Genitourinary syndrome of menopause exploring the history, updates and treatment

The biological factors and physiological functions fundamental to the female anatomy delineate the complexity of reproductive phenomenon in this population. When women experience menopausal transition, genital, sexual, and urinary signs and symptoms materialize often. These longstanding signs and symptoms, presently referred to as the genitourinary syndrome (GUS) of menopause, a relatively new term, impact their quality of life and sexual health with the emergence of vulvovaginal and urogenital atrophy, typical of irritation, soreness, dryness, dyspareunia, and itching. Despite its prevalence, GUS of menopause often goes unreported due to embarrassment, leading to underdiagnoses, diminished intervention, and under-treatment. Moreover, the rising life expectancy is also emerging as a contributing factor to the increasing prevalence of GUS of menopause, directly affecting women's health. While there are notable awareness, education, and healthcare frameworks in place aimed at addressing the unique needs of menopausal women, there is a need to explore further GUS� prevalence, pathophysiology, risk factors, clinical features, diagnosis, and treatment to understand, diagnose, and effectively manage this condition.

Construction of air quality health index for respiratory diseases in Urumqi / 环境与职业医学

Background Air quality health index (AQHI) is derived from exposure-response coefficients calculated from air pollution and morbidity/mortality time series, which helps to understand the overall short-term health impacts of air pollution. Objective To study the effects of common air pollutants on respiratory diseases in Urumqi and to develop an AQHI for the risk of respiratory diseases in the city. Methods The daily outpatient volume data of respiratory diseases from The First Affiliated Hospital of Xinjiang Medical University, meteorological data (daily mean temperature and daily mean relative humidity), and air pollutants [fine particulate matter (PM2.5), inhalable particulate matter (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon dioxide (CO), and ozone (O3)] in Urumqi City, Xinjiang, China were collected from January 1, 2017 to December 31, 2021. A distributed lag nonlinear model based on quasi-Poisson distribution was constructed by time-stratified case crossover design. Adopting zero concentration of air pollutants as reference, the exposure-response coefficient (β value) was used to quantify the impact of included air pollutants on the risk of seeking medical treatment for respiratory diseases, and the AQHI was established. The association of between AQHI and the incidence of respiratory diseases and between air quality index (AQI) and the incidence of respiratory diseases was compared to evaluate the prediction effect of AQHI. Results Each 10 µg·m−3 increase in PM10, SO2, NO2, and O3 concentrations presented the highest excess risk of seeking outpatient services at 3 d cumulative lag (Lag03) and 2d cumulative lag (Lag02), with increased risks of morbidity of 0.687% (95%CI: 0.101%, 1.276%), 17.609% (95%CI: 3.253%, 33.961%), 13.344% (95%CI: 8.619%, 18.275%), and 4.921% (95%CI: 1.401%, 8.502%), respectively. There was no statistically significant PM2.5 or CO lag effect. An AQHI was constructed based on a model containing PM10, SO2, NO2, and O3, and the results showed that the excess risk of respiratory disease consultation for the whole population, different genders, ages, or seasons for each inter-quartile range increase in the AQHI was higher than the corresponding value of AQI. Conclusion PM10, SO2, NO2, and O3 impact the number of outpatient visits for respiratory diseases in Urumqi, and the constructed AQHI for the risk of respiratory diseases in Urumqi outperforms the AQI in predicting the effect of air pollution on respiratory health.

Establishment of a concise health index synthetic evaluation method for adolescents / 中国学校卫生

Objective@#The aim of this study was to establish a comprehensive concise health index (CHI) for evaluating adolescents, so as to provide a basis for determining the overall health status of adolescents in China.@*Methods@#On the basis of a literature review and consensus among core researchers, adolescent CHI indicators in the following five dimensions were assessed:physical growth, physical fitness, common diseases, mental health and behavioral health. A total of 24 experts used an analysis hierarcgy process (AHP) to calculate the indicators subjective weights. In addition, from October to December of 2021, two regions, A and B were selected to conduct empirical research, and the CRITIC method was used to calculate the objective weights of the indicators. Finally, the weight coefficients were determined through the AHP-CRITIC combination weight method, and comprehensive evaluation was performed with the TOPSIS method.@*Results@#Across academic period and genders, the combined weighted coefficients of the health indicators were as follows:BMI, 0.081-0.095; waist circumference, 0.070-0.081; relative grip strength, 0.101-0.108; myopia, 0.110-0.128; dental caries, 0.055-0.070; psychological symptoms, 0.240-0.262; physical exercise, 0.085-0.115; screen time, 0.097-0.111; and sleep duration, 0.086-0.103. The health index of middle school students in city A (0.626±0.065) was significantly higher than that in city B(0.613±0.066)( t=6.34, P <0.01).@*Conclusion@#The comprehensive adolescent CHI evaluation method has good consistency and application value, and may serve as a reference for adolescent health monitoring.

Predictive value of isoform -2 proprostate-specific antigen and its derived indexes for prostate cancer patients with PSA 4-20 ng/mL / 现代泌尿外科杂志

【Objective】 To evaluate the predictive value of isoform [-2] proprostate-specific antigen, p2 PSA (p2PSA) and its derived indexes for prostate cancer in a Chinese cohort with PSA 4-20 ng/mL. 【Methods】 A total of 139 males scheduled for biopsy were enrolled in the prospective study from Nov.2021 to Jun.2022. The total PSA (tPSA), free PSA (fPSA), fPSA/tPSA (f/t) and p2PSA were collected, and the percentage of p2PSA(%p2PSA) and prostate health index(PHI) were calculated. The predictive value of p2PSA and its derived indexes were compared with traditional indexes with receiver operating characteristic (ROC) curve and Logistic analysis. 【Results】 Prostate cancer was found in 54 cases (38.8%). There were significant statistical differences in tPSA(10.68 vs.8.14, P=0.021), f/t(0.13 vs.0.16, P=0.006), p2PSA(30.25 vs.19.81, P<0.001), %p2PSA(21.52 vs.13.15, P<0.001) and PHI(64.3vs.38.2, P<0.001) between prostate cancer patients and non-prostate cancer patients. The area under the ROC curve (AUC) of tPSA, fPSA, %fPSA, p2PSA, %p2PSA and PHI were 0.63, 0.51, 0.63, 0.71, 0.73, and 0.80, respectively. The inclusion of %p2PSA and PHI significantly increased the prediction efficiency of the basic prediction model (AUCbase+PHI=0.81, AUCbase+%p2PSA=0.78, AUCbase=0.67). With 35 as the recommended cut-off value of PHI, the incidence of meaningless puncture was reduced by 25.8%(36/139). 【Conclusion】 The application of p2PSA and its derived indexes have good predictive value for patients with PSA 4-20 ng/mL. The combined detection of %p2PSA and PHI can significantly increase the detection efficiency of prostate cancer and reduce the incidence of meaningless prostate puncture by 25.8%.

Construction of AQHI based on joint effects of multi-pollutants in 5 provinces of China / 环境与职业医学

Background Air pollution is a major public health concern. Air Quality Health Index (AQHI) is a very important air quality risk communication tool. However, AQHI is usually constructed by single-pollutant model, which has obvious disadvantages. Objective To construct an AQHI based on the joint effects of multiple air pollutants (J-AQHI), and to provide a scientific tool for health risk warning and risk communication of air pollution. Methods Data on non-accidental deaths in Yunnan, Guangdong, Hunan, Zhejiang, and Jilin provinces from January 1, 2013 to December 31, 2018 were obtained from the corresponding provincial disease surveillance points systems (DSPS), including date of death, age, gender, and cause of death. Daily meteorological (temperature and relative humidity) and air pollution data (SO2, NO2, CO, PM2.5, PM10, and maximum 8 h O3 concentrations) at the same period were respectively derived from China Meteorological Data Sharing Service System and National Urban Air Quality Real-time Publishing Platform. Lasso regression was first applied to select air pollutants, then a time-stratified case-crossover design was applied. Each case was matched to 3 or 4 control days which were selected on the same days of the week in the same calendar month. Then a distributed lag nonlinear model (DLNM) was used to estimate the exposure-response relationship between selected air pollutants and mortality, which was used to construct the AQHI. Finally, AQHI was classified into four levels according to the air pollutant guidance limit values from World Health Organization Global Air Quality Guidelines (AQG 2021), and the excess risks (ERs) were calculated to compare the AQHI based on single-pollutant model and the J-AQHI based on multi-pollutant model. Results PM2.5, NO2, SO2, and O3 were selected by Lasso regression to establish DLNM model. The ERs for an interquartile range (IQR) increase and 95% confidence intervals (CI) for PM2.5, NO2, SO2 and O3 were 0.71% (0.34%–1.09%), 2.46% (1.78%–3.15%), 1.25% (0.9%–1.6%), and 0.27% (−0.11%–0.65%) respectively. The distribution of J-AQHI was right-skewed, and it was divided into four levels, with ranges of 0-1 for low risk, 2-3 for moderate risk, 4-5 for high health risk, and ≥6 for severe risk, and the corresponding proportions were 11.25%, 64.61%, 19.33%, and 4.81%, respectively. The ER (95%CI) of mortality risk increased by 3.61% (2.93–4.29) for each IQR increase of the multi-pollutant based J-AQHI , while it was 3.39% (2.68–4.11) for the single-pollutant based AQHI . Conclusion The J-AQHI generated by multi-pollutant model demonstrates the actual exposure health risk of air pollution in the population and provides new ideas for further improvement of AQHI calculation methods.

Tonstruction of AHI based on the integrating health risks of exposure to air pollution and ambient temperature in Tianjin / 公共卫生与预防医学

Objective To construct an air health index (AHI) based on the exposure-response relationships of air pollution and ambient temperature with the years of life lost (YLL) in Tianjin. Methods The time series database of air pollution, meteorological factors, and non-accidental YLL from 2014-2019 in six urban areas of Tianjin were established. The data from 2014 to 2017 were used as the construction set to establish the exposure-response relationships of air pollution and ambient temperature with non-accidental YLL and establish the AHI model. The data from 2018 to 2019 were used as the validation set for verifying AHI. The generalized additive model (GAM) and weighted quantile sum (WQS) model were used to establish the exposure-response relationship between air pollution mixtures and non-accidental YLL. The distributed lag nonlinear model (DLNM) was fitted to assess the exposure-response relationship between ambient temperature and non-accidental YLL. Based on these obtained coefficients, the AHI and air quality health index (AQHI) were built. By comparing the associations between AHI, air quality health index (AQHI), and air quality index (AQI) with daily mortality and YLL and model goodness of fit to evaluate the validity of AHI. Results The formula for AHIt=EYLLt,air pollution+ambient temperature/475.11*10. The validation results showed that each IQR increase in AHI was associated with a higher increase in non-accidental mortality and YLL (10.61% and 353.37 person-year) compared with the corresponding values of AQHI and AQI. In addition, the model goodness of AHI was better than AQHI and AQI model. Conclusion Compared with AQHI and AQI, the AHI based on the integrating health effects of air pollution and ambient temperature has a better health risk prediction ability.

Can we omit systematic biopsies in patients undergoing MRI fusion-targeted prostate biopsies? / 亚洲男科学杂志(英文版)

Magnetic resonance imaging (MRI)-targeted prostate biopsy is the recommended investigation in men with suspicious lesion(s) on MRI. The role of concurrent systematic in addition to targeted biopsies is currently unclear. Using our prospectively maintained database, we identified men with at least one Prostate Imaging-Reporting and Data System (PI-RADS) ≥3 lesion who underwent targeted and/or systematic biopsies from May 2016 to May 2020. Clinically significant prostate cancer (csPCa) was defined as any Gleason grade group ≥2 cancer. Of 545 patients who underwent MRI fusion-targeted biopsy, 222 (40.7%) were biopsy naïve, 247 (45.3%) had previous prostate biopsy(s), and 76 (13.9%) had known prostate cancer undergoing active surveillance. Prostate cancer was more commonly found in biopsy-naïve men (63.5%) and those on active surveillance (68.4%) compared to those who had previous biopsies (35.2%; both P < 0.001). Systematic biopsies provided an incremental 10.4% detection of csPCa among biopsy-naïve patients, versus an incremental 2.4% among those who had prior negative biopsies. Multivariable regression found age (odds ratio [OR] = 1.03, P = 0.03), prostate-specific antigen (PSA) density ≥0.15 ng ml-2 (OR = 3.24, P < 0.001), prostate health index (PHI) ≥35 (OR = 2.43, P = 0.006), higher PI-RADS score (vs PI-RADS 3; OR = 4.59 for PI-RADS 4, and OR = 9.91 for PI-RADS 5; both P < 0.001) and target lesion volume-to-prostate volume ratio ≥0.10 (OR = 5.26, P = 0.013) were significantly associated with csPCa detection on targeted biopsy. In conclusion, for men undergoing MRI fusion-targeted prostate biopsies, systematic biopsies should not be omitted given its incremental value to targeted biopsies alone. The factors such as PSA density ≥0.15 ng ml-2, PHI ≥35, higher PI-RADS score, and target lesion volume-to-prostate volume ratio ≥0.10 can help identify men at higher risk of csPCa.

Research progress of prostate health index in the diagnosis and treatment of prostate cancer / 中华泌尿外科杂志

Despite the increasing number of patients was diagnosed with prostate cancer due to widespread cancer screening, PSA testing does not differentiate between lethal and slow-growing inert prostate cancers. This leads to a proportion of patients being over-diagnosed and consequently over-treated.The current study has found that PSA exists as a precursor to post-translational modification, and that [-2]proPSA originates only from the peripheral zone of the prostate. Furthermore, the study has shown that prostate health index (PHI) calculated from [-2]proPSA, fPSA, and PSA has a higher positive predictive value for prostate cancer, making it useful in the diagnosis of clinically significant prostate cancer. This article reviews the progress of research related to PHI in prostate cancer diagnosis and treatment.

Application of free/total prostate-specific antigen ratio, peripheral blood neutrophil-to-lymphocyte ratio, interleukin-6 and prostate health index density detection in early diagnosis of prostate cancer / 肿瘤研究与临床

Objective:To investigate the clinical application of free/total prostate-specific antigen (f/tPSA), peripheral blood neutrophil-to-lymphocyte ratio (NLR), interleukin-6 (IL-6) and prostate health index density (PHID) detection in the early diagnosis of prostate cancer.Methods:The clinical data of 160 patients with abnormal prostate specific antigen (PSA) who were admitted to the Second Affiliated Hospital of Xuzhou Medical University from January 2020 to January 2022 were retrospectively analyzed. According to the pathological results of prostate biopsy or electrical resection, the patients were divided into prostate cancer group (68 cases) and benign prostatic hyperplasia group (92 cases), and 50 male healthy physical examiners in the Second Affiliated Hospital of Xuzhou Medical University during the same period were selected as healthy control group. All enrolled members were tested for total prostate-specific antigen (tPSA), free prostate-specific antigen (fPSA), and prostate specific antigen isoform 2 (p2PSA), IL-6 and other indicators, and the f/tPSA, prostate health index (PHI), PHID and NLR were calculated. Receiver operating characteristic (ROC) curve was plotted to compare the efficacy of each index in diagnosing and differentially diagnosing prostate cancer and benign prostatic hyperplasia.Results:The serum levels of tPSA, fPSA, p2PSA, PHI and PHID in the prostate cancer group were higher than those in the benign prostatic hyperplasia group and the healthy control group (all P < 0.05), and the serum f/tPSA was lower than that in the benign prostatic hyperplasia group and the healthy control group ( P < 0.05). The area under the curve (AUC) of PHID for the diagnosis of early stage prostate cancer was the largest [0.915 (95% CI 0.864-0.966)], followed by PHI [0.884 (95% CI 0.823-0.944)]. The sensitivity of both f/tPSA and PHI in diagnosing early stage prostate cancer was 86.80%, which was higher than other indicators; the specificity of PHID in diagnosing early stage prostate cancer was 94.00%, which was higher than other indicators. The AUC of f/tPSA for the diagnosis of benign prostatic hyperplasia was the largest [0.828 (95% CI 0.739-0.917)], followed by PHID [0.826 (95% CI 0.760-0.892)]. The sensitivity of f/tPSA in diagnosing benign prostatic hyperplasia (85.90%) was higher than other indicators, and the specificity of PHI in diagnosing benign prostatic hyperplasia (94.00%) was higher than other indicators. The AUC of fPSA, PHID, f/tPSA and p2PSA in differentiating early stage prostate cancer and benign prostatic hyperplasia were 0.752 (95% CI 0.663-0.841), 0.730 (95% CI 0.647-0.812), 0.713 (95% CI 0.623-0.803), 0.710 (95% CI 0.629-0.791), respectively, and there was no significant difference in each pairwise comparison (all P > 0.05). The sensitivity of NLR in differentiating early stage prostate cancer and benign prostatic hyperplasia was 91.20%, which was higher than other indicators, and the specificity of fPSA in differentiating early stage prostate cancer and benign prostatic hyperplasia was 94.00%, which was higher than other indicators. Conclusions:The f/tPSA, PHI and PHID detection have certain clinical values in the early diagnosis of prostate cancer, and can provide references for early diagnosis, early treatment and prognosis evaluation of high-risk population of prostate cancer.

Mapeando as disparidades socioeconômicas de saúde urbana: um estudo comparativo entre seis capitais brasileiras / Mapping socioeconomic disparities in urban health: a comparative study of six Brazilian capitals / Mapeando las disparidades socioeconómicas de salud urbana: un estudio comparativo entre seis capitales brasileñas

Resumo A cidade é um modo de viver, pensar e sentir. O modo de vida urbano é capaz de produzir ideias, comportamentos, valores e conhecimentos, mas também pode acirrar disparidades socioeconômicas e de saúde da população que ali reside. Este artigo examina as disparidades em saúde urbana em seis capitais brasileiras: São Paulo, Rio de Janeiro, Salvador, Fortaleza, Belo Horizonte e Manaus. Para quantificar e mapear as disparidades intraurbanas nesses espaços, foram utilizados os dados do Censo Demográfico de 2010 para a aplicação do índice de saúde urbana (ISU), uma métrica que sintetiza oito diferentes variáveis socioeconômicas e de saneamento desagregadas por setores censitários. Os resultados são discutidos à luz de três vertentes teóricas: a diferenciação centro-periferia; abordagem econômica da saúde; e epidemiologia social. As descobertas desse estudo revelam que os setores censitários que abrangem populações com maior status socioeconômico e melhores condições de saneamento apresentaram índices de saúde urbana mais elevados do que os da periferia da cidade. Há indícios de melhores indicadores de saúde urbana para o Rio de Janeiro e São Paulo, em comparação com as demais capitais analisadas. No entanto, há importantes nuances em cada uma das seis cidades estudadas, especialmente quando se atribuem diferentes pesos às variáveis que compõem o ISU, apesar da marcada segregação espacial comum a todas elas. Considerar as distinções dentro do espaço urbano é uma estratégia fundamental para a compreensão desses aspectos sociais e econômicos e seus potenciais desdobramentos nas condições de saúde da população.

Abstract A city is a way of living, thinking, and feeling. The urban lifestyle can produce ideas, behaviors, values, and knowledge. Still, it can also intensify socioeconomic and health disparities in the population. This article examines urban health disparities in six Brazilian capitals: São Paulo, Rio de Janeiro, Salvador, Fortaleza, Belo Horizonte, and Manaus. To quantify and map intra-urban disparities in these spaces, data from the 2010 Demographic Census are used to apply the Urban Health Index, a metric that synthesizes eight different socio-economic and sanitation variables disaggregated by census tracts. The results are discussed in light of three theoretical perspectives: center-periphery differentiation, the economic approach to health, and social epidemiology. The findings of this study reveal that census tracts covering populations with higher socio-economic status and better sanitation conditions exhibited higher urban health index scores than those in the city's periphery. Results indicate better urban health indicators for Rio de Janeiro and São Paulo, compared to the other capitals analyzed. However, there are important nuances in each of the six cities, especially when assigning different weights to the variables that compose the Urban Health Index, despite the marked spatial segregation common to all. Considering distinctions within urban space is a fundamental strategy to understand these social and economic aspects and their potential implications for population health conditions.

Resumen La ciudad es una forma de vivir, pensar y sentir. El modo de vida urbano es capaz de producir ideas, comportamientos, valores y conocimientos, pero también lo es de intensificar las disparidades socioeconómicas y de salud de la población que reside en ella. Este artículo examina las disparidades en salud urbana en seis capitales brasileñas: São Paulo, Río de Janeiro, Salvador, Fortaleza, Belo Horizonte y Manaus. Para cuantificar y mapear las disparidades intraurbanas en estos espacios, se utilizan datos del censo demográfico de 2010 para aplicar el índice de salud urbana, una métrica que sintetiza ocho diferentes variables socioeconómicas y de saneamiento desagregadas por sectores censales. Los resultados se discuten a la luz de tres perspectivas teóricas: la diferenciación centro-periferia, el enfoque económico de la salud y la epidemiología social. Los hallazgos de este estudio revelan que los sectores censales que abarcan poblaciones con un mayor estatus socioeconómico y mejores condiciones de saneamiento presentaron puntajes más altos en el índice de salud urbana que los de la periferia de la ciudad. Hay indicios de mejores indicadores de salud urbana para Río de Janeiro y São Paulo, en comparación con las demás capitales analizadas. Sin embargo, se observan matices importantes en cada una de las seis ciudades analizadas, especialmente al asignar diferentes pesos a las variables que componen el pindice de salud urbana, a pesar de la marcada segregación espacial común a todas ellas. Considerar las distinciones dentro del espacio urbano es una estrategia fundamental para comprender estos aspectos sociales y económicos y sus posibles implicaciones en las condiciones de salud de la población.

Construction of an air quality health index for pediatric respiratory diseases in Shanghai / 环境与职业医学

Background Air quality health index (AQHI) has been widely used to quantify the health effects of multiple pollutants observed in population-based epidemiological studies, and can better reflect the widespread linear non-threshold between air pollution and health effects. Objective To explore an AQHI for pediatric respiratory diseases (AQHIr) in Shanghai and evaluate its feasibility. Methods The daily numbers of hospital outpatient visits for pediatric respiratory diseases from 2015 to 2019 were obtained from five general hospitals in Xuhui, Baoshan, Hongkou, Jinshan, and Chongming Districts of Shanghai. Monitoring data on air pollutants (PM2.5, PM10, SO2, NO2, and O3), air quality index (AQI), and meteorological variables (temperature, relative humidity, air pressure, and wind speed) were collected from five air quality monitoring sites nearest to selected hospitals. Time-series analysis using generalized additive model (GAM) was conducted to estimate the associations between respiratory-related pediatric outpatient visits and the concentrations of air pollutants. The sum of excess risk (ER) of hospital outpatient visits was used to construct AQHIr. To assess the predictive power of AQHIr, the associations of AQHIr and AQI with the number of pediatric respiratory outpatient visits in three hospitals in Xuhui, Hongkou, and Chongming districts were compared. Results Air pollutants had various effects on respiratory diseases outpatient visits. PM2.5, NO2, and O3 had most significant impacts on lag0 day and the associated ERs of hospital outpatient visits for each 10 μg·m−3 increase in pollutant concentration were 1.27% (95%CI: 0.88%-1.66%), 0.75% (95%CI: 0.40%-1.11%), and 0.36% (95%CI: 0.10%-0.62%), respectively. PM10 and SO2 had most significant impacts on lag3 day and the associated ERs of hospital outpatient visits for each 10 μg·m−3 increase in pollutant concentration were 0.81% (95%CI: 0.51%-1.12%) and 5.64% (95%CI: 3.37%-7.96%), respectively. There were significant effects of combinations of two pollutants among PM2.5, PM10, NO2, SO2, and O3 except for PM10+NO2, SO2+PM2.5, and SO2+NO2 (P<0.05). According to the results of single-pollutant and two-pollutant models, PM2.5, NO2, SO2, and O3 were selected to construct AQHIr. The comparison showed that for every interquartile range increase in AQHIr, the ER for pediatric outpatient visits was higher than that for the value corresponding to AQI. Conclusion Air pollutants in Shanghai have an impact on the number of pediatric respiratory outpatient visits. The AQHIr based on and outpatient visits for pediatric respiratory diseases can be a sensitive index to predict the effects of air pollution on children's respiratory health.

Establishment of air quality health index based on cumulative health risk of air pollution in Tianjin / 环境与职业医学

Background Cumulative risk index (CRI), as a commonly used approach to estimate the joint effects of multiple air pollutants on health, has been used by few studies to construct an air quality health index (AQHI). Objective To construct an AQHI based on the CRI of air pollution in Tianjin and evaluate the validity of the AQHI. Methods Daily data on air pollutants, meteorological factors, and non-accidental deaths during 2015–2019 in Tianjin were collected to create a time-series object. Descriptive statistical analyses were used to describe the characteristics of the data. To determine the best lag day and indicative pollutant, single-pollutant and two-pollutant generalized additive models were fitted to construct the exposure-response relationships between air pollutants and non-accidental deaths. After that we evaluated a CRI of air pollution using multi-pollutant models and constructed an AQHI and its classifications based on the CRI. Finally, we compared the exposure-response associations and coefficients of the AQHI and the conventional air quality index (AQI) with non-accidental deaths, and evaluated the health risk communication validity of the AQHI using generalized cross validation (GCV) values and R2 values. Results We selected lag1 as the best lag day and PM2.5, SO2, NO2 and O3 as the appropriate pollutants according to the unqualified rates of pollutants and significant statistical results. One μg·m−3 increase of PM2.5, SO2, NO2, and O3 was associated with −0.00002, 0.00079, 0.00015, and 0.00042 increase in effect size b of the non-accidental mortality, respectively. Based on these coefficients, we calculated the CRI and AQHI. According to a pre-determined classification scheme of the AQHI, the air quality of 63% study days was low risks and that of 34% study days was median risks. The associations of AQHI and AQI with non-accidental deaths in different populations were evaluated. The results showed that the excess risks of non-accidental deaths in total, female, and male populations for per interquartile range (IQR) increase in AQHI were higher than the corresponding values of AQI. The GCV values of the AQHI model (2.694, 1.819, and 1.938, respectively) were lower than those of the AQI model (2.747, 1.850, and 1.961, respectively), and the R2 values of the AQHI model (0.849, 0.780, and 0.820, respectively) were higher than those of the AQI model (0.846, 0.776, and 0.817, respectively). Conclusion Compared with AQI, the CRI-based AQHI may communicate the air pollution-related health risk to the public more effectively in Tianjin.

Rendimiento del ASAS Health Index en pacientes con Espondiloartritis de la vida real / Performance of ASAS Health Index in real life spondyloarthritis patients

Introducción: El cuestionario "Assessment of Spondyloarthritis International Society Health Index" (ASAS-HI) fue desarrollado para medir de manera global la funcionalidad y el estado de salud en pacientes con espondiloartritis (EspA). Se han propuesto puntos de corte para determinar diferentes estados de salud que fueron poco evaluados en pacientes de la vida real. Objetivos: Describir el estado de salud medido por ASAS-HI en pacientes argentinos con EspA axial (EspAax) y periférica (EspAp) en la práctica diaria y evaluar los factores asociados al pobre estado de salud. Materiales y métodos: Estudio de corte transversal, analítico y multicéntrico. Se incluyeron consecutivamente pacientes con EspAax y EspAp según criterios ASAS, de 15 centros argentinos. Análisis estadístico: Se realizó estadística descriptiva, análisis bivariado y multivariado (regresión logística múltiple) para evaluar los factores asociados al pobre estado de salud (ASAS-HI ≥12). Para analizar la validez de constructo de la herramienta se realizó correlación de Spearman entre el ASAS-HI y otros parámetros de evaluación de la enfermedad. Resultados: Se incluyeron 274 pacientes con EspA, con una edad media de 49 (±14) años y una duración mediana de la enfermedad de 62 meses (p25-75: 24-135), 155 (56,6%) de los pacientes eran de sexo masculino, 129 pacientes (47%) con EspAax y 145 (52,9%) EspAp. Según el ASAS-HI 119 pacientes (43,4%) presentaban buen estado de salud, 117 (42,7%) tenían estado de salud moderado y 38 (13.9%) pobre estado de salud. En los pacientes con EspAp el valor de ASAS-HI mediano fue de 7 (p25-75: 3-10). El ASAS-HI correlacionó positivamente con: DAS28: rho: 0.5 (p<0.001) y HAQ: rho: 0.54 (p<0.001). La variable asociada de manera independiente con pobre estado de salud fue el DAS28 (OR: 1.9, IC95% 1.1-3.4, p: 0.029). En los pacientes con EspAax el valor de ASAS-HI mediano fue de 6 (p25-75: 2.75-10). El ASAS-HI mostró correlación con: BASDAI: rho: 0.7 (p<0.001), ASDAS-ERS: rho: 0.7 (p<0,001), ASQoL: rho: 0.8 (p<0.001), BASFI rho: 0.75 (p<0.001). La variable que se asoció de manera independiente a pobre estado de salud fue el ASDAS-ERS (OR 6.6, IC95% 2-22, p 0.002). Conclusión: Un pobre estado de salud se asoció independientemente a mayor actividad de la enfermedad en pacientes con EspAax y EspAp. El ASAS-HI correlacionó con otros parámetros de la enfermedad, lo que refuerza la validez de constructo de esta nueva herramienta.

Introduction: The "Assessment of Spondyloarthritis International Society Health Index" (ASAS-HI) questionnaire was developed to globally measure function and health status in patients with spondyloarthritis (SpA). Cut-off points have been proposed to determine different health states that were poorly evaluated in real-life patients. Objectives: To describe the health status measured by ASAS-HI in Argentine patients with axial SpA (AxSpA) and peripheral SpA (SpAp) in daily practice and to evaluate the factors associated with poor health. Materials and methods: Cross-sectional, analytical and multicenter study. Patients with SpAax and SpAp were consecutively included according to ASAS criteria, from 15 Argentine centers. Statistical analysis: Descriptive statistics, bivariate and multivariate analysis (multiple logistic regression) were performed to evaluate the factors associated with poor health status (ASAS-HI ≥12). To analyze the construct validity of the tool, Spearman correlation was performed between the ASAS-HI and other disease evaluation parameters. Results: 274 patients with SpA were included, with a mean age of 49 (± 14) years and a median duration of the disease of 62 months (p25-75: 24-135), 155 (56.6%) were male, 129 patients (47%) with AxSpA and 145 (52.9%) SpAp. According to the ASAS-HI, 119 patients (43.4%) had good health, 117 (42.7%) had moderate health and 38 (13.9%) had poor health. In patients with SpAp, the mean ASAS-HI value was 7 (p25-75: 3-10). The ASAS-HI positively correlated with: DAS28: rho: 0.5 (p <0.001) and HAQ: rho: 0.54 (p <0.001). The variable independently associated with poor health status was DAS28 (OR: 1.9, 95% CI 1.1-3.4, p: 0.029). In patients with AxSpA, the mean ASAS-HI value was 6 (p25-75: 2.75-10). The ASAS-HI showed correlation with: BASDAI: rho: 0.7 (p <0.001), ASDAS-ERS: rho: 0.7 (p <0.001), ASQoL: rho: 0.8 (p <0.001), BASFI rho: 0.75 (p <0.001). The variable that was independently associated with poor health was the ASDAS-ERS (OR 6.6, 95% CI 2-22, p 0.002). Conclusion: Poor health status was independently associated with higher disease activity in patients with AxSpA and SpAp. The ASAS-HI correlated with other parameters of the disease, which reinforces the construct validity of this new tool.

Prostate health index for the diagnosis of prostate cancer in Asians in Macau with a PSA level of 4－10 μg/L / 中华男科学杂志

Objective@#To evaluate the prostate health index (PHI) as a tool for the diagnosis of PCa with a PSA level of 4－10 μg/L and determine the best cut-off value of PHI.@*METHODS@#Fifty-eight patients with a PSA level of 4－10 μg/L underwent transrectal ultrasound-guided prostatic biopsy in our hospital between April 2017 and June 2019. We constructed receiver operating characteristic (ROC) curves for the relationship of the biopsy results with the level of PSA, the ratio of ［－2］ proPSA to fPSA and PHI, and calculated the area under the ROC curves (AUC).@*RESULTS@#Prostatic biopsy revealed 18 cases of PCa in the 58 patients (31.0%). Statistically significant differences were observed between the PCa and non-PCa groups in ［－2］ proPSA, %［－2］ proPSA and PHI, but not in tPSA, % fPSA and PSA-density. The AUCs of PSA, % fPSA, PSA-density, ［－2］ proPSA, %［－2］ proPSA and PHI were 0.556, 0.407, 0.533, 0.746, 0.751 and 0.774, respectively. The specificity of PHI was 27.50% (95% CI: 14.6%－43.9%), the highest among the above predictors at 90% sensitivity. By applying PHI to this cohort, 13 cases (22.4%) of unnecessary biopsy could be avoided.@*CONCLUSIONS@#The application of PHI can increase the accuracy of PCa prediction and reduce unnecessary prostatic biopsy.、.

Diagnostic performance of prostate health index (PHI) in predicting prostate cancer on prostate biopsy

@# Prostate health index (PHI) has been shown to have better diagnostic accuracy in predicting prostate cancer (PCa) in men with total prostate-specific antigen (PSA) levels between 4-10ng/ml. However, little is known of its value in men with elevated PSA beyond this range. This study aimed to evaluate the diagnostic performance of PHI in Malaysian men with elevated PSA values ≤ 20ng/ml. Materials and Methods: From March 2015 to August 2016, all men consecutively undergoing transrectal ultrasound (TRUS)-guided prostate biopsy with total PSA values ≤ 20ng/ ml were recruited. Blood samples were taken immediately before undergoing prostate biopsy. The performance of total PSA, %fPSA, %p2PSA and PHI in determining the presence of PCa on prostate biopsy were compared. Results: PCa was diagnosed in 25 of 84 patients (29.7%). %p2PSA and PHI values were significantly higher (p<0.05) in patients with PCa than those without PCa. The areas under the receiver operating characteristic curves for total PSA, %fPSA, %p2PSA and PHI were 0.558, 0.560, 0.734 and 0.746, respectively. At 90% sensitivity, the specificity of PHI (42.4%) was five times better than total PSA (8.5%) and two times better than %fPSA (20.3%). By utilising PHI cut-off >22.52, 27 of 84 (32.1%) patients could have avoided undergoing biopsy. Conclusion: Findings of our study support the potential clinical effectiveness of PHI in predicting PCa in a wider concentration range of total PSA up to 20ng/ml.

Prostate volume does not provide additional predictive value to prostate health index for prostate cancer or clinically significant prostate cancer: Results from a multicenter study in China / 亚洲男科学杂志(英文版)

To evaluate whether prostate volume (PV) would provide additional predictive utility to the prostate health index (phi) for predicting prostate cancer (PCa) or clinically significant prostate cancer, we designed a prospective, observational multicenter study in two prostate biopsy cohorts. Cohort 1 included 595 patients from three medical centers from 2012 to 2013, and Cohort 2 included 1025 patients from four medical centers from 2013 to 2014. Area under the receiver operating characteristic curves (AUC) and logistic regression models were used to evaluate the predictive performance of PV-based derivatives and models. Linear regression analysis showed that both total prostate-specific antigen (tPSA) and free PSA (fPSA) were significantly correlated with PV (all P 0.05). In conclusion, PV-based derivatives (both PHIV and PHID) and models incorporating PV did not improve the predictive abilities of phi for either PCa or clinically significant PCa.

Phi-based risk calculators performed better in the prediction of prostate cancer in the Chinese population / 亚洲男科学杂志(英文版)

Risk prediction models including the Prostate Health Index (phi) for prostate cancer have been well established and evaluated in the Western population. The aim of this study is to build phi-based risk calculators in a prostate biopsy population and evaluate their performance in predicting prostate cancer (PCa) and high-grade PCa (Gleason score ≥7) in the Chinese population. We developed risk calculators based on 635 men who underwent initial prostate biopsy. Then, we validated the performance of prostate-specific antigen (PSA), phi, and the risk calculators in an additional observational cohort of 1045 men. We observed that the phi-based risk calculators (risk calculators 2 and 4) outperformed the PSA-based risk calculator for predicting PCa and high-grade PCa in the training cohort. In the validation study, the area under the receiver operating characteristic curve (AUC) for risk calculators 2 and 4 reached 0.91 and 0.92, respectively, for predicting PCa and high-grade PCa, respectively; the AUC values were better than those for risk calculator 1 (PSA-based model with an AUC of 0.81 and 0.82, respectively) (all P < 0.001). Such superiority was also observed in the stratified population with PSA ranging from 2.0 ng ml-1to 10.0 ng ml-1. Decision curves confirmed that a considerable proportion of unnecessary biopsies could be avoided while applying phi-based risk calculators. In this study, we showed that, compared to risk calculators without phi, phi-based risk calculators exhibited superior discrimination and calibration for PCa in the Chinese biopsy population. Applying these risk calculators also considerably reduced the number of unnecessary biopsies for PCa.

Phi-based risk calculators performed better in the prediction of prostate cancer in the Chinese population / 亚洲男科学杂志(英文版)

Risk prediction models including the Prostate Health Index (phi) for prostate cancer have been well established and evaluated in the Western population. The aim of this study is to build phi-based risk calculators in a prostate biopsy population and evaluate their performance in predicting prostate cancer (PCa) and high-grade PCa (Gleason score ≥7) in the Chinese population. We developed risk calculators based on 635 men who underwent initial prostate biopsy. Then, we validated the performance of prostate-specific antigen (PSA), phi, and the risk calculators in an additional observational cohort of 1045 men. We observed that the phi-based risk calculators (risk calculators 2 and 4) outperformed the PSA-based risk calculator for predicting PCa and high-grade PCa in the training cohort. In the validation study, the area under the receiver operating characteristic curve (AUC) for risk calculators 2 and 4 reached 0.91 and 0.92, respectively, for predicting PCa and high-grade PCa, respectively; the AUC values were better than those for risk calculator 1 (PSA-based model with an AUC of 0.81 and 0.82, respectively) (all P < 0.001). Such superiority was also observed in the stratified population with PSA ranging from 2.0 ng ml-1to 10.0 ng ml-1. Decision curves confirmed that a considerable proportion of unnecessary biopsies could be avoided while applying phi-based risk calculators. In this study, we showed that, compared to risk calculators without phi, phi-based risk calculators exhibited superior discrimination and calibration for PCa in the Chinese biopsy population. Applying these risk calculators also considerably reduced the number of unnecessary biopsies for PCa.

Block-wise comprehensive health index in Gadchiroli: A tribal district in Maharashtra

Background: The National Health Mission expects bottom-up approach for preparing Project Implementation Plan and also expects special attention toward tribal areas. Some district-level health information is available from national health surveys, but subdistrict-level information is mostly not available. Gadchiroli is the farthest district from the state capital. There are 12 blocks in the district. It is a notified tribal district having 8.61%�.50% tribal population in different blocks and block-wise urbanization varies from 0.00% to 37.10%. Objectives: The objective was to assess community health status at block level in Gadchiroli district and then develop comprehensive health index for ranking the blocks. Methods: The author has used available secondary data sources including Census, Survey of Cause of Death scheme, health management information system, Directorate of Economics and Statistics, and Maharashtra Medical Council. Ten indicators were selected after discussion with public health specialists to evolve comprehensive health index. Blocks having best statistic in each indicator were given 100 marks and other blocks were given proportionate marks. Thus, the highest possible score for any block was 1000. Results: The range of block-wise score was from 424 to 781. The highest scoring block was Gadchiroli and was an outlier. The comprehensive score was having correlation with urbanization, r = 0.63 (95% confidence limits, 0.09�88). After principal component analysis, the extracted three components were responsible for most of the variations. Conclusions: Reasonably reliable and valid block-wise data are available to carry out community health assessment and develop comprehensive health index. The index is useful for comparison among blocks.

IDENTIFYING THE CRITERIA FOR ORAL HEALTH INDEX IN MONGOLIA / Шинэ санаа Шинэ нээлт

@#BACKGROUND. The measurement of oral health is recognized as a critical feature of numerous dental activities: describing normal biologic processes, understanding the natural history of disease, testing hypotheses regarding preventive agents, and planning and evaluation of health services. In modern times, statistical methods are widely being used to describe the probability of caries formation by calculating the progress and progression of dental caries for each individual by means of investigational correlations to detect and control risk factors for dental caries and periodontal diseases. OBJECTIVE. The purpose of this study is to identify the correlation between the incidences of dental caries (DMFT) and its influencing risk factors for Mongolians in order to establish the fundamental criteria for oral health index. MATERIAL AND METHODS. Data were collected from 240 volunteers in six different age groups by using a questionnaire and an intra oral examination combined with laboratory tests. The oral health index is divided into 5 major categorical factors including the residual number of natural tooth, caries state, periodontal state, other oral health state and oral health management habits and systemic condition that determines the relationship between the incidences of dental caries and influencing risk factor for each item. RESULTS. Significantly different results were observed for Mongolian people in terms of prevalence and proportion of oral disease and oral state which led to the establishment of criteria for oral health index by statistical significance factors in all age groups (p <0.05). CONCLUSION. It is possible to create and introduce a scoring system of individual oral health index that could be applied to the evaluation oral health program that is suitable for calculating future illnesses and prognosis of oral diseases.