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).

It was estimated that as of 2023, around **** million people in the United States had been diagnosed with diabetes. The number of people diagnosed with diabetes in the U.S. has increased in recent years and the disease is now a major health issue. Diabetes is now the seventh leading cause of death in the United States, accounting for ******percent of all deaths. What is prediabetes? A person is considered to have prediabetes if their blood sugar levels are higher than normal but not high enough to be diagnosed with type 2 diabetes. As of 2021, it was estimated that around ** million men and ** million women in the United States had prediabetes. However, according to the CDC, around ** percent of these people do not know they have this condition. Not only does prediabetes increase the risk of developing type 2 diabetes, but also increases the risk of heart disease and stroke. The states with the highest share of adults who had ever been told they have prediabetes are California, Hawaii, and New Mexico. The prevalence of diabetes in the United States As of 2023, around *** percent of adults in the United States had been diagnosed with diabetes, an increase from ****percent in the year 2000. Diabetes is much more common among older adults, with around ** percent of those aged 60 years and older diagnosed with diabetes, compared to just ****percent of those aged 20 to 39 years. The states with the highest prevalence of diabetes among adults are West Virginia, Mississippi, and Louisiana, while Utah and Colorado report the lowest rates. In West Virginia, around ** percent of adults have been diagnosed with diabetes.

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).

Age-adjusted rate of diabetes deaths by sex, race/ethnicity, age; trends if available. Source: Santa Clara County Public Health Department, VRBIS, 2007-2016. Data as of 05/26/2017; U.S. Census Bureau; 2010 Census, Tables PCT12, PCT12H, PCT12I, PCT12J, PCT12K, PCT12L, PCT12M; generated by Baath M.; using American FactFinder; Accessed June 20, 2017. METADATA:Notes (String): Lists table title, notes and sources.Year (Numeric): Year of data.Category (String): Lists the category representing the data: Santa Clara County is for total population, sex: Male and Female, race/ethnicity: African American, Asian/Pacific Islander, Latino and White (non-Hispanic White only); age categories as follows: 18 to 24, 25 to 34, 35 to 44, 45 to 54, 55 to 64, 65 to 74, 75 to 84, 85+; United States.Rate per 100,000 people (Numeric): Rate of diabetes deaths. Rates for age groups are reported as age-specific rates per 100,000 people. All other rates are age-adjusted rates per 100,000 people.

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.

In 2023, West Virginia had the highest share of adults (**** percent) who had ever been told by a doctor that they had diabetes. This statistic represents the percentage of adults in the United States who had ever been diagnosed diabetes as of 2023, by state.

Around ** percent of the global adult population suffered from diabetes in 2024 - by the year 2050 this number is expected to rise to ** percent. Diabetes, or diabetes mellitus, refers to a group of metabolic disorders that result in chronic high blood sugar levels. Diabetes can lead to serious health complications, such as cardiovascular disease, chronic kidney disease, and stroke, and is now among the top ten leading causes of death worldwide. Prevalence Diabetes is a global problem affecting many countries. China currently has the largest number of diabetics worldwide, with some *** million people suffering from the disease. However, the highest prevalence of diabetes is found in Pakistan, followed by the Marshall Islands and Kuwait. Rates of diabetes have increased in many countries in recent years, as have rates of obesity, one of the leading risk factors for the disease. Outlook It is predicted that diabetes will continue to be a problem in the future. Africa is expected to see a *** percent increase in the number of diabetics in the region from 2024 to 2050, while North America and the Caribbean are expected to see an increase of ** percent. In 2050, China is predicted to be the country with the highest number of diabetics worldwide, with the United States accounting for the fourth-highest number.

United States US: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70 data was reported at 14.600 % in 2016. This records an increase from the previous number of 14.300 % for 2015. United States US: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70 data is updated yearly, averaging 14.600 % from Dec 2000 (Median) to 2016, with 5 observations. The data reached an all-time high of 18.000 % in 2000 and a record low of 14.300 % in 2015. United States US: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70 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. Mortality from CVD, cancer, diabetes or CRD is the percent of 30-year-old-people who would die before their 70th birthday from any of cardiovascular disease, cancer, diabetes, or chronic respiratory disease, assuming that s/he would experience current mortality rates at every age and s/he would not die from any other cause of death (e.g., injuries or HIV/AIDS).; ; World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).; Weighted Average;

The country projected to have the highest number of diabetics in 2050 is China, with some *** million people between the ages of 20 and 79 expected to be suffering from diabetes at that time. It is forecast that the number of adults with the condition in Pakistan will exceed that of the United States by 2050. Number of diabetics worldwide Diabetes is a condition that causes the pancreas to stop (or severely reduce) the production of insulin, a hormone needed to regulate blood sugar levels. Many people worldwide live with diabetes: in 2024, a total of almost *** million people had diabetes. This number is projected to reach an estimated ***** million by the year 2050. Global diabetes costs In 2024, just over************* U.S. dollars were spent on diabetes-related healthcare treatment worldwide. Of 2024’s global total, around ***** billion U.S. dollars were spent in the United States alone. Global healthcare expenditures for diabetes are expected to increase by ** billion U.S. dollars by 2050.

The US diabetes devices market, a significant segment of the global market, is experiencing robust growth, driven by the rising prevalence of diabetes and an aging population. The market, valued at approximately $25.16 billion in 2025, is projected to expand at a Compound Annual Growth Rate (CAGR) of 6.27% from 2025 to 2033. This growth is fueled by several key factors: increasing adoption of continuous glucose monitoring (CGM) systems offering improved diabetes management, technological advancements leading to smaller, more user-friendly devices, and growing awareness of the benefits of proactive diabetes care. The market is segmented into monitoring and management devices. Within monitoring, self-monitoring blood glucose (SMBG) devices, including glucometers, test strips, and lancets, remain a substantial portion, although CGM is experiencing faster growth due to its real-time data capabilities and improved patient outcomes. The management device segment is dominated by insulin delivery systems like insulin pumps, syringes, pens, and jet injectors, with insulin pumps showing particularly strong growth prospects due to their convenience and efficacy in managing insulin delivery. Competitive intensity is high, with major players like Abbott, Medtronic, Dexcom, and Novo Nordisk vying for market share through continuous innovation and strategic partnerships. The North American region, particularly the US, holds a significant market share owing to high diabetes prevalence, advanced healthcare infrastructure, and strong regulatory support for innovative medical technologies. The market's growth trajectory is expected to remain positive through 2033, although certain restraints could influence the pace. These include high costs associated with some devices, particularly CGMs and insulin pumps, creating access barriers for some patients. However, increasing insurance coverage and the development of more affordable alternatives are mitigating these challenges. Furthermore, technological advancements, such as the integration of artificial intelligence and machine learning in diabetes management, are expected to further enhance the market's potential. Companies are focusing on developing integrated systems that combine CGM with insulin delivery, providing a more holistic approach to diabetes management. This trend will likely drive further growth and consolidation within the market. Future success will depend on companies' ability to innovate, offer competitive pricing strategies, and address the evolving needs of patients. Recent developments include: August 2023: The US Food and Drug Administration (FDA) has granted clearance for Roche's Accu-Chek Solo micropump system, a tubing-free "patch" pump for people with diabetes who use insulin., March 2022: Dexcom released G7 first in the U.K. and was expected to expand the launch across Europe throughout 2022. Meanwhile, the CGM system currently is under review by the Food and Drug Administration for an eventual U.S. release.. Key drivers for this market are: Increasing Number of Preterm and Low-weight Births, Advanced Technology in Fetal and Prenatal Monitoring. Potential restraints include: Stringent Regulatory Procedures. Notable trends are: Growing Diabetes and Obesity Population in the United States.

Snapshot img

Type 2 Diabetes Market Size 2025-2029

The type 2 diabetes market size is forecast to increase by USD 53.8 billion, at a CAGR of 12.2% between 2024 and 2029.

The market is experiencing significant growth due to the rising prevalence of diabetes, fueled by an increasing geriatric population. This demographic trend, coupled with the underdiagnosis of the condition, presents both opportunities and challenges for market participants. The large, untapped population of undiagnosed individuals represents a significant market opportunity for innovative diagnostic solutions. However, the low diagnosis rate also poses a challenge, as untreated diabetes can lead to severe health complications and increased healthcare costs. The geriatric population, with its higher susceptibility to diabetes, further exacerbates this challenge.
To capitalize on the market opportunities and navigate these challenges effectively, companies must focus on developing innovative diagnostic tools and effective patient education programs. By addressing the unmet needs in diabetes diagnosis and management, market participants can differentiate themselves and capture a larger share of this growing market.

What will be the Size of the Type 2 Diabetes Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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The market continues to evolve, driven by advancements in diabetes management, public health, and weight management. Pancreatic beta-cell destruction and the subsequent need for insulin therapy remain at the forefront of market dynamics. Diabetes burden is a significant concern, with diabetic nephropathy, diabetic retinopathy, and diabetic neuropathy posing significant health risks. SGLT2 inhibitors have emerged as a promising treatment option, addressing both glycemic control and cardiovascular disease. Healthcare access and access to care are crucial factors shaping the market. Global health initiatives and healthcare costs are influencing the development of diabetes technology, including insulin delivery devices, glucose sensors, and remote monitoring.

Precision medicine, data analytics, and machine learning are transforming diabetes education and patient engagement. Lifestyle modifications, such as dietary changes and physical activity, remain essential components of diabetes management. Insulin resistance, insulin therapy, and HbA1c testing continue to be key areas of focus in the medical device industry. Ongoing research in areas like Genetic Testing, Stem Cell Therapy, and primary care physician engagement is further shaping the market. Health disparities, early detection, and glycemic control are critical public health concerns. Diabetes prevention and healthcare provider collaboration are essential to improving health outcomes. The market is continually unfolding, with ongoing drug development, clinical trials, and advancements in diabetes technology driving innovation.

How is this Type 2 Diabetes Industry segmented?

The type 2 diabetes industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Drug Class

  Insulin
  DPP-4 inhibitor
  GLP-1 receptor agonists
  SGLT-2 inhibitors
  Others


Distribution Channel

  Retail pharmacies
  Hospital pharmacies
  Online pharmacies


Delivery Mode

  Oral
  Injectable
  Continuous glucose monitoring (CGM)


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan
    South Korea


  Rest of World (ROW)

.

By Drug Class Insights

The insulin segment is estimated to witness significant growth during the forecast period.

Type 2 diabetes is a complex condition characterized by insulin resistance and pancreatic beta-cell dysfunction, leading to elevated blood glucose levels. Metabolic syndrome, an autoimmune disease, and a precursor to type 2 diabetes, affects millions worldwide. The medical device industry is responding with innovative solutions, such as artificial pancreas systems and insulin delivery devices, to improve glycemic control and enhance quality of life. Artificial intelligence and machine learning are revolutionizing diabetes management by facilitating early detection, personalized treatment plans, and remote monitoring. Diabetes burden is significant, with complications including diabetic foot ulcers, diabetic retinopathy, diabetic neuropathy, and diabetic nephropathy.

Prevention and management involve lifestyle modifications, such as dietary changes and physical activity, as well as pharmacological interventions, including oral antidiabetic medications, insulin therapy, and newer classes like GLP-1 re

The Western Pacific region had the highest number of diabetics worldwide in 2024, with approximately 215 million people aged 20-79 suffering from the disease. This staggering figure underscores the global diabetes epidemic, which affected nearly 589 million individuals worldwide in 2024. The prevalence of diabetes is forecasted to rise to about 13 percent of the global population by 2050, highlighting the urgent need for improved prevention and treatment strategies. Regional disparities and future projections While the Western Pacific leads in absolute numbers, other regions also face significant diabetes burdens. Southeast Asia follows with 106.9 million diabetics, while the Middle East and North Africa have 84.7 million cases. Looking ahead, Africa is expected to see a 142 percent increase in diabetes cases from 2024 to 2050, while North America and the Caribbean may experience a 21 percent rise. China is anticipated to maintain its position as the country with the highest number of diabetics worldwide by 2050. Global health concerns and diabetes impact Diabetes remains a critical health issue globally, contributing to various complications such as cardiovascular disease and chronic kidney disease. In 2021, diabetes was the eighth leading cause of death worldwide. That year, around 1.62 million people died due to diabetes. The financial impact of diabetes is also significant and expected to increase in the coming decades.

Decrease the percentage of people with Type 2 diabetes from 11.2% in 2014 to 10.1% by 2019.

United States US: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70: Female data was reported at 11.800 NA in 2016. This records an increase from the previous number of 11.600 NA for 2015. United States US: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70: Female data is updated yearly, averaging 11.800 NA from Dec 2000 (Median) to 2016, with 5 observations. The data reached an all-time high of 14.600 NA in 2000 and a record low of 11.600 NA in 2015. United States US: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70: Female data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Health Statistics. Mortality from CVD, cancer, diabetes or CRD is the percent of 30-year-old-people who would die before their 70th birthday from any of cardiovascular disease, cancer, diabetes, or chronic respiratory disease, assuming that s/he would experience current mortality rates at every age and s/he would not die from any other cause of death (e.g., injuries or HIV/AIDS).; ; World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).; Weighted average;

IntroductionMany have argued that a “one-size-fits-all” approach to designing digital health is not optimal and that personalisation is essential to achieve targeted outcomes. Yet, most digital health practitioners struggle to identify which design aspect require personalisation. Personas are commonly used to communicate patient needs in consumer-oriented digital health design, however there is often a lack of reproducible clarity on development process and few attempts to assess their accuracy against the targeted population. In this study, we present a transparent approach to designing and validating personas, as well as identifying aspects of “patient work,” defined as the combined total of work tasks required to manage one's health and the contextual factors influencing such tasks, that are sensitive to an individual's context and may require personalisation.MethodsA data-driven approach was used to develop and validate personas for people with Type 2 diabetes mellitus (T2DM), focusing on patient work. Eight different personas of T2DM patient work were constructed based physical activity, dietary control and contextual influences of 26 elderly Australian participants (median age = 72 years) via wearable camera footage, interviews, and self-reported diaries. These personas were validated for accuracy and perceived usefulness for design, both by the original participants and a younger (median age bracket = 45–54 years) independent online cohort f 131 T2DM patients from the United Kingdom and the United States.ResultsBoth the original participants and the independent online cohort reported the personas to be accurate representations of their patient work routines. For the independent online cohort, 74% (97/131) indicated personas stratified to their levels of exercise and diet control were similar to their patient work routines. Findings from both cohorts highlight aspects that may require personalisation include daily routine, use of time, and social context.ConclusionPersonas made for a specific purpose can be very accurate if developed from real-life data. Our personas retained their accuracy even when tested against an independent cohort, demonstrating their generalisability. Our data-driven approach clarified the often non-transparent process of persona development and validation, suggesting it is possible to systematically identify whether persona components are accurate or. and which aspects require more personalisation and tailoring.

The size of the United States Self-Monitoring Blood Glucose Market was valued at USD 7.62 Million in 2023 and is projected to reach USD 11.76 Million by 2032, with an expected CAGR of 6.40% during the forecast period. SMBG, therefore, is an essential blood glucose tool for a person to take good care of his or her diabetes. In this technique, patients are empowered to choose their insulin dosage and diet, while making wise decisions in terms of exercise. This would prevent the complications of diabetes, such as heart disease, stroke, and renal failure. Rapidly increasing incidence of diabetes has accounted for significant growth in the market for SMBG in the United States. This market is also accelerated by heightened awareness of the requirement for tight blood sugar control and advancements in SMBG devices technology. SMBG devices-glucose meters, test strips, and lancets-are available over-the-counter through pharmacies and clinics as well as online merchants. The use of SMBG is very vital in the management of diabetes. Constant monitoring of blood glucose has benefited both type 1 and type 2 diabetic patients. Blood sugar fluctuation monitoring helps a person understand the fluctuations of blood sugar levels in their body and, by adapting lifestyle and medication, helps them gain control over their health, thus leading a better quality of life. 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.. 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: Rising Diabetes Prevalence in the United States.

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Blood Glucose Monitoring System Market Size 2024-2028

The blood glucose monitoring system market size is forecast to increase by USD 3.68 billion at a CAGR of 6.3% between 2023 and 2028.

The market is witnessing significant growth due to several key factors. One of the primary drivers is the increasing patient pool, particularly those with Type 1 and Type 2 diabetes. The demand for continuous glucose monitoring (CGM) systems is on the rise, as these devices provide real-time monitoring of blood glucose levels, enabling better management of diabetes. Additionally, medical organizations and healthcare professionals are advocating for regular blood glucose testing to improve patient outcomes. Reimbursement for blood glucose monitoring devices is also becoming more common, making them more affordable for patients. The cost of devices, including glucose meters and continuous glucose monitoring systems, is a critical factor influencing market growth. Non-invasive glucose monitoring technologies are also gaining popularity due to their convenience and ease of use. Overall, the blood glucose monitoring market is expected to continue growing due to the increasing prevalence of diabetes and the availability of advanced monitoring technologies.

What will the size of the market be during the forecast period?

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The diabetes population in North America continues to expand, necessitating advanced solutions for effective blood glucose management. Diabetes management is a critical aspect of maintaining overall health and preventing complications. This market analysis focuses on the latest trends and innovations in blood glucose monitoring systems. Blood glucose monitoring plays a pivotal role in diabetes self-management, enabling individuals to closely monitor their blood sugar levels and adjust insulin dosages accordingly. Machine learning and artificial intelligence are being integrated into monitoring devices to improve accuracy and patient comfort. The importance of regular monitoring cannot be overstated, as maintaining optimal blood sugar control is essential for preventing complications and ensuring clinical efficiency.
Gestational diabetes, a form of diabetes that develops during pregnancy, also requires consistent monitoring to ensure proper care for both mother and baby. Hospitals and clinics have adopted various blood glucose monitoring systems, including both wearable and non-wearable options, to cater to the unique needs of their patients. Wearable glucose monitors, such as continuous glucose monitoring systems (CGMs), offer real-time data, enabling patients to make informed decisions about their diabetes care. Non-wearable glucose monitors, on the other hand, provide accurate readings through minimally invasive procedures, like alternate site testing or fingertip testing. The market for blood glucose monitoring systems is witnessing growth due to the increasing demand for less invasive technologies. These technologies offer convenience and improved patient experience, making them a popular choice among patients. Invasive technologies, while effective, may not be preferred due to their discomfort and potential complications. Diabetes care extends beyond monitoring blood sugar levels. Patients require access to comprehensive resources, including diabetes education, dietary guidance, and lifestyle modifications.

How is this market segmented and which is the largest segment?

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Product

  Self monitoring
  Continuous glucose monitoring devices


Geography

  North America

    Canada
    US


  Europe

    Germany
    UK


  Asia

    China


  Rest of World (ROW)

By Product Insights

The self monitoring segment is estimated to witness significant growth during the forecast period.

The self-monitoring segment of the market is poised for substantial growth due to the rising prevalence of diabetes worldwide. In North America, the United States and Canada have a significant diabetic patient population, and diabetes incidences are projected to increase at a notable rate in these regions during the forecast period. Patients in North America predominantly opt for Self-Monitoring Blood Glucose (SMBG) devices over other types for their convenience, portability, and accuracy.

Consequently, these devices are non-invasive and consume minimal power, making them a preferred choice for patients. With the advent of advanced SMBG technology, the market is anticipated to experience a considerable increase in sales during the forecast period. Healthcare professionals and medical organizations recommend real-time monitoring of blood glucose levels to effectively manage diabetes, further

analyze the national health and nutrition examination survey (nhanes) with r nhanes is this fascinating survey where doctors and dentists accompany survey interviewers in a little mobile medical center that drives around the country. while the survey folks are interviewing people, the medical professionals administer laboratory tests and conduct a real doctor's examination. the b lood work and medical exam allow researchers like you and me to answer tough questions like, "how many people have diabetes but don't know they have diabetes?" conducting the lab tests and the physical isn't cheap, so a new nhanes data set becomes available once every two years and only includes about twelve thousand respondents. since the number of respondents is so small, analysts often pool multiple years of data together. the replication scripts below give a few different examples of how multiple years of data can be pooled with r. the survey gets conducted by the centers for disease control and prevention (cdc), and generalizes to the united states non-institutional, non-active duty military population. most of the data tables produced by the cdc include only a small number of variables, so importation with the foreign package's read.xport function is pretty straightforward. but that makes merging the appropriate data sets trickier, since it might not be clear what to pull for which variables. for every analysis, start with the table with 'demo' in the name -- this file includes basic demographics, weighting, and complex sample survey design variables. since it's quick to download the files directly from the cdc's ftp site, there's no massive ftp download automation script. this new github repository co ntains five scripts: 2009-2010 interview only - download and analyze.R download, import, save the demographics and health insurance files onto your local computer load both files, limit them to the variables needed for the analysis, merge them together perform a few example variable recodes create the complex sample survey object, using the interview weights run a series of pretty generic analyses on the health insurance ques tions 2009-2010 interview plus laboratory - download and analyze.R download, import, save the demographics and cholesterol files onto your local computer load both files, limit them to the variables needed for the analysis, merge them together perform a few example variable recodes create the complex sample survey object, using the mobile examination component (mec) weights perform a direct-method age-adjustment and matc h figure 1 of this cdc cholesterol brief replicate 2005-2008 pooled cdc oral examination figure.R download, import, save, pool, recode, create a survey object, run some basic analyses replicate figure 3 from this cdc oral health databrief - the whole barplot replicate cdc publications.R download, import, save, pool, merge, and recode the demographics file plus cholesterol laboratory, blood pressure questionnaire, and blood pressure laboratory files match the cdc's example sas and sudaan syntax file's output for descriptive means match the cdc's example sas and sudaan synta x file's output for descriptive proportions match the cdc's example sas and sudaan syntax file's output for descriptive percentiles replicate human exposure to chemicals report.R (user-contributed) download, import, save, pool, merge, and recode the demographics file plus urinary bisphenol a (bpa) laboratory files log-transform some of the columns to calculate the geometric means and quantiles match the 2007-2008 statistics shown on pdf page 21 of the cdc's fourth edition of the report click here to view these five scripts for more detail about the national health and nutrition examination survey (nhanes), visit: the cdc's nhanes homepage the national cancer institute's page of nhanes web tutorials notes: nhanes includes interview-only weights and interview + mobile examination component (mec) weights. if you o nly use questions from the basic interview in your analysis, use the interview-only weights (the sample size is a bit larger). i haven't really figured out a use for the interview-only weights -- nhanes draws most of its power from the combination of the interview and the mobile examination component variables. if you're only using variables from the interview, see if you can use a data set with a larger sample size like the current population (cps), national health interview survey (nhis), or medical expenditure panel survey (meps) instead. confidential to sas, spss, stata, sudaan users: why are you still riding around on a donkey after we've invented the internal combustion engine? time to transition to r. :D

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.