These fishing communities experienced high HIV infection, which was mainly explained by high-risk behavior. There is an urgent need to target HIV prevention and research efforts to this vulnerable and neglected group.

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S everity scores are helpful in predicting mortality in patients presenting with communityacquired pneumonia (CAP). They enable physicians to decide their management strategies and site of care according to the expected mortality risk. In Europe, most cases of lower respiratory tract infections (LRTIs) and CAP are managed in primary care settings by general practitioners (GPs). However, most severity scores have been derived and validated in a hospital setting. The best accepted tools to discriminate patients with CAP into high or low risk are the CURB-65 score (confusion, serum urea nitrogen level Ͼ19.6 mg/dL [to convert to millimoles per liter, multiply by 0.357], respiratory rate Ն30/min, low blood pressure, and age Ն65 years) and the Pneumonia Severity Index (PSI). 1,2 The CURB-65 score consists of 5 easily accessible data, while the PSI includes many tests that are not accessible in primary care. This latter score is therefore not useful for the GP. Recently, several studies evaluated the CURB-65 score and confirmed its validity, but validation was not yet done in an unselected primary care population. 3-9 A modification of the CURB-65 score, the CRB-65 score, is recommended by GPs in the communities where serum urea nitrogen measurements are often unavailable. It is expected to support GP judgment in stratifying patients into different management groups, ranging from home treatment to urgent hospital admission (Figure). 1 This management model seems unlikely to be practical in a primary care population because it recommends hospital referral for a score of 1 or higher and thus for all patients older than 65 years (Figure). We therefore conducted a study to evaluate the validity of the CRB-65 score in primary care. Methods. Study Population. Between November 2005 and May 2006, Dutch GPs prospectively included patients with CAP who were 65 years or older. Community-acquired pneumonia was diagnosed on the basis of the presence of 1 or more features, including new localizing signs present during chest examination, new infiltrates on a

BackgroundFishing communities are potentially suitable for Human immunodeficiency virus (HIV) efficacy trials due to their high risk profile. However, high mobility and attrition could decrease statistical power to detect the impact of a given intervention. We report dropout and associated factors in a fisher-folk observational cohort in Uganda.MethodsHuman immunodeficiency virus-uninfected high-risk volunteers aged 13–49 years living in five fishing communities around Lake Victoria were enrolled and followed every 6 months for 18 months at clinics located within each community. Volunteers from two of the five communities had their follow-up periods extended to 30 months and were invited to attend clinics 10–40 km (km) away from their communities. Human immunodeficiency virus counseling and testing was provided, and data on sexual behaviour collected at all study visits. Study completion was defined as completion of 18 or 30 months or visits up to the date of sero-conversion and dropout as missing one or more visits. Discrete time survival models were fitted to find factors independently associated with dropout.ResultsA total of 1000 volunteers (55 % men) were enrolled. Of these, 91.9 % completed 6 months, 85.2 % completed 12 months and 76.0 % completed 18 months of follow-up. In the two communities with additional follow-up, 76.9 % completed 30 months. In total 299 (29.9 %) volunteers missed at least one visit (dropped out). Dropout was independently associated with age (volunteers aged 13–24 being most likely to dropout), gender [men being more likely to dropout than women [adjusted hazard ratio (aHR) 1.4; 95 % confidence interval (CI) 1.1–1.8)], time spent in the fishing community (those who stayed <1 year being most likely to dropout), History of marijuana use (users being more likely to dropout than non-users [1.7; (1.2–2.5)], ethnicity (non-Baganda being more likely to dropout than Baganda [1.5; (1.2–1.9)], dropout varied between the five fishing communities, having a new sexual partner in the previous 3 months [1.3 (1.0–1.7)] and being away from home for ≥2 nights in the month preceding the interview [1.4 (1.1–1.8)].ConclusionDespite a substantial proportion dropping out, retention was sufficient to suggest that by incorporating retention strategies it will be possible to conduct HIV prevention efficacy trials in this community.Electronic supplementary materialThe online version of this article (doi:10.1186/s13104-015-1804-6) contains supplementary material, which is available to authorized users.

Prognostic scores for lower respiratory tract infections (LRTI) have been mainly derived in a hospital setting. The current authors have developed and validated a prediction rule for the prognosis of acute LRTI in elderly primary-care patients.Data including demographics, medication use, healthcare use and comorbid conditions from 3,166 episodes of patients aged o65 yrs visiting the general practitioner (GP) with LRTI were collected. Multiple logistic regression analysis was used to construct a predictive model. The main outcome measure was 30-day hospitalisation or death. The Second Dutch Survey of GPs was used for validation.The following were independent predictors of 30-day hospitalisation or death: increasing age; previous hospitalisation; heart failure; diabetes; use of oral glucocorticoids; previous use of antibiotics; a diagnosis of pneumonia; and exacerbation of chronic obstructive pulmonary disease. A prediction rule based on these variables showed that the outcome increased directly with increasing scores: 3, 10 and 31% for scores of ,2 points, 3-6 and o7 points, respectively. Corresponding figures for the validation cohort were 3, 11 and 26%, respectively. This simple prediction rule can help the primary-care physician to differentiate between highand low-risk patients. As a possible consequence, low-risk patients may be suitable for home treatment, whereas high-risk patients might be monitored more closely in a homecare or hospital setting. Future studies should assess whether information on signs and symptoms can further improve this prediction rule.