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 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).

NY State Community Health Indicators

Obesity and Diabetes Related Indicators 2008–2012

By Health Data New York [source]

About this dataset

This dataset contains New York State county-level data on obesity and diabetes related indicators from 2008 - 2012. It includes information about counties' population health status, such as the number of events, percentage/rate, 95% confidence interval, measured units and more. Analyzing this data provides insight into how communities across New York State are impacted by these diseases and how we can work together to create healthier living environments for everyone. This dataset is released under a Terms of Service license agreement – make sure to read through and understand the details if you plan to use it in any research or commercial application

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How to use the dataset

This dataset contains county-level data on obesity and diabetes related indicators in New York State. As such, it can be used to research indicators related to general health in various counties of the state.

To use this dataset effectively, first become familiar with the columns included and their meanings: - County Name: The name of the county. (String) - County Code: The code of the county. (Integer) - Region Name: The name of the region. (String) - Indicator Number: The number of the indicator. (Integer) - Total Event Counts: The total number of events related to the indicator.(Integer)
- Denominator: The denominator used to calculate the percentage/rate.(Integer) - Denominator Note: Any additional notes related to the denominator.(String) - Measure Unit :The unit of measure used for this rate/percentage .(String). - Percentage/Rate :The percentage/rate calculated using denominator and observed count data .(Float). - 95% CI :The 95% confidence interval associated with any defined rate or percentage.(Float). - Data Comments :Any additional comments relevant to this data source or indicator .(String ). - Data Years :Years covered by this particular indicator observation .(String ). - Data Sources :Sources from which we have drawn our data for indicators involving counties from different regions .(Strings). - Quartile :Quartiles are derived when all geographic entities are ranked according to a specific metric score ,and are then cut into quartiles based on speed score =0= bottom quarter; =1= middle two quarters combined; =2= top quarter..(Integer). - Mapping Distribution ;A visual representation that includes mapping details regarding how Indicators relating either disease rates or characteristics are positioned across States, regions and counties as well as any trends plus other pertinent mapping information ,such as health resource availability.(In pair plot form form otherwise text will present an informational string.). Location ;Area where distribution around space occurs..e point feature with a single location ID retrieved from geoplanet proxy service.. (string ).

Using these columns, you can find out demographic information about your chosen county such as obesity rate and diabetes incidence etc., enabling you better understand its health situation overall. Additionally,this dataset also provides important comparison features such as quartiles rankings

Research Ideas

• Analysing the geographic distribution of obesity and diabetes related indicators by county in New York State, in order to identify areas which may require greater levels of intervention and preventative health measures.

• Evaluating trends over time for different counties to assess whether policies or programs have had an impact on indicators relating to obesity and diabetes within the given area.

• Using machine learning techniques such as clustering analysis or predictive modelling, to identify patterns within the data which can be used to better inform preventative health interventions across New York State

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: community-health-obesity-and-diabetes-related-indicators-2008-2012-1.csv | Column name | Description | |:-------------------------|:-----------------------------------------------------------------------------------------| | **Count...

This is the Google Search interest data that powers the Visualisation Searching For Health. Google Trends data allows us to see what people are searching for at a very local level. This visualization tracks the top searches for common health issues in the United States, from Cancer to Diabetes, and compares them with the actual location of occurrences for those same health conditions to understand how search data reflects life for millions of Americans.

How does search interest for top health issues change over time? From 2004–2017, the data shows that search interest gradually increased over the past few years. Certain regions show a more significant increase in search interest than others. The increase in search activity is greatest in the Midwest and Northeast, while the changes are noticeably less dramatic in California, Texas, and Idaho. Are people generally becoming more aware of health conditions and health risks?

The search interest data was collected using the Google Trends API. The visualisation also brings in incidences of each condition so they can be compared. The health conditions were hand-selected from the Community Health Status Indicators (CHSI) which provides key indicators for local communities in the United States. The CHSI dataset includes more than 200 measures for each of the 3,141 United States counties. More information about the CHSI can be found on healthdata.gov.

Many striking similarities exist between searches and actual conditions—but the relationship between the Obesity and Diabetes maps stands out the most. “There are many risk factors for type 2 diabetes such as age, race, pregnancy, stress, certain medications, genetics or family history, high cholesterol and obesity. However, the single best predictor of type 2 diabetes is overweight or obesity. Almost 90% of people living with type 2 diabetes are overweight or have obesity. People who are overweight or have obesity have added pressure on their body's ability to use insulin to properly control blood sugar levels, and are therefore more likely to develop diabetes.” —Obesity Society via obesity.org

Gross-Profit Time Series for Tandem Diabetes Care Inc. Tandem Diabetes Care, Inc. designs, develops, and commercializes technology solutions for people living with diabetes in the United States and internationally. The company's flagship product is the t:slim X2 insulin delivery system; and Tandem Mobi insulin pump, an automated insulin delivery system. It also sells single-use products, including cartridges for storing and delivering insulin, and infusion sets that connect the insulin pump to the user's body. In addition, the company offers Tandem Device Updater used to update the pump software from a personal computer; Tandem Source, a web-based data management platform, which provides a visual way to display diabetes therapy management data from the pumps, integrated CGMs; and Sugarmate, a mobile app used to help people visualize diabetes therapy data. It has collaboration agreement with the University of Virginia Center for Diabetes Technology for research and development of fully automated closed-loop insulin delivery systems. The company was formerly known as Phluid Inc. and changed its name to Tandem Diabetes Care, Inc. in January 2008. Tandem Diabetes Care, Inc. was incorporated in 2006 and is headquartered in San Diego, California.

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. 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.

The State Snapshots provide graphical representations of State-specific health care quality information, including strengths, weaknesses, and opportunities for improvement. The goal is to help State officials and their public- and private-sector partners better understand health care quality and disparities in their State. State-level information used to create the State Snapshots is based on data collected for the National Healthcare Quality Report (NHQR). The State Snapshots include summary measures of quality of care and States' performances relative to all States, the region, and best performing States by overall health care quality, types of care (preventive, acute, and chronic), settings of care (hospitals, ambulatory care, nursing home, and home health), and clinical conditions (cancer, diabetes, heart disease, maternal and child health, and respiratory diseases). Special focus areas on diabetes, asthma, Healthy People 2010, clinical preventive services, disparities, payer, and variation over time are also featured. The Agency for Healthcare Research and Quality (AHRQ) has released the State Snapshots each year in conjunction with the 2004 NHQR through the 2009 NHQR.

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.

Net-Income-Including-Non-Controlling-Interests Time Series for Tandem Diabetes Care Inc. Tandem Diabetes Care, Inc. designs, develops, and commercializes technology solutions for people living with diabetes in the United States and internationally. The company's flagship product is the t:slim X2 insulin delivery system; and Tandem Mobi insulin pump, an automated insulin delivery system. It also sells single-use products, including cartridges for storing and delivering insulin, and infusion sets that connect the insulin pump to the user's body. In addition, the company offers Tandem Device Updater used to update the pump software from a personal computer; Tandem Source, a web-based data management platform, which provides a visual way to display diabetes therapy management data from the pumps, integrated CGMs; and Sugarmate, a mobile app used to help people visualize diabetes therapy data. It has collaboration agreement with the University of Virginia Center for Diabetes Technology for research and development of fully automated closed-loop insulin delivery systems. The company was formerly known as Phluid Inc. and changed its name to Tandem Diabetes Care, Inc. in January 2008. Tandem Diabetes Care, Inc. was incorporated in 2006 and is headquartered in San Diego, California.

Rationale:

The accrual of data from the laboratory and from epidemiologic and prevention trials has improved the understanding of the etiology and pathogenesis of type 1 diabetes mellitus (T1DM). Genetic and immunologic factors play a key role in the development of T1DM, and characterization of the early metabolic abnormalities in T1DM is steadily increasing. However, information regarding the natural history of T1DM remains incomplete. The TrialNet Natural History Study of the Development of T1DM (Pathway to Prevention Study) has been designed to clarify this picture, and in so doing, will contribute to the development and implementation of studies aimed at prevention of and early treatment in T1DM.

Purpose:

TrialNet is an international network dedicated to the study, prevention, and early treatment of type 1 diabetes. TrialNet sites are located throughout the United States, Canada, Finland, United Kingdom, Italy, Germany, Sweden, Australia, and New Zealand. TrialNet is dedicated to testing new approaches to the prevention of and early intervention for type 1 diabetes.

The goal of the TrialNet Natural History Study of the Development of Type 1 Diabetes is to enhance our understanding of the demographic, immunologic, and metabolic characteristics of individuals at risk for developing type 1 diabetes.

The Natural History Study will screen relatives of people with type 1 diabetes to identify those at risk for developing the disease. Relatives of people with type 1 diabetes have about a 5% percent chance of being positive for the antibodies associated with diabetes. TrialNet will identify adults and children at risk for developing diabetes by testing for the presence of these antibodies in the blood. A positive antibody test is an early indication that damage to insulin-secreting cells may have begun. If this test is positive, additional testing will be offered to determine the likelihood that a person may develop diabetes. Individuals with antibodies will be offered the opportunity for further testing to determine their risk of developing diabetes over the next 5 years and to receive close monitoring for the development of diabetes.

Total-Cash-From-Operating-Activities Time Series for Tandem Diabetes Care Inc. Tandem Diabetes Care, Inc. designs, develops, and commercializes technology solutions for people living with diabetes in the United States and internationally. The company's flagship product is the t:slim X2 insulin delivery system; and Tandem Mobi insulin pump, an automated insulin delivery system. It also sells single-use products, including cartridges for storing and delivering insulin, and infusion sets that connect the insulin pump to the user's body. In addition, the company offers Tandem Device Updater used to update the pump software from a personal computer; Tandem Source, a web-based data management platform, which provides a visual way to display diabetes therapy management data from the pumps, integrated CGMs; and Sugarmate, a mobile app used to help people visualize diabetes therapy data. It has collaboration agreement with the University of Virginia Center for Diabetes Technology for research and development of fully automated closed-loop insulin delivery systems. The company was formerly known as Phluid Inc. and changed its name to Tandem Diabetes Care, Inc. in January 2008. Tandem Diabetes Care, Inc. was incorporated in 2006 and is headquartered in San Diego, California.

The share of the population with overweight in the United States was forecast to continuously increase between 2024 and 2029 by in total 1.6 percentage points. After the fifteenth consecutive increasing year, the overweight population share is estimated to reach 77.43 percent and therefore a new peak in 2029. Notably, the share of the population with overweight of was continuously increasing over the past years.Overweight is defined as a body mass index (BMI) of more than 25.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the share of the population with overweight in countries like Canada and Mexico.

Key Indicators of Heart Disease

2022 annual CDC survey data of 400k+ adults related to their health status

What subject does the dataset cover?

According to the CDC, heart disease is a leading cause of death for people of most races in the U.S. (African Americans, American Indians and Alaska Natives, and whites). About half of all Americans (47%) have at least 1 of 3 major risk factors for heart disease: high blood pressure, high cholesterol, and smoking. Other key indicators include diabetes status, obesity (high BMI), not getting enough physical activity, or drinking too much alcohol. Identifying and preventing the factors that have the greatest impact on heart disease is very important in healthcare. In turn, developments in computing allow the application of machine learning methods to detect "patterns" in the data that can predict a patient's condition.

Where did the data set come from and what treatments has it undergone?

The dataset originally comes from the CDC and is a major part of the Behavioral Risk Factor Surveillance System (BRFSS), which conducts annual telephone surveys to collect data on the health status of U.S. residents. As described by the CDC: "Established in 1984 with 15 states, BRFSS now collects data in all 50 states, the District of Columbia, and three U.S. territories. BRFSS completes more than 400,000 adult interviews each year, making it the largest continuously conducted health survey system in the world. The most recent dataset includes data from 2023. In this dataset, I noticed many factors (questions) that directly or indirectly influence heart disease, so I decided to select the most relevant variables from it. I also decided to share with you two versions of the most recent dataset: with NaNs and without it.

What can you do with this data set?

As described above, the original dataset of nearly 300 variables was reduced to 40variables. In addition to classical EDA, this dataset can be used to apply a number of machine learning methods, especially classifier models (logistic regression, SVM, random forest, etc.). You should treat the variable "HadHeartAttack" as binary ("Yes" - respondent had heart disease; "No" - respondent did not have heart disease). Note, however, that the classes are unbalanced, so the classic approach of applying a model is not advisable. Fixing the weights/undersampling should yield much better results. Based on the data set, I built a logistic regression model and embedded it in an application that might inspire you: https://share.streamlit.io/kamilpytlak/heart-condition-checker/main/app.py. Can you indicate which variables have a significant effect on the likelihood of heart disease?

What steps did you use to convert the dataset?

Check out this notebook in my GitHub repository: https://github.com/kamilpytlak/data-science-projects/blob/main/heart-disease-prediction/2022/notebooks/data_processing.ipynb

Aim: Non-alcoholic fatty liver disease (NAFLD) exhibits a racial disparity. We examined the prevalence and the association between race, gender, and NAFLD among prediabetes and diabetes populations among adults in the United States.Methods: We analyzed data for 3,190 individuals ≥18 years old from the National Health and Nutrition Examination Survey (NHANES) 2017–2018. NAFLD was diagnosed by FibroScan® using controlled attenuation parameter (CAP) values: S0 (none) < 238, S1 (mild) = 238–259, S2 (moderate) = 260–290, S3 (severe) > 290. Data were analyzed using Chi-square test and multinomial logistic regression, adjusting for confounding variables and considering the design and sample weights.Results: Of the 3,190 subjects, the prevalence of NAFLD was 82.6%, 56.4%, and 30.5% (p < 0.0001) among diabetes, prediabetes and normoglycemia populations respectively. Mexican American males with prediabetes or diabetes had the highest prevalence of severe NAFLD relative to other racial/ethnic groups (p < 0.05). In the adjusted model, among the total, prediabetes, and diabetes populations, a one unit increase in HbA1c was associated with higher odds of severe NAFLD [adjusted odds ratio (AOR) = 1.8, 95% confidence level (CI) = 1.4–2.3, p < 0.0001; AOR = 2.2, 95% CI = 1.1–4.4, p = 0.033; and AOR = 1.5, 95% CI = 1.1–1.9, p = 0.003 respectively].Conclusion: We found that prediabetes and diabetes populations had a high prevalence and higher odds of NAFLD relative to the normoglycemic population and HbA1c is an independent predictor of NAFLD severity in prediabetes and diabetes populations. Healthcare providers should screen prediabetes and diabetes populations for early detection of NAFLD and initiate treatments including lifestyle modification to prevent the progression to non-alcoholic steatohepatitis or liver cancer.

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.

The size of the Diabetes Care Devices Market in Latin America market was valued at USD XX Million in 2023 and is projected to reach USD XXX Million by 2032, with an expected CAGR of 5.64% during the forecast period. 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.. Key drivers for this market are: Increasing Demand for Better Dentistry and Better Aesthetic outcomes, Increase in the Disposable Income. Potential restraints include: High Cost Associated with the Digital Dentistry. Notable trends are: The Continuous Glucose Monitoring Segment is expected to witness the highest growth rate over the forecast period.

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.

Research-and-Development Time Series for Embecta Corp. Embecta Corp., a medical device company, focuses on the provision of various solutions to enhance the health and wellbeing of people living with diabetes in the United States and internationally. The company's products include pen needles, syringes, and safety injection devices, as well as digital applications to assist people with managing patient's diabetes. It primarily sells its products to wholesalers and distributors. The company was formerly known as Berra Newco, Inc. Embecta Corp. was founded in 1924 and is headquartered in Parsippany, New Jersey.

The goal is to predict the rate of heart disease (per 100,000 individuals) across the United States at the county-level from other socioeconomic indicators. The data is compiled from a wide range of sources and made publicly available by the United States Department of Agriculture Economic Research Service (USDA ERS).

There are 33 variables in this dataset. Each row in the dataset represents a United States county, and the dataset we are working with covers two particular years, denoted a, and b We don't provide a unique identifier for an individual county, just a row_id for each row.

The variables in the dataset have names that of the form category_variable, where category is the high level category of the variable (e.g. econ or health). variable is what the specific column contains.

We're trying to predict the variable heart_disease_mortality_per_100k (a positive integer) for each row of the test data set.

Columns

area — information about the county

area_rucc — Rural-Urban Continuum Codes "form a classification scheme that distinguishes metropolitan counties by the population size of their metro area, and nonmetropolitan counties by degree of urbanization and adjacency to a metro area. The official Office of Management and Budget (OMB) metro and nonmetro categories have been subdivided into three metro and six nonmetro categories. Each county in the U.S. is assigned one of the 9 codes." (USDA Economic Research Service, https://www.ers.usda.gov/data-products/rural-urban-continuum-codes/)

area_urban_influence — Urban Influence Codes "form a classification scheme that distinguishes metropolitan counties by population size of their metro area, and nonmetropolitan counties by size of the largest city or town and proximity to metro and micropolitan areas." (USDA Economic Research Service, https://www.ers.usda.gov/data-products/urban-influence-codes/)

econ — economic indicators

econ_economic_typology — County Typology Codes "classify all U.S. counties according to six mutually exclusive categories of economic dependence and six overlapping categories of policy-relevant themes. The economic dependence types include farming, mining, manufacturing, Federal/State government, recreation, and nonspecialized counties. The policy-relevant types include low education, low employment, persistent poverty, persistent child poverty, population loss, and retirement destination." (USDA Economic Research Service, https://www.ers.usda.gov/data-products/county-typology-codes.aspx)

econ_pct_civilian_labor — Civilian labor force, annual average, as percent of population (Bureau of Labor Statistics, http://www.bls.gov/lau/)

econ_pct_unemployment — Unemployment, annual average, as percent of population (Bureau of Labor Statistics, http://www.bls.gov/lau/)

econ_pct_uninsured_adults — Percent of adults without health insurance (Bureau of Labor Statistics, http://www.bls.gov/lau/) econ_pct_uninsured_children — Percent of children without health insurance (Bureau of Labor Statistics, http://www.bls.gov/lau/)

health — health indicators

health_pct_adult_obesity — Percent of adults who meet clinical definition of obese (National Center for Chronic Disease Prevention and Health Promotion)

health_pct_adult_smoking — Percent of adults who smoke (Behavioral Risk Factor Surveillance System)

health_pct_diabetes — Percent of population with diabetes (National Center for Chronic Disease Prevention and Health Promotion, Division of Diabetes Translation)

health_pct_low_birthweight — Percent of babies born with low birth weight (National Center for Health Statistics)

health_pct_excessive_drinking — Percent of adult population that engages in excessive consumption of alcohol (Behavioral Risk Factor Surveillance System, )

health_pct_physical_inacticity — Percent of adult population that is physically inactive (National Center for Chronic Disease Prevention and Health Promotion)

health_air_pollution_particulate_matter — Fine particulate matter in µg/m³ (CDC WONDER, https://wonder.cdc.gov/wonder/help/pm.html)

health_homicides_per_100k — Deaths by homicide per 100,000 population (National Center for Health Statistics)

health_motor_vehicle_crash_deaths_per_100k — Deaths by motor vehicle crash per 100,000 population (National Center for Health Statistics)

health_pop_per_dentist — Population per dentist (HRSA Area Resource File)

health_pop_per_primary_care_physician — Population per Primary Care Physician (HRSA Area Resource File)

demo — demographics information

demo_pct_female — Percent of population that is female (US Census Population Estimates)

demo_pct_below_18_years_of_age — Percent of population that is below 18 years of age (US Census Population Estimates)

demo_pct_aged_65_years_and_older — Percent of population that is aged 65 years or older (US Census Population Estimates)

dem...

West Virginia, Mississippi, and Arkansas are the U.S. states with the highest percentage of their population who are obese. The states with the lowest percentage of their population who are obese include Colorado, Hawaii, and Massachusetts. Obesity in the United States Obesity is a growing problem in many countries around the world, but the United States has the highest rate of obesity among all OECD countries. The prevalence of obesity in the United States has risen steadily over the previous two decades, with no signs of declining. Obesity in the U.S. is more common among women than men, and overweight and obesity rates are higher among African Americans than any other race or ethnicity. Causes and health impacts Obesity is most commonly the result of a combination of poor diet, overeating, physical inactivity, and a genetic susceptibility. Obesity is associated with various negative health impacts, including an increased risk of cardiovascular diseases, certain types of cancer, and diabetes type 2. As of 2022, around 8.4 percent of the U.S. population had been diagnosed with diabetes. Diabetes is currently the eighth leading cause of death in the United States.