Perfil dos consumidores de hortaliças minimamente processadas de Belo Horizonte Profile of minimally processed vegetable consumers in Belo Horizonte, Brazil

O consumidor vem apresentando cada vez maior consciência na escolha de sua alimentação, porém com menor tempo disponível para preparar refeições saudáveis. Diante disso, o mercado de hortaliças minimamente processadas tem aumentado rapidamente, proporcionando o surgimento de produtos convenientes, ou seja, produtos frescos que podem ser preparados e consumidos em pouco tempo. Assim, neste trabalho estudou-se o comportamento de compra e o perfil dos consumidores de hortaliças minimamente processadas em supermercados de Belo Horizonte. Foram aplicados questionários aos consumidores de cinco estabelecimentos que vendiam hortaliças minimamente processadas (HMP) e os entrevistados foram escolhidos aleatoriamente no momento das compras, durante o mês de novembro de 2004. Foram entrevistadas 246 pessoas, das quais apenas 23% consumiam HMP. Os principais motivos para consumir foram comodidade e praticidade (46%), pouco tempo para o preparo (21%) das refeições e higiene (11%) dos produtos, e para não consumir foram preço elevado (31,9%), gosta de preparar e/ou escolher (23%) e desconfiança (17,8%) dos produtos ofertados. Os maiores consumidores são as pessoas com maior escolaridade e nível de renda mais elevado. A sobrevalorização dos HMP em relação aos produtos comuns variou de 2,4 vezes (batata) até 39,5 vezes (cenoura), sendo a média de sobrevalorização encontrada no mercado para as onze HMP de 10 vezes.<br>Consumer's food consciousness has increased, however, time for preparing healthy food has decreased. Therefore, the market of minimally processed vegetable has increased rapidly, giving rise to the appearance of fresh products that can be prepared and consumed faster than conventional foods. Thus, the objective of this work was to establish the behavior and profile of minimally processed vegetable consumers in supermarkets of Belo Horizonte, Minas Gerais State, Brazil. Based on the answers to a survey, the consumers profile was traced, highlighting preferences, habits, complaints and demands. The survey was conducted with consumers of five supermarkets which sell minimally processed vegetables (MPV). Consumers were chosen randomly when they were buying in November 2004. Of two hundred and forty six interviewed persons, 23% were consumers of MPV. The main reasons for consumption were: easiness and feasibility (46%); short preparation time (21%) of meals and hygiene (11%) of the products; and the main reasons against consumption were: the high price of the precuts (31,9%); easy to prepare and/or to choose (23%) and distrusting (17,8%) the offered products. The major consumers were those with a better education. Comparing the overrating of MPV with other vegetables, a variation of 2,4 (potato) to 39,5 times (carrots) was found. On average the overrating found in markets for eleven MPV was of 10 times

Appropriate statistical methods for analysing partially nested randomised controlled trials with continuous outcomes: a simulation study

BACKGROUND: In individually randomised trials we might expect interventions delivered in groups or by care providers to result in clustering of outcomes for participants treated in the same group or by the same care provider. In partially nested randomised controlled trials (pnRCTs) this clustering only occurs in one trial arm, commonly the intervention arm. It is important to measure and account for between-cluster variability in trial design and analysis. We compare analysis approaches for pnRCTs with continuous outcomes, investigating the impact on statistical inference of cluster sizes, coding of the non-clustered arm, intracluster correlation coefficient (ICCs), and differential variance between intervention and control arm, and provide recommendations for analysis. METHODS: We performed a simulation study assessing the performance of six analysis approaches for a two-arm pnRCT with a continuous outcome. These include: linear regression model; fully clustered mixed-effects model with singleton clusters in control arm; fully clustered mixed-effects model with one large cluster in control arm; fully clustered mixed-effects model with pseudo clusters in control arm; partially nested homoscedastic mixed effects model, and partially nested heteroscedastic mixed effects model. We varied the cluster size, number of clusters, ICC, and individual variance between the two trial arms. RESULTS: All models provided unbiased intervention effect estimates. In the partially nested mixed-effects models, methods for classifying the non-clustered control arm had negligible impact. Failure to account for even small ICCs resulted in inflated Type I error rates and over-coverage of confidence intervals. Fully clustered mixed effects models provided poor control of the Type I error rates and biased ICC estimates. The heteroscedastic partially nested mixed-effects model maintained relatively good control of Type I error rates, unbiased ICC estimation, and did not noticeably reduce power even with homoscedastic individual variances across arms. CONCLUSIONS: In general, we recommend the use of a heteroscedastic partially nested mixed-effects model, which models the clustering in only one arm, for continuous outcomes similar to those generated under the scenarios of our simulations study. However, with few clusters (3-6), small cluster sizes (5-10), and small ICC (≤0.05) this model underestimates Type I error rates and there is no optimal model