Dataset Information

Meteorological variability and infectious disease in Central Africa: a review of meteorological data quality.

ABSTRACT: Central African countries may bear high climate change-related infectious disease burdens because of preexisting high rates of disease, poor healthcare infrastructure, land use changes, and high environmental change vulnerabilities. However, making connections between climate and infectious diseases in this region is hampered by the paucity of high-quality meteorological data. This review analyzes the sources and quality of meteorological data used to study the interactions between weather and infectious diseases in Central African countries. Results show that 23% of studies used meteorological data that mismatched with the disease spatial scale of interest. Use of inappropriate weather data was most frequently identified in analyses using meteorological station data or gridded data products. These findings have implications for the interpretation of existing analyses and provide guidance for the use of climate data in future analyses of the connections between meteorology and infectious diseases in Central Africa.

SUBMITTER: Heaney A

PROVIDER: S-EPMC5184398 | biostudies-literature | 2016 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Meteorological variability and infectious disease in Central Africa: a review of meteorological data quality.

Heaney Alexandra A Little Eliza E Ng Sophia S Shaman Jeffrey J

Annals of the New York Academy of Sciences 20160531 1

Central African countries may bear high climate change-related infectious disease burdens because of preexisting high rates of disease, poor healthcare infrastructure, land use changes, and high environmental change vulnerabilities. However, making connections between climate and infectious diseases in this region is hampered by the paucity of high-quality meteorological data. This review analyzes the sources and quality of meteorological data used to study the interactions between weather and i ...[more]

PMID: 27244461

Similar Datasets

Project description:BackgroundChronic obstructive pulmonary disease (COPD) is predicted to become a major cause of death worldwide. Studies on the variability in the estimates of key epidemiological parameters of COPD may contribute to better assessment of the burden of this disease and to helpful guidance for future research and public policies. In the present study, we examined differences in the main epidemiological characteristics of COPD derived from studies across countries of the European Union, focusing on prevalence, severity, frequency of exacerbations and mortality, as well as on differences between the studies' methods.MethodsThis systematic review was based on a search for the relevant literature in the Science Citation Index database via the Web of Science and on COPD mortality rates issued from national statistics. Analysis was finally based on 65 articles and Eurostat COPD mortality data for 21 European countries.ResultsEpidemiological characteristics of COPD varied widely from country to country. For example, prevalence estimates ranged between 2.1% and 26.1%, depending on the country, the age group and the methods used. Likewise, COPD mortality rates ranged from 7.2 to 36.1 per 10(5) inhabitants. The methods used to estimate these epidemiological parameters were highly variable in terms of the definition of COPD, severity scales, methods of investigation and target populations. Nevertheless, to a large extent, several recent international guidelines or research initiatives, such as GOLD, BOLD or PLATINO, have boosted a substantial standardization of methodology in data collection and have resulted in the availability of more comparable epidemiological estimates across countries. On the basis of such standardization, severity estimates as well as prevalence estimates present much less variation across countries. The contribution of these recent guidelines and initiatives is outlined, as are the problems remaining in arriving at more accurate COPD epidemiological estimates across European countries.ConclusionsThe accuracy of COPD epidemiological parameters is important for guiding decision making with regard to preventive measures, interventions and patient management in various health care systems. Therefore, the recent initiatives for standardizing data collection should be enhanced to result in COPD epidemiological estimates of improved quality. Moreover, establishing international guidelines for reporting research on COPD may also constitute a major contribution.

Project description:BackgroundThe novel coronavirus SARS-CoV-2 rapidly spread around the world, causing the disease COVID-19. To contain the virus, much hope is placed on participatory surveillance using mobile apps, such as automated digital contact tracing, but broad adoption is an important prerequisite for associated interventions to be effective. Data protection aspects are a critical factor for adoption, and privacy risks of solutions developed often need to be balanced against their functionalities. This is reflected by an intensive discussion in the public and the scientific community about privacy-preserving approaches.ObjectiveOur aim is to inform the current discussions and to support the development of solutions providing an optimal balance between privacy protection and pandemic control. To this end, we present a systematic analysis of existing literature on citizen-centered surveillance solutions collecting individual-level spatial data. Our main hypothesis is that there are dependencies between the following dimensions: the use cases supported, the technology used to collect spatial data, the specific diseases focused on, and data protection measures implemented.MethodsWe searched PubMed and IEEE Xplore with a search string combining terms from the area of infectious disease management with terms describing spatial surveillance technologies to identify studies published between 2010 and 2020. After a two-step eligibility assessment process, 27 articles were selected for the final analysis. We collected data on the four dimensions described as well as metadata, which we then analyzed by calculating univariate and bivariate frequency distributions.ResultsWe identified four different use cases, which focused on individual surveillance and public health (most common: digital contact tracing). We found that the solutions described were highly specialized, with 89% (24/27) of the articles covering one use case only. Moreover, we identified eight different technologies used for collecting spatial data (most common: GPS receivers) and five different diseases covered (most common: COVID-19). Finally, we also identified six different data protection measures (most common: pseudonymization). As hypothesized, we identified relationships between the dimensions. We found that for highly infectious diseases such as COVID-19 the most common use case was contact tracing, typically based on Bluetooth technology. For managing vector-borne diseases, use cases require absolute positions, which are typically measured using GPS. Absolute spatial locations are also important for further use cases relevant to the management of other infectious diseases.ConclusionsWe see a large potential for future solutions supporting multiple use cases by combining different technologies (eg, Bluetooth and GPS). For this to be successful, however, adequate privacy-protection measures must be implemented. Technologies currently used in this context can probably not offer enough protection. We, therefore, recommend that future solutions should consider the use of modern privacy-enhancing techniques (eg, from the area of secure multiparty computing and differential privacy).

Dataset Information

Meteorological variability and infectious disease in Central Africa: a review of meteorological data quality.

Publications

Meteorological variability and infectious disease in Central Africa: a review of meteorological data quality.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets