Suchergebnisse

BACKGROUND: The main purpose was to determine basic epidemiological data on CKD among hospitalized pediatric patients in China. METHODS: Data from pediatric inpatients with CKD hospitalized from June 1, 2013 to May 31, 2017 were extracted from the electronic records of HQMS database, which includes over 14 million inpatients. Codes from the 10th revision of the International Classification of Diseases (ICD-10) were used to search the database. RESULTS: A total of 524 primary diseases of CKD were included in this study. In all, there were 278 231 pediatric inpatients with CKD, which accounted for 1.95 % of the 14 250 594 pediatric inpatients registered in the HQMS database. The number of pediatric inpatients with CKD was 67 498 in 2013, 76 810 in 2014, 81 665 in 2015 and 82 649 in 2016, which accounted for 1.93 %, 1.93 %, 1.99 and 2.09 %, respectively, of the total population of pediatric inpatients. The etiology of CKD was secondary nephrosis in 37.95 % of cases, which ranked first and followed by CAKUT with a percentage of 24.61 %. Glomerular diseases and cystic kidney disease accounted for 21.18 and 5.07 %, respectively. Among all 278 231 patients, 6 581 (2.37 %) had a primary discharge diagnosis of CKD. The renal pathology findings of CKD showed that IgA accounted for 51.17 %. CONCLUSIONS: This study provides a descriptive analysis of the hospitalized population of pediatric CKD patients. Our study provides important, fundamental data for policy making and legislation, registry implementation and the diagnosis, treatment and prevention of CKD in China. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12882-021-02383-1.

The expansion in the scope, scale, and sources of data on the wider social determinants of health (SDH) in the last decades could bridge gaps in information available for decision-making. However, challenges remain in making data widely available, accessible, and useful towards improving population health. While traditional, government-supported data sources and comparable data are most often used to characterize social determinants, there are still capacity and management constraints on data availability and use. Conversely, privately held data may not be shared. This study reviews and discusses the nature, sources, and uses of data on SDH, with illustrations from two middle-income countries: Kenya and the Philippines. The review highlights opportunities presented by new data sources, including the use of big data technologies, to capture data on social determinants that can be useful to inform population health. We conducted a search between October 2010 and September 2020 for grey and scientific publications on social determinants using a search strategy in PubMed and a manual snowball search. We assessed data sources and the data environment in both Kenya and the Philippines. We found limited evidence of the use of new sources of data to study the wider SDH, as most of the studies available used traditional sources. There was also no evidence of qualitative big data being used. Kenya has more publications using new data sources, except on the labor determinant, than the Philippines. The Philippines has a more consistent distribution of the use of new data sources across the HEALTHY determinants than Kenya, where there is greater variation of the number of publications across determinants. The results suggest that both countries use limited SDH data from new data sources. This limited use could be due to a number of factors including the absence of standardized indicators of SDH, inadequate trust and acceptability of data collection methods, and limited infrastructure to pool, analyze, and translate data.

Depression accounts for a large share of the global disease burden, with an estimated 264 million people globally suffering from depression. Despite being one of the most common kinds of mental health (MH) disorders, much about depression remains unknown. There are limited data about depression, in terms of its occurrence, distribution, and wider social determinants. This work examined the use of novel data sources for assessing the scope and social determinants of depression, with a view to informing the reduction of the global burden of depression.This study focused on new and traditional sources of data on depression and its social determinants in two middle-income countries (LMICs), namely, Brazil and India. We identified data sources using a combination of a targeted PubMed search, Google search, expert consultations, and snowball sampling of the relevant literature published between October 2010 and September 2020. Our search focused on data sources on the following HEALTHY subset of determinants: healthcare (H), education (E), access to healthy choices (A), labor/employment (L), transportation (T), housing (H), and income (Y).Despite the emergence of a variety of data sources, their use in the study of depression and its HEALTHY determinants in India and Brazil are still limited. Survey-based data are still the most widely used source. In instances where new data sources are used, the most commonly used data sources include social media (twitter data in particular), geographic information systems/global positioning systems (GIS/GPS), mobile phone, and satellite imagery. Often, the new data sources are used in conjunction with traditional sources of data. In Brazil, the limited use of new data sources to study depression and its HEALTHY determinants may be linked to (a) the government's outsized role in coordinating healthcare delivery and controlling the data system, thus limiting innovation that may be expected from the private sector; (b) the government routinely collecting data on depression and other MH disorders (and therefore, does not see the need for other data sources); and (c) insufficient prioritization of MH as a whole. In India, the limited use of new data sources to study depression and its HEALTHY determinants could be a function of (a) the lack of appropriate regulation and incentives to encourage data sharing by and within the private sector, (b) absence of purposeful data collection at subnational levels, and (c) inadequate prioritization of MH. There is a continuing gap in the collection and analysis of data on depression, possibly reflecting the limited priority accorded to mental health as a whole. The relatively limited use of data to inform our understanding of the HEALTHY determinants of depression suggests a substantial need for support of independent research using new data sources. Finally, there is a need to revisit the universal health coverage (UHC) frameworks, as these frameworks currently do not include depression and other mental health-related indicators so as to enable tracking of progress (or lack thereof) on such indicators.

OBJECTIVE To evaluate the associations between adiposity measures (body mass index, waist circumference, and waist-to-height ratio) with decline in glomerular filtration rate (GFR) and with all cause mortality. DESIGN Individual participant data meta-analysis. SETTING Cohorts from 40 countries with data collected between 1970 and 2017. PARTICIPANTS Adults in 39 general population cohorts (n=5 459 014), of which 21 (n=594 496) had data on waist circumference; six cohorts with high cardiovascular risk (n=84 417); and 18 cohorts with chronic kidney disease (n=91 607). MAIN OUTCOME MEASURES GFR decline (estimated GFR decline ≥40%, initiation of kidney replacement therapy or estimated GFR <10 mL/ min/1.73 m2) and all cause mortality. RESULTS Over a mean follow-up of eight years, 246 607 (5.6%) individuals in the general population cohorts had GFR decline (18 118 (0.4%) end stage kidney disease events) and 782 329 (14.7%) died. Adjusting for age, sex, race, and current smoking, the hazard ratios for GFR decline comparing body mass indices 30, 35, and 40 with body mass index 25 were 1.18 (95% confidence interval 1.09 to 1.27), 1.69 (1.51 to 1.89), and 2.02 (1.80 to 2.27), respectively. Results were similar in all subgroups of estimated GFR. Associations weakened after adjustment for additional comorbidities, with respective hazard ratios of 1.03 (0.95 to 1.11), 1.28 (1.14 to 1.44), and 1.46 (1.28 to 1.67). The association between body mass index and death was J shaped, with the lowest risk at body mass index of 25. In the cohorts with high cardiovascular risk and chronic kidney disease (mean follow-up of six and four years, respectively), risk associations between higher body mass index and GFR decline were weaker than in the general population, and the association between body mass index and death was also J shaped, with the lowest risk between body mass index 25 and 30. In all cohort types, associations between higher waist circumference and higher waist-to-height ratio with GFR decline were similar to that of body mass index; however, increased risk of death was not associated with lower waist circumference or waist-to-height ratio, as was seen with body mass index. CONCLUSIONS Elevated body mass index, waist circumference, and waist-to-height ratio are independent risk factors for GFR decline and death in individuals who have normal or reduced levels of estimated GFR. ; The CKD-PC data coordinating centre is funded partly by a programme grant from the US National Kidney Foundation and the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK; R01DK100446-01). Various sources have supported enrolment and data collection including laboratory measurements, and follow-up in the collaborating cohorts of the CKD-PC; these funding sources include government agencies such as national institutes of health and medical research councils as well as foundations and industry sponsors listed in eAppendix 3. The funders of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report. ARC was supported by NIDDK (1K23DK106515-01).