Trends in Diabetes Mortality in Urban and Rural China, 1987–2019: A Joinpoint Regression Analysis

PurposeDiabetes mellitus is emerging as an epidemic worldwide, and the incidence and prevalence of diabetes have drastically changed in China over the past 30 years, but data on its mortality rate are scarce. This study aimed to analyze the time trends of mortality rates among patients with diabetes in the rural and urban population in China between 1987 and 2019.MethodsThe research data come from China’s annual report on national health statistics and the Chinese Health Statistics Yearbook. Age-standardized mortality rates were calculated by using the direct method based on the World Standard Population from the WHO. Joinpoint regression analysis was employed to estimate the annual percent change and average annual percentage changes of mortality rates of diabetes mellitus.ResultsAn overall trend for increment in diabetes mortality was observed. The crude mortality rates and age-standardized mortality rates of diabetes for urban and rural residents in China showed a significant increasing trend between 1987 and 2019. Mortality due to diabetes in urban areas has been higher than in rural areas for 30 years. However, due to the rapid increase of rural diabetes mortality in the past decade, the gap between the two gradually narrowed. The age-standardized mortality rates of diabetes increased by about 38.5% in urban areas and 254.9% in rural areas over the whole study period. In addition, the age-standardized mortality rate of females with diabetes was higher than that of males, but this pattern began to change in urban areas in 2012. Finally, the age-standardized mortality rates in the elderly population in China are higher with a faster growth rate, especially in rural areas.ConclusionThe mortality rate of diabetes is on the rise in China. The rapid growth of the mortality rate of diabetes in rural areas leads to the reduction of the urban–rural gap. Male mortality rates in urban areas have surpassed those of women. At the same time, the mortality rate of diabetes showed obvious elder-group orientation. As China’s population ages, the burden of death and disability caused by diabetes and its complications will continue to increase. These results indicate that diabetes has become a significant public health problem in China. Such an effect increases the demand for strategies aimed at the prevention and treatment of diabetes mellitus. In addition to the prevention and intervention of diabetes in high-risk groups, it is also necessary to establish diabetes screening networks to identify patients with mild symptoms. Early detection and timely intervention can effectively reduce the incidence and mortality of diabetes.

The Research and Development Survey (RANDS) during COVID-19

The National Center for Health Statistics’ (NCHS) Research and Development Survey (RANDS) is a series of commercial panel surveys collected for methodological research purposes. In response to the COVID-19 pandemic, NCHS expanded the use of RANDS to rapidly monitor aspects of the public health emergency. The RANDS during COVID-19 survey was designed to include COVID-19 related health outcome and cognitive probe questions. Rounds 1 and 2 were fielded June 9–July 6, 2020 and August 3–20, 2020 using the AmeriSpeak® Panel. Existing and new approaches were used to: 1) evaluate question interpretation and performance to improve future COVID-19 data collections and 2) to produce a set of experimental estimates for public release using weights which were calibrated to NCHS’ National Health Interview Survey (NHIS) to adjust for potential bias in the panel. Through the expansion of the RANDS platform and ongoing methodological research, NCHS reported timely information about COVID-19 in the United States and demonstrated the use of recruited panels for reporting national health statistics. This report describes the use of RANDS for reporting on the pandemic and the associated methodological survey design decisions including the adaptation of question evaluation approaches and calibration of panel weights.

Infodemia: Concept, Social and Political Consequences, Methods of Managing

The article is devoted to the consideration of the infodemia phenomenon as a result of massive fakes injections associated with the 2019-nCoV pandemic. Author analyzes the global social and political consequences of disinformation in social networks and messengers on the topic of health, official health statistics and government methods to combat the spread of the virus. There is a decrease in trust to government authorities and official information sources and also an increase in the popularity of conspiracy narratives. Author identifies methods to deal with infodemia and analyzes their effectiveness.

Advancements in the National Vital Statistics System to Meet the Real-Time Data Needs of a Pandemic

The National Center for Health Statistics’ (NCHS’s) National Vital Statistics System (NVSS) collects, processes, codes, and reviews death certificate data and disseminates the data in annual data files and reports. With the global rise of COVID-19 in early 2020, the NCHS mobilized to rapidly respond to the growing need for reliable, accurate, and complete real-time data on COVID-19 deaths. Within weeks of the first reported US cases, NCHS developed certification guidance, adjusted internal data processing systems, and stood up a surveillance system to release daily updates of COVID-19 deaths to track the impact of the COVID-19 pandemic on US mortality. This report describes the processes that NCHS took to produce timely mortality data in response to the COVID-19 pandemic. (Am J Public Health. 2021;111(12):2133–2140. https://doi.org/10.2105/AJPH.2021.306519 )

Development of Local Percentile Growth Charts of Children between Birth to Ten Years

Growth pattern of human population changes with time and place. Particularly developing countries, country like India, is in a stage of nutritional transition hence it is necessary to update growth references regularly. The present study is carried out on 0-10 years from Baramati from Pune district of Maharashtra. We considered that children from maternity homes, BCG camps, well baby clinics, immunization camps, private clinics, ‘Anganwadis and Balwadis’, Nurseries’ and schools etc. Our study shows that growth performance of Anthropometric indices for Baramati children is much less than National Centre of Health Statistics (NCHS) and slightly less than Indian Council of Medical Research ICMR and Affluent Indians (AI). We have proposed growth charts for Baramati region to monitor growth parameters. Keywords: Anthropometric Indices, NCHS, ICMR, AI.

Learning from health information challenges in the Central African Republic: where documenting health and humanitarian needs requires fresh approaches

The Central African Republic (CAR) is one of the world’s poorest and most fragile countries. Maybe there is no nation on the planet where the official health statistics are so poor. Evidence presented in this Conflict and Health themed collection to document humanitarian needs in CAR, suggests that UN statistics dramatically under-estimate the birth and death rates in conflict settings. To be current and valid, health indicator data in violent settings require more frequent measurement, more triangulation and granular exploration, and creative approaches based on few assumptions. In a world increasingly dependent on model driven data—data often inaccurate in conflict settings—we hope that this collection will allow those service providers and researchers operating in CAR to share their work and help us better learn how to learn. We particularly invite research from professionals working in CAR that documents humanitarian needs and presents indicators of population health where official estimates might not articulate the true extent of the health crisis.

610Official statistics on health outcomes and risk factors for small geographic areas

Background The 2017-18 National Health Survey (NHS) is an Australia-wide detailed health survey conducted by the Australian Bureau of Statistics (ABS). Although the survey enables reliable National and State official health statistics, the sample size is too small to produce reliable data for smaller population areas. To produce such data the ABS applies an innovative Small Area Estimation (SAE) approach, combining the survey data and several population data sources. Methods We predict prevalence of each health outcome variable by fitting a logistic mixed model. The modelled NHS data are enhanced by data from the ABS 2016 Census, Estimated Resident Population, and several administrative sources including Medical and Pharmaceutical transactions. Models are selected using a bespoke stepwise selection process; where the predictor variables have a strong association with the health outcome, whilst also ensuring that the estimated rates maintain consistency with published national data for that health outcome. Results Health statistics were produced for over 25 health outcomes and risk factors for 1134 Population Health Areas (PHAs) across Australia. The data show significant variation in rates between areas that are not evident in National and State level data. For example, the prevalence of adult current smokers in PHAs ranged from 4.4% to 34.6%, compared to 15.1% nationally. Conclusions The ABS SAE approach is an innovative method that enables production of reliable official health statistics, meeting a known data gap of local level health data. Key messages The ABS SAE approach delivers reliable official local health statistics, meeting an important data need not met using survey data alone.

Can routine health facility data be used to monitor subnational coverage of maternal, newborn and child health services in Uganda?

Background Routine health facility data are a critical source of local monitoring of progress and performance at the subnational level. Uganda has been using district health statistics from facility data for many years. We aimed to systematically assess data quality and examine different methods to obtain plausible subnational estimates of coverage for maternal, newborn and child health interventions. Methods Annual data from the Uganda routine health facility information system 2015–2019 for all 135 districts were used, as well as national surveys for external comparison and the identification of near-universal coverage interventions. The quality of reported data on antenatal and delivery care and child immunization was assessed through completeness of facility reporting, presence of extreme outliers and internal data consistencies. Adjustments were made when necessary. The denominators for the coverage indicators were derived from population projections and health facility data on near-universal coverage interventions. The coverage results with different denominators were compared with the results from household surveys. Results Uganda’s completeness of reporting by facilities was near 100% and extreme outliers were rare. Inconsistencies in reported events, measured by annual fluctuations and between intervention consistency, were common and more among the 135 districts than the 15 subregions. The reported numbers of vaccinations were improbably high compared to the projected population of births or first antenatal visits – and especially so in 2015–2016. There were also inconsistencies between the population projections and the expected target population based on reported numbers of antenatal visits or immunizations. An alternative approach with denominators derived from facility data gave results that were more plausible and more consistent with survey results than based on population projections, although inconsistent results remained for substantive number of subregions and districts. Conclusion Our systematic assessment of the quality of routine reports of key events and denominators shows that computation of district health statistics is possible with transparent adjustments and methods, providing a general idea of levels and trends for most districts and subregions, but that improvements in data quality are essential to obtain more accurate monitoring.

817Official small area health statistics using the stratified reweighting method

Background The Australian Bureau of Statistics (ABS) presently produces health data for small population groups using a Generalised Linear Mixed Model (GLMM) method. Although this method is highly effective at producing reliable local level health data, it takes several months to compile data once it’s collected. The Stratified Reweighting Method (SRM) was investigated as an innovative efficient method for producing local level health data. Methods The SRM harnesses information from both health survey and Census data. A cluster analysis of 12 Census data items creates 13 area groups with similar population demographics. A replicated survey data set is then created where each small area is bolstered by the other small areas within its area group. The survey weights from this dataset are adjusted to match Census data of each small area across several demographic variables. A final survey weight adjustment ensures consistency of the small area predictions with national survey estimates. Results Health statistics were produced for over 20 health outcomes in the latest ABS National Health Survey; and the ABS Survey of Disability, Ageing and Carers. It was found that, compared to the GLMM method: the models had lower, but still acceptable quality; the errors of prevalence estimates were similar magnitude; and the data compilation time was reduced to within two weeks. Conclusions The SRM is an efficient approach for producing acceptable quality official local health statistics. Key messages The SRM is an innovative and efficient weight-based method using health survey and population Census data to produce official local health statistics.