Demographic, clinical and virologic data obtained from 20 MSM providing rectal swabs over 18 weeks. “Positive swabs” indicate swabs in which adenovirus was detected by real-time PCR. Baseline HIV viral load provided as log10 copies/ml plasma on day 1.

Department of State Hospitals Patient Population Demographic (Fiscal Effective Dates: 2010-2020)

This dataset compiles demographic data on race, ethnicity, sex, and age eligibility from neuromuscular disease studies initiated between January 1, 2004, and December 31, 2024. It includes studies listed on ClinicalTrials.gov that are classified as ‘completed,’ ‘terminated,’ ‘suspended,’ ‘withdrawn,’ or ‘unknown’ under ‘Study Status’ as of December 31, 2024. When data were unavailable on ClinicalTrials.gov, a manual search on PubMed/MEDLINE using National Clinical Trial (NCT) numbers was conducted to retrieve data from relevant publications. The dataset provides structured information to support research on population diversity, health disparities, and epidemiological trends in neuromuscular diseases. Its aim is to facilitate analyses of demographic representation and promote more inclusive, equitable research in this field.

Demographic data of participants in NCI clinical trials at the NIH Clinical Center, 2005-2020.

Multiple sclerosis (MS) results in an extensive use of the health care system, even within the first years of diagnosis. The effectiveness and accessibility of the health care system may affect patients' quality of life. The aim of the present study was to evaluate the health care resource use of MS patients under interferon beta-1b (EXTAVIA) treatment in Greece, the demographic or clinical factors that may affect this use and also patient satisfaction with the health care system. Structured interviews were conducted for data collection. In total, 204 patients (74.02% females, mean age (SD) 43.58 (11.42) years) were enrolled in the study. Analysis of the reported data revealed that during the previous year patients made extensive use of health services in particular neurologists (71.08% visited neurologists in public hospitals, 66.67% in private offices and 48.53% in insurance institutes) and physiotherapists. However, the majority of the patients (52.45%) chose as their treating doctor private practice neurologists, which may reflect accessibility barriers or low quality health services in the public health system. Patients seemed to be generally satisfied with the received health care, support and information on MS (84.81% were satisfied from the information provided to them). Patients' health status (as denoted by disease duration, disability status and hospitalization needs) and insurance institute were found to influence their visits to neurologists. Good adherence (up to 70.1%) to the study medication was reported. Patients' feedback on currently provided health services could direct these services towards the patients' expectations.

About this dataset

Context

The dataset tabulates the Medical Lake population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Medical Lake. The dataset can be utilized to understand the population distribution of Medical Lake by age. For example, using this dataset, we can identify the largest age group in Medical Lake.

Key observations

The largest age group in Medical Lake, WA was for the group of age 30 to 34 years years with a population of 580 (11.77%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Medical Lake, WA was the 85 years and over years with a population of 24 (0.49%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Medical Lake is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Medical Lake total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Medical Lake Population by Age. You can refer the same here

1) Data Introduction • The Diabetes Clinical Dataset(100k rows) Dataset is a detailed dataset that contains health and demographic data for 100,000 people. It contains information on gender, age, location, race, high blood pressure, heart disease, smoking history, body mass index (BMI), glycated hemoglobin (HbA1c), blood sugar, and diabetes.

2) Data Utilization (1) Diabetes Clinical Dataset(100k rows) Dataset has characteristics that: • This dataset consists of 100,000 items, each of which represents an individual's health and demographic data related to diabetes research. (2) Diabetes Clinical Dataset(100k rows) Dataset can be used to: • Predictive modeling : Builds a model to predict the likelihood of diabetes based on demographics and health-related features. • Health Analysis : Analyze the correlation between diabetes and various health indicators (e.g., BMI, HbA1c levels). • Demographic study : investigate the distribution of diabetes in various demographic groups and regions. • Public Health Study : Identify Diabetes Risk Factors and Aim for Interventions in High-Risk Groups.

12011 population data for individuals 18 years and older in Canada was obtained from Statistics Canada [44].22010 population data for individuals 18 years and older in the US was obtained from the US Census Bureau [46].3Regions were:Midwest (Illinois, Indiana, Iowa, Kansas, Michigan, Minnesota, Missouri, Nebraska, North Dakota, Ohio, South Dakota, Wisconsin);Northeast (Connecticut, Maine, Massachusetts, New Hampshire, New Jersey, New York, Pennsylvania, Rhode Island, Vermont);South (Alabama, Arkansas, Delaware, District of Columbia, Florida, Georgia, Kentucky, Louisiana, Maryland, Mississippi, North Carolina, Oklahoma, South Carolina, Tennessee, Texas, Virginia, West Virginia);West (Alaska, Arizona, California, Colorado, Hawaii, Idaho, Montana, Nevada, New Mexico, Oregon, Utah, Washington, Wyoming).42006 education data for individuals 20 years and over in Canada (most current and available data) [43].52010 education data for individuals 18 years and over in the US [45].*Significant at p

The GERAS Study-US was a prospective, multicenter, observational study that aimed to assess societal costs and resource use associated with AD among patients and their primary caregivers across 76 sites in the United States. Data includes demographics/clinical characteristics; current medication; patient cognitive, functional, and behavioral assessments; patient and caregiver health-related quality of life; and patient and caregiver resource use. The data are available via the ADDI AD Workbench.

This dataset contains records of patients diagnosed with gallbladder cancer. It includes 21 features covering patient demographics (age, gender, ethnicity), lifestyle factors (smoking, alcohol consumption), medical history (diabetes, gallstones, family history), clinical symptoms (abdominal pain, jaundice, weight loss), tumor characteristics (size, stage, lymph node involvement), biomarker levels (CEA, CA19-9), treatment types, and survival outcomes.

The dataset is useful for machine learning applications, predictive modeling, statistical analysis, and biomedical research related to gallbladder cancer. Researchers can use this data to analyze risk factors, survival rates, and treatment effectiveness.

Table 1 demonstrates the clinical demographics for the patient population studied in this manuscript.

Objective: Perform a longitudinal analysis of clinical features associated with Neurofibromatosis Type 1 (NF1) based on demographic and clinical characteristics, and to apply a machine learning strategy to determine feasibility of developing exploratory predictive models of optic pathway glioma (OPG) and attention-deficit/hyperactivity disorder (ADHD) in a pediatric NF1 cohort.

Methods: Using NF1 as a model system, we perform retrospective data analyses utilizing a manually-curated NF1 clinical registry and electronic health record (EHR) information, and develop machine-learning models. Data for 798 individuals were available, with 578 comprising the pediatric cohort used for analysis.

Results: Males and females were evenly represented in the cohort. White children were more likely to develop OPG (OR: 2.11, 95%CI: 1.11-4.00, p=0.02) relative to their non-white peers. Median age at diagnosis of OPG was 6.5 years (1.7-17.0), irrespective of sex. Males were more likely than females to have a diagnosis of ADHD (OR: 1.90, 95%CI: 1.33-2.70, p<0.001), and earlier diagnosis in males relative to females was observed. The gradient boosting classification model predicted diagnosis of ADHD with an AUROC of 0.74, and predicted diagnosis of OPG with an AUROC of 0.82.

Conclusions: Using readily available clinical and EHR data, we successfully recapitulated several important and clinically-relevant patterns in NF1 semiology specifically based on demographic and clinical characteristics. Naïve machine learning techniques can be potentially used to develop and validate predictive phenotype complexes applicable to risk stratification and disease management in NF1.

Methods Patients and Data Description

This study was performed using retrospective clinical data extracted from two sources within the Washington University Neurofibromatosis (NF) Center. First, data were extracted from an existing longitudinal clinical registry that was manually curated using clinical data obtained from patients followed in the Washington University NF Clinical Program at St. Louis Children’s Hospital. All individuals included in this database had a clinical diagnosis of NF1 based on current National Institutes of Health Consensus Development Conference diagnostic criteria,9 and had been assessed over multiple visits from 2002 to 2016 for the presence of clinical features associated with NF1. Data points in this registry included demographic information, such as age, race, and sex, in addition to NF1-related clinical features and associated conditions, such as café-au-lait macules, skinfold freckling, cutaneous neurofibromas, Lisch nodules, OPG, hypertension, ADHD, and cognitive impairment. These data were maintained in a semi-structured format containing textual and binary fields, capturing each individual’s data over multiple clinical visits. From these data, clinical features and phenotypes were extracted using data manipulation, imputation, and text mining techniques. Data obtained from this NF1 clinical registry were converted to data tables, which captured each patient visit and the presence/absence of specific clinical features at each visit. Clinical features which were once marked as present were assumed to be present for all future visits, and missing data were assumed absent for that specific visit. Categorical variables are reported as frequencies and proportions, and compared using odds ratios (ORs). Continuously distributed traits, adhering to both conventional normality assumptions and homogeneity of variances, are reported as mean and standard deviations, and compared using analysis of variance methods. Non-parametric equivalents were used for data with non-normative distributions.

Clinical Feature Extraction from Clinical Registry and EHR

The NF1 Clinical Registry comprised string-based clinical feature values, such as ADHD, OPG, and asthma. From these data, we extracted 27 unique clinical features in addition to longitudinal data on the development of NF1-related clinical features and associated diagnoses. For each clinical feature, age at initial presentation and/or diagnosis was computed, and median age of occurrence was calculated for each sex. The exact age of presentation and/or diagnosis could not be definitively ascertained for any feature that was present at a child’s initial clinic visit. As such, we computed the age of diagnosis only for those clinical features for which we have at least one visit documenting feature absence prior to the manifestation of that feature.

Diagnosis codes from the EHR-derived data set were also extracted. Diagnosis codes were recorded as 15,890 unique ICD 9/10 codes. Given the large number of ICD 9/10 codes, a consistent, concept-level “roll up” of relevant codes to a single phenotype description was created by mapping the extracted ICD 9/10 values to phenome-wide association (PheWAS) codes called Phecodes, which have been demonstrated to better align with clinical disease compared to individual ICD codes.

Machine Learning Analyses

Using a combination of clinical features obtained from the NF1 Clinical Registry and EHR-derived data sets, we developed prediction models using a gradient boosting platform for identifying patients with specific NF1-related diagnoses to establish the usefulness of clinical history and documentation of clinical findings in predicting phenotypic variability of NF1. Initial analyses used a state-of-the-art classification algorithm, gradient boosting model, which uses a tree-based algorithm to produce a predictive model from an ensemble of weak predictive models. Gradient boosting model was selected, as it supports identifying importance of features used in the final prediction model. Subsequent analyses employed training each model for three different feature sets: (1) demographic features for all patients, including race, sex, and family history of NF1 [5 features]; (2) clinical features associated with NF1 [27 features] extracted from the NF1 Clinical Registry; and (3) diagnosis codes extracted from the EHR data, which were reduced to 50 Phecodes. Four-fold cross validation was then applied for the three models, and comparisons for the prediction accuracies of each model determined. Positive predictive value (PPV), F1 score and the area under the receiver operator characteristic (AUROC) curve were used as evaluation metrics. Scikit Learn, a machine learning library in Python, was employed to implement all analyses.

Standard Protocol Approvals, Registrations, and Patient Consents

The NF1 Clinical Registry is an existing longitudinal clinical registry that was manually curated using clinical data obtained from patients followed in the Washington University NF Clinical Program at St. Louis Children’s Hospital. All individuals included in this database have a clinical diagnosis of NF1 based on current National Institutes of Health criteria and have provided informed consent for participation in the clinical registry. All data collection, usage and analysis for this study were approved by the Institutional Review Board (IRB) at the Washington University School of Medicine.

About this dataset

Context

The dataset tabulates the Medical Lake population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Medical Lake across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Medical Lake was 4,957, a 1.27% decrease year-by-year from 2022. Previously, in 2022, Medical Lake population was 5,021, an increase of 2.95% compared to a population of 4,877 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Medical Lake increased by 1,086. In this period, the peak population was 5,064 in the year 2010. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Medical Lake is shown in this column.
• Year on Year Change: This column displays the change in Medical Lake population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Medical Lake Population by Year. You can refer the same here

a Lung disease was significantly (p

About this dataset

Context

The dataset tabulates the data for the Medical Lake, WA population pyramid, which represents the Medical Lake population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Medical Lake, WA, is 22.4.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Medical Lake, WA, is 17.1.
• Total dependency ratio for Medical Lake, WA is 39.5.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Medical Lake, WA is 5.8.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group for the Medical Lake population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Medical Lake for the selected age group is shown in the following column.
• Population (Female): The female population in the Medical Lake for the selected age group is shown in the following column.
• Total Population: The total population of the Medical Lake for the selected age group is shown in the following column.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Medical Lake Population by Age. You can refer the same here

The BrANCH program is a group of UCSF Memory and Aging Center projects with the common goal of a better understanding of the biological drivers of brain aging.

About this dataset

Context

The dataset tabulates the population of Medical Lake by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Medical Lake. The dataset can be utilized to understand the population distribution of Medical Lake by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Medical Lake. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Medical Lake.

Key observations

Largest age group (population): Male # 30-34 years (355) | Female # 35-39 years (308). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Medical Lake population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Medical Lake is shown in the following column.
• Population (Female): The female population in the Medical Lake is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Medical Lake for each age group.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Medical Lake Population by Gender. You can refer the same here

This dataset provides detailed records of patient health risk assessments, including demographic data, clinical measurements, and calculated risk factor scores for chronic disease prediction. It is ideal for population health analytics, risk stratification, and supporting proactive care management in healthcare settings.

Clinical and Demographic Patient Data.