Social disorganization and history of child sexual abuse against girls in sub-Saharan Africa : a multilevel analysis

Background: Child sexual abuse (CSA) is a considerable public health problem. Less focus has been paid to the role of community level factors associated with CSA. The aim of this study was to examine the association between neighbourhood-level measures of social disorganization and CSA. Methods: We applied multiple multilevel logistic regression analysis on Demographic and Health Survey data for 6,351 adolescents from six countries in sub-Saharan Africa between 2006 and 2008. Results: The percentage of adolescents that had experienced CSA ranged from 1.04% to 5.84%. There was a significant variation in the odds of reporting CSA across the communities, suggesting 18% of the variation in CSA could be attributed to community level factors. Respondents currently employed were more likely to have reported CSA than those who were unemployed (odds ratio [OR] = 2.05, 95% confidence interval [CI] 1.48 to 2.83). Respondents from communities with a high family disruption rate were 57% more likely to have reported CSA (OR=1.57, 95% CI 1.14 to 2.16). Conclusion: We found that exposure to CSA was associated with high community level of family disruption, thus suggesting that neighbourhoods may indeed have significant important effects on exposure to CSA. Further studies are needed to explore pathways that connect the individual and neighbourhood levels, that is, means through which deleterious neighbourhood effects are transmitted to individuals

Thromboelastography results on citrated whole blood from clinically healthy cats depend on modes of activation

<p>Abstract</p> <p>Background</p> <p>During the last decade, thromboelastography (TEG) has gained increasing acceptance as a diagnostic test in veterinary medicine for evaluation of haemostasis in dogs, however the use of TEG in cats has to date only been described in one previous study and a few abstracts. The objective of the present study was to evaluate and compare three different TEG assays in healthy cats, in order to establish which assay may be best suited for TEG analyses in cats.</p> <p>Methods</p> <p>90 TEG analyses were performed on citrated whole blood samples from 15 clinically healthy cats using assays without activator (native) or with human recombinant tissue factor (TF) or kaolin as activators. Results for reaction time (R), clotting time (K), angle (α), maximum amplitude (MA) and clot lysis (LY30; LY60) were recorded.</p> <p>Results</p> <p>Coefficients of variation (CVs) were highest in the native assay and comparable in TF and kaolin activated assays. Significant differences were observed between native and kaolin assays for all measured parameters, between kaolin and TF for all measured parameters except LY60 and between native and TF assays for R and K.</p> <p>Conclusion</p> <p>The results indicate that TEG is a reproducible method for evaluation of haemostasis in clinically healthy cats. However, the three assays cannot be used interchangeably and the kaolin- and TF activated assays have the lowest analytical variation indicating that using an activator may be superior for performing TEG in cats.</p

Statistical process control of mortality series in the Australian and New Zealand Intensive Care Society (ANZICS) adult patient database: implications of the data generating process

for the ANZICS Centre for Outcome and Resource Evaluation (CORE) of the Australian and New Zealand Intensive Care Society (ANZICS)BACKGROUND Statistical process control (SPC), an industrial sphere initiative, has recently been applied in health care and public health surveillance. SPC methods assume independent observations and process autocorrelation has been associated with increase in false alarm frequency. METHODS Monthly mean raw mortality (at hospital discharge) time series, 1995–2009, at the individual Intensive Care unit (ICU) level, were generated from the Australia and New Zealand Intensive Care Society adult patient database. Evidence for series (i) autocorrelation and seasonality was demonstrated using (partial)-autocorrelation ((P)ACF) function displays and classical series decomposition and (ii) “in-control” status was sought using risk-adjusted (RA) exponentially weighted moving average (EWMA) control limits (3 sigma). Risk adjustment was achieved using a random coefficient (intercept as ICU site and slope as APACHE III score) logistic regression model, generating an expected mortality series. Application of time-series to an exemplar complete ICU series (1995-(end)2009) was via Box-Jenkins methodology: autoregressive moving average (ARMA) and (G)ARCH ((Generalised) Autoregressive Conditional Heteroscedasticity) models, the latter addressing volatility of the series variance. RESULTS The overall data set, 1995-2009, consisted of 491324 records from 137 ICU sites; average raw mortality was 14.07%; average(SD) raw and expected mortalities ranged from 0.012(0.113) and 0.013(0.045) to 0.296(0.457) and 0.278(0.247) respectively. For the raw mortality series: 71 sites had continuous data for assessment up to or beyond lag ₄₀ and 35% had autocorrelation through to lag ₄₀; and of 36 sites with continuous data for ≥ 72 months, all demonstrated marked seasonality. Similar numbers and percentages were seen with the expected series. Out-of-control signalling was evident for the raw mortality series with respect to RA-EWMA control limits; a seasonal ARMA model, with GARCH effects, displayed white-noise residuals which were in-control with respect to EWMA control limits and one-step prediction error limits (3SE). The expected series was modelled with a multiplicative seasonal autoregressive model. CONCLUSIONS The data generating process of monthly raw mortality series at the ICU level displayed autocorrelation, seasonality and volatility. False-positive signalling of the raw mortality series was evident with respect to RA-EWMA control limits. A time series approach using residual control charts resolved these issues.John L Moran, Patricia J Solomo

Decreased respiratory system compliance on the sixth day of mechanical ventilation is a predictor of death in patients with established acute lung injury

<p>Abstract</p> <p>Background</p> <p>Multiple studies have identified single variables or composite scores that help risk stratify patients at the time of acute lung injury (ALI) diagnosis. However, few studies have addressed the important question of how changes in pulmonary physiologic variables might predict mortality in patients during the subacute or chronic phases of ALI. We studied pulmonary physiologic variables, including respiratory system compliance, P/F ratio and oxygenation index, in a cohort of patients with ALI who survived more than 6 days of mechanical ventilation to see if changes in these variables were predictive of death and whether they are informative about the pathophysiology of subacute ALI.</p> <p>Methods</p> <p>Ninety-three patients with ALI who were mechanically ventilated for more than 6 days were enrolled in this prospective cohort study. Patients were enrolled at two medical centers in the US, a county hospital and a large academic center. Bivariate analyses were used to identify pulmonary physiologic predictors of death during the first 6 days of mechanical ventilation. Predictors on day 1, day 6 and the changes between day 1 and day 6 were compared in a multivariate logistic regression model.</p> <p>Results</p> <p>The overall mortality was 35%. In multivariate analysis, the PaO₂/FiO₂(OR 2.09, p < 0.04) and respiratory system compliance (OR 3.61, p < 0.01) were predictive of death on the 6^thday of acute lung injury. In addition, a decrease in respiratory system compliance between days 1 and days 6 (OR 2.14, p < 0.01) was independently associated with mortality.</p> <p>Conclusions</p> <p>A low respiratory system compliance on day 6 or a decrease in the respiratory system compliance between the 1^stand 6^thday of mechanical ventilation were associated with increased mortality in multivariate analysis of this cohort of patients with ALI. We suggest that decreased respiratory system compliance may identify a subset of patients who have persistent pulmonary edema, atelectasis or the fibroproliferative sequelae of ALI and thus are less likely to survive their hospitalization.</p

Deathly Drool: Evolutionary and Ecological Basis of Septic Bacteria in Komodo Dragon Mouths

Komodo dragons, the world's largest lizard, dispatch their large ungulate prey by biting and tearing flesh. If a prey escapes, oral bacteria inoculated into the wound reputedly induce a sepsis that augments later prey capture by the same or other lizards. However, the ecological and evolutionary basis of sepsis in Komodo prey acquisition is controversial. Two models have been proposed. The “bacteria as venom” model postulates that the oral flora directly benefits the lizard in prey capture irrespective of any benefit to the bacteria. The “passive acquisition” model is that the oral flora of lizards reflects the bacteria found in carrion and sick prey, with no relevance to the ability to induce sepsis in subsequent prey. A third model is proposed and analyzed here, the “lizard-lizard epidemic” model. In this model, bacteria are spread indirectly from one lizard mouth to another. Prey escaping an initial attack act as vectors in infecting new lizards. This model requires specific life history characteristics and ways to refute the model based on these characteristics are proposed and tested. Dragon life histories (some details of which are reported here) prove remarkably consistent with the model, especially that multiple, unrelated lizards feed communally on large carcasses and that escaping, wounded prey are ultimately fed on by other lizards. The identities and evolutionary histories of bacteria in the oral flora may yield the most useful additional insights for further testing the epidemic model and can now be obtained with new technologies

The Incidence of Molluscum contagiosum among American Indians and Alaska Natives

The epidemiology of Molluscum contagiosum (MC) in the United States is largely unknown, despite the fact that the virus is directly communicable and large outbreaks occur. This study provides population-based estimates to describe the epidemiology of MC in the United States among American Indian and Alaska Native (AI/AN) persons. This population was selected because of the comprehensiveness and quality of available data describing utilization of out-patient services.Outpatient visits listing MC as a diagnosis in the Indian Health Service National Patient Information Reporting System during 2001-2005 were analyzed to assess patient characteristics, visit frequency and concurrent skin conditions. Outpatient visit rates and incidence rates were calculated based on known population denominators (retrospective cohort). Overall outpatient visit rates were also calculated for the general US population using national data. The average annual rate of MC-associated outpatient visits was 20.15/10,000 AI/AN persons for 2001-2005 (13,711 total visits), which was similar to the rate for the general US population (22.0/10,000 [95% CI: 16.9-27.1]). The incidence of MC-associated visits was 15.34/10,000. AI/AN children 1-4 years old had the highest incidence (77.12), more than twice that for children 5-14 years old (30.79); the incidence for infants (<1 year) was higher than that for adults. AI/AN persons living in the West region had the highest incidence, followed by those in the East and Alaska regions (26.96, 22.88 and 21.38, respectively). There were age-specific associations between MC and concurrent skin conditions (e.g., atopic dermatitis, eczema).This study highlights the need for periodic population-based measurements to assess trends in incidence and healthcare utilization for MC in the United States. High rates of MC were found among AI/AN persons, especially among children <15 years old. The AI/AN population would benefit from greater availability of effective strategies for prevention and treatment of MCV infection

Description and validation of a Markov model of survival for individuals free of cardiovascular disease that uses Framingham risk factors

BACKGROUND: Estimation of cardiovascular disease risk is increasingly used to inform decisions on interventions, such as the use of antihypertensives and statins, or to communicate the risks of smoking. Crude 10-year cardiovascular disease risk risks may not give a realistic view of the likely impact of an intervention over a lifetime and will underestimate of the risks of smoking. A validated model of survival to act as a decision aid in the consultation may help to address these problems. This study aims to describe the development of such a model for use with people free of cardiovascular disease and evaluates its accuracy against data from a United Kingdom cohort. METHODS: A Markov cycle tree evaluated using cohort simulation was developed utilizing Framingham estimates of cardiovascular risk, 1998 United Kingdom mortality data, the relative risk for smoking related non-cardiovascular disease risk and changes in systolic blood pressure and serum total cholesterol total cholesterol with age. The model's estimates of survival at 20 years for 1391 members of the Whickham survey cohort between the ages of 35 and 65 were compared with the observed survival at 20-year follow-up. RESULTS: The model estimate for survival was 75% and the observed survival was 75.4%. The correlation between estimated and observed survival was 0.933 over 39 subgroups of the cohort stratified by estimated survival, 0.992 for the seven 5-year age bands from 35 to 64, 0.936 for the ten 10 mmHg systolic blood pressure bands between 100 mmHg and 200 mmHg, and 0.693 for the fifteen 0.5 mmol/l total cholesterol bands between 3.0 and 10.0 mmol/l. The model significantly underestimated mortality in those people with a systolic blood pressure greater than or equal to 180 mmHg (p = 0.006). The average gain in life expectancy from the elimination of cardiovascular disease risk as a cause of death was 4.0 years for all the 35 year-old men in the sample (n = 24), and 1.8 years for all the 35 year-old women in the sample (n = 32). CONCLUSIONS: This model accurately estimates 20-year survival in subjects from the Whickham cohort with a systolic blood pressure below 180 mmHg

Use of deep learning to develop continuous-risk models for adverse event prediction from electronic health records

Early prediction of patient outcomes is important for targeting preventive care. This protocol describes a practical workflow for developing deep-learning risk models that can predict various clinical and operational outcomes from structured electronic health record (EHR) data. The protocol comprises five main stages: formal problem definition, data pre-processing, architecture selection, calibration and uncertainty, and generalizability evaluation. We have applied the workflow to four endpoints (acute kidney injury, mortality, length of stay and 30-day hospital readmission). The workflow can enable continuous (e.g., triggered every 6 h) and static (e.g., triggered at 24 h after admission) predictions. We also provide an open-source codebase that illustrates some key principles in EHR modeling. This protocol can be used by interdisciplinary teams with programming and clinical expertise to build deep-learning prediction models with alternate data sources and prediction tasks