URBANIZATION, POPULATION PRESSURE AND AGRICULTURAL INTENSIFICATION: EVIDENCES FROM TAMIL NADU IN INDIA

This paper analyzes the phenomenon that the transfer of agricultural land and water resources for urban use with adverse consequences to agricultural production has not been adequately addressed in the Indian context, through a case study of the state of Tamil Nadu which stands in the forefront of industrialization and urbanization. Results show that urbanization and land put to non-agricultural uses have strong negative impact on agricultural sector. Therefore, it is important to give priority for decentralized growth through rural industrialization, and public investment on road network should be increased not only to reduce the growth of urbanization but also to promote rural industrialization. The impact of industrialization especially around a few large cities is undesirable both from the point of view of balanced regional development and also from the viewpoint of the serious negative impacts of the growth of large urban centres on the neighbouring rural economies

Evaluating Potential Changes in Price Reporting Accuracy

Non-cash-market transactions for fed cattle have increased. Price discovery depends in part on the accuracy of reported cash market prices. Cattlemen and others have expressed concern that as non-cash-market transactions increase, reported cash market prices may no longer accurately reflect supply-demand conditions. Equations based on Chebyschev's inequality are used in conjunction with experimental market data from the Fed Cattle Market Simulator to explore relationships related to price reporting accuracy for several subpopulations of prices versus the known population. Price means and variances and distribution of prices were invariant to number of transaction prices. Mean prices and variance of prices also were invariant to number of observations. Only when the reduction in prices reached 80% was there a significant relationship between number of observations and two pairs of variables, i.e., reported price precision and confidence of a given level of precision. With the exception of the smallest reduction in transactions, no differences were found between the subpopulations and population for reported price precision versus probability of a given level of precision, for reported price precision versus estimated number of observations with a given degree of confidence, and for probability of a given level of precision versus estimated number of observations with a given level of precision. Results suggest the possibility that number of non-reported fed cattle transaction prices could increase significantly before the industry faces serious concerns regarding the accuracy of reported prices certis paribus

Validation of Operational Definition to Identify Patients with Osteoporotic Hip Fractures in Administrative Claims Data

As incidences of osteoporotic hip fractures (OHFs) have increased, identifying OHFs has become important to establishing the medical guidelines for their management. This study was conducted to develop an operational definition to identify patients with OHFs using two diagnosis codes and eight procedure codes from health insurance claims data and to assess the operational definition’s validity through a chart review. The study extracted data on OHFs from 522 patients who underwent hip surgeries based on diagnosis codes. Orthopedic surgeons then reviewed these patients’ medical records and radiographs to identify those with true OHFs. The validities of nine different algorithms of operational definitions, developed using a combination of three levels of diagnosis codes and eight procedure codes, were assessed using various statistics. The developed operational definition showed an accuracy above 0.97 and an area under the receiver operating characteristic curve above 0.97, indicating excellent discriminative power. This study demonstrated that the operational definition that combines diagnosis and procedure codes shows a high validity in detecting OHFs and can be used as a valid tool to detect OHFs from big health claims data