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We show that the determination of a minimum cut-set of odd cardinality in a graph with even and odd vertices can be dealt with by a minor modification of the polynomially bounded algorithm of Gomory and Hu for multi-terminal networks. We connect this problem to the problem of identifying a matching (or blossom) constraint that chops off a point which is not contained in the convex hull of matchings or proving that no such inequality exists. Both the b-matching problems without and with upper bounds are considered. We discuss how the results of this paper can be used in conjunction with commercial LP packages lo solve b-matching problems.

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ObjectivesTo compare the effects of the Rajiv Aarogyasri Health Insurance Scheme of Andhra Pradesh (AP) with health financing innovations including the Rashtriya Swasthya Bima Yojana (RSBY) in Maharashtra (MH) over time on access to and out-of-pocket expenditure (OOPE) on hospital inpatient care.Study designA difference-in-differences (DID) study using repeated cross-sectional surveys with parallel control.SettingNational Sample Survey Organisation of India (NSSO) urban and rural ‘first stratum units’, 863 in AP and 1008 in MH.MethodsWe used two cross-sectional surveys: as a baseline, the data from the NSSO 2004 survey collected before the Aarogyasri and RSBY schemes were launched; and as postintervention, a survey using the same methodology conducted in 2012.Participants8623 households in AP and 10 073 in MH.Main outcome measuresAverage OOPE, large OOPE and large borrowing per household per year for inpatient care, hospitalisation rate per 1000 population per year.ResultsAverage expenditure, large expenditures and large borrowings on inpatient care had increased in MH and AP, but the increase was smaller in AP across these three measures. DIDs for average expenditure and large borrowings were significant and in favour of AP for the rural and the poorest households. Hospitalisation rates also increased in both states but more so in AP, although the DID was not significant and the subgroup analysis presented a mixed picture.ConclusionsHealth innovations in AP had a greater beneficial effect on inpatient care-related expenditures than innovations in MH. The Aarogyasri scheme is likely to have contributed to these impacts in AP, at least in part. However, OOPE increased in both states over time. Schemes such as the Aarogyasri and RSBY may result in some positive outcomes, but additional interventions may be required to improve access to care for the most vulnerable sections of the population.

dedicated to the memory of ray fulkerson, who planted the seeds of this research twenty five years ago, in a graduate course at cornell university A 0, 1 matrix is balanced if it does not contain a square submatrix of odd order with two ones per row and per column. We show that a balanced 0, 1 matrix is either totally unimodular or its bipartite representation has a cutset consisting of two adjacent nodes and some of their neighbors. This result yields a polytime recognition algorithm for balancedness. To prove the result, we first prove a decomposition theorem for balanced 0, 1 matrices that are not strongly balanced. 1999Academic Press

This chapter provides an overview of the role of primary care in the context of global health. Universal health coverage is a key priority for WHO and its member states, and provision of accessible and safe primary care is recognised as essential to meet this important international policy goal. Nevertheless, more than three decades after Alma Ata, the provision of primary health care remains inadequate, indicating that primary care has not received the priority it deserves, in many parts of the world. This is despite the proven health benefits that result from access to comprehensive primary health care. We highlight some examples of good practice and discuss the relevance of primary care in the context of health equity and cost-effectiveness. Challenges that influence the success of primary care include the availability of a qualified workforce, financing and system design and quality assurance and patient safety.

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Applying Fishburn's [4] conditions for convex stochastic dominance, exact linear programming algorithms are proposed and implemented for assigning discrete return distributions into the first-and second-order stochastic dominance optimal sets. For third-order stochastic dominance, a superconvex stochastic dominance approach is defined which allows classification of choice elements into superdominated, mixed, and superoptimal sets. For a choice set of 896 security returns treated previously in the literature, 454, 25, and 13 distributions are in the first-, second-, and third-order convex stochastic dominance optimal sets, respectively. These optimal sets compare with admissible first-, second-, and third-order stochastic dominance sets of 682, 35, and 19 distributions, respectively.The applicability of superconvex stochastic dominance for continuous distributions defined over a bounded interval is then shown. The difficulties in identifying the elements of the superdominated set for distributions defined over the entire real line are demonstrated in the determination of the dominated choices for a set of normally distributed mutual fund returns previously examined by Meyer [9]. Specifically, we find that the dominated set determined by Meyer is too large. FINANCIAL DECISION MAKING UNDER uncertainty boils down to the following two elements:(i) characterization of the choice set of securities by a joint probability distribution of returns, and (ii) a preference ordering that ranks the above alternatives by a utility function defined over the probability distribution characterizing the choice set. Simply stated, decision making under uncertainty may be viewed as ranking alternative probability distributions of returns, based on a consistent set of preferences. A significant breakthrough for decision making was the development of the von Neumann-Morgenstern expected utility paradigm. This choice criteria posits that the decision maker chooses the strategy which maximizes the expected