Objective. Current studies of serum sclerostin levels in AS and RA patients are inconsistent. This meta-analysis was performed to identify the association of serum sclerostin level with AS and RA patients. Methods. Embase, PubMed, MEDLINE, and Cochrane Library databases (up to 25 January 2017) were used to collect all relevant published articles. Studies were pooled and standard mean difference (SMD) with 95% confidence interval (CI) was calculated. All data analyses were performed using RevMan 5.3. Results. Totally eight studies of AS including 420 AS patients and 317 healthy controls (HC) and three studies of RA including 145 RA patients and 127 HC were finally included in this meta-analysis. The results revealed that the serum sclerostin levels in both AS patients  and RA patients  were not significantly different from those in HC. Conclusion. The difference of serum sclerostin levels in AS and RA patients was not significantly different from HC, indicating that the sclerostin may not associate with the development of AS and RA. Ankylosing spondylitis (AS) and rheumatoid arthritis (RA) are both chronic systemic autoimmune diseases. AS is a chronic inflammatory disease that mainly involves axial skeleton and sacroiliac joint, which is characterized by entheses inflammation, resulting in uncontrolled osteoproliferation that usually leads to fusion and rigidity of the affected spine. RA is characterized by persistent inflammation of synovium, which eventually gives rise to joint destruction and deformation . Progresβ-catenin pathway, which had been considered as a crucial modulating pathway for bone formation ; P = 0.61  . The sha = 0.28) . In ordeP = 0.61 .P = 0.41; I2 = 0), and fixed-effects model was used, and it demonstrated that serum sclerostin levels between RA patients and HC were not significantly different   . The shaSclerostin had been considered to work as a suppressor during bone formation, which was backed up by the observation that the differentiation and proliferation of human and mouse osteoblastic cells were suppressed as exogenous sclerostin was added to the cultures –23. FurtIn this present study, we retrieved seven articles to estimate the serum sclerostin levels in AS patients by meta-analysis. These results demonstrated no difference in serum sclerostin levels between AS patients and HC, suggesting that serum sclerostin levels may be irrelevant to the pathogenesis of AS in patients. Dickkopf-1 (Dkk-1), another Wnt signal pathway antagonist, had been reported to increase in AS patients in a meta-analysis . A recenConsidering the suppression of sclerostin in bone formation, the role of sclerostin was also investigated in RA patients. Vis et al. had firstly shown that serum sclerostin level related to the disease activity and radiographic joint damage in RA patients; however, we only get the abstract of their study. Sclerostin inhibition is considered as a powerful tool to enhance bone repair in inflammatory arthritis in mice reported by Chen et al., indicating that sclerostin plays a pivotal role in the development of RA . RecentlI2 was, respectively, high and low in the meta-analysis of serum sclerostin in AS patients and RA patients. To our knowledge, no data have been published comparing the BD Biosciences kit and R&D systems kit, Biomedica kit, or TECOmedical kit. The variability in values generated from these sclerostin ELISA kits raises questions regarding the accuracy and specificity of the assays.As far as we know, this is the first comprehensive meta-analysis of serum sclerostin levels in AS and RA patients. However, several limitations should be considered in this study. First, there are very few studies after the first sorting, which account for just 4%, and the number of patients is relatively small; thus the limited size might affect the conclusion. Second, the articles, which only support median and range, were excluded. The method of transformation had been reported by Hozo et al. ; howeverIn conclusion, our meta-analysis demonstrated that serum sclerostin levels in AS and RA patients were not significantly different from those in HC. These results suggest that sclerostin may not be associated with the development of AS and RA in patients. Nonetheless, determining the underlying mechanisms of sclerostin in AS and RA patients still awaits further analysis using larger samples.


Background: The Centers for Disease Control and Prevention reported that deaths from chronic respiratory diseases (CRDs) in Thailand increased by almost 13% in 2010, along with an increased burden related to the disease. Evaluating the geographical heterogeneity of CRDs is important for surveillance. Previous studies have indicated that socioeconomic status has an effect on disease, and that this can be measured with variables such as night-time lights (NTLs) and industrial density (ID). However, there is no understanding of how NTLs and ID correlate with CRDs. We compared spatial heterogeneity obtained by using local cluster detection methods for CRDs and by correlating NTLs and ID with CRDs.Methods: We applied the spatial scan statistic in SaTScan, as well as local indices of spatial association (LISA), Getis and Ord’s local Gi*(d) statistic, and Pearson correlation. In our analysis, data were collected on gender, age, household income, education, family size, occupation, region, residential area, housing construction materials, cooking fuels, smoking status and previously diagnosed CRDs by a physician from the National Socioeconomic Survey, which is a cross-sectional study conducted by the National Statistical Office of Thailand in 2010.Results: According to our findings, the spatial scan statistic, LISA, and the local Gi*(d) statistic revealed similar results for areas with the highest clustering of CRDs. However, the hotspots for the spatial scan statistic covered a wider area than LISA and the local Gi*(d) statistic. In addition, there were persistent hotspots in Bangkok and the perimeter provinces. NTLs and ID have a positive correlation with CRDs.Conclusions: This study demonstrates that all the statistical methods used could detect spatial heterogeneity of CRDs. NTLs and ID can serve as new parameters for determining disease hotspots by representing the population and industrial boom that typically contributes to epidemics. According to the World Health Organization, CRDs are the world’s leading cause of death. In Thailand, the Centers for Disease Control and Prevention reported that deaths from CRDs increased by almost 13% in 2010, and there was an increased burden resulting from these diseases2.Chronic respiratory diseases (CRDs) are a public health issue worldwide3. These methods differ from standard methods4 used to identify the presence of spatial clustering in a whole study area. Local clustering methods in spatial epidemiology that are commonly applied to understanding spatial clustering (heterogeneity) are the Getis and Ord’s local Gi*(d) statistic, spatial scan statistics, and local indices of spatial association (LISA)8.Spatial clustering methods are important for statistical consideration, to develop models for prediction of disease risk positions. Disease risk positions are areas located near one another in statistically significant clusters. In this study, we indicate whether the geographical aggregation of CRDs is explained by chance or is statistically significant. It is likely that geographical areas share similar disease risk factors, since they are in a similar environment. Local spatial clustering methods are useful to identify the characteristics of clusters in terms of location, size, and prevalence of the disease9. A local version of Moran’s I was one of our selective cluster detection methods, due to its great performance on outlier detection, despite not being a good method for detecting large clusters10. The local Gi*(d) statistic was used to indicate locations surrounded by a cluster of high or low values. It shows areas where lower than average values tend to be found near each other, or areas where higher than average values tend to be found near each other, including the value at the location in which the spatial autocorrelation is being measured.At present, there are no studies comparing the performance of these methods in epidemiological studies involving CRDs. In this study, we used local clustering methods to detect the spatial heterogeneity of CRDs and compared the performance of these local spatial clustering methods, including the local Gi*(d) statistic, the spatial scan statistic, and LISA. Spatial scan statistics performed in SaTScan circular version (version 9.4.2) were considered the best choice for detecting small, compact clusters15 and population density16. These data have been employed to monitor the speed of urbanization18, as well as electricity used19. Moreover, NTLs have been utilized as a substitute for Gross Domestic Product (GDP), which works as an indirect indicator of administrative areas24. Remotely sensed NTL data can also be applied in epidemiological studies26. A 2011 study entitled “Explaining seasonal fluctuations of measles in Niger using night-time lights imagery” in Niger, by Bhartiet al., found the concentration of the population affected by outbreaks of measles, and recommended that NTLs should be applied to public health studies27. Some studies in Israel have found that the incidence of breast and lung cancer in females is associated with NTL intensity in Israel28.In a socioeconomic context, previous literature has recommended that night-time lights (NTLs) can substitute for other variables, such as urbanization, density, and economic growth. Data on NTLs have been used to indicate the actual scope of urban incorporations and appraise population scale in urban areas29. Industrial density (ID) may also be related to disease. However, previous studies have not investigated how NTLs and ID correlate with CRDs.In Thailand, NTLs are used to estimate Gross Provincial Products (GPPs), because the luminosity of NTLs is normally dependent on the amount of economic activity in each area. The results showed that NTLs and GPP growth are significantly correlated and can represent the relationship between economic values and spatial inequalityThe government of Thailand has utilized various measures to attempt to prevent, diagnose, and treat CRDs. However, spatial clustering of diseases, particularly at a national level, has rarely been applied to examine characteristics of CRD clusters in terms of their location, size, and magnitude in Thailand. In addition, there has been limited use of socioeconomic indicators, such as NTLs and ID, in the epidemiology of CRDs.In this study, commonly used methods for spatial clustering of CRDs were compared, including the use of the spatial scan statistic, LISA, and the local Gi*(d) statistic. We also analysed whether there was a correlation between NTLs and ID with CRDs. Our findings could help decision makers implement more relevant disease control policies using the performance of these local cluster detection methods, as well as the correlation of CRDs with NTLs and ID. The availability of a suitable strategy for spatial clustering of CRDs may allow for a better allocation of health care resources.Thailand occupies an area of 514,000 square kilometres, which consists of an area of 511,770 square kilometres of land and 2,230 square kilometres of water. Thailand shares a border with Myanmar, Cambodia, Laos and Malaysia. In 2010, there were 76 provinces, 878 districts (Amphoe), 7,225 sub-districts (Tambon), and 74,965 villages (Mooban) in Thailand.The Ethical Committee of Khon Kaen University deemed this study to be exempt from requiring ethical approval (reference no. HE 582315).Spatial data. The geographical coordinates of administrative areas of Thailand were retrieved from DIVA-GIS online (http://www.diva-gis.org/gdata), which is publicly available. This database of provincial maps of Thailand was processed with Quantum GIS for further GIS based analysis. For GIS based analysis, we applied zonal statistics to calculate mean NTLs and ID. The spatial distribution of CRD prevalence was obtained from the province-level polygon map, which contains information regarding the latitudes and longitudes of each province. The calculated mean NTLs, ID and CRD prevalence were further visualized with province-level layers on the polygon map and labelled with the administrative code of each province.CRD, NTL and ID data. This study used data from the National Socioeconomics Survey (NSS), a cross-sectional study conducted by the National Statistical Office (NSO), Thailand, in 2010. Using stratified two-stage sampling, the survey selected a nationally representative sample to respond to a structured questionnaire. The questionnaires collected information on gender, age, household income, education, family size, occupation, region, residential area, housing construction materials, cooking fuels, smoking status and previous diagnosis of CRDs. A total of 17,040 individuals who met the inclusion criteria (aged between 18–59 years and diagnosed with having CRDs by a physician) were included in this analysis. The NSO administrative board officially allowed the research team to use the data (reference no.050601/1441).https://www.ngdc.noaa.gov/eog/dmsp/downloadV4composites.html). The ID data were calculated from GPPs, which represent the official statistics on provincial economic activity published by the National Economic and Social Development Board (NESDB). GPP data are also publicly available (http://www.nesdb.go.th/nesdb_en/more_news.php?cid=156&filename=index).The NTLs of Thailand used in this study were based on global stable lights imagery from 2010, acquired from the Operational Linescan System (OLS) sensor on-board satellite F18 under the Defense Meteorological Satellite Program (DMSP). All NTL data are publicly available  and the local Gi*(d) statistic31. Stata version 10.0  was used to calculate CRD prevalence. SaTScan (version 9.0.1) was used to determine the presence of statistically significant CRD spatial clusters and identify their approximate locations.The Open GIS software Quantum GIS (version 2.8.5) was used to conduct exploratory spatial data analysisLocal indices of spatial association. The local Moran’s I investigates the local level of spatial autocorrelation of provinces with a high and low prevalence of CRDs32. Computation of LISA assesses the local version of Moran’s I for each location to determine the variation in spatial autocorrelation over the study area. Its significance is evaluated in five categories: High-High, Low-Low, Low-High, High-Low, and Not Significant. Spatial autocorrelation occurs when a high prevalence of CRDs correlates with a high prevalence in neighbouring areas , or when a low prevalence of CRDs correlates with a low prevalence in neighbouring areas 33. This study set the spatial weight matrix of 3 k-Nearest Neighbours around each province, meaning that the clustering relation was identified with three neighbouring provinces. The statistical significance level was 0.05, and the simulation used 999 permutations34 to evaluate the sensitivity of the results.Getis and Ord’s local Gi*(d) statistic. The local Gi*(d) statistic is used to test the statistical significance of local clusters and to determine the spatial dependence and relative magnitude between observations. In this study, the local Gi*(d) statistic was used to test statistically significant CRD prevalence and local autocorrelation, as well as to determine the dependence of neighbouring observation35 and local clustering. We used k-Nearest Neighbours, and contiguity was set as three provinces for a polygon contiguity spatial weight matrix, which was created depending on three provinces that share common boundaries and vertices. A 0.05 significance level and 999 permutations were used to identify significant clusters of local autocorrelation. A high value for the local Gi*(d) statistic indicates a hotspot, and a low value indicates a cold spot37.Spatial scan statistic. The spatial scan statistic, calculated in SaTScan38, was applied to analyse the geographic distribution of CRD prevalence in Thailand and determine whether there are any spatial geographical clusters of CRD prevalence as for high or low CRDs. We used the retrospective purely spatial Poisson model to identify spatially high or low clusters of CRDs, since it fit the assumption that the number of events in an area was Poisson distributed according to a known underlying population at risk. Purely spatial analysis, which ignores the time dimension of the cases, was performed to detect CRD clusters in the study areas. The purely spatial Poisson model imposed circular windows on the map and let the circle move over the area. Each circle moved until reaching a maximum population at risk. The radius of each circle increased continuously from to a maximum radius. Therefore, the window never encompassed more than 50% of the total population at risk, i.e., the maximum spatial cluster size, in this investigation. The likelihood function was maximized over all window positions and sizes, and the one with the maximum likelihood constituted the most likely cluster. For each window, we used a Monte Carlo simulation to test the null hypothesis that no significant clusters were created, supposing that the relative risk (RR) of CRDs was no different within the window compared to outside the window. The maximum number of random Monte Carlo replications was defined as 999. The likelihood function was maximized in the overall window locations and sizes39. The window with the maximum likelihood value of the spatial window was set to 50% of the population at risk.Pearson correlation assessed the relationship between NTLs and ID with CRDs in all provinces. There was a moderate positive correlation of 0.34  and 0.36  with CRDs, which indicated that NTLs and ID had similar relationships to CRDs .The Univariate Moran’s I scatter of annual CRD prevalence among provinces in Thailand in 2010 showed a slightly positive spatial autocorrelation, as the Moran’s I was 0.072, with statistical significance at 0.05. The Moran’s I indicated clustering patterns. There were hotspots in Bangkok and the perimeter provinces, namely, Samut Prakan, Nonthaburi, Pathum Thani and Nakhon Pathom. Cold spots and low prevalence areas, were located in the Nan and Phitsanulok provinces. Two high/low clusters were located in Trang, Songkhla, and part of the Phatthalung provinces, indicating that the distribution of the CRD in affected provinces was spatially autocorrelated (low clustering), even though the overall tendencies were not clear. This could be due to the nature of local Moran’s I indices; the statistic cannot accommodate extreme values or outliers in the data set, such as the very low CRD prevalence in some provinces that were also included in the model. Therefore, its sensitivity in cluster detection may be reduced. Using results from other local spatial analysis methods could help explain the autocorrelations.Considering NTLs and ID with CRDs in terms of bivariate Moran’s I scatter for annualized prevalence in 2010, the results showed a positive spatial autocorrelation, with Moran’s I values of 0.345 and 0.374, respectively. These values were statistically significant, with a significance level of 0.05. Based on these results, hotspots were located in Bangkok and perimeter provinces, consisting of Samut Prakan, Nonthaburi, Pathum Thani and Nakhon Pathom. The results showed a high, significant correlation between the NTLs with CRDs, suggesting that brighter lights, together with high population densities of exclusively wealthier people, can be used to define the spatial extent of CRD development. Therefore, ID, which reflects urbanization, was correlated with CRDs .For the local Gi*(d) statistics, hotspots were located in Bangkok and perimeter provinces, consisting of Samut Prakan, Nonthaburi, Pathum Thani and Nakhon Pathom . The spatial scan statistics revealed two spatial clusters. The primary cluster was located in 23 provinces. There were 157 cases compared to 213 expected cases. Thus, the ratio between observed and expected cases was 0.74. The p-value was smaller than 0.05, which indicated that the cluster was highly significant. The relative risk for the population inside the cluster compared to the population outside the cluster was 0.65, indicating that the risk of CRDs within this area was higher than locations outside it. This number is the estimated risk within the cluster, divided by the estimated risk outside the cluster. The analysis also revealed that a secondary cluster, which is the most likely cluster, was located in 32 provinces. The number of observed cases was 348, compared to 297 expected cases. Thus, the ratio between observed and expected cases was 1.17. According to these calculations, the relative risk inside the cluster is 1.37. The p-value was smaller than 0.05, indicating that the cluster was highly significant. The relative risk for the population inside the cluster compared to the population outside the cluster was 1.37, indicating that the risk of CRDs in this area was higher than locations outside it . In summary, as seen in26. The findings of this study suggest that NTLs and ID can substitute for some variables, such as urbanization, density, economic growth, and industrial concentration. NTLs and ID serve as a new tool for specifying disease hotspots by representing the population and industrial booms typically contributing to epidemics. In urban areas with migration, NTLs can indicate where populations are clustering, through the captured expansion and the increase brightness of lighted areas. The technique indicates fluctuations in population density, which affect epidemic risk and can replace current methods of outbreak surveillance40.In this study, NTL and ID data were utilized to assess growth in provinces based on geography, economic growth, urbanization, or health metrics, as well as concentration of NTLs and ID. Areas with a high CRD prevalence also experienced high light growth and a high concentration of ID, and therefore NTLs and ID were probably induced CRDs. In other words, the results indicated that CRDs are affected by population density and industrial concentration. This finding was consistent with a previous study that stated that it is possible to use NTLs in public health studies41. In this study, all of these methods provided comparable results in the detection of geographic areas of CRDs in both high and low- rate clusters. However, there were some inconsistencies. The identification of clusters by spatial scan statistics was more localized, compared to LISA and local Gi*(d) statistics, possibly due to their dissimilar role in defining clusters. LISA detected the neighbouring provinces with rates significantly correlated to each other, and areas with similar values were thus determined as a cluster32. In this study, LISA explored the correlation between the value of a certain area and the average value of neighbouring areas. Spatial scan statistic methods were used, and larger clusters were detected; the calculation of the maximum likelihood ratio of cases was made together with the underlying population in the area to investigate the cluster of provinces considered to have higher or lower rates. Using spatial scan statistics methods, larger clusters were detected, and the calculation of maximum likelihood ratio of cases was made simultaneously with the underlying population in the area to investigate the cluster of provinces and consider them for higher or lower rates42. Meanwhile, the local Gi*(d) statistic and LISA reported that the two methods shared similar results in terms of rising clusters (hotspots). Hanson and Wieczorek7 compared LISA to spatial scan statistics by exploring alcohol-related mortality in New York, USA (which identified some differences between two methods). It was concluded that the spatial scan statistic is the more sensitive method. A multi-method approach is adopted for cluster detection, since different methods tend to identify different characteristics in clusters, i.e., LISA identifies the core of the cluster, and the scan statistic identifies its extent. Furthermore, Jacquez and Greiling43 used LISA to analyse spatial clustering for diagnosis of breast, lung and colorectal cancers in Long Island, USA, and to identify significant spatial patterns for all of these diseases. Their analysis confirms the clustering of breast cancer mortality previously revealed by Kulldorff’s spatial scan statistic44, but they found that the two methods identified slightly different clusters. As a result, they recommended that a combination of statistics be used when studying local clustering to assure that different aspects of spatial patterns are fully identified and that the results from the suite of analyses are logically consistent.LISA, spatial scan statistics, and local Gi*(d) statistics are common methods applied to investigate local clusters of diseases. These techniques help identify clusters and assess their statistical significance45.Due to its own principle for cluster detection, each method selected different geographic areas. The intersection of the selections indicated that these three methods resulted in different features in the same clusters, suggesting that various methods should be applied to identify clusters of CRDs at the provincial level in Thailand. These spatial cluster detection methods can be utilized collectively rather than individually. Furthermore, because each method has its own advantages and disadvantages, no single method is deemed the “gold standard” for cluster analysis46, whereas the mortality may relate with the accessibility to healthcare service in Bangkok metropolitan and some surrounding area.We study the CRDs morbidity which is mainly linked to the epidemic of tobacco exposure and indoor and outdoor air pollution in Asian countriesThe strengths of this study included the methods used to analyse CRD cases, including spatial scan statistics, LISA, and local Gi*(d) statistics. All of these have been thoroughly investigated and compared to determine the appropriate methods for evaluating spatial clustering and cluster detection. NTLs and ID were included to investigate a correlation with CRDs. This study contained a large sample size that represents a nationally representative sample. Therefore, the results could represent the Thai population in general.There are two aspects of limitations. The first is based on the availability of data. Therefore, there are limited resolutions in both spatial and time dimensions of data. The second limitation is caused by the characteristic of data and analytical method that do not permit the cause-and-effect conclusion, particularly the connection between individual exposure histories and individual outcome events.Abbreviations: CRDs: Chronic respiratory diseases, NSO: National Statistical Office, NSS: National Socioeconomics Survey, LISA: Local Indices of Spatial Association, ID: Industrial density, NTLs: Night time lights.Pearson correlation with CRDs datasetClick here for additional data file.Copyright: © 2018 Laohasiriwong W et al.2018Data associated with the article are available under the terms of the Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication).NTLs and ID datasetClick here for additional data file.Copyright: © 2018 Laohasiriwong W et al.2018Data associated with the article are available under the terms of the Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication).LISA (Univariate) cluster CRDs datasetClick here for additional data file.Copyright: © 2018 Laohasiriwong W et al.2018Data associated with the article are available under the terms of the Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication).LISA  cluster datasetClick here for additional data file.Copyright: © 2018 Laohasiriwong W et al.2018Data associated with the article are available under the terms of the Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication).LISA  cluster datasetClick here for additional data file.Copyright: © 2018 Laohasiriwong W et al.2018Data associated with the article are available under the terms of the Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication).Local Gi*(d) cluster CRDs datasetClick here for additional data file.Copyright: © 2018 Laohasiriwong W et al.2018Data associated with the article are available under the terms of the Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication).Spatial scan statistics in SaTScan analysis of CRDs datasetClick here for additional data file.Copyright: © 2018 Laohasiriwong W et al.2018Data associated with the article are available under the terms of the Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication).This study utilized a geographic information system and spatial analyses, which can be applied to several epidemiological studies to investigate and clarify the spatial heterogeneity of CRDs in highly affected provinces in Thailand. LISA, spatial scan statistics, and local Gi*(d) statistics were calculated, with the aim of revealing the spatial characteristics of the CRDs. Spatial scan statistics performed much better in outlier detection in terms of power, compared to LISA and local Gi*(d) statistics. Based on our simulation, there was a large relative risk difference  and significant spatial heterogeneity. The findings suggested heterogeneity in the spatial pattern of CRDs in the study. Purely spatial retrospective analysis revealed persistence of CRD clusters in some geographical locations of the provinces on an annual basis. CRDs were affected by the concentration of NTLs and ID. We believe that NTLs and ID serve as a new tool for specifying disease hotspots by representing the population and industrial booms that typically contribute to epidemics. The results of the study provide helpful information on the common epidemiological situation of CRDs. Demonstrating the existence of CRDs hotspots in different provinces may enable the Ministry of Public Health or provincial health officers to launch remedial measures in the affected areas and formulate strategies for more effective handling of CRDs.The data referenced by this article are under copyright with the following copyright statement: Copyright: © 2018 Laohasiriwong W et al.Data associated with the article are available under the terms of the Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication).Data used in this study were from the NSS; permission to use these data can be requested from the NSO. This study got approval from the NSS (reference no.050601/1441) to use the data on gender, age, household income, education, family size, occupation, region, residential area, housing construction materials, cooking fuels, smoking status and previously diagnosed CRDs by a physician. NTL data and ID data are publicly available.https://www.ngdc.noaa.gov/eog/dmsp/downloadV4composites.html).The NTL data are from the Operational Linescan System (OLS) sensor on-board satellite F18 under the Defense Meteorological Satellite Program (DMSP) (http://www.nesdb.go.th/nesdb_en/more_news.php?cid=156&filename=index).The ID data are from the National Economic and Social Development Board (NESDB)  cluster CRDs dataset.10.5256/f1000research.12128.d17870249DOI,Dataset 4. LISA  cluster dataset.10.5256/f1000research.12128.d17870350DOI,Dataset 5. LISA  cluster dataset.10.5256/f1000research.12128.d17870451DOI,Dataset 6. Local Gi*(d) cluster CRDs dataset.10.5256/f1000research.12128.d17870552DOI,Dataset 7. Spatial scan statistics in SaTScan analysis of CRDs dataset.10.5256/f1000research.12128.d17870653DOI, I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. All suggestions were incorporated in the revised version and I recommend that the article be published in F1000Research.I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. , since they do not link individual exposure histories to individual outcome events. Thus, I believe the authors can provide a paragraph on the limitations of the study.The authors present interesting data about the possible associations between socioeconomic status  and chronic respiratory diseases. The manuscript is well written,  the methods and the results sections are adequate, and should be of great interest to the readers. Overall, it is an important study, but their limitations are not mentioned. For example, ecological studies do not permit cause-and-effect conclusions or evidence that risk-modifying factors are related to risk of the diseaseI have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. There are two aspects of limitations. The first is based on the availability of data. Therefore, there are limited resolutions in both spatial and time dimensions of data. The second limitation is caused by the characteristic of data and analytical method that do not permit the cause-and-effect conclusion, particularly the connection between individual exposure histories and individual outcome events. In the methods, the author used SatScan and used 50% of population at risk to be a radius at the province level. Using such technique at the provincial level (large geographic area) may lead to inaccurate cluster as you can see in the result that one cluster cover the whole region.In the result, Moran’ I present very low spatial correlation (0.07), although it presented statistically significant. The correlational value cannot accept the spatial correlation that exists, and therefore all following spatial data analysis may not be necessary.LISA and Local G were used for different purpose, I don’t think comparison between this two is needed.The number of CRD may relate with the accessibility to healthcare service in Bangkok metropolitan and some surrounding area. The author may explain whether this point may affect the study result.I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Explanation to question 1.We used the SaTScan, using the Poisson probability model and constrained to clusters no larger than 50% of the population at risk and 50% of this study to locate potential circular cluster areas based on the recommendation of Kulldorff M. Kulldorff M (2016) suggested that 50 percent is the default maximum, a smaller maximum may be requested. However, the recommended value is 50 percent  which reported that a window sized up to 50% of the population at risk can generally reduce negative clusters. Whereas 90%, is more appropriately interpreted as a lower disease rate in the 10% of the area outside the ‘cluster’ rather than as an excess disease rate covering almost the whole study region, therefore, it did not appropriate with our study.References:Abdurrob A., Kulldorff M. SaTScan Tutoria # 3 Advanced Options. 2016. Available: https://www.satscan.org/tutorials/advancedoptions/SaTScanTutorialAdvancedOptions.pdfKulldorff M, 2018. SaTScanTM User Guide for version 9.5Statistics in medicine. 1995;14(8):799–810. Kulldorff M, Nagarwalla N. Spatial disease clusters: detection and inference.Explanation to question 2 and 3 We conducted Global Moran I, G* test and LISA based on contents and suggestions introduced by Anselin (1995). Specifically, all three tests were undertaken because of the following reasons as clarified in Anselin (1995).(1) Global Moran I has a limitation. The statistical insignificance of Global Moran I might not indicate that the local association does not exist . Therefore, the methods of G* and LISA have been developed in order to overcome this incomplete capability of Global Moran I, and both are capable of identifying the local associations.(2) Based on statistical inference, G* can identify only two cases of localized associations, which are hotspots of high-high and cold spots of low-low. However, LISA has extended this capability, and it can indicate four categories of hotspots, which are high-high, high-low, low-high and low-low. Specifically, LISA can identify both cases of positive spatial correlation (e.g. high-high and low-low) and those of negative spatial association (e.g. high-low and low-high) Based on these clarifications, this paper included results obtained from Global Moran I, G* test, and LISA in order to generate the robust results, statically examining spatial associations for both aspects - the global  vs. local and univariate  vs. bivariate. References: Geographical Analysis. 1995; 27: 93–115.Anselin, L. Local Indicators of Spatial Association—LISA.Geographical Analysis. 1992; 24:189-206.Getis, A., and K. Ord. ‘The Analysis of Spatial Association by Use of Distance Statistics.”Explanation to question 4We study the CRDs morbidity which is mainly linked to the epidemic of tobacco exposure and indoor and outdoor air pollution in Asian countries , whereas the mortality may relate with the accessibility to healthcare service in Bangkok metropolitan and some surrounding area.References:West. Chest. 2008; 133(2):517-527.Tan WC, Ng TP. COPD in Asia: where East meets


The We Run This City (WRTC) Youth Marathon Program is a community-supported, school-based fitness program designed to increase physical activity in a large, urban school district by engaging middle school youth to train 12 to 14 weeks to run or walk 1.2 miles, 6.2 miles, or 13.1 miles of the Rite Aid Cleveland Marathon. The objective of our study was to evaluate the effect of the intervention on adolescent health.We assessed changes in obesity, health, and fitness, measured before training and postintervention, among 1,419 sixth- to eighth-grade students participating in WRTC for the first time, with particular interest in the program’s effect on overweight (85th–94th body mass index percentile) or obese (≥95th percentile) students. We collected data from 2009 through 2012, and analyzed it in 2016 and 2017. Outcomes of interest were body mass index (BMI), waist-to-hip ratio (WHR), elevated blood pressure, and fitness levels evaluated by using the Progressive Aerobic Cardiovascular Endurance Run (PACER) test and the sit-to-stand test.P < .001). WHR increased among obese participants, whereas reductions in blood pressure among those with elevated blood pressure were associated with higher amounts of training and lower baseline BMI.We saw significant improvements overall in fitness and blood pressure. Controlling for demographics, program event, and training dosage, BMI percentile increased among normal weight participants and decreased among overweight and obese participants (Even small amounts of regular physical activity can affect the health and fitness of urban youths. School–community partnerships offer a promising approach to increasing physical activity by supporting schools and making a school-based activity inclusive, fun, and connected to the broader fitness community. More than one third of adolescents in the United States are overweight or obese, and rates of obesity are significantly higher among racial/ethnic minority youths and in economically disadvantaged communities –3. DecreThe WRTC program is a school-based youth fitness program in Cleveland, Ohio, developed by a multiorganization collaborative led by the YMCA of Greater Cleveland. It is implemented across a large metropolitan school district where most (85%) students are from racial/ethnic minority groups and from low-income households. The program was designed to address the declining levels of school-based physical education across the school district, where only 20% of middle school students were receiving daily physical education and more than 40% of middle school youth were considered overweight or obese .The WRTC program encourages middle school youths (grades 6–8) at varying fitness levels to adopt active, healthy lifestyles and to engage in a goal-oriented activity: to train in a graduated manner over 12 to 14 weeks to run or walk a segment of 1.2 miles, 6.2 miles (10K), or 13.1 miles  of the Rite Aid Cleveland Marathon. We examined the effect of the WRTC program on body mass index (BMI) and other body measurements, on blood pressure, and on overall fitness. A secondary aim was to examine the program’s impact on students who were overweight or obese before enrolling in the program.We used a nonexperimental pretest–posttest study design to examine the effect of the WRTC program by aggregating WRTC program evaluation data collected from 2009 through 2012, which we analyzed in 2016 and 2017. No reference group was available. Data were derived from all first-time participants enrolled in the program from 2009 through 2012 , representing 60.4% of all WRTC participants and involving 35 schools. These aggregated data consisted of samples of 387 students in 2009, 407 in 2010, 452 in 2011, and 173 in 2012. Annual participation in the program at the school level varied, with 57% of schools participating for 1 to 2 years and 43% participating for 3 to 4 years. However, we included only first-time participants on each team in our analyses. Team sizes ranged from 2 to 72 students. Parental consent and student assent for study and program participation were obtained. The institutional review board of Case Western Reserve University approved the study.WRTC recruitment began in the fall of each year. Teams, typically led by a physical education teacher, were formed within schools and were composed of students with a range of fitness levels, athletic ability, and body composition. Other school personnel  also led or assisted with teams. Coaches were responsible for scheduling and leading practices, collecting training data, and ensuring that their teams attended prerace events . Training for the 10K or the half marathon event  began in January; training for the 1.2-mile event started mid-February. Race day is always the third Sunday of May.The goal of training was to help participants safely build up to running their chosen event, allowing each student to progress at his or her own pace. Because of safety concerns , most teams trained on school property either by running several laps around the gymnasium, creating a short course throughout school hallways, or running the perimeter of school grounds outdoors. Typically, trainings focused on endurance  rather than distance.Coaches were given a training curriculum, and trainings were held a minimum of 2 to 3 days per week depending on the event: 1.2-mile event participants trained twice per week and 10K/half participants trained at least 3 times per week. For both groups, the goal was to reach at least 25 miles of training before race day; however, the 10k/half participants were expected to log in at least 3 times that amount of training when including the required out-of-school practice races. Training focused on walking and running but also included sessions on nutrition , injury prevention, getting adequate sleep, and game-like activities . Training materials were developed by the YMCA and were drawn from national fitness initiatives such as Let’s Move . Teams aAs part of the program design, all students who completed 20 training miles would earn a pair of good-quality running shoes; however, in practice, nearly all participants received running shoes. Students who remained in the program until race day received free race registration, T-shirts, and an official medal. Coaches received a small stipend ($100–$300) at the conclusion of the program. Program size and incentives varied from year to year, depending on the level of external funding secured by the program.The evaluation team consisted of trained research staff of the Prevention Research Center for Healthy Neighborhoods and community partners, including school nurses, nursing and nutrition students, and YMCA fitness staff. Team members were trained and observed in their assigned protocol. The assessments took place at the YMCA 1 to 2 weeks before the beginning of training and again 1 to 2 weeks following race day.2). BMI percentile was calculated by using BMI-for-age growth charts, which are different for boys and girls, and then categorized at baseline as normal weight (<85th percentile), overweight (85th–94th percentile), or obese (≥95th percentile) . Height was measured to the nearest 0.1 cm by using a free-standing portable stadiometer . Weight and height were measured twice, and the average of the 2 measurements was used to calculate BMI. We calculated BMI as weight in kilograms divided by the square of the height in meters (kg/mcentile) .We calculated waist-to-hip ratio (WHR) as the average waist measurement (cm) divided by the average hip measurement (cm). Higher ratios are an indicator of visceral fat around the abdomen, which is associated with cardiovascular disease, hypertension, and diabetes ,20. MeasBy using a nationally recommended protocol  and DuraWe used the sit-to-stand test to assess lower-extremity endurance and strength . SittingWe used the Progressive Aerobic Cardiovascular Endurance Run (PACER) test, a multistage fitness test adapted from the 20-meter shuttle run test , to evalTraining dosage was measured as the total distance walked, jogged, or run during the 12- to 14-week training period. Before the program, routes within and outside the school grounds were measured by the coach and YMCA staff. Coaches documented distance completed, measured in tenths of a mile, after each training session, and they calculated accumulated mileage.P < .05.We analyzed the data by using SPSS version 19 (IBM Corp). For elevated blood pressure, we examined proportional differences by using the McNemar test; for all other outcomes, repeated measures analysis of covariance (ANCOVA) were conducted with age, sex, and race/ethnicity as covariates. Both within-subject effects (change across time) and between-subject differences based on event , training dosage (number of miles), and baseline BMI category were examined. Significance was set at The average age of participants was 13 years. Approximately half were male, and the racial/ethnic distribution was largely minority with 66% black, 13% Hispanic/Latino, 14% white, and 7% other race  . At baseP < .001) and fewer were obese (P = .03) than those in the lost to follow-up group, but did not differ by sex or race/ethnicity. When stratified by training event, only age was significant (followed students were younger) within the 1.2-mile event; no differences were found within the 10K/half event group. Primary reasons for withdrawing from the program or not attending the evaluation assessments, as reported by the coaches, were academic concerns, behavioral issues on the team or in school, transportation problems, illness, or injury.Of students assessed at baseline, 67.3% completed the postintervention assessment. Overall, followed students were younger (P = .051), obese participants logged fewer miles than did either overweight or normal-weight participants. However, 39% of obese participants logged more than 25 miles of training, compared with 49% of normal-weight participants and 48% of overweight participants. We saw no difference in training levels between male and female participants.Approximately half of participants met the goal of training 25 miles before the race . BecauseP = .02) and completed nearly 8 more laps in the PACER test (P < .001) than they did at baseline .We observed significant improvements in both the sit-to-stand and the PACER test; on average, participants completed the sit-to-stand test 0.72 seconds faster . A significant interaction was also found for WHR, but in this case, a change was observed only in obese participants, and it was an increase in WHR rather than a decrease . In the final set of analyses for all 4 indicators , the models were rerun, controlling for event and training dosage. The results  (P < .001). Similarly, as the number of miles of training increased, the proportion with reductions in elevated blood pressure increased, from 72.7% for those with less than 15 miles of training to 77.8% for those with 15 to 24 miles and 85.7% for those with 25 or more miles of training. However, new cases of elevated blood pressure within the 15 to 24 miles group made the gains nonsignificant. With regard to BMI category, both normal weight and overweight participants showed significant improvements . However, this was not the case for obese participants. Although 70% of those with elevated blood pressure at baseline had normal blood pressure postintervention, 30% continued to have elevated blood pressure and an additional 19 students  had elevated blood pressure at postintervention. Moreover, even when participating at the highest training level (≥25 miles), only 73% of those with elevated blood pressure had normal blood pressure postintervention compared with 91% of overweight and 88% of normal weight peers. An additional 18% of these highly active but obese adolescents had elevated blood pressure after the intervention even though they had normal blood pressure at baseline.In the nonstratified analyses, of the 137 participants with elevated blood pressure at baseline, 81.0% had readings in the normal range at postintervention, a substantial reduction even in light of the 6.8% of new cases of elevated blood pressure among the previously normal group ( < .001) . This efOver the past decade, the amount of time during the school day dedicated to physical education has decreased dramatically. Simultaneously, the rate of obesity has grown exponentially. Both of these trends are markedly higher in economically disadvantaged communities and communities of color such as the one involved in this study. For these reasons, our study results provide promise. All participants, regardless of obesity status, appeared to have gained strength and endurance, as indicated in the 2 fitness measures. Overweight participants appeared to have benefited the most: increases in fitness, decreases in BMI, and improved heart health via normalized blood pressure, a result found by others . Obese pAdolescents with obesity might find a school-based fitness program unwelcoming and daunting. However, when programs are developed to be inclusive and fun, we found that many overweight and obese students can and will stick to a moderate-to-vigorous physical activity program in their schools. The results also highlight the difficulty in retaining these most at-risk youth, as obese participants were more likely to drop out of the program compared with their peers.This study also highlights the challenge of making a marked and sustained difference for adolescents who are already at the high end of the BMI charts. The increase in the WHR and the nonsignificant impact on blood pressure in this group suggests a complex physiologic interplay between physical activity, blood pressure, and visceral adiposity . ModeratWe presume that part of the success of WRTC is due to the nature of a school–community partnership. In this case, the WRTC coaches are school personnel and the program is conducted during the school day, but tremendous support and resources are supplied by community partners. These partners not only support the school coaches but also provide the structure and financial backing for the program. As a result, the WRTC program appears to offer a unique experience: an opportunity that allows students to be physically active with friends and not just those in their gym class, perhaps allows them to interact with their teacher/coach in a different way than in a classroom setting, provides an opportunity to travel to different parts of the city for practice races and trainings, and has them run as a registered “runner” on race day with thousands of other runners, not to mention the experience of hearing thousands cheering as they cross the finish line. While unique, this program is one that should be replicable in other cities with a community race event and willing partners.The lack of a comparison group as well as the cross-sectional study design are the primary limitations of this study. Without a comparison group, we cannot be certain that some of the improvements, particularly the fitness tests, were not due to a practice effect. Also, a longitudinal study of program participants would determine the effects of participation over several years and would strengthen support of this already successful program.This study provides some evidence that the high rates of obesity could be reduced somewhat, or at least abated, if schools reinstated more frequent physical education programming during the school day, especially in districts where regular physical education has been drastically reduced over the past decade. One approach to bridging this gap is through strong, committed partnerships between schools and communities.


The exposure pathways of perfluoroalkyl acids (PFAAs) to humans are still not clear because of the complex living environment, and few studies have simultaneously investigated the bioaccumulative behaviour of different PFAAs in humans. In this study, serum, dust, duplicate diet, and other matrices were collected around a manufacturing plant in China, and homologous series of PFAAs were analysed. PFAA levels in dust and serum of local residents in this area were considerably higher than those in non-polluted area. Although dietary intake was the major exposure pathway in the present study, dust ingestion played an important role in this case. Serum PFAAs in local residents was significantly correlated with dust PFAAs levels in their living or working microenvironment. Serum PFAAs and dust PFAAs were significantly higher in family members of occupational workers (FM) than in ordinary residents (OR) (p < 0.01). After a careful analysis of the PFAAs exposure pathway, a potential pathway in addition to direct dust ingestion was suggested: PFAAs might transferred from occupational worker's clothes to dinners via cooking processes. The bioaccumulative potential of PFHxS and PFOS were higher than other PFAAs, which suggested a substantial difference between the bioaccumulative ability of perfluorinated sulfonic acids and perfluorinated carboxylic acids. Perfluoroalkyl acids (PFAAs) have been extensively used as surfactants, lubricants, stain- and water-resistant coatings, and fire-resistant foams, due to their unique properties233PFAAs have been detected in human blood worldwide, and in certain cases, their levels were even higher than those of other persistent organic pollutants9111314161518Our previous study investigated the transportation of PFAAs from the manufacturing facility to the surrounding environment and elevated perfluorohexanesulfonate (PFHxS), PFOS, and PFOA levels in soil and biota samples were found within three kilometres of the plantA fluorochemical manufacturing plant located in the Hubei province of China was selected as the sampling area. The main synthesis method for PFAAs in this plant is by the electrochemical fluorination process. The sampling procedure was performed in December 2011 and 2012. Fifty-eight local residents who lived around this plant within a distance of 3 km were recruited in the present study. The gender, age, weight, occupation, dietary habits, and other information were collected by questionnaires; the demographic information of studied population is listed in Furthermore, to investigate the exposure pathways of PFAAs to residents around the manufacturing facility, relevant exposure matrices , and duplicate diet) from their living environment were also collected. Dust and TSP samples were collected from the plant and the corresponding participants' residential environment. Duplicate diet and drinking water samples were directly collected from the table and tap from the home of participants. In addition, the work clothes of workers and local residents were also collected. A total of 141 samples, including serum (n = 58), dust (n = 38), TSP (n = 15), drinking water (n = 6), duplicate diet (n = 9) and clothes (n = 15), were collected in this study. The detailed information of these samples  is listed in Eleven PFAAs (perfluoro-n-pentanoic acid (PFPeA), perfluorohexanoic acid (PFHxA), perfluoroheptanoic acid (PFHpA), PFOA, perfluorononanoic acid (PFNA), perluorodecanoic acid (PFDA), perfluorododecanoic acid (PFDoDA), perfluoroundecanoic acid (PFUnDA), salts of perfluorobutanesulfonate (PFBS), PFHxS, and PFOS were determined by HPLC-ESI-MS/MS. Sample pretreatment, instrumental analysis, quality assurance, and quality control of the analytical method are based on our previous work2223foodC represents concentration of PFAAs in food (ng/g) and waterC stands for concentration of PFAAs in drinking water (ng/mL). According to the questionnaires, the average body weight of local residents was 60 kg (dust) was calculated through the following equation: DIdust = Cdust × Adust/body weight, where Cdust is the concentration of PFAAs in house dust, Adust is the average amount of the daily dust ingestion, and the mean gastrointestinal uptake of dust is set at 50 mg/day for adults according to the US EPA exposure factors handbookTSP = CTSP*Vair/body weight, where CTSP is the concentrations of PFAAs in TSP, Vair is the daily air volume inhaled by adults and was set at 16.0 m315The daily dietary intake of PFAAs is estimated by the sum of PFAAs in the ingested diet and water. Diet and drinking water consumption are set at 2500 g and 1200 ml per day, respectively. Thus, the daily dietary intake of PFAAs is calculated as as 60 kg . The nonE) and the change of PFAAs levels could be described as k is the first-order rate constant for PFAAs elimination per day . According to previous studies, dV of PFOA and PFOS were set at 170 and 230 mL/kg bw, respectively28pC is the PFAA concentrations in serum. If the serum PFAAs reached steady-state conditions, dpC/dt = 0. Thus, the former equation could be simplified as Furthermore, based on a simplified one compartment pharmacokinetic model, we estimated the total estimated daily intake (TEDI) of humans by serum PFAA levels. The model was developed by Andersen et al. and Thompson et al.283031PFPeA, PFHxA, PFNA, PFDA, PFDoDA and PFUnDA had low detection rates in all samples, and therefore, they were not included for further data analysis and discussion. PFBS, PFHxS, PFOS and PFOA were detected in all serum samples in local residents while PFHpA had a detection frequency of 67%. The average serum PFBS, PFHxS, PFOS, PFHpA, and PFOA concentrations were 22.7, 138, 445, 2.85 and 50.6 ng/mL, respectively. The detailed characteristics of PFAAs in resident demographic subgroups by gender and age (≤45 and >45 years) were provided in PFAAs in drinking waters were relatively low , the mai363740dust for occupational workers reached 1679 ng/day/kg bw, and the DIdust of PFOS and PFOA were 604 and 759 ng/day/kg body, respectively. These values were almost three orders of magnitude higher than those estimated in previous studies18dust of PFOS, PFOA, and PFHxS of local residents was 0.991, 0.295, and 0.057 ng/day/kg body, respectively. PFAAs exposure via TSP inhalation was lower than dietary intake and dust ingestion in local residents . In contrast to TSP, dust PFAA levels were significantly higher in the FM group (6775 ng/g) than in the OR group (429 ng/g) (p < 0.01). It was surprising that the high PFAA levels in dust seems not play a role in elevating the PFAA levels in TSP via resuspension in this study. It might because that the large noise of the TSP sampler kept human activities  away during TSP sampling process. High PFAA levels in dust samples collected from FM indicated that occupational workers elevated the PFAAs levels in the living environment. PFAAs levels in the duplicate diet were considerably higher in the FM group than in the OR group, PFOS level in duplicate diet from FM group was 1.41 ng/g ww, while it was only 0.077 ng/g ww in the duplicate diet from OR group. However, the food material of both groups was mainly purchased from the local market, and the drinking water was supplied by municipal water systems, so there was no special food source for the FM group according to questionnaires. Through our observation and further investigation, most of the occupational workers did not change their clothes after work. We collected clothes of both workers and residents, used distilled water to wash them, and then analysed PFAAs in the wash-off water , with the GM of 218275 and 4402 ng/g, respectively  of FM was higher than that of OR, which was consistent with the PFAAs body burden. Dietary intake was the major exposure pathway in the present study, which was consistent with previous studies151819dust of PFOS and PFOA accounted for 5.2% and 20.0% of the total daily intake (TDI) in the FM group, and 6.0% and 8.0% in OR group, respectively. Compared to that DIdust accounted for less than 2% of daily intake in the Chinese population12The total PFAAs daily intake  and two groups of serum data (FM and OR group) in this study. Thus, we cited a set of unpublished data (data only for the reviewer), the serum PFAAs levels of occupational workers from different departments in this plant in 2011 , to get more paired data of dust and serum samples and make a better significance of fit. The result indicated that logarithm serum PFAA concentrations were significantly correlated with the corresponding logarithm dust PFAA concentrations (p < 0.05), which suggested that PFAAs body burden was closely related to dust ingestion in this setting .The PFAAs profiles changed largely between the dust and serum samples of local residents. PFSAs accounted for 68.2% of the total PFAAs in dust samples, which then increased to 87.4% in serum samples in the present study . The resSerum to the proportion of PFAADust was calculated to illustrate the different accumulation potential of the studied PFAAs, and this value was defined as the potential bioaccumulative index (PBI) in later discussions. The PBI of five PFAAs were ranked as follows: PFHxS (7.41) > PFOS (3.90) > PFOA (0.359) > PFBS (0.057) > PFHpA (0.051). The PBIPFHpA and PBIPFBS exhibited a low bioaccumulation potential in the present study, and it was consistent with Conder et al.'s report that PFCAs with less than seven fluorinated carbons might not accumulate in biotaPFHxS and PBIPFOS were nearly 130 and 68.4 times higher than PBIPFBS, respectively. Olsen et al. reported that half-lives of PFHxS and PFOS were 107 and 70 times of PFBS, respectively, which were highly consistent with the PBI results in this study33833The ratio of the proportion of PFAASupplementary information


To analyzed the association of quality of life and compliance to drug treatment in chronic kidney disease patients.The Short Form Health Survey was used to evaluate the quality of life of these patients, and the therapeutic complexity index was verified. The Morisky-Green test and the Brief Medication Questionnaire were applied to check compliance to drug therapy.A total of 197 patients were included. The Morisky-Green test and Brief Medication Questionnaire showed that most patients had low compliance to treatment . Compliance was highly associated with gender  and slightly associated with complexity of therapy, mental health, and social aspects.We observed a slight association between compliance to pharmacotherapy and quality of life and complexity of therapy, and a strong association with gender. According to the Brazilian Society of Nephrology (SBN) census, the estimated total number of patients under dialytic treatment in Brazil, in 2015, was 111,303, and 90% of them were treated free of charge by the Unified Health System (SUS – Sistema Único de Saúde).Chronic kidney disease (CKD) is a worldwide public health problem due to its increased prevalence, high costs, and mortality rate.,2 Ac(–)Patients on dialysis deal with a painful long-term treatment and its complications, which impacts their quality of life and that of their families.–6Adding to the abovementioned factors, it is expected that high number of comorbidities and, consequently, a high therapeutic complexity have an important impact on compliance.7 Therefore, it is imperative to evaluate the impact on quality of life and therapeutic complexity index (TCI) on compliance to pharmacotherapy to assure drug efficacy and improve outcome in CKD patients.Regarding drug therapy, some factors may hinder compliance of these patients, such as therapeutic complexity, adverse reactions induced by the drugs, and lack of understanding regarding the prescribed therapy.8 TherTo evaluate the association between quality of life of chronic kidney disease patients, therapeutic complexity index, and compliance to pharmacotherapy.This cross-sectional study was developed in a philanthropic hospital located in the state of Minas Gerais, Brazil. Ethical approval was obtained , and an Informed Consent was obtained from all individual participants enrolled in the study. A total of 209 end-stage renal disease patients (>18 years old) on hemodialysis were eligible for inclusion. We invited these patients to participate; 22 refused and 197 patients were enrolled. Data collection was carried out from August to November of 2014.The interviews were performed individually in the hemodialysis room after the patients signing the Informed Consent Form. Each interview lasted, in average, 30 minutes.The socioeconomic and demographic variables were gender, ethnicity, age, marital status, schooling level, comorbidities, private health insurance, transportations, household income , and employment situation.The 36-Item Short Form Health Survey (SF-36), version 2, validated for Brazilian Portuguese was used to evaluate the quality of life of the CKD patients. This form has 36 items measuring eight scales of lifestyle: vitality (VIT), physical functioning (PF), bodily pain (BP), general health perceptions (GHP), physical role functioning (PRF), emotional role functioning (ERF), social role functioning (SRF), and mental health (MH). The questionnaire portrays a score from 0 to 100, in which 0 corresponds to the worst health status, and 100 to the best health status.9 The analysis was performed for 196 out of 197 patients, since one patient was not on any medication. The MGT measures compliance of patients through four questions with dichotomous answers (“yes” and “no”). “No” is considered the expected answer for patients with good compliance; thus if there were no “yes” answer (positive), patients were included in the Compliance Group, and Non-Compliance was considered one to four positive answers. On the other hand, BMQ evaluates compliance to the drug therapy through three domains that identify barriers to compliance to drug regimen (seven questions), beliefs (two questions), and recall of medication treatment (two questions). Scores ≥1 indicate a potential non-compleinace in any domain. Compliance was classified as high (no positive answer), probable (one positive answer), probable low compliance (two positive answers) and low (three or more positive answers) in any domain.In order to evaluate compliance to drug therapy, two questionnaires were used: the four item Morisky-Green Test (MGT) and the Brief Medication Questionnaire (BMQ).10 The The TCI was calculated according to Acurcio et al., including measurements of number of medicines, frequency, and type of actions required for self-administration. The TCI was calculated considering only medication regimens dependent on patient action. Thus medicines used during and/or after dialysis sessions were disregarded. One patient was excluded from the calculation since he was not on any medicine.The pharmacotherapeutic profile showed that it is possible to verify the presence of polypharmacy and to determine the TCI. The presence of polypharmacy was evaluated, considered as the simultaneous usage of five or more medicines.11 TheStatistical analysis was performed using the Statistical Package for the Social Sciences (SPSS), version 16.0 software. Normality was tested by the Kolmogorov-Smirnov test. The Mann-Whitney test was used to evaluate the difference between the questionnaire domains of the SF-36 and the gender variables, MGT and each domain of the BMQ , and the TCI with MGT and BMQ. The established significance level was 5% (p<0.05).The odds ratio (OR) was estimated using compliance as outcome, with the respective 95% of confidence interval (95%CI) using logistic regression. We included in the multivariate model the variables with p<0.2 for the univariate analysis, and those with plausibility to be inserted according to the literature. The stepwise backward method was used in order to insert the variables, and only the significant variables with p<0.5 remained. The explanatory variables used in the multivariate model were age, gender, race, number of comorbidities, hemodialysis time, functional capability, therapeutic complexity index, general state, mental health, pain, vitality, social role functioning, emotional role functioning, and physical role functioning.The majority of the population of the study was male (61.4%); the mean age was 56.5±13.2 years; 72.1% were retired due to disability, and 66.5% with monthly family income below two Brazilian minimum wages. Only 33.0% had private health insurance . The tradiabetes mellitus (29.9%), congestive heart failure (24.4%), ulcer/gastritis (10.2%), depression (8.6%), bone diseases (6.1%), and neoplasia (0.5%).The main comorbidities reported were systemic arterial hypertension (80.2%), For quality of life, measured according to the SF-36, the domains with lowest average scores were PRF (0.0) and ERF (33.3). On the other hand, the domain SRF showed the best average score (100.0). Female patients demonstrated lower quality of life scores than males in most domains, according to Regarding the variable polypharmacy 65.9% of patients took more than five medicines, and no significant difference with SF-36 domains was noticed (p>0.05). Compliance to pharmacotherapy as per MGT showed that 50.3% of patients complied with. When associating with the SF-36 domains, a significant difference was noticed, as shown on Evaluating compliance through the BMQ, the final score showed that 80.6% of patients achieved scores related to potential low compliance. In the regimen domain, 92.2% of patients showed positive potential for non-compliance (score ≥1). In the belief domain, only 12.8% of patients presented positive potential for non-compliance (score ≥1). In the “patient recall of medicine treatment” domain, which analysed a scheme of multiple doses and the difficulty of patients to remember taking their medications, 96.9% of patients showed potential non-compliance (score ≥1). However, “regimen” and “recall/memory” domains were more closely related to the potential low compliance to drug therapy. A significant difference relating BMQ and quality of life was observed, as is shown on versus 15 (2-34) for the Non-Compliance Group (p<0.001).The TCI was compared with the compliance profile and no significant difference (p=0.953) was obtained for the MGT test, with median values of 15 (0-34), for compliance, and 15 (0-29), for non-compliance. The same result was observed for BMQ beliefs, in which the Compliance Group presented a TCI of 14 (0-34) and the Non-Compliance Group, of 16 (6-25), with p=0.422. For regimen and memory domains, a significant difference was obtained. For the regimen domain, the Compliance Group showed lower TCI levels than the Non-Compliance Group, 9 (0-32) and 14.5 (4-27), respectively (p=0.04), and for the memory domain, 5 (0-7) for the Compliance Group versus TCI (OR=0.856) and age (OR=0.934); BMQ beliefs versus SRF (OR=1.021) and MH (OR=1.029); and BMQ-recall versus male sex (OR=9.354) and TCI (OR=0.524), as well as an association between MGT and general health perception (OR=1.026), MH (OR=1.026) and TCI (OR=1.053).Finally,  The relation between quality of life and treatment compliance has an important impact on the healthcare system, since non-compliance leads to an increase of new cases of hospitalizations, generating costs for this system.Until the present, the association of quality of life with compliance to pharmacotherapy has been poorly reported, despite the value of this information in the end-stage renal disease population.13 The Similar results were found for hypertensive patients regarding SRF and MH.In the present study, compliance to pharmacotherapy by MGT showed that half of the patients had high compliance, while controversially, by BMQ, the final score showed 80.6% of potentially low compliance. Comparing compliance with quality of life scores, both general state and MH of the quality of life and TCI scores slightly increased the chance of compliance by MGT. Moreover, the compliance measurement using the BMQ (regimen domain) showed that the increased age and the TCI reduced the chance of compliance. Regarding the belief domain, the increase of SRF and MH values showed a slight increase in the chance of compliance. On the other hand, regarding the memory domain, being female and having higher TCI scores were highly associated with non-compliance. This result indicates that despite the association between quality of life and compliance, the main factors associated with compliance, especially recall, are the TCI and female sex. This result indicates that the perception of patients regarding their own health, SRF, and MH have a slight association with treatment compliance. Currently, data on patients undergoing hemodialysis are rare in the literature. A recent systematic review identified a positive relation between therapeutic compliance and quality of life in diabetic patients, which suggests that health professionals should take social aspects into consideration in order to have better clinical outcomes.15 Sim According to our results in which we evaluate the compliance to treatment with oral drugs, the patient tends to comply more to the pharmacological treatment when showing the same conditions.DeOreo et al., found that patients who missed approximately two dialysis treatments per month, that is, who did not comply with the non-pharmacological treatment, showed better scores for mental and physical health, which demonstrates that when the patient is feeling mentally and physically well, he or she tends to skip the hemodialysis sessions.13 Acc Before addressing complexity, it is important to relate negative attitudes of patients regarding pharmacotherapy. In the present study, these attitudes might be related mainly to the belief domain of compliance, and be explained by poor quality of life of the patients over MH and SRF.The negative association between compliance and TCI in this study has been shown previously in a systematic review by Ghimere et al., in hemodialysis patients.17 Bef This result contradicts the data obtained in previous studies, in which no significant difference was noted in any dimension of the SF-36 regarding the variable sex or shown in the male gender as a disadvantage for better quality of life scores.Comparing the quality of life scores with the variable sex, females showed more commitment in all domains and the recall domain was strongly associated with the compliance profile. Physiological changes, such as hypothalamic and ovarian dysfunction caused by the disease and intensified by hemodialysis, made females more limited in their daily activities with a low perception as to their general health status.19 ThiRegarding the drug therapy compliance profile analyzed by MGT and BMQ, the results obtained from these instruments were contradictory. Using MGT, half of the patients presented compliance profile, while using the BMQ, the majority of patients presented with a potential low compliance to the treatment. This divergence confirms the hypothesis that there is no gold standard for measuring compliance to pharmacological treatment.,,23 The low monthly income reported by patients may be related to retirement income and government financial aid for diseases, since half of the study population was at working age, which explained retirement caused by limitations from the disease. As expected, the higher prevalent diseases (hypertension and diabetes mellitus) have been the major causes of CKD development.The population demographic and social data in this study were similar to the Brazilian Dialysis Census and other national studies, showing the prevalence of males, low educational level, and monthly income below two minimum wages.,,– Th However, Mortari et al., found low scores for the PF limitations and for the GHP domain. Another domain that showed a high impact was ERF. This result was similar to other data in the literature, in which the most affected domains were PRF and ERF. Then again, in this study, the domain SRF had the maximum score and showed the best score, indicating that physical health or emotional problems do not interfere in the social life of end-stage renal disease patients.Considering the quality of life scores, the results pointed out some problems mainly regarding PF, showing that physical status affects the performance of daily and work activities in CKD patients. Similar results were also found in another Brazilian study.30 HowThe extreme values can be justified according to the type of variable that is obtained through SF-36, which is a categorical variable and does not allow a large diversity of intermediary values, or those close to “floor” and “ceiling”. Moreover, these results highlighted that the population is heterogeneous in relation to quality of life. This can be explained according to the broad social and economic diversity of the studied population, in addition to different phases and time of treatment.Finally, the use of indirect methods of assessing compliance is assumed as a limitation, since it may present recall and information bias. Additionally, the cross-sectional design does not establish causality. Therefore, new longitudinal studies should be designed to evaluate the impact of quality of life on treatment compliance, so that interventional strategies are established in nephrology services, improving quality of life and consequently, compliance to treatment.Non-compliance to drug therapy may be associated with worse quality of life and high therapeutic complexity. Beyond improving therapeutic complexity, strategies should be based on mental health and social role functioning to improve compliance.


This study aimed to compare the effects of three commonly used varicocelectomy techniquesnamely, open retroperitoneal ligation , open inguinal ligation (Ivanissevich) and laparoscopy, inIranian infertile men.This retrospective study was conducted on 70 infertile men with palpable varicocele whounderwent one of the varicocelectomy techniques namely, Palomo, Ivanissevich, or laparoscopy. Basic informationabout semen parameters were collected and registered prior to the surgery. Three months after the surgery, semenparameters and surgical complications were investigated in all patients.The Palomo technique was significantly associated with fewer complications compared to other techniques(P=0.006). The means of sperm concentration, normal motility and normal morphology were significantly differentamong the three groups after surgery . However, after adjustment for potential confounders, in addition to the baseline values of semen parameters, significant differences were observed onlyin sperm concentration among the groups (P=0.040).Varicocelectomy improved sperm parameters. The Ivanissevich technique was more effective in improving sperm concentration compared to the laparoscopic method. The lowest rates of complications were related tothe Palomo technique. Reproductive health is a global health priority and infertilityis one of its critical components regarded as aglobal health concern . The preAbout one-sixth, two-fifths, and four-fifths of general male population and in men with primary and secondaryinfertility are diagnosed with varicocele, respectively , 8. AmajIn many developing countries, including Iran, it is notpossible for young couples to use the assisted reproductivetechnology (ART) due to its economic burden; therefore,in these countries, it is required to seek more affordableeffective approaches that are associated with fewer side ef.fects . This stThis retrospective study was conducted on infertile men with confirmed varicocele. These men had multiple abnormal semen analysis results and a 3-5 year history of primary infertility even after different medical treatments. The subjects were selected from individuals who referred to Hazrat Rasoul-e-Akram medical center in Tehran, Iran between 2009 and 2015. Cases with a history of previous scrotal or inguinal operation were excluded from our study. Using G*Power 3.1.9.2 and considering equal number in each group and 0.386 for effect size from means of postoperative sperm concentration (million/mL), 0.05 for alpha, 0.80 for power, and 3 for number of groups, a total sample size of 70 patients was calculated .Convenience sampling was done by an expert clinician. Then, patients’ medical history was recorded and physical examination  and semen analysis were done for each subject. The results of the last semen analysis before surgery, were considered as the baseline. The patients were classified into three clinical groups: grade 1 , grade 2 , and grade 3 .The status of atrophy in patients before and after surgery was examined using scrotal Doppler ultrasonography. Atrophy was defined as a testicular volume of <16 ml. According to WHO definition, normal semen samples have a volume of =1.5 mL, a sperm concentration of =15 million per mL, motility (movement of the sperm) value of =32% with forward progression (sum of type A and type B), total motility  of =40% and =4% normal morphology .The sperm concentration was measured using a haemocytometer utilizing a neubauer sperm counting chamber after immobilization of spermatozoa by neutral formalin. The sperm motility was assessed by scanning a few fields under high-dry objective, until a total of =200 spermatozoa was enrolled and the sperm morphology was assessed on the basis of differential counts of morphologically normal and abnormal spermatozoa sorts on Pap-stained slides. Different types of sperm motility were classified into four groups: Quick dynamic (type A), Slow dynamic (type B), Non-dynamic (type C), and Immotile (type D). Based on a previous study, motility was defined as the average percentage of forward progression (sum of type A and B) divided by all four types of motility .Information about time of surgery (since anesthesia time), type of anesthesia, and level of pain after surgery were collected for all the patients. Pain was measured using a visual analogue scale (VAS) ranging from zero (no pain) to 10 (severe pain). Three months after the surgery, semen parameters and surgical complications  were investigated in all patients via clinical examinations, ultrasound test, and semen analysis. An urologist who was completely blind to the medical history and semen analysis, carried out all physical examinations.Taking into consideration the clinical indication and patients’ preference, they were allocated to different groups of varicocelectomy. All surgeries were done by a single urologist. To compare the effects of surgery in each group, we used Mann-Whitney, Wilcoxon, and McNemar’s tests. To compare the effects of the type of surgery on semen parameters and to compare their side effects, we used chi-square or Kruskal-Wallis test, as appropriate.Also, univariate general linear model was utilized to compare the effects of the type of surgery on semen parameters, by considering the baseline values of semen parameters as covariates and controlling the effect of other potential confounders. For sensitivity analysis, an additional univariate general linear model was used by controlling other residual  confounders that were not different among the three groups.In order to predict the effect of varicocelectomy on semen parameters, we used linear regression in a stepwise manner. In this study, the level of statistical significance was set as a P value less than 0.05. All analyses were performed using SPSS 20 software . All surgeries performed in this study were in accordance with the institutional ethical standards and the study protocol was confirmed by Ethics Committee of Iran University of Medical Sciences, Tehran, Iran. Written informed consent was signed by all participants.There were 25, 23 and 22 cases in Palomo, Ivanissevich and laparoscopic groups with the mean age of 25.97 ± 5.7 years old which was not significantly different among the groups (P=0.352). In 76% of subjects, varicocele was in left side with no statistically significant differences among the three groups (P=0.513). Grade 3, 2 and 1 of varicocele were observed in 67, 30 and 3%, respectively. The rate of varicocele grade 3 in the Palomo group was significantly higher than that of laparoscopic (P=0.005) and Ivanissevich (P=0.047) groups. Sperm concentration was abnormal in 30 subjects accounting for 42.85% of patients population; there were no significant differences in this parameter among the three groups (P=0.138). Moreover, there were no statistically significant differences in other parameters of semen analyzed before the surgery, among the three groups. There was no atrophy before the surgery in 56% of the patients (n=39). Presence of atrophy significantly varied among different groups . There was a significant difference among the three groups in terms of the mean duration of surgery (longer in laparoscopic type than two others) and type of anesthesia  (P<0.001 for both comparisons) .The results showed that after surgery, the Palomo technique was significantly associated with fewer complications compared to other techniques . In all group, no one had significant nausea and vomiting, infection, ileus, and next organ damages specifically intestinal damage as well as need for blood transfusion, re-operation, and changing the laparoscopic surgery to open method. In general, the most common complications were hydrocele in 21.4% (n=15), recurrence in 10% (n=7), and hematoma and pneumoscrotum each in 8.60% (n=6) of the patients. Pain after surgery was similar among all groups .Post-surgery semen analysis of all 70 subjects showed decreases in sperm concentration, normal motility and normal morphology in 7 (10%), 5 (7.4%) and 4 patients (5.9%), respectively. In these patients with semen parameters worsened after the surgery (n=13), from 5 patients with atrophy at baseline, only one had atrophy after the surgery and recurrence was observed in three of them. Moreover, 30 patients had abnormal sperm concentration and 67 patients had abnormal sperm motility at baseline; following the surgery, sperm concentration and sperm motility were within the normal range in 73.3% (n=22) and 40.2% (n=27) of these individuals, respectively (P<0.001 for both). Mean values of semen parameters after surgery indicated significant improvements in all groups of varicocelectomy. The results of the univariate general linear model  revealed that the means of sperm concentration, normal motility and normal morphology were significantly different among the three groups after the surgery . Mean values of sperm concentration and normal motility in the patients in Ivanissevich and Palomo groups were better than those of patients in laparoscopic group; however, Palomo technique had significantly better effect on normal morphology only compared to the laparoscopic technique .Comparing the mean differences of semen parameters among the three groups of varicocelectomy confirmed the results of univariate general linear model. We also used a univariate general linear model for controlling other factors  which were different among the three groups. The results of this analysis showed a significant difference among the groups just in terms of sperm concentration (P=0.040). Post-hoc analysis revealed that this difference was statistically significant only when comparing Ivanissevich (15.13 ± 8.69 million/mL) and laparoscopic (8.77 ± 8.94 million/mL) groups (P=0.008) .Age distribution and mode of presentation laterality was not significantly different in the three groups. Nonetheless, the power of this study to detect differences was low. Thus, we can consider the effect of these variables as residual confounders. Controlling these variables in an additional univariate general linear model showed that there was no significant differences among the three groups in terms of improving all semen parameters.Varicocelectomy helps to improve atrophy (P<0.001). So, at all ages and all surgery groups, among 31 patients who had atrophy at baseline, improvement in this respect was seen in nearly all of them , except for 2 patients at the age of 24 and 36. Atrophy was unknown for 3 patients. Moreover, except for the patients in Ivanissevich group, this positive effect was confirmed in patients of the other groups .The results of stepwise linear regression showed that sperm concentration prior to the surgery, laparoscopic varicocelectomy, and atrophy prior to the surgery were the prognostic factors that could significantly predict the sperm concentration after the surgery. Laparoscopic varicocelectomy and presence of atrophy before the surgery have a negative impact on sperm concentration after the surgery. The values of normal motility before surgery and laparoscopic varicocelectomy were independent factors for predicting the normal motility after surgery. In addition, the number of sperms with normal morphology after the surgery, depends on the values of normal morphology before surgery and normal motility, as well as the utilization of Palomo technique. The adjusted R-square of the models  indicates the higher accuracy of regression models in predicting the morphology and concentration, regardless of the number of independent variables entered the model. In each of the models, higher standardized beta indicates higher values of a variable in predicting the dependent variable. The values of each semen parameter prior to the surgery (e.g. sperm concentration) had the highest values in prediction of these parameters (e.g. sperm concentration) after the surgery .In this study, following the surgery, sperm concentration, normal motility, and normal morphology worsened in 10, 7.4, and 5.9% of patients, respectively. Based on univariate analysis, sperm concentration, normal motility and normal morphology after surgery using Ivanissevich and Palomo techniques, were better than those of laparoscopic group; but after controlling for confounders, a significant difference was seen only between Ivanissevich and laparoscopic techniques.A similar study on 100 infertile patients who underwent varicocelectomy, showed a significant difference between open inguinal or laparoscopy methods in terms of sperm concentration and motility . A quasiAccording to other studies that had no controlling for confounding factors and did not consider the type of varicocelectomy, varicocelectomy could lead to significant improvements in sperm concentration, motility, and morphology. The results of our study are in line with the mentioned studies and confirm their findings , 17. ObThe results of this study showed that laparoscopic and Palomo surgery had a positive effect on the improvement of the atrophy. Regarding Ivanissevich technique, at least in short-term follow-up in this study, no improvement in atrophy was seen; however, at least in terms of sperm concentration, changes were in direction to improve.Nevertheless, to check the efficacy of varicocelectomy, we need to know post-surgery fertility status which was not assessed in this study. The best modality for treatment of varicocele in infertile men is a modality which highly improves semen and increases pregnancy rates with minimum complication rates . Thus, an ideal technique not only preserves the lymph nodes and spermatic vessels, but also closes all external and internal spermatic veins. Although so far, no treatment modality has been introduced as a "gold standard" of varicocele treatment. According to the literature, compared to other varicocelectomy techniques, microscopic varicocelectomy (MV), despite its need for more operative time, surgical skills and experiences, was accepted as a standard treatment which had the lowest postoperative recurrence and complication rates . The fiOverall rate of complications in open varicocelectomy has been reported to be slightly higher than laparoscopic varicocelectomy  but this difference is not significant. Recurrent symptoms of varicocele were observed only in five and two patients in laparoscopic and Ivanissevich group, respectively. Other studies have also shown that higher grades of preoperative varicocele lead to increased risk of recurrence that can be secondary to multiple collateral venous channels . This caHydrocele after the surgery has an incidence rate of up to 10% of cases regardless of type of varicocelectomy . In the There are some limitations in our study such as the retrospective nature of study, non-random sampling, small sample size, short follow-up period and no hormonal and fertility assessment. Moreover, there is a risk of selection bias. A higher proportion of men who underwent a Palomo repair had bilateral disease. Similarly, those who underwent this procedure had a higher grade of varicocele and higher incidence of atrophy, as defined in the study. This might have an impact on the results. However, we used a general linear model in our analyses to overcome this problem and fix this bias. Performing all surgeries by a single surgeon may cause dependency of the results to the surgeon; although, it removed inter-observer bias. In real scenario, most cases are like our cases with high grade of varicocele. So, our results are relatively generalizable.Varicocelectomy improves sperm parameters. Palomo, Ivanissevich, and laparoscopy methods were similar in terms of sperm normal motility and morphology. However, Ivanissevich was more effective in improving sperm concentration. Regarding complications, Palomo technique caused the lowest rate of post-surgery complications. It seems necessary to conduct further studies with longer follow-up periods to clarify the effect of different types of varicocelectomy on semen parameters and pregnancy rate in Iran.