the two disease groups wherever possible, eg, screening for diabetes in tuberculosis. Applying these lessons will require increased political will, research capacity, ownership, use of local expertise, and research funding.