IntroductionAs non-communicable disease (NCD) burden rises worldwide, community-based programmes are a promising strategy to bridge gaps in NCD care. The HealthRise programme sought to improve hypertension and diabetes management for underserved communities in nine sites across Brazil, India, South Africa and the USA between 2016 and 2018. This study presents findings from the programme’s endline evaluation.MethodsThe evaluation utilises a mixed-methods quasi-experimental design. Process indicators assess programme implementation; quantitative data examine patients’ biometric measures and qualitative data characterise programme successes and challenges. Programme impact was assessed using the percentage of patients meeting blood pressure and A1c treatment targets and tracking changes in these measures over time.ResultsAlmost 60 000 screenings, most of them in India, resulted in 1464 new hypertension and 295 new diabetes cases across sites. In Brazil, patients exhibited statistically significant reductions in blood pressure and A1c. In Shimla, India, and in South Africa, country with the shortest implementation period, there were no differences between patients served by facilities in HealthRise areas relative to comparison areas. Among participating patients with diabetes in Hennepin and Ramsey counties and hypertension patients in Hennepin County, the percentage of HealthRise patients meeting treatment targets at endline was significantly higher relative to comparison group patients. Qualitative analysis identified linking different providers, services, communities and information systems as positive HealthRise attributes. Gaps in health system capacities and sociodemographic factors, including poverty, low levels of health education and limited access to nutritious food, are remaining challenges.ConclusionsFindings from Brazil and the USA indicate that the HealthRise model has the potential to improve patient outcomes. Short implementation periods and strong emphasis on screening may have contributed to the lack of detectable differences in other sites. Community-based care cannot deliver its full potential if sociodemographic and health system barriers are not addressed in tandem.

The rational construction of an essential drug list, considering the patient's need, drug safety, availability and the best cost-benefit ratio, is based on drug safety, efficacy and quality. However, in daily practice, the prescriber's decision is mostly influenced by drug effectiveness, following criteria that increase adherence to the treatment, such as relative drug toxicity, convenience, cost and prescriber's experience. In addition, frequent launching of new molecules for the same therapeutic indication, together with wide publicity targeting prescribers, interferes with the decision-making process. Similarly, the bonuses offered by the industry for over-the-counter drug sales interfere with the consumer's choice. The confrontation between known human biological variability and the knowledge that there is no absolute similarity between drugs of the same therapeutic class, or even generic drugs, has an impact on the prescriber's drug list, which should include the concept of first and second choice drugs. Prescribers' unfamiliarity with these subjects is a determinant factor for irrational drug use: a public health issue. The objective was to introduce to drug prescribers information that can help them building up a rational drug list for their patients, based on the National Health Surveillance Agency (Anvisa) experience of drug regulation.

Odds ratio": algumas considerações. Rev. Saúde públ., S. Paulo, 20 : 251-6, 1986. RESUMO:Tem sido grande o número de estudos retrospectivos e transversais controlados que utilizam o "odds ratio" como medida de intensidade de associação. Visando melhor compreensão do significado desta medida, o "odds ratio" foi comparado com a razão de prevalências; foi estudado o comportamento desta medida em relação a variação amostrai de prevalência do fator de risco nos casos e nos controles; e a importância de expressar o "odds ratio" com o respectivo intervalo de confiança. Devido a utilização cada vez maior do "odds ratio" em epidemiologia, apresentam-se a seguir algumas considerações sobre esta medida.O "odds ratio" é uma medida antiga tendo sido usada por Snow em seu clássico trabalho de identificação do fator de risco da propagação da cólera em Londres, em 1853 6 . É utilizado como medida de associação em estudos caso-controle e em estudos transversais controlados 4 .Considerando uma tabela de acordo com a Figura 1, "odds ratio" é igual a ou e por isto é também chamado de razão de produtos cruzados.O "odds ratio" em conjunto com o coeficiente de Yule, o risco relativo e o risco atribuível são as medidas de associação mais usadas em pesquisas etiológicas 3 .Miettinen 5 especificou o 'odds ratio" em 3 tipos: "exposure odds ratio (EOR)", "risk odds ratio (ROR)" e "prevelence odds ratio (POR)" em função da exposição ao fator de risco ser num curto (EOR) ou longo período (ROR), ou o número de casos serem prevalentes (POR) e não incidentes (EOR e ROR). CONSIDERAÇÃO l O "odds ratio" e a razão de prevalênciasIntuitivamente, visando inferências causais, podemos pensar em quantas vezes a prevalência do fator de risco nos casos é maior que a prevalência do fator de risco nos controles, ou seja, conforme a Figura l: a r a z ã o . .Esta razão é denominada "prevalence ratio" 4 ou ainda "likelihood ratio" 2 , que neste trabalho denominaremos razão de prevalência (RP).Por exemplo, comparemos as tabelas A e B na Figura 2.Por este raciocínio podemos inferir que o fator de risco A está mais associado com os casos que o fator de risco B e, portanto, tem maior possibilidade de ser causa, como mostram os respectivos RPs.