From August 2000 through January 2001, a large epidemic of Ebola hemorrhagic fever occurred in Uganda, with 425 cases and 224 deaths. Starting from three laboratory-confirmed cases, we traced the chains of transmission for three generations, until we reached the primary case-patients (i.e., persons with an unidentified source of infection). We then prospectively identified the other contacts in whom the disease had developed. To identify the risk factors associated with transmission, we interviewed both healthy and ill contacts (or their proxies) who had been reported by the case-patients (or their proxies) and who met the criteria set for contact tracing during surveillance. The patterns of exposure of 24 case-patients and 65 healthy contacts were defined, and crude and adjusted prevalence proportion ratios (PPR) were estimated for different types of exposure. Contact with the patient’s body fluids (PPR = 4.61%, 95% confidence interval 1.73 to 12.29) was the strongest risk factor, although transmission through fomites also seems possible.

BackgroundAdministrative databases are widely available and have been extensively used to provide estimates of chronic disease prevalence for the purpose of surveillance of both geographical and temporal trends. There are, however, other sources of data available, such as medical records from primary care and national surveys. In this paper we compare disease prevalence estimates obtained from these three different data sources.MethodsData from general practitioners (GP) and administrative transactions for health services were collected from five Italian regions (Veneto, Emilia Romagna, Tuscany, Marche and Sicily) belonging to all the three macroareas of the country (North, Center, South). Crude prevalence estimates were calculated by data source and region for diabetes, ischaemic heart disease, heart failure and chronic obstructive pulmonary disease (COPD). For diabetes and COPD, prevalence estimates were also obtained from a national health survey. When necessary, estimates were adjusted for completeness of data ascertainment.ResultsCrude prevalence estimates of diabetes in administrative databases (range: from 4.8% to 7.1%) were lower than corresponding GP (6.2%-8.5%) and survey-based estimates (5.1%-7.5%). Geographical trends were similar in the three sources and estimates based on treatment were the same, while estimates adjusted for completeness of ascertainment (6.1%-8.8%) were slightly higher. For ischaemic heart disease administrative and GP data sources were fairly consistent, with prevalence ranging from 3.7% to 4.7% and from 3.3% to 4.9%, respectively. In the case of heart failure administrative estimates were consistently higher than GPs’ estimates in all five regions, the highest difference being 1.4% vs 1.1%. For COPD the estimates from administrative data, ranging from 3.1% to 5.2%, fell into the confidence interval of the Survey estimates in four regions, but failed to detect the higher prevalence in the most Southern region (4.0% in administrative data vs 6.8% in survey data). The prevalence estimates for COPD from GP data were consistently higher than the corresponding estimates from the other two sources.ConclusionThis study supports the use of data from Italian administrative databases to estimate geographic differences in population prevalence of ischaemic heart disease, treated diabetes, diabetes mellitus and heart failure. The algorithm for COPD used in this study requires further refinement.

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High ambient temperatures have been associated with increased mortality across the world. Several studies suggest that timely preventive measures may reduce heat-related excess mortality. The main aim of this study was to detect the temporal modification of heat-related mortality, in older adults (aged 65-74) and in elderly ≥75 years old, in the Florentine area by comparing previous (1999-2002) and subsequent (2004-2007) periods to the summer of 2003, when a regional Heat-Health Warning System (HHWS) was set up. Mortality data from 1999 to 2007 (May-September) were provided by the Mortality Registry of the Tuscany Region (n = 21,092). Weather data were used to assess daily apparent temperatures (AT). Case-crossover time-stratified designs and constrained segmented distributed lag models were applied. No significant heat-related mortality odds ratio (OR) variations were observed among the sub-periods. Nevertheless, a general OR decrease dating from 1999-2002 (OR 1.23; lack of HHWS) to 2004-2005 (OR 1.21; experimental HHWS running only for Florence) and to 2006-2007 (OR 1.12; official HHWS extended to the whole Florentine area) was observed when the maximum AT was considered. This modification was only evident in subjects ≥75 years old. The heat effect was higher and sustained for more days (until lag 9) during the period 1999-2002 than 2004-2007. The decrease of the excessive heat effect on mortality between periods with the absence and existence of a HHWS is also probably due to the mitigation of preventive measures and the implementation of a HHWS with specific interventions for safeguarding the health of the "frail elderly".

The association between air temperature and human health is described in detail in a large amount of literature. However, scientific publications estimating how climate change will affect the population's health are much less extensive. In this study current evaluations and future predictions of the impact of temperature on human health in different geographical areas have been carried out. Non-accidental mortality and hospitalizations, and daily average air temperatures have been obtained for the 1999-2008 period for the ten main cities in Tuscany (Central Italy). High-resolution city-specific climatologic A1B scenarios centered on 2020 and 2040 have been assessed. Generalized additive and distributed lag models have been used to identify the relationships between temperature and health outcomes stratified by age: general adults (<65), elderly (aged 65-74) and very elderly (≥75). The cumulative impact (over a lag-period of 30 days) of the effects of cold and especially heat, was mainly significant for mortality in the very elderly, with a higher impact on coastal plain than inland cities: 1 °C decrease/increase in temperature below/above the threshold was associated with a 2.27% (95% CI: 0.17-4.93) and 15.97% (95% CI: 7.43-24.51) change in mortality respectively in the coastal plain cities. A slight unexpected increase in short-term cold-related mortality in the very elderly, with respect to the baseline period, is predicted for the following years in half of the cities considered. Most cities also showed an extensive predicted increase in short-term heat-related mortality and a general increase in the annual temperature-related elderly mortality rate. These findings should encourage efforts to implement adaptation actions conducive to policy-making decisions, especially for planning short- and long-term health intervention strategies and mitigation aimed at preventing and minimizing the consequences of climate change on human health.

ObjectivesThe Italian project MATRICE aimed to assess how well cases of type 2 diabetes (T2DM), hypertension, ischaemic heart disease (IHD) and heart failure (HF) and their levels of severity can be automatically extracted from the Health Search/CSD Longitudinal Patient Database (HSD). From the medical records of the general practitioners (GP) who volunteered to participate, cases were extracted by algorithms based on diagnosis codes, keywords, drug prescriptions and results of diagnostic tests. A random sample of identified cases was validated by interviewing their GPs.SettingHSD is a database of primary care medical records. A panel of 12 GPs participated in this validation study.Participants300 patients were sampled for each disease, except for HF, where 243 patients were assessed.Outcome measuresThe positive predictive value (PPV) was assessed for the presence/absence of each condition against the GP's response to the questionnaire, and Cohen's κ was calculated for agreement on the severity level.ResultsThe PPV was 100% (99% to 100%) for T2DM and hypertension, 98% (96% to 100%) for IHD and 55% (49% to 61%) for HF. Cohen's kappa for agreement on the severity level was 0.70 for T2DM and 0.69 for hypertension and IHD.ConclusionsThis study shows that individuals with T2DM, hypertension or IHD can be validly identified in HSD by automated identification algorithms. Automatic queries for levels of severity of the same diseases compare well with the corresponding clinical definitions, but some misclassification occurs. For HF, further research is needed to refine the current algorithm.

Despite the limited statistical power, the results of our study show an association between HIV infection and clinical malaria; if confirmed, this finding could be important for public health in sub-Saharan Africa.

BackgroundMicrovascular complications (MC) have been claimed to increase the risk for cardiovascular disease in diabetic subjects. However, the effect of MC burden on the risk of major vascular outcomes and all-cause mortality in type 1 diabetes is still poorly explored. We evaluated the relationship between microvascular complications burden and incidence of major cardiovascular events and all-cause mortality in subjects with type 1 diabetes.MethodsWe recruited 774 participants with type 1 diabetes in a single-center observational study over a follow-up of 10.8 ± 2.5 years. Hazard ratios (HR) for cardiovascular outcomes and all-cause death associated with microvascular complications were determined by unadjusted and adjusted Cox regression analysis.ResultsOut of 774 individuals, 54.9% had no-MC, 32.3% 1 MC, 9.7% 2 MC and 3.1% 3 MC. A total of 54 deaths (7.0%) occurred. Death rate increased from no-MC 2.1% (Ref) to 1 MC 7.2% (HR 3.54 [95% CI 1.59–7.87]), 2 MC 14.7% (HR 6.41 [95% CI 2.65–15.49]) and 3 MC 66.7% (HR 41.73 [95% CI 18.42–94.57], p < 0.0001). After adjustments, HRs were: 1 MC 2.05 (95% CI 0.88–4.76), 2 MC 1.98 (95% CI 0.75–5.21), 3 MC 7.02 (95% CI 2.44–20.20, p = 0.002). Forty-nine subjects (6.7%) had at least one cardiovascular event, and cumulative incidence went from no-MC 2.2% (Ref) to 1 MC 5.0%; (HR 2.27 [95% CI 0.96–5.38]), 2 MC 26.8% (HR 12.88 [95% CI 5.82–28.50]) and 3 MC 40.9% (HR 29.34 [95% CI 11.59–74.25], p < 0.0001). Upon adjustments, HRs were: 1 MC 1.59 (95% CI 0.65–3.88), 2 MC 4.33 (95% CI 1.75–10.74), 3 MC 9.31 (95% CI 3.18–27.25, p < 0.0001). Thirty-five individuals (4.8%) had at least one coronary event, which cumulative incidence increased with MC burden (p < 0.0001).ConclusionsIn type 1 diabetes, microvascular complications burden increases in an independent dose-dependent manner the risk of major cardiovascular outcomes and all-cause mortality. The presence and number of microvascular complications should be considered in stratifying overall cardiovascular risk in type 1 diabetes.