• 2018-07
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  • Inhaled corticosteroids long acting agonists and long acting


    Inhaled corticosteroids, long-acting β-agonists, and long-acting muscarinic agonists are all expensive drugs and many patients in China cannot afford them. An alternative pharmacological treatment for COPD, phosphodiesterase-4 inhibitor, is awaiting approval by the China State Food and Drug Administration. In our 2008 study, only 4·1% of stable patients with COPD took theophylline, and did so irregularly. Despite the fact that this study was done 5 years ago, the present situation is still not optimistic. Although most people living in rural areas have joined the New Rural Co-operative Medical System of China, the proportion of reimbursement is quite low. For example, the reimbursement for admission to hospital is only 30% for tertiary hospitals and 40% for secondary hospitals in many regions. Thus, only patients with a high standard of living and a high level of education can follow the guidelines. We should have our own guidelines with patient stratification, and assessment of theophylline, carbocysteine, and other alternative drugs to make them more applicable in China. A basic version of treatment for COPD could be introduced (and would exist alongside the standard treatments), which would enable more general doctors to use available methods of diagnosis and treatment, and give patients in rural or remote areas the opportunity to have affordable and appropriate treatment.
    Across all countries, costs of care are increasing, and so increased prevention of disease is widely thought to mean reduced costs in the future. Although this assumption might be true for the disease being prevented, increased prevention will mean that populations live longer (a good thing of course), and as a result adopt different . As dna ligase profiles change, demand for health care increases and changes—but what does this mean for those committees and individuals involved in service delivery planning and policy making? Issues raised by these processes could be especially important the more constrained resources become, particularly in countries of low and middle income.
    WHO and many other organisations are very interested in implementing treatment-as-prevention as a global policy to control the HIV pandemic. Widespread treatment of HIV-infected individuals with antiretroviral therapy will reduce HIV transmission, because it decreases viral load and hence infectiousness. To implement the rollout of treatment-as-prevention in an efficient manner, estimation of the number of HIV-infected individuals and where they live is needed. This assessment will be difficult to accomplish, particularly in areas of sub-Saharan Africa with severe HIV epidemics. We propose a solution to this problem by using geospatial statistical techniques and global positioning system (GPS) data. To estimate the number of HIV-infected individuals in a particular area, a predictive map of the prevalence of infection could be constructed. This map would then be overlaid on a grid map that shows the geographical dispersion of the population. The size of the grid would determine the degree of spatial resolution of the overlay map (ie, the density-of-infection map). The density map would show the estimated number of HIV-infected individuals per km and their geographical distribution over the area of interest. The total number of HIV-infected individuals could be obtained by summing the estimates in each grid over the entire area. All of the geospatial techniques needed to construct density-of-infection maps for HIV are techniques that have been used in studies of other infectious diseases—eg, dengue fever, influenza, malaria, rotavirus, and tuberculosis. Predictive prevalence maps have been constructed by using georeferenced prevalence data and spatial interpolation techniques. The most commonly used techniques are Bayesian geostatistical modelling and Kriging. Bayesian geostatistical models are constructed in the same manner as are Bayesian statistical models, but include additional parameters to allow for spatial dependency in the data. Bayesian geostatistical models have been used to generate predictive prevalence and risk maps for malaria and tuberculosis. Kriging uses semivariograms to model spatial dependency. The standard error of the estimated prevalence at any specific location is usually calculated, irrespective of whether Bayesian geostatistical modelling or Kriging is used for spatial interpolation. The standard error is then mapped to visualise the uncertainty in the prediction at any geographical location. The standard error is always largest in areas with the lowest density of sample sites. Kriging was developed by Danie Krige in the 1950s to identify the locations of gold mines by using georeferenced samples of mineral deposits. In 1992, Carrat and Valleron were the first to apply Kriging to the spatial analysis of an infectious disease. They used surveillance data from specific geographical locations and generated predictive surfaces to identify the spatial and temporal spread of the 1989–90 influenza epidemic in France. Kriging has since been used to generate predictive prevalence maps for dengue fever, rotavirus, and malaria.