National and sub-national variation in patterns of febrile case management in sub-Saharan Africa

Alegana, Victor A.; Maina, Joseph; Ouma, Paul; Macharia, Peter M.; Wright, Jim A.; Atkinson, Peter M.; Okiro, Emelda A.; Snow, Robert W.; Tatem, Andrew J.

doi:10.1038/s41467-018-07536-9

Cited by 42 publications

(56 citation statements)

References 35 publications

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“…[3][4][5][6][7][8] Infrastructure data have also been used to define the connectivity between regions with the travel time as a proxy of human mobility and health accessibility. 51,52 Moreover, earth observation data, such as satellite imagery of night-time lights can help inform on the changing densities of populations within cities over the course of a year. 35,53 Mobile phone data are particularly promising for analysing travel-related phenomena on a scale previously impossible, providing a 'big data' approach to understanding human mobility and its changes.…”

Section: Measuring Human Mobility Using Mobile Phone Datamentioning

confidence: 99%

Measuring mobility, disease connectivity and individual risk: a review of using mobile phone data and mHealth for travel medicine

Lai

Farnham

Ruktanonchai

et al. 2019

Journal of Travel Medicine

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Rationale for review:The increasing mobility of populations allows pathogens to move rapidly and far, making endemic or epidemic regions more connected to the rest of the world than at any time in history. However, the ability to measure and monitor human mobility, health risk and their changing patterns across spatial and temporal scales using traditional data sources has been limited. To facilitate a better understanding of the use of emerging mobile phone technology and data in travel medicine, we reviewed relevant work aiming at measuring human mobility, disease connectivity and health risk in travellers using mobile geopositioning data. Key findings:Despite some inherent biases of mobile phone data, analysing anonymized positions from mobile users could precisely quantify the dynamical processes associated with contemporary human movements and connectivity of infectious diseases at multiple temporal and spatial scales. Moreover, recent progress in mobile health (mHealth) technology and applications, integrating with mobile positioning data, shows great potential for innovation in travel medicine to monitor and assess real-time health risk for individuals during travel. Conclusions:Mobile phones and mHealth have become a novel and tremendously powerful source of information on measuring human movements and origin-destination-specific risks of infectious and non-infectious health issues. The high penetration rate of mobile phones across the globe provides an unprecedented opportunity to quantify human mobility and accurately estimate the health risks in travellers. Continued efforts are needed to establish the most promising uses of these data and technologies for travel health.

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Section: Measuring Human Mobility Using Mobile Phone Datamentioning

confidence: 99%

Measuring mobility, disease connectivity and individual risk: a review of using mobile phone data and mHealth for travel medicine

Lai

Farnham

Ruktanonchai

et al. 2019

Journal of Travel Medicine

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“…Additionally, they do not consider the accessibility of services, regarding the time taken to reach a hospital or the actual population at risk, for example, WoCBA, pregnancies, or births [17][18][19]. Using high-resolution estimates of population and pregnancies [29][30][31], hospital locations, and their associated travel times [32], we estimate the availability and geographical accessibility of services across SSA and assess the suitability of these indicators for monitoring maternal health targets. In the context of these estimates, we evaluate how the guidelines for monitoring the availability and use of emergency obstetric care could be revised, to more accurately reflect the target population, accessibility of services, and equitable access to care.…”

Section: Introductionmentioning

confidence: 99%

Measuring the availability and geographical accessibility of maternal health services across sub-Saharan Africa

Wigley

Tejedor‐Garavito

Alegana

et al. 2020

BMC Med

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Background With universal health coverage a key component of the 2030 Sustainable Development Goals, targeted monitoring is crucial for reducing inequalities in the provision of services. However, monitoring largely occurs at the national level, masking sub-national variation. Here, we estimate indicators for measuring the availability and geographical accessibility of services, at national and sub-national levels across sub-Saharan Africa, to show how data at varying spatial scales and input data can considerably impact monitoring outcomes. Methods Availability was estimated using the World Health Organization guidelines for monitoring emergency obstetric care, defined as the number of hospitals per 500,000 population. Geographical accessibility was estimated using the Lancet Commission on Global Surgery, defined as the proportion of pregnancies within 2 h of the nearest hospital. These were calculated using geo-located hospital data for sub-Saharan Africa, with their associated travel times, along with small area estimates of population and pregnancies. The results of the availability analysis were then compared to the results of the accessibility analysis, to highlight differences between the availability and geographical accessibility of services. Results Despite most countries meeting the targets at the national level, we identified substantial sub-national variation, with 58% of the countries having at least one administrative unit not meeting the availability target at province level and 95% at district level. Similarly, 56% of the countries were found to have at least one province not meeting the accessibility target, increasing to 74% at the district level. When comparing both availability and accessibility within countries, most countries were found to meet both targets; however sub-nationally, many countries fail to meet one or the other. Conclusion While many of the countries met the targets at the national level, we found large within-country variation. Monitoring under the current guidelines, using national averages, can mask these areas of need, with potential consequences for vulnerable women and children. It is imperative therefore that indicators for monitoring the availability and geographical accessibility of health care reflect this need, if targets for universal health coverage are to be met by 2030.

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“…Inequalities in geographic accessibility to healthcare in the US have been documented to cause negative health outcomes for seasonal influenza transmission and other diseases 5 . Further, travel time negatively impacts healthcare-seeking behavior 6 . The deployment of SARS-CoV-2 testing within existing medical infrastructure, while logistically efficient, may exacerbate this disparity in health outcomes 7,8 and underestimate disease burden in disadvantaged populations.…”

Section: Introductionmentioning

confidence: 99%

Increased travel times to United States SARS-CoV-2 testing sites: a spatial modeling study

Rader

Astley

et al. 2020

Preprint

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Importance: Access to testing is key to a successful response to the COVID-19 pandemic. Objective: To determine the geographic accessibility to SARS-CoV-2 testing sites in the United States, as quantified by travel time. Design: Cross-sectional analysis of SARS-CoV-2 testing sites as of April 7, 2020 in relation to travel time. Setting: United States COVID-19 pandemic. Participants: The United States, including the 48 contiguous states and the District of Columbia. Exposures: Population density, percent minority, percent uninsured, and median income by county from the 2018 American Community Survey demographic data. Main Outcome: SARS-CoV-2 testing sites identified in two national databases (Carbon Health and CodersAgainstCovid), geocoded by address. Median county 1 km 2 gridded friction surface of travel times, as a measure of geographic accessibility to SARS-CoV-2 testing sites. Results: 6,236 unique SARS-CoV-2 testing sites in 3,108 United States counties were identified. Thirty percent of the U.S. population live in a county (N = 1,920) with a median travel time over 20 minutes. This was geographically heterogeneous; 86% of the Mountain division population versus 5% of the Middle Atlantic population lived in counties with median travel times over 20 min. Generalized Linear Models showed population density, percent minority, percent uninsured and median income were predictors of median travel time to testing sites. For example, higher percent uninsured was associated with longer travel time (β = 0.41 min/percent, 95% confidence interval 0.3-0.53, p = 1.2x10 -12 ), adjusting for population density. Conclusions and Relevance: Geographic accessibility to SARS-Cov-2 testing sites is reduced in counties with lower population density and higher percent of minority and uninsured, which are also risk factors for worse healthcare access and outcomes. Geographic barriers to SARS-Cov-2 testing may . CC-BY 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)

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National and sub-national variation in patterns of febrile case management in sub-Saharan Africa

Cited by 42 publications

References 35 publications

Measuring mobility, disease connectivity and individual risk: a review of using mobile phone data and mHealth for travel medicine

Measuring mobility, disease connectivity and individual risk: a review of using mobile phone data and mHealth for travel medicine

Measuring the availability and geographical accessibility of maternal health services across sub-Saharan Africa

Increased travel times to United States SARS-CoV-2 testing sites: a spatial modeling study

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