No abstract

Background Cancer atlases often provide estimates of cancer incidence, mortality or survival across small areas of a region or country. A recent example of a cancer atlas is the Australian cancer atlas (ACA), that provides interactive maps to visualise spatially smoothed estimates of cancer incidence and survival for 20 different cancer types over 2148 small areas across Australia. Methods The present study proposes a multivariate Bayesian meta-analysis model, which can model multiple cancers jointly using summary measures without requiring access to the unit record data. This new approach is illustrated by modelling the publicly available spatially smoothed standardised incidence ratios for multiple cancers in the ACA divided into three groups: common, rare/less common and smoking-related. The multivariate Bayesian meta-analysis models are fitted to each group in order to explore any possible association between the cancers in three remoteness regions: major cities, regional and remote areas across Australia. The correlation between the pairs of cancers included in each multivariate model for a group was examined by computing the posterior correlation matrix for each cancer group in each region. The posterior correlation matrices in different remoteness regions were compared using Jennrich’s test of equality of correlation matrices (Jennrich in J Am Stat Assoc. 1970;65(330):904–12. 10.1080/01621459.1970.10481133). Results Substantive correlation was observed among some cancer types. There was evidence that the magnitude of this correlation varied according to remoteness of a region. For example, there has been significant negative correlation between prostate and lung cancer in major cities, but zero correlation found in regional and remote areas for the same pair of cancer types. High risk areas for specific combinations of cancer types were identified and visualised from the proposed model. Conclusions Publicly available spatially smoothed disease estimates can be used to explore additional research questions by modelling multiple cancer types jointly. These proposed multivariate meta-analysis models could be useful when unit record data are unavailable because of privacy and confidentiality requirements.

Analysis of spatial patterns of disease is a significant field of research. However, access to unit-level disease data can be difficult for privacy and other reasons. As a consequence, estimates of interest are often published at the small area level as disease maps. This motivates the development of methods for analysis of these ecological estimates directly. Such analyses can widen the scope of research by drawing more insights from published disease maps or atlases. The present study proposes a hierarchical Bayesian meta-analysis model that analyses the point and interval estimates from an online atlas. The proposed model is illustrated by modelling the published cancer incidence estimates available as part of the online Australian Cancer Atlas (ACA). The proposed model aims to reveal patterns of cancer incidence for the 20 cancers included in ACA in major cities, regional and remote areas. The model results are validated using the observed areal data created from unit-level data on cancer incidence in each of 2148 small areas. It is found that the meta-analysis models can generate similar patterns of cancer incidence based on urban/rural status of small areas compared with those already known or revealed by the analysis of observed data. The proposed approach can be generalized to other online disease maps and atlases.

The efficiency of modified atmosphere packaging (MAP) in combination with postharvest treatment on the shelf-life, physiochemical attributes, color, and nutrition of pointed gourd was studied after storing in refrigerated (low temperature, LT) and ambient (room temperature, RT) conditions. Fresh pointed gourd fruits were dipped in NaOCl solution (0.01% w/v) and potassium metabisulphite (KMS) (0.05% w/v), blanched (100°C for 4 min), and then packed in perforated and non-perforated polythene and polypropylene packets of each type and brown paper bags as MAP before storing at LT and RT. Physiochemical attributes, color, and nutrition were measured until the marketable level of acceptance (up to shelf-life) after storage and compared with the untreated and unpacked samples (control). The results showed profound differences among the treatment variables in all the studied dependent parameters regarding the LT and RT storage conditions. Among the treatments, perforated and non-perforated polyethylene (NPE) and polypropylene (NPP) packaging performed well to retain a considerable amount of ascorbic acid, β-carotene, and greenish color (lower L*, high h*) in pointed gourd treated with NaOCl (0.01%) and KMS (0.05%) after storing at LT and RT. Furthermore, the principal component analysis suggested that five major quality attributes (L*, C*, h*, shelf-life, and ascorbic acid) were influenced remarkably in terms of non-perforated polyethylene packaging in combination with KMS treatment both in LT and RT storage conditions. However, perforated polythene and polypropylene in combination with NaOCl responded well in RT but only for the shortest storage life. Thus, a non-perforated polythene package with KMS treatment would be the best solution for retaining market quality acceptance with green color up to the extended shelf-life of 23 and 10 days, respectively, in the refrigerator (LT) and in ambient (RT) storage conditions.