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  • On the horizon: More mhealth solutions for Kenya’s poor

    This post was authored by Ben Bellows and Jared Stamm of the Population Council.

    Mobile phone use in Africa is growing fast, from 16 million handsets in use in 2000 to 246 million in 2008 and more than 500 million by early 2013. In Kenya, 78% of households have mobile phones. In spite of these high numbers, there is still concern that mobile interventions are failing to reach the most in need. We believe this will become less of an issue as mobile phone use continues to rise.

    The Population Council’s voucher survey data reflect a growing uptake of phones in poor households, and a substantial increase in the use of mobile phones between 2010 and 2012 among the voucher-eligible population (you can learn more about our study here and initial findings here). And according to another recent study in Kenya among people who have a mobile phone and make less than $2.50 per day, there is high demand for telecom services—low-income consumers will forgo spending on some necessities in order to buy airtime—and growing interest in receiving health information via mobile devices.

    That’s good news for programs like Baby Monitor, a screening tool being tested by the Population Council and Duke University among pregnant women and new mothers in rural and remote areas of Kenya—places where a mobile signal is more likely to reach them than a skilled birth attendant or community health worker.

    Baby Monitor brings low-cost clinical assessment directly to mothers and their infants through their mobile phones. With Baby Monitor, women sign up to get phone calls 90, 60, and 30 days before their due date, and on days 1, 3, 7, and 10 after birth—the most critical days for a new baby and a new mother.

    Using interactive voice-response (IVR) technology, women listen to a free phone call and respond by key press to a series of pre-recorded, algorithm-selected questions that screen for potential physical and mental health issues. The cloud-based, highly scalable program automatically flags cases that warrant additional follow-up and then sends information, makes referrals, and/or dispatches community health workers.

    Early analyses of Baby Monitor indicate that it’s better than nurses at detecting higher probable levels of depression, possibly because women feel less stigma answering recorded questions by phone.

    Programs like these hold a lot of promise for the poorest women in the poorest communities. Next stages could be coordinating transport to facility referrals; sharing screening data with receiving facilities to speed intake; beginning the process of providing informed choice for preventive care like family planning methods; helping patients adhere to treatment for HIV or tuberculosis; and, in the case of reproductive health vouchers, distributing credits by mobile phone that could pay for health care or transport services.

    With mobile phone penetration increasing in developing countries and among the poorest, we have an opportunity to create lasting change in the way valuable health services are delivered. Programs like Baby Monitor may provide the blueprint for developing additional effective mhealth programs or improving current programs like the reproductive health voucher program in Kenya.

    Harnessing the power of mobile phones to improve health will be a challenge, but with strategic investment, collaboration between public health researchers and technology developers, and a focus on creating mhealth applications that are easy to use and available to the most vulnerable, we can make great strides in improving health and saving lives.

    (Listen to an interview Ben did with Smart Monkey TV on the potential of mhealth to create greater efficiencies in the delivery of reproductive health vouchers.)


    Voucher Sites Show More Equitable Distribution of RH Service Utilization

    sachathep_fig2

    This post was authored by Karampreet K. Sachathep, a Population Council intern and a Ph.D. candidate at the Johns Hopkins Bloomberg School of Public Health.

    Since the 1970s concentration curves have increasingly been used to visually examine inequality in health outcomes and health service utilization (Kakwani, Wagstaff and van Doorslaer 1997). They provide a snapshot of how, in this case, utilization of family and maternity services varies across a distribution of individuals who are ranked from poorest to richest. The greater the distance between the concentration curve and the line of equality (the diagonal line that runs through the graph), the more concentrated the number of facility-based deliveries or use of LAPMs (Long-Acting and Permanent Method of family planning) among the richer individuals.

    These concentration curves below are calculated from 2010 baseline survey of the Population Council’s evaluation of the Government of Kenya voucher program, four years after the voucher program was introduced in 2006. The main objective behind vouchers is to target services to those who are poor and equalize access to the healthcare system. Thus, we would expect that people in areas exposed to the OBA (Output-Based Aid) program would have a more equitable distribution or even a more ‘pro-poor’ (graphically this would translate to above or at the line of equality), distribution of health service use relative to the non-OBA sites.

    Figure 1: The two concentration curves here show that the degree of inequality in OBA areas is lower than non-OBA areas for both facility-based deliveries and use of LAPMs (Long-Acting and Permanent Method of family planning).

    In Figure 1, the CC lies below the line of equality in the OBA and non-OBA areas for use of facility-based deliveries. On the other hand, we notice that the concentration curves for LAPM use  are on opposite sides of the line of equality for OBA and non-OBA sites, and that for OBA sites, LAPM use is ‘pro-poor’ and lies above the line of equality.  In OBA sites, more poor women are using long term FP methods than non-poor women while the opposite is true for the non-OBA sites. Concentration curves provide a visual sense of the distribution of inequality; these graphs show us that the degree of health service utilization in OBA sites seems to be more equitable than in non-OBA sites.

    However, in order to make a better conclusion as to whether these differences are truly significant, a numerical measure of health inequality can be used. A related measure, the concentration index (CI), quantifies the amount of inequality in a health variable (Kakwani, Wagstaff and van Doorslaer 1997).  It’s defined as twice the area between the concentration curve and the line of equality. CIs range between -1 and 1 and if there is no inequality, the index is equal to 0 (it may help to think of this as a correlation coefficient).

    To take this a step further, the concentration index (CI) is positive when the concentration curve lies below the line of equality (e.g. indicating that poor have lower healthcare use or worse health outcomes).

    Figure 2: Comparing concentration indices between OBA and non-OBA sites.

    Figure 2 quantifies the information that we saw in Figure 1– that in 2010, LAPM use in the OBA sites is concentrated among the poorer populations relative to the non-OBA sites (hence the negative CI value). Facility-based deliveries seem to also be more equitably distributed in the OBA-sites relative to the non-OBA sites; however, both sites show ‘pro-rich’ utilization of this service.

    The concentration index for the inequality of distribution in facility-based deliveries since the inception of the program in mid-2006, was 0.24 in the OBA sites and 0.13 in the non-OBA sites.  LAPM use was at -0.07 in the OBA sites and 0.03 in the non-OBA sites. These differences, however, were found to be statistically non-significant.

    Finally, in order to provide us with a sense of whether this program has been equity enhancing, we will compare these curves to those generated from the endline surveys (collected August 2012), to observe whether the distribution of health utilization has changed over two years in voucher-exposed and non-exposed sites. For more information on concentration curves, indices, and equity analysis in health, please refer to the World Bank guidance document on Analyzing Health Equity Using Household Survey Data.

    REFERENCES

    1. Kakwani, N. C., A. Wagstaff, and E. van Doorslaer. 1997. “Socioeconomic Inequalities in Health: Measurement, Computation and Statistical Inference.” Journal of Econometrics 77(1): 87–104.

    2. Wagstaff, A., and N. Watanabe. 2003. “What Difference Does the Choice of SES Make in Health Inequality Measurement?” Health Economics 12(10): 885–90.

    (Image credit: All graphs produced by Karampreet K. Sachathep, © 2012)


    Institute for Healthcare Improvement: Kadi Screening

    The Institute for Healthcare Improvement (IHI) will host a screening of Kadi on January 17, 2013 from 12:00-1:00pm for its staff. Jaspal Sandhu of the Gobee Group, an executive producer of Kadi, will be in Cambridge to present the film and to lead a discussion on vouchers.


    World Bank Institute: Kadi Screening

    Venue: The World Bank Institute
    Date: December 13, 2012

    A flagship course at the World Bank Institute in Washington, DC to be held in December will spend one day  of the two-week program focused on results-based financing (RBF). The course is targeting high-level policymakers from various countries, including Kenya. During this RBF module, Kadi will be presented in the context of a discussion about voucher schemes.


    USAID: Kadi Screening

    USAID will host a screening of Kadi at 1:00pm on November 29, 2012 at the Ronald Reagan Building in Washington, DC.

    Agenda:

    • Introduction by Joe Naimoli, USAID – Dr. Naimoli leads the Performance-Based Incentives Interest Group at USAID
    • Presentation on reproductive health vouchers by Charlotte Warren, Population Council
    • Presentation of Kadi
    • Discussion facilitated by Maggie Farrell, USAID