27 research outputs found

    Treatment adherence among sputum smear-positive pulmonary tuberculosis patients in mountainous areas in China

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    <p>Abstract</p> <p>Background</p> <p>We carried out an investigation in five provinces in China to assess treatment adherence and identify factors associated with insufficient treatment adherence in tuberculosis (TB) patients in mountainous, rural areas of China.</p> <p>Methods</p> <p>In each of the five provinces, all counties with > 80% mountainous area were stratified into three groups according to their gross domestic product. In each stratum, one county was randomly sampled. Study subjects were sampled from all smear positive TB cases registered in 2007 in the target counties. TB patients, village doctors, county doctors and directors of the TB prevention and control institutes were interviewed. Insufficient medication adherence was defined as taking less than 90% of anti-TB drug doses prescribed. Insufficient re-examination adherence was defined as having less than the recommended three sputum smear examinations during the treatment course.</p> <p>Results</p> <p>A minority of patients took drugs under direct observation: on average 29% during the intensive phase of treatment. In total, 524 TB patients were included, of whom 49 (9.4%) took less than 90% of all doses prescribed and 92 (17.6%) did not have all sputum smear examinations, with substantial variations between the provinces. In multivariable analysis, no direct observation of treatment during the intensive phase and the presence of adverse events were associated both with insufficient medication adherence and insufficient re-examination adherence. Overall, 79% of patients were adherent both to treatment and re-examinations.</p> <p>Conclusions</p> <p>In these remote and poor areas of China, the TB control program is not fully functioning according to the guidelines. The majority of patients are not treated under direct observation, while direct observation by health care staff was associated with better adherence, both to drug therapy and re-examinations. Insufficient adherence increases the risk of unsuccessful treatment outcomes and development of drug resistance. Measures should be taken urgently in these areas to strengthen implementation of the international Stop TB strategy.</p

    Evolution of contribution timing in public goods games

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    Indirect reciprocity with Bayesian reasoning and biases.

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    Reputations can foster cooperation by indirect reciprocity: if I am good to you then others will be good to me. But this mechanism for cooperation in one-shot interactions only works when people agree on who is good and who is bad. Errors in actions or assessments can produce disagreements about reputations, which can unravel the positive feedback loop between social standing and pro-social behaviour. Cooperators can end up punished and defectors rewarded. Public reputation systems and empathy are two possible mechanisms to promote agreement about reputations. Here we suggest an alternative: Bayesian reasoning by observers. By taking into account the probabilities of errors in action and observation and their prior beliefs about the prevalence of good people in the population, observers can use Bayesian reasoning to determine whether or not someone is good. To study this scenario, we develop an evolutionary game theoretical model in which players use Bayesian reasoning to assess reputations, either publicly or privately. We explore this model analytically and numerically for five social norms (Scoring, Shunning, Simple Standing, Staying, and Stern Judging). We systematically compare results to the case when agents do not use reasoning in determining reputations. We find that Bayesian reasoning reduces cooperation relative to non-reasoning, except in the case of the Scoring norm. Under Scoring, Bayesian reasoning can promote coexistence of three strategic types. Additionally, we study the effects of optimistic or pessimistic biases in individual beliefs about the degree of cooperation in the population. We find that optimism generally undermines cooperation whereas pessimism can, in some cases, promote cooperation

    Bifurcation diagrams for optimism (A) and pessimism (B) biases under Simple Standing.

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    Here AllC strategists are excluded and thus 1 − z = y. Violet curves are stable equilibria and orange curves are unstable.</p

    Ternary plots for Scoring under biases, 25% optimism (A) and 25% pessimism (B).

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    Stable, semistable, and unstable equilibria are again plotted in blue, purple, and red, respectively. Circles are singular equilibria, and lines sets of them. These biases have shifted the internal equilibria relative to Fig 1A.</p

    Ternary plots for Simple Standing, Staying, and Stern Judging under private assessment.

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    Stable and unstable equilibria are plotted in blue and red, respectively. Circles are singular equilibria and lines sets of them. A: Simple Standing gives qualitatively similar results to Public assessment. The system either evolves to AllD or a point on the interior of the AllD-Disc boundary. For this panel, e1 = e2 = 0.05 so that the plotting of the equilibria are clear. For e1 = e2 = 0.01, the interior stable equilibrium is nearly on the Disc vertex. B: Under Staying, the AllC-Disc and AllD-Disc boundaries are sets of equilibria. Further, at z* = 1, any reputation is an equilibria. Thus, the trajectory towards z* = 1 will determine reputations at it. C: Under Stern Judging, AllD is globally asymptotically stable. r = 3 for all figures, and e1 = e2 = 0.01 for panels B and C.</p

    Ternary plots for Simple Standing, Staying, and Stern Judging under private assessment of reputations with bias λ = 0.25.

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    The benefit to cost ratio is r = 3 and the error rates are e1 = e2 = 0.01. Bias has no great impact on the qualitative outcome, except that private Staying no longer has unstable equilibria on two boundaries. For optimism bias and Staying, the plot is qualitatively similar to public assessment of reputations. Negative bias results in a stable equilibrium along the AllC-Disc boundary. (PDF)</p
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