One of the challenges faced by global disease surveillance efforts is the lack of comparability across systems. Reporting commonly focuses on overall incidence, despite differences in surveillance quality between and within countries. For most immunizing infections, the age distribution of incident cases provides a more robust picture of trends in transmission. We present a framework to estimate transmission intensity for dengue virus from age-specific incidence data, and apply it to 359 administrative units in Thailand, Colombia, Brazil and Mexico. Our estimates correlate well with those derived from seroprevalence data (the gold standard), capture the expected spatial heterogeneity in risk, and correlate with known environmental drivers of transmission. We show how this approach could be used to guide the implementation of control strategies such as vaccination. Since age-specific counts are routinely collected by many surveillance systems, they represent a unique opportunity to further our understanding of disease burden and risk for many diseases.
The code to implement the model described in our study is available at https://github.com/isabelrodbar/dengue_foi. The case data used for the analyses is publicly available and can be accessed through the following links links: Brazil- http://tabnet.datasus.gov.br/cgi/deftohtm.exe?sih/cnv/mruf.def; Thailand - http://www.boe.moph.go.th/boedb/surdata/index.php; Colombia - http://www.ins.gov.co/lineas-de-accion/Subdireccion-Vigilancia/sivigila/Paginas/vigilancia-rutinaria.aspxand https://www.sispro.gov.co/Pages/Home.aspx; Mexico - http://www.epidemiologia.salud.gob.mx/anuario/html/anuarios.html.
- Derek A Cummings
- Henrik Salje
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
- Jos WM van der Meer, Radboud University Medical Centre, Netherlands
© 2019, Rodriguez-Barraquer et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
The National Institute on Minority Health and Health Disparities (NIMHD) has developed a framework to guide and orient research into health disparities and minority health. The framework depicts different domains of influence (such as biological and behavioral) and different levels of influence (such as individual and interpersonal). Here, influenced by the “One Health” approach, we propose adding two new levels of influence – interspecies and planetary – to this framework to reflect the interconnected nature of human, animal, and environmental health. Extending the framework in this way will help researchers to create new avenues of inquiry and encourage multidisciplinary collaborations. We then use the One Health approach to discuss how the COVID-19 pandemic has exacerbated health disparities, and show how the expanded framework can be applied to research into health disparities related to antimicrobial resistance and obesity.
Type 2 diabetes (T2D) accounts for ~90% of all cases of diabetes, resulting in an estimated 6.7 million deaths in 2021, according to the International Diabetes Federation. Early detection of patients with high risk of developing T2D can reduce the incidence of the disease through a change in lifestyle, diet, or medication. Since populations of lower socio-demographic status are more susceptible to T2D and might have limited resources or access to sophisticated computational resources, there is a need for accurate yet accessible prediction models.
In this study, we analyzed data from 44,709 nondiabetic UK Biobank participants aged 40–69, predicting the risk of T2D onset within a selected time frame (mean of 7.3 years with an SD of 2.3 years). We started with 798 features that we identified as potential predictors for T2D onset. We first analyzed the data using gradient boosting decision trees, survival analysis, and logistic regression methods. We devised one nonlaboratory model accessible to the general population and one more precise yet simple model that utilizes laboratory tests. We simplified both models to an accessible scorecard form, tested the models on normoglycemic and prediabetes subcohorts, and compared the results to the results of the general cohort. We established the nonlaboratory model using the following covariates: sex, age, weight, height, waist size, hip circumference, waist-to-hip ratio, and body mass index. For the laboratory model, we used age and sex together with four common blood tests: high-density lipoprotein (HDL), gamma-glutamyl transferase, glycated hemoglobin, and triglycerides. As an external validation dataset, we used the electronic medical record database of Clalit Health Services.
The nonlaboratory scorecard model achieved an area under the receiver operating curve (auROC) of 0.81 (95% confidence interval [CI] 0.77–0.84) and an odds ratio (OR) between the upper and fifth prevalence deciles of 17.2 (95% CI 5–66). Using this model, we classified three risk groups, a group with 1% (0.8–1%), 5% (3–6%), and the third group with a 9% (7–12%) risk of developing T2D. We further analyzed the contribution of the laboratory-based model and devised a blood test model based on age, sex, and the four common blood tests noted above. In this scorecard model, we included age, sex, glycated hemoglobin (HbA1c%), gamma glutamyl-transferase, triglycerides, and HDL cholesterol. Using this model, we achieved an auROC of 0.87 (95% CI 0.85–0.90) and a deciles' OR of ×48 (95% CI 12–109). Using this model, we classified the cohort into four risk groups with the following risks: 0.5% (0.4–7%); 3% (2–4%); 10% (8–12%); and a high-risk group of 23% (10–37%) of developing T2D. When applying the blood tests model using the external validation cohort (Clalit), we achieved an auROC of 0.75 (95% CI 0.74–0.75). We analyzed several additional comprehensive models, which included genotyping data and other environmental factors. We found that these models did not provide cost-efficient benefits over the four blood test model. The commonly used German Diabetes Risk Score (GDRS) and Finnish Diabetes Risk Score (FINDRISC) models, trained using our data, achieved an auROC of 0.73 (0.69–0.76) and 0.66 (0.62–0.70), respectively, inferior to the results achieved by the four blood test model and by the anthropometry models.
The four blood test and anthropometric models outperformed the commonly used nonlaboratory models, the FINDRISC and the GDRS. We suggest that our models be used as tools for decision-makers to assess populations at elevated T2D risk and thus improve medical strategies. These models might also provide a personal catalyst for changing lifestyle, diet, or medication modifications to lower the risk of T2D onset.
The funders had no role in study design, data collection, interpretation, or the decision to submit the work for publication.