Borghi Josephine, Ataguba John, Mtei Gemini, Akazili James, Meheus Filip, Rehnberg Clas, Di McIntyre
Department of Public Health and Policy, London School of Hygiene and Tropical Medicine, London, UK.
Adv Health Econ Health Serv Res. 2009;21:133-56.
Measurement of the incidence of health financing contributions across socio-economic groups has proven valuable in informing health care financing reforms. However, there is little evidence as to how to carry out financing incidence analysis (FIA) in lower income settings. We outline some of the challenges faced when carrying out a FIA in Ghana, Tanzania and South Africa and illustrate how innovative techniques were used to overcome data weaknesses in these settings.
FIA was carried out for tax, insurance and out-of-pocket (OOP) payments. The primary data sources were Living Standards Measurement Surveys (LSMS) and household surveys conducted in each of the countries; tax authorities and insurance funds also provided information. Consumption expenditure and a composite index of socioeconomic status (SES) were used to assess financing equity. Where possible conventional methods of FIA were applied. Numerous challenges were documented and solution strategies devised.
LSMS are likely to underestimate financial contributions to health care by individuals. For tax incidence analysis, reported income tax payments from secondary sources were severely under-reported. Income tax payers and shareholders could not be reliably identified. The use of income or consumption expenditure to estimate income tax contributions was found to be a more reliable method of estimating income tax incidence. Assumptions regarding corporate tax incidence had a huge effect on the progressivity of corporate tax and on overall tax progressivity. LSMS consumption categories did not always coincide with tax categories for goods subject to excise tax (e.g., wine and spirits were combined, despite differing tax rates). Tobacco companies, alcohol distributors and advertising agencies were used to provide more detailed information on consumption patterns for goods subject to excise tax by income category. There was little guidance on how to allocate fuel levies associated with 'public transport' use. Hence, calculations of fuel tax on public transport were based on individual expenditure on public transport, the average cost per kilometre and average rates of fuel consumption for each form of transport. For insurance contributions, employees will not report on employer contributions unless specifically requested to and are frequently unsure of their contributions. Therefore, we collected information on total health insurance contributions from individual schemes and regulatory authorities. OOP payments are likely to be under-reported due to long recall periods; linking OOP expenditure and illness incidence questions--omitting preventive care; and focusing on the last service used when people may have used multiple services during an illness episode. To derive more robust estimates of financing incidence, we collected additional primary data on OOP expenditures together with insurance enrolment rates and associated payments. To link primary data to the LSMS, a composite index of SES was used in Ghana and Tanzania and non-durable expenditure was used in South Africa.
We show how data constraints can be overcome for FIA in lower income countries and provide recommendations for future studies.
事实证明，衡量不同社会经济群体的卫生筹资贡献发生率，对于为医疗保健筹资改革提供信息很有价值。然而，关于如何在低收入环境中进行筹资发生率分析（FIA）的证据很少。我们概述了在加纳、坦桑尼亚和南非进行FIA时面临的一些挑战，并说明如何使用创新技术克服这些环境中的数据弱点。
对税收、保险和自付费用进行了FIA。主要数据来源是各国进行的生活水平测量调查（LSMS）和家庭调查；税务机关和保险基金也提供了信息。消费支出和社会经济地位综合指数（SES）用于评估筹资公平性。在可能的情况下，应用了传统的FIA方法。记录了许多挑战并制定了解决策略。
LSMS可能低估了个人对医疗保健的财务贡献。对于税收发生率分析，二级来源报告的所得税支付严重少报。无法可靠识别所得税纳税人及股东。发现使用收入或消费支出估计所得税贡献是估计所得税发生率的更可靠方法。关于公司税发生率的假设对公司税的累进性和总体税收累进性有巨大影响。LSMS消费类别并不总是与消费税应税商品的税目一致（例如，葡萄酒和烈酒合并在一起，尽管税率不同）。烟草公司、酒精经销商和广告机构被用来提供按收入类别划分的消费税应税商品消费模式的更详细信息。关于如何分配与“公共交通”使用相关的燃油税，几乎没有指导意见。因此，公共交通燃油税的计算基于个人在公共交通上的支出、每公里平均成本以及每种交通方式的平均燃油消耗率。对于保险缴款，除非特别要求，员工不会报告雇主的缴款情况，而且他们通常不确定自己的缴款情况。因此，我们从各个计划和监管机构收集了关于医疗保险缴款总额的信息。由于回忆期长，自付费用可能少报；将自付费用支出与疾病发生率问题联系起来——忽略预防保健；以及关注人们在患病期间可能使用了多种服务时最后使用的服务。为了得出更可靠的筹资发生率估计值，我们收集了关于自付费用支出的额外原始数据以及保险参保率和相关付款情况。为了将原始数据与LSMS联系起来，加纳和坦桑尼亚使用了SES综合指数，南非使用了非耐用支出。
我们展示了如何在低收入国家克服FIA的数据限制，并为未来研究提供了建议。
