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BackgroundBangladesh has a high proportion of households incurring catastrophic health expenditure, and very limited risk sharing mechanisms. Identifying determinants of out-of-pocket (OOP) payments and catastrophic health expenditure may reveal opportunities to reduce costs and protect households from financial risk.ObjectiveThis study investigates the determinants of high healthcare expenditure and healthcare- related financial catastrophe.MethodsA cross-sectional household survey was conducted in Rajshahi city, Bangladesh, in 2011. Catastrophic health expenditure was estimated separately based on capacity to pay and proportion of non-food expenditure. Determinants of OOP payments and financial catastrophe were estimated using double hurdle and Poisson regression models respectively.ResultsOn average households spent 11% of their total budgets on health, half the residents spent 7% of the monthly per capita consumption expenditure for one illness, and nearly 9% of households faced financial catastrophe. The poorest households spent less on health but had a four times higher risk of catastrophe than the richest households. The risk of financial catastrophe and the level of OOP payments were higher for users of inpatient, outpatient public and private facilities respectively compared to using self-medication or traditional healers. Other determinants of OOP payments and catastrophic expenses were economic status, presence of chronic illness in the household, and illness among children and adults.ConclusionHouseholds that received inpatient or outpatient private care experienced the highest burden of health expenditure. The poorest members of the community also face large, often catastrophic expenses. Chronic illness management is crucial to reducing the total burden of disease in a household and its associated increased risk of level of OOP payments and catastrophic expenses. Households can only be protected from these situations by reducing the health system's dependency on OOP payments and providing more financial risk protection.

BackgroundChronic arsenic exposure has been shown to cause liver damage. However, serum hepatic enzyme activity as recognized on liver function tests (LFTs) showing a dose-response relationship with arsenic exposure has not yet been clearly documented. The aim of our study was to investigate the dose-response relationship between arsenic exposure and major serum enzyme marker activity associated with LFTs in the population living in arsenic-endemic areas in Bangladesh.MethodsA total of 200 residents living in arsenic-endemic areas in Bangladesh were selected as study subjects. Arsenic concentrations in the drinking water, hair and nails were measured by Inductively Coupled Plasma Mass Spectroscopy (ICP-MS). The study subjects were stratified into quartile groups as follows, based on concentrations of arsenic in the drinking water, as well as in subjects' hair and nails: lowest, low, medium and high. The serum hepatic enzyme activities of alkaline phosphatase (ALP), aspartate transaminase (AST) and alanine transaminase (ALT) were then assayed.ResultsArsenic concentrations in the subjects' hair and nails were positively correlated with arsenic levels in the drinking water. As regards the exposure-response relationship with arsenic in the drinking water, the respective activities of ALP, AST and ALT were found to be significantly increased in the high-exposure groups compared to the lowest-exposure groups before and after adjustments were made for different covariates. With internal exposure markers (arsenic in hair and nails), the ALP, AST and ALT activity profiles assumed a similar shape of dose-response relationship, with very few differences seen in the higher groups compared to the lowest group, most likely due to the temporalities of exposure metrics.ConclusionsThe present study demonstrated that arsenic concentrations in the drinking water were strongly correlated with arsenic concentrations in the subjects' hair and nails. Further, this study revealed a novel exposure- and dose- response relationship between arsenic exposure metrics and serum hepatic enzyme activity. Elevated serum hepatic enzyme activities in the higher exposure gradients provided new insights into arsenic-induced liver toxicity that might be helpful for the early prognosis of arsenic-induced liver diseases.

Not all vital signs are useful in the prediction of clinical outcomes. Vital signs had high specificity but very low sensitivity as predictors of clinical outcomes. Clinicians should always remember to treat patients and not numbers.

BACKGROUND: This study was undertaken to validate the use of the modified early warning score (MEWS) as a predictor of patient mortality and intensive care unit (ICU)/ high dependency (HD) admission in an Asian population. METHODS:The MEWS was applied to a retrospective cohort of 1 024 critically ill patients presenting to a large Asian tertiary emergency department (ED) between November 2006 and December 2007. Individual MEWS was calculated based on vital signs parameters on arrival at ED. Outcomes of mortality and ICU/HD admission were obtained from hospital records. The ability of the composite MEWS and its individual components to predict mortality within 30 days from ED visit was assessed. Sensitivity, specificity, positive and negative predictive values were derived and compared with values from other cohorts. A MEWS of ≥4 was chosen as the cut-off value for poor prognosis based on previous studies. RESULTS:A total of 311 (30.4%) critically ill patients were presented with a MEWS ≥4. Their mean age was 61.4 years (SD 18.1) with a male to female ratio of 1.10. Of the 311 patients, 53 (17%) died within 30 days, 64 (20.6%) were admitted to ICU and 86 (27.7%) were admitted to HD. The area under the receiver operating characteristic curve was 0.71 with a sensitivity of 53.0% and a specifi city of 72.1% in addition to a positive predictive value (PPV) of 17.0% and a negative predictive value (NPV) of 93.4% (MEWS cut-off of ≥4) for predicting mortality. CONCLUSION:The composite MEWS did not perform well in predicting poor patient outcomes for critically ill patients presenting to an ED.

Objectives: This study aimed to determine if a deployment strategy based on geospatial-time analysis is able to reduce ambulance response times for out-of-hospital cardiac arrests (OOHCA) in an urban emergency medical services (EMS) system.Methods: An observational prospective study examining geographic locations of all OOHCA in Singapore was conducted. Locations of cardiac arrests were spot-mapped using a geographic information system (GIS). A progressive strategy of satellite ambulance deployment was implemented, increasing ambulance bases from 17 to 32 locations. Variation in ambulance deployment according to demand, based on time of day, was also implemented. The total number of ambulances and crews remained constant over the study period. The main outcome measure was ambulance response times.Results: From October 1, 2001, to October 14, 2004, a total of 2,428 OOHCA patients were enrolled into the study. Mean ± SD age for arrests was 60.6 ± 19.3 years with 68.0% male. The overall return of spontaneous circulation (ROSC) rate was 17.2% and survival to discharge rate was 1.6%. Response time decreased significantly as the number of fire stations ⁄ fire posts increased (Pearson v 2 = 108.70, df = 48, p < 0.001). Response times for OOHCA decreased from a monthly median of 10.1 minutes at the beginning to 7.1 minutes at the end of the study. Similarly, the proportion of cases with response times < 8 minutes increased from 22.3% to 47.3% and < 11 minutes from 57.6% to 77.5% at the end of the study. Conclusions:A simple, relatively low-cost ambulance deployment strategy was associated with significantly reduced response times for OOHCA. Geospatial-time analysis can be a useful tool for EMS providers.ACADEMIC EMERGENCY MEDICINE 2010; 17:951-957 ª 2010 by the Society for Academic Emergency MedicineKeywords: geographic information systems, heart arrest, emergency medical services G eographic information systems (GIS) are computer-based systems for the integration and analysis of geographic data. GIS is a multilayering mapping software that is able to portray multiple geographic-time information in an easy-to-read, graphical manner. ''Geographic data'' are spatial data that result from observation and measurement of phenomena referenced to their locations on the earth's surface.

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BackgroundFinancial risk protection and equity are major components of universal health coverage (UHC), which is defined as ensuring access to health services for all citizens without any undue financial burden. We investigated progress towards UHC financial risk indicators and assessed variability of inequalities in financial risk protection indicators by wealth quintile. We further examined the determinants of different financial hardship indicators related to healthcare costs.MethodsA cross-sectional, three-stage probability survey was conducted in Bangladesh, which collected information from 1600 households from August to November 2011. Catastrophic health payments, impoverishment, and distress financing (borrowing or selling assets) were treated as financial hardship indicators in UHC. Poisson regression models were used to identify the determinants of catastrophic payment, impoverishment and distress financing separately. Slope, relative and concentration indices of inequalities were used to assess wealth-based inequalities in financial hardship indicators.ResultsThe study found that around 9% of households incurred catastrophic payments, 7% faced distress financing, and 6% experienced impoverishing health payments in Bangladesh. Slope index of inequality indicated that the incidence of catastrophic health payment and distress financing among the richest households were 12 and 9 percentage points lower than the poorest households respectively. Multivariable Poisson regression models revealed that all UHC financial hardship indicators were significantly higher among household that had members who received inpatient care or were in the poorest quintile. The presence of a member with chronic illness in a household increased the risk of impoverishment by nearly double.ConclusionThis study identified a greater inequality in UHC financial hardship indicators. Rich households in Bangladesh were facing disproportionately less financial hardship than the poor ones. Households can be protected from financial hardship associated with healthcare costs by implementing risk pooling mechanism, increasing GDP spending on health, and properly monitoring subsidized programs in public health facilities.Electronic supplementary materialThe online version of this article (doi:10.1186/s12939-017-0556-4) contains supplementary material, which is available to authorized users.