A Smartphone App (AfyaData) for Innovative One Health Disease Surveillance from Community to National Levels in Africa: Intervention in Disease Surveillance

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Abstract

Background

We describe the development and initial achievements of a participatory disease surveillance system that relies on mobile technology to promote Community Level One Health Security (CLOHS) in Africa.

Objective

The objective of this system, Enhancing Community-Based Disease Outbreak Detection and Response in East and Southern Africa (DODRES), is to empower community-based human and animal health reporters with training and information and communication technology (ICT)–based solutions to contribute to disease detection and response, thereby complementing strategies to improve the efficiency of infectious disease surveillance at national, regional, and global levels. In this study, we refer to techno-health as the application of ICT-based solutions to enhance early detection, timely reporting, and prompt response to health events in human and animal populations.

Methods

An EpiHack, involving human and animal health experts as well as ICT programmers, was held in Tanzania in 2014 to identify major challenges facing early detection, timely reporting, and prompt response to disease events. This was followed by a project inception workshop in 2015, which brought together key stakeholders, including policy makers and community representatives, to refine the objectives and implementation plan of the DODRES project. The digital ICT tools were developed and packaged together as the AfyaData app to support One Health disease surveillance. Community health reporters (CHRs) and officials from animal and human health sectors in Morogoro and Ngorongoro districts in Tanzania were trained to use the AfyaData app. The AfyaData supports near- to real-time data collection and submission at both community and health facility levels as well as the provision of feedback to reporters. The functionality of the One Health Knowledge Repository (OHKR) app has been integrated into the AfyaData app to provide health information on case definitions of diseases of humans and animals and to synthesize advice that can be transmitted to CHRs with next step response activities or interventions. Additionally, a WhatsApp social group was made to serve as a platform to sustain interactions between community members, local government officials, and DODRES team members.

Results

Within the first 5 months (August-December 2016) of AfyaData tool deployment, a total of 1915 clinical cases in livestock (1816) and humans (99) were reported in Morogoro (83) and Ngorongoro (1832) districts.

Conclusions

These initial results suggest that the DODRES community-level model creates an opportunity for One Health engagement of people in their own communities in the detection of infectious human and animal disease threats. Participatory approaches supported by digital and mobile technologies should be promoted for early disease detection, timely reporting, and prompt response at the community, national, regional, and global levels.

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Most cited references 27

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Prevention and control of disease and injury require information about the leading medical causes of illness and exposures or risk factors. The assessment of the public-health importance of these has been hampered by the lack of common methods to investigate the overall, worldwide burden. The Global Burden of Disease Study (GBD) provides a standardised approach to epidemiological assessment and uses a standard unit, the disability-adjusted life year (DALY), to aid comparisons. DALYs for each age-sex group in each GBD region for 107 disorders were calculated, based on the estimates of mortality by cause, incidence, average age of onset, duration, and disability severity. Estimates of the burden and prevalence of exposure in different regions of disorders attributable to malnutrition, poor water supply, sanitation and personal and domestic hygiene, unsafe sex, tobacco use, alcohol, occupation, hypertension, physical inactivity, use of illicit drugs, and air pollution were developed. Developed regions account for 11.6% of the worldwide burden from all causes of death and disability, and account for 90.2% of health expenditure worldwide. Communicable, maternal, perinatal, and nutritional disorders explain 43.9%; non-communicable causes 40.9%; injuries 15.1%; malignant neoplasms 5.1%; neuropsychiatric conditions 10.5%; and cardiovascular conditions 9.7% of DALYs worldwide. The ten leading specific causes of global DALYs are, in descending order, lower respiratory infections, diarrhoeal diseases, perinatal disorders, unipolar major depression, ischaemic heart disease, cerebrovascular disease, tuberculosis, measles, road-traffic accidents, and congenital anomalies. 15.9% of DALYs worldwide are attributable to childhood malnutrition and 6.8% to poor water, and sanitation and personal and domestic hygiene. The three leading contributors to the burden of disease are communicable and perinatal disorders affecting children. The substantial burdens of neuropsychiatric disorders and injuries are under-recognised. The epidemiological transition in terms of DALYs has progressed substantially in China, Latin America and the Caribbean, other Asia and islands, and the middle eastern crescent. If the burdens of disability and death are taken into account, our list differs substantially from other lists of the leading causes of death. DALYs provide a common metric to aid meaningful comparison of the burden of risk factors, diseases, and injuries.

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Evaluation of reporting timeliness of public health surveillance systems for infectious diseases

Ruth Ann Jajosky, Samuel Groseclose (2004)

Background Timeliness is a key performance measure of public health surveillance systems. Timeliness can vary by disease, intended use of the data, and public health system level. Studies were reviewed to describe methods used to evaluate timeliness and the reporting timeliness of National Notifiable Diseases Surveillance System (NNDSS) data was evaluated to determine if this system could support timely notification and state response to multistate outbreaks. Methods Published papers that quantitatively measured timeliness of infectious disease surveillance systems operating in the U.S. were reviewed. Median reporting timeliness lags were computed for selected nationally notifiable infectious diseases based on a state-assigned week number and various date types. The percentage of cases reported within the estimated incubation periods for each disease was also computed. Results Few studies have published quantitative measures of reporting timeliness; these studies do not evaluate timeliness in a standard manner. When timeliness of NNDSS data was evaluated, the median national reporting delay, based on date of disease onset, ranged from 12 days for meningococcal disease to 40 days for pertussis. Diseases with the longer incubation periods tended to have a higher percentage of cases reported within its incubation period. For acute hepatitis A virus infection, which had the longest incubation period of the diseases studied, more than 60% of cases were reported within one incubation period for each date type reported. For cryptosporidiosis, Escherichia coli O157:H7 infection, meningococcal disease, salmonellosis, and shigellosis, less than 40% of cases were reported within one incubation period for each reported date type. Conclusion Published evaluations of infectious disease surveillance reporting timeliness are few in number and are not comparable. A more standardized approach for evaluating and describing surveillance system timeliness should be considered; a recommended methodology is presented. Our analysis of NNDSS reporting timeliness indicated that among the conditions evaluated (except for acute hepatitis A infection), the long reporting lag and the variability across states limits the usefulness of NNDSS data and aberration detection analysis of those data for identification of and timely response to multistate outbreaks. Further evaluation of the factors that contribute to NNDSS reporting timeliness is warranted.

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Author and article information

Contributors

Esron Daniel Karimuribo:

Department of Veterinary Medicine and Public HealthCollege of Veterinary Medicine and Biomedical SciencesSokoine University of AgriculturePO Box 3021 Chuo KikuuMorogoro, TanzaniaUnited Republic Of Tanzania255 695760ekarimu@yahoo.co.uk

ORCID: http://orcid.org/0000-0002-5517-1769

Journal

Journal ID (nlm-ta): JMIR Public Health Surveill

Journal ID (iso-abbrev): JMIR Public Health Surveill

Journal ID (publisher-id): JPH

Title: JMIR Public Health and Surveillance

Publisher: JMIR Publications (Toronto, Canada )

ISSN (Electronic): 2369-2960

Publication date Collection: Oct-Dec 2017

Publication date (Electronic): 18 December 2017

Volume: 3

Issue: 4

Electronic Location Identifier: e94

Affiliations

[1] ¹ Department of Veterinary Medicine and Public Health College of Veterinary Medicine and Biomedical Sciences Sokoine University of Agriculture Morogoro United Republic Of Tanzania

[2] ² Southern African Centre for Infectious Disease Surveillance Sokoine University of Agriculture Morogoro United Republic Of Tanzania

[3] ³ National Institute for Medical Research Dar es Salaam United Republic Of Tanzania

[4] ⁴ Kenya Medical Research Institute Nairobi Kenya

[5] ⁵ Innovative Support to Emergencies Diseases and Disasters San Francisco, CA United States

[6] ⁶ Skoll Global Threats Fund San Francisco, CA United States

Author notes

Corresponding Author: Esron Daniel Karimuribo ekarimu@ 123456yahoo.co.uk

Author information

Esron Daniel Karimuribo http://orcid.org/0000-0002-5517-1769

Eric Mutagahywa http://orcid.org/0000-0001-7530-0276

Calvin Sindato http://orcid.org/0000-0003-3040-165X

Leonard Mboera http://orcid.org/0000-0001-5746-3776

Mpoki Mwabukusi http://orcid.org/0000-0002-6165-9824

M Kariuki Njenga http://orcid.org/0000-0002-7629-7002

Scott Teesdale http://orcid.org/0000-0002-5541-4038

Jennifer Olsen http://orcid.org/0000-0001-6547-8791

Mark Rweyemamu http://orcid.org/0000-0001-7329-567X

Article

Publisher ID: v3i4e94

DOI: 10.2196/publichealth.7373

PMC ID: 5748470

PubMed ID: 29254916

SO-VID: cdf08396-dbe8-40b4-9d5a-fb45e4b1c3e2

Copyright © ©Esron Daniel Karimuribo, Eric Mutagahywa, Calvin Sindato, Leonard Mboera, Mpoki Mwabukusi, M Kariuki Njenga, Scott Teesdale, Jennifer Olsen, Mark Rweyemamu. Originally published in JMIR Public Health and Surveillance (http://publichealth.jmir.org), 18.12.2017.

License:

This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Public Health and Surveillance, is properly cited. The complete bibliographic information, a link to the original publication on http://publichealth.jmir.org, as well as this copyright and license information must be included.

History

Date received : 20 January 2017

Date revision requested : 20 March 2017

Date revision received : 12 July 2017

Date accepted : 20 October 2017

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