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Measurements of health indicators are rarely available for every population and period of interest, and available data may not be comparable. The Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER) define best reporting practices for studies that calculate health estimates for multiple populations (in time or space) using multiple information sources. Health estimates that fall within the scope of GATHER include all quantitative population-level estimates (including global, regional, national, or subnational estimates) of health indicators, including indicators of health status, incidence and prevalence of diseases, injuries, and disability and functioning; and indicators of health determinants, including health behaviours and health exposures. GATHER comprises a checklist of 18 items that are essential for best reporting practice. A more detailed explanation and elaboration document, describing the interpretation and rationale of each reporting item along with examples of good reporting, is available on the GATHER website (http://gather-statement.org).

Measurements of health indicators are rarely available for every population and period of interest, and available data may not be comparable. The Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER) define best reporting practices for studies that calculate health estimates for multiple populations (in time or space) using multiple information sources. Health estimates that fall within the scope of GATHER include all quantitative population-level estimates (including global, regional, national, or subnational estimates) of health indicators, including indicators of health status, incidence and prevalence of diseases, injuries, and disability and functioning; and indicators of health determinants, including health behaviours and health exposures. GATHER comprises a checklist of 18 items that are essential for best reporting practice. A more detailed explanation and elaboration document, describing the interpretation and rationale of each reporting item along with examples of good reporting, is available on the GATHER website., (This is an Open Access article published under the CC BY 3.0 IGO license which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. In any use of this article, there should be no suggestion that WHO endorses any specific organisation, products or services. The use of the WHO logo is not permitted. This notice should be preserved along with the article's original URL.)

Gretchen Stevens and colleagues present the GATHER statement, which seeks to promote good practice in the reporting of global health estimates.

Background: Pneumonia and diarrhea are leading causes of death for children under five (U5). It is challenging to estimate the total number of deaths and cause-specific mortality fractions. Two major efforts, one led by the Institute for Health Metrics and Evaluation (IHME) and the other led by the World Health Organization (WHO)/Child Health Epidemiology Reference Group (CHERG) created estimates for the burden of disease due to these two syndromes, yet their estimates differed greatly for 2010., Methods: This paper discusses three main drivers of the differences: data sources, data processing, and covariates used for modelling. The paper discusses differences in the model assumptions for etiology-specific estimates and presents recommendations for improving future models., Results: IHME's Global Burden of Disease (GBD) 2010 study estimated 6.8 million U5 deaths compared to 7.6 million U5 deaths from CHERG. The proportional differences between the pneumonia and diarrhea burden estimates from the two groups are much larger; GBD 2010 estimated 0.847 million and CHERG estimated 1.396 million due to pneumonia. Compared to CHERG, GBD 2010 used broader inclusion criteria for verbal autopsy and vital registration data. GBD 2010 and CHERG used different data processing procedures and therefore attributed the causes of neonatal death differently. The major difference in pneumonia etiologies modeling approach was the inclusion of observational study data; GBD 2010 included observational studies. CHERG relied on vaccine efficacy studies., Discussion: Greater transparency in modeling methods and more timely access to data sources are needed. In October 2013, the Bill & Melinda Gates Foundation (BMGF) hosted an expert meeting to examine possible approaches for better estimation. The group recommended examining the impact of data by systematically excluding sources in their models. GBD 2.0 will use a counterfactual approach for estimating mortality from pathogens due to specific etiologies to overcome bias of the methods used in GBD 2010 going forward.

Objective: To determine whether observed changes in HIV prevalence in countries with generalised HIV epidemics are associated with changes in sexual risk behaviour., Methods: A mathematical model was developed to explore the relation between prevalence recorded at antenatal clinics (ANCs) and the pattern of incidence of infection throughout the population. To create a null model a range of assumptions about sexual behaviour, natural history of infection, and sampling biases in ANC populations were explored to determine which factors maximised declines in prevalence in the absence of behaviour change. Modelled prevalence, where possible based on locally collected behavioural data, was compared with the observed prevalence data in urban Haiti, urban Kenya, urban Cote d'Ivoire, Malawi, Zimbabwe, Rwanda, Uganda, and urban Ethiopia., Results: Recent downturns in prevalence observed in urban Kenya, Zimbabwe, and urban Haiti, like Uganda before them, could only be replicated in the model through reductions in risk associated with changes in behaviour. In contrast, prevalence trends in urban Cote d'Ivoire, Malawi, urban Ethiopia, and Rwanda show no signs of changed sexual behaviour., Conclusions: Changes in patterns of HIV prevalence in urban Kenya, Zimbabwe, and urban Haiti are quite recent and caution is required because of doubts over the accuracy and representativeness of these estimates. Nonetheless, the observed changes are consistent with behaviour change and not the natural course of the HIV epidemic.