BackgroundThe shortage in human resources for health affects most dramatically developing countries which frequently use community health workers (CHW) as the basis for health programmes and services. The traditional definition refers CHWs as members of the community who are recruited and trained in health prevention and promotion to provide services within their community. In Guinea-Bissau, CHWs play a fundamental role in the diagnosis and treatment of childhood diarrheal diseases - one of the main health problems in the country.MethodsThis study is based on 22 CHW, 79% of the total number of CHW in the Sanitary Region of Bolama. The main goal was to assess how training CHW on diarrheal diseases impacted the accuracy of the diagnosis and treatment of these diseases in children under the age of 5 years. Three evaluations were made throughout time - one evaluation before the training and two follow-up evaluations.An observation grid was developed to evaluate the identified signs, symptoms, diagnosis and treatments prescribed by the CHW in consultations to children with a suspicion of diarrhoeal disease. A similar grid was filled by a medical doctor who took the role of the external validation standard.Friedman’s variance analysis and Cochran’s Q test were performed to compare the accuracy depicted by CHWs in identifying items throughout time. A logistic regression model was also used to check the possible influence of socio-demographic characteristics of CHWs on the accuracy of the diagnosis and treatment prescribed by the CHW.ResultsThe results show that CHWs improve significantly their performance in identifying the correct diagnosis in the first follow-up moment after the training (P = 0.001, n = 22) but, 3 months later, the effectiveness decreases. No statistical evidence was found for the logistic regression models applied.This progressive loss of performance after training may occur because CHWs fail to apply treatment algorithms and guidelines over time.A limited set of socio-demographic characteristics of the CHWs can influence their performance and should not be disregarded when selecting CHW candidates.ConclusionThe selection, supervision, support and continuous training of CHW are as important as the training provided.
Housing conditions can impact physical and mental health. In 2013, Portugal was still the fourth European Union country with the highest percentage of population without an adequately heated home in winter. Other adverse conditions are, for instance, overcrowding and living in older buildings. Some studies stress the relationship between stroke and poor living conditions in the elderly population, especially cold homes. Univariate and multivariate spatial cluster analysis were used to explore the relationship between excess stroke death risk, from 1998 to 2004, measured by the standardized mortality ratio (SMR) for several cohorts (including persons over 64 years old), and poor housing variables from the 2001 census, at the parish level in continental Portugal. A multivariate cluster of parishes, with population without any form of heating their homes as dominant condition, was detected in northwest Portugal. Mean and median SMR values across all cohorts were consistently higher within this cluster. This strengthens the hypothesis that cold homes deserve more attention in stroke prevention and mitigation amongst elderly persons, especially in northwestern continental Portugal.
Stroke risk has been shown to display varying patterns of geographic distribution amongst countries but also between regions of the same country. Traditionally a disease of older persons, a global 25% increase in incidence instead was noticed between 1990 and 2010 in persons aged 20-≤64 years, particularly in low-and medium-income countries. Understanding spatial disparities in the association between socioeconomic factors and stroke is critical to target public health initiatives aiming to mitigate or prevent this disease, including in younger persons. We aimed to identify socioeconomic determinants of geographic disparities of stroke risk in people 65 years old, in municipalities of mainland Portugal, and the spatiotemporal variation of the association between these determinants and stroke risk during two study periods (1992-1996 and 2002-2006). Poisson and negative binomial global regression models were used to explore determinants of disease risk. Geographically weighted regression (GWR) represents a distinctive approach, allowing estimation of local regression coefficients. Models for both study periods were identified. Significant variables included education attainment, work hours per week and unemployment. Local Poisson GWR models achieved the best fit and evidenced spatially varying regression coefficients. Spatiotemporal inequalities were observed in significant variables, with dissimilarities between men and women. This study contributes to a better understanding of the relationship between stroke and socioeconomic factors in the population <65 years of age, one age group seldom analysed separately. It can thus help to improve the targeting of public health initiatives, even more in a context of economic crisis.
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