In an integrating sphere multispectral imaging system, measurement inconsistency can arise when acquiring the spectral reflectances of samples. This is because the lighting condition can be changed by the measured samples, due to the multiple light reflections inside the integrating sphere. Besides, owing to non-uniform light transmission of the lens and narrow-band filters, the measured reflectance is spatially dependent. To deal with these problems, we propose a correction method that consists of two stages. The first stage employs a white board to correct non-uniformity and a small white patch to correct lighting deviation, both under the assumption of ideal Lambertian reflection. The second stage uses a polynomial regression model to further remove the lighting inconsistency when measuring non-Lambertian samples. The method is evaluated on image data acquired in a real multispectral imaging system. Experimental results illustrate that our method eliminates the measurement inconsistency considerably. This consequently improves the spectral and colorimetric accuracy in color measurement, which is crucial to practical applications. Integrating spheres are widely used in radiometric and photometric measurements, among which reflectance measurement is a typical application . By collAbsolute or relative reflectance measurements can be conducted by using integrating spheres. In absolute measurements ,3, the rThe spectrophotometer is a typical measurement system that uses the integrating sphere. It measures the average spectral reflectance from the sample port whose diameter is relatively large. Accordingly, it has a very coarse spatial resolution. A multispectral imaging system, when appropriately calibrated, can measure the spectral reflectance of a sample with spatially-fine resolution ,7,8,9. CIn a single-beam integrating sphere, substituting the standard with a sample will alter the throughput of the sphere, especially when the sample is of much lower reflectance than the standard. This usually leads to a lower measurement quantity. The measurement error caused by lighting deviation is known as the single-beam substitution error. Typical solutions to this problem include increasing the sphere size, decreasing the sample port area, using multiple standards with different reflectance levels and introducing an additional reference beam. The methods for eliminating substitution error have been investigated in ,16.In an integrating sphere multispectral imaging system, the sample port has a relatively large area and holds the standard and sample in turn, which introduces non-negligible lighting deviation. However, the solutions just mentioned focus on systems measuring the average spectral reflectance from the sample port and cannot be applied directly to the imaging system. Previous calibration methods ,18,19,20The novelty of the proposed lighting correction is mainly two-fold. First, a small reference white patch is employed to compensate for the lighting deviation based on the assumption of Lambertian reflection. Second, a polynomial regression model is used to further reduce the measurement inconsistency of non-Lambertian samples in practical measurement. The improvement of measurement consistency is finally validated using intensive experiments.Lighting correction is needed for accurate and stable color measurement in an integrating sphere multispectral imaging system. The correction procedure mainly deals with two problems, i.e., spatial non-uniformity and lighting deviation. First, even if the integrating sphere provides a uniform illumination, the reflected fluxes reaching the image sensor from a uniform sample at different positions can still be different. This is due to the non-uniform light transmission of the lens and filters. This problem can be solved by acquiring an image of a white board that has the same size of the sample.Second, lighting deviation can occur when measuring different samples. Due to the multiple light reflections inside the sphere, the sample itself can change the lighting condition; this is referred to as the background effect hereafter. This means that, for a certain small color patch, its measurements will be different if this patch is placed on different background samples. In addition to the imaged sample, the fluctuation of the lamp irradiance also leads to the lighting deviation.To deal with the mentioned problems, we propose a two-stage lighting calibration method to correct the measurements of individual bands. In the first stage, we assume the sample surface is of ideal Lambertian diffuse reflection. We then characterize the spatial non-uniformity by acquiring the image of a white board, and correct lighting deviation by employing a small white patch for reference. In the second stage, we use a polynomial regression model to further reduce the background effect for practical color measurement.The lighting deviation correction is performed on the camera response . The spectral reflectance is reconstructed from the corrected camera responses using Wiener estimation . To be mLambertian reflection is a common assumption in eliminating substitution errors for an integrating sphere ,16. In SAs illustrated in We denote the radiant flux entering the sphere in the wavelength range of Among the flux reflected by the sphere wall, the percentages of the part striking the sphere wall and the part incident on the sample holder are denoted by For calibration, the white standard was placed on the sample holder to acquire the spatial distribution of the lighting. The standard had the same size as the sample and was much larger than the white patch illustrated in A part of the flux reflected by the standard strikes the sphere wall, at a percentage of The flux in the second reflection of the standard is:Then, the flux in the sphere wall’s third reflection is computed as:The flux in the N is the number of corresponding pixels of the standard, and We first consider the imaging model of the white standard. For a pixel position puted as:ur=eThen, we consider the imaging model of a Lambertian sample whose average reflectance is The background variation, which corresponds to the different reflectances Equation .The camera response of the sample Note that Thanks to the uniformity of the white standard, we actually have Equation . HoweverWe correct the lighting deviation using a reference white patch as illustrated in Our aim is to characterize lighting deviation when imaging various samples; thus, we normalize Then, the normalized camera response of the sample in Equation  can be fWhen compared with Equation , it is oBased on the above theoretical analysis, we present the procedure of lighting correction using the white standard and white patch as follows. We first acquired the image of the white standard, obtaining the camera response We note that the lighting deviation cannot be fully corrected by using a white patch. This is due to the non-Lambertian nature of real surface reflection of the imaged sample. In ,23, the Owing to the bidirectional reflectance distribution function (BRDF)  of a nont effect ,28,29 alt effect . In the The lighting deviation correction aims to produce similar camera responses for a given color regardless of lighting change. Since the influence of the aforementioned factors is rather complicated, it is difficult to introduce a complete physical model to correct lighting deviation. We instead resorted to polynomial regression ,31, whicIn the polynomial model, the normalized camera response We collected training data using the procedure illustrated in j, we set the corrected response For the We concatenated the model coefficients into a vector Note that the variables Equation  to simplEquation , the proAfter obtaining Equation , which gWe built an integrating sphere multispectral imaging system whose structure was identical to that in Experiments were carried out to validate the proposed method on both training and test data acquired in our system. The improvement of measurement consistency was evaluated using the standard deviation  of camera responses, as well as the spectral and colorimetric errors of spectral reflectances. Comparison with the state-of-the-art method was also performed on the test data.We first evaluated the improvement of response consistency using the training data shown in We evaluated the model accuracy using the test data in By reconstructing the 31-channel spectral reflectance from corresponding camera responses at different bands, we further quantified the improvement in spectral color measurement with our method.For a patch placed on different background samples, the consistency of color measurement can be evaluated using both spectral and colorimetric errors. The spectral error was computed as the root-mean-square (rms) between two spectral reflectances. The colorimetric errors were computed using the CIEDE2000 color difference formula  under CIFor illustration, The Markov model  is the mWe have proposed a lighting correction method to eliminate the measurement inconsistency caused by spatial non-uniformity and lighting deviation in integrating sphere multispectral imaging systems. We first modeled the imaging process of Lambertian surfaces in the integrating sphere, which explained the effect of these on measurements. Based on the theoretical analysis, we eliminated the spatial non-uniformity by acquiring the image of a white board and compensated for the lighting deviation by employing a reference white patch. We further introduced a polynomial regression model to correct the measurement inconsistency of real non-Lambertian samples. Experimental results validated that our method could improve measurement consistency considerably on both camera response and spectral reflectance. It also performed better than the state-of-the-art method.


Localization is a critical issue for Underwater Acoustic Sensor Networks (UASNs). Existing localization algorithms mainly focus on localizing unknown nodes (location-unaware) by measuring their distances to beacon nodes (location-aware), whereas ignoring additional challenges posed by harsh underwater environments. Especially, underwater nodes move constantly with ocean currents and measurement noises vary with distances. In this paper, we consider a special drifting-restricted UASN and propose a novel beacon-free algorithm, called MAP-PSO. It consists of two steps: MAP estimation and PSO localization. In MAP estimation, we analyze nodes’ mobility patterns, which provide the priori knowledge for localization, and characterize distance measurements under the assumption of additive and multiplicative noises, which serve as the likelihood information for localization. Then the priori and likelihood information are fused to derive the localization objective function. In PSO localization, a swarm of particles are used to search the best location solution from local and global views simultaneously. Moreover, we eliminate the localization ambiguity using a novel reference selection mechanism and improve the convergence speed using a bound constraint mechanism. In the simulations, we evaluate the performance of the proposed algorithm under different settings and determine the optimal values for tunable parameters. The results show that our algorithm outperforms the benchmark method with high localization accuracy and low energy consumption. Underwater Acoustic Sensor Networks (UASNs) have been widely applied in many fields such as underwater surveillance, pollution detection and disaster prevention ,2. GenerGenerally, an UASN consists of two types of nodes: beacon nodes and unknown nodes. The locations of beacon nodes are known in advance using GPS module or fixed deployment. They are powerful and can emit beacon signals to help unknown nodes’ localization. In contrast, unknown nodes are relatively cheap with limited energy and computation ability. The localization procedure aims to obtain the ranging measurements between unknown nodes and beacon nodes, and figure out the locations of unknown nodes from these measurements using specific localization algorithms. Therefore, the localization performance significantly depends on the ranging accuracy and the algorithm efficiency.In UASNs, the ranging methods mainly include Time of Arrival (TOA), Time Difference of Arrival, Angle of Arrival (AOA) and Received Signal Strength Indicator (RSSI). Since underwater acoustic channel is time-varying, accurate attenuation models do not exist and thus RSSI method is rarely used . AOA metIn addition, the harsh underwater environments have a great impact on the accuracy of the TOA method. First, the signals can arrive from line of sight (LOS) or non-line of sight (NLOS) communication links due to the existence of reflective obstacles  . The LOSAnother important factor on localization performance is the network topology. Due to the fluid property of underwater environment, nodes usually drift with currents and tides. Consequently, the network topology may change continuously. According to the degree of the restriction of nodes’ movements, there exist two drifting modes, which are free drifting and restricted drifting, respectively. For free drifting mode, nodes move without restrictions ,18,19,20This paper proposes a novel localization method without the presence of beacon nodes for DR-UASNs, which achieves higher localization accuracy and lower computational cost compared with the benchmark method.The noises varying with distances are taken into account, which is modeled by additive and multiplicative noises. Hence the noises in distance measurements can be efficiently filtered for improving localization accuracy.The reference selection and bound constraint mechanisms are proposed to combat the problems of localization ambiguity and low convergence speed in the PSO step.Aiming at the above mentioned problems, this paper proposes a new localization method based on Maximum a Posteriori (MAP) estimation and Particle Swarm Optimization (PSO) techniques. Considering a portion of nodes may move out of the surveillance area due to free drifting, which leads to insufficient network coverage and increases the difficulties of network recycling and maintenance, we follow a Drifting-Restricted Underwater Acoustic Sensor Network (DR-UASN) ,22, in wThe rest of this paper is organized as follows: in Existing localization schemes can be divided into two categories: range-free and range-based. While range-free schemes provide coarse-grained location estimations with low communication cost, range-based schemes can achieve a relatively high localization accuracy, but with additional communication and hardware cost. In this work, we are interested in range-based schemes. Next, we briefly review some works related to our method, and more detailed research review can be found in ,27.In general, range-based localization consists of two stages: distance estimation and location calculation. The ranging method commonly used is TOA, which gets the difference between packet sending and receiving time and estimates the distance by multiplying the time difference and the acoustic speed. However, it suffers from low accuracy due to multiple factors such as time synchronization, multipath effect and stratification effect. In ,25 , theIn a 3D UASN, the locations of unknown nodes can be figured out by using classic multilateration algorithm and the ranging measurements to at least four beacon nodes. Further, the number of beacon nodes can be reduced to three by projecting them onto unknown node’s horizontal plane ,12. In , a hyperAll of these algorithms mentioned above assume the nodes drift freely with ocean currents and need the presence of beacon nodes prior to localization. Moreover, the number of beacon nodes often increases with the network scale. This increases both the network cost and the difficulties of network recycling and maintenance. In , the autIn this section, we present the network model, analyze the drifting characteristic of underwater nodes and present formal definition of the localization problem.We consider an UASN that consists of a number of nodes deployed at the surveillance area. To reduce the network cost, each node is low-complexity with constrained energy and limited computational ability. Due to the intrinsic fluid property of underwater environments, the nodes move continuously with ocean currents. This requires the localization should be accomplished in a short time, otherwise the estimated locations will become obsolete as the nodes move to new locations. Hence, it is necessary to design a fast and energy-efficient algorithm to provide real-time localization in this resource-constrained network.i for example, let i and j should be longer than the sum of the length of their cables:As a consequence of the continuous mobility, some nodes may drift out of the deployment area, which increases the difficulties of network recycling and maintenance. Aiming at this problem, we follow a drifting-restricted UASN. Its network architecture is shown in re areas  and hundre areas ,35). At H. Due to the impact of tide rise and fall, the sea depth should satisfy:To ensure the nodes float in the water all the time, the length of the cables should be less than the sea depth At the same time, due to the tension of the cable, the depth of each node satisfies:Generally, the movement of the nodes is controlled by joint forces from ocean current, water buoyancy and the cables. The buoyancy of each node is related to its volume, the water density and the acceleration of gravity. The water density is further influenced by ocean temperature and salinity. Hence the buoyancy of each node is a constant value in a certain spatial and temporal extent due to slow change of ocean temperature and salinity. The force of ocean current is mainly influenced by the current speed.i:r is At a specific depth, the three forces reach a balance state. The node can drift on the plane of this depth and move along a circle centered at its anchor point. According to Pythagorean theorem, we have the following equation for node i is constrained on a circle centered at the anchor point In practice, the depth Equation , the radTo exploit the spatial relationship between nodes for localization, a node communicates with its neighbor nodes and measures the distances to them by multiplying the propagation delay by the acoustic speed. Most researches adopt one-way TOA method for distance estimation . Howeverc represents the speed of acoustic signals. In each packet exchange, a node sends a packet at the power level After that, the distance between A and B can be estimated asSimilarly, we can estimate the distances between a node and all of its neighbor nodes and calculate the overall energy consumption. Based on the priori localization information and spatial relationship between nodes, our purpose is to accurately estimate the locations of all the nodes with low computational and communication costs as fast as possible. Next we will present formal definition of the localization problem.N nodes in the whole network and each node needs to get its location periodically. We define T as the localization period in which each node needs to estimate its location. Note that, the localization period can be tuned according to practical requirements . For siFor each cluster, we now have represented the two-dimensional location of the nodes and obtained the distance estimation between any two nodes. Given these data, the localization problem is to fuse them using a proper model and figure out the locations of all the cluster nodes. Specifically, our purpose is to minimize the sum of the squares of the errors between estimated distances and real distances. The objective function can be written asM nodes can be resolved using efficient optimization methods. The whole localization process lasts until all the clusters have been localized. Next, we will present the algorithm that can fast localize the nodes without ambiguity.The locations of In this section, we present MAP-PSO, a novel localization scheme without the presence of beacon nodes for DR-UASNs. The whole localization process consists of MAP estimation and PSO localization. In the following, we describe the details of our MAP-PSO algorithm starting from the MAP estimation step and followed by the PSO localization step.Recall the localization problem defined in Equation , we didni along X- and Y-axes, and follow a Gaussian distribution with zero mean and precision i, i follows a joint Gaussian distribution with mean To characterize the nodes’ mobility patterns, we assume the location of each node is randomly distributed around its corresponding movement circle, which can be represented asxi=xAi+riHere, the depth Equation . It is ii and j, We now discuss how to model the noises in distance measurements. In , it has For all the distance measurements Based on Equation , the priUsing the Bayesian theorem, the posterior probability distribution of Then the locations Substituting Equations (9) and (11) into Equation , the maxNow let us analyze the ratio Multiplying both numerator and denominator with In the simulations, we have assigned approximate values to Then we can replace Equation  and execThe minimization problem of the objective function in Equation  has no aIn Equation , besidesk at iteration t, t, k at iteration t, respectively. The parameter Once the particles are initialized, they move to new positions with a certain speed in the search space. By calculating the value of the objective function, they can find the local best position a weight  is commoThe optimization repeats the operations in Equations (19)–(21) until the termination condition is satisfied. There are two termination conditions in our method. One is the relative change of the best objective function value over M is prone to localization ambiguity. As shown in M).In the above optimization process, there exist two problems that limit the localization accuracy and the convergence speed. First, a cluster with a small i and i. However, this confidence mechanism does not hold true in our method. Take the nodes in T, i.e., i can act as a reference node when the following two conditions are satisfied: (1) the node should be localized in every localization period of a sliding window; (2) the change of the estimated locations of the node in any two consecutive localization periods should be below the maximum movement distance, To solve the first problem, we propose a novel reference selection mechanism to eliminate the ambiguity in localization. In practice, different clusters may overlap, that is, for two clusters j at the localization period m, respectively. Our approach is to set the tight bound of the location of the reference node at the localization period Aiming at the second problem, we propose to further confine the particles’ positions on the basis of their values initialized in Equation . As we hj, we can set the lower bound of its X- and Y-axes location as Then the azimuth angle Equation , the nodIn this section, we evaluate the performance of the MAP-PSO algorithm using simulations.In our simulations, 100 unknown nodes are deployed in a cubic region with the range 1000 m × 1000 m × 20 m. MAP-PSO does not need beacon nodes. Each node is linked to a fixed anchor point through a cable with the length 20 m. The anchor points of any two neighbor nodes should satisfy the Equation .Equation , where tEvery simulation lasts for 100 s. We set the localization period as The performance of the MAP-PSO algorithm depends on many important parameters, including the penalty factor, the maximum iteration number, the particle number and the minimum cluster node number. We aim to determine the optimal values of these parameters by simulations.The penalty factor has an important impact on the localization accuracy. As we discussed above, assigning a proper value to the penalty factor will make a balance between the priori knowledge and the likelihood information. Therefore, it is necessary to determine the value of the penalty factor by simulations. The maximum iteration number is another important factor that impacts the localization performance. On one hand, if the maximum iteration number is set to be too small, the optimization may stop too early to converge to the optimal solution, resulting in low localization accuracy; on the other hand, if setting the maximum iteration number to be large helps to improve the localization accuracy, the optimization needs more computational resource, which is infeasible for resource-constrained UASNs. By choosing the penalty factor as 0.56 according to The number of particles is a critical parameter to determine the search ability of PSO localization. In theory, the search ability can be improved by simply increasing the particle number. However, a large number of particles have to be updated according to Equations (19) and (20), which leads to high computational cost. Hence, it is necessary to find a critical point, where a balance between the localization error and the localization time can be reached. By choosing the penalty factor and the maximum iteration number as 0.56 and 400 respectively, the simulation results using 50 to 400 particles are shown in In the above simulations, we ignore the localization coverage because it is rarely affected by the penalty factor, the iteration number and the particle number. We now evaluate the performance under the minimum cluster node number. This parameter means that a cluster can’t be localized if the number of its nodes is less than the minimum cluster node number. By choosing the optimal values of the penalty factor, the maximum iteration number, the particle number and the minimum cluster node number, we set the node density as 8 by adjusting the communication range of the nodes as 160 m and evaluate the localization result of each node. In this section, we compare the localization accuracy and time of MAP-PSO with that of AFLA under different parameters, including the measurement noise level and the node density. The localization coverage is not evaluated because both algorithms have the same localization coverage under the two parameters. According to the above simulations, we choose the penalty factor, the maximum iteration number, the particle number and the minimum cluster node number as 0.56, 400, 200 and 4, respectively. The unique parameters of AFAL are set according to .We first compare the performance of both algorithms under different measurement noise levels. As we stated before, the measurement noise is composed of the additive and multiplicative noises. Due to the additive noise is assumed to follow a Gaussian distribution with zero mean, it is reasonable to use the mean of the multiplicative noise to represent the measurement noise level. We change the mean of the multiplicative noise from 0.01 to 0.06. The results for localization accuracy and localization coverage are illustrated in From We next compare the performance of both algorithms under different node densities. Node density is defined as the expected number of nodes in a node’s neighborhood. We change the node density from 4 to 12 by adjusting the communication range of the nodes. The results for localization accuracy and localization coverage are illustrated in As shown in Besides the localization accuracy and time, we evaluate the energy consumption under different node densities. For simplicity, we follow LDSN  to set tIn this paper, we considered the localization problem in UASNs where nodes are permanently moving with restrictions and measurement noises vary with the distances. We introduced a localization algorithm that uses the priori knowledge provided by nodes’ mobility patterns and the likelihood information offered by distance measurements. Under the Bayesian framework, the algorithm fuses the two types of information in a weighted way. In the localization phase, the algorithm fits the components in the localization objective function according to their weights. To improve localization accuracy and convergence speed, we further propose novel reference selection and bound constraint mechanisms. We evaluate the localization performance of our algorithm under different unique parameters. The simulation results give the suggestions of selecting proper values for these parameters. We also compare the localization performance with the benchmark AFAL algorithm. The simulation results show that our algorithm can use less localization time and energy consumption to achieve higher localization accuracy.In the future, our work focuses on two aspects: on one hand, considering the depth measurements may be obtained with low accuracy from a cheap pressure sensor due to the variation of atmosphere and tides, we will study the accurate localization algorithm with coarse depth measurements; on the other hand, we will extend the proposed method to improve the localization performance by using temporal dependency between a node’s locations of consecutive time instants.


Inequality of health services for different specialty categories not only occurs in different areas in the world, but also happens in the online service platform. In the online health community (OHC), health services often display inequality for different specialty categories, including both online views and medical consultations for offline registered services. Moreover, how the city-level factors impact the inequality of health services in OHC is still unknown. We designed a causal inference study with data on distributions of serviced patients and online views in over 100 distinct specialty categories on one of the largest OHCs in China. To derive the causal effect of the city-levels (two levels inducing 1 and 0) on the Gini coefficient, we matched the focus cases in cities with rich healthcare resources with the potential control cities. For each of the specialty categories, we first estimated the average treatment effect of the specialty category’s Gini coefficient (SCGini) with the balanced covariates. For the Gini coefficient of online views, the average treatment effect of level-1 cities is 0.573, which is 0.016 higher than that of the matched group. Similarly, for the Gini coefficient of serviced patients, the average treatment effect of level-1 cities is 0.470, which is 0.029 higher than that of the matched group. The results support the argument that the total Gini coefficient of the doctors in OHCs shows that the inequality in health services is still very serious. This study contributes to the development of a theoretically grounded understanding of the causal effect of city-level factors on the inequality of health services in an online to offline health service setting. In the future, heterogeneous results should be considered for distinct groups of doctors who provide different combinations of online contributions and online attendance. With the development of health services worldwide , the ineAs our previous findings  suggesteAlthough the provision of OHCs can mitigate the low levels of medical resources in rural areas, few studies have focused on the inequality of online health services, especially on the inequality of health services for different specialty categories. The OHC platform can be regarded as an online-to-offline (O2O) system that provides both communication channels (interaction) for online medical services and records (or feedback) for offline medical services. Although many pieces of research have suggested a long tail phenomenon exists in the online product sale platform, seldom have they simultaneously taken both the inequalities in online views and in the offline service (patients’ consultation) into consideration. This study attempts to bridge this knowledge gap. We examine whether the online health community reduces the inequality of health service for different specialty categories through a retrospective study of the Lorenz curve of doctors’ service diversity. Our motivation is trying to answer the following issues: (1) What kind of patterns characterize the distribution of medical service delivery in distinct specialty categories in the online health community? (2) How does the factor ‘city-level’ impact the inequality of health services in OHCs? (3) How to identify the difference of the response of the Gini coefficient with the treatment variable of the city-level and other confounding variables?Among OHC platform users, the three types of services with the highest usage rate  are mediStudies have investigated the influence of the general structure of inequality on the service value and practices in society. One study  investigThe Lorenz curve was first proposed as a graphical statistic tool to express the concentration of wealth in a population in 1905 . Thus onA number of approaches are capable of revealing the associative relationships between the outcomes and the related independent variables at a significant statistic level. The causal inference method takes the advantages of non-significant related covariates, which assigns treatment experiments on different units. However, challenges lie in the identification of the population average causal effect of the treatment on the dependent variables. Average treatment effect is a term which refers to the measure comparing treatments (or policies) in randomized experiments . AlthougIn the research design, the treatment variable (city-level) represents the doctor’s location status at a specific time. Second, the mean and variance of the number of doctors’ articles across the specialty categories, mean in the degree of voted diversity, mean of doctors’ review rating and mean in doctors’ online contribution as independent variables are considered as the covariates. Based on this framework, we can verify whether doctors’ average treatment effect of cities with rich healthcare resources on the inequality of health service is the same for online service (online reviews) and offline service delivery (serviced patients) in different specialty categories.Gini coefficient  was intr2), health technical personnel, beds in health care institutions and health investment, while Beijing occupied the second place [The difference of Gini coefficients (of serviced patients or online views) was the dependent variable of interest, and the average number of articles, average breadth of service diversity, average doctor review rating and average doctor online contribution are set as the covariate variables and the city-level .Through web crawler technology, data from the Good Doctor website were collected (on 26 July 2017) and filtered for the purposes of the study. In the data set of 142,448 samples, 140,344 doctors with personal homepages were commonly considered to be genuinely involved in this OHC. The collected data set contained all the values of this study as well as the doctor’s identity document  and other de-identified information. The following filtering criterion was set to design an observational retrospective study; (a) Amount of served patients for doctor After filtering, 9644 samples of doctors remained from the original data set. Meanwhile, 114 specialty categories were filtered from the original 132 categories. The data acquired and filtering process is illustrated in The filtered samples have the following characteristics. First, our samples were from a large heterogeneous population with diverse backgrounds. The 9644 doctors came from 127 different specialty categories and 1338 different hospitals widely distributed throughout China. Second, the number of service deliveries and the number of patient reviews were collected for the retrieved doctors on the OHC. Although their usage time was different, the corresponding values of the independent variables were also collected during the same period for their usage time. Third, the number of doctors’ articles were collected without distinguishing between the original articles and reprinted long articles (not the communication posts with patients). We also collected the doctors’ review ratings (regarded as online word-of-mouth) from the stars labeled on the OHC. The average score of these ratings is 2.756 for all the sample data on a scale from 1 (the lowest) to 5 (the highest). Moreover, despite the association with the posted articles on the website, the contribution scores of the doctors were also impacted by many other factors, including the posted articles communicating with the patients online on the website. The other values we collected were the patient votes, which were different from the doctors’ review votes for the word-of-mouth rating and the case records of doctors’ accumulated clinical experience. Finally, the values of the location of hospitals were also collected for those doctors clustered in the samples. After filtering, 2603 (27%) of all the doctors were from Beijing or Shanghai, which are China’s two largest developed cities (level-1), while the other 7041(73%) doctors from the other cities (level-0). Thus, a causal inference study can be designed with those collected and filtered data samples.Before examining the OHC platform’ effects, it is necessary to distinguish between service delivery and service diversity. Service diversity typically measures how many different services a doctor offers. It is a supply-side measure of breadth. In contrast, we use the diversity of service delivery to describe the concentration of market shares conditional on doctors’ assortment decisions .Here we To identify the causal effect of cities of rich healthcare resources on service inequality, our research framework is designed as a retrospective observational study. We aim to investigate the outcomes from two aspects: (a) Gini coefficient of service delivery: offline registered patients, and (b) Gini coefficient of patient reviews: online service. Thus, the dependent variable will be used to reveal the patterns  of the doctors’ online service and reveal the relationship between specialty category’s Gini coefficient  of all the doctors in the same specialty area:i in the specialty category (discipline) j, j.First, the volume of patient online reviews for doctors clustered in the same specialty area during a fixed time period, j is defined as:A value To test this main conjecture, we use the mean and variance of the number of doctors’ articles across the specialty categories, mean in the degree of voted diversity, mean of doctors’ review rating and mean in doctors’ online contribution as independent variables.a) Mean of the number of doctors’ articles:In this study, we measured the number of doctors’ articles through a cumulative count of the articles of each doctor listed on the Good Doctor website. b) Degree of voted diversity:Given the voting states i in specialty category j, and c) Mean of the doctors’ review rating:In this study, we measured the physicians’ ratings in user reviews through the star scores listed on the Good Doctor website. d) Mean of the doctors’ online contribution:Essentially, the existence of online contributions means that members are involved in community-related activities, such as sharing information actively, responding positively to other members’ questions, and intuitively interacting with other members ,19. In tThe propensity score is often employed to reduce the dimensionality of the causal influence problem. The propensity score is the conditional probability of assignment to a particular treatment gen a vector of observed covariates .x, Let fined as :(11)p(Xigression :(12)logiTo achieve a balanced control-treatment case dataset, matching on pre-treatment covariates is one popular method. We match control-treatment cases on pre-treatment covariates with the propensity score. In the matching process, the scalar can be preset for the number of matches which should be found, i.e., the default value 1 is for one-to-one matching. More similar units are more likely to experience more similar trends so the parallel path assumption may be more plausible. Finally, we run the causal effect regression model with the matched data-set.Having defined our two main variables—service diversity and Gini—we now turn to our empirical analysis. To test the main conjecture of whether doctors’ patient votes will affect service usage, it’s easy to think about the associative relationship between the covariates and the outcomes. We first fit these data for ten specialty areas by examining how an increase in its influence might enhance or diminish the long tail of medical service demand, rather than fit the size of serviced patients and scale of vote data for the individual doctors. However, we are not only investigating the associative relationship of main effects but also revealing the causal effect of the treatment variable on the outcome, the inequality of health service for different specialty categories.x.To further reveal the causal effect, the statistical analysis is designed and conducted for those doctors, respectively. The average treatment effect  refers tFor all the specialty categories, the In the form of regression , the cauThe coefficient for the treatment indicator gression  on the tWith the propensity score matching theory , we analFirst, as the literature usually has done ,31, grapSecond, plots in To diagnose the balance of the control-case data, we also compared the focus cases and matched control cases. The OHC system associated the average influence of the reputation award on the doctors’ serviced patients and online views in each category, with the inequality measure (Gini coefficient) derived from the category’s Lorenz curve.t-test, as demonstrated in To diagnosis the difference of the cases in those two groups, we examined the data with Welch two sample With the cases of control-case matching, the Gini coefficients of the empirical experimental data were compared among focus cases, control cases after matching and those before matching. We also compared the Gini of all the cases after matching and those of all the cases before matching, shown as in The results in We first identified the causal effects of cities of rich healthcare resources on online service and offline service with equation (13). Here we deduced the causal effect with the definition, which is different from the identification process of average treatment effect using regression. This is because the experimental data were provided with complete observations  on the covariates. For Gini coefficients the specialty categories, 101 entities remained after filtering the NA values in the Gini coefficient table. The distribution of those Gini coefficients was deployed by the Gini coefficient of serviced patients and the views. For the Gini coefficient of serviced patients, 95% quantile of NDAMea, DRRMea, and BVSMea are, the inequality of DOCMea would increase the inequality of Although this paper is designed as a causal inference about the inequality of health service between online views and offline serviced patients for specialty categories, we also analyzed the associative relationship between those covariates and the , occupational disease and prosthodontics, with their average number of doctors’ serviced patients over 5000. However, CTM surgery, plant medicines, CTM infectious disease medicines, osteoporosis, and periodontitis are the lowest five specialty categories with a number of average doctors’ serviced patients under 800. The Gini coefficient of serviced patients ranges from 0.136–0.759 with a mean 0.564, which suggested that the inequality of health service in the online health community is relatively serious for the specialty categories. The total Gini coefficients of all the doctors in OHC are 0.632 for serviced patients and 0.774 for online views after control-case matching, and the Gini coefficient in level-1 cities is much higher (0.006 for serviced patients and −0.031 for online views) than those in the other cities.Essentially, we should first realize that our empirical results cannot be used to explain all of the doctors’ specialties to serve patients but to interpret the causal effect of the city-level on the inequality of health service. As shown in Finally, we make specific recommendations for OHC managers to reduce the inequality in the distribution of doctors’ service delivery among specialty categories based on our findings. For example, platform managers should make an effort to reduce the service inequality, improving the referral system and assigning the patients to the matched doctors with the appropriate service diversity. Holding average influence constant, the association between the influence of the specificity diversity and the distributions service delivery was enhanced when the influence was spread more evenly across the doctors in the clinical title, rather than concentrated on a few doctors within the clinical title. For example, when the doctor encountered a not well-experienced disease case , she/he may directly refuse to provide the online medical consultation service and suggested the patient to referral to another doctor or go to the hospital.Although the difference of inequalities between the units of cases from the level-1 cities and the others in OHC were reflected, more investigations need to be designed on the causality and policy evaluation. In the future, heterogeneity of the results should be considered for distinct groups of doctors who devoted different combinations of online contributions and online attendance. According to the scholarly commonsense of the coauthors, the samples may be grouped by their mean online contributions and online attendance values. As the samples did not completely conform to the standard normal distributions but were nevertheless supported, the mean value was used to represent the entire data set.First, the number of doctors’ articles was collected at a specific time for this study. To further investigate the contribution of doctors’ articles, more properties of doctors’ articles could be abstracted in the future from the website, including the number of doctors’ articles written by themselves, number of doctors’ articles copies from others, the average count of words in a doctor’ articles, the average times of reviewing for a doctor’ articles, etc. Second, the measure of serviced patients used to rank experimental units when estimating the empirical Lorenz curve, and the corresponding Gini coefficient was subject to random error. This error could also lead to an incorrect ranking of experimental units that inevitably results in a curve that exaggerates the degree of diversity (variation) among doctors. Furthermore, all the data were collected from one single OHC, the Good Doctor website. Since the size of each individual doctor’ specialty was calculated in the patient voting process from 26–27 August 2017, there exists a bias in the measurement time interval. Moreover, propensity score matching (PSM)  in this The causal inference method takes the advantages of non-significant related covariates, which assigns treatment experiments on different units. The research design in this paper avoids selection bias in the estimation of treatment effects. The Lorenz curve has been documented for a number of service diversities enrolled in OHCs. The distribution of the online service delivery  across the physicians in a specialty category was characterized by a Lorenz curve in which the cumulative proportion of the volume of service delivery was plotted against the cumulative proportion of physicians in the same specialty category in the OHC. We designed a causal inference study with data on distributions of serviced patients and online views in over 100 distinct specialty categories on one of the largest OHCs in China. For the Gini coefficient of serviced patients in over 100 specialty categories, the average treatment effect of level-1 cities is 0.470, which is 0.029 higher than that of the matched group. Similarly, for the Gini coefficient of online views, the average treatment effect of level-1 cities is 0.573, which is 0.016 higher than that of the matched group. The results support the argument that the total Gini coefficient of all the doctors in the OHC shows that the inequality of health service is still very serious. The inequality of health service in level-1 cities is much higher (more serious) than that outside of those level-1 cities for different specialty categories. Such ac differential effect at the city-level on the Gini coefficient of health service delivery also provides evidence that the patients are more likely to be aggregated in level-1 cities, and they are more likely to be served by the doctors.


Understanding the changes induced by body weight support (BWS) systems when non-disabled adults walk can help develop appropriate rehabilitation protocols. The purpose of this study was to investigate spatial-temporal gait alterations during walking with BWS on a treadmill and over the ground. Fourteen non-disabled young adults (including seven women) walked over the ground and on a treadmill with 0%, 10%, and 20% of BWS at 80% of their self-selected comfortable walking speed (baseline). The stride length and speed, step length, and stance and double-limb support durations were calculated and compared among the different conditions. The non-disabled adults modulated their spatial-temporal gait parameters according to the surface and percentage of BWS. They walked with shorter and slower strides and shorter steps and spent more time in contact with the support surface as they walked on the treadmill than as they did over the ground. Walking on the treadmill promoted less variability and a higher rate of change than did walking over the ground. Both the surface and amount of BWS should be taken into consideration when using BWS systems for (re)learning and/or reestablishing gait. Further, some researchers have examined the changes induced by BWS when non-disabled young adults walk in an attempt to develop an appropriate training protocol for those with gait impairment17. However, previous studies have investigated the use of BWS either on treadmills or over the ground18. To the best of our knowledge, only Barela et al.14 investigated gait with BWS on both surfaces in non-disabled adults, but were not able to control the walking speed when walking over the ground because of the incapability of the BWS system they used and the fact that only 30% of BWS was assessed.Partial body weight support (BWS) systems have been widely used as an alternative therapeutic strategy for gait training of different populations, including individuals with stroke20. However, as young adults walk over the ground with BWS, the cadence, duration of double-limb support, maximum flexion and extension of the knee, maximum flexion of the hip, and maximum dorsiflexion of the ankle decrease, while the duration of single-limb support increases15. These results show that the use of a BWS system either on a treadmill or over the ground can modify the gait pattern of individuals with no gait impairment.As non-disabled adults walk on a treadmill using a BWS system, the cadence, duration of double-limb support, maximum extension of the hip, maximum flexion of the knee at toe-off, and hip flexion at initial contact decrease; conversely, the duration of single-limb support, dorsiflexion at initial contact, and maximum plantarflexion of the ankle at toe-off increase22. However, when walking over the ground with a BWS system, 30% of BWS does not seem to be the most appropriate percentage of body weight unloading mainly because it would decrease the generation of posterior muscle energy by the lower limb at the end of the terminal stance23. Thus, it is important to investigate the different aspects of gait patterns in non-disabled young adults walking with a body weight unloading of less than 30% of BWS on both treadmills and over the ground while controlling their walking speed to establish and implement better gait training protocols for those with gait impairment.Among the different percentages of BWS for gait intervention, 30% of body weight unloading is the most employed for individuals with stroke on treadmillsIn the present investigation, we employed BWS on both walking surfaces  with similar speeds to examine the possible gait parameter changes depending on surfaces and/or body weight unloading in non-disabled young adults. First, we examined the spatio-temporal changes in non-disabled young adults walking on a treadmill and over the ground with different amounts of body weight unloading. We investigated whether the support surface on which the BWS system is employed and the amount of body weight unloading would modify gait patterns. Second, we examined gait variability during walking with different amounts of body weight unloading on both the surfaces. We investigated whether the surface on which the BWS system is employed and the amount of BWS would influence the variability in walking performance. Finally, we examined the rate of change between walking over the ground with no BWS at self-selected comfortable speed and walking with different amounts of body weight unloading on both the treadmill and over the ground. We investigated the amount of change between walking at a self-selected comfortable speed along a pathway and walking with different amounts of body weight unloading on the treadmill and over the ground.Fourteen non-disabled young adults (seven men and seven women) were included in this study. Their mean (standard deviation [SD]) age, height, and mass were 26 (4.4) years, 1.73 (0.08) m, and 73.4 (14.4) kg, respectively. All participants reported no known injury, disease, or musculoskeletal disorder, which could alter their walking performance. They wore flat shoes during their participation in the study.This study was conducted in accordance with the Declaration of Helsinki principles. The Institutional Ethics Committee of Cruzeiro do Sul University approved the study experimental protocol. An informed consent was obtained from all participants.25. The participants were asked to walk at a self-selected comfortable speed along a 10-m walkway back and forth for five times in a total of 10 trials (“baseline”). The time they took to walk in each trial was recorded using two photocells  placed 8 m apart along the walkway, and the mean walking speed was calculated. Thereafter, they walked with the assistance of two different BWS systems used in previous studies26 on a treadmill  and over the ground  with 0%, 10%, and 20% of BWS at approximately 80% of their baseline walking speed. This velocity percentage was used, as a previous investigation has revealed that non-disabled individuals walked 20% slower than their self-selected comfortable speed with the overground BWS system (Finix Tecnologia)19. The surface order  and the amount of body weight unloading were randomized among the participants. The speed of both the treadmill and the overground BWS systems was carefully controlled. The overground BWS system specifically contains a moving cart attached to the bottom of a suspended 7-m-long rail that allows backward and forward movements and is controlled by a belt system linked to a servomotor. A customized program  was developed to control the displacement, velocity, and acceleration of the moving cart.The spatio-temporal parameters of walking were assessed and registered using two inertial sensors  placed on each foot of the participants for the entire session. The use of these sensors has been described previously, and they exhibit good accuracy and precision for gait analysesBefore evaluating the walking performance with each of the BWS systems, the percentage of body weight unloading was set according to the experimental condition , and the participants practiced for a few trials over the ground and for 1 min on the treadmill, providing similar amount of practice for familiarization with each of the six experimental conditions. Thereafter, the participants performed 10 trials over the ground and for 1 min on the treadmill under each experimental condition. All participants were instructed not to hold onto the handrails during treadmill walking.In this study, the spatio-temporal parameters selected for analyses were the stride length, stride speed, and percentage of the stance period duration, in which the measurements are conducted for a single lower limb, and the step length and percentage of the total double-limb support duration, in which the measurements are conducted for both lower limbs. Using the Gait Analysis Software PRO , the stride length, stride speed, stance period duration, and step length raw data were obtained. The percentage of double-limb support duration was calculated on the basis of the instants of heel strikes from each foot and the stance duration time; it was then converted into a percentage value, taking into consideration the stride duration.For the overground conditions, data from the turns and first and last strides were discarded; for the treadmill conditions, data from the first 30 s and the last five strides were discarded. The means and SDs of all the spatio-temporal parameters were then calculated for each participant under each condition. Finally, the rate of change was calculated to examine the differences between walking with BWS  on each surface (over the ground and on a treadmill) and walking at a self-selected comfortable speed with no BWS system (baseline). The value of each stride under each experimental condition was divided by the mean value of the baseline.As there was no difference between the male and female participants and between sides, the data of all participants were pooled together, and those on the right and left sides were averaged together before making comparisons among the different experimental conditions. Multivariate analyses of variance (MANOVAs) for repeated measurements were employed using the surface (treadmill and over the ground) and BWS  as factors. The dependent variables were the stride length, stride speed, and percentage of stance duration in the first MANOVA and the step length and percentage of the total double-limb support duration in the second MANOVA.The same procedures were adopted for comparing the mean, variability, and rate of change among the different experimental conditions . When necessary, univariate analyses, Tukey Honest Significant Difference post hoc tests, and pairwise comparisons with Bonferroni adjustments were employed. An alpha level of 0.05 was adopted for the statistical tests, which were conducted using SPSS.All participants could walk under all the experimental conditions. The mean walking speed at baseline and with the BWS systems was 1.28 (0.14) m/s and 1.03 (0.10) m/s, respectively. Table 3,11 = 327.53, p < 0.01), BWS , and surface-BWS interaction . The univariate analyses revealed a surface effect for the stride length , stride speed , and stance duration ; BWS effect for the stride length  and stance duration ; and surface-BWS interaction for the stance duration . The participants had shorter and slower strides and a longer stance duration when they walked with BWS on the treadmill than when they did over the ground , BWS , and surface-BWS interaction . The univariate analyses revealed a surface effect for the step length  and double-limb support duration ; BWS effect for the step length  and double-limb support duration ; and surface-BWS interaction for the double-limb support duration . The participants had shorter steps and a longer double-limb support duration when they walked with BWS on the treadmill than when they did over the ground , but not of BWS  and surface-BWS interaction . The univariate analyses revealed a surface effect for the stride length , stride speed , and stance duration . The participants presented higher variabilities in their stride length, stride speed, and stance duration as they walked with BWS over the ground than as they did on the treadmill , BWS , and surface-BWS interaction . The univariate analyses revealed a surface effect for the step length ; BWS effect for the step length  and double-limb support duration ; and surface-BWS interaction for the step length  and double-limb support duration . The post hoc tests revealed that the participants presented a higher variability in their step length as they walked over the ground than as they did on the treadmill, at 20% of BWS than at 0% of BWS when walking on the treadmill, and at 10% and 20% of BWS than at 0% of BWS when walking over the ground , BWS , and surface-BWS interaction . The univariate analyses revealed a surface effect for the stride length , stride speed , and stance duration . In terms of the surface, the participants presented a higher rate of change in all parameters as they walked on the treadmill than as they did over the ground , BWS , and surface-BWS interaction . The univariate analyses revealed a surface effect for the step length  and double-limb support duration ; BWS effect for the step length  and double-limb support duration ; and surface-BWS interaction for the double-limb support duration . The participants presented a higher rate of change in the step length and double-limb support duration when they walked with BWS on the treadmill than when they did over the ground learning and/or reestablishing gait in individuals with gait impairment.Regarding gait variability during walking on the treadmill and over the ground and the amount of BWS, the non-disabled young adults presented higher variabilities when they walked over the ground than when they did on the treadmill. Similar results have been found when comparing walking on a treadmill and over the ground with no BWS37. The lower the rate of change, the easier the generalization from intervention settings on a daily life basis. Therefore, walking over the ground with a BWS would promote better and more generalizable practice for individuals seeking to improve their walking capabilities in a clustered environment. This is an important issue that should be taken into consideration in any intervention protocol.Finally, our study revealed that the non-disabled young adults presented a higher rate of change as they walked on the treadmill than as they did over the ground, even if the walking speed was controlled through a servo-motor in the overground BWS system and the treadmill’s belt. These results are particularly important if we consider that the main goal of a gait intervention program is to improve ambulation in daily life38. Future investigations should aim to include other measurements to provide a full description of gait pattern changes. Second, the participants walked at 80% of their self-selected walking speed with the BWS systems on both surfaces, which might have influenced their performance. However, we decided to employ similar walking speeds on both surfaces because a previous investigation revealed that the non-disabled young adults’ self-selected walking speed with BWS over the ground was 20% slower than their free self-selected walking speed19. Thus, the effects of manipulation of the walking speed and use of BWS on different surfaces remain to be investigated. Lastly, only a short period of familiarization was provided for the participants, which might not have been enough to reveal other effects of BWS and the surfaces during walking. Such a short period of familiarization was necessary owing to all manipulations employed in this study; this prevented any longer periods spent in the laboratory, although the period was similar for all participants in all experimental conditions. This issue still requires further investigation for a better understanding.This study presents new insights related to the use of BWS on treadmill and over the ground; however, there are some limitations that should be considered. First, this study only examined spatio-temporal parameters, which might not account for all the changes in gait patterns with the use of BWS on different surfaces. However, we selected parameters related to the time and distance aspects of walking that constitute the performers’ stride characteristics. As such, these parameters represent their basic walking capabilityThe overall results of this study showed that the surface and the amount of body weight unloading modify gait patterns. The use of a BWS system over the ground seems to be more appropriate for gait intervention than that on a treadmill. However, future studies should investigate the effects on different populations with gait impairment.


Dear Editor;To increase patient safety, it is the direct responsibility of all healthcare providers to migrate from negligence and approach how to prevent mistakes . In deveMortality and Morbidity conferences (M&M) focusing on learning from errors are required educational series in all residency programs . Based oWorking effectively in different health care delivery situations, awareness of cost and risk-benefit analysis, working in inter-professional teams and actively recognizing system errors and applying systems solutions are parts of systems-based practice. Accordingly, Interpersonal and communication skills need the ability to successfully communicate with physicians, other healthcare professionals, and health-related organizations .Morbidity & Mortality conferences, which are described in different methods –9, are aThe inattention of operating resident to the level of expertise of the optometrist who provides the biometric data required for surgery.Crowded environments in optometry division and operating room.Shortage of required equipment including optical biometry which enhances the accuracy of the test and paucity of the health workforce population in relation to the patient.Lack of cooperation of patients during ultrasound biometry.The selected case was a 62-year-old otherwise healthy woman who had undergone uneventful phacoemulsification with one-piece intraocular lens implantation in the right eye 2 weeks earlier. She was not satisfied with the operation and complained of blurry vision afterward. Her refraction before surgery had been − 1.00 D and − 1.50 D for right and left eye, respectively. After the operation, uncorrected distance visual acuity (UDVA) in the right eye was 20/40 which improved to 20/20 with + 2 diopter spectacle correction. The anterior segment, intraocular pressures, optic nerve, and fundus examinations were all unremarkable. Macular optical coherence tomography was normal as well. The induced refractive surprise was attributed to the possible biometry error. The case selected to be scrutinized by further analysis of other possible sources of errors and was referred to the morbidity & mortality committee. This committee’s members consisted of the residents involved, the engaged attending physicians, the head of the optometry department, the optometrist measured the biometry and the operating room staff. The causative factors recognized in the session are shown as a fishbone diagram. (Fig. To implement the change, we asked the optometrists to set a new policy for optometry division. The A-scan forms should be signed by the experienced technicians as a sign of supervision. The surgeons should know the level of expertise of the personnel of the optometry division and it is their direct responsibility to confirm the validity of the A-scans.Providing a sincere atmosphere in which the main focus is on finding the root causes of error rather than blaming the individuals.Increasing the ability of intercommunication skills among residents and the team who are critical for the excellence of outcome.Practicing systems-based thinking through knowing all procedures involved in the context of the working area.The benefits of running M&M using this method of analysis include:In conclusion, we recommend that holding Morbidity and Mortality conferences regularly using the root cause analysis method is crucial to strengthen patient safety and to increase the residents’ intercomminucation skills and systems-based thinking. These conferences are effective methods to provide the unique opportunity of learning from mistakes in a sincere atmosphere in which the main focus is on the recognition of the root causes of error and prevention of them in the future.


Dispatcher-assisted cardiopulmonary resuscitation (DA-CPR) is highlighted in 2015 resuscitation guidelines , reduce Medical dispatchers have also indicated that it could be a benefit to receive visual information based on simulations of OHCA scenarios . This isThe aim of the present study was to explore the medical dispatchers’ perception of the bystanders’ responses and dispatchers’ reflections about the added value of visual information in real out-of-hospital cardiac arrest (OHCA) situations investigated with CCTV recordings.The Emergency Medical Dispatch Centre (EMDC), Copenhagen, Denmark serves 1.8 million inhabitants. All 112 emergency calls are initially handled by a call centre that identifies the location and then forwards all medical calls to an EMDC. An emergency medical dispatcher answers the call and determines the appropriate response, while a technical dispatcher handles the logistics of simultaneously dispatching ambulances. The medical dispatchers are specially trained registered nurses and paramedics. The decision-making process is supported by a standardised national criteria-based dispatch tool (Danish Index for Emergency Care) . In caseThe study was a qualitative study designed as an explorative interview study including medical dispatchers who had previously handled a case of OHCA captured on CCTV. The CCTV recordings were collected in relation to a previous study 11). The primary researcher invited by e-mailed 10 out of 21 relevant medical dispatchers to participate. We chose medical dispatchers who were available (still employed and not sick or on maternity leave) and where the CCTV footage of the OHCA showed most information about the victim and the resuscitation attempt. No relationship to the medical dispatchers were established before the study. The medical dispatchers were interviewed individually at the EMDC with a timeframe from 6 months to one year after collection of the CCTV recordings. The duration of the interview was approximately one hour. First, the medical dispatcher listened to the audio recording until a natural break occurred in the conversation between the caller and the medical dispatcher – for example, when the medical dispatcher had to register information in the electronic system or changed the subject. The interviewer then asked the medical dispatcher to visualise his or her perception of the situation using mini-mannequins , median (range). None of the contacted medical dispatchers declined to participate. The characteristics of the situations in the 10 CCTV recordings are shown in Table The medical dispatchers expressed an improved understanding of the OHCA scenario after they watched the CCTV recording. The CCTV footage provided more information about the victim, the physical setting and the bystander response. Table In cases where the CCTV contributed with more close-up pictures of the victim, the dispatchers were able to observe the victim’s condition including consciousness, skin color, and respiration patterns. In all cases, the dispatcher could observe when the victim stopped having voluntary movements, as an indicator of unconsciousness. Furthermore, the dispatcher received information about the victim’s position: if the victim was held in sitting position, lying on the back, or lying on the side. The dispatchers sometimes thought the victim was sitting independently when he or she was actually being held in the sitting position, because the caller stated that the victim was “sitting.” In one situation, the dispatcher thought the victim was sitting by himself and therefore did not identify OHCA.The dispatcher also perceived more information about the physical setting with the CCTV recording, including the location of the victim and hence, the bystanders’ working conditions. In one case, the victim was lying in a doorway, which made it difficult for the bystanders to assist the victim. The dispatcher was not aware of this before he watched the CCTV recording.All OHCAs were in public places. If the caller did not mention the number of bystanders directly, all medical dispatchers thought there were several bystanders in the surroundings when the OHCA happened in daytime. The medical dispatchers’ perceptions sometimes failed when it came to estimating the number of bystanders helping the victim or performing CPR. Often the dispatchers thought more bystanders were involved.The medical dispatchers often presumed that the quality of CPR performed was adequate, and they asked few or no questions to get information about this, so in case of inadequate bystander CPR, the medical dispatchers’ perceptions were not in accordance with reality.The CCTV recordings provided an improved understanding of how the CPR was performed, including shift among laypersons to provide compressions, hands-of-time, hand position, compression rate, and continuous evaluation. Furthermore, most medical dispatchers assumed that the callers were very close to the victims. In many cases, however, the caller was standing some distance from the victim.The medical dispatchers expressed that their improved perception of the OHCA scenario could improve the communication with the bystanders, their ability to guide more bystanders and the quality of CPR in the future. . (DOCX 15 kb)


Inequalities in diabetes are widespread and are exacerbated by differences in lifestyle. Many studies that have estimated inequalities in diabetes make use of self-reported diabetes which is often biased by differences in access to health care and diabetes awareness. This study adds to this literature by making use of a more objective standardised measure of diabetes in South Africa. The study estimates socio-economic inequalities in undiagnosed diabetes, diagnosed diabetes (self-reported), as well as total diabetes (undiagnosed diabetics + diagnosed diabetics). The study also examines the contribution of lifestyle factors to diabetes inequalities in South Africa.This cross sectional study uses data from the 2012 South African National Health and Nutrition Examination Survey (SANHANES-1) and applies the Erreygers Concentration Indices to assess socio-economic inequalities in diabetes. Contributions of lifestyle factors to inequalities in diabetes are assessed using a decomposition method.p < 0.05 and CI = 0.0859; p < 0.05). The concentration index for undiagnosed diabetes was insignificant but pro-poor. The decomposition showed that lifestyle factors contributed 22% and 35% to socioeconomic inequalities in self-reported and total diabetes, respectively.Self-reported diabetes and total diabetes (undiagnosed diabetics + diagnosed diabetics) were significantly concentrated amongst the rich  when compared to the self-reported diabetes outcome. Although in comparison to other determinants, the contribution of lifestyle factors was modest, these contributions are important in the development of policies that address socio-economic inequalities in the prevalence of diabetes. Non-communicable diseases (NCDs) are currently the leading cause of death globally. According to the World Health Organisation, NCDs are projected to overtake all other causes of death in Africa by the year 2030 . In the Research has been consistent in showing that there is an association between socioeconomic inequalities and diabetes prevalence –10. WhilThe use of self-reported data on illness without any other standardised measures is reported to result in the exclusion of undiagnosed individuals, especially those in groups with low socio-economic status  who mighWhilst the causes of type 1 diabetes are unknown, the risk of type 2 diabetes is determined by an interplay of factors such as ethnicity, age, socio-economic status and various lifestyle factors , 20, whiEstimating inequalities in diabetes and determining the contributions of avoidable diabetes risk factors to these inequalities can help South Africa in working towards meeting the 2030 sustainable development goal 3 (SDG 3), which targets a reduction in premature deaths due to NCDs (including diabetes). This study therefore aims to (1) describe the prevalence, treatment and control of diabetes among South Africans across various socio-economic groups; (2) to determine socio-economic inequalities in the prevalence of diabetes using the CI; and (3) to examine the contribution of dietary, lifestyle and metabolic risk factors to socio-economic inequalities in diabetes prevalence by conducting a decomposition analysis. Our study makes important contributions to the body of literature on inequalities in diabetes. To the best of our knowledge this is the first South African study to make use of clinical outcomes in addition to self-reported data in the estimation of socio-economic inequalities in diabetes and the only study that allows a more in-depth analysis into the contribution of a number of lifestyle factors to inequalities in diabetes.Data are taken from the 2012 South African National Health and Nutrition Examination Survey (SANHANES-1). SANHANEThe SANHANES-1 survey comprised a questionnaire component and a clinical examination. Three questionnaires were administered during the survey: a household questionnaire, an adult questionnaire and a child questionnaire. In this study, the household questionnaire is the source of data for the wealth index. Information on self-reported diabetes and lifestyle factors is drawn from the adult questionnaire. The adult and child questionnaires were administered in the individual’s households. The SANHANES-1 survey did not draw a distinction between type 1 and type 2 diabetes.Blood samples were collected during the clinical examinations which were conducted at various facilities such as school halls, church halls, primary healthcare facilities, community centres and city halls. The blood samples were collected from individuals, aged 6 years and older, and used for biomarker analysis. The clinical examinations were conducted by experienced medical doctors and nurses on consenting individuals. The blood samples were collected and stored in cooler boxes and delivered to a laboratory within 24 hours. No deviations from established quality control measures were reported.Fig 1.Our analysis is restricted to individuals above the age of 15 who had a blood sample taken and had non missing information on HbA1c; 17.8% had missing data on the wealth index and were also excluded. The final analytical sample used in this study is 3 438. Details of our exclusion criteria are shown in To measure inequalities in diabetes, this study makes use of the CI. The calculation of the CI requires a measure of socioeconomic status. In this study, a wealth index is used for this purpose. The wealth index was constructed with the aid of Multiple Correspondence Analysis (MCA). The household and living conditions considered in the creation of the wealth index are housing type, water and sanitation services, and a set of thirteen household assets. The full list of thirteen household assets is as follows: ownership of a fridge, television, stove, mobile phone, radio, DVD , washing machine, computer, DSTV , motorcar, vacuum cleaner, telephone (landline) and internet access. Imputation by iterative binomial and multinomial logistic regression analysis, applied using Stata’s mi function, is employed to deal with item non-response. Asset ownership is imputed as a function of the ownership of the twelve other assets, whereas housing type is imputed from information on the material of the wall and roof of a dwelling. The percentage inertia explained by the first dimension is approximately 90%.μ):The CI is derived from the concentration curve (CC) which plots the cumulative percentage of the health variable against the cumulative percentage of the population ranked by the living standards measure  and the Since all the health variables in our study are binary, a normalisation process is required to measure inequality. This study makes use of the Erreygers corrected concentration index which is algebraically expressed as shown below .μ is the mean of the health variable, CI is the standard CI, b is the maximum value of the health variable (in this case 1) and a is the minimum value of the health variable (in this case 0). Similar to previous studies [conindex command in STATA to estimate inequalities.Where  studies , 27 we mThe CI can be decomposed into the factors that contribute to the measured inequality . A reviehi (diabetes), is linked to a set of explanatory variable xij by the following linear model.Following Wagstaff et al.  our healhi can be written as [If we have such linear model as shown in Eq  Wagstaffitten as :CI(h)=∑CI(h) is the CI for the health variable h (diabetes), xj, μh is the mean of the health variable, CI(xj) is the CI for xj, GCε is the generalised CI for the error term. In this equation the first part is the weighted sum of the CI for the variable xj. The weight of each regressor is determined by the elasticity  iEq  can now The absolute contribution a variable makes to socio-economic inequality is a product of the elasticity , we calculated the proportion of diabetics that reported being on diabetic treatment. Diabetic individuals were considered to be taking treatment if they reported currently taking insulin or tablets to lower their blood glucose levels. A binary variable was created which took on the following values, 0 = if a self-reported diabetic individual reported not taking insulin or tablets for the lowering of blood glucose levels, and 1 = if a self-reported diabetic individual reported taking insulin or tablets for the lowering of blood glucose levels see .Among the sample on diabetes treatment we calculated the proportion of individuals with controlled diabetes. Diabetes was defined as controlled if the respondent reported taking diabetes treatment (insulin or tablets) and had an HbA1c test of < 6.5%. A binary variable was then created for diabetes control amongst the treated sample, taking on the following values: 0 = individual was on diabetes treatment and had an HbA1c test of > 6.5%, 1 = if the individual was on diabetes treatment and had an HbA1c test of < 6.5% see .A systematic review and meta-analysis by Aune et al. shows that all types of physical activity are beneficial in reducing the risks of type two diabetes . PhysicaApart from the lifestyle factors and the wealth index, we also included a range of other variables which past literature has shown to influence health –48. ThesIn each diabetes health outcome category the sample is predominantly female, resides in urban areas, is unemployed, has no health insurance and is overweight or obese. Our sample is predominantly within the age group of 36 to 60, with the exception of controlled diabetes. Approximately 63% of the undiagnosed sample was made up of Africans. The group that self-reported diabetes was predominantly non-African. However, the majority of respondents under treatment and with controlled diabetes were African. Based on the lifestyle factors, our sample predominantly consumed fruits or vegetable portions less than four times a day and did not consume alcohol. A majority of the individuals who self-reported diabetes did not drink alcohol, and conducted weekly exercise equivalent to more than 2 000 MET-minutes. With the exception of diagnosed diabetes a majority of the respondents within the health outcomes were obese.In order to better understand the lifestyle factors that contribute to inequalities in the diabetes outcomes we conducted a decomposition analysis. The decomposition analysis was conducted only for the measured inequalities in self-reported and total diabetes. Our study does not decompose inequalities in undiagnosed diabetes because our findings show that the measured inequalities in this diabetes outcome was statistically insignificant.As shown in The marginal effects in Our paper provides evidence on the socio-economic inequalities in various diabetes outcomes using the CI and identifies the contribution of lifestyle factors to socio-economic inequalities in diabetes prevalence by conducting a decomposition analysis. To the best of our knowledge this is the first paper to incorporate biomarker analysis in the measurement of diabetes inequalities in South Africa and the first to attempt to measure the contribution of various lifestyle factors to socio-economic related inequalities in diabetes. Consistent with the study by Stokes et al., our study documents the high levels of undiagnosed diabetes in South Africa . This stOur findings corroborate other related literature that demonstrates the existence of socio-economic inequalities in diabetes , 15. FurThe decomposition of inequalities has become an important tool in influencing policy in inequality studies. Decompositions provide important information on the sources of the observed inequalities. Whilst the largest contributions to the inequalities in diabetes in this study came from residence and socio-economics status, the contributions of lifestyle factors further exacerbate these inequalities and are the focus of this analysis. Various studies have attempted to estimate the contribution of lifestyle factors to health in general , 50–52 aAmongst the lifestyle factors in our study obesity makes the largest contribution to socio-economic inequalities in both self-reported and total diabetes variable. Stringhini et al, using data from the London Whitehall II Study also finds that amongst the health behaviours in the study obesity was the most important contributor to the relationship between socio economic status and diabetes . ObesityAlthough evidence suggests that diets rich in fruits and vegetable are associated with a reduced risk of type 2 diabetes –59, the Findings within the literature on the association between alcohol and diabetes have not been consistent . Some stIn our study smoking and physical activity make the smallest contributions to inequalities in diabetes. This is a result of the very small marginal effects and elasticities. Similar to the existing literature , we alsoA major strength of this study is that it made use of an HbA1c test, an objective measure of diabetes. This measure allowed us to measure the prevalence of undiagnosed and total diabetes. The study has some limitations that must be acknowledged. Whilst there are several regression based decomposition methods within the literature our study makes use of the Wagstaff method. Results may differ depending on the decomposition method applied . The AmeThis paper provides an analysis of the socio-economic inequalities in the prevalence of diabetes and determines the sources of these inequalities with a focus on modifiable lifestyle factors. The paper contributes to the literature on diabetes by making use of a more objective measure of diabetes and highlighting the magnitude of undiagnosed diabetes in South Africa. The study provides evidence that inequality in self-reported and total diabetes is concentrated among the rich. The magnitude of inequality estimates based on self-reported data only would be different when compared to inequality estimates based both on self-reported plus clinical data. The measured inequalities are mostly explained by residence and wealth. The contributions made by lifestyle factors to inequalities in diabetes, are less than the overall contributions of other factors within our model. Although modest, the contributions made by lifestyle factors to inequalities in diabetes provide important information for use in planning of interventions to reduce the burden of diabetes. Our study shows that in comparison to all other lifestyle factors obesity, alcohol consumption and vegetable consumption make large contributions to inequalities in diabetes. These findings are important to policy makers in terms of informing the design of effective strategies and policies for encouraging healthy lifestyles. Future national health surveillance surveys that capture larger numbers of individuals who provide blood samples are an ideal conduit for the monitoring of diabetes and the tracking of socio-economics inequalities in the prevalence, diagnosis and treatment of diabetes.