@Article{info:doi/10.2196/67047,
author="Chauhan, Anshul
and Goyal, Anju
and Masih, Ritika
and Kaur, Gagandeep
and Kumar, Lakshay
and Neha, ?.
and Rastogi, Harsh
and Kumar, Sonam
and Singh, Lord Bidhi
and Syal, Preeti
and Gupta, Vishali
and Vale, Luke
and Duggal, Mona",
title="Barriers and Determinants of Referral Adherence in AI-Enabled Diabetic Retinopathy Screening for Older Adults in Northern India During the COVID-19 Pandemic: Mixed Methods Pilot Study",
journal="JMIR Form Res",
year="2025",
month="Mar",
day="31",
volume="9",
pages="e67047",
keywords="diabetic retinopathy",
keywords="diabetes",
keywords="gerontology",
keywords="geriatric",
keywords="old",
keywords="aging",
keywords="aged",
keywords="artificial intelligence",
keywords="retinopathy",
keywords="retinal",
keywords="referral",
keywords="screening",
keywords="optometry",
keywords="ophthalmology",
keywords="adherence",
keywords="barriers",
abstract="Background: Diabetic retinopathy (DR) is a leading cause of blindness globally. DR has increasingly affected both individuals and health care systems as the population ages. Objective: This study aims to explore factors and identify barriers associated with nonadherence to referral recommendations among older adult participants after DR screening (DRS) during the COVID-19 pandemic. Method: This paper presents findings from a pilot study on artificial intelligence--enabled DRS conducted in two districts in Punjab, India (Moga and Mohali) during the COVID-19 pandemic. The screenings were conducted from March to June 2022 at community health center Badhani Kalan in Moga and from March to June 2021 in community settings (homes) in Block Boothgarh, Mohali. Participants were referred to the district hospital for an ophthalmological review based on artificial intelligence--enabled screening. After 1 month, the participants were contacted by telephone to assess adherence to the referral recommendations. Participants who did not adhere to the referral were then interviewed alongside health care providers to understand the barriers explaining their nonadherence. Results: We aimed to recruit 346 and 600 older adult participants from 2 sites but enrolled 390. Key challenges included health facility closures due to COVID-19, low motivation among health personnel for recruitment, incomplete nonparticipation data, and high participant workloads. Approximately 45\% of the participants were male and 55\% female. Most participants (62.6\%) were between 60 and 69 years old, while 37.4\% were 70 or older, with a mean age of 67.2 (SD 6.2) years. In total, 159 participants (40.8\%) were referred, while 231 participants (59.2\%) were not. Only 23 (14.5\%) of those referred followed through and visited a health facility for ophthalmological review, while 136 (85.5\%) did not pursue further evaluation. Our analysis revealed no significant differences in the characteristics between adherent and nonadherent participants, suggesting that demographic and health factors alone do not predict adherence behavior in patients with DR. Interviews identified limited knowledge about DR, logistical challenges, financial constraints, and attitudinal barriers as the primary challenges. Conclusions: This study, conducted during the COVID-19 pandemic, showed suboptimal adherence to referral recommendations among older adult patients due to knowledge gaps, logistical challenges, and health system issues. Quantifying and understanding adherence factors are crucial for targeted interventions addressing barriers to referral recommendations after DRS. Integrating teleophthalmology into and strengthening infrastructure for artificial intelligence--enabled diabetic retinopathy screening to enhance access and outcomes. ",
doi="10.2196/67047",
url="https://formative.jmir.org/2025/1/e67047"
}


@Article{info:doi/10.2196/66184,
author="Ram, Sharan
and Corbin, Marine
and 't Mannetje, Andrea
and Eng, Amanda
and Kvalsvig, Amanda
and Baker, G. Michael
and Douwes, Jeroen",
title="Antibiotic Use In Utero and Early Life and Risk of Chronic Childhood Conditions in New Zealand: Protocol for a Data Linkage Retrospective Cohort Study",
journal="JMIR Res Protoc",
year="2025",
month="Feb",
day="28",
volume="14",
pages="e66184",
keywords="early childhood",
keywords="chronic childhood conditions",
keywords="antibiotics",
keywords="data linkage",
keywords="study protocol",
keywords="routine data",
abstract="Background: The incidence of many common chronic childhood conditions has increased globally in the past few decades, which has been suggested to be potentially attributed to antibiotic overuse leading to dysbiosis in the gut microbiome. Objective: This linkage study will assess the role of antibiotic use in utero and in early life in the development of type 1 diabetes (T1D), attention-deficit/hyperactive disorder (ADHD), and inflammatory bowel disease. Methods: The study design involves several retrospective cohort studies using linked administrative health and social data from Statistics New Zealand's Integrated Data Infrastructure. It uses data from all children who were born in New Zealand between October 2005 and December 2010 (N=334,204) and their mothers. Children's antibiotic use is identified for 4 time periods (at pregnancy, at ?1 year, at ?2 years, and at ?5 years), and the development of T1D, ADHD, and inflammatory bowel disease is measured from the end of the antibiotic use periods until death, emigration, or the end of the follow-up period (2021), whichever came first. Children who emigrated or died before the end of the antibiotic use period are excluded. Cox proportional hazards regression models are used while adjusting for a range of potential confounders. Results: As of September 2024, data linkage has been completed, involving the integration of antibiotic exposure and outcome variables for 315,789 children. Preliminary analyses show that both prenatal and early life antibiotic consumption is associated with T1D. Full analyses for all 3 outcomes will be completed by the end of 2025. Conclusions: This series of linked cohort studies using detailed, complete, and systematically collected antibiotic prescription data will provide critical new knowledge regarding the role of antibiotics in the development of common chronic childhood conditions. Thus, this study has the potential to contribute to the development of primary prevention strategies through, for example, targeted changes in antibiotic use. International Registered Report Identifier (IRRID): DERR1-10.2196/66184 ",
doi="10.2196/66184",
url="https://www.researchprotocols.org/2025/1/e66184",
url="http://www.ncbi.nlm.nih.gov/pubmed/40053783"
}


@Article{info:doi/10.2196/64479,
author="Beuken, JM Maik
and Kleynen, Melanie
and Braun, Susy
and Van Berkel, Kees
and van der Kallen, Carla
and Koster, Annemarie
and Bosma, Hans
and Berendschot, TJM Tos
and Houben, JHM Alfons
and Dukers-Muijrers, Nicole
and van den Bergh, P. Joop
and Kroon, A. Abraham
and 
and Kanera, M. Iris",
title="Identification of Clusters in a Population With Obesity Using Machine Learning: Secondary Analysis of The Maastricht Study",
journal="JMIR Med Inform",
year="2025",
month="Feb",
day="5",
volume="13",
pages="e64479",
keywords="Maastricht Study",
keywords="participant clusters",
keywords="cluster analysis",
keywords="factor probabilistic distance clustering",
keywords="FPDC algorithm",
keywords="statistically equivalent signature",
keywords="SES feature selection",
keywords="unsupervised machine learning",
keywords="obesity",
keywords="hypothesis free",
keywords="risk factor",
keywords="physical inactivity",
keywords="poor nutrition",
keywords="physical activity",
keywords="chronic disease",
keywords="type 2 diabetes",
keywords="diabetes",
keywords="heart disease",
keywords="long-term behavior change",
abstract="Background: Modern lifestyle risk factors, like physical inactivity and poor nutrition, contribute to rising rates of obesity and chronic diseases like type 2 diabetes and heart disease. Particularly personalized interventions have been shown to be effective for long-term behavior change. Machine learning can be used to uncover insights without predefined hypotheses, revealing complex relationships and distinct population clusters. New data-driven approaches, such as the factor probabilistic distance clustering algorithm, provide opportunities to identify potentially meaningful clusters within large and complex datasets. Objective: This study aimed to identify potential clusters and relevant variables among individuals with obesity using a data-driven and hypothesis-free machine learning approach. Methods: We used cross-sectional data from individuals with abdominal obesity from The Maastricht Study. Data (2971 variables) included demographics, lifestyle, biomedical aspects, advanced phenotyping, and social factors (cohort 2010). The factor probabilistic distance clustering algorithm was applied in order to detect clusters within this high-dimensional data. To identify a subset of distinct, minimally redundant, predictive variables, we used the statistically equivalent signature algorithm. To describe the clusters, we applied measures of central tendency and variability, and we assessed the distinctiveness of the clusters through the emerged variables using the F test for continuous variables and the chi-square test for categorical variables at a confidence level of $\alpha$=.001 Results: We identified 3 distinct clusters (including 4128/9188, 44.93\% of all data points) among individuals with obesity (n=4128). The most significant continuous variable for distinguishing cluster 1 (n=1458) from clusters 2 and 3 combined (n=2670) was the lower energy intake (mean 1684, SD 393 kcal/day vs mean 2358, SD 635 kcal/day; P<.001). The most significant categorical variable was occupation (P<.001). A significantly higher proportion (1236/1458, 84.77\%) in cluster 1 did not work compared to clusters 2 and 3 combined (1486/2670, 55.66\%; P<.001). For cluster 2 (n=1521), the most significant continuous variable was a higher energy intake (mean 2755, SD 506.2 kcal/day vs mean 1749, SD 375 kcal/day; P<.001). The most significant categorical variable was sex (P<.001). A significantly higher proportion (997/1521, 65.55\%) in cluster 2 were male compared to the other 2 clusters (885/2607, 33.95\%; P<.001). For cluster 3 (n=1149), the most significant continuous variable was overall higher cognitive functioning (mean 0.2349, SD 0.5702 vs mean --0.3088, SD 0.7212; P<.001), and educational level was the most significant categorical variable (P<.001). A significantly higher proportion (475/1149, 41.34\%) in cluster 3 received higher vocational or university education in comparison to clusters 1 and 2 combined (729/2979, 24.47\%; P<.001). Conclusions: This study demonstrates that a hypothesis-free and fully data-driven approach can be used to identify distinguishable participant clusters in large and complex datasets and find relevant variables that differ within populations with obesity. ",
doi="10.2196/64479",
url="https://medinform.jmir.org/2025/1/e64479"
}


@Article{info:doi/10.2196/64992,
author="Wang, Jiao
and Chen, Jianrong
and Liu, Ying
and Xu, Jixiong",
title="Use of the FHTHWA Index as a Novel Approach for Predicting the Incidence of Diabetes in a Japanese Population Without Diabetes: Data Analysis Study",
journal="JMIR Med Inform",
year="2025",
month="Jan",
day="28",
volume="13",
pages="e64992",
keywords="prediction",
keywords="diabetes",
keywords="risk",
keywords="index",
keywords="population without diabetes",
abstract="Background: Many tools have been developed to predict the risk of diabetes in a population without diabetes; however, these tools have shortcomings that include the omission of race, inclusion of variables that are not readily available to patients, and low sensitivity or specificity. Objective: We aimed to develop and validate an easy, systematic index for predicting diabetes risk in the Asian population. Methods: We collected the data from the NAGALA (NAfld [nonalcoholic fatty liver disease] in the Gifu Area, Longitudinal Analysis) database. The least absolute shrinkage and selection operator model was used to select potentially relevant features. Multiple Cox proportional hazard analysis was used to develop a model based on the training set. Results: The final study population of 15464 participants had a mean age of 42 (range 18-79) years; 54.5\% (8430) were men. The mean follow-up duration was 6.05 (SD 3.78) years. A total of 373 (2.41\%) participants showed progression to diabetes during the follow-up period. Then, we established a novel parameter (the FHTHWA index), to evaluate the incidence of diabetes in a population without diabetes, comprising 6 parameters based on the training set. After multivariable adjustment, individuals in tertile 3 had a significantly higher rate of diabetes compared with those in tertile 1 (hazard ratio 32.141, 95\% CI 11.545?89.476). Time receiver operating characteristic curve analyses showed that the FHTHWA index had high accuracy, with the area under the curve value being around 0.9 during the more than 12 years of follow-up. Conclusions: This research successfully developed a diabetes risk assessment index tailored for the Japanese population by utilizing an extensive dataset and a wide range of indices. By categorizing the diabetes risk levels among Japanese individuals, this study offers a novel predictive tool for identifying potential patients, while also delivering valuable insights into diabetes prevention strategies for the healthy Japanese populace. ",
doi="10.2196/64992",
url="https://medinform.jmir.org/2025/1/e64992"
}


@Article{info:doi/10.2196/58137,
author="Wang, Xuan
and Plantinga, M. Anna
and Xiong, Xin
and Cromer, J. Sara
and Bonzel, Clara-Lea
and Panickan, Vidul
and Duan, Rui
and Hou, Jue
and Cai, Tianxi",
title="Comparing Insulin Against Glucagon-Like Peptide-1 Receptor Agonists, Dipeptidyl Peptidase-4 Inhibitors, and Sodium-Glucose Cotransporter 2 Inhibitors on 5-Year Incident Heart Failure Risk for Patients With Type 2 Diabetes Mellitus: Real-World Evidence Study Using Insurance Claims",
journal="JMIR Diabetes",
year="2024",
month="Oct",
day="22",
volume="9",
pages="e58137",
keywords="type 2 diabetes mellitus",
keywords="diabetes",
keywords="diabetes complications",
keywords="heart failure",
keywords="antidiabetic drug",
keywords="diabetes pharmacotherapy",
keywords="insulin",
keywords="GLP-1 RA",
keywords="DPP-4I",
keywords="SGLT2I",
keywords="real-world data",
keywords="insurance data",
keywords="claims data",
keywords="glucagon-like peptide-1 receptor agonist",
keywords="dipeptidyl peptidase-4 inhibitor",
keywords="sodium-glucose cotransporter 2 inhibitor",
abstract="Background: Type 2 diabetes mellitus (T2DM) is a common health issue, with heart failure (HF) being a common and lethal long-term complication. Although insulin is widely used for the treatment of T2DM, evidence regarding the efficacy of insulin compared to noninsulin therapies on incident HF risk is missing among randomized controlled trials. Real-world evidence on insulin's effect on long-term HF risk may supplement existing guidelines on the management of T2DM. Objective: This study aimed to compare insulin therapy against other medications on HF risk among patients with T2DM using real-world data extracted from insurance claims. Methods: A retrospective, observational study was conducted based on insurance claims data from a single health care network. The study period was from January 1, 2016, to August 11, 2021. The cohort was defined as patients having a T2DM diagnosis code. The inclusion criteria were patients who had at least 1 record of a glycated hemoglobin laboratory test result; full insurance for at least 1 year (either commercial or Medicare Part D); and received glucose-lowering therapy belonging to 1 of the following groups: insulin, glucagon-like peptide 1 receptor agonists (GLP-1 RAs), dipeptidyl peptidase-4 inhibitors (DPP-4Is), or sodium-glucose cotransporter-2 inhibitors (SGLT2Is). The main outcome was the 5-year incident HF rate. Baseline covariates, including demographic characteristics, comorbidities, and laboratory test results, were adjusted to correct for confounding. Results: After adjusting for a broad list of confounders, patients receiving insulin were found to be associated with an 11.8\% (95\% CI 11.0\%?12.7\%), 12.0\% (95\% CI 11.5\%?12.4\%), and 15.1\% (95\% CI 14.3\%?16.0\%) higher 5-year HF rate compared to those using GLP-1 RAs, DPP-4Is, and SGLT2Is, respectively. Subgroup analysis showed that insulin's effect of a higher HF rate was significant in the subgroup with high HF risk but not significant in the subgroup with low HF risk. Conclusions: This study generated real-world evidence on the association of insulin therapy with a higher 5-year HF rate compared to GLP-1 RAs, DPP-4Is, and SGLT2Is based on insurance claims data. These findings also demonstrated the value of real-world data for comparative effectiveness studies to complement established guidelines. On the other hand, the study shares the common limitations of observational studies. Even though high-dimensional confounders are adjusted, remaining confounding may exist and induce bias in the analysis. ",
doi="10.2196/58137",
url="https://diabetes.jmir.org/2024/1/e58137"
}


@Article{info:doi/10.2196/58463,
author="Tan, Kuan Joshua
and Quan, Le
and Salim, Mohamed Nur Nasyitah
and Tan, Hong Jen
and Goh, Su-Yen
and Thumboo, Julian
and Bee, Mong Yong",
title="Machine Learning--Based Prediction for High Health Care Utilizers by Using a Multi-Institutional Diabetes Registry: Model Training and Evaluation",
journal="JMIR AI",
year="2024",
month="Oct",
day="17",
volume="3",
pages="e58463",
keywords="diabetes mellitus",
keywords="type 2 diabetes",
keywords="health care utilization",
keywords="population health management",
keywords="population health",
keywords="machine learning",
keywords="artificial intelligence",
keywords="predictive model",
keywords="predictive system",
keywords="practical model",
abstract="Background: The cost of health care in many countries is increasing rapidly. There is a growing interest in using machine learning for predicting high health care utilizers for population health initiatives. Previous studies have focused on individuals who contribute to the highest financial burden. However, this group is small and represents a limited opportunity for long-term cost reduction. Objective: We developed a collection of models that predict future health care utilization at various thresholds. Methods: We utilized data from a multi-institutional diabetes database from the year 2019 to develop binary classification models. These models predict health care utilization in the subsequent year across 6 different outcomes: patients having a length of stay of ?7, ?14, and ?30 days and emergency department attendance of ?3, ?5, and ?10 visits. To address class imbalance, random and synthetic minority oversampling techniques were employed. The models were then applied to unseen data from 2020 and 2021 to predict health care utilization in the following year. A portfolio of performance metrics, with priority on area under the receiver operating characteristic curve, sensitivity, and positive predictive value, was used for comparison. Explainability analyses were conducted on the best performing models. Results: When trained with random oversampling, 4 models, that is, logistic regression, multivariate adaptive regression splines, boosted trees, and multilayer perceptron consistently achieved high area under the receiver operating characteristic curve (>0.80) and sensitivity (>0.60) across training-validation and test data sets. Correcting for class imbalance proved critical for model performance. Important predictors for all outcomes included age, number of emergency department visits in the present year, chronic kidney disease stage, inpatient bed days in the present year, and mean hemoglobin A1c levels. Explainability analyses using partial dependence plots demonstrated that for the best performing models, the learned patterns were consistent with real-world knowledge, thereby supporting the validity of the models. Conclusions: We successfully developed machine learning models capable of predicting high service level utilization with strong performance and valid explainability. These models can be integrated into wider diabetes-related population health initiatives. ",
doi="10.2196/58463",
url="https://ai.jmir.org/2024/1/e58463",
url="http://www.ncbi.nlm.nih.gov/pubmed/39418089"
}


@Article{info:doi/10.2196/58085,
author="Conderino, Sarah
and Anthopolos, Rebecca
and Albrecht, S. Sandra
and Farley, M. Shannon
and Divers, Jasmin
and Titus, R. Andrea
and Thorpe, E. Lorna",
title="Addressing Information Biases Within Electronic Health Record Data to Improve the Examination of Epidemiologic Associations With Diabetes Prevalence Among Young Adults: Cross-Sectional Study",
journal="JMIR Med Inform",
year="2024",
month="Oct",
day="1",
volume="12",
pages="e58085",
keywords="information bias",
keywords="electronic health record",
keywords="EHR",
keywords="epidemiologic method",
keywords="confounding factor",
keywords="diabetes",
keywords="epidemiology",
keywords="young adult",
keywords="cross-sectional study",
keywords="risk factor",
keywords="asthma",
keywords="race",
keywords="ethnicity",
keywords="diabetic",
keywords="diabetic adult",
abstract="Background: Electronic health records (EHRs) are increasingly used for epidemiologic research to advance public health practice. However, key variables are susceptible to missing data or misclassification within EHRs, including demographic information or disease status, which could affect the estimation of disease prevalence or risk factor associations. Objective: In this paper, we applied methods from the literature on missing data and causal inference to assess whether we could mitigate information biases when estimating measures of association between potential risk factors and diabetes among a patient population of New York City young adults. Methods: We estimated the odds ratio (OR) for diabetes by race or ethnicity and asthma status using EHR data from NYU Langone Health. Methods from the missing data and causal inference literature were then applied to assess the ability to control for misclassification of health outcomes in the EHR data. We compared EHR-based associations with associations observed from 2 national health surveys, the Behavioral Risk Factor Surveillance System (BRFSS) and the National Health and Nutrition Examination Survey, representing traditional public health surveillance systems. Results: Observed EHR-based associations between race or ethnicity and diabetes were comparable to health survey-based estimates, but the association between asthma and diabetes was significantly overestimated (OREHR 3.01, 95\% CI 2.86-3.18 vs ORBRFSS 1.23, 95\% CI 1.09-1.40). Missing data and causal inference methods reduced information biases in these estimates, yielding relative differences from traditional estimates below 50\% (ORMissingData 1.79, 95\% CI 1.67-1.92 and ORCausal 1.42, 95\% CI 1.34-1.51). Conclusions: Findings suggest that without bias adjustment, EHR analyses may yield biased measures of association, driven in part by subgroup differences in health care use. However, applying missing data or causal inference frameworks can help control for and, importantly, characterize residual information biases in these estimates. ",
doi="10.2196/58085",
url="https://medinform.jmir.org/2024/1/e58085"
}


@Article{info:doi/10.2196/55261,
author="Johar, Hamimatunnisa
and Ang, Way Chiew
and Ismail, Roshidi
and Kassim, Zaid
and Su, Tin Tin",
title="Changes in 10-Year Predicted Cardiovascular Disease Risk for a Multiethnic Semirural Population in South East Asia: Prospective Study",
journal="JMIR Public Health Surveill",
year="2024",
month="Sep",
day="26",
volume="10",
pages="e55261",
keywords="cardiovascular risk trajectory",
keywords="Framingham risk score",
keywords="population-based study",
keywords="low- and middle-income countries",
abstract="Background: Cardiovascular disease (CVD) risk factors tend to cluster and interact multiplicatively and have been incorporated into risk equations such as the Framingham risk score, which can reasonably predict CVD over short- and long-term periods. Beyond risk factor levels at a single time point, recent evidence demonstrated that risk trajectories are differentially related to CVD risk. However, factors associated with suboptimal control or unstable CVD risk trajectories are not yet established. Objective: This study aims to examine factors associated with CVD risk trajectories in a semirural, multiethnic community-dwelling population. Methods: Data on demographic, socioeconomic, lifestyle, mental health, and cardiovascular factors were measured at baseline (2013) and during follow-up (2018) of the South East Asia Community Observatory cohort. The 10-year CVD risk change transition was computed. The trajectory patterns identified were improved; remained unchanged in low, moderate, or high CVD risk clusters; and worsened CVD risk trajectories. Multivariable regression analyses were used to examine the association between risk factors and changes in Framingham risk score and predicted CVD risk trajectory patterns with adjustments for concurrent risk factors. Results: Of the 6599 multiethnic community-dwelling individuals (n=3954, 59.92\% female participants and n=2645, 40.08\% male participants; mean age 55.3, SD 10.6 years), CVD risk increased over time in 33.37\% (n=2202) of the sample population, while 24.38\% (n=1609 remained in the high-risk trajectory pattern, which was reflected by the increased prevalence of all major CVD risk factors over the 5-year follow-up. Meanwhile, sex-specific prevalence data indicate that 21.44\% (n=567) of male and 41.35\% (n=1635) of female participants experienced an increase in CVD risk. However, a stark sex difference was observed in those remaining in the high CVD risk cluster, with 45.1\% (n=1193) male participants and 10.52\% (n=416) female participants. Regarding specific CVD risk factors, male participants exhibited a higher percentage increase in the prevalence of hypertension, antihypertensive medication use, smoking, and obesity, while female participants showed a higher prevalence of diabetes. Further regression analyses identified that Malay compared to Chinese (P<.001) and Indian (P=.04) ethnicity, nonmarried status (P<.001), full-time employment (P<.001), and depressive symptoms (P=.04) were all significantly associated with increased CVD risk scores. In addition, lower educational levels and frequently having meals from outside were significantly associated to higher odds of both worsening and remaining in high CVD risk trajectories. Conclusions: Sociodemographics and mental health were found to be differently associated with CVD risk trajectories, warranting future research to disentangle the role of psychosocial disparities in CVD. Our findings carry public health implications, suggesting that the rise in major risk factors along with psychosocial disparities could potentially elevate CVD risk among individuals in underserved settings. More prevention efforts that continuously monitor CVD risk and consider changes in risk factors among vulnerable populations should be emphasized. ",
doi="10.2196/55261",
url="https://publichealth.jmir.org/2024/1/e55261"
}


@Article{info:doi/10.2196/56993,
author="Oyebola, Kolapo
and Ligali, Funmilayo
and Owoloye, Afolabi
and Erinwusi, Blessing
and Alo, Yetunde
and Musa, Z. Adesola
and Aina, Oluwagbemiga
and Salako, Babatunde",
title="Machine Learning--Based Hyperglycemia Prediction: Enhancing Risk Assessment in a Cohort of Undiagnosed Individuals",
journal="JMIRx Med",
year="2024",
month="Sep",
day="11",
volume="5",
pages="e56993",
keywords="hyperglycemia",
keywords="diabetes",
keywords="machine learning",
keywords="hypertension",
keywords="random forest",
abstract="Background: Noncommunicable diseases continue to pose a substantial health challenge globally, with hyperglycemia serving as a prominent indicator of diabetes. Objective: This study employed machine learning algorithms to predict hyperglycemia in a cohort of individuals who were asymptomatic and unraveled crucial predictors contributing to early risk identification. Methods: This dataset included an extensive array of clinical and demographic data obtained from 195 adults who were asymptomatic and residing in a suburban community in Nigeria. The study conducted a thorough comparison of multiple machine learning algorithms to ascertain the most effective model for predicting hyperglycemia. Moreover, we explored feature importance to pinpoint correlates of high blood glucose levels within the cohort. Results: Elevated blood pressure and prehypertension were recorded in 8 (4.1\%) and 18 (9.2\%) of the 195 participants, respectively. A total of 41 (21\%) participants presented with hypertension, of which 34 (83\%) were female. However, sex adjustment showed that 34 of 118 (28.8\%) female participants and 7 of 77 (9\%) male participants had hypertension. Age-based analysis revealed an inverse relationship between normotension and age (r=?0.88; P=.02). Conversely, hypertension increased with age (r=0.53; P=.27), peaking between 50?59 years. Of the 195 participants, isolated systolic hypertension and isolated diastolic hypertension were recorded in 16 (8.2\%) and 15 (7.7\%) participants, respectively, with female participants recording a higher prevalence of isolated systolic hypertension (11/16, 69\%) and male participants reporting a higher prevalence of isolated diastolic hypertension (11/15, 73\%). Following class rebalancing, the random forest classifier gave the best performance (accuracy score 0.89; receiver operating characteristic--area under the curve score 0.89; F1-score 0.89) of the 26 model classifiers. The feature selection model identified uric acid and age as important variables associated with hyperglycemia. Conclusions: The random forest classifier identified significant clinical correlates associated with hyperglycemia, offering valuable insights for the early detection of diabetes and informing the design and deployment of therapeutic interventions. However, to achieve a more comprehensive understanding of each feature's contribution to blood glucose levels, modeling additional relevant clinical features in larger datasets could be beneficial. ",
doi="10.2196/56993",
url="https://xmed.jmir.org/2024/1/e56993"
}


@Article{info:doi/10.2196/54429,
author="Shi, Lixin
and Xue, Yaoming
and Yu, Xuefeng
and Wang, Yangang
and Hong, Tianpei
and Li, Xiaoying
and Ma, Jianhua
and Zhu, Dalong
and Mu, Yiming",
title="Prevalence and Risk Factors of Chronic Kidney Disease in Patients With Type 2 Diabetes in China: Cross-Sectional Study",
journal="JMIR Public Health Surveill",
year="2024",
month="Aug",
day="30",
volume="10",
pages="e54429",
keywords="awareness rate",
keywords="chronic kidney disease",
keywords="prevalence",
keywords="risk factor",
keywords="screening rate",
keywords="type 2 diabetes",
abstract="Background: Chronic kidney disease (CKD) is a significant long-term complication of diabetes and is a primary contributor to end-stage kidney disease. Objective: This study aimed to report comprehensive nationwide data on the prevalence, screening, and awareness rates of CKD in Chinese patients with type 2 diabetes, along with associated risk factors. Methods: Baseline data analysis of the ongoing prospective, observational IMPROVE study was conducted. The study cohort comprised patients who had been diagnosed with type 2 diabetes more than 12 months prior, received at least 1 hypoglycemic medication, and were aged ?18 years. The participants completed questionnaires and underwent laboratory assessments, including blood and urine samples. The data encompassed patient demographics, medical history, concurrent medications, and comorbidities. Comprehensive evaluations involved physical examinations, urinary albumin-to-creatinine ratio (UACR), estimated glomerular filtration rate (eGFR), glycated hemoglobin (HbA1c), fasting blood glucose, 2-hour postprandial blood glucose, fasting blood lipid profile, and urinalysis. Descriptive statistics were applied for data interpretation, and logistic regression analyses were used to identify the CKD-associated risk factors in patients with type 2 diabetes. Results: A national study from December 2021 to September 2022 enlisted 9672 participants with type 2 diabetes from 45 hospitals that had endocrinology departments. The enrollees were from diverse regions in China, as follows: central (n=1221), east (n=3269), south (n=1474), north (n=2219), and west (n=1489). The prevalence, screening, and awareness rates of CKD among patients with type 2 diabetes were 31\% (2997/9672), 27\% (810/2997), and 54.8\% (5295/9672), respectively. Multivariate binary regression analysis revealed that the CKD risk factors were screening, awareness, smoking, age, diabetes duration, concurrent antihypertensive and microcirculation medications, diabetic complications (foot, retinopathy, and neuropathy), hypertension, elevated low-density lipoprotein (LDL) cholesterol, and suboptimal glycemic control. Subgroup analysis highlighted an increased CKD prevalence among older individuals, those with prolonged diabetes durations, and residents of fourth-tier cities. Residents of urban areas that had robust educational and economic development exhibited relatively high awareness and screening rates. Notably, 24.2\% (1717/7107) of patients with an eGFR ?90 mL/min/1.73 m2 had proteinuria, whereas 3.4\% (234/6909) who had a UACR <30 mg/g presented with an eGFR <60 mL/min/1.73 m2. Compared with patients who were cognizant of CKD, those who were unaware of CKD had increased rates of HbA1c ?7\%, total cholesterol >5.18 $\mu$mol/L, LDL cholesterol >3.37 $\mu$mol/L, BMI ?30 kg/m2, and hypertension. Conclusions: In a Chinese population of adults with type 2 diabetes, the CKD prevalence was notable, at 31\%, coupled with low screening and awareness rates. Multiple risk factors for CKD have been identified. Trial Registration: ClinicalTrials.gov NCT05047471; https://clinicaltrials.gov/study/NCT05047471 ",
doi="10.2196/54429",
url="https://publichealth.jmir.org/2024/1/e54429"
}


@Article{info:doi/10.2196/59571,
author="Jang, Wonwoo
and Kim, Seokjun
and Son, Yejun
and Kim, Soeun
and Kim, Jin Hyeon
and Jo, Hyesu
and Park, Jaeyu
and Lee, Kyeongmin
and Lee, Hayeon
and Tully, A. Mark
and Rahmati, Masoud
and Smith, Lee
and Kang, Jiseung
and Woo, Selin
and Kim, Sunyoung
and Hwang, Jiyoung
and Rhee, Youl Sang
and Yon, Keon Dong",
title="Prevalence, Awareness, Treatment, and Control of Type 2 Diabetes in South Korea (1998 to 2022): Nationwide Cross-Sectional Study",
journal="JMIR Public Health Surveill",
year="2024",
month="Aug",
day="27",
volume="10",
pages="e59571",
keywords="disease management",
keywords="epidemiology",
keywords="prevalence",
keywords="Republic of Korea",
keywords="type 2 diabetes mellitus",
abstract="Background: Type 2 diabetes poses an increasing disease burden in South Korea. The development and management of type 2 diabetes are closely related to lifestyle and socioeconomic factors, which have undergone substantial changes over the past few decades, including during the COVID-19 pandemic. Objective: This study aimed to investigate long-term trends in type 2 diabetes prevalence, awareness, treatment, and control. It also aimed to determine whether there were substantial alterations in the trends during the pandemic and whether these changes were more pronounced within specific demographic groups. Methods: This study examined the prevalence, awareness, treatment, and control of type 2 diabetes in a representative sample of 139,786 South Koreans aged >30 years, using data from the National Health and Nutrition Examination Survey and covering the period from 1998 to 2022. Weighted linear regression and binary logistic regression were performed to calculate weighted $\beta$ coefficients or odds ratios. Stratified analyses were performed based on sex, age, region of residence, obesity status, educational background, household income, and smoking status. $\beta$ (difference) was calculated to analyze the trend difference between the prepandemic period and the COVID-19 pandemic. To identify groups more susceptible to type 2 diabetes, we estimated interaction terms for each factor and calculated weighted odds ratios. Results: From 1998 to 2022, a consistent increase in the prevalence of type 2 diabetes was observed among South Koreans, with a notable rise to 15.61\% (95\% CI 14.83-16.38) during the pandemic. Awareness followed a U-shaped curve, bottoming out at 64.37\% (95\% CI 61.79-66.96) from 2013 to 2015 before increasing to 72.56\% (95\% CI 70.39-74.72) during the pandemic. Treatment also increased over time, peaking at 68.33\% (95\% CI 65.95-70.71) during the pandemic. Control among participants with diabetes showed no substantial change, maintaining a rate of 29.14\% (95\% CI 26.82-31.47) from 2020 to 2022, while control among treated participants improved to 30.68\% (95\% CI 27.88-33.48). During the pandemic, there was a steepening of the curves for awareness and treatment. However, while the slope of control among participants being treated increased, the slope of control among participants with diabetes showed no substantial change during the pandemic. Older populations and individuals with lower educational level exhibited less improvement in awareness and control trends than younger populations and more educated individuals. People with lower income experienced a deceleration in prevalence during the pandemic. Conclusions: Over the recent decade, there has been an increase in type 2 diabetes prevalence, awareness, treatment, and control. During the pandemic, a steeper increase in awareness, treatment, and control among participants being treated was observed. However, there were heterogeneous changes across different population groups, underscoring the need for targeted interventions to address disparities and improve diabetes management for susceptible populations. ",
doi="10.2196/59571",
url="https://publichealth.jmir.org/2024/1/e59571"
}


@Article{info:doi/10.2196/56756,
author="Arueyingho, Oritsetimeyin
and Aprioku, Sydney Jonah
and Marshall, Paul
and O'Kane, Ann Aisling",
title="Insights Into Sociodemographic Influences on Type 2 Diabetes Care and Opportunities for Digital Health Promotion in Port Harcourt, Nigeria: Quantitative Study",
journal="JMIR Diabetes",
year="2024",
month="Aug",
day="21",
volume="9",
pages="e56756",
keywords="type 2 diabetes",
keywords="digital health",
keywords="t2d in nigeria",
keywords="technologies for diabetes",
keywords="pharmaceutical care for t2d",
abstract="Background: A significant percentage of the Nigerian population has type 2 diabetes (T2D), and a notable portion of these patients also live with comorbidities. Despite its increasing prevalence in Nigeria due to factors such as poor eating and exercise habits, there are insufficient reliable data on its incidence in major cities such as Port Harcourt, as well as on the influence of sociodemographic factors on current self-care and collaborative T2D care approaches using technology. This, coupled with a significant lack of context-specific digital health interventions for T2D care, is our major motivation for the study. Objective: This study aims to (1) explore the sociodemographic profile of people with T2D and understand how it directly influences their care; (2) generate an accurate understanding of collaborative care practices, with a focus on nuances in the contextual provision of T2D care; and (3) identify opportunities for improving the adoption of digital health technologies based on the current understanding of technology use and T2D care. Methods: We designed questionnaires aligned with the study's objectives to obtain quantitative data, using both WhatsApp (Meta Platforms, Inc) and in-person interactions. A social media campaign aimed at reaching a hard-to-reach audience facilitated questionnaire delivery via WhatsApp, also allowing us to explore its feasibility as a data collection tool. In parallel, we distributed surveys in person. We collected 110 responses in total: 83 (75.5\%) from in-person distributions and 27 (24.5\%) from the WhatsApp approach. Data analysis was conducted using descriptive and inferential statistical methods on SPSS Premium (version 29; IBM Corp) and JASP (version 0.16.4; University of Amsterdam) software. This dual approach ensured comprehensive data collection and analysis for our study. Results: Results were categorized into 3 groups to address our research objectives. We found that men with T2D were significantly older (mean 61 y), had higher household incomes, and generally held higher academic degrees compared to women (P=.03). No statistically significant relationship was found between gender and the frequency of hospital visits (P=.60) or pharmacy visits (P=.48), and cultural differences did not influence disease incidence. Regarding management approaches, 75.5\% (83/110) relied on prescribed medications; 60\% (66/110) on dietary modifications; and 35.5\% (39/110) and 20\% (22/110) on traditional medicines and spirituality, respectively. Most participants (82/110, 74.5\%) were unfamiliar with diabetes care technologies, and 89.2\% (98/110) of those using technology were only familiar with glucometers. Finally, participants preferred seeking health information in person (96/110, 87.3\%) over digital means. Conclusions: By identifying the influence of sociodemographic factors on diabetes care and health or information seeking behaviors, we were able to identify context-specific opportunities for enhancing the adoption of digital health technologies. ",
doi="10.2196/56756",
url="https://diabetes.jmir.org/2024/1/e56756"
}


@Article{info:doi/10.2196/54373,
author="Khalilnejad, Arash
and Sun, Ruo-Ting
and Kompala, Tejaswi
and Painter, Stefanie
and James, Roberta
and Wang, Yajuan",
title="Proactive Identification of Patients with Diabetes at Risk of Uncontrolled Outcomes during a Diabetes Management Program: Conceptualization and Development Study Using Machine Learning",
journal="JMIR Form Res",
year="2024",
month="Apr",
day="26",
volume="8",
pages="e54373",
keywords="diabetes",
keywords="diabetic",
keywords="DM",
keywords="diabetes mellitus",
keywords="type 2 diabetes",
keywords="type 1 diabetes",
keywords="self-monitoring",
keywords="predictive model",
keywords="predictive models",
keywords="predictive analytics",
keywords="predictive system",
keywords="practical model",
keywords="practical models",
keywords="ML",
keywords="machine learning",
keywords="AI",
keywords="artificial intelligence",
keywords="algorithm",
keywords="algorithms",
keywords="behavior",
keywords="behaviour",
keywords="telehealth",
keywords="tele-health",
keywords="chronic condition",
keywords="chronic conditions",
keywords="chronic disease",
keywords="chronic diseases",
keywords="chronic illness",
keywords="chronic illnesses",
abstract="Background: The growth in the capabilities of telehealth have made it possible to identify individuals with a higher risk of uncontrolled diabetes and provide them with targeted support and resources to help them manage their condition. Thus, predictive modeling has emerged as a valuable tool for the advancement of diabetes management. Objective: This study aimed to conceptualize and develop a novel machine learning (ML) approach to proactively identify participants enrolled in a remote diabetes monitoring program (RDMP) who were at risk of uncontrolled diabetes at 12 months in the program. Methods: Registry data from the Livongo for Diabetes RDMP were used to design separate dynamic predictive ML models to predict participant outcomes at each monthly checkpoint of the participants' program journey (month-n models) from the first day of onboarding (month-0 model) up to the 11th month (month-11 model). A participant's program journey began upon onboarding into the RDMP and monitoring their own blood glucose (BG) levels through the RDMP-provided BG meter. Each participant passed through 12 predicative models through their first year enrolled in the RDMP. Four categories of participant attributes (ie, survey data, BG data, medication fills, and health signals) were used for feature construction. The models were trained using the light gradient boosting machine and underwent hyperparameter tuning. The performance of the models was evaluated using standard metrics, including precision, recall, specificity, the area under the curve, the F1-score, and accuracy. Results: The ML models exhibited strong performance, accurately identifying observable at-risk participants, with recall ranging from 70\% to 94\% and precision from 40\% to 88\% across the 12-month program journey. Unobservable at-risk participants also showed promising performance, with recall ranging from 61\% to 82\% and precision from 42\% to 61\%. Overall, model performance improved as participants progressed through their program journey, demonstrating the importance of engagement data in predicting long-term clinical outcomes. Conclusions: This study explored the Livongo for Diabetes RDMP participants' temporal and static attributes, identification of diabetes management patterns and characteristics, and their relationship to predict diabetes management outcomes. Proactive targeting ML models accurately identified participants at risk of uncontrolled diabetes with a high level of precision that was generalizable through future years within the RDMP. The ability to identify participants who are at risk at various time points throughout the program journey allows for personalized interventions to improve outcomes. This approach offers significant advancements in the feasibility of large-scale implementation in remote monitoring programs and can help prevent uncontrolled glycemic levels and diabetes-related complications. Future research should include the impact of significant changes that can affect a participant's diabetes management. ",
doi="10.2196/54373",
url="https://formative.jmir.org/2024/1/e54373",
url="http://www.ncbi.nlm.nih.gov/pubmed/38669074"
}


@Article{info:doi/10.2196/54852,
author="Andrikopoulou, Elisavet
and Chatzistergos, Panagiotis
and Chockalingam, Nachiappan",
title="Exploring the Pathways of Diabetes Foot Complications Treatment and Investigating Experiences From Frontline Health Care Professionals: Protocol for a Mixed Methods Study",
journal="JMIR Res Protoc",
year="2024",
month="Apr",
day="24",
volume="13",
pages="e54852",
keywords="diabetic foot",
keywords="first-ever diabetic foot ulcer",
keywords="qualitative research",
keywords="quantitative evaluation",
keywords="surveys and questionnaires",
keywords="telephone interviews",
keywords="primary care",
keywords="community care",
keywords="acute care",
keywords="education of patients",
keywords="foot ulcer",
keywords="exploration",
keywords="diabetes",
keywords="foot ulceration",
keywords="United Kingdom",
keywords="diabetic foot ulceration",
keywords="DFU",
keywords="amputation",
keywords="complication",
keywords="perspectives",
keywords="experiences",
keywords="health care professionals",
keywords="barrier",
keywords="barriers",
keywords="effective care",
keywords="foot care",
keywords="primary ulcers",
keywords="quality of life",
abstract="Background: Diabetes affects more than 4.3 million individuals in the United Kingdom, with 19\% to 34\% developing diabetes-related foot ulceration (DFU) during their lifespan, which can lead to an amputation. In the United Kingdom, every week, approximately 169 people have an amputation due to diabetes. Preventing first-ever ulcers is the most effective strategy to reduce the occurrence of diabetes-related amputations, but research in this space is lacking. Objective: This protocol seeks to document the experiences and perspectives of frontline health care professionals who work with people who have diabetes and diabetes-related foot problems. Special attention is given to their perceptions of barriers to effective care, their views about barriers to effective and inclusive engagement with people with diabetes, and their experience with the first-ever DFU. Another aspect of the study is the focus on whether clinical management is affected by data sharing, data availability, and interoperability issues. Methods: This is a mixed methods explanatory protocol, which is sequential, and its purpose is to use the qualitative data to explain the initial quantitative data collected through a survey of frontline health care professionals. Data analysis of quantitative data will be completed first and then synthesized with the qualitative data analysis. Qualitative data will be analyzed using the framework method. This study will use joint displays to integrate the data. Ethical approval has been granted by the ethics committee of Staffordshire University. Results: The quantitative data collection started in March 2023 and will close in May 2024. The qualitative interviews commenced in November 2023 with volunteer participants who initially completed the survey. Conclusions: This study's survey focuses on data interoperability and the interviews focus more on the perspectives and experiences of clinicians and their perceived barriers for the effective management of diabetes foot ulcers. Including a geographically relevant and diverse cohort of health care professionals that spans a wide range of roles and care settings involved in diabetes-related foot care is very important for the successful application of this protocol. Special care is given to advertise and promote participation as widely as possible. The qualitative part of this protocol is also limited to 30-40 interview participants, as it is not realistic to interview higher numbers, due to time and resource constraints. International Registered Report Identifier (IRRID): DERR1-10.2196/54852 ",
doi="10.2196/54852",
url="https://www.researchprotocols.org/2024/1/e54852",
url="http://www.ncbi.nlm.nih.gov/pubmed/38656782"
}


@Article{info:doi/10.2196/55285,
author="Beverly, A. Elizabeth
and Koopman-Gonzalez, Sarah
and Wright, Jackson
and Dungan, Kathleen
and Pallerla, Harini
and Gubitosi-Klug, Rose
and Baughman, Kristin
and Konstan, W. Michael
and Bolen, D. Shari",
title="Assessing Priorities in a Statewide Cardiovascular and Diabetes Health Collaborative Based on the Results of a Needs Assessment: Cross-Sectional Survey Study",
journal="JMIR Form Res",
year="2024",
month="Apr",
day="12",
volume="8",
pages="e55285",
keywords="health collaborative",
keywords="cardiovascular disease",
keywords="type 2 diabetes",
keywords="needs assessment",
abstract="Background: The Ohio Cardiovascular and Diabetes Health Collaborative (Cardi-OH) unites general and subspecialty medical staff at the 7 medical schools in Ohio with community and public health partnerships to improve cardiovascular and diabetes health outcomes and eliminate disparities in Ohio's Medicaid population. Although statewide collaboratives exist to address health improvements, few deploy needs assessments to inform their work. Objective: Cardi-OH conducts an annual needs assessment to identify high-priority clinical topics, screening practices, policy changes for home monitoring devices and referrals, and preferences for the dissemination and implementation of evidence-based best practices. The results of the statewide needs assessment could also be used by others interested in disseminating best practices to primary care teams. Methods: A cross-sectional survey was distributed electronically via REDCap (Research Electronic Data Capture; Vanderbilt University) to both Cardi-OH grant-funded and non--grant-funded members (ie, people who have engaged with Cardi-OH but are not funded by the grant). Results: In total, 88\% (103/117) of Cardi-OH grant-funded members and 8.14\% (98/1204) of non--grant-funded members completed the needs assessment survey. Of these, 51.5\% (53/103) of Cardi-OH grant-funded members and 47\% (46/98) of non--grant-funded members provided direct clinical care. The top cardiovascular medicine and diabetes clinical topics for Cardi-OH grant-funded members (clinical and nonclinical) were lifestyle prescriptions (50/103, 48.5\%), atypical diabetes (38/103, 36.9\%), COVID-19 and cardiovascular disease (CVD; 38/103, 36.9\%), and mental health and CVD (38/103, 36.9\%). For non--grant-funded members, the top topics were lifestyle prescriptions (53/98, 54\%), mental health and CVD (39/98, 40\%), alcohol and CVD (27/98, 28\%), and cardiovascular complications (27/98, 28\%). Regarding social determinants of health, Cardi-OH grant-funded members prioritized 3 topics: weight bias and stigma (44/103, 42.7\%), family-focused interventions (40/103, 38.8\%), and adverse childhood events (37/103, 35.9\%). Non--grant-funded members' choices were family-focused interventions (51/98, 52\%), implicit bias (43/98, 44\%), and adverse childhood events (39/98, 40\%). Assessment of other risk factors for CVD and diabetes across grant- and non--grant-funded members revealed screening for social determinants of health in approximately 50\% of patients in each practice, whereas some frequency of depression and substance abuse screening occurred in 80\% to 90\% of the patients. Access to best practice home monitoring devices was challenging, with 30\% (16/53) and 41\% (19/46) of clinical grant-funded and non--grant-funded members reporting challenges in obtaining home blood pressure monitoring devices and 68\% (36/53) and 43\% (20/46) reporting challenges with continuous glucose monitors. Conclusions: Cardi-OH grant- and non--grant-funded members shared the following high-priority topics: lifestyle prescriptions, CVD and mental health, family-focused interventions, alcohol and CVD, and adverse childhood experiences. Identifying high-priority educational topics and preferred delivery modalities for evidence-based materials is essential for ensuring that the dissemination of resources is practical and useful for providers. ",
doi="10.2196/55285",
url="https://formative.jmir.org/2024/1/e55285",
url="http://www.ncbi.nlm.nih.gov/pubmed/38607661"
}


@Article{info:doi/10.2196/45536,
author="Dreyer, A. Nancy
and Knuth, B. Kendall
and Xie, Yiqiong
and Reynolds, W. Matthew
and Mack, D. Christina",
title="COVID-19 Vaccination Reactions and Risk of Breakthrough Infections Among People With Diabetes: Cohort Study Derived From Community Reporters",
journal="JMIR Diabetes",
year="2024",
month="Feb",
day="27",
volume="9",
pages="e45536",
keywords="COVID-19",
keywords="diabetes",
keywords="vaccine",
keywords="vaccine hesitancy",
keywords="registry",
keywords="person-generated health data",
keywords="patient-reported outcomes",
keywords="side effects",
keywords="vaccination",
keywords="infection",
keywords="nondiabetic adult",
keywords="clinical data",
keywords="fatigue",
keywords="headache",
keywords="risk",
keywords="patient data",
keywords="medication",
keywords="community health",
abstract="Background: This exploratory study compares self-reported COVID-19 vaccine side effects and breakthrough infections in people who described themselves as having diabetes with those who did not identify as having diabetes. Objective: The study uses person-reported data to evaluate differences in the perception of COVID-19 vaccine side effects between adults with diabetes and those who did not report having diabetes. Methods: This is a retrospective cohort study conducted using data provided online by adults aged 18 years and older residing in the United States. The participants who voluntarily self-enrolled between March 19, 2021, and July 16, 2022, in the IQVIA COVID-19 Active Research Experience project reported clinical and demographic information, COVID-19 vaccination, whether they had experienced any side effects, test-confirmed infections, and consented to linkage with prescription claims. No distinction was made for this study to differentiate prediabetes or type 1 and type 2 diabetes nor to verify reports of positive COVID-19 tests. Person-reported medication use was validated using pharmacy claims and a subset of the linked data was used for a sensitivity analysis of medication effects. Multivariate logistic regression was used to estimate the adjusted odds ratios of vaccine side effects or breakthrough infections by diabetic status, adjusting for age, gender, education, race, ethnicity (Hispanic or Latino), BMI, smoker, receipt of an influenza vaccine, vaccine manufacturer, and all medical conditions. Evaluations of diabetes medication-specific vaccine side effects are illustrated graphically to support the examination of the magnitude of side effect differences for various medications and combinations of medications used to manage diabetes. Results: People with diabetes (n=724) reported experiencing fewer side effects within 2 weeks of vaccination for COVID-19 than those without diabetes (n=6417; mean 2.7, SD 2.0 vs mean 3.1, SD 2.0). The adjusted risk of having a specific side effect or any side effect was lower among those with diabetes, with significant reductions in fatigue and headache but no differences in breakthrough infections over participants' maximum follow-up time. Diabetes medication use did not consistently affect the risk of specific side effects, either using self-reported medication use or using only diabetes medications that were confirmed by pharmacy health insurance claims for people who also reported having diabetes. Conclusions: People with diabetes reported fewer vaccine side effects than participants not reporting having diabetes, with a similar risk of breakthrough infection. Trial Registration: ClinicalTrials.gov NCT04368065; https://clinicaltrials.gov/study/NCT04368065 ",
doi="10.2196/45536",
url="https://diabetes.jmir.org/2024/1/e45536",
url="http://www.ncbi.nlm.nih.gov/pubmed/38412008"
}


@Article{info:doi/10.2196/46708,
author="Yang, Wenyi
and Wang, Baohua
and Ma, Shaobo
and Wang, Jingxin
and Ai, Limei
and Li, Zhengyu
and Wan, Xia",
title="Optimal Look-Back Period to Identify True Incident Cases of Diabetes in Medical Insurance Data in the Chinese Population: Retrospective Analysis Study",
journal="JMIR Public Health Surveill",
year="2023",
month="Nov",
day="6",
volume="9",
pages="e46708",
keywords="diabetes",
keywords="incident cases",
keywords="administrative data",
keywords="look-back period",
keywords="retrograde survival function",
abstract="Background: Accurate estimation of incidence and prevalence is vital for preventing and controlling diabetes. Administrative data (including insurance data) could be a good source to estimate the incidence of diabetes. However, how to determine the look-back period (LP) to remove cases with preceding records remains a problem for administrative data. A short LP will cause overestimation of incidence, whereas a long LP will limit the usefulness of a database. Therefore, it is necessary to determine the optimal LP length for identifying incident cases in administrative data. Objective: This study aims to offer different methods to identify the optimal LP for diabetes by using medical insurance data from the Chinese population with reference to other diseases in the administrative data. Methods: Data from the insurance database of the city of Weifang, China from between January 2016 and December 2020 were used. To identify the incident cases in 2020, we removed prevalent patients with preceding records of diabetes between 2016 and 2019 (ie, a 4-year LP). Using this 4-year LP as a reference, consistency examination indexes (CEIs), including positive predictive values, the $\kappa$ coefficient, and overestimation rate, were calculated to determine the level of agreement between different LPs and an LP of 4 years (the longest LP). Moreover, we constructed a retrograde survival function, in which survival (ie, incident cases) means not having a preceding record at the given time and the survival time is the difference between the date of the last record in 2020 and the most recent previous record in the LP. Based on the survival outcome and survival time, we established the survival function and survival hazard function. When the survival probability, S(t), remains stable, and survival hazard converges to zero, we obtain the optimal LP. Combined with the results of these two methods, we determined the optimal LP for Chinese diabetes patients. Results: The $\kappa$ agreement was excellent (0.950), with a high positive predictive value (92.2\%) and a low overestimation rate (8.4\%) after a 2-year LP. As for the retrograde survival function, S(t) dropped rapidly during the first 1-year LP (from 1.00 to 0.11). At a 417-day LP, the hazard function reached approximately zero (ht=0.000459), S(t) remained at 0.10, and at 480 days, the frequency of S(t) did not increase. Combining the two methods, we found that the optimal LP is 2 years for Chinese diabetes patients. Conclusions: The retrograde survival method and CEIs both showed effectiveness. A 2-year LP should be considered when identifying incident cases of diabetes using insurance data in the Chinese population. ",
doi="10.2196/46708",
url="https://publichealth.jmir.org/2023/1/e46708",
url="http://www.ncbi.nlm.nih.gov/pubmed/37930785"
}


@Article{info:doi/10.2196/44073,
author="Li, Wenzhen
and Chen, Dajie
and Peng, Ying
and Lu, Zuxun
and Kwan, Mei-Po
and Tse, Ah Lap",
title="Association Between Metabolic Syndrome and Mortality: Prospective Cohort Study",
journal="JMIR Public Health Surveill",
year="2023",
month="Sep",
day="5",
volume="9",
pages="e44073",
keywords="metabolic syndrome",
keywords="mortality",
keywords="heart disease",
keywords="diabetes mellitus",
keywords="cancer",
abstract="Background: Metabolic syndrome (MetS) is a common metabolic disorder that results from the increasing prevalence of obesity, which has been an increasing concern in recent years. Previous evidence indicated that MetS was associated with mortality; however, different definitions of MetS were used. In 2005, the National Cholesterol Education Program (NCEP) Adult Treatment Panel (ATP) III updated the definition of MetS, which has since been widely adopted. Therefore, it is necessary to conduct a novel study among other populations and countries with a larger sample size using the updated definition of MetS and death code to examine the association of MetS with all-cause and cause-specific mortality. Objective: We aimed to examine the associations of MetS with all-cause and cause-specific mortality. Methods: A total of 36,414 adults were included in this study, using data from the National Health and Nutrition Examination Survey (NHANES) III (1988-1994) and the continuous NHANES (1999-2014) in the United States. Death outcomes were ascertained by linkage to National Death Index records through December 31, 2015. MetS was defined by the NCEP ATP III-2005 criterion. Complex survey design factors including sample weights, clustering, and stratification were considered for all analyses with instructions for using NHANES data. Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95\% CIs for mortality from all causes, heart disease, diabetes, and cancer. Results: We observed 8494 deaths during the 16.71 years of follow-up. Compared with those without MetS, individuals with MetS were associated with a significantly elevated multiadjusted HR of 1.24 (95\% CI 1.16-1.33), 1.44 (95\% CI 1.25-1.66), and 5.15 (95\% CI 3.15-8.43) for all cause, heart diseases, and diabetes mellitus, respectively, whereas no significant association was found for cancer mortality (HR 1.17, 95\% CI 0.95-1.43). Conclusions: Our study provides additional evidence that MetS and its components are significantly associated with all-cause, heart disease, and diabetes mortality, but not with cancer mortality. Health care professionals should pay more attention to MetS and its individual component. ",
doi="10.2196/44073",
url="https://publichealth.jmir.org/2023/1/e44073",
url="http://www.ncbi.nlm.nih.gov/pubmed/37669100"
}


@Article{info:doi/10.2196/43687,
author="Li, Xiaopan
and Liu, Ru
and Chen, Yichen
and Han, Yan
and Wang, Qizhe
and Xu, Yaxin
and Zhou, Jing
and Jiang, Sunfang",
title="Patterns and Trends in Mortality Associated With and Due to Diabetes Mellitus in a Transitioning Region With 3.17 Million People: Observational Study",
journal="JMIR Public Health Surveill",
year="2023",
month="Sep",
day="4",
volume="9",
pages="e43687",
keywords="diabetes mellitus",
keywords="mortality",
keywords="years of life lost",
keywords="multimorbidity",
keywords="trend analysis",
keywords="diabetes",
keywords="disease",
keywords="urbanization",
keywords="aging",
keywords="epidemiology",
abstract="Background: Diabetes mellitus (DM) imposes a significant disease burden in economically transitioning regions. Most transitioning regions share similar experience in urbanization processes. Shanghai's Pudong district serves as a representative area of such regions. Objective: We aimed to assess the burden of and trends in DM mortality in Shanghai's Pudong district and analyze the impact of aging and multimorbidity. Methods: A longitudinal, population-based study was conducted to analyze DM mortality in Pudong from 2005 to 2020. We used joinpoint regression to analyze epidemiological features and long-term trends in crude mortality rate (CMR), age-standardized mortality rate worldwide (ASMRW), and years of life lost (YLL). Furthermore, the decomposition method was used to evaluate the contribution of demographic and nondemographic factors associated with mortality. Results: There were 49,414 deaths among individuals with DM, including 15,512 deaths due to DM. The CMR and ASMRW were 109.55/105 and 38.01/105 person-years, respectively. Among the mortality associated with and due to DM, the total annual ASMRW increased by 3.65\% (95\% CI 3.25\%-4.06\%) and 1.38\% (95\% CI 0.74\%-2.02\%), respectively. Additionally, the total annual YLL rate increased by 4.98\% (95\% CI 3.92\%-6.05\%) and 2.68\% (95\% CI 1.34\%-4.04\%). The rates of YLL increase in persons aged 30 to 44 years (3.98\%, 95\% CI 0.32\%-7.78\%) and 45 to 59 years (4.31\%, 95\% CI 2.95\%-5.69\%) were followed by the increase in persons aged 80 years and older (10.53\%, 95\% CI 9.45\%-11.62\%) for deaths associated with DM. The annual CMR attributable to demographic factors increased by 41.9\% (95\% CI 17.73\%-71.04\%) and 36.72\% (95\% CI 16.69\%-60.2\%) for deaths associated with and due to DM, respectively. Hypertension, cerebrovascular disease, and ischemic heart disease were the top 3 comorbidities. Conclusions: Aging and multimorbidity played essential roles in changing the burden of DM in an urbanizing and transitioning region. There is an increasing disease burden among young and middle-aged people, emphasizing the need for greater attention to these groups. Health management is an emerging method that holds important implications for alleviating the future burden of DM. ",
doi="10.2196/43687",
url="https://publichealth.jmir.org/2023/1/e43687",
url="http://www.ncbi.nlm.nih.gov/pubmed/37665630"
}


@Article{info:doi/10.2196/41902,
author="Te, Vannarath
and Chhim, Srean
and Buffel, Veerle
and Van Damme, Wim
and van Olmen, Josefien
and Ir, Por
and Wouters, Edwin",
title="Evaluation of Diabetes Care Performance in Cambodia Through the Cascade-of-Care Framework: Cross-Sectional Study",
journal="JMIR Public Health Surveill",
year="2023",
month="Jun",
day="22",
volume="9",
pages="e41902",
keywords="diabetes",
keywords="cascade of care",
keywords="implementation research",
keywords="population-based survey",
keywords="care continuum",
keywords="mobile phone",
abstract="Background: Cambodia has seen an increase in the prevalence of type 2 diabetes (T2D) over the last 10 years. Three main care initiatives for T2D are being scaled up in the public health care system across the country: hospital-based care, health center--based care, and community-based care. To date, no empirical study has systematically assessed the performance of these care initiatives across the T2D care continuum in Cambodia. Objective: This study aimed to assess the performance of the 3 care initiatives---individually or in coexistence---and determine the factors associated with the failure to diagnose T2D in Cambodia. Methods: We used a cascade-of-care framework to assess the T2D care continuum. The cascades were generated using primary data from a cross-sectional population-based survey conducted in 2020 with 5072 individuals aged ?40 years. The survey was conducted in 5 operational districts (ODs) selected based on the availability of the care initiatives. Multiple logistic regression analysis was used to identify the factors associated with the failure to diagnose T2D. The significance level of P<.05 was used as a cutoff point. Results: Of the 5072 individuals, 560 (11.04\%) met the definition of a T2D diagnosis (fasting blood glucose level ?126 mg/dL and glycated hemoglobin level ?6.5\%). Using the 560 individuals as the fixed denominator, the cascade displayed substantial drops at the testing and control stages. Only 63\% (353/560) of the participants had ever tested their blood glucose level in the last 3 years, and only 10.7\% (60/560) achieved blood glucose level control with the cutoff point of glycated hemoglobin level <8\%. The OD hosting the coexistence of care displayed the worst cascade across all bars, whereas the OD with hospital-based care had the best cascade among the 5 ODs. Being aged 40 to 49 years, male, and in the poorest category of the wealth quintile were factors associated with the undiagnosed status. Conclusions: The unmet needs for T2D care in Cambodia were large, particularly in the testing and control stages, indicating the need to substantially improve early detection and management of T2D in the country. Rapid scale-up of T2D care components at public health facilities to increase the chances of the population with T2D of being tested, diagnosed, retained in care, and treated, as well as of achieving blood glucose level control, is vital in the health system. Specific population groups susceptible to being undiagnosed should be especially targeted for screening through active community outreach activities. Future research should incorporate digital health interventions to evaluate the effectiveness of the T2D care initiatives longitudinally with more diverse population groups from various settings based on routine data vital for integrated care. Trial Registration: International Standard Randomized Controlled Trials Number (ISRCTN) ISRCTN41932064; https://www.isrctn.com/ISRCTN41932064 International Registered Report Identifier (IRRID): RR2-10.2196/36747 ",
doi="10.2196/41902",
url="https://publichealth.jmir.org/2023/1/e41902",
url="http://www.ncbi.nlm.nih.gov/pubmed/37347529"
}


@Article{info:doi/10.2196/36523,
author="Zeng, Qibing
and Yang, Jingyuan
and Wang, Ziyun
and Liu, Haiyan
and Wang, Junhua
and Yang, Tingting
and Hu, Jin
and Guan, Han
and Lu, Yun
and Liu, Huijuan
and Hong, Feng",
title="The Epidemiological Characteristics of Noncommunicable Diseases and Malignant Tumors in Guiyang, China: Cross-sectional Study",
journal="JMIR Public Health Surveill",
year="2022",
month="Oct",
day="28",
volume="8",
number="10",
pages="e36523",
keywords="epidemiological characteristics",
keywords="noncommunicable diseases",
keywords="malignant tumors",
keywords="cross-sectional study",
keywords="Guiyang",
abstract="Background: Studies that address the changing characteristics of diseases are of great importance for preventing and controlling the occurrence and development of diseases and for improving health. However, studies of the epidemiological characteristics of noncommunicable diseases (NCDs) and malignant tumors (MTs) of the residents in Guiyang, China, are lacking. Objective: The aim of this study was to evaluate the prevalences of NCDs and MTs in residents of Guiyang, Guizhou Province, China, and analyze differences among ages, genders, and regions. Methods: A multistage stratified cluster sampling method was used. Based on the inclusion and exclusion criteria, 81,517 individuals were selected for the study. Of these, 77,381 (94.9\%) participants completed the study. Structured questionnaires were used to collect information on demographic characteristics, NCDs, and MTs. The chi-square test (with 95\% confidence intervals) was used to analyze differences in disease prevalence among genders, ages, and geographical regions. Results: The major chronic NCDs of Guiyang residents are obesity, hypertension, and diabetes. MTs in women are mostly breast cancer, cervical cancer, and endometrial cancer, whereas in men, MTs are mainly lung cancer, rectal cancer, and gastric cancer. The prevalences of hypertension and diabetes in women are higher than in men, but the prevalences of lung cancer and gastric cancer in men are higher than in women. The epidemiological characteristics of individuals in different life stages are dissimilar. In terms of regional distribution, the prevalences of the above diseases in the Baiyun and Yunyan districts of Guiyang are relatively high. Conclusions: Several NCDs (obesity, hypertension, and diabetes) and MTs (women: breast cancer, cervical cancer, and endometrial cancer; men: lung cancer, rectal cancer, and gastric cancer) should be the focus for the prevention and control of chronic diseases in the future. In particular, the Baiyun and Yunyan districts of Guiyang are the important regions to emphasize. ",
doi="10.2196/36523",
url="https://publichealth.jmir.org/2022/10/e36523",
url="http://www.ncbi.nlm.nih.gov/pubmed/36306160"
}


@Article{info:doi/10.2196/37572,
author="de Leon, Brosina Elisa
and Campos, Morais H{\'e}rcules L{\'a}zaro
and Brito, Almeida Fabiana
and Almeida, Araujo Fabio",
title="Study of Health in Primary Care of the Amazonas Population: Protocol for an Observational Study on Diabetes Management in Brazil",
journal="JMIR Res Protoc",
year="2022",
month="Sep",
day="15",
volume="11",
number="9",
pages="e37572",
keywords="health management",
keywords="diabetes",
keywords="patient activation",
keywords="health care",
keywords="T2DM",
keywords="Amazon",
keywords="patient profile",
keywords="behavioral change",
keywords="health policy",
keywords="epidemiological profile",
keywords="epidemiology",
keywords="management",
abstract="Background: Changes in the profiles of patients have significant impacts on the health care system. Diabetes mellitus type 2 (T2DM) prevention and management should be studied in different contexts. Objective: The Study of Health in Primary Care for the Amazonas Population (SAPPA) primarily aims to describe T2DM prevention and management actions offered by primary health care settings in Brazil and whether the care delivered is consistent with the chronic care model (CCM). Second, the study aims to examine the impact of T2DM management actions on health and lifestyle, and third, to understand how sociodemographic characteristics, health, and subjective outcomes impact diabetes management. Methods: As part of this observational study, managers and health professionals complete a questionnaire containing information about T2DM prevention and management actions and CCM dimensions. During in-home visits, patients are asked about their health, lifestyle, sociodemographics, diabetes care, and subjective variables. Results: A total of 34 managers, 1560 professional health workers, and 955 patients will be recruited. The data collection will be completed in October 2022. Conclusions: The SAPPA is an observational study that intends to understand the T2DM management process in primary health care, including planning, execution, reach, and impact on patient motivation and adherence. International Registered Report Identifier (IRRID): DERR1-10.2196/37572 ",
doi="10.2196/37572",
url="https://www.researchprotocols.org/2022/9/e37572",
url="http://www.ncbi.nlm.nih.gov/pubmed/36107477"
}


@Article{info:doi/10.2196/34681,
author="Zheng, Yaguang
and Dickson, Vaughan Victoria
and Blecker, Saul
and Ng, M. Jason
and Rice, Campbell Brynne
and Melkus, D'Eramo Gail
and Shenkar, Liat
and Mortejo, R. Marie Claire
and Johnson, B. Stephen",
title="Identifying Patients With Hypoglycemia Using Natural Language Processing: Systematic Literature Review",
journal="JMIR Diabetes",
year="2022",
month="May",
day="16",
volume="7",
number="2",
pages="e34681",
keywords="hypoglycemia",
keywords="natural language processing",
keywords="electronic health records",
keywords="diabetes",
abstract="Background: Accurately identifying patients with hypoglycemia is key to preventing adverse events and mortality. Natural language processing (NLP), a form of artificial intelligence, uses computational algorithms to extract information from text data. NLP is a scalable, efficient, and quick method to extract hypoglycemia-related information when using electronic health record data sources from a large population. Objective: The objective of this systematic review was to synthesize the literature on the application of NLP to extract hypoglycemia from electronic health record clinical notes. Methods: Literature searches were conducted electronically in PubMed, Web of Science Core Collection, CINAHL (EBSCO), PsycINFO (Ovid), IEEE Xplore, Google Scholar, and ACL Anthology. Keywords included hypoglycemia, low blood glucose, NLP, and machine learning. Inclusion criteria included studies that applied NLP to identify hypoglycemia, reported the outcomes related to hypoglycemia, and were published in English as full papers. Results: This review (n=8 studies) revealed heterogeneity of the reported results related to hypoglycemia. Of the 8 included studies, 4 (50\%) reported that the prevalence rate of any level of hypoglycemia was 3.4\% to 46.2\%. The use of NLP to analyze clinical notes improved the capture of undocumented or missed hypoglycemic events using International Classification of Diseases, Ninth Revision (ICD-9), and International Classification of Diseases, Tenth Revision (ICD-10), and laboratory testing. The combination of NLP and ICD-9 or ICD-10 codes significantly increased the identification of hypoglycemic events compared with individual methods; for example, the prevalence rates of hypoglycemia were 12.4\% for International Classification of Diseases codes, 25.1\% for an NLP algorithm, and 32.2\% for combined algorithms. All the reviewed studies applied rule-based NLP algorithms to identify hypoglycemia. Conclusions: The findings provided evidence that the application of NLP to analyze clinical notes improved the capture of hypoglycemic events, particularly when combined with the ICD-9 or ICD-10 codes and laboratory testing. ",
doi="10.2196/34681",
url="https://diabetes.jmir.org/2022/2/e34681",
url="http://www.ncbi.nlm.nih.gov/pubmed/35576579"
}


@Article{info:doi/10.2196/33213,
author="Cooper, Drew
and Ubben, Tebbe
and Knoll, Christine
and Ballhausen, Hanne
and O'Donnell, Shane
and Braune, Katarina
and Lewis, Dana",
title="Open-source Web Portal for Managing Self-reported Data and Real-world Data Donation in Diabetes Research: Platform Feasibility Study",
journal="JMIR Diabetes",
year="2022",
month="Mar",
day="31",
volume="7",
number="1",
pages="e33213",
keywords="diabetes",
keywords="type 1 diabetes",
keywords="automated insulin delivery",
keywords="diabetes technology",
keywords="open-source",
keywords="patient-reported outcomes",
keywords="real-world data",
keywords="research methods",
keywords="mixed methods",
keywords="insulin",
keywords="digital health",
keywords="web portal",
abstract="Background: People with diabetes and their support networks have developed open-source automated insulin delivery systems to help manage their diabetes therapy, as well as to improve their quality of life and glycemic outcomes. Under the hashtag \#WeAreNotWaiting, a wealth of knowledge and real-world data have been generated by users of these systems but have been left largely untapped by research; opportunities for such multimodal studies remain open. Objective: We aimed to evaluate the feasibility of several aspects of open-source automated insulin delivery systems including challenges related to data management and security across multiple disparate web-based platforms and challenges related to implementing follow-up studies. Methods: We developed a mixed methods study to collect questionnaire responses and anonymized diabetes data donated by participants---which included adults and children with diabetes and their partners or caregivers recruited through multiple diabetes online communities. We managed both front-end participant interactions and back-end data management with our web portal (called the Gateway). Participant questionnaire data from electronic data capture (REDCap) and personal device data aggregation (Open Humans) platforms were pseudonymously and securely linked and stored within a custom-built database that used both open-source and commercial software. Participants were later given the option to include their health care providers in the study to validate their questionnaire responses; the database architecture was designed specifically with this kind of extensibility in mind. Results: Of 1052 visitors to the study landing page, 930 participated and completed at least one questionnaire. After the implementation of health care professional validation of self-reported clinical outcomes to the study, an additional 164 individuals visited the landing page, with 142 completing at least one questionnaire. Of the optional study elements, 7 participant--health care professional dyads participated in the survey, and 97 participants who completed the survey donated their anonymized medical device data. Conclusions: The platform was accessible to participants while maintaining compliance with data regulations. The Gateway formalized a system of automated data matching between multiple data sets, which was a major benefit to researchers. Scalability of the platform was demonstrated with the later addition of self-reported data validation. This study demonstrated the feasibility of custom software solutions in addressing complex study designs. The Gateway portal code has been made available open-source and can be leveraged by other research groups. ",
doi="10.2196/33213",
url="https://diabetes.jmir.org/2022/1/e33213",
url="http://www.ncbi.nlm.nih.gov/pubmed/35357312"
}


@Article{info:doi/10.2196/27486,
author="Ryan, Irene
and Herrick, Cynthia
and Ebeling, E. Mary F.
and Foraker, Randi",
title="Constructing an Adapted Cascade of Diabetes Care Using Inpatient Admissions Data: Cross-sectional Study",
journal="JMIR Diabetes",
year="2022",
month="Mar",
day="25",
volume="7",
number="1",
pages="e27486",
keywords="diabetes mellitus",
keywords="cascade of care",
keywords="EHR data",
keywords="health care monitoring",
keywords="inpatient care",
abstract="Background: The diabetes mellitus cascade of care has been constructed to evaluate diabetes care at a population level by determining the percentage of individuals diagnosed and linked to care as well as their reported glycemic control. Objective: We sought to adapt the cascade of care to an inpatient-only setting using the electronic health record (EHR) data of 81,633 patients with type 2 diabetes. Methods: In this adaptation, linkage to care was defined as prescription of diabetes medications within 3 months of discharge, and control was defined as hemoglobin A1c (HbA1c) below individual target levels, as these are the most reliably captured items in the inpatient setting. We applied the cascade model to assess differences in demographics and percent loss at each stage of the cascade; we then conducted two-sample chi-square equality of proportions tests for each demographic. Based on findings in the previous literature, we hypothesized that women, Black patients, younger patients (<45 years old), uninsured patients, and patients living in an economically deprived area called the Promise Zone would be disproportionately unlinked and uncontrolled. We also predicted that patients who received inpatient glycemic care would be more likely to reach glycemic control. Results: We found that out of 81,633 patients, 28,716 (35.2\%) were linked to care via medication prescription. Women and younger patients were slightly less likely to be linked to care than their male and older counterparts, while Black patients (n=19,141, 23.4\% of diagnosed population vs n=6741, 23.5\% of the linked population) were as proportionately part of the linked population as White patients (n=58,291, 71.4\% of diagnosed population vs n=20,402, 71.0\% of the linked population). Those living in underserved communities (ie, the Promise Zone) and uninsured patients were slightly overrepresented (n=6789, 8.3\% of diagnosed population vs n=2773, 9.7\% of the linked population) in the linked population as compared to patients living in wealthier zip codes and those who were insured. Similar patterns were observed among those more likely to reach glycemic control via HbA1c. However, conclusions are limited by the relatively large amount of missing glycemic data. Conclusions: We conclude that inpatient EHR data do not adequately capture the care cascade as defined in the outpatient setting. In particular, missing data in this setting may preclude assessment of glycemic control. Future work should integrate inpatient and outpatient data sources to complete the picture of diabetes care. ",
doi="10.2196/27486",
url="https://diabetes.jmir.org/2022/1/e27486",
url="http://www.ncbi.nlm.nih.gov/pubmed/35333182"
}


@Article{info:doi/10.2196/31941,
author="Vaidya, Rama
and Vaidya, B. Ashok D.
and Sheth, Jayesh
and Jadhav, Shashank
and Mahale, Umakant
and Mehta, Dilip
and Popko, Janusz
and Badmaev, Vladimir
and Stohs, J. Sidney",
title="Vitamin K Insufficiency in the Indian Population: Pilot Observational Epidemiology Study",
journal="JMIR Public Health Surveill",
year="2022",
month="Feb",
day="3",
volume="8",
number="2",
pages="e31941",
keywords="phylloquinone",
keywords="menaquinone-7",
keywords="vitamin K1",
keywords="vitamin K2",
keywords="insufficiency",
keywords="deficiency",
keywords="Indian population",
keywords="diabetes",
keywords="healthy people",
abstract="Background: The fat-soluble K vitamins K1 and K2 play an essential role in the blood coagulation cascade and are made available predominantly through selective dietary intakes. They are less known for their nonessential roles in a family of vitamin K--dependent proteins that promote various functions of organs and systems in the body. A lack of vitamin K can characterize vitamin and nutritional element insufficiency, which is different from a clinically apparent vitamin deficiency. Objective: This epidemiological study evaluated the nutritional status of vitamin K in a sample of the Indian population and vitamin K content in staple Indian foods. Methods: Serum levels of vitamin K1 and vitamin K2 in the form of menaquinone-7 (MK-7) were assessed via high-performance liquid chromatography coupled with fluorescence detection in 209 patients with type 2 diabetes, 50 healthy volunteers, and common staple foods in India. Results: After comparing populations with high and low serum vitamin K levels from various geographical regions, our results indicated that the sample of healthy Indian individuals and the sample of Indian patients with type 2 diabetes had low (insufficient) levels of vitamin K2 (MK-7; range 0.3-0.4 ng/mL). No significant differences existed in vitamin K1--related and MK-7--related values between healthy male and female subjects, between male and female subjects with diabetes, and between the healthy sample and the sample of patients with diabetes. The staple, commonly consumed Indian foods that were tested in this study had undetectable levels of vitamin K2, while levels of vitamin K1 varied widely (range 0-37 {\textmu}g/100 g). Conclusions: Based on our sample's low serum levels of vitamin K2 (MK-7) as well as the low levels of vitamin K2 in their typical diet, we propose that the general Indian population could benefit from the consumption of vitamin K2 in the form of MK-7 supplements. Trial Registration: Clinical Trials Registry - India CTRI/2019/05/014246; http://ctri.nic.in/Clinicaltrials/showallp.php?mid1=21660\&EncHid=\&userName=014246; Clinical Trials Registry - India CTRI/2019/03/018278; http://ctri.nic.in/Clinicaltrials/showallp.php?mid1=32349\&EncHid=\&userName=018278 ",
doi="10.2196/31941",
url="https://publichealth.jmir.org/2022/2/e31941",
url="http://www.ncbi.nlm.nih.gov/pubmed/35113033"
}


@Article{info:doi/10.2196/27299,
author="Menon, Jaideep
and Numpeli, Mathews
and Kunjan, P. Sajeev
and Karimbuvayilil, V. Beena
and Sreedevi, Aswathy
and Panniyamakkal, Jeemon
and Suseela, P. Rakesh
and Thachathodiyil, Rajesh
and Banerjee, Amitava",
title="A Sustainable Community-Based Model of Noncommunicable Disease Risk Factor Surveillance (Shraddha-Jagrithi Project): Protocol for a Cohort Study",
journal="JMIR Res Protoc",
year="2021",
month="Oct",
day="22",
volume="10",
number="10",
pages="e27299",
keywords="non-communicable diseases",
keywords="surveillance",
keywords="accredited social health activist",
keywords="panchayat (village)",
keywords="primary health centre",
keywords="spoke and hub",
keywords="cardiovascular",
keywords="public health",
keywords="hypertension",
keywords="health services",
keywords="health center",
keywords="diabetes",
abstract="Background: India has a massive noncommunicable disease (NCD) burden, at an enormous cost to the individual, family, society, and health system at large, despite which prevention and surveillance are relatively neglected. If diagnosed early and treated adequately, risk factors for atherosclerotic cardiovascular disease would help decrease the mortality and morbidity burden. Surveillance for NCDs, creating awareness, positive lifestyle changes, and treatment are the proven measures known to prevent the progression of the disease. India is in a stage of rapid epidemiological transition, with the state of Kerala being at the forefront, pointing us towards likely disease burden and outcomes for the rest of the country in the future. A previous study done by the same investigators in a population of >100,000 revealed poor awareness, treatment of NCDs, and poor adherence to medicines in individuals with CVD. Objective: This study aimed at assessing a sustainable, community-based surveillance model for NCDs with corporate support fully embedded in the public health system. Methods: Frontline health workers will check all individuals in the target group (?age 30 years) with further follow-up and treatment planned in a ``spoke and hub'' model using the public health system of primary health centers as spokes to the hubs of taluk or district hospitals. All data entry done by frontline health workers will be on a tablet PC, ensuring rapid acquisition and transfer of participant health details to primary health centers for further follow-up and treatment. Results: The model will be evaluated based on the utilization rate of various services offered at all tier levels. The proportions of the target population screened, eligible individuals who reached the spoke or hub centers for risk stratification and care, and community-level control for hypertension and diabetes in annual surveys will be used as indicator variables. The model ensures diagnosis and follow-up treatment at no cost to the individual entirely through the tiered public health system of the state and country. Conclusions: Surveillance for NCDs is an essential facet of health care presently lacking in India. Atherosclerotic cardiovascular disease has a long gestation period in progression to the symptomatic phase of the disease, during which timely preventive and lifestyle measures would help prevent disease progression if implemented. Unfortunately, several asymptomatic individuals have never tested their plasma glucose, serum lipid levels, or blood pressure and are unaware of their disease status. Our model, implemented through the public health system using frontline health workers, would ensure individuals aged?30 years at risk of disease are identified, and necessary lifestyle modifications and treatments are given. In addition, the surveillance at the community level would help create a general awareness of NCDs and lead to healthier lifestyle habits. Trial Registration: Clinical Trial Registry India CTRI/2018/07/014856; https://tinyurl.com/4saydnxf International Registered Report Identifier (IRRID): DERR1-10.2196/27299 ",
doi="10.2196/27299",
url="https://www.researchprotocols.org/2021/10/e27299",
url="http://www.ncbi.nlm.nih.gov/pubmed/34677141"
}


@Article{info:doi/10.2196/24681,
author="Golder, Su
and Bach, Millie
and O'Connor, Karen
and Gross, Robert
and Hennessy, Sean
and Gonzalez Hernandez, Graciela",
title="Public Perspectives on Anti-Diabetic Drugs: Exploratory Analysis of Twitter Posts",
journal="JMIR Diabetes",
year="2021",
month="Jan",
day="26",
volume="6",
number="1",
pages="e24681",
keywords="diabetes",
keywords="insulin",
keywords="Twitter",
keywords="social media",
keywords="infodemiology",
keywords="infoveillance",
keywords="social listening",
keywords="cost",
keywords="rationing",
abstract="Background: Diabetes mellitus is a major global public health issue where self-management is critical to reducing disease burden. Social media has been a powerful tool to understand public perceptions. Public perception of the drugs used for the treatment of diabetes may be useful for orienting interventions to increase adherence. Objective: The aim of this study was to explore the public perceptions of anti-diabetic drugs through the analysis of health-related tweets mentioning such medications. Methods: This study uses an infoveillance social listening approach to monitor public discourse using Twitter data. We coded 4000 tweets from January 1, 2019 to October 1, 2019 containing key terms related to anti-diabetic drugs by using qualitative content analysis. Tweets were coded for whether they were truly about an anti-diabetic drug and whether they were health-related. Health-related tweets were further coded based on who was tweeting, which anti-diabetic drug was being tweeted about, and the content discussed in the tweet. The main outcome of the analysis was the themes identified by analyzing the content of health-related tweets on anti-diabetic drugs. Results: We identified 1664 health-related tweets on 33 anti-diabetic drugs. A quarter (415/1664) of the tweets were confirmed to have been from people with diabetes, 17.9\% (298/1664) from people posting about someone else, and 2.7\% (45/1664) from health care professionals. However, the role of the tweeter was unidentifiable in two-thirds of the tweets. We identified 13 themes, with the health consequences of the cost of anti-diabetic drugs being the most extensively discussed, followed by the efficacy and availability. We also identified issues that patients may conceal from health care professionals, such as purchasing medications from unofficial sources. Conclusions: This study uses an infoveillance approach using Twitter data to explore public perceptions related to anti-diabetic drugs. This analysis gives an insight into the real-life issues that an individual faces when taking anti-diabetic drugs, and such findings may be incorporated into health policies to improve compliance and efficacy. This study suggests that there is a fear of not having access to anti-diabetic drugs due to cost or physical availability and highlights the impact of the sacrifices made to access anti-diabetic drugs. Along with screening for diabetes-related health issues, health care professionals should also ask their patients about any non--health-related concerns regarding their anti-diabetic drugs. The positive tweets about dietary changes indicate that people with type 2 diabetes may be more open to self-management than what the health care professionals believe. ",
doi="10.2196/24681",
url="http://diabetes.jmir.org/2021/1/e24681/",
url="http://www.ncbi.nlm.nih.gov/pubmed/33496671"
}


@Article{info:doi/10.2196/20532,
author="Alqabandi, Naeema
and Al-Ozairi, Ebaa
and Ahmed, Adel
and Ross, L. Edgar
and Jamison, N. Robert",
title="Secondary Impact of Social Media via Text Message Screening for Type 2 Diabetes Risk in Kuwait: Survey Study",
journal="JMIR Diabetes",
year="2020",
month="Nov",
day="12",
volume="5",
number="4",
pages="e20532",
keywords="SMS",
keywords="Short text message interventions",
keywords="mHealth",
keywords="smartphone",
keywords="Type 2 diabetes mellitus",
keywords="prevention",
abstract="Background: Type 2 diabetes mellitus (T2DM) is an international problem of alarming epidemic proportions. T2DM can develop due to multiple factors, and it usually begins with prediabetes. Fortunately, this disease can be prevented by following a healthy lifestyle. However, many health care systems fail to properly educate the public on disease prevention and to offer support in embracing behavioral interventions to prevent diabetes. SMS messaging has been combined with cost-effective ways to reach out to the population at risk for medical comorbidities. To our knowledge, the use of nationwide SMS messaging in the Middle East as a screening tool to identify individuals who might be at risk of developing T2DM has not been reported in the literature. Objective: The primary aim of this study was to assess the feasibility of conducting a series of SMS messaging campaigns directed at random smartphone users in Kuwait for the detection and prevention of T2DM. It was predicted that 1\% of those receiving the text message would find it relevant and participate in the study. The secondary aim of this study was to assess the incidence of participation of those who were forwarded the initial text message by family members and friends. Methods: In this study, 5 separate text message screening campaigns were launched inviting recipients to answer 6 questions to determine the risk of developing T2DM. If subjects agreed to participate, a link to the prediabetes screening test devised by the Centers for Disease Control and Prevention was automatically transmitted to their mobile devices. Those identified as high risk were invited to participate in a diabetes prevention program. Results: A total of 180,000 SMSs were sent to approximately 6\% of the adult population in Kuwait. Of these, 0.14\% (260/180,000) of the individuals who received the SMS agreed to participate, of whom 58.8\% (153/260) completed the screening. Surprisingly, additional surveys were completed by 367 individuals who were invited via circulated SMS messages forwarded by family members and friends. Altogether, 23.3\% (121/520) qualified and agreed to participate in a diabetes prevention program. The majority of those who chose to participate in the prevention program were overweight, aged 45-65 years, and reported being less physically active than those who chose not to participate ($\chi$22=42.1, P<.001). Conclusions: Although health care screening via text messaging was found to have limited effectiveness by itself, it exhibited increased reach through shared second-party social media messaging. Despite the fact a subpopulation at possible risk of developing T2DM could be reached via text messaging, most responders were informed about the screening campaign by family and friends. Future research should be designed to tap into the benefits of social media use in health risk campaigns. ",
doi="10.2196/20532",
url="https://diabetes.jmir.org/2020/4/e20532",
url="http://www.ncbi.nlm.nih.gov/pubmed/33180021"
}


@Article{info:doi/10.2196/14431,
author="Griffis, Heather
and Asch, A. David
and Schwartz, Andrew H.
and Ungar, Lyle
and Buttenheim, M. Alison
and Barg, K. Frances
and Mitra, Nandita
and Merchant, M. Raina",
title="Using Social Media to Track Geographic Variability in Language About Diabetes: Infodemiology Analysis",
journal="JMIR Diabetes",
year="2020",
month="Feb",
day="11",
volume="5",
number="1",
pages="e14431",
keywords="social media",
keywords="epidemiology",
keywords="infodemiology",
keywords="diabetes",
keywords="prevalence",
keywords="twitter",
abstract="Background: Social media posts about diabetes could reveal patients' knowledge, attitudes, and beliefs as well as approaches for better targeting of public health messages and care management. Objective: This study aimed to characterize the language of Twitter users' posts regarding diabetes and describe the correlation of themes with the county-level prevalence of diabetes. Methods: A retrospective study of diabetes-related tweets identified from a random sample of approximately 37 billion tweets from the United States from 2009 to 2015 was conducted. We extracted diabetes-specific tweets and used machine learning to identify statistically significant topics of related terms. Topics were combined into themes and compared with the prevalence of diabetes by US counties and further compared with geography (US Census Divisions). Pearson correlation coefficients are reported for each topic and relationship with prevalence. Results: A total of 239,989 tweets from 121,494 unique users included the term diabetes. The themes emerging from the topics included unhealthy food and drink, treatment, symptoms/diagnoses, risk factors, research, recipes, news, health care, management, fundraising, diet, communication, and supplements/remedies. The theme of unhealthy foods most positively correlated with geographic areas with high prevalence of diabetes (r=0.088), whereas tweets related to research most negatively correlated (r=?0.162) with disease prevalence. Themes and topics about diabetes differed in overall frequency across the US geographical divisions, with the East South Central and South Atlantic states having a higher frequency of topics referencing unhealthy food (r range=0.073-0.146; P<.001). Conclusions: Diabetes-related tweets originating from counties with high prevalence of diabetes have different themes than tweets originating from counties with low prevalence of diabetes. Interventions could be informed from this variation to promote healthy behaviors. ",
doi="10.2196/14431",
url="http://diabetes.jmir.org/2020/1/e14431/"
}


@Article{info:doi/10.2196/diabetes.7535,
author="Al-Thani, Mohammed
and Al-Thani, Al-Anoud
and Al-Chetachi, Walaa
and Khalifa, Eldin Shams
and Vinodson, Benjamin
and Al-Malki, Badria
and Haj Bakri, Ahmad
and Akram, Hammad",
title="Situation of Diabetes and Related Factors Among Qatari Adults: Findings From a Community-Based Survey",
journal="JMIR Diabetes",
year="2017",
month="May",
day="03",
volume="2",
number="1",
pages="e7",
keywords="diabetes mellitus",
keywords="obesity",
keywords="public health",
keywords="Qatar",
abstract="Background: Diabetes mellitus (DM) is a prominent public health problem in Qatar with one of the highest prevalence in the Gulf Cooperation Council region. Obesity continues to be a challenging public health problem in Qatar along with other social determinants contributing to the high DM prevalence. Objective: This paper examines the data from Qatar National STEPS survey (2012) to determine diabetes prevalence among Qatari adults and identify the effect of both generalized and central obesity on it. The article also describes the contribution of selected social and demographic factors on diabetes prevalence in Qatar. Methods: Secondary data analysis of 1471 Qatari adults (18-64 years) from STEP 3 component of the 2012 STEPS Survey was executed. Multivariate binary logistic regression analysis was carried out to assess the role of social and biomedical factors in the prevalence of DM. Results: Among participants, 18.97\% (279/1471) of the study population had DM. Both generalized (OR 1.8, P=.005) and central obesity (OR 1.9, P<.001) were significantly associated with DM when adjusted for various respondent characteristics. Older age (P<.001), marital status of ever married (P<.001), and lower educational status (P=.01) were associated with DM. Hypertension (OR 1.5, P=.003 total cholesterol level ?190 mg/dL (OR 2.2, P<.001) and triglyceride level ?150 mg/dL (OR 3.6, P<.001) were significantly associated with DM among the study participants. Although family history of DM was significantly associated with development of DM (OR 1.7, P=.01), parental consanguinity was not associated with DM (OR 0.96, P=.80). Conclusions: The DM prevalence in Qatar seems to be highly associated with obesity; however, various additional population characteristics and comorbidity factors should also require attention and should be incorporated while developing intervention strategies. ",
doi="10.2196/diabetes.7535",
url="http://diabetes.jmir.org/2017/1/e7/",
url="http://www.ncbi.nlm.nih.gov/pubmed/30291095"
}