AIDS Care, 2015 Vol. 27, No. 2, 240–243, http://dx.doi.org/10.1080/09540121.2014.952219
Rethinking HIV prevalence determination in developing countries Olusesan A. Makindea* and Kolawole A. Oyediranb a
MEASURE Evaluation/John Snow Inc., Abuja, Nigeria; bJohn Snow Inc., Rosslyn, VA, USA
(Received 3 December 2013; accepted 31 July 2014) The process for HIV prevalence determination using antenatal clinic (ANC) sentinel surveillance data has been plagued by criticisms of its biasness. Exploring other means of HIV prevalence determination is necessary to validate that estimates are near actual values or to replace the current system. We propose a data collection model that leverages the increasing adoption and penetration of the Internet and mobile technology to collect and archive routine data from HIV counseling and testing (HCT) client intake forms from all HCT centers and prevention of mother-to-child transmission (PMTCT) sites in a country. These data will then be mined to determine prevalence rates and risk factors at the community level. The need to improve the method for the generation of HIV prevalence rates has been repeatedly echoed by researchers though no one has been able to fashion out a better and more reliable way to the current ANC sentinel surveillance method at a reasonable cost. The chance of using routinely generated data during HCT and PMTCT is appealing and needs to be envisioned as the technology to achieve this is increasingly becoming available and affordable in countries worst hit by the pandemic. Triangulating data generated from routine HCT and PMTCT sites with data from sentinel surveillance and where the confidence of its quality is assured, as the sole source of HIV prevalence rate determination and behavioral risk assessment will improve the acceptance by communities and drive evidence-based interventions at the community level.
Keywords: data collection; public health informatics; sero-prevalence; surveillance; telecommunications
Introduction The importance of high-quality data for the derivation of HIV prevalence rates in developing countries cannot be overemphasized (Ghys, Walker, McFarland, Miller, & Garnett, 2008; Lyerla, Gouws, & Garcia-Calleja, 2008; Makinde, Ezomike, Lehmann, & Ibanga, 2011; Sun et al., 2007; UNAIDS, 2007). Many countries in subSaharan Africa (SSA) rely on antenatal clinic (ANC) sentinel surveillance to estimate their HIV prevalence. The results generated from these data may vary based on the underlying assumptions and the statistical methods used in the estimation process (AbouZahr & Boerma, 2005; Brookmeyer, 2010). Furthermore, the usefulness of ANC sentinel surveillance data for prevalence determination varies with changes in ANC attendance rate (Dzekedzeke & Fylkesnes, 2006; B. Zaba, Slaymaker, Urassa, & Boerma, 2005). In the absence of populationderived estimates and routine HIV surveillance data, these methods despite being the best means of estimating HIV prevalence can still be plagued by errors and uncertainty, and the outcome can be doubtful (Francoise Kayibanda et al., 2011; Gouws, Mishra, & Fowler, 2008; Health Metrics Network, 2008; Jia, Lu, Sun, & Vermund, 2007; B. W. Zaba et al., 2000). In 2007, the Joint United Nations Program on HIV/AIDS (UNAIDS) revised downward the estimated number of people living *Corresponding author. Email: [email protected] © 2014 Taylor & Francis
with HIV in the world (Ghys et al., 2008; UNAIDS, 2007). Several reasons for the new estimates were given which include improved methodology of data collection, better surveillance by countries, and changes in the key epidemiological assumptions (UNAIDS, 2007). This revision though justified further shows that assumptionbased estimates can be cynical. In countries like Nigeria where ANC attendance is dismally under 60% (“WHO/ World Health Statistics 2013,” 2013), it further calls to question the reliability of ANC surveillance-based prevalence rates. Based on the aforementioned disadvantages of estimating HIV prevalence using statistical assumptions, it is therefore imperative to explore other means of deriving HIV prevalence which may provide true rates or provide better estimates and improve the acceptance of estimates. One way to achieve this will be to strengthen surveillance through the collection of data generated during routine HIV counseling and testing (HCT) and routine prevention of mother-to-child transmission (PMTCT) and the utilization of these as a bedrock for prevalence determination and behavioral risk assessment (B. Zaba et al., 2005). Several studies have proposed the utilization of routine HCT and PMTCT program data for determining the prevalence of HIV in countries, but no foundational way for achieving this has been developed (Baryarama et al., 2008; Fabiani, Nattabi, Ayella, Ogwang, & Declich,
AIDS Care 2005; Getachew, Gotu, & Enquselassie, 2010; Makinde et al., 2011). Some studies have argued that the large number of participants will help in improving the precision of the prevalence rates. Furthermore, the community level of the data collection will make available prevalence rates at that level as well as the predisposing behavioral factors for that locality. Such data will help improve the design of intervention programs for achieving a high impact. One of the challenges that we identified could deter the success of a routine HCT and PMTCT surveillance system is the lack of a process for the collection of this significant data and its incorporation in the knowledge generation process. In this paper, we propose a data collection model that will explore the advancing growth of mobile communication technologies and the Internet in developing countries to collect routine HCT and PMTCT data which include behavioral risk factors, into a centralized national database from where the data can be mined and utilized for HIV prevalence determination, and in identifying the predominant risk factors in each geographic precinct. We further discuss the advantages and the challenges to achieving success of this system.
Proposed model The proposed model will explore advances and growth in information and communications technology (ICT), the Internet and the mobile telephony system in SSA to connect universally, HCT, and PMTCT centers to a national database. With an exponential growth rate of mobile phones and mobile service penetration reaching over half the population in several SSA countries, mobile phones and the Internet are poised to be explored as the media for delivering data across regional boundaries (Aker & Mbiti, 2010; Asangansi & Braa, 2010; Asongu, 2013; Twin Pine and iHub Research, 2012). Data from the national database archive will then be mined intermittently for disease prevalence estimation and geographic risk assessment. The geographic risk will be achieved by aggregating all sites providing HCT and PMTCT services enrolled into the system with specific data that identifies their geographic location and using this geographic identifier as a unit of analysis. The analysis of the behavioral data for each geographic zone will provide community-specific risks and epidemic drivers for that community. The utilization of these results for designing intervention programs for each district will help target the pertinent predisposing behavioral factors for an area, thereby improving the efficiency of the funds spent on HIV control. The data collection template will mirror the approved HCT client intake form for each country that will be adopting the model.
241
Figure 1 maps the different processes (Table 1) for the collection of the HCT and PMTCT data for HIV surveillance and link them to a national database archive. Discussion International programs like the US Presidents Emergency Plan for AIDS Relief and the Global Fund to Fight AIDS, Tuberculosis and Malaria have supported several countries in improving and expanding the reach of HCT and PMTCT services, incorporating quality checks, and improving program monitoring and evaluation. For instance, in Nigeria, a HCT client intake form serves as a routine tool for collecting behavioral data and recording the outcome of the counseling and testing session (Federal Ministry of Health Nigeria, 2006; Makinde et al., 2011). This tool was mandated as a fundamental tool for data collection on all clients to be tested for HIV. This follows the principle of the three ones (one national M&E system) which was proposed by the United Nations in 2004 (UNAIDS, 2004). Similarly, Ghana, Kenya, South Africa, and Rwanda among others have HCT and PMTCT client intake forms that collect behavioral and outcome of counseling and testing visits. Despite the presence of these national tools, few countries (like China) have been able to incorporate this important data source in their HIV prevalence determination process. This data collection model and its subsequent adoption and implementation will tackle this gap and serve as a country-level planning architecture for developing such an important surveillance system. Defining the processes and monitoring the conformance to the steps defined will be paramount to the success of this intervention. Though these are not new processes, the enormity of using HCT and PMTCT data for prevalence determination necessitates improving vigilance. Since the data collection tool is nationally mandated, there will be no new data collection template. The only difference will be the data collection platform, electronic vs. paper for some centers. The advantages of using routine HCT and PMTCT data for prevalence determination are promising. However, it is difficult to estimate the possible benefits as no one has been able to achieve this at a national level. We envision that this new system will meet with several challenges which include technical (power, Internet connectivity, integration, and interoperability issues) and nontechnical factors (political will, low ICT user skills, poor technical support, and financial limitations; Blantz, 2014). For the system to thrive, it must be designed to address the technical issues highlighted above. It must be able to work on both mobile and nonmobile devices. In addition, the system must incorporate standards that will facilitate integration and interoperability of various platforms which can then be interconnected through a health
242
O.A. Makinde and K.A. Oyediran
Figure 1. Routine HCT and PMTCT surveillance system architecture.
HCT and PMTCT services in several developing countries as well as the increasing utilization of ICT for health surveillance. In addition, countries with low ANC attendance will most benefit from this system in providing additional data that can be triangulated with the current ANC surveillance and population-based estimates currently utilized. The large number and diversity of respondents from HCT and PMTCT centers when compared to the sample size of surveillance studies will also be a main acceptance
information exchange. Furthermore, the system should be able to store data locally for onward migration to the data server whenever Internet connection becomes available. These will alleviate some of the major technical issues that have been identified. Nontechnical challenges of the information system equally need to be planned for as they are critical to its success. We believe that this surveillance system is a necessary step in order to take advantage of the grounds that have been gained as a result of an expanded access to Table 1. Description of processes entailed at each step of the model. Step
Process
A, B
The paper-based HCT forms currently in use will continue to be used by some facilities. Upon completion of the form, it is passed on to another unit in the same facility or to an agreed third party (B) who enters the client information into the national database. This process is explained in detail in an earlier publication. Remote facilities utilize a mobile platform for feeding the HCT client intake form into the central database via real-time connection using their mobile networks. Real-time data entry into the central database as clients are being counseled and tested at HCT and PMTCT centers using touch screen computers. The central server houses data from mobile and nonmobile applications and updates the database appropriately. The users – National AIDS Coordinating Agencies, Ministry of Health at the national and subnational levels, and Donors and Implementing organizations – have direct access to the information in the system based on an agreed level of data authority granted.
C D E F
AIDS Care factor. In addition, the data will be collected all year-round and across male and female populations and as such will eliminate barriers faced by intermittent population surveillance studies and ANC-based sentinel surveillance studies. This system promises to provide knowledge for community level interventions that will target epidemic drivers which may vary by community and needs to begin to be envisioned in response to the increasing utilization of ICT for health surveillance in developing countries. Acknowledgments The authors would like to thank Dr John S. Osika and Dr Carlos Avila for helping them review an earlier draft of the manuscript and providing feedback for its improvement.
References AbouZahr, C., & Boerma, T. (2005). Health information systems: The foundations of public health. Bulletin of the World Health Organization, 83, 578–583. Aker, J., & Mbiti, I. (2010). Mobile phones and economic development in Africa (Working Paper). Washington, DC: Center for Global Development. Asangansi, I., & Braa, K. (2010). The emergence of mobilesupported national health information systems in developing countries. Studies in Health Technology and Informatics, 160(Pt 1), 540–544. Asongu, S. A. (2013). How has mobile phone penetration stimulated financial development in Africa? Journal of African Business, 14(1), 7–18. doi:10.1080/15228916. 2013.765309 Baryarama, F., Bunnell, R. E., Montana, L., Hladik, W., Opio, A., Musinguzi, J., … Mermin, J. H. (2008). HIV prevalence in voluntary counseling and testing centers compared with national HIV serosurvey data in Uganda. Journal of Acquired Immune Deficiency Syndromes, 49, 183–189. doi:10.1097/QAI.0b013e31818455b8 Blantz, E. (2014, March 8). 4 Key challenges and solutions to ICT deployments for rural healthcare. San Francisco, CA: ICT Works. Brookmeyer, R. (2010). Measuring the HIV/AIDS epidemic: Approaches and challenges. Epidemiologic Reviews, 32(1), 26–37. doi:10.1093/epirev/mxq002 Dzekedzeke, K., & Fylkesnes, K. (2006). Reducing uncertainties in global HIV prevalence estimates: The case of Zambia. BMC Public Health, 6(1), 83. doi:10.1186/14712458-6-83 Fabiani, M., Nattabi, B., Ayella, E. O., Ogwang, M., & Declich, S. (2005). Using prevalence data from the programme for the prevention of mother-to-child-transmission for HIV-1 surveillance in North Uganda. AIDS, 19, 823–827. doi:10.1097/01.aids.0000168977.51843.d6 Federal Ministry of Health Nigeria. (2006). National guidelines for HIV counseling and testing. Abuja: Author. Francoise Kayibanda, J., Alary, M., Bitera, R., Mutagoma, M., Kabeja, A., Hinda, R., … Asiimwe, A. (2011). Use of
243
routine data collected by the prevention of mother-to-child transmission program for HIV surveillance among pregnant women in Rwanda: Opportunities and limitations. AIDS Care, 23, 1570–1577. doi:10.1080/09540121.2011.579941 Getachew, Y., Gotu, B., & Enquselassie, F. (2010). An estimate of the magnitude and trend of HIV/AIDS epidemic using data from the routine VCT services as an alternative data source to ANC sentinel surveillance in Addis Ababa, Ethiopia. Ethiopian Medical Journal, 48, 257–266. Ghys, P. D., Walker, N., McFarland, W., Miller, R., & Garnett, G. P. (2008). Improved data, methods and tools for the 2007 HIV and AIDS estimates and projections. Sexually Transmitted Infections, 84(Suppl. 1), i1–i4. doi:10.1136/ sti.2008.032573 Gouws, E., Mishra, V., & Fowler, T. B. (2008). Comparison of adult HIV prevalence from national population-based surveys and antenatal clinic surveillance in countries with generalised epidemics: Implications for calibrating surveillance data. Sexually Transmitted Infections, 84 (Suppl. 1), i17–i23. doi:10.1136/sti.2008.030452 Health Metrics Network. (2008). Framework and standards for country health information systems (2nd ed.). Geneva: World Health Organization. Jia, Y., Lu, F., Sun, X., & Vermund, S. H. (2007). Sources of data for improved surveillance of HIV/AIDS in China. The Southeast Asian Journal of Tropical Medicine and Public Health, 38, 1041–1052. Lyerla, R., Gouws, E., & Garcia-Calleja, J. M. (2008). The quality of sero-surveillance in low- and middle-income countries: Status and trends through 2007. Sexually Transmitted Infections, 84(Suppl. 1), i85–i91. doi:10.1136/sti.2008.030593 Makinde, O. A., Ezomike, C. F., Lehmann, H. P., & Ibanga, I. J. (2011). Lessons learned in the deployment of a HIV counseling and testing management information system on a new project. AIDS (London, England), 25, 2289–2293. doi:10.1097/QAD.0b013e32834d6ad2 Sun, X., Wang, N., Li, D., Zheng, X., Qu, S., Wang, L., … Poundstone, K. (2007). The development of HIV/AIDS surveillance in China. AIDS, 21(Suppl. 8), S33–S38. doi:10.1097/01.aids.0000304694.54884.06 Twin Pine and iHub Research. (2012). An analysis of Mobile Technology in West Africa. Retrieved April 30, 2013, from http://research.ihub.co.ke/uploads/2012/october/135 1001605_819_249.pdf UNAIDS. (2004). “Three Ones” key principles. Geneva: Author. UNAIDS. (2007). AIDS epidemic update. Geneva: Author. WHO/World Health Statistics 2013. (2013, October 4). WHO. Zaba, B., Slaymaker, E., Urassa, M., & Boerma, J. T. (2005). The role of behavioral data in HIV surveillance. AIDS, 19 (Suppl. 2), S39–S52. doi:10.1097/01.aids.0000172876. 74886.86 Zaba, B. W., Carpenter, L. M., Boerma, J. T., Gregson, S., Nakiyingi, J., & Urassa, M. (2000). Adjusting ante-natal clinic data for improved estimates of HIV prevalence among women in sub-saharan Africa. AIDS, 14, 2741–2750. doi:10.1097/00002030-200012010-00014
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Rethinking HIV prevalence determination in developing countries Olusesan A. Makindea* and Kolawole A. Oyediranb a
MEASURE Evaluation/John Snow Inc., Abuja, Nigeria; bJohn Snow Inc., Rosslyn, VA, USA
(Received 3 December 2013; accepted 31 July 2014) The process for HIV prevalence determination using antenatal clinic (ANC) sentinel surveillance data has been plagued by criticisms of its biasness. Exploring other means of HIV prevalence determination is necessary to validate that estimates are near actual values or to replace the current system. We propose a data collection model that leverages the increasing adoption and penetration of the Internet and mobile technology to collect and archive routine data from HIV counseling and testing (HCT) client intake forms from all HCT centers and prevention of mother-to-child transmission (PMTCT) sites in a country. These data will then be mined to determine prevalence rates and risk factors at the community level. The need to improve the method for the generation of HIV prevalence rates has been repeatedly echoed by researchers though no one has been able to fashion out a better and more reliable way to the current ANC sentinel surveillance method at a reasonable cost. The chance of using routinely generated data during HCT and PMTCT is appealing and needs to be envisioned as the technology to achieve this is increasingly becoming available and affordable in countries worst hit by the pandemic. Triangulating data generated from routine HCT and PMTCT sites with data from sentinel surveillance and where the confidence of its quality is assured, as the sole source of HIV prevalence rate determination and behavioral risk assessment will improve the acceptance by communities and drive evidence-based interventions at the community level.
Keywords: data collection; public health informatics; sero-prevalence; surveillance; telecommunications
Introduction The importance of high-quality data for the derivation of HIV prevalence rates in developing countries cannot be overemphasized (Ghys, Walker, McFarland, Miller, & Garnett, 2008; Lyerla, Gouws, & Garcia-Calleja, 2008; Makinde, Ezomike, Lehmann, & Ibanga, 2011; Sun et al., 2007; UNAIDS, 2007). Many countries in subSaharan Africa (SSA) rely on antenatal clinic (ANC) sentinel surveillance to estimate their HIV prevalence. The results generated from these data may vary based on the underlying assumptions and the statistical methods used in the estimation process (AbouZahr & Boerma, 2005; Brookmeyer, 2010). Furthermore, the usefulness of ANC sentinel surveillance data for prevalence determination varies with changes in ANC attendance rate (Dzekedzeke & Fylkesnes, 2006; B. Zaba, Slaymaker, Urassa, & Boerma, 2005). In the absence of populationderived estimates and routine HIV surveillance data, these methods despite being the best means of estimating HIV prevalence can still be plagued by errors and uncertainty, and the outcome can be doubtful (Francoise Kayibanda et al., 2011; Gouws, Mishra, & Fowler, 2008; Health Metrics Network, 2008; Jia, Lu, Sun, & Vermund, 2007; B. W. Zaba et al., 2000). In 2007, the Joint United Nations Program on HIV/AIDS (UNAIDS) revised downward the estimated number of people living *Corresponding author. Email: [email protected] © 2014 Taylor & Francis
with HIV in the world (Ghys et al., 2008; UNAIDS, 2007). Several reasons for the new estimates were given which include improved methodology of data collection, better surveillance by countries, and changes in the key epidemiological assumptions (UNAIDS, 2007). This revision though justified further shows that assumptionbased estimates can be cynical. In countries like Nigeria where ANC attendance is dismally under 60% (“WHO/ World Health Statistics 2013,” 2013), it further calls to question the reliability of ANC surveillance-based prevalence rates. Based on the aforementioned disadvantages of estimating HIV prevalence using statistical assumptions, it is therefore imperative to explore other means of deriving HIV prevalence which may provide true rates or provide better estimates and improve the acceptance of estimates. One way to achieve this will be to strengthen surveillance through the collection of data generated during routine HIV counseling and testing (HCT) and routine prevention of mother-to-child transmission (PMTCT) and the utilization of these as a bedrock for prevalence determination and behavioral risk assessment (B. Zaba et al., 2005). Several studies have proposed the utilization of routine HCT and PMTCT program data for determining the prevalence of HIV in countries, but no foundational way for achieving this has been developed (Baryarama et al., 2008; Fabiani, Nattabi, Ayella, Ogwang, & Declich,
AIDS Care 2005; Getachew, Gotu, & Enquselassie, 2010; Makinde et al., 2011). Some studies have argued that the large number of participants will help in improving the precision of the prevalence rates. Furthermore, the community level of the data collection will make available prevalence rates at that level as well as the predisposing behavioral factors for that locality. Such data will help improve the design of intervention programs for achieving a high impact. One of the challenges that we identified could deter the success of a routine HCT and PMTCT surveillance system is the lack of a process for the collection of this significant data and its incorporation in the knowledge generation process. In this paper, we propose a data collection model that will explore the advancing growth of mobile communication technologies and the Internet in developing countries to collect routine HCT and PMTCT data which include behavioral risk factors, into a centralized national database from where the data can be mined and utilized for HIV prevalence determination, and in identifying the predominant risk factors in each geographic precinct. We further discuss the advantages and the challenges to achieving success of this system.
Proposed model The proposed model will explore advances and growth in information and communications technology (ICT), the Internet and the mobile telephony system in SSA to connect universally, HCT, and PMTCT centers to a national database. With an exponential growth rate of mobile phones and mobile service penetration reaching over half the population in several SSA countries, mobile phones and the Internet are poised to be explored as the media for delivering data across regional boundaries (Aker & Mbiti, 2010; Asangansi & Braa, 2010; Asongu, 2013; Twin Pine and iHub Research, 2012). Data from the national database archive will then be mined intermittently for disease prevalence estimation and geographic risk assessment. The geographic risk will be achieved by aggregating all sites providing HCT and PMTCT services enrolled into the system with specific data that identifies their geographic location and using this geographic identifier as a unit of analysis. The analysis of the behavioral data for each geographic zone will provide community-specific risks and epidemic drivers for that community. The utilization of these results for designing intervention programs for each district will help target the pertinent predisposing behavioral factors for an area, thereby improving the efficiency of the funds spent on HIV control. The data collection template will mirror the approved HCT client intake form for each country that will be adopting the model.
241
Figure 1 maps the different processes (Table 1) for the collection of the HCT and PMTCT data for HIV surveillance and link them to a national database archive. Discussion International programs like the US Presidents Emergency Plan for AIDS Relief and the Global Fund to Fight AIDS, Tuberculosis and Malaria have supported several countries in improving and expanding the reach of HCT and PMTCT services, incorporating quality checks, and improving program monitoring and evaluation. For instance, in Nigeria, a HCT client intake form serves as a routine tool for collecting behavioral data and recording the outcome of the counseling and testing session (Federal Ministry of Health Nigeria, 2006; Makinde et al., 2011). This tool was mandated as a fundamental tool for data collection on all clients to be tested for HIV. This follows the principle of the three ones (one national M&E system) which was proposed by the United Nations in 2004 (UNAIDS, 2004). Similarly, Ghana, Kenya, South Africa, and Rwanda among others have HCT and PMTCT client intake forms that collect behavioral and outcome of counseling and testing visits. Despite the presence of these national tools, few countries (like China) have been able to incorporate this important data source in their HIV prevalence determination process. This data collection model and its subsequent adoption and implementation will tackle this gap and serve as a country-level planning architecture for developing such an important surveillance system. Defining the processes and monitoring the conformance to the steps defined will be paramount to the success of this intervention. Though these are not new processes, the enormity of using HCT and PMTCT data for prevalence determination necessitates improving vigilance. Since the data collection tool is nationally mandated, there will be no new data collection template. The only difference will be the data collection platform, electronic vs. paper for some centers. The advantages of using routine HCT and PMTCT data for prevalence determination are promising. However, it is difficult to estimate the possible benefits as no one has been able to achieve this at a national level. We envision that this new system will meet with several challenges which include technical (power, Internet connectivity, integration, and interoperability issues) and nontechnical factors (political will, low ICT user skills, poor technical support, and financial limitations; Blantz, 2014). For the system to thrive, it must be designed to address the technical issues highlighted above. It must be able to work on both mobile and nonmobile devices. In addition, the system must incorporate standards that will facilitate integration and interoperability of various platforms which can then be interconnected through a health
242
O.A. Makinde and K.A. Oyediran
Figure 1. Routine HCT and PMTCT surveillance system architecture.
HCT and PMTCT services in several developing countries as well as the increasing utilization of ICT for health surveillance. In addition, countries with low ANC attendance will most benefit from this system in providing additional data that can be triangulated with the current ANC surveillance and population-based estimates currently utilized. The large number and diversity of respondents from HCT and PMTCT centers when compared to the sample size of surveillance studies will also be a main acceptance
information exchange. Furthermore, the system should be able to store data locally for onward migration to the data server whenever Internet connection becomes available. These will alleviate some of the major technical issues that have been identified. Nontechnical challenges of the information system equally need to be planned for as they are critical to its success. We believe that this surveillance system is a necessary step in order to take advantage of the grounds that have been gained as a result of an expanded access to Table 1. Description of processes entailed at each step of the model. Step
Process
A, B
The paper-based HCT forms currently in use will continue to be used by some facilities. Upon completion of the form, it is passed on to another unit in the same facility or to an agreed third party (B) who enters the client information into the national database. This process is explained in detail in an earlier publication. Remote facilities utilize a mobile platform for feeding the HCT client intake form into the central database via real-time connection using their mobile networks. Real-time data entry into the central database as clients are being counseled and tested at HCT and PMTCT centers using touch screen computers. The central server houses data from mobile and nonmobile applications and updates the database appropriately. The users – National AIDS Coordinating Agencies, Ministry of Health at the national and subnational levels, and Donors and Implementing organizations – have direct access to the information in the system based on an agreed level of data authority granted.
C D E F
AIDS Care factor. In addition, the data will be collected all year-round and across male and female populations and as such will eliminate barriers faced by intermittent population surveillance studies and ANC-based sentinel surveillance studies. This system promises to provide knowledge for community level interventions that will target epidemic drivers which may vary by community and needs to begin to be envisioned in response to the increasing utilization of ICT for health surveillance in developing countries. Acknowledgments The authors would like to thank Dr John S. Osika and Dr Carlos Avila for helping them review an earlier draft of the manuscript and providing feedback for its improvement.
References AbouZahr, C., & Boerma, T. (2005). Health information systems: The foundations of public health. Bulletin of the World Health Organization, 83, 578–583. Aker, J., & Mbiti, I. (2010). Mobile phones and economic development in Africa (Working Paper). Washington, DC: Center for Global Development. Asangansi, I., & Braa, K. (2010). The emergence of mobilesupported national health information systems in developing countries. Studies in Health Technology and Informatics, 160(Pt 1), 540–544. Asongu, S. A. (2013). How has mobile phone penetration stimulated financial development in Africa? Journal of African Business, 14(1), 7–18. doi:10.1080/15228916. 2013.765309 Baryarama, F., Bunnell, R. E., Montana, L., Hladik, W., Opio, A., Musinguzi, J., … Mermin, J. H. (2008). HIV prevalence in voluntary counseling and testing centers compared with national HIV serosurvey data in Uganda. Journal of Acquired Immune Deficiency Syndromes, 49, 183–189. doi:10.1097/QAI.0b013e31818455b8 Blantz, E. (2014, March 8). 4 Key challenges and solutions to ICT deployments for rural healthcare. San Francisco, CA: ICT Works. Brookmeyer, R. (2010). Measuring the HIV/AIDS epidemic: Approaches and challenges. Epidemiologic Reviews, 32(1), 26–37. doi:10.1093/epirev/mxq002 Dzekedzeke, K., & Fylkesnes, K. (2006). Reducing uncertainties in global HIV prevalence estimates: The case of Zambia. BMC Public Health, 6(1), 83. doi:10.1186/14712458-6-83 Fabiani, M., Nattabi, B., Ayella, E. O., Ogwang, M., & Declich, S. (2005). Using prevalence data from the programme for the prevention of mother-to-child-transmission for HIV-1 surveillance in North Uganda. AIDS, 19, 823–827. doi:10.1097/01.aids.0000168977.51843.d6 Federal Ministry of Health Nigeria. (2006). National guidelines for HIV counseling and testing. Abuja: Author. Francoise Kayibanda, J., Alary, M., Bitera, R., Mutagoma, M., Kabeja, A., Hinda, R., … Asiimwe, A. (2011). Use of
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