Early Detection for Dengue Using Local Indicator of Spatial Association (LISA) Analysis

Liu

et al. 2018

BMC Health Serv Res

BackgroundHealth workforce misdistribution leads to severe inequity and low-efficiency in health services in the developing countries. Targeting at China, this research aims to reveal, visualize and compare the geographical distribution patterns of different subtypes of urban and rural health workforce and identify the priority regions for health workforce planning and allocation policies designing.MethodsThe health workforce density (workforce-to-population ratio) is adopted to represent the accessibility to health workforce in each geographical unit. Besides a descriptive geography of health workforce as a whole, the local indicators of spatial association (LISA) are used to explore the spatial clusters of different subtypes of health workforce, which are visualized by geographical tools.ResultsResults reveal that regional disparities and spatial clusters exist in China’s health workforce distribution, with different types of workforce exhibiting relatively different spatial distribution characteristics. Besides, huge urban-rural disparities are found in the distribution of health workforce in China. Unexpectedly but intriguingly, most of the high-high and high-low cluster area of urban health workforce are concentrated in the western China (Xinjiang, Xizang etc.), indicating the relative abundant stock of urban health workforce in these units, while the low-low and low-high cluster area of different types of urban health workforce are mainly distributed in middle China. Regarding the rural health workforce, there is an obvious and similar low-low and low-high clustering pattern in western provinces (Sichuan, Yunnan) for the licensed doctors, pharmacists, technologists, which play a critical role in health services delivery.ConclusionsDifferent types of health workforce displayed distinct spatial distribution patterns, while the misdistribution of rural health workforce imposed more challenges to the Chinese health sector due to its poorer stock and more disadvantaged positions of backward regions (i.e., low-low and low-high cluster area). Subtype-specific and region-oriented health workforce planning and allocation policies are suggested to be made, aiming at the urban and rural health workforce respectively, by prioritizing the identified low-low and low-high cluster areas.Electronic supplementary materialThe online version of this article (10.1186/s12913-018-3737-y) contains supplementary material, which is available to authorized users.

Section: Methodsmentioning

confidence: 99%

Detecting the priority areas for health workforce allocation with LISA functions: an empirical analysis for China

Liu

et al. 2018

BMC Health Serv Res

“…The value of Moran's I ranges from -1 to 1, where a 0 indicates that the RIDs cases are randomly distributed in space and no clusters are detected [26]. A value approaching 1 indicates the unit clusters with a similar value [27]. A value approximating -1 indicates an opposing situation: the units with high values and low values are adjacent to each other in space.…”

Section: Spatial Autocorrelation Analysismentioning

confidence: 99%

“…Based on its value and significance, the local Moran's I can detect four types of clusters, reflecting the high-high (HH, geographical units with high value surrounded by geographical units with low value), high-low (HL, geographical units with high value surrounded by geographical units with high value), low-low (LL), and low-high (LH) clustering patterns, respectively [28]. The local Moran's I is a kind of Local Indicator of Spatial Association (LISA); thus, the maps that display the clusters detected by the local Moran's I are always termed as univariate LISA cluster maps [27]. The number of permutation tests was set to 9999, and the significance level was set as 0.05.…”

Section: Spatial Autocorrelation Analysismentioning

confidence: 99%

Notifiable Respiratory Infectious Diseases in China: A Spatial–Temporal Epidemiology Analysis

Mao

et al. 2020

IJERPH

Nowadays, tuberculosis, scarlet fever, measles, influenza, and mumps are five major notifiable respiratory infectious diseases (RIDs) in China. The objective of this study was to describe, visualize, and compare the spatial-temporal distributions of these five RIDs from 2006 to 2016. In addition to descriptive epidemiology analysis, seasonality and spatial autocorrelation analysis were also applied to explore the epidemiologic trends and spatial changing patterns of the five RIDs, respectively. The results indicated that the incidence of tuberculosis, measles, and mumps presented a downtrend trend, while those of scarlet fever and influenza was in a strong uptrend across the research period. The incidences of the five diseases all peaked in spring. There were significant spatial disparities in the distribution of tuberculosis, scarlet fever, and measles cases, with the hotspots mainly located in the western plateau region, northern plain region, and southern mountainous region. To conclude, notable epidemiological differences were observed across regions, indicating that some provincial units should pay more attention to prevent and control respiratory infectious diseases.

“…Global Moran’s I is a measure describing the overall spatial distribution characteristics in an area as whole. Local Moran’s I, which is also referred to as a decomposition of global Moran’s I, is an indicator for a particular area, which can be used to detect the spatial clusters of infectious diseases [ 28 ]. Based on the incidence of one certain infectious diseases, we will calculate the global and local Moran’s I for each infectious disease and develop corresponding graphics.…”

Section: Methodsmentioning

confidence: 99%

Spatial distribution of 12 class B notifiable infectious diseases in China: A retrospective study

Liu

2018

PLoS ONE

BackgroundChina is the largest developing country with a relatively developed public health system. To further prevent and eliminate the spread of infectious diseases, China has listed 39 notifiable infectious diseases characterized by wide prevalence or great harm, and classified them into classes A, B, and C, with severity decreasing across classes. Class A diseases have been almost eradicated in China, thus making class B diseases a priority in infectious disease prevention and control. In this retrospective study, we analyze the spatial distribution patterns of 12 class B notifiable infectious diseases that remain active all over China.MethodsGlobal and local Moran’s I and corresponding graphic tools are adopted to explore and visualize the global and local spatial distribution of the incidence of the selected epidemics, respectively. Inter-correlations of clustering patterns of each pair of diseases and a cumulative summary of the high/low cluster frequency of the provincial units are also provided by means of figures and maps.ResultsOf the 12 most commonly notifiable class B infectious diseases, viral hepatitis and tuberculosis show high incidence rates and account for more than half of the reported cases. Almost all the diseases, except pertussis, exhibit positive spatial autocorrelation at the provincial level. All diseases feature varying spatial concentrations. Nevertheless, associations exist between spatial distribution patterns, with some provincial units displaying the same type of cluster features for two or more infectious diseases. Overall, high–low (unit with high incidence surrounded by units with high incidence, the same below) and high–high spatial cluster areas tend to be prevalent in the provincial units located in western and southwest China, whereas low–low and low–high spatial cluster areas abound in provincial units in north and east China.ConclusionDespite the various distribution patterns of 12 class B notifiable infectious diseases, certain similarities between their spatial distributions are present. Substantial evidence is available to support disease-specific, location-specific, and disease-combined interventions. Regarding provinces that show high–high/high–low patterns of multiple diseases, comprehensive interventions targeting different diseases should be established. As to the adjacent provincial units revealing similar patterns, coordinated actions need to be taken across borders.