Population-based county-level estimates for prevalence of DC were obtained from the Institute for Health Metrics and Evaluation (IHME) for the years 2004-2012 (16). DC prevalence rate was defined as the propor-tion of people within a county who had previously been diagnosed with diabetes (high fasting plasma glu-cose 126 mg/dL, hemoglobin A1c (HbA1c) of 6.5%, or diabetes diagnosis) but do not currently have high fasting plasma glucose or HbA1c for the period 2004-2012. DC prevalence estimates were calculated using a two-stage approach. The first stage used National Health and Nutrition Examination Survey (NHANES) data to predict high fasting plasma glucose (FPG) levels (≥126 mg/dL) and/or HbA1C levels (≥6.5% [48 mmol/mol]) based on self-reported demographic and behavioral characteristics (16). This model was then applied to Behavioral Risk Factor Surveillance System (BRFSS) data to impute high FPG and/or HbA1C status for each BRFSS respondent (16). The second stage used the imputed BRFSS data to fit a series of small area models, which were used to predict county-level prevalence of diabetes-related outcomes, including DC (16). The EQI was constructed for 2006-2010 for all US counties and is composed of five domains (air, water, built, land, and sociodemographic), each composed of variables to represent the environmental quality of that _domain. Domain-specific EQIs were developed using principal components analysis (PCA) to reduce these variables within each _domain while the overall EQI was constructed from a second PCA from these individual domains (L. C. Messer et al., 2014). To account for differences in environment across rural and urban counties, the overall and _domain-specific EQIs were stratified by rural urban continuum codes (RUCCs) (U.S. Department of Agriculture, 2015). Results are reported as prevalence rate differences (PRD) with 95% confidence intervals (CIs) comparing the highest quintile/worst environmental quality to the lowest quintile/best environmental quality expo-sure metrics. PRDs are representative of the entire period of interest, 2004-2012. Due to availability of DC data and covariate data, not all counties were captured, however, the majority, 3134 of 3142 were utilized in the analysis. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: Human health data are not available publicly. EQI data are available at: https://edg.epa.gov/data/Public/ORD/NHEERL/EQI. Format: Data are stored as csv files. This dataset is associated with the following publication: Jagai, J., A. Krajewski, K. Price, D. Lobdell, and R. Sargis. Diabetes control is associated with environmental quality in the USA. Endocrine Connections. BioScientifica Ltd., Bristol, UK, 10(9): 1018-1026, (2021).
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United States US: Diabetes Prevalence: % of Population Aged 20-79 data was reported at 10.790 % in 2017. United States US: Diabetes Prevalence: % of Population Aged 20-79 data is updated yearly, averaging 10.790 % from Dec 2017 (Median) to 2017, with 1 observations. United States US: Diabetes Prevalence: % of Population Aged 20-79 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Health Statistics. Diabetes prevalence refers to the percentage of people ages 20-79 who have type 1 or type 2 diabetes.; ; International Diabetes Federation, Diabetes Atlas.; Weighted average;
Population-based county-level estimates for diagnosed (DDP), undiagnosed (UDP), and total diabetes prevalence (TDP) were acquired from the Institute for Health Metrics and Evaluation (IHME) for the years 2004-2012 (Evaluation 2017). Prevalence estimates were calculated using a two-stage approach. The first stage used National Health and Nutrition Examination Survey (NHANES) data to predict high fasting plasma glucose (FPG) levels (≥126 mg/dL) and/or hemoglobin A1C (HbA1C) levels (≥6.5% [48 mmol/mol]) based on self-reported demographic and behavioral characteristics (Dwyer-Lindgren, Mackenbach et al. 2016). This model was then applied to Behavioral Risk Factor Surveillance System (BRFSS) data to impute high FPG and/or A1C status for each BRFSS respondent (Dwyer-Lindgren, Mackenbach et al. 2016). The second stage used the imputed BRFSS data to fit a series of small area models, which were used to predict the county-level prevalence of each of the diabetes-related outcomes (Dwyer-Lindgren, Mackenbach et al. 2016). Diagnosed diabetes was defined as the proportion of adults (age 20+ years) who reported a previous diabetes diagnosis, represented as an age-standardized prevalence percentage. Undiagnosed diabetes was defined as proportion of adults (age 20+ years) who have a high FPG or HbA1C but did not report a previous diagnosis of diabetes. Total diabetes was defined as the proportion of adults (age 20+ years) who reported a previous diabetes diagnosis and/or had a high FPG/HbA1C. The age-standardized diabetes prevalence (%) was used as the outcome. The EQI was constructed for 2000-2005 for all US counties and is composed of five domains (air, water, built, land, and sociodemographic), each composed of variables to represent the environmental quality of that domain. Domain-specific EQIs were developed using principal components analysis (PCA) to reduce these variables within each domain while the overall EQI was constructed from a second PCA from these individual domains (L. C. Messer et al., 2014). To account for differences in environment across rural and urban counties, the overall and domain-specific EQIs were stratified by rural urban continuum codes (RUCCs) (U.S. Department of Agriculture, 2015). This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: Human health data are not available publicly. EQI data are available at: https://edg.epa.gov/data/Public/ORD/NHEERL/EQI. Format: Data are stored as csv files. This dataset is associated with the following publication: Jagai, J., A. Krajewski, S. Shaikh, D. Lobdell, and R. Sargis. Association between environmental quality and diabetes in the U.S.A.. Journal of Diabetes Investigation. John Wiley & Sons, Inc., Hoboken, NJ, USA, 11(2): 315-324, (2020).
This dataset is from UCI machine learning repository: 130 US hospital diabetes dataset However, I did several cleaning and this is the output. How I did the cleaning, you can read more here https://github.com/rischanlab/Cleaning_diabetes_130_US_hospital_dataset
Diabetic retinopathy is the leading cause of blindness in the working-age population of the developed world. It is estimated to affect over 93 million people.
retina
The US Center for Disease Control and Prevention estimates that 29.1 million people in the US have diabetes and the World Health Organization estimates that 347 million people have the disease worldwide. Diabetic Retinopathy (DR) is an eye disease associated with long-standing diabetes. Around 40% to 45% of Americans with diabetes have some stage of the disease. Progression to vision impairment can be slowed or averted if DR is detected in time, however this can be difficult as the disease often shows few symptoms until it is too late to provide effective treatment.
Currently, detecting DR is a time-consuming and manual process that requires a trained clinician to examine and evaluate digital color fundus photographs of the retina. By the time human readers submit their reviews, often a day or two later, the delayed results lead to lost follow up, miscommunication, and delayed treatment.
Clinicians can identify DR by the presence of lesions associated with the vascular abnormalities caused by the disease. While this approach is effective, its resource demands are high. The expertise and equipment required are often lacking in areas where the rate of diabetes in local populations is high and DR detection is most needed. As the number of individuals with diabetes continues to grow, the infrastructure needed to prevent blindness due to DR will become even more insufficient.
The need for a comprehensive and automated method of DR screening has long been recognized, and previous efforts have made good progress using image classification, pattern recognition, and machine learning. With color fundus photography as input, the goal of this competition is to push an automated detection system to the limit of what is possible – ideally resulting in models with realistic clinical potential. The winning models will be open sourced to maximize the impact such a model can have on improving DR detection.
Acknowledgements This competition is sponsored by the California Healthcare Foundation.
Retinal images were provided by EyePACS, a free platform for retinopathy screening.
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The size of the North America Self-monitoring Blood Glucose Market was valued at USD 8.10 Million in 2023 and is projected to reach USD 12.99 Million by 2032, with an expected CAGR of 6.98% during the forecast period. Self-monitoring blood glucose is a relevant tool for patients with diabetes. Its process includes the regular testing of blood sugar to gauge how the body reacts to food, medicine, or physical activity. It helps increase peoples' awareness of their own choices and helps them avoid some complications that may come as a result of diabetes. The North American SMBG market is driven by the increasing incidences of diabetes, especially Type 2. The size and risk profile of the population are critical factors in North America. The region suffers from a rather large population that is susceptible to developing the disease primarily because of obesity, sedentary lifestyles, and aging. With the growth in awareness of diabetes, and with preventive and controlling features forming an important aspect of healthcare systems, the SMBG device and supplies market will continue to grow. The advances in technology, especially the available kinds of continuous glucose monitors, give market expansion way to more convenient and accurate blood sugar tracking. Recent developments include: May 2023: LifeScan announced positive data from a study of real-world evidence supporting its Bluetooth-connected blood glucose meter. Evidence from more than 55,000 people with diabetes demonstrated sustained improvements in readings in range. The analysis focuses on changes over 180 days. LifeScan published results in the peer-reviewed journal Diabetes Therapy. The company’s OneTouch Bluetooth-connected blood glucose meter and mobile diabetes app provide simplicity, accuracy, and trust., January 2023: LifeScan announced that the peer-reviewed Journal of Diabetes Science and Technology published Improved Glycemic Control Using a Bluetooth-Connected Blood Glucose Meter and a Mobile Diabetes App: Real-World Evidence from Over 144,000 People With Diabetes, detailing results from a retrospective analysis of real-world data from over 144,000 people with diabetes is one of the largest combined blood glucose meter and mobile diabetes app datasets ever published.. Key drivers for this market are: Rising Prevalence of Cancer Worldwide, Technological Advancements in Diagnostic Testing; Increasing Demand for Point-of-care Treatment. Potential restraints include: High Cost of Molecular Diagnostic Tests, Lack of Skilled Workforce and Stringent Regulatory Framework. Notable trends are: Blood Glucose Test Strips Held the Largest Market Share in Current Year.
Data for cities, communities, and City of Los Angeles Council Districts were generated using a small area estimation method which combined the survey data with population benchmark data (2022 population estimates for Los Angeles County) and neighborhood characteristics data (e.g., U.S. Census Bureau, 2017-2021 American Community Survey 5-Year Estimates). This indicator is based on self-report of ever being diagnosed with diabetes (type 1 or 2).Diabetes is associated with decreased life expectancy, heart disease and stroke, lower limb amputations, kidney disease, and blindness. It is also closely linked with obesity. Cities and communities can help prevent diabetes by adopting policies that support healthy food retail and physical activity and improve access to preventive care services.For more information about the Community Health Profiles Data Initiative, please see the initiative homepage.
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Background: Arab Americans are a historically understudied minority group in the United States and their health needs and risks have been poorly documented. We aim to provide an updated comprehensive review of the literature on Arab American physical and mental health and provide suggestions for future work in this field.Methods: A comprehensive review of the English language medical and public health literature published prior to 2017 identified through multiple database searches was conducted with search terms describing Arab Americans and health outcomes and behaviors. The literature was qualitatively summarized by health behavior (vaccination, tobacco use, drug and alcohol use, and physical activity), health outcome (diabetes, mental health, cardiovascular disease, cancer, women's, and child health), and populations at increased risk of poor health outcomes (adolescents and the elderly).Results: The majority of studies identified exploring Arab American health have been published since 2009 with an increase in the number of longitudinal and intervention studies done with this population. The majority of research is being undertaken among individuals living in ethnic enclaves due to the lack of an ethnic or racial identifier that may help identify Arab Americans from population-based studies. Studies highlight the conflicting evidence in the prevalence of diabetes and cardiovascular disease based on study sample, an increased understanding of cancer incidence and barriers to identification, and an increased level of knowledge regarding mental health and sexual health needs in the population. Information on health behaviors has also increased, with a better understanding of physical activity, alcohol and drug use, and vaccination.Conclusion: More research on Arab American health is needed to identify risks and needs of this marginalized population given the current social and political climate in the United States, especially with regard to acculturation status and immigrant generation status. We provide recommendations on approaches that may help improve our understanding of Arab American health.
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Montenegro ME: Diabetes Prevalence: % of Population Aged 20-79 data was reported at 10.080 % in 2017. Montenegro ME: Diabetes Prevalence: % of Population Aged 20-79 data is updated yearly, averaging 10.080 % from Dec 2017 (Median) to 2017, with 1 observations. Montenegro ME: Diabetes Prevalence: % of Population Aged 20-79 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Montenegro – Table ME.World Bank: Health Statistics. Diabetes prevalence refers to the percentage of people ages 20-79 who have type 1 or type 2 diabetes.; ; International Diabetes Federation, Diabetes Atlas.; Weighted average;
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Background: Diabetes mellitus affects over 3.9 million people in the United Kingdom (UK), with over 2.6 million people in England alone. More than 1 million people living with diabetes are acutely admitted to hospital due to complications of their illness every year. Cardiovascuar disease is the most prevalent cause of morbidity and mortality in people with diabetes. Diabetic retinopathy (DR) is a common microvascular complication of type 1 and type 2 diabetes and remains a major cause of vision loss and blindness in those of working age. This dataset includes the national screening diabetic grade category (seven categories from R0M0 to R3M1) from the Birmingham, Solihull and Black Country DR screening program (a member of the National Health Service (NHS) Diabetic Eye Screening Programme) and the University Hospitals Birmingham NHS Trust cardiac outcome data.
Geography: The West Midlands has a population of 5.9 million. The region includes a diverse ethnic, and socio-economic mix, with a higher than UK average of minority ethnic groups. It has a large number of elderly residents but is the youngest population in the UK. There are particularly high rates of diabetes, physical inactivity, obesity, and smoking.
Data sources:
1. The Birmingham, Solihull and Black Country Data Set, University Hospitals Birmingham NHS Foundation Trust, Birmingham, United Kingdom. They manage over 200,000 diabetic patients, with longitudinal follow-up up to 15 years, making this the largest urban diabetic eye screening scheme in Europe.
2. The Electronic Health Records held at University Hospitals Birmingham NHS Foundation Trust is one of the largest NHS Trusts in England, providing direct acute services and specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds and 100 ITU beds. UHB runs a fully electronic healthcare record for systemic disease.
Scope: All Birmingham, Solihull and Black Country diabetic eye screened participants who have been admitted to UHB with a cardiac related health concern from 2006 onwards. Longitudinal and individually linked with their diabetic eye care from primary screening data and secondary care hospital cardiac outcome data including • Demographic information (including age, sex and ethnicity) • Diabetes status • Diabetes type • Length of time since diagnosis of diabetes • Visual acuity • The national screening diabetic screening grade category (seven categories from R0M0 to R3M1) • Diabetic eye clinical features • Reason for sight and severe sight impairment • ICD-10 and SNOMED-CT codes pertaining to cardiac disease • Outcome
Website: https://www.retinalscreening.co.uk/
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BackgroundCancer and diabetes are among the leading causes of morbidity and mortality worldwide. Several studies have reported diabetes as a risk factor for developing cancer, a relationship that may be explained by associated factors shared with both diseases such as age, sex, body weight, smoking, and alcohol consumption. Social factors referred to as social determinants of health (SDOH) were shown to be associated with the risk of developing cancer and diabetes. Despite that diabetes and social factors were identified as significant determinants of cancer, no studies examined their combined effect on the risk of developing cancer. In this study, we aim at filling this gap in the literature by triangulating the association between diabetes, indices of SDOH, and the risk of developing cancer.MethodsWe have conducted a quantitative study using data from the Behavioral Risk Factor Surveillance System (BRFSS), whereby information was collected nationally from residents in the United States (US) with respect to their health-related risk behaviors, chronic health conditions, and the use of preventive services. Data analysis using weighted regressions was conducted on 389,158 study participants.ResultsOur findings indicated that diabetes is a risk factor that increases the likelihood of cancer by 13% (OR 1.13; 95%CI: 1.05–1.21). People of White race had higher odds for cancer compared to African Americans (OR 0.44; 95%CI: 0.39–0.49), Asians (OR 0.27; 95%CI: 0.20–0.38), and other races (OR 0.56; 95%CI: 0.46–0.69). The indices of SDOH that were positively associated with having cancer encompassed unemployment (OR 1.78; 95%CI: 1.59–1.99), retirement (OR 1.54; 95%CI: 1.43–1.67), higher income levels with ORs ranging between 1.16–1.38, college education (OR 1.10; 95%CI: 1.02–1.18), college graduates (OR 1.31; 95%CI: 1.21–1.40), and healthcare coverage (OR 1.44; 95%CI: 1.22–1.71). On the other hand, the indices of SDOH that were protective against having cancer were comprised of renting a home (OR 0.86; 95%CI: 0.79–0.93) and never married (OR 0.73; 95%CI: 0.65–0.81).ConclusionThis study offers a novel social dimension for the association between diabetes and cancer that could guide setting strategies for addressing social inequities in disease prevention and access to healthcare.
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The United States self-monitoring blood glucose (SMBG) market, valued at $7.62 billion in 2025, is projected to experience robust growth, driven by the increasing prevalence of diabetes and the rising adoption of advanced SMBG devices. The market's Compound Annual Growth Rate (CAGR) of 6.40% from 2019 to 2024 indicates a steady expansion, which is expected to continue through 2033. Key growth drivers include the increasing diabetic population, particularly among older adults, the rising awareness about diabetes management, and technological advancements leading to more accurate, user-friendly, and convenient glucose monitoring devices. The market is segmented into glucometer devices, test strips, and lancets, with glucometer devices likely representing the largest segment due to the technological innovations leading to smaller, more sophisticated devices and integration with mobile apps for data tracking and management. Furthermore, the growing demand for continuous glucose monitoring (CGM) systems, though not explicitly stated in the provided data, is a significant emerging trend that is likely contributing to market expansion. While challenges exist, such as the high cost of treatment and the potential for inaccuracies with some devices, the market's overall trajectory remains positive due to the sustained need for effective diabetes management. The competitive landscape is characterized by established players like Abbott Diabetes Care, Roche Holding AG, and LifeScan, who hold significant market share. These companies are engaged in continuous innovation to maintain their market dominance by developing technologically advanced devices and expanding their global reach. Smaller companies contribute significantly to innovation and competition, particularly in the development of less expensive and more accessible devices. However, the market's success is closely tied to the broader healthcare landscape, including government regulations, insurance coverage policies, and public health initiatives aimed at diabetes prevention and management. Further research is required to fully quantify the impact of these factors on specific market segments and individual companies within the US SMBG market. Recent developments include: January, 2023: LifeScan announced that the peer-reviewed Journal of Diabetes Science and Technology published Improved Glycemic Control Using a Bluetooth Connected Blood Glucose Meter and a Mobile Diabetes App: Real-World Evidence From Over 144,000 People With Diabetes, detailing results from a retrospective analysis of real-world data from over 144,000 people with diabetes-one of the largest combined blood glucose meter and mobile diabetes app datasets ever published., January 20, 2022: Roche announced the launch of the COBAS pulse system in selected countries accepting the CE mark. The COBAS pulse system marks Roche Diagnostics' newest generation of connected point-of-care solutions for professional blood glucose management. The COBAS pulse system combines the form factor of a high-performance blood glucose meter with simple usability and expanded digital capabilities like those of a smartphone. Following first commercial availability under the CE mark in select markets, Roche plans to seek CE IVDR and FDA clearance for the Cobas Pulse System in other global markets.. Notable trends are: Rising Diabetes Prevalence in the United States.
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The Latin American diabetes care devices market, valued at approximately $XX million in 2025, is projected to experience robust growth, exhibiting a compound annual growth rate (CAGR) of 5.64% from 2025 to 2033. This expansion is fueled by several key factors. The rising prevalence of diabetes across Brazil, Mexico, and the rest of Latin America, coupled with increasing awareness of effective disease management, is driving demand for both self-monitoring and management devices. Technological advancements, such as the introduction of more user-friendly continuous glucose monitoring (CGM) systems and improved insulin delivery technologies like insulin pumps, are further stimulating market growth. Government initiatives promoting diabetes awareness and affordable access to healthcare are also contributing positively. However, high costs associated with advanced devices, limited healthcare infrastructure in certain regions, and a lack of patient education in some areas pose challenges to market penetration. The market is segmented by device type (self-monitoring blood glucose devices – including glucometers, test strips, and lancets; CGMs – encompassing sensors, receivers, and transmitters; and management devices like insulin pumps, syringes, pens, and jet injectors) and geography (Brazil, Mexico, and the rest of Latin America). Brazil and Mexico are expected to be the largest markets due to their significant populations and relatively higher prevalence of diabetes. The competitive landscape is characterized by the presence of major global players like Abbott, Dexcom, Medtronic, and Roche, alongside several regional players. The future of the Latin American diabetes care devices market appears bright, although further growth hinges on several factors. Addressing affordability concerns through government subsidies and insurance coverage is critical. Furthermore, improving healthcare infrastructure and expanding patient education programs will be crucial to maximizing market potential. Strategic partnerships between manufacturers, healthcare providers, and government bodies can facilitate wider adoption of advanced technologies. Specific focus should be placed on improving access to CGM technology, which offers significant improvements in diabetes management compared to traditional self-monitoring methods. The growing adoption of telehealth and remote monitoring will also contribute to the market's future trajectory. Competitive strategies will likely focus on innovation, product differentiation, and expanding distribution networks to reach diverse patient populations across the region. Recent developments include: January 2023: LifeScan announced that the peer-reviewed Journal of Diabetes Science and Technology published Improved Glycemic Control Using a Bluetooth Connected Blood Glucose Meter and a Mobile Diabetes App: Real-World Evidence From Over 144,000 People With Diabetes, detailing results from a retrospective analysis of real-world data from over 144,000 people with diabetes - one of the largest combined blood glucose meter and mobile diabetes app datasets ever published., October 2022: Becton, Dickinson, and Company and Biocorp announced that they had signed an agreement to use connected technology to track adherence to self-administered drug therapies, like biologics. To support biopharmaceutical companies in their efforts to improve the adherence and outcomes of injectable drugs, the two companies will integrate Biocorp's Injay technology-a solution designed to capture and transmit injection events using Near Field Communication technology to the BD UltraSafe Plus Passive Needle Guard used with pre-fillable syringes.. Notable trends are: The Continuous Glucose Monitoring Segment is expected to witness the highest growth rate over the forecast period.
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Heart Disease is among the most prevalent chronic diseases in the United States, impacting millions of Americans each year and exerting a significant financial burden on the economy. In the United States alone, heart disease claims roughly 647,000 lives each year — making it the leading cause of death. The buildup of plaques inside larger coronary arteries, molecular changes associated with aging, chronic inflammation, high blood pressure, and diabetes are all causes of and risk factors for heart disease. While there are different types of coronary heart disease, the majority of individuals only learn they have the disease following symptoms such as chest pain, a heart attack, or sudden cardiac arrest. This fact highlights the importance of preventative measures and tests that can accurately predict heart disease in the population prior to negative outcomes like myocardial infarctions (heart attacks) taking place
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The North American self-monitoring blood glucose (SMBG) market, valued at $8.10 billion in 2025, is projected to experience robust growth, driven by the increasing prevalence of diabetes and the rising geriatric population. The market's Compound Annual Growth Rate (CAGR) of 6.98% from 2019 to 2025 indicates a consistent upward trend. Key drivers include advancements in SMBG device technology, such as the development of more accurate, user-friendly, and cost-effective glucometers and lancets. Furthermore, increased awareness of diabetes management and the growing adoption of remote patient monitoring contribute significantly to market expansion. While data for individual countries within North America (United States, Canada, and Rest of North America) are not provided, we can infer that the United States likely holds the largest market share given its substantial population and higher prevalence of diabetes compared to Canada. The Rest of North America segment will likely exhibit moderate growth, reflecting the combined market dynamics of smaller countries in the region. Market restraints may include the potential for reimbursement challenges and the emergence of alternative glucose monitoring technologies, such as continuous glucose monitors (CGMs). However, the overall market outlook remains positive, fueled by the persistent need for effective diabetes management. Major players like Abbott, Roche, and LifeScan continue to innovate and compete, further shaping the market landscape. The forecast period (2025-2033) promises continued growth driven by technological advancements and an aging population requiring consistent blood glucose monitoring. The competitive landscape is characterized by established players like Abbott, Roche, and LifeScan, along with other significant contributors like Arkray, Ascensia, and Agamatrix. These companies are continuously striving for innovation to maintain their market share. This involves introducing technologically advanced devices, improving user experience, and developing more comprehensive diabetes management solutions. The increasing demand for accurate, convenient, and affordable SMBG devices, coupled with the growing awareness of diabetes self-management, will drive further market expansion. The incorporation of data analytics and connectivity features in SMBG devices also plays a crucial role in fostering better diabetes management and patient outcomes, thereby bolstering the market's growth trajectory. The focus on improving patient outcomes and reducing healthcare costs will likely shape future market developments, attracting further investments and technological advancements within the sector. Recent developments include: May 2023: LifeScan announced positive data from a study of real-world evidence supporting its Bluetooth-connected blood glucose meter. Evidence from more than 55,000 people with diabetes demonstrated sustained improvements in readings in range. The analysis focuses on changes over 180 days. LifeScan published results in the peer-reviewed journal Diabetes Therapy. The company’s OneTouch Bluetooth-connected blood glucose meter and mobile diabetes app provide simplicity, accuracy, and trust., January 2023: LifeScan announced that the peer-reviewed Journal of Diabetes Science and Technology published Improved Glycemic Control Using a Bluetooth-Connected Blood Glucose Meter and a Mobile Diabetes App: Real-World Evidence from Over 144,000 People With Diabetes, detailing results from a retrospective analysis of real-world data from over 144,000 people with diabetes is one of the largest combined blood glucose meter and mobile diabetes app datasets ever published.. Notable trends are: Blood Glucose Test Strips Held the Largest Market Share in Current Year.
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The global Diabetes Care Devices market, valued at approximately $XX million in 2025, is projected to experience robust growth, driven by rising diabetes prevalence globally, an aging population, and increasing adoption of advanced technologies like continuous glucose monitoring (CGM). The market's Compound Annual Growth Rate (CAGR) of 5.10% from 2025 to 2033 indicates a significant expansion, with substantial opportunities across various segments. Self-monitoring blood glucose (SMBG) devices, including glucometers, test strips, and lancets, continue to dominate the market due to their widespread accessibility and affordability. However, the CGM segment is witnessing exponential growth, fueled by its enhanced accuracy, convenience, and ability to provide real-time glucose data, leading to improved diabetes management. The increasing demand for insulin delivery devices, such as insulin pumps, syringes, pens, and jet injectors, also contributes significantly to market expansion. Technological advancements, including the development of integrated devices and improved sensor technologies, are further propelling market growth. The market is geographically diverse, with North America and Europe currently holding the largest market share due to high diabetes prevalence and developed healthcare infrastructure. However, Asia Pacific is emerging as a high-growth region, driven by rising disposable incomes and increasing healthcare awareness. Market restraints include the high cost of advanced devices, particularly CGMs and insulin pumps, which can limit accessibility in low- and middle-income countries. Furthermore, the need for regular calibration and potential inaccuracies associated with some devices present challenges. Despite these restraints, the continuous development of more affordable and user-friendly devices, coupled with supportive government initiatives and rising awareness campaigns, are expected to mitigate these challenges and drive market expansion throughout the forecast period. Key players in the market, including Abbott, Roche, Dexcom, Medtronic, and Novo Nordisk, are heavily invested in research and development, further fueling innovation and market competition. This competitive landscape will continue to shape the market dynamics, leading to advancements in technology and more accessible and affordable diabetes care solutions. Recent developments include: March 2023: Abbott announced that the U.S. Food and Drug Administration cleared its FreeStyle Libre 2 and FreeStyle Libre 3 integrated continuous glucose monitoring system sensors for integration with automated insulin delivery (AID) systems. Abbott modified the sensors to enable integration with AID systems., January 2023: LifeScan announced that the peer-reviewed Journal of Diabetes Science and Technology published Improved Glycemic Control Using a Bluetooth Connected Blood Glucose Meter and a Mobile Diabetes App: Real-World Evidence From Over 144,000 People With Diabetes, detailing results from a retrospective analysis of real-world data from over 144,000 people with diabetes - one of the largest combined blood glucose meter and mobile diabetes app datasets ever published.. Notable trends are: The continuous glucose monitoring segment is expected to witness a healthy growth rate over the forecast period.
Data for cities, communities, and City of Los Angeles Council Districts were generated using a small area estimation method which combined the survey data with population benchmark data (2022 population estimates for Los Angeles County) and neighborhood characteristics data (e.g., U.S. Census Bureau, 2017-2021 American Community Survey 5-Year Estimates). The current Physical Activity Guidelines for Americans is issued by the US Department of Health and Human Services. To meet physical activity guidelines, adults must meet aerobic physical activity guidelines (vigorous activity for at least 75 minutes a week, or moderate activity for at least 150 minutes a week, or a combination of vigorous and moderate activity for at least 150 minutes a week) and muscle-strengthening physical activity guidelines (exercise all major muscle groups on 2 or more days a week).Physical inactivity contributes to our current obesity epidemic and is a major risk factor for heart disease, diabetes, cancer, and many other chronic health conditions. It can be difficult for people to be physically active if their communities do not have available and safe places for recreation.For more information about the Community Health Profiles Data Initiative, please see the initiative homepage.
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Abstract (en): Overview: The goal of the project was to develop a unique database linking chronic disease clinical data from an electronic medical record (EMR) of a large academic healthcare system to multi-payer claims data. The longitudinal relational database can be used to study clinical effectiveness of many diagnostic and treatment interventions. The population of patients used consisted of those patients who were attributed to the University of Michigan Health System (UMHS) as continuing care patients, who are also in adjudicated and validated chronic disease registries. Data Access: These data are not available from ICPSR. The data are restricted to use by the principal investigator and cannot be shared. This project concerned AHRQ priority populations, including low income and uninsured patients, older adult patients, and patients with diabetes. The population of patients used consisted of those patients who were attributed to the UMHS as continuing care patients, who were also in adjudicated and validated chronic disease registries. These registries organize EMR diagnostic and management information for patients with physician adjudicated chronic disease diagnoses. Complete claims are available for most of the relevant patient population. Funding insitution(s): United States Department of Health and Human Services. Agency for Healthcare Research and Quality (R24 HS019459).
Data for cities, communities, and City of Los Angeles Council Districts were generated using a small area estimation method which combined the survey data with population benchmark data (2022 population estimates for Los Angeles County) and neighborhood characteristics data (e.g., U.S. Census Bureau, 2017-2021 American Community Survey 5-Year Estimates). Data for this indicator are based on self-reported height and weight. Body Mass Index (BMI) is calculated by dividing a person’s weight in kilograms by the square of their height in meters. Individuals with a BMI ≥ 30 are considered to have obesity. Note, while BMI can be helpful in screening for individuals with obesity or overweight, it does not measure how much body fat an individual has or provide any diagnostic information about their overall health.Obesity is associated with increased risk for heart disease, diabetes, and cancer. Cities and communities can help curb the current obesity epidemic by adopting policies that support healthy food retail and physical activity and improve access to preventive care services.For more information about the Community Health Profiles Data Initiative, please see the initiative homepage.
This map was developed by the University of New Mexico Prevention Research Center in partnership with the Step Into Cuba Alliance and New Mexico Community Data Collaborative. For more information about this map, please contact Andrea Cantarero (arcantarero@unm.edu).The map displays annual death rates per 100,000 population (age-adjusted) due to chronic disease over the 13 year period from 1999 to 2011. Rates are displayed overall as well as for each race/ethnicity and by sex, for all 109 NM Small Area geographies. Death rates are explored in context of the Village of Cuba proposal to increase access to the Continental Divide Trail to better serve the local community as well as the broader Hwy 550 Corridor population.NEW MEXICO STATE AVERAGESRate per 100,000 of Diabetes Deaths: 30.2Rate per 100,000 of Diabetes Deaths, Hispanic: 42.2Rate per 100,000 of Diabetes Deaths, White: 20.3Rate per 100,000 of Diabetes Deaths, Native American: 73.6Rate per 100,000 of Diabetes Deaths, African American: 47.8Rate per 100,000 of Diabetes Deaths, Asian or Pacific Islander: 23.2Diabetes deaths were defined as Diabetes mellitus (ICD10: E10-E14).Data Sources: New Mexico Death Certificate Database, Office of Vital Records and Statistics, New Mexico Department of Health; Population Estimates: University of New Mexico, Geospatial and Population Studies (GPS) Program, http://bber.unm.edu/bber_research_demPop.html. Retrieved Mon, 19 May 2014 from New Mexico Department of Health, Indicator-Based Information System for Public Health Web site: http://ibis.health.state.nm.usNOTES:New Mexico Small Areas are 109 geographic areas across the state with approximately equal population sizes (~20,000) that are just large enough to calculate rates for selected health events. For more information, please visit http://ibis.health.state.nm.us/resources/SmallAreaMethods.html.
Population-based county-level estimates for prevalence of DC were obtained from the Institute for Health Metrics and Evaluation (IHME) for the years 2004-2012 (16). DC prevalence rate was defined as the propor-tion of people within a county who had previously been diagnosed with diabetes (high fasting plasma glu-cose 126 mg/dL, hemoglobin A1c (HbA1c) of 6.5%, or diabetes diagnosis) but do not currently have high fasting plasma glucose or HbA1c for the period 2004-2012. DC prevalence estimates were calculated using a two-stage approach. The first stage used National Health and Nutrition Examination Survey (NHANES) data to predict high fasting plasma glucose (FPG) levels (≥126 mg/dL) and/or HbA1C levels (≥6.5% [48 mmol/mol]) based on self-reported demographic and behavioral characteristics (16). This model was then applied to Behavioral Risk Factor Surveillance System (BRFSS) data to impute high FPG and/or HbA1C status for each BRFSS respondent (16). The second stage used the imputed BRFSS data to fit a series of small area models, which were used to predict county-level prevalence of diabetes-related outcomes, including DC (16). The EQI was constructed for 2006-2010 for all US counties and is composed of five domains (air, water, built, land, and sociodemographic), each composed of variables to represent the environmental quality of that _domain. Domain-specific EQIs were developed using principal components analysis (PCA) to reduce these variables within each _domain while the overall EQI was constructed from a second PCA from these individual domains (L. C. Messer et al., 2014). To account for differences in environment across rural and urban counties, the overall and _domain-specific EQIs were stratified by rural urban continuum codes (RUCCs) (U.S. Department of Agriculture, 2015). Results are reported as prevalence rate differences (PRD) with 95% confidence intervals (CIs) comparing the highest quintile/worst environmental quality to the lowest quintile/best environmental quality expo-sure metrics. PRDs are representative of the entire period of interest, 2004-2012. Due to availability of DC data and covariate data, not all counties were captured, however, the majority, 3134 of 3142 were utilized in the analysis. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: Human health data are not available publicly. EQI data are available at: https://edg.epa.gov/data/Public/ORD/NHEERL/EQI. Format: Data are stored as csv files. This dataset is associated with the following publication: Jagai, J., A. Krajewski, K. Price, D. Lobdell, and R. Sargis. Diabetes control is associated with environmental quality in the USA. Endocrine Connections. BioScientifica Ltd., Bristol, UK, 10(9): 1018-1026, (2021).