Sensitivity analysis and methodological choices on health-related impoverishment estimates in Cambodia, 2009–17

Antunes, Adélio Fernandes; Jacobs, Bart; Jithitikulchai, Theepakorn; Nagpal, Somil; Tong, Kimsun; Fleßa, Steffen

doi:10.1093/heapol/czac028

Cited by 2 publications

(1 citation statement)

References 28 publications

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“…number of days wages of the lowest-paid government worker that is needed to purchase a medicine) present the same challenge in children. The validity of the present and other methods of expressing affordability in reporting on access for children should be subject of future research [ 16 ].…”

Section: Discussionmentioning

confidence: 99%

SDG indicator 3.b.3 – an analysis of its robustness and challenges for measuring access to medicines for children

Joosse,

Wirtz,

van Mourik

et al. 2023

BMC Health Serv Res

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Background Sustainable Development Goal (SDG) indicator 3.b.3 monitors progress in medicines’ accessibility for adults and has significant limitations when applying to medicines for children. An adapted indicator methodology was developed to fill this gap, but no proof of its robustness exists. We provide this evidence through sensitivity analyses. Methods Data on availability and prices of child medicines from ten historical datasets were combined to create datasets for analysis: Dataset 1 (medicines selected at random) and Dataset 2 (preference given to available medicines, to better capture affordability of medicines). A base case scenario and univariate sensitivity analyses were performed to test critical components of the methodology, including the new variable of number of units needed for treatment (NUNT), disease burden (DB) weighting, and the National Poverty Line (NPL) limits. Additional analyses were run on a continuously smaller basket of medicines to explore the minimum number of medicines required. Mean facility scores for access were calculated and compared. Results The mean facility score for Dataset 1 and Dataset 2 within the base case scenario was 35.5% (range 8.0–58.8%) and 76.3% (range 57.2–90.6%). Different NUNT scenarios led to limited variations in mean facility scores of + 0.1% and -0.2%, or differences of + 4.4% and -2.1% at the more critical NPL of $5.50 (Dataset 1). For Dataset 2, variations to the NUNT generated differences of + 0.0% and -0.6%, at an NPL of $5.50 the differences were + 5.0 and -2.0%. Different approaches for weighting for DB induced considerable fluctuations of 9.0% and 11.2% respectively. Stable outcomes with less than 5% change in mean facility score were observed for a medicine basket down to 12 medicines. For smaller baskets, scores increased more rapidly with a widening range. Conclusion This study has confirmed that the proposed adaptations to make SDG indicator 3.b.3 appropriate for children are robust, indicating that they could be an important addition to the official Global Indicator Framework. At least 12 child-appropriate medicines should be surveyed to obtain meaningful outcomes. General concerns that remain about the weighting of medicines for DB and the NPL should be considered at the 2025 planned review of this framework.

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Section: Discussionmentioning

confidence: 99%

SDG indicator 3.b.3 – an analysis of its robustness and challenges for measuring access to medicines for children

Joosse,

Wirtz,

van Mourik

et al. 2023

BMC Health Serv Res

View full text Add to dashboard Cite

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Revisiting a decade of inequality in healthcare financial burden in Cambodia, 2009–19: trends, determinants and decomposition

Fernandes Antunes,

Jithitikulchai,

Hohmann

et al. 2024

Int J Equity Health

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Background Out-of-pocket healthcare expenditure (OOPHE) without adequate social protection often translates to inequitable financial burden and utilization of services. Recent publications highlighted Cambodia’s progress towards Universal Health Coverage (UHC) with reduced incidence of catastrophic health expenditure (CHE) and improvements in its distribution. However, departing from standard CHE measurement methods suggests a different storyline on trends and inequality in the country. Objective This study revisits the distribution and impact of OOPHE and its financial burden from 2009–19, employing alternative socio-economic and economic shock metrics. It also identifies determinants of the financial burden and evaluates inequality-contributing and -mitigating factors from 2014–19, including coping mechanisms, free healthcare, and OOPHE financing sources. Methods Data from the Cambodian Socio-Economic Surveys of 2009, 2014, and 2019 were utilized. An alternative measure to CHE is proposed: Excessive financial burden (EFB). A household was considered under EFB when its OOPHE surpassed 10% or 25% of total consumption, excluding healthcare costs. A polychoric wealth index was used to rank households and measure EFB inequality using the Erreygers Concentration Index. Inequality shifts from 2014–19 were decomposed using the Recentered Influence Function regression followed by the Oaxaca-Blinder method. Determinants of financial burden levels were assessed through zero-inflated ordered logit regression. Results Between 2009–19, EFB incidence increased from 10.95% to 17.92% at the 10% threshold, and from 4.41% to 7.29% at the 25% threshold. EFB was systematically concentrated among the poorest households, with inequality sharply rising over time, and nearly a quarter of the poorest households facing EFB at the 10% threshold. The main determinants of financial burden were geographic location, household size, age and education of household head, social health protection coverage, disease prevalence, hospitalization, and coping strategies. Urbanization, biased disease burdens, and preventive care were key in explaining the evolution of inequality. Conclusion More efforts are needed to expand social protection, but monitoring those through standard measures such as CHE has masked inequality and the burden of the poor. The financial burden across the population has risen and become more unequal over the past decade despite expansion and improvements in social health protection schemes. Health Equity funds have, to some extent, mitigated inequality over time. However, their slow expansion and the reduced reliance on coping strategies to finance OOPHE could not outbalance inequality.

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Sensitivity analysis and methodological choices on health-related impoverishment estimates in Cambodia, 2009–17

Cited by 2 publications

References 28 publications

SDG indicator 3.b.3 – an analysis of its robustness and challenges for measuring access to medicines for children

SDG indicator 3.b.3 – an analysis of its robustness and challenges for measuring access to medicines for children

Revisiting a decade of inequality in healthcare financial burden in Cambodia, 2009–19: trends, determinants and decomposition

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