Variables used in analysis of determinants of OOPS for OPD (means/proportions).

<p>Variables used in analysis of determinants of OOPS for OPD (means/proportions).</p

Switch to second provider.

<p>Switch to second provider.</p

Key indicators of survey states.

<p>Key indicators of survey states.</p

Selection of first provider, reasons for selection and mean distance travelled.

<p>Selection of first provider, reasons for selection and mean distance travelled.</p

Expenditure quintile wise variation in the share (%) of OPD in total consumption expenditure.

<p>Expenditure quintile wise variation in the share (%) of OPD in total consumption expenditure.</p

OPD rates and out-of-pocket expenditures.

<p>OPD rates and out-of-pocket expenditures.</p

Sample characteristics of individuals.

<p>Sample characteristics of individuals.</p

Determinants of out-of-pocket expenditure for out-patient care in India (Standard errors in parenthesis) (Dependent variable: log of out-of-pocket expenditure).

<p>Determinants of out-of-pocket expenditure for out-patient care in India (Standard errors in parenthesis) (Dependent variable: log of out-of-pocket expenditure).</p

Real-Time Quantitative PCR Measurements of Fecal Indicator Bacteria and Human-Associated Source Tracking Markers in a Texas River following Hurricane Harvey

Hurricane Harvey has caused unprecedented devastation to huge parts of southeastern Texas, particularly damaging the wastewater infrastructure resulting in release of sewage contamination into environmental waters. The purpose of this study was to conduct a preliminary assessment of fecal indicator bacteria (<i>Escherichia coli</i> and enterococci) and human-associated fecal genetic markers (human-associated Bacteroidales), measured using qPCR assays, across a Texas river impacted by Hurricane Harvey. Water samples were collected along the Guadalupe River during September–December 2017. The most heavily flooded sites showed the highest abundance of fecal indicator bacteria and human-associated Bacteroidales markers, indicating that a large number of sewage overflows and stormwater runoff occurred during Harvey flooding. These findings suggest that high levels of human fecal contamination were introduced into waterways draining into the Gulf of Mexico and impaired surface water quality. The human-associated Bacteroidales markers exhibited a low to slightly strong correlation with conventional fecal indicators, suggesting the variable occurrence of different markers and uncertainty of enterococci and <i>E. coli</i> for detection of human fecal pollution. In general, results of this initial microbiological contaminant assessment will serve as baseline information for follow-on studies to monitor existing and emerging public health risks to residents of Texas and potential long-term environmental impacts on the water resources in the impacted regions

Air quality mapping using GIS and economic evaluation of health impact for Mumbai City, India

<p>Mumbai, a highly populated city in India, has been selected for air quality mapping and assessment of health impact using monitored air quality data. Air quality monitoring networks in Mumbai are operated by National Environment Engineering Research Institute (NEERI), Maharashtra Pollution Control Board (MPCB), and Brihanmumbai Municipal Corporation (BMC). A monitoring station represents air quality at a particular location, while we need spatial variation for air quality management. Here, air quality monitored data of NEERI and BMC were spatially interpolated using various inbuilt interpolation techniques of ArcGIS. Inverse distance weighting (IDW), Kriging (spherical and Gaussian), and spline techniques have been applied for spatial interpolation for this study. The interpolated results of air pollutants sulfur dioxide (SO₂), nitrogen dioxide (NO₂) and suspended particulate matter (SPM) were compared with air quality data of MPCB in the same region. Comparison of results showed good agreement for predicted values using IDW and Kriging with observed data. Subsequently, health impact assessment of a ward was carried out based on total population of the ward and air quality monitored data within the ward. Finally, health cost within a ward was estimated on the basis of exposed population. This study helps to estimate the valuation of health damage due to air pollution.</p> <p><i>Implications</i>: Operating more air quality monitoring stations for measurement of air quality is highly resource intensive in terms of time and cost. The appropriate spatial interpolation techniques can be used to estimate concentration where air quality monitoring stations are not available. Further, health impact assessment for the population of the city and estimation of economic cost of health damage due to ambient air quality can help to make rational control strategies for environmental management. The total health cost for Mumbai city for the year 2012, with a population of 12.4 million, was estimated as USD8000 million.</p