Alleviating the burden of diabetes with Health Equity Funds: Economic evaluation of the health and financial risk protection benefits in Cambodia

Abstract

In Cambodia, diabetes caused nearly 3% of the country’s mortality in 2016 and became the fourth highest cause of disability in 2017. Providing sufficient financial risk protection from health care expenditures may be part of the solution towards effectively tackling the diabetes burden and motivating individuals to appropriately seek care to effectively manage their condition. In this study, we aim to estimate the distributional health and financial impacts of strategies providing financial coverage for diabetes services through the Health Equity Funds (HEF) in Cambodia. The trajectory of diabetes was represented using a Markov model to estimate the societal costs, health impacts, and individual out-of-pocket expenditures associated with six strategies of HEF coverage over a time horizon of 45 years. Input parameters for the model were compiled from published literature and publicly available household survey data. Strategies covered different combinations of types of diabetes care costs (i.e., diagnostic services, medications, and management of diabetes-related complications). Health impacts were computed as the number of disability-adjusted life-years (DALYs) averted and financial risk protection was analyzed in terms of cases of catastrophic health expenditure (CHE) averted. Model simulations demonstrated that coverage for medications would be cost-effective, accruing health benefits ($27 per DALY averted) and increases in financial risk protection ($2 per case of CHE averted) for the poorest in Cambodia. Women experienced particular gains in health and financial risk protection. Increasing the number of individuals eligible for financial coverage also improved the value of such investments. For HEF coverage, the government would pay between an estimated $28 and $58 per diabetic patient depending on the extent of coverage and services covered. Efforts to increase the availability of services and capacity of primary care facilities to support diabetes care could have far-reaching impacts on the burden of diabetes and contribute to long-term health system strengthening.

Application of decision analytical models to diabetes in low- and middle-income countries: a systematic review

“…Screening interventions targeted at-risk populations except for two studies [ 27 , 29 ] that investigated diabetes retinopathy screening strategies among diabetes patients aged 40 years and above under clinical treatment. Three studies each investigated interventions related to fiscal policies [ 16 , 22 , 28 ] and patient education [ 21 , 24 , 25 ]. Specific interventions under the formal include taxes on sugar-sweetened beverages [ 16 ], food aid [ 22 ] and financial coverage for clinical diabetes care [ 28 ].…”

“…Three studies each investigated interventions related to fiscal policies [ 16 , 22 , 28 ] and patient education [ 21 , 24 , 25 ]. Specific interventions under the formal include taxes on sugar-sweetened beverages [ 16 ], food aid [ 22 ] and financial coverage for clinical diabetes care [ 28 ]. Specific patient education-related interventions reported include lifestyle-focused planned diabetes programs [ 24 , 25 ] and the delivery of short message services on diabetes care [ 21 ].…”

“…Seven studies (41%) used several methods: five combined one-way and PSA [ 16 , 18 , 26 , 27 , 29 ], one combined one-way and multiway analysis [ 21 ] and one combined one-way, multiway and PSA [ 23 ]. Nine studies (54%) used a single method: four each used one-way only [ 19 , 20 , 25 , 30 ] and PSA only [ 17 , 22 , 28 , 31 ] and one used multiway analysis only [ 15 ].…”

Extended cost-effectiveness analysis of interventions to improve uptake of diabetes services in South Africa

“…A Markov model ( Figure 1 ) previously developed by Feldhaus et al. to conduct an ECEA for diabetes interventions in Cambodia was updated to model a CCT programme designed to increase utilization of diabetes services in South Africa for the lowest two income quintiles ( Feldhaus et al ., 2021 ). A population of 12 million individuals was simulated, representing the South African population aged between 25 to 69 years in 2020 in the two lowest income quintiles ( Knoema, 2020 ; Statistics South Africa, 2020b ).…”

Sociodemographic and behavioural risk factors associated with low awareness of diabetes mellitus medication in Indonesia: Findings from the Indonesian Family Life Survey (IFLS-5)

“…At least 45% of treatment failures among DM patients are caused by low adherence to anti-diabetic medical treatments ( 12 ). This could lead to increases in health expenses yearly at both patient and societal levels ( 13 ).…”