Demographics of the patient population.

Baseline Demographic and Clinical Characteristics for Total Patient Population.

Data for this publication are extracted each month as a snapshot in time from the Primary Care Registration database within the PDS (Personal Demographics Service) system. This release is an accurate snapshot as at 1 May 2025. GP Practice; Primary Care Network (PCN); Sub Integrated Care Board Locations (SICBL); Integrated Care Board (ICB) and NHS England Commissioning Region level data are released in single year of age (SYOA) and 5-year age bands, both of which finish at 95+, split by gender. In addition, organisational mapping data is available to derive PCN; SICBL; ICB and Commissioning Region associated with a GP practice and is updated each month to give relevant organisational mapping. Quarterly publications in January, April, July and October will include Lower Layer Super Output Area (LSOA) populations.

Demographics and geographic locations of eligible patients and enrolled participants. The patients were grouped based on disease state and age.

The dataset contains counts of inpatient visits leading to a discharge to hospice care. Inpatient visits included in the counts consist of individuals aged 18 or over with a discharge disposition leading to home or facility hospice care. The total counts per each individual year can be viewed based on different patient characteristics, including patient age groups, individual counties of residence, primary payer type, diagnosis category, and patient sex/race/ethnicity. The disease categories include circulatory conditions, diabetes, malignant/benign neoplasms, malnutrition, neurodegenerative disease, renal failure or other kidney diagnoses, respiratory conditions and circulatory conditions. The categories represent common groupings of diagnoses seen in other studies related to hospice care and were created by grouping together relevant medical MSDRG codes in the HCAI inpatient data.

Demographics of the EB TNGP TeleED patient population.

The COVID-19 pandemic and subsequent expansion of telehealth may be exacerbating inequities in ambulatory care access due to institutional and structural barriers. We conduct a repeat cross-sectional analysis of ambulatory patients to evaluate for demographic disparities in the utilization of telehealth modalities. The ambulatory patient population at Oregon Health & Science University (Portland, OR) is examined from June 1 through September 30, in 2019 (reference period) and in 2020 (study period). We first assess for changes in demographic representation and then evaluate for disparities in the utilization of telephone and video care modalities using logistic regression. Between the 2019 and 2020 periods, patient video utilization increased from 0.2% to 31%, and telephone use increased from 2.5% to 25%. There was also a small but significant decline in the representation males, Asians, Medicaid, Medicare, and non-English speaking patients. Amongst telehealth users, adjusted odds of video participation were significantly lower for those who were Black, American Indian, male, prefer a non-English language, have Medicaid or Medicare, or older. A large portion of ambulatory patients shifted to telehealth modalities during the pandemic. Seniors, non-English speakers, and Black patients were more reliant on telephone than video for care. The differences in telehealth adoption by vulnerable populations demonstrate the tendency towards disparities that can occur in the expansion of telehealth and suggest structural biases. Organizations should actively monitor the utilization of telehealth modalities and develop best-practice guidelines in order to mitigate the exacerbation of inequities.

Methods A repeat cross-sectional study was conducted of patients who utilized the ambulatory clinics at Oregon Health & Science University (OHSU) from June 1 through September 30, in 2019 (reference period) and 2020 (study period). The study period was chosen because it exhibited a relatively stable rate of in-person, telephone, and video ambulatory visits. The initial months of the pandemic in March through May 2020 were marked by shifting state and institutional policies that affected appointment availability. By the summer of 2020, clinics were more open to scheduling in-person visits. We chose to investigate a later, more stable time-frame for disparities because we believe that the analysis would be more indicative of ongoing trends.

Unique patient counts were extracted from ambulatory provider-led visits, defined as outpatient visits with physicians, nurse practitioners, or physician assistants. Visits modalities included in-person, video, or telephone, the latter two comprising telehealth. Patient demographics included ethnicity, race, preferred language, payer, age, and sex. The encounter-level data was aggregated by unique patient identifier into patient counts for the study period of June 1 through Sept 30, 2020. Table 1 displays unique patient counts of ambulatory care modality utilization (in-person, video, telephone, and any telehealth) for each demographic group (race, ethnicity, sex, preferred language, insurance, and age). There is also a column for total patients in that demographic group. In the main article, we performed logistic regression to evaluate the association of patient demographics with telehealth utilization. Table 2 displays unique patient counts of ambulatory care modality utilization for each demographic group only within primary care clinics.

Table 3 displays unique patient counts for each demographic group within the time periods before and during the COVID-19 pandemic: June 1 through Sept 30, 2019 and June 1 through Sept 30, 2020. In the study, we compared the proportional representation of demographic groups between before and during the pandemic to assess for overall changes in our patient population.

This dataset contains information about the demographics of all US cities and census-designated places with a population greater or equal to 65,000. This data comes from the US Census Bureau's 2015 American Community Survey. This product uses the Census Bureau Data API but is not endorsed or certified by the Census Bureau.

Estimated number of persons on July 1, by 5-year age groups and gender, and median age, for Canada, provinces and territories.

Demographic information of Mesa residents sourced by US Census American Community Survey (ACS) 1-Year Estimates. Race estimates includes survey responses for "One Race" and "Not Hispanic or Latino"). This dataset is manually updated. Sources include: https://data.census.gov/table?q=dp05&g=160XX00US0446000 Number of Households and Veteran Status Only: https://data.census.gov/cedsci/table?text=dp02&g=1600000US0446000

About this dataset

Context

The dataset tabulates the population of Spring Hill by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of Spring Hill across both sexes and to determine which sex constitutes the majority.

Key observations

There is a slight majority of female population, with 50.62% of total population being female. 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.

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. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the Spring Hill is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Spring Hill 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 Spring Hill Population by Race & Ethnicity. You can refer the same here

This statistic shows the total population of Brazil from 2020 to 2023, with a forecast through 2030. In 2023, the total population of Brazil was estimated at around 211.7 million inhabitants. Population of Brazil Brazil has a surprisingly low (and decreasing) population growth rate; despite it being home to the largest number of Catholics in the world, the majority of women in Brazil use some form of contraception, which is often government-subsidized or free, even though the Catholic Church retains its stance that the use of contraceptives is inherently wrong. Within the space of just one generation, families have gone from having more than six children to having just one or two, and the share of Catholics in the population is dwindling, too. The influence of 'telenovelas' — the overwhelmingly popular soap operas often with strong women figures and fewer than three children — could also be helping shape the population’s view of what an ideal family is. The fertility rate in Brazil fell below the replacement rate in 2006 and is still decreasing. The impending population imbalance in Brazil can be seen in the decreasing lower tier of the country’s age distribution. This follows a trend similar to the one Japan and many European countries are experiencing, which are now facing the problems of providing for an aging population with fewer young and working taxpayers. The trend is not quite as extreme in Brazil, giving it time to prepare for the fallout of decreasing family size. This preparation will be important to help the country maintain its emerging economic strength, which is watched with interest by many economists who have said that Brazil’s is one to watch — thus its position as one of the pillars of the “big four” BRIC countries.

About this dataset

Context

The dataset tabulates the population of Parks by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of Parks across both sexes and to determine which sex constitutes the majority.

Key observations

There is a slight majority of female population, with 52.97% of total population being female. Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

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. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the Parks is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Parks 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 Parks Population by Gender. You can refer the same here

Data for this publication are extracted each month as a snapshot in time from the Primary Care Registration database within the NHAIS (National Health Application and Infrastructure Services) system. This release is an accurate snapshot as at 1 December 2022. GP Practice; Primary Care Network (PCN); Sub Integrated Care Board Locations (SICBL); Integrated Care Board (ICB) and NHS England Commissioning Region level data are released in single year of age (SYOA) and 5-year age bands, both of which finish at 95+, split by gender. In addition, organisational mapping data is available to derive PCN; SICBL; ICB and Commissioning Region associated with a GP practice and is updated each month to give relevant organisational mapping. Quarterly publications in January, April, July and October will include Lower Layer Super Output Area (LSOA) populations. The outbreak of Coronavirus (COVID-19) has led to changes in the work of General Practices and subsequently the data within this publication. Until activity in this healthcare setting stabilises, we urge caution in drawing any conclusions from these data without consideration of the country's circumstances and would recommend that any uses of these data are accompanied by an appropriate caveat.

Demographics, characteristics and comorbidities of patients hospitalized with a SARS-CoV-2 infection or COVID-19 diagnosis, total and stratified by rural/urban zip codes.

About this dataset

Context

The dataset tabulates the population of Willing town by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Willing town. The dataset can be utilized to understand the population distribution of Willing town by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Willing town. 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 Willing town.

Key observations

Largest age group (population): Male # 40-44 years (125) | Female # 15-19 years (88). 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 Willing town population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Willing town is shown in the following column.
• Population (Female): The female population in the Willing town 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 Willing town 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 Willing town Population by Gender. You can refer the same here

Data for this publication are extracted each month as a snapshot in time from the GP Payments system (Open Exeter) maintained by NHS Digital. This release is an accurate snapshot as at 1 September 2019. GP Practice; Sustainability and transformation partnership (STP); Clinical Commissioning Group (CCG); NHS England Region and NHS England Commissioning Region level data are released in single year of age (SYOA) and 5-year age bands, both of which finish at 95+, split by gender. In addition, organisational mapping data is available to derive STP; CCG; Region and Commissioning Region associated with a GP practice and is updated each month to give relevant organisational mapping. Quarterly publications in January, April, July and October will include Lower Layer Super Output Area (LSOA) populations and a topic of interest.

Comparison of demographics and disease characteristics between different health literacy profiles (n = 243).

The American Community Survey (ACS) is designed to estimate the characteristic distribution of populations* and estimated counts should only be used to calculate percentages. They do not represent the actual population counts or totals. Beginning in 2019, the Washington Student Achievement Council (WSAC) has measured educational attainment for the Roadmap Progress Report using one-year American Community Survey (ACS) data from the United States Census Bureau. These public microdata represents the most current data, but it is limited to areas with larger populations leading to some multi-county regions**.

*The American Community Survey is not the official source of population counts. It is designed to show the characteristics of the nation's population and should not be used as actual population counts or housing totals for the nation, states or counties. The official population count — including population by age, sex, race and Hispanic origin — comes from the once-a-decade census, supplemented by annual population estimates (which do not typically contain educational attainment variables) from the following groups and surveys:
-- Washington State Office of Financial Management (OFM): https://www.ofm.wa.gov/washington-data-research/population-demographics -- US Census Decennial Census: https://www.census.gov/programs-surveys/decennial-census.html and Population Estimates Program: https://www.census.gov/programs-surveys/popest.html

**In prior years, WSAC used both the five-year and three-year (now discontinued) data. While the 5-year estimates provide a larger sample, they are not recommended for year to year trends and also are released later than the one-year files.

Detailed information about the ACS at https://www.census.gov/programs-surveys/acs/guidance.html

About this dataset

Context

The dataset tabulates the population of Gratiot by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of Gratiot across both sexes and to determine which sex constitutes the majority.

Key observations

There is a slight majority of female population, with 51.69% of total population being female. 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.

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. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the Gratiot is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Gratiot 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 Gratiot Population by Race & Ethnicity. You can refer the same here