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Most research papers in psychology study the behaviour of a sample of participants. To characterise this sample, authors report various characteristics, frequently including the mean age and the associated standard deviation. However, based on reports from authors who publish in Acta Psychologica and from respondents on X/Twitter, the present paper shows that some authors use rounded-down ages whereas others don't, which lead to an uncertainty of 0.5 year in the average age. The results furthermore show that the authors tend to report the average age with two decimals precision, irrespective of the uncertainty of this average. I recommend that publications should explicitly mention how the average age is determined and report its value using a number of decimals that reflects its uncertainty.

Abstract Child stunting prevalence is primarily used as an indicator of impeded physical growth due to undernutrition and infections, which also increases the risk of mortality, morbidity and cognitive problems, particularly when occurring during the 1000 days from conception to age 2 years. This paper estimated the relationship between stunting prevalence and age for children 0–59 months old in 94 low‐ and middle‐income countries. The overall stunting prevalence was 32%. We found higher stunting prevalence among older children until around 28 months of age—presumably from longer exposure times and accumulation of adverse exposures to undernutrition and infections. In most countries, the stunting prevalence was lower for older children after around 28 months—presumably mostly due to further adverse exposures being less detrimental for older children, and catch‐up growth. The age for which stunting prevalence was the highest was fairly consistent across countries. Stunting prevalence and gradient of the rise in stunting prevalence by age varied across world regions, countries, living standards and sex. Poorer countries and households had a higher prevalence at all ages and a sharper positive age gradient before age 2. Boys had higher stunting prevalence but had peak stunting prevalence at lower ages than girls. Stunting prevalence was similar for boys and girls after around age 45 months. These results suggest that programmes to prevent undernutrition and infections should focus on younger children to optimise impact in reducing stunting prevalence. Importantly, however, since some catch‐up growth may be achieved after age 2, screening around this time can be beneficial.

In the European Green Deal, EU Commission has set a goal to reduce greenhouse gas emissions in the transport sector by 90% by 2050 compared to the 1990 level. Most likely, transport decarbonization will rely on a rapid expansion of electric and hydrogen vehicle fleet, which would seriously affect not just overall electricity demand, but also the shape of the electricity consumption curve. Consequently, our research focuses on integrated energy and transport modelling when analyzing its development pathways up to 2050 and beyond. This paper describes how already established transport modeling practices can be further improved by differentiating vehicles by age groups and setting vehicle age distributions to improve the representation of vehicle stock, fuel efficiencies and emissions, especially for countries that have non-declining vehicle age distributions. Modeling results using proposed and traditional approaches were compared for the Lithuanian case. It shows that the transport fuel shift using the proposed approach is more gradual than the traditional one. Diesel cars are phased out by 2050 versus 2040. Furthermore, the proposed approach provided more realistic CO2 emissions, 7% lower emissions for 2018 than estimated based on statistical data, while traditional approach was 27% lower.

O objetivo deste artigo é examinar a importância da composição etária da população sobre os gastos com internação hospitalar no Brasil. São utilizados dados do Sistema de Informação Hospitalar de Curitiba (Paraná) e Belém (Pará), em 2004, para separar o efeito de diferenças na composição etária sobre os gastos com internação no Sistema Único de Saúde, dos efeitos preço (diferenças nos gastos médios por idade) e taxa (diferenças na taxas de utilização por idade). Além disso, o artigo examina o papel do envelhecimento populacional sobre o efeito de composição com base nas mudanças projetadas para a estrutura etária do Brasil em 2050. Os resultados indicam que as diferenças na composição etária explicam apenas 13% das diferenças atuais dos gastos entre as duas cidades. Aproximadamente 72% das diferenças nos gastos totais devem-se ao efeito preço. No entanto, caso a taxa de utilização e o gasto médio por idade mantenham-se constantes no futuro, o envelhecimento populacional explicará mais da metade da diferença nos gastos totais com internação hospitalar, sugerindo o papel significativo da dinâmica demográfica sobre os gastos futuros com saúde no Brasil.This paper examines the impact of changes in population age distribution on inpatient care expenditures in Brazil. The authors use data from two highly distinct metropolitan areas, Curitiba (Paraná State) and Belém (Paraná State), in order to determine to what extent current differences in expenditures are explained by differences in: (i) age distributions, (ii) age-specific expenditures (price effects), and (iii) utilization rates (rate effects). The study also looks at the consequences of future changes in age distribution by simulating each of the effects (age distribution, price, and rate) under the projected population age distribution for Brazil in 2050. The results show that only 13% of current differences in health expenditures in Curitiba and Belém are explained by differences in age distribution. Most of the difference is due to price effect (72%), probably because of large socioeconomic and epidemiological discrepancies in these two metropolitan areas. However, simulations for 2050 suggest that most future changes in inpatient care will be explained by population aging effects.

The effect of the changing age distribution on national saving is estimated empirically in this paper. We have specified a saving function based on Ando – Modigliani’s Life Cycle Hypothesis which incorporates the portion of population in 11 different age groups to represent the age structure of the population. Auto_ Regressive Distributed Lag (ARDL) Model estimation technique is based derived from the ARDL model. An Error Correction Model (ECM) of national saving behavior in also constructed and estimated in order to reveal the short run dynamic adjustments of saving towards its long run equilibrium. Data used in the estimation process are time series data within the period 1345 and 1383. Data are tested for stationary and co-integration estimation technique in used for estimating the coefficients of the model. The results indicate that the age distribution in an influential factor an saving behavior of the population. A relative population increase in the age group of 15-24 and 55- and over, tends to lower national saving, while a relative increase in population within the age group of 25-29, 30-34, … and 50-54 has a positive impact on national saving. These in the age group of 35-39 and 40-44 have the highest saving rate.

The infectivity function is a function giving a measure of how infectious a given individual is t time units after becoming infected. Today, no feasible and ethically acceptable study design is known, which would lead to estimates of HIV-infection probability within steady heterosexual partnerships, using standard statistical methodology. In this paper a transmission model is used as a link between the infectivity function and data sets which already exist or can be generated with standard methods and moderate expenses. This model suggests that the distribution of HIV-infections by age and sex depends on the infectivity function as well as on age-dependent patterns of sexual partner choice. Application of the model requires populationbased data of age-specific HIV-incidences in men and women of the general heterosexual population. At present, the only known data set suitable for this purpose is a set of HIV-test results from a sample of 8690 Colombian women in pregnancy who attended prenatal care. The prevalence of HIV was 0.33% in the group of 12-24 years, but only 0.16% in the group of 25-34 years. The model can explain this strange result. A data set of age-specific HIV-prevalences in heterosexual Colombian men would be useful, but is not known. Therefore, further research and data collecting is required in order to arrive at well founded conclusions.La función de infectividad es una función que dice qué tan infecciosa es una persona transcurrido un tiempo t después de haberse infectado. Hoy en día no se conoce ningún diseño de estudio, que sea factible y éticamente acceptable y que conduzca a estimados de la infectividad del VIH entre uniones heterosexuales estables, usando los métodos estadísticos corrientes. Por eso,aquí se utiliza un modelo de transmisión como puente entre la función de infectividad y datos que ya existen o pueden ser generados con pocos gastos. El modelo sugiere que la distribución según sexo y edad de la infección por VIH en la población heterosexual de bajo riesgo depende de la función de infectividad así como de los patrones de mezcla entre diferentes grupos de edad. La aplicación del modelo requiere datos poblacionales sobre la incidencia de la infección en los grupos de edad de hombres y mujeres heterosexuales. Actualmente el único conjunto de datos adecuado consiste en los resultados de pruebas por VIH en 8690 mujeres colombianas en embarazo que asistieron a consulta de control prenatal. La prevalencia del VIH era 0.33% en el grupo de 12-24 años, y 0.16% en el grupo de 25-34 años. El modelo puede explicar esta diferencia. Pero no existen datos sobre prevalencias del VIH en hombres heterosexuales de diferentes edades que se necesitan en este contexto. Por eso, se requieren otras investigaciones y recolecciones de datos para llegar a conclusiones seguras.

Cohort measures, describing a lifetime random variable are easily and unambiguously obtained using standard tools. On the contrary, the lifetime random variable, and therefore life expectancy, for the period setting cannot be unambiguously defined without additional simplifying assumptions. For non-stationary populations the corresponding conventional period measures should be justified in some way. Our paper is based on Bongaarts and Feeney (2002). We consider different measures of life expectancy and compare them for specific populations using stochastic ordering of the corresponding random variables. This gives possibility to look at the problem in a more general way.