Geographical mortality patterns in Italy: A Bayesian analysis

Estimation and probabilistic projection of age- and sex-specific mortality rates across Brazilian municipalities between 2010 and 2030

BACKGROUND

Mortality rate estimation in small areas can be difficult due the low number of events/exposure (i.e. stochastic error). If the death records are not completed, it adds a systematic uncertainty on the mortality estimates. Previous studies in Brazil have combined demographic and statistical methods to partially overcome these issues. We estimated age- and sex-specific mortality rates for all 5,565 Brazilian municipalities in 2010 and forecasted probabilistic mortality rates and life expectancy between 2010 and 2030.

METHODS

We used a combination of the Tool for Projecting Age-Specific Rates Using Linear Splines (TOPALS), Bayesian Model, Spatial Smoothing Model and an ad-hoc procedure to estimate age- and sex-specific mortality rates for all Brazilian municipalities for 2010. Then we adapted the Lee-Carter model to forecast mortality rates by age and sex in all municipalities between 2010 and 2030.

RESULTS

The adjusted sex- and age-specific mortality rates for all Brazilian municipalities in 2010 reveal a distinct regional pattern, showcasing a decrease in life expectancy in less socioeconomically developed municipalities when compared to estimates without adjustments. The forecasted mortality rates indicate varying regional improvements, leading to a convergence in life expectancy at birth among small areas in Brazil. Consequently, a reduction in the variability of age at death across Brazil's municipalities was observed, with a persistent sex differential.

CONCLUSION

Mortality rates at a small-area level were successfully estimated and forecasted, with associated uncertainty estimates also generated for future life tables. Our approach could be applied across countries with data quality issues to improve public policy planning.

Temporal and spatial trends of adult mortality in small areas of Brazil, 1980–2010

To determine the variations and spatial patterns of adult mortality across regions, over time, and by sex for 137 small areas in Brazil, we first apply TOPALS to estimate and smooth mortality rates and then use death distribution methods to evaluate the quality of the mortality data. Lastly, we employ spatial autocorrelation statistics and cluster analysis to identify the adult mortality trends and variations in these areas between 1980 and 2010. We find not only that regions in Brazil’s South and Southeast already had complete death registration systems prior to the study period, but that the completeness of death count coverage improved over time across the entire nation—most especially in lesser developed regions—probably because of public investment in health data collection. By also comparing adult mortality by sex and by region, we document a mortality sex differential in favor of women that remains high over the entire study period, most probably as a result of increased morbidity from external causes, especially among males. This increase also explains the concentration of high male mortality levels in some areas.

Bayesian demography 250 years after Bayes

Bayesian statistics offers an alternative to classical (frequentist) statistics. It is distinguished by its use of probability distributions to describe uncertain quantities, which leads to elegant solutions to many difficult statistical problems. Although Bayesian demography, like Bayesian statistics more generally, is around 250 years old, only recently has it begun to flourish. The aim of this paper is to review the achievements of Bayesian demography, address some misconceptions, and make the case for wider use of Bayesian methods in population studies. We focus on three applications: demographic forecasts, limited data, and highly structured or complex models. The key advantages of Bayesian methods are the ability to integrate information from multiple sources and to describe uncertainty coherently. Bayesian methods also allow for including additional (prior) information next to the data sample. As such, Bayesian approaches are complementary to many traditional methods, which can be productively re-expressed in Bayesian terms.

The Association of Geographic Coordinates with Mortality in People with Lower and Higher Education and with Mortality Inequalities in Spain

Objective

Geographic patterns in total mortality and in mortality by cause of death are widely known to exist in many countries. However, the geographic pattern of inequalities in mortality within these countries is unknown. This study shows mathematically and graphically the geographic pattern of mortality inequalities by education in Spain.

Methods

Data are from a nation-wide prospective study covering all persons living in Spain's 50 provinces in 2001. Individuals were classified in a cohort of subjects with low education and in another cohort of subjects with high education. Age- and sex-adjusted mortality rate from all causes and from leading causes of death in each cohort and mortality rate ratios in the low versus high education cohort were estimated by geographic coordinates and province.

Results

Latitude but not longitude was related to mortality. In subjects with low education, latitude had a U-shaped relation to mortality. In those with high education, mortality from all causes, and from cardiovascular, respiratory and digestive diseases decreased with increasing latitude, whereas cancer mortality increased. The mortality-rate ratio for all-cause death was 1.27 in the southern latitudes, 1.14 in the intermediate latitudes, and 1.20 in the northern latitudes. The mortality rate ratios for the leading causes of death were also higher in the lower and upper latitudes than in the intermediate latitudes. The geographic pattern of the mortality rate ratios is similar to that of the mortality rate in the low-education cohort: the highest magnitude is observed in the southern provinces, intermediate magnitudes in the provinces of the north and those of the Mediterranean east coast, and the lowest magnitude in the central provinces and those in the south of the Western Pyrenees.

Conclusion

Mortality inequalities by education in Spain are higher in the south and north of the country and lower in the large region making up the central plateau. This geographic pattern is similar to that observed in mortality in the low-education cohort.

Progress in Spatial Demography

BACKGROUND

Demography is an inherently spatial science, yet the application of spatial data and methods to demographic research has tended to lag that of other disciplines. In recent years, there has been a surge in interest in adding a spatial perspective to demography. This sharp rise in interest has been driven in part by rapid advances in geospatial data, new technologies, and methods of analysis.

OBJECTIVES

We offer a brief introduction to four of the advanced spatial analytic methods: spatial econometrics, geographically weighted regression, multilevel modeling, and spatial pattern analysis. We look at both the methods used and the insights that can be gained by applying a spatial perspective to demographic processes and outcomes. To help illustrate these substantive insights, we introduce six papers that are included in a Special Collection on Spatial Demography. We close with some predictions for the future, as we anticipate that spatial thinking and the use of geospatial data, technology, and analytical methods will change how many demographers address important demographic research questions.

CONCLUSION

Many important demographic questions can be studied and framed using spatial approaches. This will become even more evident as changes in the volume, source, and form of available demographic data-much of it geocoded-further alter the data landscape, and ultimately the conceptual models and analytical methods used by demographers. This overview provides a brief introduction to a rapidly changing field.