Međupopisne procene u Srbiji 2011-2022 - da li su neophodne?

Censuses in Serbia often introduce new methodological solutions, making it increasingly challenging to analyse population changes. Methodological differences in the results of the last three censuses (2002, 2011, and 2022) hinder accurate data comparisons and lead to significantly varied interpretations of census data. Assessing net migration in the inter-census period and generating annual postcensal estimates of the total population, which form the basis for calculating demographic indicators, rely on census-derived data. Annual population estimates can deviate significantly from actual values, particularly when the base year (census year) is distant. As a result, estimates for 2021 and 2022 differ by over 180 thousand due to being based on different censuses. Intercensal estimates have not been conducted in Serbia to date, but they can substantially impact the correction of demographic indicators, especially at lower territorial-administrative levels. Intercensal estimates refer to population estimates between two censuses and are deemed more precise than postcensal estimates as they approximate data between two specific points in time. Leveraging census results (2002, 2011, and 2022), vital events in the inter-census period, and internal migration data, annual population figures at the municipal level and the extent of external migration will be computed. These calculations will enable the estimation of population figures and total net migration for all higher territorial-administrative units, starting from the municipal level. Consequently, the fundamental demographic indicators for 2002–2022 will be reevaluated based on the acquired data. The outcomes are expected to reveal the extent to which population estimates were overstated in official reports due to the exclusion of external migration in their computation. This imprecision led to inaccuracies in basic demographic indicators. By rectifying the data, it is anticipated that death rates and fertility levels will slightly surpass the officially published values during the inter-census period. The data obtained from intercensal estimates should demonstrate how the migration component influences population fluctuations at the municipal level.

Prevalence estimates often use U.S. Census Bureau estimates of the population as denominator data. Postcensal estimates are population estimates produced following a decennial census. Intercensal estimates are surrounded by 2 census years and supersede postcensal estimates. In this report we describe prevalence estimates in Atlanta for mental retardation, cerebral palsy, and hearing and vision loss for 8 year olds from 1991-1994 and 1996. We used calculations of postcensal and intercensal population estimates. Intercensal population data were consistently higher than postcensal data, and prevalence estimates for developmental disabilities were lower using intercensal population data. This discrepancy varied by race and ethnicity. Comparison of population estimates, particularly at state and local levels, should be considered to assess meaningful differences in published prevalence estimates using intercensal data.

IntroductionPopulation estimation techniques are often used to provide updated data for a current year. However, estimates for small geographic units, such as census tracts in the United States, are typically not available. Yet there are growing demands from local policy making, program planning and evaluation practitioners for such data because small area population estimates are more useful than those for larger geographic areas.ObjectivesTo estimate the population sizes at the census block level by subgroups (age, sex, and race/ethnicity) so that the population data can be aggregated up to any target small geographic areas.MethodsWe estimated the population sizes by subgroups at the census block level using an intercensal approach for years between 2000 and 2010 and a postcensal approach for the years following the 2010 decennial census (2011-2017). Then we aggregated the data to the county level (intercensal approach) and incorporated place level (postcensal approach) and compared our estimates to corresponding US Census Bureau (the Census) estimates.ResultsOverall, our intercensal estimates were close to the Census’ population estimates at the county level for the years 2000-2010; yet there were substantive errors in counties where population sizes experienced sudden changes. Our postcensal estimates were also close to the Census’ population estimates at the incorporated place level for years closer to the 2010 decennial census.ConclusionThe approaches presented here can be used to estimate population sizes for any small geographic areas based on census blocks. The advantages and disadvantages of their application in public health practice should be considered.

Our purpose in this chapter is to provide a general roadmap of subnational population estimation methods, which are covered in detail in subsequent chapters. We set the stage for this overview by differentiating between pre-censal, inter-censal, and post-censal estimates and classifying population estimation methods. Finally we describe the variety of methods currently used to estimate population. This focus of this chapter is on population estimates based on usual residence or de jure. We cover de facto (physically present) population estimates in Chapter 16.

To verify the correlation between overweight and socioeconomic and demographic indicators among Brazilian adults, using data from the Brazilian Household Budget Survey from 2008/2009. We analyzed the joint relationships between overweight and socioeconomic and demographic indicators in the Brazilian adult population (99,532,672 individuals), through the multiple correspondence analysis technique. The featured profile of the Brazilian adult population with regard to overweight was correlated with ages from 30 years of the most developed and economically social geographic regions of Brazil; however between genders, the correlation was in the opposite direction in the variables income, education, and subjective issues about life conditions related to food and nutrition security. By the joint relationship between overweight and selected demographic and socioeconomic indicators, the urgency of the development of strategies and/or preventive public health programs of health problems with overweight in the Brazilian adult population is suggested.

Human immunodeficiency virus (HIV) may affect the risk of death due to cardiovascular disease (CVD) differently in men versus women. We examined CVD mortality rates between 2007 and 2017 among all New York City residents living with HIV and aged 13+ by sex, using data from city HIV surveillance and vital statistics and the National Death Index. Residents without HIV were enumerated using modified US intercensal estimates. We determined associations of HIV status with CVD mortality by sex and neighborhood poverty, defined as the percent of residents living below the federal poverty level, after accounting for age, race/ethnicity, and year. There were 3234 CVD deaths reported among 147 915 New Yorkers living with HIV, with the proportion of deaths due to CVD increasing from 11% in 2007 to 22% in 2017. The age-standardized CVD mortality rate was 2.7/1000 person-years among both men and women with HIV. The relative rate of CVD mortality associated with HIV status was significantly higher among women (adjusted rate ratio [aRR] 1.7, 95% confidence interval [CI] 1.6-1.8) than men (aRR 1.2, 95% CI 1.1-1.3) overall, and within strata defined by neighborhood poverty. Sex differences in CVD mortality rates were the greatest when comparing individuals living with HIV and having detectable HIV RNA and CD4+ T-cell counts <500 cells/uL with individuals living without HIV. Among people with HIV, 1 in 5 deaths is now associated with CVD. HIV providers should recognize the CVD risk among women with HIV, and reinforce preventive measures (eg, smoking cessation, blood pressure control, lipid management) and viremic control among people living with HIV regardless of neighborhood poverty to reduce CVD mortality.Human immunodeficiency virus (HIV) increases cardiovascular disease mortality risks to a greater degree among women than men, even after accounting for neighborhood poverty. HIV providers should emphasize cardiovascular disease prevention (eg, smoking cessation, hypertension control, lipid management) and viremic control.

From 1991 through 1995, all Latin American countries maintained cholera surveillance systems to track the epidemic that entered the region through Peru in January 1991. These data were used to assess correlations between socioeconomic and demographic indices that might serve as national risk predictors for epidemic cholera in Latin America. Correlations between country-specific cumulative cholera incidence rates from 1991 through 1995 and infant mortality, the Human Development Index [HDI] a numerical value based on life expectancy, education, and income), gross national product (GNP) per capita, and female literacy were tested using the Pearson correlation coefficient. A total of 1,339,834 cholera cases with a cumulative incidence rate of 183 per 100,000 population were reported from affected Western Hemisphere countries from 1991 through 1995. Infant mortality rates were the most strongly correlated with cumulative cholera incidence based on the Pearson correlation coefficient. The HDI had a less strong negative correlation with cumulative cholera incidence. The GNP per capita and female literacy rates were weakly and negatively correlated with cholera cumulative incidence rates. Infant mortality and possibly the HDI may be useful indirect indices of the risk of sustained transmission of cholera within a Latin American country. Cumulative cholera incidence is decreased particularly in countries with infant mortality below 40 per 1000 live births. The lack of reported cholera cases in Uruguay and the Caribbean may reflect a low risk for ongoing transmission, consistent with socioeconomic and demographic indices. Cholera surveillance remains an important instrument for determining cholera trends within individual countries and regions.

Molecular analysis of yeast population dynamics: Effect of sulphur dioxide and inoculum on must fermentation

ObjectivesTo investigate how socioeconomic indicators and individual variables from woman, pregnancy and delivery, can interact to influence rates of adolescent pregnancy in South Brazil.MethodsInformation from the Live Birth National Information...

Preface. Notations. 1. Introduction. 2. Analysis of Age-Dependent Population Dynamics. 3. Optimal Control of Population Dynamics. 4. Analysis of Population Dynamics with Diffusion. 5. Control of Population Dynamics with Diffusion. Appendix 1: Elements of Nonlinear Analysis. Appendix 2: The Linear Heat Equation. References. Index.

Two Zymomonas mobilis wild strains (UMB478 and 479) isolated from water kefir were characterized for their biomass production levels and leavening performance when used as the inoculum of a real bread-like dough formulation. The obtained baked product would be consumable by people with adverse responses to Saccharomyces cerevisiae. In liquid cultures, the two strains reached similar biomass concentration (0.7 g CDW/L). UMB479 showed an interesting resistance to NaCl (MBC 30 g/L), that may be useful in the bakery sector. When inoculated in doughs, UMB479 produced the maximum dough volume (650 mL) after 5 h, glucose was almost consumed and 1 g/100 g of ethanol produced, +200% respective to UMB478. Using S. cerevisiae for comparison purposes, the dough doubled its volume fast, in only 2 h, but reached a final level of 575 mL, lower than that achieved by Z. mobilis. The analysis of bacterial and fungal population dynamics during dough leavening was performed through the Automated Ribosomal Intergenic Spacer Analysis (ARISA); doughs leavened by UMB479 showed an interesting decrease in fungal richness after leavening. S. cerevisiae, instead, created a more complex fungal community, similar before and after leavening. Results will pave the way for the use of Z. mobilis UMB479 in commercial yeast-free leavened products.

Suburban rural areas are characterized by a number of various relationships between rural and urban populations. A common feature of suburban areas in the post-Soviet period is the intensification of two types of spatially expressed relationships, namely labour migrations of rural population to the city centre and dacha recreational trips of city residents to the countryside. The article considers the regional specifics of the Chuvash Republic (a multi-ethnic region with an increased share of rural population and a rapid expansion of the regional centre agglomeration), and identifies factors of spatial heterogeneity of the post-Soviet transformation of the population of villages in the suburban Cheboksary district. The analysis of the evolution of settlements was preceded by a spatial analysis of the regional rural population dynamics by inter-census periods since 1979. Territorial differences in the intensity of both the emergence of gardening partnerships, and individual housing construction allow the identification of three spatial zones according to the nature of the interaction of rural areas with the city centre: 1) “immediately suburban”; 2) “transitional” and 3) “traditional rural”. The settlements of the “immediately suburban” zone are characterized by the highest intensity of relocation of the urban dwellers to permanent residence i.e. the classical suburbanization. The “transitional” zone experiences the largest migration influx of population from peripheral rural areas, and the “traditional rural” zone demonstrates the largest relative differences between the permanent and summer seasonal population numbers. Rural settlements of different types are characterized within each zone, such as local rural centres, “dormitory settlements” with farms and “dormitory settlements” without places of collective labour. The post-Soviet population dynamics of settlements located at different distances from the city was analyzed, and a conclusion was made about the primary role of transport accessibility of the city centre as a factor in the spatial differentiation of the distribution of the rural population. The availability of jobs in a settlement within the “immediately suburban” zone is not important for its sustainability, while on the contrary, the role of this factor increases in the “traditional rural” zone of the district. As a result, “dormitory settlements” without jobs are both the best in the region in terms of post-Soviet population dynamics (if they are less than 20 km from the city centre, they are transformed into cottage estates) and the worst (if they are more than 40 km away).

This study employs spatial and semantic modeling to formally specify the intersection of environmental concerns and social justice, focusing on the unequal impact of environmental hazards on the economically disadvantaged people living in the Proctor Creek watershed within the Atlanta Metropolitan area. Our 'Public Health-Urban-Socio-economic-Environmental Justice' (PUSH-EJ) ontology formally integrates environmental justice indices and all concepts from the EPA&amp;#8217;s EJScreen, such as Environmental Indicator, Demographic Indicator, EJ Index, Particulate Matter, Risk, and Proximity to hazardous sites. PUSH-EJ also formalizes the National Air Toxics Assessment (NATA)&amp;#8217;s Air Toxics Cancer Risk, Air Toxics Respiratory Hazard Index, and Diesel Particulate Matter. The modeled environmental indicators include levels of ozone, particulate matter 2.5 (micrometer or smaller-sized particles), and lead paint (for houses built before 1960) in the air, count of underground leaking storage tanks, and count and proximity to wastewater discharge areas. The ontology also models proximity of housing units to waste and hazardous chemical facilities or sites related to National Priorities List (NPL) Superfund Program, Risk Management Plan (RMP) sites, Treatment, Storage, and Disposal Facilities (TSDFs), and Traffic volume. Environmental, demographic, and socioeconomic indicators are mapped to the objectives of UN SDGs 1, 3, 4, 5, 8, and 10, bridging the gap between environmental justice, public health, urban dynamics, and socio-economic aspects. Our analysis of Proctor Creek's socioeconomic indicators reveals a combined Demographic Index of 73%, driven by Low Income (61%) and People of Color (90%). These findings indicate that Proctor Creek exhibits the lowest scores across all categories when compared to other regions in Georgia, EPA Region 4, and the nation. Our results call for minimizing contamination in the Proctor Creek area and uplifting socioeconomic conditions by the authorities responsible for the watershed. Our spatial analysis highlights urgent priorities in the Proctor Creek area, for the management of air toxic sources, emissions, and addressing proximity issues linked to environmental pollutants from hazardous waste sites, lead paint, and traffic.

Since car ownership is an important determinant to analyze car travel behavior especially in developing countries, this paper deals with modeling and forecasting car ownership in Turkey based on socio-economic and demographic indicators such as Gross Domestic Product (GDP) per capita, Gasoline Price (GP), car price and number of employees by using multiple nonlinear regression analysis. Although most of the studies on this subject prefer using annual data, we use monthly data for the analysis of car ownership since all explanatory variables and exchange rates used for the modeling are unstable and vary even in a short period in developing countries such as Turkey. Thus, it may be possible to reflect the effects of socio-economic and demographic indicators on car ownership more properly. During the modeling process, exponential and polynomial nonlinear regression models are set up and then tested to investigate their applicability for car ownership forecasting. Based on results of the Kolmogorov-Smirnov test, the polynomial models has been selected to forecast car ownership for the year 2035. In order to reveal the possible different trends of the independent variables in future, car ownership is forecasted along the scenarios which are related to the GDP per capita and GP. Results show that Turkey’s car ownership may vary between 230 and 325 per thousand capita in 2035 depending on economic achievements, global oil prices and national taxation policies. The lowest and the highest values of the car ownership may provide insight to car producers and transport planners in Turkey. Another significant result presented in this study is that car ownership rate will be substantially lower in Turkey than that in the European Union countries despite it has an increasing trend in the past two decades.

Introduction. The COVID-19 emergency caused a pandemic with a great impact on mortality in Peru. Objective. To analyze the magnitude of mortality due to COVID-19 and identify some sociodemographic characteristics. Methods. A descriptive ecological study was carried out, analyzing data on deaths due to COVID-19 during the period 2020-2023, as well as publicly accessible socioeconomic and demographic indicators. The variables included in the study were: year of death, number of deaths, sex, age group, province of origin, natural region, macroregion, human development index (HDI), unmet basic needs (UBN) and population density. Results. 220,628 deaths due to COVID-19 were reported, of which 219,374 (99.43%) occurred during the pandemic and 1,254 (0.57%) in the post-pandemic period; The cumulative mortality rate was 6.76 deaths/1000 inhabitants. The COVID-19 death curve shows that between 2020-2023, five waves occurred, with the second wave having the highest mortality, followed by the first wave. Mortality predominated in men, older adults (69.77%) and adults (28.11%). The macroregion of Metropolitan Lima and Callao had the highest mortality rates with 9.36 deaths/1000 inhabitants. The provinces with the highest cumulative mortality rates were those located on the coast, in quintiles with less NBI, in quintiles with a higher HDI and in those provinces with a higher population density. Conclusions. The COVID-19 pandemic caused high mortality in Peru, being higher in males, older adults, from coastal cities, with higher population density and better socioeconomic indicators.