This dataset contains information on the proportion by age, total number, male and female and sex of adults of adults diagnosed with diabetes, collected from the system of health-related telephone surveys, the Behavioral Risk Factor Surveillance System (BRFSS), conducted in more than 400,000 patients, from 50 states in the US, the District of Columbia and three US territories.

It was estimated that as of 2022 around 24.4 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 eighth leading cause of death in the United States, accounting for three 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 53 million men and 44 million women in the United States had prediabetes. However, according to the CDC, around 80 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 Hawaii, California, and Alaska.

The prevalence of diabetes in the United States As of 2022, around 8.4 percent of adults in the United States had been diagnosed with diabetes, an increase from six percent in the year 2000. Diabetes is much more common among older adults, with almost a quarter of those aged 65 years and older diagnosed with diabetes, compared to just three percent of those aged 18 to 44 years. The states with the highest prevalence of diabetes among adults are Alabama, Mississippi, and West Virginia, while Colorado and Alaska report the lowest rates. In Alabama, around 17 percent of adults have been diagnosed with diabetes.

Health, United States is an annual report on trends in health statistics, find more information at http://www.cdc.gov/nchs/hus.htm.

Visualizations and data related to diagnosed Diabetes among various age groups, genders, race and ethnicity, education and stratifications.

This is a source dataset for a Let's Get Healthy California indicator at "https://letsgethealthy.ca.gov/. This table displays the prevalence of diabetes in California. It contains data for California only. The data are from the California Behavioral Risk Factor Surveillance Survey (BRFSS). The California BRFSS is an annual cross-sectional health-related telephone survey that collects data about California residents regarding their health-related risk behaviors, chronic health conditions, and use of preventive services. The BRFSS is conducted by Public Health Survey Research Program of California State University, Sacramento under contract from CDPH. This prevalence rate does not include pre-diabetes, or gestational diabetes. This is based on the question: "Has a doctor, or nurse or other health professional ever told you that you have diabetes?" The sample size for 2014 was 8,832. NOTE: Denominator data and weighting was taken from the California Department of Finance, not U.S. Census. Values may therefore differ from what has been published in the national BRFSS data tables by the Centers for Disease Control and Prevention (CDC) or other federal agencies.

Context Diabetes is one of the most prevalent chronic diseases in the United States, affecting millions of Americans each year and placing a substantial financial burden on the economy. It is a serious chronic condition in which the body loses the ability to effectively regulate blood glucose levels, leading to a reduced quality of life and decreased life expectancy. During digestion, food is broken down into sugars, which enter the bloodstream. This triggers the pancreas to release insulin, a hormone that helps cells in the body use these sugars for energy. Diabetes is typically characterized by either insufficient insulin production or the body's inability to use insulin effectively.

Chronic high blood sugar levels in individuals with diabetes can lead to severe complications, including heart disease, vision loss, kidney disease, and lower-limb amputation. Although there is no cure for diabetes, strategies such as maintaining a healthy weight, eating a balanced diet, staying physically active, and receiving medical treatments can help mitigate its effects. Early diagnosis is crucial, as it allows for lifestyle modifications and more effective treatment, making predictive models for assessing diabetes risk valuable tools for public health officials.

The scale of the diabetes epidemic is significant. According to the Centers for Disease Control and Prevention (CDC), as of 2018, approximately 34.2 million Americans have diabetes, while 88 million have prediabetes. Alarmingly, the CDC estimates that 1 in 5 individuals with diabetes and about 8 in 10 individuals with prediabetes are unaware of their condition. Type II diabetes is the most common form, and its prevalence varies based on factors such as age, education, income, geographic location, race, and other social determinants of health. The burden of diabetes disproportionately affects those with lower socioeconomic status. The economic impact is also substantial, with the cost of diagnosed diabetes reaching approximately $327 billion annually, and total costs, including undiagnosed diabetes and prediabetes, nearing $400 billion each year.

Content The Behavioral Risk Factor Surveillance System (BRFSS) is a health-related telephone survey that is collected annually by the CDC. Each year, the survey collects responses from over 400,000 Americans on health-related risk behaviors, chronic health conditions, and the use of preventative services. It has been conducted every year since 1984. For this project, a XPT of the dataset available on CDC website for the year 2023 was used. This original dataset contains responses from 433,323 individuals and has 345 features. These features are either questions directly asked of participants, or calculated variables based on individual participant responses.

I have selected 20 features from this dataset that are suitable for working on the topic of diabetes, and I have saved them in a CSV file without making any changes to the data. The goal of this is to make it easier to work with the data. For more information or to access updated data, you can refer to the CDC website. I initially examined the original dataset from the CDC and found no duplicate entries. That dataset contains 330 columns and features. Therefore, the duplicate cases in this dataset are not due to errors but rather represent individuals with similar conditions. In my opinion, removing these entries would both introduce errors and reduce accuracy.

Explore some of the following research questions: - Can survey questions from the BRFSS provide accurate predictions of whether an individual has diabetes? - What risk factors are most predictive of diabetes risk? - Can we use a subset of the risk factors to accurately predict whether an individual has diabetes? - Can we create a short form of questions from the BRFSS using feature selection to accurately predict if someone might have diabetes or is at high risk of diabetes?

Acknowledgements It is important to reiterate that I did not create this dataset, it is simply a summarized and reformatted dataset derived from the BRFSS 2023 dataset available on the CDC website. It is also worth noting that none of the data in this dataset discloses individuals' identities.

Inspiration Zidian Xie et al for Building Risk Prediction Models for Type 2 Diabetes Using Machine Learning Techniques using the 2014 BRFSS, and Alex Teboul for building Diabetes Health Indicators dataset based on BRFSS 2015 were the inspiration for creating this dataset and exploring the BRFSS in general.

The crude prevalence rate of diabetes is defined as the ratio of respondents that are 18 years or older who have ever been told by a health professional that they had diabetes (other than during pregnancy) over the total number of respondents in the study (excluding those who refused to answer, had a missing answer, or answered “don’t know/not sure”).Prevalence data are derived from Behavioral Risk Factor Surveillance System (BRFSS) (numerator) and population estimates from the U.S. Census Bureau (denominator).The 500 Cities Project seeks to provide city- and census tract-level small area estimates for chronic disease risk factors, health outcomes, and clinical preventive service use for the largest 500 cities in the United States.Data source: CDC (Division of Population Health, National Center for Chronic Disease Prevention and Health Promotion)Date: 2015

Provisional death counts of diabetes, coronavirus disease 2019 (COVID-19) and other select causes of death, by month, sex, and age.

Context

The National Health and Nutrition Examination Survey (NHANES) is a program of studies designed to assess the health and nutritional status of adults and children in the United States. The survey is unique in that it combines interviews and physical examinations. NHANES is a major program of the National Center for Health Statistics (NCHS). NCHS is part of the Centers for Disease Control and Prevention (CDC) and has the responsibility for producing vital and health statistics for the Nation.

The NHANES program began in the early 1960s and has been conducted as a series of surveys focusing on different population groups or health topics. In 1999, the survey became a continuous program that has a changing focus on a variety of health and nutrition measurements to meet emerging needs. The survey examines a nationally representative sample of about 5,000 persons each year. These persons are located in counties across the country, 15 of which are visited each year.

The NHANES interview includes demographic, socioeconomic, dietary, and health-related questions. The examination component consists of medical, dental, and physiological measurements, as well as laboratory tests administered by highly trained medical personnel.

To date, thousands of research findings have been published using the NHANES data.

Content

The 2013-2014 NHANES datasets include the following components:

1. Demographics dataset:

• A complete variable dictionary can be found here

1. Examinations dataset, which contains:

• Blood pressure

• Body measures

• Muscle strength - grip test

• Oral health - dentition

• Taste & smell

• A complete variable dictionary can be found here

1. Dietary data - total nutrient intake, first day:

• A complete variable dictionary can be found here

1. Laboratory dataset, which includes:

• Albumin & Creatinine - Urine

• Apolipoprotein B

• Blood Lead, Cadmium, Total Mercury, Selenium, and Manganese

• Blood mercury: inorganic, ethyl and methyl

• Cholesterol - HDL

• Cholesterol - LDL & Triglycerides

• Cholesterol - Total

• Complete Blood Count with 5-part Differential - Whole Blood

• Copper, Selenium & Zinc - Serum

• Fasting Questionnaire

• Fluoride - Plasma

• Fluoride - Water

• Glycohemoglobin

• Hepatitis A

• Hepatitis B Surface Antibody

• Hepatitis B: core antibody, surface antigen, and Hepatitis D antibody

• Hepatitis C RNA (HCV-RNA) and Hepatitis C Genotype

• Hepatitis E: IgG & IgM Antibodies

• Herpes Simplex Virus Type-1 & Type-2

• HIV Antibody Test

• Human Papillomavirus (HPV) - Oral Rinse

• Human Papillomavirus (HPV) DNA - Vaginal Swab: Roche Cobas & Roche Linear Array

• Human Papillomavirus (HPV) DNA Results from Penile Swab Samples: Roche Linear Array

• Insulin

• Iodine - Urine

• Perchlorate, Nitrate & Thiocyanate - Urine

• Perfluoroalkyl and Polyfluoroalkyl Substances (formerly Polyfluoroalkyl Chemicals - PFC)

• Personal Care and Consumer Product Chemicals and Metabolites

• Phthalates and Plasticizers Metabolites - Urine

• Plasma Fasting Glucose

• Polycyclic Aromatic Hydrocarbons (PAH) - Urine

• Standard Biochemistry Profile

• Tissue Transglutaminase Assay (IgA-TTG) & IgA Endomyseal Antibody Assay (IgA EMA)

• Trichomonas - Urine

• Two-hour Oral Glucose Tolerance Test

• Urinary Chlamydia

• Urinary Mercury

• Urinary Speciated Arsenics

• Urinary Total Arsenic

• Urine Flow Rate

• Urine Metals

• Urine Pregnancy Test

• Vitamin B12

• A complete data dictionary can be found here

1. Questionnaire dataset, which includes information on:

• Acculturation

• Alcohol Use

• Blood Pressure & Cholesterol

• Cardiovascular Health

• Consumer Behavior

• Current Health Status

• Dermatology

• Diabetes

• Diet Behavior & Nutrition

• Disability

• Drug Use

• Early Childhood

• Food Security

• Health Insurance

• Hepatitis

• Hospital Utilization & Access to Care

• Housing Characteristics

• Immunization

• Income

• Medical Conditions

• Mental Health - Depression Screener

• Occupation

• Oral Health

• Osteoporosis

• Pesticide Use

• Physical Activity

• Physical Functioning

• Preventive Aspirin Use

• Reproductive Health

• Sexual Behavior

• Sleep Disorders

• Smoking - Cigarette Use

• Smoking - Household Smokers

• Smoking - Recent Tobacco Use

• Smoking - Secondhand Smoke Exposure

• Taste & Smell

• Weight History

• Weight History - Youth

• A complete variable dictionary can be found here

1. Medication dataset, which includes prescription medications:

• A complete variable dictionary can be found here

Acknowledgements

Original data and additional documents related to the datasets or NHANES can be found here.

Obesity rate for adults 20+ years old. Data from US Diabetes Surveillance System; www.cdc.gov/diabetes/data; Division of Diabetes Translation - Centers for Disease Control and Prevention. Obesity is defined with BMI >30.

PLACES (Population Level Analysis and Community Estimates) is an expansion of the original 500 Cities project and is a collaboration between the Centers for Disease Control and Prevention (CDC), the Robert Wood Johnson Foundation, and the CDC Foundation. The original 500 Cities Project provided city- and census tract-level estimates for the 500 largest US cities. PLACES extends these estimates to all counties, places (incorporated and census designated places), census tracts, and ZIP Code Tabulation Areas (ZCTA) across the United States.This service includes 40 measures for chronic disease related health outcomes (12), prevention measures (7), health risk behaviors (4), disability (7), health status (3), and health-related social needs (7). Data were provided by CDC Division of Population Health, Epidemiology and Surveillance Branch. Data sources used to generate these measures include BRFSS data (2022 or 2021), Census Bureau 2020 census population data or annual population estimates for county vintage 2022, and American Community Survey (ACS) 2018-2022 estimates. The health outcomes include arthritis, current asthma, high blood pressure, cancer (non-skin) or melanoma, high cholesterol, chronic kidney disease, chronic obstructive pulmonary disease (COPD), coronary heart disease, diagnosed diabetes, depression, obesity, all teeth lost, and stroke. The prevention measures are lack of health insurance, routine checkup within the past year, visited dentist or dental clinic in the past year, taking medicine to control high blood pressure, cholesterol screening, mammography use for women, and colorectal cancer screening. The health risk behaviors are binge drinking, current cigarette smoking, physical inactivity, and short sleep duration.The disability measures are six disability types (hearing, vision, cognitive, mobility, self-care, and independent living) and any disability.The health status measures are frequent mental distress, frequent physical distress, and poor or fair health.The health-related social needs measures are social isolation, food stamps, food insecurity, housing insecurity, utility services threat, transportation barriers, and lack of social and emotional support.For more information, please visit https://www.cdc.gov/places or contact places@cdc.gov.

CDC's Division of Population Health provides cross-cutting set of 124 indicators that were developed by consensus and that allows states and territories and large metropolitan areas to uniformly define, collect, and report chronic disease data that are important to public health practice and available for states, territories and large metropolitan areas. In addition to providing access to state-specific indicator data, the CDI web site serves as a gateway to additional information and data resources.

This map shows where obesity and diabetes are happening in the US, by county. It shows each component of the map as its own layer, and also shows the patterns overlapping. Diabetes prevalence (% of adults)Obesity prevalence (% of adults)This data can be used to assess the health factors, and answer questions such as:Are certain counties more/less at risk in regards to diabetes and obesity?Are diabetes, obesity, and physical inactivity happening within the same areas of the US?According to the CDC: "These data can help the public to better use existing resources for diabetes management and prevention efforts." The data comes from the Behavioral Risk Factor Surveillance System (BRFSS) through the Centers for Disease Control and Prevention (CDC), and the data vintage is 2013. To explore other county indicators, different vintages, or the original data, click here. To view the interactive map through the CDC website, click here. To learn more about the methodology of how county-level estimates are calculated, see this PDF.

Companies in this industry produce insulin, a treatment for diabetes, which is one of the top health concerns for the US population. In 2020, 11.3% of the US population, equivalent to 37.3 million people, were diagnosed with diabetes, according to the CDC. Thanks to medical advancements, insulin has become more widely accessible to a larger share of the diabetic population over the past years. But industry revenue is expected to fall at a CAGR of 5.9% to $2.6 billion over the five years to 2023, including an expected 3.5% growth in 2023 alone. Over the past five years, more US adults have experienced obesity, one of the causes of diabetes. Despite more adults becoming active and adopting a healthier diet, the US obesity rate still increases annually. As a result, 38.0% of US adults have prediabetes, while 3.4% of all US adults experience undiagnosed diabetes, according to the CDC. Overall, the US diabetic population translates to growing demand for insulin, a treatment used to lower blood glucose levels. The US diabetic population dealt with overpriced insulin until 2023, when the $35.00 cap for insulin Medicare Part D was enforced. Although this will increase insulin affordability to diabetic patients, it will also reduce industry revenue. As the younger generation increasingly adopts a healthier lifestyle and diet, the diabetes rate is expected to slow. Nonetheless, genetics and pregnancy can also be the cause of diabetes, but to a lesser extent. As a result, demand for insulin will continue to remain robust. Over the five years to 2028, industry revenue is expected to recover, rising at a CAGR of 1.9% to $2.9 billion.

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.

This is a source dataset for a Let's Get Healthy California indicator at https://letsgethealthy.ca.gov/. Adult smoking prevalence in California, males and females aged 18+, starting in 2012. Caution must be used when comparing the percentages of smokers over time as the definition of ‘current smoker’ was broadened in 1996, and the survey methods were changed in 2012. Current cigarette smoking is defined as having smoked at least 100 cigarettes in lifetime and now smoking every day or some days. Due to the methodology change in 2012, the Centers for Disease Control and Prevention (CDC) recommend not conducting analyses where estimates from 1984 – 2011 are compared with analyses using the new methodology, beginning in 2012. This includes analyses examining trends and changes over time. (For more information, please see the narrative description.) The California Behavioral Risk Factor Surveillance System (BRFSS) is an on-going telephone survey of randomly selected adults, which collects information on a wide variety of health-related behaviors and preventive health practices related to the leading causes of death and disability such as cardiovascular disease, cancer, diabetes and injuries. Data are collected monthly from a random sample of the California population aged 18 years and older. The BRFSS is conducted by Public Health Survey Research Program of California State University, Sacramento under contract from CDPH. The survey has been conducted since 1984 by the California Department of Public Health in collaboration with the Centers for Disease Control and Prevention (CDC). In 2012, the survey methodology of the California BRFSS changed significantly so that the survey would be more representative of the general population. Several changes were implemented: 1) the survey became dual-frame, with both cell and landline random-digit dial components, 2) residents of college housing were eligible to complete the BRFSS, and 3) raking or iterative proportional fitting was used to calculate the survey weights. Due to these changes, estimates from 1984 – 2011 are not comparable to estimates from 2012 and beyond. Center for Disease Control and Policy (CDC) and recommend not conducting analyses where estimates from 1984 – 2011 are compared with analyses using the new methodology, beginning in 2012. This includes analyses examining trends and changes over time.Current cigarette smoking was defined as having smoked at least 100 cigarettes in lifetime and now smoking every day or some days. Prior to 1996, the definition of current cigarettes smoking was having smoked at least 100 cigarettes in lifetime and smoking now.

The prevalence of obesity in the United States has risen gradually over the past decade. As of 2023, around 33 percent of the population aged 18 years and older was obese. Obesity is a growing problem in many parts of the world, but is particularly troubling in the United States. Obesity in the United States The states with the highest prevalence of obesity are West Virginia, Mississippi, and Arkansas. As of 2023, a shocking 41 percent of the population in West Virginia were obese. The percentage of adults aged 65 years and older who are obese has grown in recent years, compounding health issues that develop with age. Health impacts of obesity Obesity is linked to several negative health impacts including cardiovascular disease, diabetes, and certain types of cancer. Unsurprisingly, the prevalence of diagnosed diabetes has increased in the United States over the years. As of 2022, around 8.4 percent of the population had been diagnosed with diabetes. Some of the most common types of cancers caused by obesity include breast cancer in postmenopausal women, colon and rectum cancer, and corpus and uterus cancer.

The crude prevalence rate of diabetes is the ratio of respondents that are 18 years or older who have ever been told by a health professional that they had diabetes (other than during pregnancy) over the total number of respondents in the study (excluding those who refused to answer, had a missing answer, or answered “don’t know/not sure”). Prevalence data are derived from Behavioral Risk Factor Surveillance System (BRFSS).

The 500 Cities Project seeks to provide city- and census tract-level small area estimates for chronic disease risk factors, health outcomes, and clinical preventive service use for the largest 500 cities in the United States.

Data source: CDC (Division of Population Health, National Center for Chronic Disease Prevention and Health Promotion)

Date: 2017

We have used a publicly available dataset, COVID-19 Tweets Dataset, consisting of an extensive collection of 1,091,515,074 tweet IDs, and continuously expanding. The dataset was compiled by tracking over 90 distinct keywords and hashtags commonly associated with discussions about the COVID-19 pandemic. From this massive dataset, we focused on a specific time frame, encompassing data from August 05, 2020, to August 26, 2020, to meet our research objectives. As this dataset contains only tweet IDs, we have used the Twitter developer API to retrieve the corresponding tweets from Twitter. This retrieval process involved searching for tweet IDs and extracting the associated tweet texts, and it was implemented using the Twython library. In total, we successfully collected 21,890 tweets during this data extraction phase.

Following guidelines set by the CDC and WHO, we categorized tweets into five distinct classes for classification: health risks, prevention, symptoms, transmission, and treatment. Specifically, individuals aged over sixty, or those with pre-existing health conditions such as heart disease, lung problems, weakened immune systems, or diabetes, are at higher risk of severe COVID-19 complications. Therefore, tweets categorized as ‘health risks’ pertain to the elevated risks associated with COVID-19 due to age or specific health conditions. ‘Prevention’ related tweets encompass discussions on preventive and precautionary measures regarding the COVID-19 pandemic. Tweets discussing common COVID-19 symptoms, including cough, congestion, breathing issues, fever, body aches, and more, are classified as ‘symptoms’ related tweets. Conversations pertaining to the spread of COVID-19 between individuals, between animals and humans, and contact with virus-contaminated objects or surfaces are categorized as ‘transmission’ related tweets. Lastly, tweets indicating vaccine development and drugs used for COVID-19 treatment fall under the ‘treatment’ related category.

We determined specific keywords for each of the five classes (health risks, prevention, symptoms, transmission, and treatment) based on the definitions provided by the CDC and WHO on their official websites. These definitions, along with their associated keywords, are detailed in Table 1. For instance, the CDC and WHO indicate that individuals over the age of sixty with conditions like heart disease, lung problems, weak immune systems, or diabetes face a higher risk of severe COVID-19 complications. In accordance with this definition, we selected relevant keywords such as “lung disease”, “heart disease”, “diabetes”, “weak immunity”, and others to identify tweets related to health risks within the larger tweet dataset. This approach was consistently applied to define keywords for the remaining four classes. Subsequently, we filtered the initial dataset of 21,890 tweets to extract tweets relevant to our predefined classes, resulting in a total of 6,667 tweets based on the selected keywords.

To ensure the accuracy of our dataset, two separate annotators individually assigned the 6,667 tweets to the five classes. A third annotator, a natural language expert, meticulously cross-checked the dataset and provided necessary corrections. Subsequently, the two annotators resolved any discrepancies through mutual agreement, resulting in the final annotated dataset. Our dataset comprises a total of 6,667 data points categorized into five classes: 978, 2046, 1402, 802, and 1439 tweets annotated as ‘health risk’, ‘prevention’, ‘symptoms’, ‘transmission’, and ‘treatment’, respectively

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Insulin Pump Market Size 2024-2028

The insulin pump market size is forecast to increase by USD 9.43 billion at a CAGR of 21.81% between 2023 and 2028. The market is witnessing robust growth due to the escalating global diabetes prevalence, favorable government initiatives geared towards diabetes treatment, and the availability of comprehensive diabetes self-management education programs. This expansion is primarily driven by the increasing demand for innovative solutions, technological advancements, and evolving consumer preferences. Key market dynamics include the transition towards more efficient systems, improved accessibility, and higher industry standards. In response, industry players are adapting their strategies to align with these trends, prioritizing sustainability and operational efficiency to maintain a competitive edge. As the market continues to evolve, these factors are shaping its trajectory, ensuring long-term growth. The industry's focus on delivering advanced solutions is expanding its scope, addressing the growing demand for sophisticated diabetes management tools. The global diabetes burden is on the rise, necessitating the development of more effective and accessible treatment options. Government initiatives aimed at improving diabetes care and increasing public awareness are further fueling market growth.

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

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Insulin Pump Market Segmentation

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

Type

  Tethered pumps
  Patch pumps


Distribution Channel

  Offline
  Online


Geography

  North America

    US


  Europe

    Germany
    UK


  Asia

    China
    Japan


  Rest of World (ROW)

Which is the largest segment driving market growth?

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

Insulin pumps are computerized devices designed to deliver insulin to diabetic persons, both those with Type 1 and Type 2 diabetes, in a more controlled and efficient manner than traditional insulin injections. These devices have gained popularity due to their ability to provide automatic insulin administration based on real-time glucose monitoring and advanced features like Artificial Intelligence (AI) and Machine Learning (ML) algorithms. Tethered insulin pumps, which use a flexible tube connecting the pump to the cannula, are commonly used. Notable examples include the Accu-Chek Spirit Combo, which integrates a smartphone for sugar monitoring, diagnosis, and bolus calculation. The market for insulin pumps is expanding due to the increasing incidence of diabetes, aging population, unhealthy lifestyles, and risk factors like obesity, sedentary lifestyle, and diseases such as cataract, glaucoma, hypertension, and diabetes indications.

Insulin pumps are available through retail and hospital pharmacies, home infusion treatments, and electronic versions. Closed-loop pumps, also known as artificial pancreas systems, are the latest advancement in insulin pump technology. The market for insulin pumps is significant in developed nations, with a growing focus on tubeless insulin pumps and AI-enabled pumps to improve accuracy and convenience for diabetic patients.

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The Tethered pumps segment was valued at USD 2.38 billion in 2018 and showed a gradual increase during the forecast period.

Which region is leading the market?

North America is estimated to contribute 58% to the growth of the global market during the forecast period.

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Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.The market in North America is experiencing growth due to the rising prevalence of type 1 and type 2 diabetes and technological advancements in healthcare. According to the Centers for Disease Control and Prevention (CDC), over 130 million adults In the US have diabetes or prediabetes, with type 2 diabetes accounting for 90-95% of cases. Factors contributing to this increase include an aging population, sedentary lifestyles, and obesity. The CDC reports that new diabetes diagnoses are equal among women and men aged 45-64 years. Insulin pumps, computerized devices that deliver insulin automatically, are becoming increasingly popular for diabetes management. These pumps can be integrated with AI and machine learning technologies, smartphones, and continuous sugar monitoring systems for closed-loop insulin delivery.

Additionally, AI-enabled pumps are being developed for type 2 diabet