Procurement of Category 2 Vaccines in China.

Internationally, vaccine pricing is relatively opaque, although many low- or lower-middle-income countries belong to international consortiums that jointly procure vaccines. China procures vaccines domestically, and vaccines that require payment from the public ("category 2 vaccines"), have undergone several regulatory changes over the past 15 years. This study aims to describe the vaccine procurement method changes in China since 2005 and to analyze how the procurement method impacted vaccine price. This review of vaccine procurement reforms found that a shift to provincial-level Group Purchasing Organizations after 2016 was accompanied by an increase in most prices. There was more variability in vaccine prices across provinces for vaccines with only one supplier, and these vaccines have a higher price than what is found in United Nations Children's Fund (UNICEF)-supported countries. China's current procurement system for non-mandatory vaccines leaves these vaccines costing several-fold more than in other countries, and in particular those supported by Gavi, the Vaccine Alliance. Exploring a variety of methods to reduce vaccine purchase prices will not only directly benefit the general population, but also the government, as they aim to implement more programs to benefit public health in a cost-effective manner.

[Elephantiasis of bilateral lower limbs and scrotum: one case report].

This paper reports the process of diagnosis and treatment of a patient with elephantiasis of bilateral lower limbs and scrotum and the result of epidemiological investigation.

[Spatial distribution pattern of Oncomelania hupensis population in marshland and lake area].

OBJECTIVE: To explore the spatial pattern of the distribution of Oncomelania hupensis population in marshland and lake region. METHODS: Four bottomlands were selected randomly for the investigation along the Qiupu River in Guichi District, Anhui Province. The quadrats were placed randomly for snail survey, and the mean crowding, mean density, aggregation indices (diffusion coefficient (C), morisita index (Idelta), index of patchiness (m*/m) ), Taylor's power law and Iwao's m*-m regression index were analyzed. RESULTS: For 4 bottomlands in different time, the diffusion coefficients, Morisita indices, and m*/m indices were all more than one. There was a close linear relationship between the logarithm of the snail density and its variance (P < 0.01), and the correlation coefficient was 0.972. In Taylor's power law, the lg a was 0.602 and b was 1.427. There was a close linear relationship between the mean crowding (m*) and the snail density (m) (P < 0.01), and the correlation coefficient, alpha and beta was 0.984, 2.367 and 1.617 respectively. CONCLUSION: The spatial pattern of Oncomelania hupensis population shows an aggregation distribution in marshland and lake region, and the basic components of snail distribution are in colonies.

[Linear models for predicting of Oncomelania hupensis in the lake and marsh regions].

OBJECTIVE: To study the prediction model of O. hupensis in the lake and marshland regions in order to provide methodological basis for quantitative study of O. hupensis. METHODS: The research sites were randomly selected from the bottomlands along Qiupu River in the Guichi District, Anhui Province. A random and stratified sampling method was administrated according to the type of vegetation; the frame size of snail survey was 0.11 m2. Snail data was collected by crosscheck-random sampling inspection survey. Elevation, soil temperature and air temperature, height of vegetation, soil humidity and types of vegetation were measured through GPS machine, T&D Recorder for Windows, tape measure and attemperator. All the data were doubly inputted into the computer and checked. The final dataset for developing the prediction model was set up after necessary data preprocessing, such as, recoding the variable of elevation. The generalized linear models were used to develop the prediction model, and the statistics of deviance and AIC were used to determine the best model structure. Model diagnostics and model evaluation of efficiency were performed with the determined best model structure. RESULTS: The sample size was 162, and there were 6 explanatory variable including 2 categorical variables and 4 quantitative variables. A complicated relationship was observed among all the variables. Snail was positively associated with height of vegetation (r = 0.36), while negatively associated with soil humidity (r = - 0.22), and the air temperature had a close positive relations with soil temperature (r = 0.59), and the soil temperature was negatively associated with height of vegetation (r = - 0.36), the soil humidity had negative relations with the soil and air temperature (r = -0.34 and -0.12). The best structure fitting for the liner model selected in gamma distribution was the error distribution, reciprocal as the conjunction function in mathematics, and the mean square as the variance function. The results showed that the elevation, soil humidity, soil temperature, types and the height of vegetation were statistically significant to predict the O. hupensis, while t-values were -3.202, 3.124, -1.989, 2.668 and -2.371, respectively, and P-values were 0.00166, 0.00214, 0.04849, 0.00846 and 0.01897 respectively. CONCLUSION: The generalized linear models can be used to develop the predictive model, which could broaden the area of quantitative study for O. hupensis.