Describing mortality trends for major cancer sites in 133 intermediate regions of Brazil and an ecological study of its causes

BACKGROUND

In Brazil, 211 thousand (16.14%) of all death certificates in 2016 identified cancer as the underlying cause of death, and it is expected that around 320 thousand will receive a cancer diagnosis in 2019. We aimed to describe trends of cancer mortality from 1996 to 2016, in 133 intermediate regions of Brazil, and to discuss macro-regional differences of trends by human development and healthcare provision.

METHODS

This ecological study assessed georeferenced official data on population and mortality, health spending, and healthcare provision from Brazilian governmental agencies. The regional office of the United Nations Development Program provided data on the Human Development Index in Brazil. Deaths by misclassified or unspecified causes (garbage codes) were redistributed proportionally to known causes. Age-standardized mortality rates used the world population as reference. Prais-Winsten autoregression allowed calculating trends for each region, sex and cancer type.

RESULTS

Trends were predominantly on the increase in the North and Northeast, whereas they were mainly decreasing or stationary in the South, Southeast, and Center-West. Also, the variation of trends within intermediate regions was more pronounced in the North and Northeast. Intermediate regions with higher human development, government health spending, and hospital beds had more favorable trends for all cancers and many specific cancer types.

CONCLUSIONS

Patterns of cancer trends in the country reflect differences in human development and the provision of health resources across the regions. Increasing trends of cancer mortality in low-income Brazilian regions can overburden their already fragile health infrastructure. Improving the healthcare provision and reducing socioeconomic disparities can prevent increasing trends of mortality by all cancers and specific cancer types in Brazilian more impoverished regions.

Health geography and the 'performative' turn: making space for the audio-visual in ethnographic health research

The purpose of this paper is to critically reflect on the added value of video in ethnographic research that seeks to understand peoples' lived experiences of health and place. Of particular interest is the potential for video to elicit the embodied, multisensory and relational nature of people's place experiences that are the focus of much recent health geography research. We draw on our experiences of using video in an ethnographic study that sought to explore the experiences of people with intellectual disabilities engaged in nature based (or 'green care') therapeutic interventions for health and wellbeing. We argue that video has the potential to capture aspects of people's wellbeing experiences that may be lost using other methods, such as observational field noting. Consideration is also given to how researchers using video methods should seek to (re)present people's wellbeing experiences, as well as the practical and ethical challenges that this approach has for those working in the field of health geography.

Geostatistical predictive modeling for asthma and chronic obstructive pulmonary disease using socioeconomic and environmental determinants

The spatial distribution of the prevalence of asthma and chronic obstructive pulmonary disease (COPD) remains under the influence of a wide array of environmental, climatic, and socioeconomic determinants. However, a large proportion of these influences remain unexplained. In completion, this study examined the spatial associations between asthma/COPD morbidity and their determinants using ordinary least squares (OLS) and geographically weighted regressions (GWR). Inpatient records collected from the secondary and tertiary care hospitals in Kandy from 2010 to 2014 were considered as the dependent variable. Potential risk factors (explanatory variables) were identified in four distinguished classes: 1) meteorological factors, (2) direct and indirect factors of air pollution, (3) socioeconomic factors, and (4) characteristics of the physical environment. All possible combinations of candidate explanatory variables were evaluated through an exploratory regression. A comparison between the regression models was also explored. The best OLS regression models revealed about 55% of asthma variation and 62% of COPD variation while GWR models yielded 78% and 74% of the variation of asthma and COPD occurrences respectively. Relative humidity, proximity to roads (0-200 m), road density, use of firewood as a source of fuel, and elevation play a vital role in predicting morbidity from asthma and COPD. Both local and global regression models are important in assessing spatial relationships of asthma and COPD. However, the local models exhibit a better prediction capability for assessing non-stationary relationships of asthma and COPD than global models. The geostatistical aspects used in this study may also provide insights for evaluating heterogeneous environmental risk factors in other epidemiological studies across different spatial settings.

Geo-spatial Informatics in International Public Health Nursing Education

This poster describes results of an undergraduate nursing informatics experience. Students applied geo-spatial methods to community assessments in two urban regions of New Zealand and the United States. Students used the Omaha System standardized language to code their observations during a brief community assessment activity and entered their data into a mapping program developed in Esri ArcGIS Online, a geographic information system. Results will be displayed in tables and maps to allow comparison among the communities. The next generation of nurses can employ geo-spatial informatics methods to contribute to innovative community assessment, planning and policy development.

Therapeutic micro-environments in the Edgelands: A thematic analysis of Richard Mabey's The Unofficial Countryside

The concept of therapeutic landscapes, as introduced by Gesler, has had a significant impact on what has become a reformed geography (or geographies) of health. Research in this field has developed the number and type of sites that have been characterised as therapeutic landscapes. A wide range of environments have now been explored through the analytical lens of the 'therapeutic landscape'. This research further expands current descriptions of such environments by exploring Edgelands as therapeutic micro landscapes. Edgelands refer to the neglected and routinely ignored interfacial zone between urban and rural that are a routine characteristic of the urban fringe resulting from dynamic cycles of urban development and decay. Using a hybrid method of thematic analysis incorporating both inductive and deductive approaches, this research explores Richard Mabey's seminal work on this topic, The Unofficial Countryside. Previous examinations of the features of therapeutic environments are therefore scrutinised to explore both scale and the possibility of further extending the kind of environments that may be described as therapeutic to include Edgelands. This approach is informed, in part, by principles of mindfulness, a historically Eastern, but increasingly Western approach to exploring oneself and the environment. This research identifies that these overlooked and neglected landscapes are in fact vibrant, resilient and enthralling environments teeming with life, renewal and re-birth. Examination reveals that there are three crucial outcomes of this research. The first relates to the issue of scale. Mabey's book provides evidence of the importance of micro environments in providing a therapeutic environmental focus. Secondly, this research explores the potential of mindfulness as an approach in Geography. Lastly, this research also identifies Edgelands as therapeutic sites and calls for an increased understanding and appreciation of their potential.

Can Italian healthcare administrative databases be used to compare regions with respect to compliance with standards of care for chronic diseases?

Background

Italy has a population of 60 million and a universal coverage single-payer healthcare system, which mandates collection of healthcare administrative data in a uniform fashion throughout the country. On the other hand, organization of the health system takes place at the regional level, and local initiatives generate natural experiments. This is happening in particular in primary care, due to the need to face the growing burden of chronic diseases. Health services research can compare and evaluate local initiatives on the basis of the common healthcare administrative data.However reliability of such data in this context needs to be assessed, especially when comparing different regions of the country. In this paper we investigated the validity of healthcare administrative databases to compute indicators of compliance with standards of care for diabetes, ischaemic heart disease (IHD) and heart failure (HF).

Methods

We compared indicators estimated from healthcare administrative data collected by Local Health Authorities in five Italian regions with corresponding estimates from clinical data collected by General Practitioners (GPs). Four indicators of diagnostic follow-up (two for diabetes, one for IHD and one for HF) and four indicators of appropriate therapy (two each for IHD and HF) were considered.

Results

Agreement between the two data sources was very good, except for indicators of laboratory diagnostic follow-up in one region and for the indicator of bioimaging diagnostic follow-up in all regions, where measurement with administrative data underestimated quality.

Conclusion

According to evidence presented in this study, estimating compliance with standards of care for diabetes, ischaemic heart disease and heart failure from healthcare databases is likely to produce reliable results, even though completeness of data on diagnostic procedures should be assessed first. Performing studies comparing regions using such indicators as outcomes is a promising development with potential to improve quality governance in the Italian healthcare system.

When homogeneity meets heterogeneity: the geographically weighted regression with spatial lag approach to prenatal care utilization

Using geographically weighted regression (GWR), a recent study by Shoff and colleagues (2012) investigated the place-specific risk factors for prenatal care utilisation in the United States of America (USA) and found that most of the relationships between late or no prenatal care and its determinants are spatially heterogeneous. However, the GWR approach may be subject to the confounding effect of spatial homogeneity. The goal of this study was to address this concern by including both spatial homogeneity and heterogeneity into the analysis. Specifically, we employed an analytic framework where a spatially lagged (SL) effect of the dependent variable is incorporated into the GWR model, which is called GWR-SL. Using this framework, we found evidence to argue that spatial homogeneity is neglected in the study by Shoff et al. (2012) and that the results change after considering the SL effect of prenatal care utilisation. The GWR-SL approach allowed us to gain a placespecific understanding of prenatal care utilisation in USA counties. In addition, we compared the GWR-SL results with the results of conventional approaches (i.e., ordinary least squares and spatial lag models) and found that GWR-SL is the preferred modelling approach. The new findings help us to better estimate how the predictors are associated with prenatal care utilisation across space, and determine whether and how the level of prenatal care utilisation in neighbouring counties matters.

Child mortality in the Democratic Republic of Congo: cross-sectional evidence of the effect of geographic location and prolonged conflict from a national household survey

BACKGROUND

The child mortality rate is a good indicator of development. High levels of infectious diseases and high child mortality make the Democratic Republic of Congo (DRC) one of the most challenging environments for health development in Sub-Saharan Africa (SSA). Recent conflicts in the eastern part of the country and bad governance have compounded the problem. This study aimed to examine province-level geographic variation in under-five mortality (U5M), accounting for individual- and household-level risk factors including environmental factors such as conflict.

METHODS

Our analysis used the nationally representative cross-sectional household sample of 8,992 children under five in the 2007 DRC Demographic and Health Survey. In the survey year, 1,005 deaths among this group were observed. Information on U5M was aggregated to the 11 provinces, and a Bayesian geo-additive discrete-time survival mixed model was used to map the geographic distribution of under-five mortality rates (U5MRs) at the province level, accounting for observable and unobservable risk factors.

RESULTS

The overall U5MR was 159 per 1,000 live births. Significant associations with risk of U5M were found for <24 month birth interval [posterior odds ratio and 95% credible region: 1.14 (1.04, 1.26)], home birth [1.13 (1.01, 1.27)] and living with a single mother [1.16 (1.03, 1.33)]. Striking variation was also noted in the risk of U5M by province of residence, with the highest risk in Kasaï-Oriental, a non-conflict area of the DRC, and the lowest in the conflict area of North Kivu.

CONCLUSION

This study reveals clear geographic patterns in rates of U5M in the DRC and shows the potential role of individual child, household and environmental factors, which are unexplained by the ongoing conflict. The displacement of mothers to safer areas may explain the lower U5MR observed at the epicentre of the conflict in North Kivu, compared with rates in conflict-free areas. Overall, the U5M maps point to a lack of progress towards the Millennium Development Goal of reducing U5M by half by 2015.

[The modern sources for making a medical geography description]

The current article is dedicated to application of Internet for acquisition of medical geography information. The vast majority of the modern domestic reference manuals are neither reliable nor up-to-date. At the time when the foreign printed sources are not easily accessible the foreign web resources often become the main source of information. The article possesses some practical advice on how to find the general, medical and military medical data on the web. It is emphasized the necessity of careful cross validation of all the obtained data to be confident in their reliability.

Modeling type 1 and type 2 diabetes mellitus incidence in youth: an application of Bayesian hierarchical regression for sparse small area data

Sparse count data violate assumptions of traditional Poisson models due to the excessive amount of zeros, and modeling sparse data becomes challenging. However, since aggregation to reduce sparseness may result in biased estimates of risk, solutions need to be found at the level of disaggregated data. We investigated different statistical approaches within a Bayesian hierarchical framework for modeling sparse data without aggregation of data. We compared our proposed models with the traditional Poisson model and the zero-inflated model based on simulated data. We applied statistical models to type 1 and type 2 diabetes in youth 10-19 years known as rare diseases, and compared models using the inference results and various model diagnostic tools. We showed that one of the models we proposed, a sparse Poisson convolution model, performed better than other models in the simulation and application based on the deviance information criterion (DIC) and the mean squared prediction error.

Generalized index for spatial data sets as a measure of complete spatial randomness

Spatial data sets, generated from a wide range of physical systems can be analyzed by counting the number of objects in a set of bins. Previous work has been limited to equal-sized bins, which are inappropriate for some domains (e.g., circular). We consider a nonequal size bin configuration whereby overlapping or nonoverlapping bins cover the domain. A generalized index, defined in terms of a variance between bin counts, is developed to indicate whether or not a spatial data set, generated from exclusion or nonexclusion processes, is at the complete spatial randomness (CSR) state. Limiting values of the index are determined. Using examples, we investigate trends in the generalized index as a function of density and compare the results with those using equal size bins. The smallest bin size must be much larger than the mean size of the objects. We can determine whether a spatial data set is at the CSR state or not by comparing the values of a generalized index for different bin configurations-the values will be approximately the same if the data is at the CSR state, while the values will differ if the data set is not at the CSR state. In general, the generalized index is lower than the limiting value of the index, since objects do not have access to the entire region due to blocking by other objects. These methods are applied to two applications: (i) spatial data sets generated from a cellular automata model of cell aggregation in the enteric nervous system and (ii) a known plant data distribution.

Global animal disease surveillance

Development and implementation of global animal disease surveillance has been limited by the lack of information systems that enable near real-time data capturing, sharing, analysis, and related decision- and policy-making. The objective of this paper is to describe requirements for global animal disease surveillance, including design and functionality of tools and methods for visualization and analysis of animal disease data. The paper also explores the potential application of techniques for spatial and spatio-temporal analysis on global animal disease surveillance, including for example, landscape genetics, social network analysis, and Bayesian modeling. Finally, highly pathogenic avian influenza data from Denmark and Sweden are used to illustrate the potential application of a novel system (Disease BioPortal) for data sharing, visualization, and analysis for regional and global surveillance efforts.