Predictor's Factors of Mortality of Patients Suffering From Severe Head Injury in Emergency Department at General Hospital Tugurejo Semarang

This research is a descriptive correlation study with cross sectional design to search the relation factors affecting mortality among severe head injury patients. Data of the study was collected from medical record of patients suffering from severe head injury who admitted at energency department of Tugurejo general hospital Semarang during Nopember 2010 until Oktober 2011. From 57 respondents, 19 people passed away during obtaining treatment at emergency department. There were three factors related to mortality of severe head injury patients. Theese were blood pressure on emergency department (ED) admission (p = 0,000), GCS on admission (p = 0,000), and Injury Severity Score (ISS) (p = 0,000). The logistic regression biner resulted that there were no dominant factors related to mortality of severe head injury patients (p>0,05). The odd ration of blood pressure variable is the hightes (0,688) compare to other variables such as GCS (OR = 0,281) and ISS (0,594)

Ep25-335-23 It’s not TB but what could it be? Abnormalities on chest X-rays from the 2016 Kenya National Tuberculosis Prevalence Survey

Background: The prevalence of diseases other than tuberculosis(TB) detected on chest-Xray(CXR) during TB screening in Kenya is unknown. Our study aimed to characterise and quantify non-TB abnormalities on CXR and to compare radiologist interpretation with Computer-Aided Detection for Tuberculosis (CAD4TB). We hypothesized that non-TB abnormalities requiring further clinical input are prevalent and may be missed using CAD4TB. Design/Methods: We undertook a cross-sectional study from May 2019-February 2020, analyzing CXRs from the 2016 Kenya National TB Prevalence Survey, sam- pling films classified either as “abnormal, suggestive of TB” or “abnormal other”. We developed a reporting tool which comprised four anatomical categories and a list of common diagnoses. Readers were blinded, films double reported and discordant results resolved by a third reader. We used CAD4TB 6.0. and R v3.6.2. for analysis. Results: Of 1123 films sampled, 600(53.4%) were ab- normal (Figure-1). Prevalence of abnormalities in major categories: 26.3% (95% CI 23.7%-28.9%) heart and/ or great vessels, 26.1% (95% CI23.5%-28.8%) lung parenchyma, 7.6% (95% CI 6.1%-9.3%) pleura and 3% (95% CI 2.1%-4.2%) mediastinum. Prevalence of active-TB 4% (95% CI 2%-4%), severe post TB lung changes (bronchiectasis/destroyed lung) 2% (95% CI 0-2%). Non-TB related diagnoses: cardiomegaly 23.1% (95% CI 20.6%-25.6%), suspected cardiac failure 1.9% (95% CI1.2-2.8%), non-specific airspace opacification/ interstitial disease 6% (95% CI 4%-8%), suspected emphysema 2% (95% CI 2%-4%) and mediastinal masses 0.8% (95% CI 0.4%-1.5%). Median CAD4TB scores: Severe post TB lung changes 76 (IQR 71-81), active-TB 66 (IQR 55-72), suspected emphysema 57 (IQR 54-59), non-specific airspace opacification/interstitial disease 56(IQR 50-61), mediastinal mass 52 (IQR 47-59) and cardiomegaly 50(IQR 46-56). Conclusions: Abnormalities unrelated to TB were prev- alent, most notably cardiomegaly. These non-TB ab- normalities will go undetected using CAD stratification based on threshold scores alone. Further refinement of CAD algorithms to include non-TB diagnoses could attenuate this risk. Incorporation of blood pressure monitoring and spirometry should be considered in TB screening activities

Forecasting societies' adaptive capacities through a demographic metabolism model

In seeking to understand how future societies will be affected by climate change we cannot simply assume they will be identical to those of today, because climate and societies are both dynamic. Here we propose that the concept of demographic metabolism and the associated methods of multi-dimensional population projections provide an effective analytical toolbox to forecast important aspects of societal change that affect adaptive capacity. We present an example of how the changing educational composition of future populations can influence societies' adaptive capacity. Multi-dimensional population projections form the human core of the Shared Socioeconomic Pathways scenarios, and knowledge and analytical tools from demography have great value in assessing the likely implications of climate change on future human well-being