This statistic shows the age structure in Brazil from 2013 to 2023. In 2023 about 19.94 percent of Brazil's total population were aged 0 to 14 years. Population of Brazil Brazil is the fifth largest country in the world by area and population and the largest in both South America and the Latin American region. With a total population of more than 200 million inhabitants in 2013, Brazil also ranks fifth in terms of population numbers. Brazil is a founding member of the United Nations, the G20, CPLP, and a member of the BRIC countries. BRIC is an acronym for Brazil, Russia, India, and China, the four major emerging market countries. The largest cities in Brazil are São Paulo, Rio de Janeiro and Salvador. São Paulo alone reports over 11.1 million inhabitants. Due to a steady increase in the life expectancy in Brazil, the average age of the population has also rapidly increased. From 1950 until 2015, the average age of the population increased by an impressive 12 years; in 2015, the average age of the population in Brazil was reported to be around 31 years. As a result of the increasing average age, the percentage of people aged between 15 and 64 years has also increased: In 2013, about 68.4 percent of the population in Brazil was aged between 15 and 64 years.

1- Temporal fluctuations in growth rates can arise from both variation in age-specific vital rates and temporal fluctuations in age structure (i.e., the relative abundance of individuals in each age-class). However, empirical assessments of temporal fluctuations in age structure and their effects on population growth rate are rare. Most research has focused on understanding the contribution of changing vital rates to population growth rates and these analyses routinely assume that: (i) populations have stable age distributions, (ii) environmental influences on vital rates and age structure are stationary (i.e., the mean and/or variance of these processes does not change over time), and (iii) dynamics are independent of density. 2- Here we quantified fluctuations in age structure and assessed whether they were stationary for four populations of free-ranging vertebrates: moose (observed for 48 years), elk (15 years), tawny owls (15 years) and gray wolves (17 years). We also assessed the extent that fluctuations in age structure were useful for predicting annual population growth rates using models which account for density-dependence. 3- Fluctuations in age structure were of a similar magnitude to fluctuations in abundance. For three populations (moose, elk, owls), the mean and the skew of the age distribution fluctuated without stabilizing over the observed time periods. More precisely, the sample variance (interannual variance) of age structure indices increased with the length of the study period which suggests that fluctuations in age structure were non-stationary for these populations – at least over the 15-48 year periods analysed. 4- Fluctuations in age structure were associated with population growth rate for two populations. In particular, population growth varied from positive to negative for moose and from near zero to negative for elk as the average age of adults increased over its observed range. 5- Non-stationarity in age structure may represent an important mechanism by which abundance becomes non-stationary – and therefore difficult to forecast – over time scales of concern to wildlife managers. Overall, our results emphasize the need for vertebrate populations to be modelled using approaches that consider transient dynamics and density-dependence, and that do not rely on the assumption that environmental processes are stationary.

This table provides the age-standardized mortality rates per 100,000 population, for the three selected causes of death and all causes combined. The three selected causes of death are Circulatory System, Neoplasms and External Causes (Injury). Age standardization is a technique applied to make rates comparable across groups with different age distributions. A simple rate is defined as the number of people with a particular condition divided by the whole population. An age-standardized rate is defined as the number of people with a condition divided by the population within each age group. Standardizing (adjusting) the rate across age groups allows a more accurate comparison between populations that have different age structures. Age standardization is typically done when comparing rates across time periods, different geographic areas, and or population sub-groups (e.g. ethnic group). This indicator dataset contains information at both Local Geographic Area (for example, Lacombe, Red Deer - North, Calgary - West Bow, etc.) and Alberta levels. Local geographic area refers to 132 geographic areas created by Alberta Health (AH) and Alberta Health Services (AHS) based on census boundaries. This table is the part of "Alberta Health Primary Health Care - Community Profiles" report published March 2019

Nigeria's population structure reveals a youthful demographic, with those aged **** years comprising the largest age group compared to the total of those between the ages of 30 and 84 years. The majority of the young population are men. This demographic trend has significant implications for Nigeria's future, particularly in terms of economic development and social services. It has the potential to offer a large future workforce that could drive economic growth if it is adequately educated and employed. However, without sufficient investment in health, education, and job creation, this youth bulge could strain public resources and fuel unemployment and social unrest. Poverty challenges amid population growth Despite Nigeria's large youth population, the country faces substantial poverty challenges. This is largely due to its youth unemployment rate, which goes contrary to the expectation that the country’s large labor force would contribute to employment and the economic development of the nation. In 2022, an estimated **** million Nigerians lived in extreme poverty, defined as living on less than **** U.S. dollars a day. This number is expected to rise in the coming years, indicating a growing disparity between population growth and economic opportunities. The situation is particularly dire in rural areas, where **** million people live in extreme poverty compared to *** million in urban centers. Linguistic and ethnic diversity Nigeria's population is characterized by significant linguistic and ethnic diversity. Hausa is the most commonly spoken language at home, used by ** percent of the population, followed by Yoruba at ** percent and Igbo at ** percent. This linguistic variety reflects Nigeria's complex ethnic composition, with major groups including Hausa, Yoruba, Igbo, and Fulani. English, the country's official language, serves as the primary language of instruction in schools, promoting literacy across diverse communities.

Definition: The long-term demographic change and the associated changes in the age structure of the population are represented by the numerical ratio of certain age groups. The youth ratio compares the child and youth generation, which is predominantly in the education and training phase, with the middle generation, which is predominantly in the labour market. The age limit for children and adolescents is ‘less than 20 years’ and the age limit for the middle generation is ‘20 to less than 65 years’. The old-age dependency ratio contrasts the older generation, which has largely left the labour force, with the middle generation. For the older generation, the age limit “from 65 years” is chosen. In order to be able to assess the financial burdens of the social system, an overall quotient is formed that places the “young” and “old” in relation to the population in middle age. Data source: IT.NRW, Population update

Provides the age-standardized mortality rates per 100,000 population, for the three selected causes of death and all causes combined. The three selected causes of death are Circulatory System, Neoplasms and External Causes (Injury). Age standardization is a technique applied to make rates comparable across groups with different age distributions. A simple rate is defined as the number of people with a particular condition divided by the whole population. An age-standardized rate is defined as the number of people with a condition divided by the population within each age group. Standardizing (adjusting) the rate across age groups allows a more accurate comparison between populations that have different age structures. Age standardization is typically done when comparing rates across time periods, different geographic areas, and or population sub-groups (e.g. ethnic group). This indicator dataset contains information at both Local Geographic Area (for example, Lacombe, Red Deer - North, Calgary - West Bow, etc.) and Alberta levels. Local geographic area refers to 132 geographic areas created by Alberta Health (AH) and Alberta Health Services (AHS) based on census boundaries. This table is the part of "Alberta Health Primary Health Care - Community Profiles" report published March 2015

Footnotes: 1 Sources: Statistics Canada, Canadian Vital Statistics, Death Database and Demography Division (population estimates). The table 13-10-0743-01 is an update of table 13-10-0412-01. This is because of the adoption of the 2015 version of the Health Region Geography. For more information, consult Statistics Canada's publication Health Regions: Boundaries and Correspondence with Census Geography" (catalogue number 82-402-X)." 2 Mortality is the death rate, which can be measured as total mortality (all causes of death combined) or by selected cause of death. All counts and rates are calculated using the total population (all age groups). 3 Potential years of life lost (PYLL) is the number of years of potential life not lived when a person dies prematurely" defined for this indicator as before age 75. All counts and rates in this table are calculated using the population aged 0 to 74."4 Counts and rates in this table are based on three consecutive years of death data. Rates are per 100,000 population and were calculated by dividing the counts by three consecutive years of population data. 5 Rates are age-standardized using the direct method and the 2011 Canadian Census population structure. The use of a standard population results in more meaningful rate comparisons because it adjusts for variations in population age distributions over time and across geographic areas. 6 Counts and rates in this table exclude: deaths of non-residents of Canada; deaths of residents of Canada whose province or territory of residence was unknown; deaths for which age of decedent was unknown. 7 Rates in this table are based on place of residence for indicators derived from death events. 8 The number of deaths in Ontario for 2016 is considered preliminary. 9 Health regions are administrative areas defined by provincial ministries of health according to provincial legislation. The health regions presented in this table are based on boundaries and names in effect as of December 2017. For complete Canadian coverage, each northern territory represents a health region. 10 Peer groups are aggregations of health regions that share similar socio-economic and demographic characteristics, based on data from the 2011 Census of Population and 2011 National Household Survey. These are useful in the analysis of health regions, where important differences may be detected by comparing health regions within a peer group. The nine peer groups are identified by the letters A through I, which are appended to the health region 4-digit code. Caution should be taken when comparing data for the Peer Groups over time due to changes in the Peer Groups. In an analysis involving the peer groups, only one level of geography in Ontario should be used. For more information on the peer groups classification, consult Statistics Canada's publication Health Regions: Boundaries and Correspondence with Census Geography" (catalogue number 82-402-X)." 11 Before 2010, missing data on sex of the deceased were imputed based on death registration number. Starting with 2010 data year, missing data on sex of the deceased were imputed based on the cause of death information and a logistic regression. 12 The cause of death tabulated is the underlying cause of death. This is defined as (a) the disease or injury which initiated the train of events leading directly to death, or (b) the circumstances of the accident or violence which produced the fatal injury. The underlying cause is selected from the conditions listed on the medical certificate of cause of death. 13 Confidence intervals for age-standardized rates for selected causes of death data were produced using the Spiegelman method. Source: Spiegelman, M., Introduction to Demography" Revised Edition14 Confidence intervals for crude rates for selected causes of death data were produced using the Fleiss method. Source: Fleiss, JL., Statistical Methods for Rates and Proportions" Second Edition15 The 95% confidence interval (CI) illustrates the degree of variability associated with a number or a rate. 16 Wide confidence intervals (CIs) indicate high variability, thus, these numbers or rates should be interpreted and compared with due caution. 17 The following standard symbols are used in this Statistics Canada table: (..) for figures not available for a specific reference period, (...) for figures not applicable and (x) for figures suppressed to meet the confidentiality requirements of the Statistics Act. 18 The figures shown in the tables have been subjected to a confidentiality procedure known as controlled rounding to prevent the possibility of associating statistical data with any identifiable individual. Under this method, all figures, including totals and margins, are rounded either up or down to a multiple of 5. Controlled rounding has the advantage over other types of rounding of producing additive tables as well as offering more protection. 19 Premature deaths are those of individuals who are younger than age 75.

Footnotes: 1 Sources: Statistics Canada, Canadian Vital Statistics, Death Database and Demography Division (population estimates). The table 13-10-0743-01 is an update of table 13-10-0412-01. This is because of the adoption of the 2015 version of the Health Region Geography. For more information, consult Statistics Canada's publication Health Regions: Boundaries and Correspondence with Census Geography" (catalogue number 82-402-X)." 2 Mortality is the death rate, which can be measured as total mortality (all causes of death combined) or by selected cause of death. All counts and rates are calculated using the total population (all age groups). 3 Potential years of life lost (PYLL) is the number of years of potential life not lived when a person dies prematurely" defined for this indicator as before age 75. All counts and rates in this table are calculated using the population aged 0 to 74." 4 Counts and rates in this table are based on three consecutive years of death data. Rates are per 100,000 population and were calculated by dividing the counts by three consecutive years of population data. 5 Rates are age-standardized using the direct method and the 2011 Canadian Census population structure. The use of a standard population results in more meaningful rate comparisons because it adjusts for variations in population age distributions over time and across geographic areas. 6 Counts and rates in this table exclude: deaths of non-residents of Canada; deaths of residents of Canada whose province or territory of residence was unknown; deaths for which age of decedent was unknown. 7 Rates in this table are based on place of residence for indicators derived from death events. 8 The number of deaths in Ontario for 2016 is considered preliminary. 9 Health regions are administrative areas defined by provincial ministries of health according to provincial legislation. The health regions presented in this table are based on boundaries and names in effect as of December 2017. For complete Canadian coverage, each northern territory represents a health region. 10 Peer groups are aggregations of health regions that share similar socio-economic and demographic characteristics, based on data from the 2011 Census of Population and 2011 National Household Survey. These are useful in the analysis of health regions, where important differences may be detected by comparing health regions within a peer group. The nine peer groups are identified by the letters A through I, which are appended to the health region 4-digit code. Caution should be taken when comparing data for the Peer Groups over time due to changes in the Peer Groups. In an analysis involving the peer groups, only one level of geography in Ontario should be used. For more information on the peer groups classification, consult Statistics Canada's publication Health Regions: Boundaries and Correspondence with Census Geography" (catalogue number 82-402-X)." 11 Before 2010, missing data on sex of the deceased were imputed based on death registration number. Starting with 2010 data year, missing data on sex of the deceased were imputed based on the cause of death information and a logistic regression. 12 The cause of death tabulated is the underlying cause of death. This is defined as (a) the disease or injury which initiated the train of events leading directly to death, or (b) the circumstances of the accident or violence which produced the fatal injury. The underlying cause is selected from the conditions listed on the medical certificate of cause of death. 13 Confidence intervals for age-standardized rates for selected causes of death data were produced using the Spiegelman method. Source: Spiegelman, M., Introduction to Demography" Revised Edition Cambridge14 Confidence intervals for crude rates for selected causes of death data were produced using the Fleiss method. Source: Fleiss, JL., Statistical Methods for Rates and Proportions" Second Edition New York15 The 95% confidence interval (CI) illustrates the degree of variability associated with a number or a rate. 16 Wide confidence intervals (CIs) indicate high variability, thus, these numbers or rates should be interpreted and compared with due caution. 17 The following standard symbols are used in this Statistics Canada table: (..) for figures not available for a specific reference period, (...) for figures not applicable and (x) for figures suppressed to meet the confidentiality requirements of the Statistics Act. 18 The figures shown in the tables have been subjected to a confidentiality procedure known as controlled rounding to prevent the possibility of associating statistical data with any identifiable individual. Under this method, all figures, including totals and margins, are rounded either up or down to a multiple of 5. Controlled rounding has the advantage over other types of rounding of producing additive tables as well as offering more protection. 19 Premature deaths are those of individuals who are younger than age 75.

This table provides the age-standardized mortality rates per 100,000 population, for the three selected causes of death and all causes combined for both the local geographic area and Alberta for the most recent three-year period available. The three selected causes of death are Circulatory System, Neoplasms and External Causes (Injury). Age standardization is a technique applied to make rates comparable across groups with different age distributions. A simple rate is defined as the number of people with a particular condition divided by the whole population. An age-standardized rate is defined as the number of people with a condition divided by the population within each age group. Standardizing (adjusting) the rate across age groups allows a more accurate comparison between populations that have different age structures. Age standardization is typically done when comparing rates across time periods, different geographic areas, and or population sub-groups (e.g. ethnic group). This indicator dataset contains information at both Local Geographic Area (for example, Lacombe, Red Deer - North, Calgary - West Bow, etc.) and Alberta levels. Local geographic area refers to 132 geographic areas created by Alberta Health (AH) and Alberta Health Services (AHS) based on census boundaries. This table is the part of "Alberta Health Primary Health Care - Community Profiles" report published February 2013

Age structure data is essential for single species stock assessments but length-frequency data can provide complementary information. In south-western Australia, the majority of these data for exploited species are derived from line caught fish. However, baited remote underwater stereo-video systems (stereo-BRUVS) surveys have also been found to provide accurate length measurements. Given that line fishing tends to be biased towards larger fish, we predicted that, stereo-BRUVS would yield length-frequency data with a smaller mean length and skewed towards smaller fish than that collected by fisheries-independent line fishing. To assess the biases and selectivity of stereo-BRUVS and line fishing we compared the length-frequencies obtained for three commonly fished species, using a novel application of the Kernel Density Estimate (KDE) method and the established Kolmogorov–Smirnov (KS) test. The shape of the length-frequency distribution obtained for the labrid Choerodon rubescens by stereo-BRUVS and line fishing did not differ significantly, but, as predicted, the mean length estimated from stereo-BRUVS was 17% smaller. Contrary to our predictions, the mean length and shape of the length-frequency distribution for the epinephelid Epinephelides armatus did not differ significantly between line fishing and stereo-BRUVS. For the sparid Pagrus auratus, the length frequency distribution derived from the stereo-BRUVS method was bi-modal, while that from line fishing was uni-modal. However, the location of the first modal length class for P. auratus observed by each sampling method was similar. No differences were found between the results of the KS and KDE tests, however, KDE provided a data-driven method for approximating length-frequency data to a probability function and a useful way of describing and testing any differences between length-frequency samples. This study found the overall size selectivity of line fishing and stereo-BRUVS were unexpectedly similar.

Footnotes: 1 Sources: Statistics Canada, Canadian Vital Statistics, Death Database and Demography Division (population estimates). The table 13-10-0743-01 is an update of table 13-10-0412-01. This is because of the adoption of the 2015 version of the Health Region Geography. For more information, consult Statistics Canada's publication Health Regions: Boundaries and Correspondence with Census Geography" (catalogue number 82-402-X)." 2 Mortality is the death rate, which can be measured as total mortality (all causes of death combined) or by selected cause of death. All counts and rates are calculated using the total population (all age groups). 3 Potential years of life lost (PYLL) is the number of years of potential life not lived when a person dies prematurely" defined for this indicator as before age 75. All counts and rates in this table are calculated using the population aged 0 to 74." 4 Counts and rates in this table are based on three consecutive years of death data. Rates are per 100,000 population and were calculated by dividing the counts by three consecutive years of population data. 5 Rates are age-standardized using the direct method and the 2011 Canadian Census population structure. The use of a standard population results in more meaningful rate comparisons because it adjusts for variations in population age distributions over time and across geographic areas. 6 Counts and rates in this table exclude: deaths of non-residents of Canada; deaths of residents of Canada whose province or territory of residence was unknown; deaths for which age of decedent was unknown. 7 Rates in this table are based on place of residence for indicators derived from death events. 8 The number of deaths in Ontario for 2016 is considered preliminary. 9 Health regions are administrative areas defined by provincial ministries of health according to provincial legislation. The health regions presented in this table are based on boundaries and names in effect as of December 2017. For complete Canadian coverage, each northern territory represents a health region. 10 Peer groups are aggregations of health regions that share similar socio-economic and demographic characteristics, based on data from the 2011 Census of Population and 2011 National Household Survey. These are useful in the analysis of health regions, where important differences may be detected by comparing health regions within a peer group. The nine peer groups are identified by the letters A through I, which are appended to the health region 4-digit code. Caution should be taken when comparing data for the Peer Groups over time due to changes in the Peer Groups. In an analysis involving the peer groups, only one level of geography in Ontario should be used. For more information on the peer groups classification, consult Statistics Canada's publication Health Regions: Boundaries and Correspondence with Census Geography" (catalogue number 82-402-X)." 11 Before 2010, missing data on sex of the deceased were imputed based on death registration number. Starting with 2010 data year, missing data on sex of the deceased were imputed based on the cause of death information and a logistic regression. 12 The cause of death tabulated is the underlying cause of death. This is defined as (a) the disease or injury which initiated the train of events leading directly to death, or (b) the circumstances of the accident or violence which produced the fatal injury. The underlying cause is selected from the conditions listed on the medical certificate of cause of death. 13 Confidence intervals for age-standardized rates for selected causes of death data were produced using the Spiegelman method. Source: Spiegelman, M., Introduction to Demography" Revised Edition Cambridge14 Confidence intervals for crude rates for selected causes of death data were produced using the Fleiss method. Source: Fleiss, JL., Statistical Methods for Rates and Proportions" Second Edition New York15 The 95% confidence interval (CI) illustrates the degree of variability associated with a number or a rate. 16 Wide confidence intervals (CIs) indicate high variability, thus, these numbers or rates should be interpreted and compared with due caution. 17 The following standard symbols are used in this Statistics Canada table: (..) for figures not available for a specific reference period, (...) for figures not applicable and (x) for figures suppressed to meet the confidentiality requirements of the Statistics Act. 18 The figures shown in the tables have been subjected to a confidentiality procedure known as controlled rounding to prevent the possibility of associating statistical data with any identifiable individual. Under this method, all figures, including totals and margins, are rounded either up or down to a multiple of 5. Controlled rounding has the advantage over other types of rounding of producing additive tables as well as offering more protection. 19 Premature deaths are those of individuals who are younger than age 75.

Protogynous fishes are often assumed to be more sensitive than gonochorists to exploitation, primarily because of potential sperm limitation and/or social disruption of mating if larger, mostly male individuals are selected for. Almost 4000 red porgy were collected year-round, Mar 1998-Sep 2001, in the NE Gulf, primarily using standardized hook and line gear in depths of 22-92 m. Most (n=2,586) were collected at 9 sites off NW Florida in 30-68 m sampled repeatedly. Fish were aged using whole and broken and burned sagittae, and sexed and staged histologically (females and transitionals)or macroscopically (males). An early objective was to determine if the behaviorally-related size and/or sex selectivity observed in other protogynous species occurred in red porgy, i.e., were larger individuals or males at a site more aggressive and more likely to bite a hook and be caught before smaller ones or females. Non-parametric runs tests of ordered size and sex data showed no evidence of such selectivity, indicating that hook-and-line gear is a fair way to sample red porgy. More importantly, any evidence of truncation in size structure or skewing of sex ratios in exploited populations should not be attributed to greater aggression or '"hook attraction" in males but can be easily explained as the results of simple size-selective harvesting. Histological evidence indicated that red porgy in depths of 20 to 78 m in the NE Gulf spawn wherever they occur, primarily Dec to Feb. Estimated size and age at 50% maturity for females was 211 - 216 mm TL and <2 yr. Sex change occurred wherever they were found, almost exclusively Mar-Nov, and across a wide range of sizes (206-417 mm TL) and ages (2-9 yr), strong evidence it is socially controlled. Red porgy are permanently sexually dichromatic. The premaxilla is green or bluish-green in males and pink or reddish in females. Observations of captive fish suggest they pair spawn. Seasonal patterns in catch rates and sex ratios, and widespread occurrence of spawning females indicated that red porgy do not form large, predictable spawning aggregations. There was no evidence found that protogyny or their reproductive ecology might 1) explain the apparent crash of the red porgy stock(s) in the SAB or 2) make the species more sensitive to exploitation than gonochorists - in fact they are probably less sensitive in some cases. Many aspects of their biology and behavior, including widespread spawning grounds, no tendency to form spawning aggregations, absence of behaviorally-related size or sex selectivity, socially controlled sex change, co-occurrence of sexes year-round, and an extended period of transition, should stabilize or enable rapid compensation of sex ratios (preventing sperm limitation or disruption of mating). Socially controlled sex change also enables size and age of transition to slide downward as fishing truncates the size structure, similar to the declines in size and age at maturity seen in many gonochorists. Several population traits differed significantly among the 9 regular sites (which ranged from 1.3 to 58.4 km (0 = 28.7) apart), including size and age composition (K-S 2 sample test); means ranged from 261 to 309 mm TL and 2.7 to 4.1 yr. Size at age varied considerably, primarily because of significant differences among sites. The relationships of mean size at age among sites were consistent across ages and temporally stable. Robson-Chapman maximum likelihood estimates of annual survival ranged from 38 to 65 % among sites, and 95% CI?s did not overlap for 6 of the 8 sites with estimates. Logistic regression indicated that the proportion of females changing sex differed significantly among sites (medians: 12 - 33%) and depths. Sizes and ages at transition also varied spatially, with site-specific means of 266 - 313 mm and 3.1 - 4.6 yr. Sex ratio was yet another demographic that differed among sites: 28 of 36 pairwise comparisons were significant (log. regress.). These persistent differences in population traits at such a small scale likely reflect phenotypic, not genetic, effects. Two factors - spatial heterogeneity of their environment and site fidelity - probably explain most of those differences. The live bottom habitat preferred by red porgy is widespread but very patchy. These patches, grossly similar, have variable hydrological, geological, biological, and ecological characteristics; and they range from unexploited to heavily exploited. Biological and ecological characteristics likely to vary among patches include density, predator and prey composition and density, and competition. Adult redporgy exhibit considerable site fidelity, so once recruited to a given patch of habitat, they are exposed to a unique suite of many factors which could affect growth, mortality, and reproduction. The consistent, persistent, significant differences in size and age structure, growth, xvi mortality, transition rates, size and ages at transition, and sex ratios among sites separated by only 10?s of kilometers strongly suggests that red porgy in the NE Gulf have a complex population structure composed of many local subpopulations. These subpopulations closely resemble Crowder et al. (2000) definition of sources and sinks areas of differing demographic rates dictated by underlying differences in habitat quality?. This complex structure is not the classical metapopulation of Levins (1970), i.e.,a A population of populations that go extinct and recolonize @and which are exposed to the same conditions in each habitat patch. It does, however, fit the broader definition of metapopulation espoused by Hastings and Harrison (1994), Hanski and Simberloff (1997), and Kritzer and Sale (2004), which relaxes the requirement for extinctions and recolonizations and does not require uniform conditions across patches. Whether the population subunits are called local subpopulations, sources and sinks, or members of a metapopulation, the critical point is that many may have significantly different demographics and life history traits, which has potentially significant implications regarding stock assessment and management of red porgy. Data pooled from several subpopulations may yield skewed parameter estimates, which in turn could bias stock assessments and the models used to predict responses to exploitation. It could also introduce excessive variability to the parameter estimates. Such complexity could certainly frustrate and confound the efforts of those trying to assess the status of these stocks and predict the effects of fishing on them, as it requires examination of population biology at much smaller spatial scales than typically done and use of more complex, spatially-explicit population models. It is likely that small scale population complexity has played some part in the failure of some southeastern U.S. reef fish fisheries to respond to management measures in recent years

This table provides the age-standardized inpatient separation rates per 100,000 population for selected conditions for most recent fiscal year. An inpatient separation from a health care facility occurs anytime a patient (or resident) leaves because of death, discharge, sign-out against medical advice or transfer. The number of separations is the most commonly used measure of the utilization of hospital services. Separations, rather than admissions, are used because hospital abstracts for inpatient care are based on information gathered at the time of discharge. The selected conditions are Asthma, Diabetes, Influenza, Ischemic Heart Diseases, Mental and Behavioural Disorders due to Psychoactive Substance Use, Pneumonia, Pulmonary Heart and Pulmonary Circulation Diseases. Age standardization is a technique applied to make rates comparable across groups with different age distributions. A simple rate is defined as the number of people with a particular condition divided by the whole population. An age-standardized rate is defined as the number of people with a condition divided by the population within each age group. Standardizing (adjusting) the rate across age groups allows a more accurate comparison between populations that have different age structures. Age standardization is typically done when comparing rates across time periods, different geographic areas, and or population sub-groups (e.g. ethnic group). This indicator dataset contains information at both Local Geographic Area (for example, Lacombe, Red Deer, Calgary West Bow, etc.) and Alberta levels. Local geographic area refers to 132 geographic areas created by Alberta Health (AH) and Alberta Health Services (AHS) based on census boundaries. This table is the part of "Alberta Health Primary Health Care - Community Profiles" report published March 2015.

Abstract copyright UK Data Service and data collection copyright owner. The Organisation for Economic Co-operation and Development (OECD) Health Statistics offers the most comprehensive source of comparable statistics on health and health systems across OECD countries. It is an essential tool for health researchers and policy advisors in governments, the private sector and the academic community, to carry out comparative analyses and draw lessons from international comparisons of diverse health care systems. Within UKDS.Stat the data are presented in the following databases: Health status This datasets presents internationally comparable statistics on morbidity and mortality with variables such as life expectancy, causes of mortality, maternal and infant mortality, potential years of life lost, perceived health status, infant health, dental health, communicable diseases, cancer, injuries, absence from work due to illness. The annual data begins in 2000. Non-medical determinants of health This dataset examines the non-medical determinants of health by comparing food, alcohol, tobacco consumption and body weight amongst countries. The data are expressed in different measures such as calories, grammes, kilo, gender, population. The data begins in 1960. Healthcare resources This dataset includes comparative tables analyzing various health care resources such as total health and social employment, physicians by age, gender, categories, midwives, nurses, caring personnel, personal care workers, dentists, pharmacists, physiotherapists, hospital employment, graduates, remuneration of health professionals, hospitals, hospital beds, medical technology with their respective subsets. The statistics are expressed in different units of measure such as number of persons, salaried, self-employed, per population. The annual data begins in 1960. Healthcare utilisation This dataset includes statistics comparing different countries’ level of health care utilisation in terms of prevention, immunisation, screening, diagnostics exams, consultations, in-patient utilisation, average length of stay, diagnostic categories, acute care, in-patient care, discharge rates, transplants, dialyses, ICD-9-CM. The data is comparable with respect to units of measures such as days, percentages, population, number per capita, procedures, and available beds. Health Care Quality Indicators This dataset includes comparative tables analyzing various health care quality indicators such as cancer care, care for acute exacerbation of chronic conditions, care for chronic conditions and care for mental disorders. The annual data begins in 1995. Pharmaceutical market This dataset focuses on the pharmaceutical market comparing countries in terms of pharmaceutical consumption, drugs, pharmaceutical sales, pharmaceutical market, revenues, statistics. The annual data begins in 1960. Long-term care resources and utilisation This dataset provides statistics comparing long-term care resources and utilisation by country in terms of workers, beds in nursing and residential care facilities and care recipients. In this table data is expressed in different measures such as gender, age and population. The annual data begins in 1960. Health expenditure and financing This dataset compares countries in terms of their current and total expenditures on health by comparing how they allocate their budget with respect to different health care functions while looking at different financing agents and providers. The data covers the years starting from 1960 extending until 2010. The countries covered are Australia, Austria, Belgium, Canada, Chile, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Israel, Italy, Japan, Korea, Luxembourg, Mexico, Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, Turkey, United Kingdom, and United States. Social protection This dataset introduces the different health care coverage systems such as the government/social health insurance and private health insurance. The statistics are expressed in percentage of the population covered or number of persons. The annual data begins in 1960. Demographic references This dataset provides statistics regarding general demographic references in terms of population, age structure, gender, but also in term of labour force. The annual data begins in 1960. Economic references This dataset presents main economic indicators such as GDP and Purchasing power parities (PPP) and compares countries in terms of those macroeconomic references as well as currency rates, average annual wages. The annual data begins in 1960. These data were first provided by the UK Data Service in November 2014.

This table provides the age-standardized inpatient separation rates per 100,000 population for selected conditions. An inpatient separation from a health care facility occurs anytime a patient (or resident) leaves because of death, discharge, sign-out against medical advice or transfer. The number of separations is the most commonly used measure of the utilization of hospital services. Separations, rather than admissions, are used because hospital abstracts for inpatient care are based on information gathered at the time of discharge. The selected conditions are Asthma, Diabetes, Influenza, Ischemic Heart Diseases, Mental and Behavioural Disorders due to Psychoactive Substance Use, Pneumonia, Pulmonary Heart and Pulmonary Circulation Diseases. Age standardization is a technique applied to make rates comparable across groups with different age distributions. A simple rate is defined as the number of people with a particular condition divided by the whole population. An age-standardized rate is defined as the number of people with a condition divided by the population within each age group. Standardizing (adjusting) the rate across age groups allows a more accurate comparison between populations that have different age structures. Age standardization is typically done when comparing rates across time periods, different geographic areas, and or population sub-groups (e.g. ethnic group). This indicator dataset contains information at both Local Geographic Area (for example, Lacombe, Red Deer, Calgary West Bow, etc.) and Alberta levels. Local geographic area refers to 132 geographic areas created by Alberta Health (AH) and Alberta Health Services (AHS) based on census boundaries. This table is the part of "Alberta Health Primary Health Care - Community Profiles" report published February 2013.

Annual change in mean age, age composition, mean size, size-at-age and female composition were estimated within each Chonook salmon population using linear regressions and age-specific patterns across populations and geographical regions. The clearest trends observed were in mean size and size-at-age. Both showed declines across most populations with statistical significance. Declines varied in magnitude across populations and age classes. Mean age showed variable, yet statistically significant declines across most stocks, and appeared to be driven by reductions in the relative proportion of the oldest (ocean-4) and increases in the youngest age classes (ocean-2). Low-enhancement stocks (stocks with mean pHOS < 10%) showed mixed trends in mean age and age composition. The proportion of females showed variable declines across populations, of which only a small proportion were significant. There was no consistent pattern for the few wild and low-enhancement populations. Our study shows that the size and age of BC Chinook salmon have declined since the 1970s. Declines such as these may be caused by fishing, predation, competition and hatchery enhancement, however in this review we did not explore quantitative models that incorporate these factors. Simple analyses show some evidence for the effect of hatchery enhancement on declining mean size and size-at-age, with some populations showing increases in size for the youngest age class but declines for the oldest.

Figure 9.2 provides the age-standardized inpatient separation rates per 100,000 population for selected conditions for most recent fiscal year. An inpatient separation from a health care facility occurs anytime a patient (or resident) leaves because of death, discharge, sign-out against medical advice or transfer. The number of separations is the most commonly used measure of the utilization of hospital services. Separations, rather than admissions, are used because hospital abstracts for inpatient care are based on information gathered at the time of discharge. The selected conditions are Asthma, Diabetes, Influenza, Ischemic Heart Diseases, Mental and Behavioural Disorders due to Psychoactive Substance Use, Pneumonia, Pulmonary Heart and Pulmonary Circulation Diseases. Age standardization is a technique applied to make rates comparable across groups with different age distributions. A simple rate is defined as the number of people with a particular condition divided by the whole population. An age-standardized rate is defined as the number of people with a condition divided by the population within each age group. Standardizing (adjusting) the rate across age groups allows a more accurate comparison between populations that have different age structures. Age standardization is typically done when comparing rates across time periods, different geographic areas, and or population sub-groups (e.g. ethnic group). This indicator dataset contains information at both Local Geographic Area (for example, Lacombe, Red Deer, Calgary West Bow, etc.) and Alberta levels. Local geographic area refers to 132 geographic areas created by Alberta Health (AH) and Alberta Health Services (AHS) based on census boundaries. This figure is the part of "Alberta Health Primary Health Care - Community Profiles" report published August 2022.

Four-yearly Wage Structure Survey: Means and percentiles by sex and worker age. Four-yearly. National.

Statistical significance of differences in the distribution (χ2 test) and mean (Tukey test) of Ovarian Categories for consecutive months.

The youngest ice marginal zone between the White Sea and the Ural mountains is the W-E trending belt of moraines called the Varsh-Indiga-Markhida-Harbei-Halmer-Sopkay, here called the Markhida line. Glacial elements show that it was deposited by the Kara Ice Sheet, and in the west, by the Barents Ice Sheet. The Markhida moraine overlies Eemian marine sediments, and is therefore of Weichselian age. Distal to the moraine are Eemian marine sediments and three Palaeolithic sites with many C-14 dates in the range 16-37 ka not covered by till, proving that it represents the maximum ice sheet extension during the Weichselian. The Late Weichselian ice limit of M. G. Grosswald is about 400 km (near the Urals more than 700 km) too far south. Shorelines of ice dammed Lake Komi, probably dammed by the ice sheet ending at the Markhida line, predate 37 ka. We conclude that the Markhida line is of Middle/Early Weichselian age, implying that no ice sheet reached this part of Northern Russia during the Late Weichselian. This age is supported by a series of C-14 and OSL dates inside the Markhida line all of >45 ka. Two moraine loops protrude south of the Markhida line; the Laya-Adzva and Rogavaya moraines. These moraines are covered by Lake Komi sediments, and many C-14 dates on mammoth bones inside the moraines are 26-37 ka. The morphology indicates that the moraines are of Weichselian age, but a Saalian age cannot be excluded. No post-glacial emerged marine shorelines are found along the Barents Sea coast north of the Markhida line.