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      Assessing the impoverishing effects, and factors associated with the incidence of catastrophic health care payments in Kenya

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          Abstract

          Background

          Monitoring the incidence and intensity of catastrophic health expenditure, as well as the impoverishing effects of out of pocket costs to access healthcare, is a key part of benchmarking Kenya’s progress towards reducing the financial burden that households experience when accessing healthcare.

          Methods

          The study relies on data from the nationally-representative Kenya Household Expenditure and Utilization Survey conducted in 2013 ( n =33,675). We undertook health equity analysis to estimate the incidence and intensity of catastrophic expenditure. Households were considered to have incurred catastrophic expenditures if their annual out of-pocket health expenditures exceeded 40% of their annual non-food expenditure. We assessed the impoverishing effects of out of pocket payments using the Kenya national poverty line. We distinguished between direct payments for healthcare such as payments for consultation, medicines, medical procedures, and total healthcare expenditure that includes direct healthcare payments and the cost of transportation to and from health facilities. We used logistic regression analysis to explore the factors associated with the incidence of catastrophic expenditures.

          Results

          When only direct payments to healthcare providers were considered, the incidence of catastrophic expenditures was 4.52%. When transport costs are included, the incidence of catastrophic expenditure increased to 6.58%. 453,470 Kenyans are pushed into poverty annually as a result of direct payments for healthcare. When the cost of transport is included, that number increases by more than one third to 619,541. Unemployment of the household head, presence of an elderly person, a person with a chronic ailment, a large household size, lower household social-economic status, and residence in marginalized regions of the country are significantly associated with increased odds of incurring catastrophic expenditures.

          Conclusions

          Kenyan policy makers should prioritize extending pre-payment mechanisms to more vulnerable groups, specifically the poor, the elderly, those suffering from chronic ailments and those living in marginalized regions of the country. The range of services covered under these mechanisms should also be extended such that the proportion of direct costs paid to access care is reduced. Policy makers should also prioritize reducing supply side bottlenecks such as availability of healthcare facilities in close proximity to the population, especially in rural and marginalized areas, and improvements in quality of care. For the poor and the vulnerable, initiatives to cover the cost of transport to and from a health facility, such as transport vouchers could also be explored.

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          Most cited references27

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          The bounds of the concentration index when the variable of interest is binary, with an application to immunization inequality.

          When the health sector variable whose inequality is being investigated is binary, the minimum and maximum possible values of the concentration index are equal to micro-1 and 1-micro, respectively, where micro is the mean of the variable in question. Thus as the mean increases, the range of the possible values of the concentration index shrinks, tending to zero as the mean tends to one and the concentration index tends to zero. Examples are presented on levels of and inequalities in immunization across 41 developing countries, and on changes in coverage and inequalities in selected countries. Copyright (c) 2004 John Wiley & Sons, Ltd.
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            Correcting the concentration index.

            In recent years attention has been drawn to several shortcomings of the Concentration Index, a frequently used indicator of the socioeconomic inequality of health. Some modifications have been suggested, but these are only partial remedies. This paper proposes a corrected version of the Concentration Index which is superior to the original Concentration Index and its variants, in the sense that it is a rank-dependent indicator which satisfies four key requirements (transfer, level independence, cardinal invariance, and mirror). The paper also shows how the corrected Concentration Index can be decomposed and generalized.
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              Extending health insurance to the rural population: an impact evaluation of China's new cooperative medical scheme.

              In 2003, China launched a heavily subsidized voluntary health insurance program for rural residents. We combine differences-in-differences with matching methods to obtain impact estimates, using data collected from program administrators, health facilities and households. The scheme has increased outpatient and inpatient utilization, and has reduced the cost of deliveries. But it has not reduced out-of-pocket expenses per outpatient visit or inpatient spell. Out-of-pocket payments overall have not been reduced. We find heterogeneity across income groups and implementing counties. The program has increased ownership of expensive equipment among central township health centers but has had no impact on cost per case.
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                Author and article information

                Contributors
                +254722129757 , edwinebarasa@gmail.com
                thomas.maina@thepalladiumgroup.com
                nravishankar@gmail.com
                Journal
                Int J Equity Health
                Int J Equity Health
                International Journal for Equity in Health
                BioMed Central (London )
                1475-9276
                6 February 2017
                6 February 2017
                2017
                : 16
                : 31
                Affiliations
                [1 ]ISNI 0000 0001 0155 5938, GRID grid.33058.3d, , Health Economics Research Unit, KEMRI Wellcome Trust Research Programme, ; P.O Box 43640-00100, Nairobi, Kenya
                [2 ]ISNI 0000 0004 1936 8948, GRID grid.4991.5, Nuffield department of Medicine, , University of Oxford, ; Oxford, UK
                [3 ]ISNI 0000 0001 1955 0561, GRID grid.420285.9, , USAID Health Policy Plus Project, Palladium group, ; 1331 Pennsylvania Ave NW, Suite 600, Washington, DC 20004 USA
                [4 ]Independent consultant, Nairobi, Kenya
                Article
                526
                10.1186/s12939-017-0526-x
                5294805
                28166779
                669c0a8e-359b-4395-a11a-90e8db6caf1e
                © The Author(s). 2017

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 17 June 2016
                : 1 February 2017
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100004440, Wellcome Trust;
                Award ID: 107527
                Award Recipient :
                Categories
                Research
                Custom metadata
                © The Author(s) 2017

                Health & Social care
                household health spending,out of pocket health spending,catastrophic health spending,financial risk protection,kenya

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