Population by age groups, race and gender, and the poverty by race is attached to the split tract geography to create this split tract with population and poverty data. Split tract data is the product of 2020 census tracts split by 2023 incorporated city boundaries and unincorporated community/countywide statistical areas (CSA) boundaries as of July 1, 2023. The census tract boundaries have been altered and aligned where necessary with legal city boundaries and unincorporated areas, including shoreline/coastal areas. Census Tract:Every 10 years the Census Bureau counts the population of the United States as mandated by Constitution. The Census Bureau (https://www.census.gov/)released 2020 geographic boundaries data including census tracts for the analysis and mapping of demographic information across the United States. City Boundary:City Boundary data is the base map information for the County of Los Angeles. These City Boundaries are based on the Los Angeles County Seamless Cadastral Landbase. The Landbase is jointly maintained by the Los Angeles County Assessor and the Los Angeles County Department of Public Works (DPW). This layer represents current city boundaries within Los Angeles County. The DPW provides the most current shapefiles representing city boundaries and city annexations. True, legal boundaries are only determined on the ground by surveyors licensed in the State of California.Countywide Statistical Areas (CSA): The countywide Statistical Area (CSA) was defined to provide a common geographic boundary for reporting departmental statistics for unincorporated areas and incorporated Los Angeles city to the Board of Supervisors. The CSA boundary and CSA names are established by the CIO and the LA County Enterprise GIS group worked with the Los Angeles County Board of Supervisors Unincorporated Area and Field Deputies that reflect as best as possible the general name preferences of residents and historical names of areas. This data is primarily focused on broad statistics and reporting, not mapping of communities. This data is not designed to perfectly represent communities, nor jurisdictional boundaries such as Angeles National Forest. CSA represent board approved geographies comprised of Census block groups split by cities.Data Fields:CT20: 2020 Census tractFIP22: 2023 City FIP CodeCITY: City name for incorporated cities and “Unincorporated” for unincorporated areas (as of July 1, 2023) CSA: Countywide Statistical Area (CSA) - Unincorporated area community names and LA City neighborhood names.CT20FIP23CSA: 2020 census tract with 2023 city FIPs for incorporated cities and unincorporated areas and LA neighborhoods. SPA22: 2022 Service Planning Area (SPA) number.SPA_NAME: Service Planning Area name.HD22: 2022 Health District (HD) number: HD_NAME: Health District name.POP23_AGE_0_4: 2023 population 0 to 4 years oldPOP23_AGE_5_9: 2023 population 5 to 9 years old POP23_AGE_10_14: 2023 population 10 to 14 years old POP23_AGE_15_17: 2022 population 15 to 17 years old POP23_AGE_18_19: 2023 population 18 to 19 years old POP23_AGE_20_44: 2023 population 20 to 24 years old POP23_AGE_25_29: 2023 population 25 to 29 years old POP23_AGE_30_34: 2023 population 30 to 34 years old POP23_AGE_35_44: 2023 population 35 to 44 years old POP23_AGE_45_54: 2023 population 45 to 54 years old POP23_AGE_55_64: 2023 population 55 to 64 years old POP23_AGE_65_74: 2023 population 65 to 74 years old POP23_AGE_75_84: 2023 population 75 to 84 years old POP23_AGE_85_100: 2023 population 85 years and older POP23_WHITE: 2023 Non-Hispanic White POP23_BLACK: 2023 Non-Hispanic African AmericanPOP23_AIAN: 2023 Non-Hispanic American Indian or Alaska NativePOP23_ASIAN: 2023 Non-Hispanic Asian POP23_HNPI: 2023 Non-Hispanic Hawaiian Native or Pacific IslanderPOP23_HISPANIC: 2023 HispanicPOP23_MALE: 2023 Male POP23_FEMALE: 2023 Female POV23_WHITE: 2023 Non-Hispanic White below 100% Federal Poverty Level POV23_BLACK: 2023 Non-Hispanic African American below 100% Federal Poverty Level POV23_AIAN: 2023 Non-Hispanic American Indian or Alaska Native below 100% Federal Poverty Level POV23_ASIAN: 2023 Non-Hispanic Asian below 100% Federal Poverty Level POV23_HNPI: 2023 Non-Hispanic Hawaiian Native or Pacific Islander below 100% Federal Poverty Level POV23_HISPANIC: 2023 Hispanic below 100% Federal Poverty Level POV23_TOTAL: 2023 Total population below 100% Federal Poverty Level POP23_TOTAL: 2023 Total PopulationAREA_SQMil: Area in square mile.POP23_DENSITY: 2023 Population per square mile.POV23_PERCENT: 2023 Poverty rate/percentage.How this data created?Population by age groups, ethnic groups and gender, and the poverty by ethnic groups is attributed to the split tract geography to create this data. Split tract polygon data is created by intersecting 2020 census tract polygons, LA Country City Boundary polygons and Countywide Statistical Areas (CSA) polygon data. The resulting polygon boundary aligned and matched with the legal city boundary whenever possible. Notes:1. Population and poverty data estimated as of July 1, 2023. 2. 2010 Census tract and 2020 census tracts are not the same. Similarly, city and community boundaries are as of July 1, 2023.

2020 Census Tract Demographics for Sacramento County.Decennial Census 2020 includes tabulations of housing units, total population and adult population by race and by Hispanic or Latino origin, and total group quarters population. Data are summary statistics for population and housing from a "100% count." The Census Bureau attempts to survey or interview all known addresses. Geographies nationwide can be obtained from Census, with disaggregate geographic detail down to Block-level.The Decennial Census PL94-171 reports summary statistics on population and housing for use in redistricting. The Census Bureau attempts to survey or interview all known addresses. Still, the data are subject to error. The errors derive from survey data collection (response errors, field follow-up for missing cases) and processing by the Census Bureau (geolocation of population and housing, data coding, compilation processes, and imputation of missing cases).

This web map displays data from the voter registration database as the percent of registered voters by census tract in King County, Washington. The data for this web map is compiled from King County Elections voter registration data for the years 2013-2019. The total number of registered voters is based on the geo-location of the voter's registered address at the time of the general election for each year. The eligible voting population, age 18 and over, is based on the estimated population increase from the US Census Bureau and the Washington Office of Financial Management and was calculated as a projected 6 percent population increase for the years 2010-2013, 7 percent population increase for the years 2010-2014, 9 percent population increase for the years 2010-2015, 11 percent population increase for the years 2010-2016 & 2017, 14 percent population increase for the years 2010-2018 and 17 percent population increase for the years 2010-2019. The total population 18 and over in 2010 was 1,517,747 in King County, Washington. The percentage of registered voters represents the number of people who are registered to vote as compared to the eligible voting population, age 18 and over. The voter registration data by census tract was grouped into six percentage range estimates: 50% or below, 51-60%, 61-70%, 71-80%, 81-90% and 91% or above with an overall 84 percent registration rate. In the map the lighter colors represent a relatively low percentage range of voter registration and the darker colors represent a relatively high percentage range of voter registration. PDF maps of these data can be viewed at King County Elections downloadable voter registration maps. The 2019 General Election Voter Turnout layer is voter turnout data by historical precinct boundaries for the corresponding year. The data is grouped into six percentage ranges: 0-30%, 31-40%, 41-50% 51-60%, 61-70%, and 71-100%. The lighter colors represent lower turnout and the darker colors represent higher turnout. The King County Demographics Layer is census data for language, income, poverty, race and ethnicity at the census tract level and is based on the 2010-2014 American Community Survey 5 year Average provided by the United States Census Bureau. Since the data is based on a survey, they are considered to be estimates and should be used with that understanding. The demographic data sets were developed and are maintained by King County Staff to support the King County Equity and Social Justice program. Other data for this map is located in the King County GIS Spatial Data Catalog, where data is managed by the King County GIS Center, a multi-department enterprise GIS in King County, Washington. King County has nearly 1.3 million registered voters and is the largest jurisdiction in the United States to conduct all elections by mail. In the map you can view the percent of registered voters by census tract, compare registration within political districts, compare registration and demographic data, verify your voter registration or register to vote through a link to the VoteWA, Washington State Online Voter Registration web page.

As of July 2nd, 2024 the COVID-19 Deaths by Population Characteristics Over Time dataset has been retired. This dataset is archived and will no longer update. We will be publishing a cumulative deaths by population characteristics dataset that will update moving forward.

A. SUMMARY This dataset shows San Francisco COVID-19 deaths by population characteristics and by date. This data may not be immediately available for recently reported deaths. Data updates as more information becomes available. Because of this, death totals for previous days may increase or decrease. More recent data is less reliable.

Population characteristics are subgroups, or demographic cross-sections, like age, race, or gender. The City tracks how deaths have been distributed among different subgroups. This information can reveal trends and disparities among groups.

B. HOW THE DATASET IS CREATED As of January 1, 2023, COVID-19 deaths are defined as persons who had COVID-19 listed as a cause of death or a significant condition contributing to their death on their death certificate. This definition is in alignment with the California Department of Public Health and the national https://preparedness.cste.org/wp-content/uploads/2022/12/CSTE-Revised-Classification-of-COVID-19-associated-Deaths.Final_.11.22.22.pdf">Council of State and Territorial Epidemiologists. Death certificates are maintained by the California Department of Public Health.

Data on the population characteristics of COVID-19 deaths are from: *Case reports *Medical records *Electronic lab reports *Death certificates

Data are continually updated to maximize completeness of information and reporting on San Francisco COVID-19 deaths.

To protect resident privacy, we summarize COVID-19 data by only one characteristic at a time. Data are not shown until cumulative citywide deaths reach five or more.

Data notes on each population characteristic type is listed below.

Race/ethnicity * We include all race/ethnicity categories that are collected for COVID-19 cases.

Gender * The City collects information on gender identity using these guidelines.

C. UPDATE PROCESS Updates automatically at 06:30 and 07:30 AM Pacific Time on Wednesday each week.

Dataset will not update on the business day following any federal holiday.

D. HOW TO USE THIS DATASET Population estimates are only available for age groups and race/ethnicity categories. San Francisco population estimates for race/ethnicity and age groups can be found in a view based on the San Francisco Population and Demographic Census dataset. These population estimates are from the 2016-2020 5-year American Community Survey (ACS).

This dataset includes many different types of characteristics. Filter the “Characteristic Type” column to explore a topic area. Then, the “Characteristic Group” column shows each group or category within that topic area and the number of deaths on each date.

New deaths are the count of deaths within that characteristic group on that specific date. Cumulative deaths are the running total of all San Francisco COVID-19 deaths in that characteristic group up to the date listed.

This data may not be immediately available for more recent deaths. Data updates as more information becomes available.

To explore data on the total number of deaths, use the COVID-19 Deaths Over Time dataset.

E. CHANGE LOG

• 9/11/2023 - on this date, we began using an updated definition of a COVID-19 death to align with the California Department o

A. SUMMARY This dataset shows San Francisco COVID-19 deaths by population characteristics. This data may not be immediately available for recently reported deaths. Data updates as more information becomes available. Because of this, death totals may increase or decrease.

Population characteristics are subgroups, or demographic cross-sections, like age, race, or gender. The City tracks how deaths have been distributed among different subgroups. This information can reveal trends and disparities among groups.

B. HOW THE DATASET IS CREATED As of January 1, 2023, COVID-19 deaths are defined as persons who had COVID-19 listed as a cause of death or a significant condition contributing to their death on their death certificate. This definition is in alignment with the California Department of Public Health and the national https://preparedness.cste.org/wp-content/uploads/2022/12/CSTE-Revised-Classification-of-COVID-19-associated-Deaths.Final_.11.22.22.pdf">Council of State and Territorial Epidemiologists. Death certificates are maintained by the California Department of Public Health.

Data on the population characteristics of COVID-19 deaths are from: *Case reports *Medical records *Electronic lab reports *Death certificates

Data are continually updated to maximize completeness of information and reporting on San Francisco COVID-19 deaths.

To protect resident privacy, we summarize COVID-19 data by only one population characteristic at a time. Data are not shown until cumulative citywide deaths reach five or more.

Data notes on select population characteristic types are listed below.

Race/ethnicity * We include all race/ethnicity categories that are collected for COVID-19 cases.

Gender * The City collects information on gender identity using these guidelines.

C. UPDATE PROCESS Updates automatically at 06:30 and 07:30 AM Pacific Time on Wednesday each week.

Dataset will not update on the business day following any federal holiday.

D. HOW TO USE THIS DATASET Population estimates are only available for age groups and race/ethnicity categories. San Francisco population estimates for race/ethnicity and age groups can be found in a dataset based on the San Francisco Population and Demographic Census dataset.These population estimates are from the 2018-2022 5-year American Community Survey (ACS).

This dataset includes several characteristic types. Filter the “Characteristic Type” column to explore a topic area. Then, the “Characteristic Group” column shows each group or category within that topic area and the number of cumulative deaths.

Cumulative deaths are the running total of all San Francisco COVID-19 deaths in that characteristic group up to the date listed.

To explore data on the total number of deaths, use the COVID-19 Deaths Over Time dataset.

E. CHANGE LOG

Millennials were the largest generation group in the United States in 2024, with an estimated population of ***** million. Born between 1981 and 1996, Millennials recently surpassed Baby Boomers as the biggest group, and they will continue to be a major part of the population for many years. The rise of Generation Alpha Generation Alpha is the most recent to have been named, and many group members will not be able to remember a time before smartphones and social media. As of 2024, the oldest Generation Alpha members were still only aging into adolescents. However, the group already makes up around ***** percent of the U.S. population, and they are said to be the most racially and ethnically diverse of all the generation groups. Boomers vs. Millennials The number of Baby Boomers, whose generation was defined by the boom in births following the Second World War, has fallen by around ***** million since 2010. However, they remain the second-largest generation group, and aging Boomers are contributing to steady increases in the median age of the population. Meanwhile, the Millennial generation continues to grow, and one reason for this is the increasing number of young immigrants arriving in the United States.

Analysis of ‘COVID-19 Cases by Population Characteristics Over Time’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/a3291d85-0076-43c5-a59c-df49480cdc6d on 13 February 2022.

--- Dataset description provided by original source is as follows ---

Note: On January 22, 2022, system updates to improve the timeliness and accuracy of San Francisco COVID-19 cases and deaths data were implemented. You might see some fluctuations in historic data as a result of this change. Due to the changes, starting on January 22, 2022, the number of new cases reported daily will be higher than under the old system as cases that would have taken longer to process will be reported earlier.

A. SUMMARY This dataset shows San Francisco COVID-19 cases by population characteristics and by specimen collection date. Cases are included on the date the positive test was collected.

Population characteristics are subgroups, or demographic cross-sections, like age, race, or gender. The City tracks how cases have been distributed among different subgroups. This information can reveal trends and disparities among groups.

Data is lagged by five days, meaning the most recent specimen collection date included is 5 days prior to today. Tests take time to process and report, so more recent data is less reliable.

B. HOW THE DATASET IS CREATED Data on the population characteristics of COVID-19 cases and deaths are from: * Case interviews * Laboratories * Medical providers

These multiple streams of data are merged, deduplicated, and undergo data verification processes. This data may not be immediately available for recently reported cases because of the time needed to process tests and validate cases. Daily case totals on previous days may increase or decrease. Learn more.

Data are continually updated to maximize completeness of information and reporting on San Francisco residents with COVID-19.

Data notes on each population characteristic type is listed below.

Race/ethnicity * We include all race/ethnicity categories that are collected for COVID-19 cases. * The population estimates for the "Other" or “Multi-racial” groups should be considered with caution. The Census definition is likely not exactly aligned with how the City collects this data. For that reason, we do not recommend calculating population rates for these groups.

Sexual orientation * Sexual orientation data is collected from individuals who are 18 years old or older. These individuals can choose whether to provide this information during case interviews. Learn more about our data collection guidelines. * The City began asking for this information on April 28, 2020.

Gender * The City collects information on gender identity using these guidelines.

Comorbidities * Underlying conditions are reported when a person has one or more underlying health conditions at the time of diagnosis or death.

Transmission type * Information on transmission of COVID-19 is based on case interviews with individuals who have a confirmed positive test. Individuals are asked if they have been in close contact with a known COVID-19 case. If they answer yes, transmission category is recorded as contact with a known case. If they report no contact with a known case, transmission category is recorded as community transmission. If the case is not interviewed or was not asked the question, they are counted as unknown.

Homelessness Persons are identified as homeless based on several data sources: * self-reported living situation
* the location at the time of testing * Department of Public Health homelessness and health databases * Residents in Single-Room Occupancy hotels are not included in these figures.
These methods serve as an estimate of persons experiencing homelessness. They may not meet other homelessness definitions.

Skilled Nursing Facility (SNF) occupancy * A Skilled Nursing

--- Original source retains full ownership of the source dataset ---

Abstract

The Ouagadougou Health and Demographic Surveillance System (Ouagadougou HDSS), located in five neighborhoods at the northern periphery of the capital of Burkina Faso, was established in 2008. Data on vital events (births, deaths, unions, migration events) are collected during household visits that have taken place every 10 months.

The areas were selected to contrast informal neighborhoods (40,000 residents) with formal areas (40,000 residents), with the aims of understanding the problems of the urban poor, and testing innovative programs that promote the well-being of this population. People living in informal areas tend to be marginalized in several ways: they are younger, poorer, less educated, farther from public services and more often migrants. Half of the residents live in the Sanitary District of Kossodo and the other half in the District of Sig-Nonghin.

The Ouaga HDSS has been used to study health inequalities, conduct a surveillance of typhoid fever, measure water quality in informal areas, study the link between fertility and school investments, test a non-governmental organization (NGO)-led program of poverty alleviation and test a community-led targeting of the poor eligible for benefits in the urban context. Key informants help maintain a good rapport with the community.

The areas researchers follow consist of 55 census tracks divided into 494 blocks. Researchers mapped all the census tracks and blocks using fieldworkers with handheld global positioning system (GPS) receivers and ArcGIS. During a first census (October 2008 to March 2009), the demographic surveillance system was explained to every head of household and a consent form was signed; during subsequent censuses, new households were enrolled in the same way.

Geographic coverage

Ouagadougou is the capital city of Burkina Faso and lies at the centre of this country, located in the middle of West Africa (128 North of the Equator and 18 West of the Prime Meridian).

Analysis unit

Individual

Universe

Resident household members of households resident within the demographic surveillance area. Inmigrants (visitors) are defined by intention to become resident, but actual residence episodes of less than six months (180 days) are censored. Outmigrants are defined by intention to become resident elsewhere, but actual periods of non-residence less than six months (180 days) are censored. Children born to resident women are considered resident by default, irrespective of actual place of birth. The dataset contains the events of all individuals ever residents during the study period (03 Oct. 2009 to 31 Dec. 2014).

Kind of data

Event history data

Frequency of data collection

This dataset contains rounds 0 to 7 of demographic surveillance data covering the period from 07 Oct. 2008 to 31 December 2014.

Sampling procedure

This dataset is not based on a sample, it contains information from the complete demographic surveillance area of Ouagadougou in Burkina Faso.

Reponse units (households) by Round: Round Households
2008 4941
2009 19159 2010 21168
2011 12548 2012 24174 2013 22326

Sampling deviation

None

Mode of data collection

Proxy Respondent [proxy]

Research instrument

List of questionnaires:

Collective Housing Unit (UCH) Survey Form - Used to register characteristics of the house - Use to register Sanitation installations - All registered house as at previous round are uploaded behind the PDA or tablet.

Household registration (HHR) or update (HHU) Form - Used to register characteristics of the HH - Used to update information about the composition of the household - All registered households as at previous rounds are uploaded behind the PDA or tablet.

Household Membership Registration (HMR) or update (HMU) - Used to link individuals to households. - Used to update information about the household memberships and member status observations - All member status observations as at previous rounds are uploaded behind the PDA or tablet.

Presences registration form (PDR) - Used to uniquely identify the presence of each individual in the household and to identify the new individual in the household - Mainly to ensure members with multiple household memberships are appropriately captured - All presences observations as at previous rounds are uploaded behind the PDA or tablet.

Visitor registration form (VDR) - Used register the characteristics of the new individual in the household - Used to capt the internal migration - Use matching form to facilitate pairing migration

Out Migration notification form (MGN) - Used to record change in the status of residency of individuals or households - Migrants are tracked and updated in the database

Pregnancy history form (PGH) & pregnancy outcome notification form (PON) - Records details of pregnancies and their outcomes - Only if woman is a new member - Only if woman has never completed WHL or WGH - All member pregnancy without pregnancy outcome as at previous rounds are uploaded behind the PDA or tablet.

Death notification form (DTN) - Records all deaths that have recently occurred - Includes information about time, place, circumstances and possible cause of death

Updated Basic information Form (UBIF) - Use to change the individual basic information

Health questionnaire (adults, women, child, elder) - Family planning - Chronic illnesses - Violence and accident - Mental health - Nutrition, alcohol, tobacco - Access to health services - Anthropometric measures - Physical limitations - Self-rated health - Food security

Variability of climate and water accessibility - accessibility to water - child health outcomes - gender outcomes - data on rainfall, temperatures, water quality

Cleaning operations

The data collection system is composed by two databases: - A temporary database, which contains data collected and transferred each day during the round. - A reference database, which contains all data of Ouagadougou Health and Demographic Surveillance System, in which is transferred the data of the temporary database to the end of each round. The temporary database is emptied at the end of the round for a new round.

The data processing takes place in two ways:

1) When collecting data with PDAs or tablets and theirs transfers by Wi-Fi, data consistency and plausibility are controlled by verification rules in the mobile application and in the database. In addition to these verifications, the data from the temporary database undergo validation. This validation is performed each week and produces a validation report for the data collection team. After the validation, if the error is due to an error in the data collection, the field worker equipped with his PDA or tablet go back to the field to revisit and correct this error. At the end of this correction, the field worker makes again the transfer of data through the wireless access points on the server. If the error is due to data inconsistencies that might not be directly related to an error in data collection, the case is remanded to the scientific team of the main database that could resolve the inconsistency directly in the database or could with supervisors perform a thorough investigation in order to correct the error.

2) At the end of the round, the data from the temporary database are automatically transferred into the reference database by a transfer program. After the success of this transfer, further validation is performed on the data in the database to ensure data consistency and plausibility. This still produces a validation report for the data collection team. And the same process of error correction is taken.

Response rate

Household response rates are as follows (assuming that if a household has not responded for 2 years following the last recorded visit to that household, that the household is lost to follow-up and no longer part of the response rate denominator):

Year Response Rate
2008 100%
2009 100%
2010 100%
2011 98% 2012 100% 2013 95%

Sampling error estimates

Not applicable

Data appraisal

CentreId MetricTable QMetric Illegal Legal Total Metric RunDate BF041 MicroDataCleaned Starts 151624 2017-05-16 13:36
BF041 MicroDataCleaned Transitions 0 314778 314778 0 2017-05-16 13:36
BF041 MicroDataCleaned Ends 151624 2017-05-16 13:36
BF041 MicroDataCleaned SexValues 314778 2017-05-16 13:36
BF041 MicroDataCleaned DoBValues 314778 2017-05-16 13:36

In a time of global change, having an understanding of the nature of biotic and abiotic factors that drive a species’ range may be the sharpest tool in the arsenal of conservation and management of threatened species. However, such information is lacking for most tropical and epiphytic species due to the complexity of life history, the roles of stochastic events, and the diversity of habitat across the span of a distribution. In this study, we conducted repeated censuses across the core and peripheral range of Trichocentrum undulatum, a threatened orchid that is found throughout the island of Cuba (species core range) and southern Florida (the northern peripheral range). We used demographic matrix modeling as well as stochastic simulations to investigate the impacts of herbivory, hurricanes, and logging (in Cuba) on projected population growth rates (? and ?s) among sites. Methods Field methods Censuses took place between 2013 and 2021. The longest census period was that of the Peripheral population with a total of nine years (2013–2021). All four populations in Cuba used in demographic modeling that were censused more than once: Core 1 site (2016–2019, four years), Core 2 site (2018–2019, two years), Core 3 (2016 and 2018 two years), and Core 4 (2018–2019, two years) (Appendix S1: Table S1). In November 2017, Hurricane Irma hit parts of Cuba and southern Florida, impacting the Peripheral population. The Core 5 population (censused on 2016 and 2018) was small (N=17) with low survival on the second census due to logging. Three additional populations in Cuba were visited only once, Core 6, Core 7, and Core 8 (Table 1). Sites with one census or with a small sample size (Core 5) were not included in the life history and matrix model analyses of this paper due to the lack of population transition information, but they were included in the analysis on the correlation between herbivory and fruit rate, as well as the use of mortality observations from logging for modeling. All Cuban sites were located between Western and Central Cuba, spanning four provinces: Mayabeque (Core 1), Pinar del Rio (Core 2 and Core 6), Matanzas (Core 3 and Core 5), and Sancti Spiritus (Core 4, Core 7, Core 8). At each population of T. undulatum presented in this study, individuals were studied within ~1-km strips where T. undulatum occurrence was deemed representative of the site, mostly occurring along informal forest trails. Once an individual of T. undulatum was located, all trees within a 5-m radius were searched for additional individuals. Since tagging was not permitted, we used a combination of information to track individual plants for the repeated censuses. These include the host species, height of the orchid, DBH of the host tree, and hand-drawn maps. Individual plants were also marked by GPS at the Everglades Peripheral site. If a host tree was found bearing more than one T. undulatum, then we systematically recorded the orchids in order from the lowest to highest as well as used the previous years’ observations in future censuses for individualized notes and size records. We recorded plant size and reproductive variables during each census including: the number of leaves, length of the longest leaf (cm), number of inflorescence stalks, number of flowers, and the number of mature fruits. We also noted any presence of herbivory, such as signs of being bored by M. miamensis, and whether an inflorescence was partially or completely affected by the fly, and whether there was other herbivory, such as D. boisduvalii on leaves. We used logistic regression analysis to examine the effects of year (at the Peripheral site) and sites (all sites) on the presence or absence of inflorescence herbivory at all the sites. Cross tabulation and chi-square analysis were done to examine the associations between whether a plant was able to fruit and the presence of floral herbivory by M. miamensis. The herbivory was scored as either complete or partial. During the orchid population scouting expeditions, we came across a small population in the Matanzas province (Core 5, within 10 km of the Core 3 site) and recorded the demographic information. Although the sampled population was small (N = 17), we were able to observe logging impacts at the site and recorded logging-associated mortality on the subsequent return to the site. Matrix modeling Definition of size-stage classes To assess the life stage transitions and population structures for each plant for each population’s census period we first defined the stage classes for the species. The categorization for each plant’s stage class depended on both its size and reproductive capabilities, a method deemed appropriate for plants (Lefkovitch 1965, Cochran and Ellner 1992). A size index score was calculated for each plant by taking the total number of observed leaves and adding the length of the longest leaf, an indication of accumulated biomass (Borrero et al. 2016). The smallest plant size that attempted to produce an inflorescence is considered the minimum size for an adult plant. Plants were classified by stage based on their size index and flowering capacity as the following: (1) seedlings (or new recruits), i.e., new and small plants with a size index score of less than 6, (2) juveniles, i.e., plants with a size index score of less than 15 with no observed history of flowering, (3) adults, plants with size index scores of 15 or greater. Adult plants of this size or larger are capable of flowering but may not produce an inflorescence in a given year. The orchid’s population matrix models were constructed based on these stages. In general, orchid seedlings are notoriously difficult to observe and easily overlooked in the field due to the small size of protocorms. A newly found juvenile on a subsequent site visit (not the first year) may therefore be considered having previously been a seedling in the preceding year. In this study, we use the discovered “seedlings” as indicatory of recruitment for the populations. Adult plants are able to shrink or transition into the smaller juvenile stage class, but a juvenile cannot shrink to the seedling stage. Matrix elements and population vital rates calculations Annual transition probabilities for every stage class were calculated. A total of 16 site- and year-specific matrices were constructed. When seedling or juvenile sample sizes were < 9, the transitions were estimated using the nearest year or site matrix elements as a proxy. Due to the length of the study and variety of vegetation types with a generally large population size at each site, transition substitutions were made with the average stage transition from all years at the site as priors. If the sample size of the averaged stage was still too small, the averaged transition from a different population located at the same vegetation type was used. We avoided using transition values from populations found in different vegetation types to conserve potential environmental differences. A total of 20% (27/135) of the matrix elements were estimated in this fashion, the majority being seedling stage transitions (19/27) and noted in the Appendices alongside population size (Appendix S1: Table S1). The fertility element transitions from reproductive adults to seedlings were calculated as the number of seedlings produced (and that survived to the census) per adult plant. Deterministic modeling analysis We used integral projection models (IPM) to project the long-term population growth rates for each time period and population. The finite population growth rate (?), stochastic long-term growth rate (?s), and the elasticity were projected for each matrices using R Popbio Package 2.4.4 (Stubben and Milligan 2007, Caswell 2001). The elasticity matrices were summarized by placing each element into one of three categories: fecundity (transition from reproductive adults to seedling stage), growth (all transitions to new and more advanced stage, excluding the fecundity), and stasis (plants that transitioned into the same or a less advanced stage on subsequent census) (Liu et al. 2005). Life table response experiments (LTREs) were conducted to identify the stage transitions that had the greatest effects on observed differences in population growth between select sites and years (i.e., pre-post hurricane impact and site comparisons of same vegetation type). Due to the frequent disturbances that epiphytes in general experience as well as our species’ distribution in hurricane-prone areas, we ran transient dynamic models that assume that the populations censused were not at stable stage distributions (Stott et al. 2011). We calculated three indices for short-term transient dynamics to capture the variation during a 15-year transition period: reactivity, maximum amplification, and amplified inertia. Reactivity measures a population’s growth in a single measured timestep relative to the stable-stage growth, during the simulated transition period. Maximum amplification and amplified inertia are the maximum of future population density and the maximum long-term population density, respectively, relative to a stable-stage population that began at the same initial density (Stott et al. 2011). For these analyses, we used a mean matrix for Core 1, Core 2 Core 3, and Core 4 sites and the population structure of their last census. For the Peripheral site, we averaged the last three matrices post-hurricane disturbance and used the most-recent population structure. We standardized the indices across sites with the assumption of initial population density equal to 1 (Stott et al. 2011). Analysis was done using R Popdemo version 1.3-0 (Stott et al. 2012b). Stochastic simulation We created matrices to simulate the effects of episodic recruitment, hurricane impacts, herbivory, and logging (Appendix S1: Table S2). The Peripheral population is the longest-running site with nine years of censuses (eight

Among all age groups worldwide, those aged 30 to 39 years had the highest percentage of fitness mobile app users. It was estimated that in 2016, around 41 percent of those aged 30 to 39 years used fitness tracking apps to track their health. The popularity of fitness tracking apps has increased significantly within recent years.

App usage

Fitness apps are used to track many aspects of health and wellbeing. Not only are they used for health data such as fitness and nutrition, they can also be used for mental health and chronic illness tracking. Health app users access their apps in a variety of ways. The most common way that health app users accessed their apps was through their mobile phone. When app publishers were asked who they target their apps for, people with chronic illness were most commonly chosen as a target demographic for health apps.

Health app market

Fitness tracking app downloads have dramatically increased in recent years. App diversity and availability have also increased since the popularity of health apps has risen. Since 2015, the number of apps available for download from the Apple store has almost doubled. The top health and fitness app downloaded through the Google Play store in March 2019 was Calorie Counter by MyFitnessPal. The second most downloaded health app at that time was Headspace: Meditation & Sleep.

This layer shows Household Pulse Survey data on gender identity and sexual orientation. Gender identity is the internal perception of gender, and how one identifies based on how one aligns or doesn’t align with cultural options for gender. This is a different concept than sex assigned at birth. Sexual orientation is the type of sexual attraction one has the capacity to feel for others, generally labeled based on the gender relationship between the person and the people they are attracted to. This is not the same as sexual behavior or preference.Learn more about how the Census Bureau survey measures sexual orientation and gender identity. This page includes nation-wide characteristics such as age, Hispanic origin and race, and educational attainment. Also read some of their findings about experiences during the COVID-19 pandemic, such as lesbian, gay, bisexual, or transgender (LGBT) adults experiencing higher rates of both economic hardship and mental health hardship. See the questionnaire used in phase 3.2 of the Household Pulse Survey.Source: Household Pulse Survey Data Tables. Data values in this layer are from Week 34 (July 21 - August 2, 2021), the first week that gender identity and sexual orientation questions were part of this survey. Top 15 metros are based on total population and are the same 15 metros available for all Household Pulse Data Tables.This layer is symbolized to show the percent of adults who are lesbian, gay, bisexual, or transgender (LGBT) as well as adults whose gender or sexual orientation was not listed on the survey (LGBTQIA+). The color of the symbol depicts the percentage and the size of the symbol depicts the count. *Percent calculations do not use those who did not report either their gender or sexual orientation in either the numerator or denominator, consistent with methodology used by the source.*Data Prep Steps:Data prep used Table 1 (Child Tax Credit Payment Status and Use, by Select Characteristics) to perform tabular data transformation. SAS to Table conversion tool was used to bring the tables into ArcGIS Pro.The data is joined to 2019 TIGER boundaries from the U.S. Census Bureau.Using the counties in each metro according to the Metropolitan and Micropolitan Statistical Area Reference Files, metro boundaries created via Merge and Dissolve tools in ArcGIS Pro.In preparing the field aliases and long descriptions, "none of these" and "something else" were generally modified to "not listed."

How did the 1918 influenza pandemic affect female labor force participation in India over the short run and the medium run? We use an event-study approach at the district level and four waves of decadal census data in order to answer this question. We find that districts most adversely affected by influenza mortality saw a temporary increase in female labor force participation in 1921, an increase that was concentrated in the service sector. We find suggestive evidence that distress labor supply by widows and rising wages help account for this result.

As of July 2024, Nigeria's population was estimated at around 229.5 million. Between 1965 and 2024, the number of people living in Nigeria increased at an average rate of over two percent. In 2024, the population grew by 2.42 percent compared to the previous year. Nigeria is the most populous country in Africa. By extension, the African continent records the highest growth rate in the world. Africa's most populous country Nigeria was the most populous country in Africa as of 2023. As of 2022, Lagos held the distinction of being Nigeria's biggest urban center, a status it also retained as the largest city across all of sub-Saharan Africa. The city boasted an excess of 17.5 million residents. Notably, Lagos assumed the pivotal roles of the nation's primary financial hub, cultural epicenter, and educational nucleus. Furthermore, Lagos was one of the largest urban agglomerations in the world. Nigeria's youthful population In Nigeria, a significant 50 percent of the populace is under the age of 19. The most prominent age bracket is constituted by those up to four years old: comprising 8.3 percent of men and eight percent of women as of 2021. Nigeria boasts one of the world's most youthful populations. On a broader scale, both within Africa and internationally, Niger maintains the lowest median age record. Nigeria secures the 20th position in global rankings. Furthermore, the life expectancy in Nigeria is an average of 62 years old. However, this is different between men and women. The main causes of death have been neonatal disorders, malaria, and diarrheal diseases.

This seventeenth Orange County Annual Survey continues to track trends over time in the county's important social, economic and political issues. This year, there is a special focus on understanding the impacts of incresing urbanization and the changing demographics of Orange County. The sample size is 1,000 Orange County adult residents. Online data analysis & additional documentation in Link below. Methods The 1998 Orange County Annual Survey was co-directed by Mark Baldassare, professor at UCI and senior fellow at the Public Policy Institute of California, and Cheryl Katz, research associate. The random telephone survey included interviews with 2,002 Orange County adult residents conducted Sept. 1-13, 1998. We follow the methods used in the 16 previous surveys, with two exceptions. This year, we doubled the sample size of the Orange County Annual Survey, which is usually about 1,000 interviews, so that we could expand our analysis of the Latino and Asian populations. We also conducted interviews in Vietnamese as well as in English and Spanish. Interviewing was conducted on weekend days and weekday nights, using a computer-generated random sample of telephone numbers. Within a household, adult respondents were randomly chosen for interview. Each interview took an average of 20 minutes to complete. The interviewing was conducted in English, Spanish or Vietnamese, as needed. The completion rate was 74 percent. The telephone interviewing was conducted by Interviewing Services of America in Van Nuys, CA. The survey sample was compared with the U.S. Census and state figures by city for Orange County, and was found to represent the actual regional distribution of Orange County residents. The sample's demographic characteristics also were closely comparable to the census and other survey data, including previous Orange County Annual Surveys. The sampling error for this survey is +/2% at the 95% confidence level. This means that 95 times out of 100, the results will be within two percentage points of what they would be if all adults in Orange County were interviewed. The sampling error for any subgroup would be larger. Sampling error is just one type of error to which surveys are subject. Results may also be affected by question wording, ordering, and survey timing. Throughout the report, we refer to two geographic regions. North County includes Anaheim, Orange, Villa Park, La Habra, Brea, Buena Park, Fullerton, Placentia, Yorba Linda, La Palma, Cypress, Los Alamitos, Rossmoor, Seal Beach, Westminster, Midway City, Stanton, Fountain Valley, Huntington Beach, Santa Ana, Garden Grove, Tustin, Tustin Foothills and Costa Mesa. South County includes Newport Beach, Irvine, Lake Forest, Aliso Viejo, Laguna Hills, Laguna Niguel, Mission Viejo, Portola Hills, Rancho Santa Margarita, Foothill Ranch, Coto de Caza, Trabuco Highlands, El Toro Station, Laguna Beach, Dana Point, San Clemente, Capistrano Beach and San Juan Capistrano. In the analysis of questions on the proposed El Toro airport, we include Newport Beach in North County.

This thirteenth Orange County Annual Survey, UCI, spotlights several top concerns of Orange County residents, most notably: crime and the economy. In addition, the survey continues to track topics from previous years. The 1994 survey was conducted August 19 to 29 and included interviews with 1,000 adults.Online data analysis & additional documentation in Link below. Methods The Orange County Annual Survey, UCI was co-directed by Mark Baldassare, professor and chair of urban and regional planning, and Cheryl Katz, research associate. The random telephone survey included interviews with 1,000 Orange County adult residents conducted August 19 to 29, 1994. Interviewing was conducted on weekend days and weekday nights, using a computer-generated random sample of telephone numbers. Within a household, adult respondents were randomly chosen for interview. Each interview included 94 questions and took an average of 20 minutes to complete. The interviewing was conducted in English and Spanish, as needed.The completion rate for the survey was 61 percent. This rate is consistent with earlier Orange County Annual Surveys. Of the telephone numbers called, 20 percent resulted in completed interviews and 13 percent were refusals. The field work was conducted by Interviewing Services of America of Van Nuys, CA.The sample was compared to the 1990 U.S. Census population figures by city for Orange County and was found to represent the actual regional distribution of Orange County residents. The sample's demographic characteristics were also closely comparable to the Census and other data available on Orange County residents.The sampling error for this survey is +/3 percent at the 95 percent confidence level. This means that 95 times out of 100, the results will be within 3 percentage points of what they would be if all adults in Orange County were interviewed. The sampling error for any subgroup would be larger. Sampling error is just one type of error to which surveys are subject. Results may also be affected by question wording, ordering, and survey timing.

The Orange County Annual Survey is in its fourth year. The 1985 survey represents the first as a university-based research program with all its funding from external sources. It aims (1) to provide decision makers in the private and public sectors with valid and current information on Orange County residents; (2) to track shifts in important attitudes and population characteristics over time; (3) to study the social, economic, and political issues of Orange County from a nonadvocacy position; and (4) to help establish public discussion and enlightened debate in future public policy. The theme of this year's survey is "Orange County at Mid-Decade." The 1985 survey continues the analysis of attitudes on national issues by considering views towards social welfare spending. This survey analyzes geographical divisions of Orange County that represent distinguishable communities in terms of social, housing, and public policy concerns. The sample size is 1,008 Orange County adult residents.Online data analysis & additional documentation in Link below. Methods The sample for the 1985 Orange County Annual Survey consists of 1,008 randomly selected residents who were interviewed by telephone. The sample stratified geographically, with half of the sample selected from north of the Santa Ana River and half from the south. For data analyses, the sample is statistically weighted to represent the actual distribution of the Orange County population. The sample in each area was chosen using a computer program which randomly generates telephone numbers from among working blocks of telephone exchanges. A working block is one that contains numbers in use. The total of telephone numbers generated within an exchange was in proportion to the number of residential phones represented by that exchange in the northern art of the county or the southern part of the county. Using this procedure, approximately 2,500 telephone numbers from the south and approximately 2,500 telephone numbers from the north were drawn. This procedure of random digit dialing ensures that unlisted as well as listed numbers are included the sample. Also, since over 97% of the households in Orange County have telephones, random dialing yields a sample representative of the population Orange County. The Troldahl-Carter Method was used in randomly selecting which adult member of the household was to be interviewed. This method consists of numerating the total number of adults in the household and the total number men in the household. Then, using a prearranged grid, the interviewer selects the individual in the household for interviewing. As further evidence of the representativeness of the sample chosen by the above methods, characteristics of the sample were compared to characteristics of the total Orange County population using the 1980 Census. On the basis of age, household composition, marital status, household size, and homeownership, the sample is representative of the population of Orange County. Characteristics of the 1982, 1983, and 1984 Orange County Annual Survey samples were also contrasted with the characteristics of the 1985 orange County Annual Survey sample. Marital status, ethnicity, age, sex, and education were closely comparable in the four surveys. The sampling error for this survey is plus or minus three percentage points. This means that if this survey were to be repeated 100 times, in 95 out of the 100 times the answers obtained for a particular question would match those we obtained in this survey within three points. The sampling error for any particular subgroup would be greater. These calculations assume that the data were collected under ideal circumstances. Since there are a large number of practical problems in conducting social surveys, the actual sampling error for any particular result might be slightly higher.

This is the fifth year of the Orange County Annual Survey. The 1986 report asks questions found in national surveys and compares Orange County to the nation. It examines key trends over time with questions repeated annually in our survey. The design, analysis, and interpretation of the 1986 survey are guided by goals (1) to provide decisionmakers in the private and public sectors with valid and current information on Orange County residents; (2) to track shifts in important attitudes and population characteristics over time; (3) to study the social, economic and political issues of Orange County from a non-advocacy positions and (4) to help establish public discussion and enlightened debate in future public policy. The sample size is 1,008 Orange County adult residents.Online data analysis & additional documentation in Link below. Methods The Orange County Annual Survey was conducted by Mark Baldassare, an associate professor of social ecology at UC Irvine. The telephone interviews with 1,008 adult residents took place between September 2 and 19. Telephone interviews provide representative data in Orange County, because more than 97 percent of the households have telephones.The calls were made on weekend days and weekday nights using a random sample of listed and unlisted telephone numbers. Telephone numbers were randomly generated by computer from a list of working blocks of telephone exchanges. Within a household, respondents were chosen for interview using the Troldahl-Carter method, which randomly selects a househoId member from a grid. The field work was conducted by the Center for Survey Research, Public Policy Research Organization, at UC Irvine. The sample was generated by Pijacki Associates of Shoreham, New York.Each interview contained 90 questions and took an average of 20 minutes to complete. The surveys were designed in three stages over several months. In the first stage, amail questionnaire and personal interviews with community leaders were used to define topics and questions. The second stage involved meetings with the Research Advisory Committee. In this stage, Mark Baldassare drafted questions and made revisions based upon comments from the group. The final stage involved extensive consultation between Baldassare and the Center for Survey Research staff, followed by pre-tests and final revisions of the questions.The interview began with questions about housing, economics, growth and general evaluations of Orange County. These were followed by questions on transportation issues. Later in the interview, we turned to the topics of media use, civic and social responsibility and civil liberties. The conclusion of the survey was devoted to questions about respondents' household, financial, personal and political characteristics.The survey also included questions devoted to the concerns of two private sponsors. These are not part of the Orange County Annual Survey report.The survey's validity was checked by comparing the sample's characteristics to available information on Orange County's population. We compared the 1986 survey results to the 1980 Census, previous Orange County Annual Surveys, and other recent polling data. Age, marital status, and other demographic features of our sample were close to those noted in other studies. For data analyses, we statistically weighted the sample to represent the actual regional distribution of Orange County residents.The sampling error for this survey is +/3 percent. This means it is 95 percent certain that the results are within 3 percentage points of what they would be if all adults in Orange County were interviewed. The sampling error for any subgroup would be larger. Sampling error is just one type of error to which surveys are subject. Results may be influenced by many other factors, such as question wording, survey timing and interview design.

Graph and download economic data for Real Median Personal Income in the United States (MEPAINUSA672N) from 1974 to 2023 about personal income, personal, median, income, real, and USA.

Abstract

This report presents the main results of the 2017 Namibia Financial Inclusion Survey. The survey was conducted by the Namibia Statistics Agency, in all 14 regions of Namibia, with funding from the Bank of Namibia and the World Bank. By design, the NFIS surveys was intended to involve a range of stakeholders through syndicate membership to enrich the entire survey process through cross-cutting learning, sharing of information, and to facilitate the extended utilization of the final data. A nationally representative sample of Namibians 16 years and older was employed. During October and November 2017 1863 face-to-face interviews were conducted, one interview per selected household. The data was captured into a tablet-based questionnaire using the Survey-To-Go application. The data collected was weighted to reflect the adult/eligible population (i.e. aged 16 years or older) in Namibia, as this is the minimum age legally allowed for any individual to make use of formal financial products in their own capacity. It is also important to note that the results of 2017 are representative only at national and urban/rural areas levels, but not regional.

· To measure the levels of financial inclusion (inclusive of formal and informal usage) · To describe the landscape of access (type of products and services used by financially included individuals) · To identify the drivers of, and barriers to the usage of financial products and services · To track and compare results and provide an assessment of changes and reasons thereof (including possible impacts of interventions to enhance access) · To stimulate evidence-based dialogue that will ultimately lead to effective public/private sector interventions that will increase and deepen financial inclusion strategies · Provide information on new opportunities for increased financial inclusion and usage.

Geographic coverage

National sampling frame is a list of small geographical areas called Primary Sampling Units (PSUs). There are a total of 6453 PSUs in Namibia that were created using the enumeration areas (EA) of the 2011 Population and Housing Census. The measure of size in the frame is the number of households within the PSU as reflected in the 2011 Census. The frame units were stratified first by region, and then by urban/rural areas within each region.

The results are only representative at national level, but not at regional level.

Analysis unit

Individuals, households

Universe

The target population for the NFIS 2017 was all people aged 16 and above who live in private households in Namibia. The eligible population living in institutions, such as hospitals, hostels, police barracks and prisons were not covered in this survey. However, private households within institutional settings such as teachers' houses in school premises were covered.

Kind of data

Sample survey data [ssd]

Sampling procedure

The target population for the NFIS 2017 was eligible members of private households in Namibia. The eligible population living in institutions, such as hospitals, hostels, police barracks and prisons were not covered in this survey. However, private households within institutional settings such as teachers' houses in school premises were covered. The sample design was a stratified three-stage cluster sample, where the first stage units were the PSUs, the second stage units were the households and the third stage were the eligible members, that is individuals who, by the time of the survey were 16 years or older, available during the duration of survey, mentally/physically capable to be interviewed and have resided in the selected household for at least six month preceding the survey. The age limit for the eligibility criteria was based on the fact that only individuals aged 16 years or above are officially authorized to get personal formal financial products (such as open a personal bank account) from formal financial institutions in Namibia, which makes them the target population of the financial sector. Only one individual was interviewed per selected household

The national sampling frame was used to select the first stage units (PSUs). The national sampling frame is a list of small geographical areas called Primary Sampling Units (PSUs) created using the enumeration areas (EAs) of 2011 Population and Housing Census. There are a total of 6 453 PSUs in Namibia. A total of 151 PSUs were selected from all the 14 regions, and 2 114 households were drawn from them, constituting the sample size. Power allocation procedures were adopted to distribute the samples across the regions so that the smaller regions will get adequate samples.

Mode of data collection

Face-to-face [f2f]

Research instrument

The 2017 NFIS questionnaire was made up of 13 sections in total. The questionnaire was transmitted onto CAPI (Computer aided Personal Interview) using the Survey-To-Go application.

Cleaning operations

The data processing methodology that was adopted for this study was the Computer Assisted Personal Interview. Data management series of operations to collect, transmit, clean and store the survey data were designed using SurveyToGo computer system onto the Dubloo platform.

Data entry is very crucial, since the quality of data collected impact heavily on the output. The collection process was designed to ensure that the data gathered are both defined and accurate, so that subsequent decisions based on the findings are valid.

Response rate

After data processing, 1863 out of 2114 sampled households were successfully interviewed, resulting in 88.1 percent response rate which is highly satisfactory given that the NSA subscribes to a response rate of 80 percent for all data collection in the social statistics domain. Overall, the rural response is higher than the urban response.

It was not possible to interview all the selected households when the household sample was implemented, due to refusals or non-contacts.

Sampling error estimates

The most common measure of quality of the survey estimates reported from the sample surveys was the level of precision of the estimates. The quality indicators are meant to ascertain the analysis about the level of precision of the estimates at different domains. The statistical precision of the survey estimates were expressed using different types of statistics such as Standard errors (SE), the coefficient of variation (CV) and the Confidence Interval (CI). These statistics were used to indicate the level of precision of the survey estimates in estimating the population parameters of interest. There are a number of factors that can affect the precision of the survey estimates namely the size of the sample relative to the population size, the sample design and the variability of the characteristics of interest in the population. The data quality indicators were discussed in details in the following sub-section.

Abstract

The 2022 Ghana Demographic and Health Survey (2022 GDHS) is the seventh in the series of DHS surveys conducted by the Ghana Statistical Service (GSS) in collaboration with the Ministry of Health/Ghana Health Service (MoH/GHS) and other stakeholders, with funding from the United States Agency for International Development (USAID) and other partners.

The primary objective of the 2022 GDHS is to provide up-to-date estimates of basic demographic and health indicators. Specifically, the GDHS collected information on: - Fertility levels and preferences, contraceptive use, antenatal and delivery care, maternal and child health, childhood mortality, childhood immunisation, breastfeeding and young child feeding practices, women’s dietary diversity, violence against women, gender, nutritional status of adults and children, awareness regarding HIV/AIDS and other sexually transmitted infections, tobacco use, and other indicators relevant for the Sustainable Development Goals - Haemoglobin levels of women and children - Prevalence of malaria parasitaemia (rapid diagnostic testing and thick slides for malaria parasitaemia in the field and microscopy in the lab) among children age 6–59 months - Use of treated mosquito nets - Use of antimalarial drugs for treatment of fever among children under age 5

The information collected through the 2022 GDHS is intended to assist policymakers and programme managers in designing and evaluating programmes and strategies for improving the health of the country’s population.

Geographic coverage

National coverage

Analysis unit

• Household
• Individual
• Children age 0-5
• Woman age 15-49
• Man age 15-59

Universe

The survey covered all de jure household members (usual residents), all women aged 15-49, men aged 15-59, and all children aged 0-4 resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

To achieve the objectives of the 2022 GDHS, a stratified representative sample of 18,450 households was selected in 618 clusters, which resulted in 15,014 interviewed women age 15–49 and 7,044 interviewed men age 15–59 (in one of every two households selected).

The sampling frame used for the 2022 GDHS is the updated frame prepared by the GSS based on the 2021 Population and Housing Census.1 The sampling procedure used in the 2022 GDHS was stratified two-stage cluster sampling, designed to yield representative results at the national level, for urban and rural areas, and for each of the country’s 16 regions for most DHS indicators. In the first stage, 618 target clusters were selected from the sampling frame using a probability proportional to size strategy for urban and rural areas in each region. Then the number of targeted clusters were selected with equal probability systematic random sampling of the clusters selected in the first phase for urban and rural areas. In the second stage, after selection of the clusters, a household listing and map updating operation was carried out in all of the selected clusters to develop a list of households for each cluster. This list served as a sampling frame for selection of the household sample. The GSS organized a 5-day training course on listing procedures for listers and mappers with support from ICF. The listers and mappers were organized into 25 teams consisting of one lister and one mapper per team. The teams spent 2 months completing the listing operation. In addition to listing the households, the listers collected the geographical coordinates of each household using GPS dongles provided by ICF and in accordance with the instructions in the DHS listing manual. The household listing was carried out using tablet computers, with software provided by The DHS Program. A fixed number of 30 households in each cluster were randomly selected from the list for interviews.

For further details on sample design, see APPENDIX A of the final report.

Mode of data collection

Face-to-face computer-assisted interviews [capi]

Research instrument

Four questionnaires were used in the 2022 GDHS: the Household Questionnaire, the Woman’s Questionnaire, the Man’s Questionnaire, and the Biomarker Questionnaire. The questionnaires, based on The DHS Program’s model questionnaires, were adapted to reflect the population and health issues relevant to Ghana. In addition, a self-administered Fieldworker Questionnaire collected information about the survey’s fieldworkers.

The GSS organized a questionnaire design workshop with support from ICF and obtained input from government and development partners expected to use the resulting data. The DHS Program optional modules on domestic violence, malaria, and social and behavior change communication were incorporated into the Woman’s Questionnaire. ICF provided technical assistance in adapting the modules to the questionnaires.

Cleaning operations

DHS staff installed all central office programmes, data structure checks, secondary editing, and field check tables from 17–20 October 2022. Central office training was implemented using the practice data to test the central office system and field check tables. Seven GSS staff members (four male and three female) were trained on the functionality of the central office menu, including accepting clusters from the field, data editing procedures, and producing reports to monitor fieldwork.

From 27 February to 17 March, DHS staff visited the Ghana Statistical Service office in Accra to work with the GSS central office staff on finishing the secondary editing and to clean and finalize all data received from the 618 clusters.

Response rate

A total of 18,540 households were selected for the GDHS sample, of which 18,065 were found to be occupied. Of the occupied households, 17,933 were successfully interviewed, yielding a response rate of 99%. In the interviewed households, 15,317 women age 15–49 were identified as eligible for individual interviews. Interviews were completed with 15,014 women, yielding a response rate of 98%. In the subsample of households selected for the male survey, 7,263 men age 15–59 were identified as eligible for individual interviews and 7,044 were successfully interviewed.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: (1) nonsampling errors and (2) sampling errors. Nonsampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the 2022 Ghana Demographic and Health Survey (2022 GDHS) to minimize this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2022 GDHS is only one of many samples that could have been selected from the same population, using the same design and identical size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability between all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results. A sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall within a range of plus or minus two times the standard error of that statistic in 95% of all possible samples of identical size and design.

If the sample of respondents had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the 2022 GDHS sample was the result of a multistage stratified design, and, consequently, it was necessary to use more complex formulas. The computer software used to calculate sampling errors for the GDHS 2022 is an SAS program. This program used the Taylor linearization method to estimate variances for survey estimates that are means, proportions, or ratios. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.

A more detailed description of estimates of sampling errors are presented in APPENDIX B of the survey report.

Data appraisal

Data Quality Tables

• Age distribution of eligible and interviewed women
• Age distribution of eligible and interviewed men
• Age displacement at age 14/15
• Age displacement at age 49/50
• Pregnancy outcomes by years preceding the survey
• Completeness of reporting
• Standardisation exercise results from anthropometry training
• Height and weight data completeness and quality for children
• Height measurements from random subsample of measured children
• Interference in height and weight measurements of children
• Interference in height and weight measurements of women and men
• Heaping in anthropometric measurements for children (digit preference)
• Observation of mosquito nets
• Observation of handwashing facility
• School attendance by single year of age
• Vaccination cards photographed
• Number of