Table_1_Inequality in benefit distribution of reducing the outpatient cost-sharing: evidence from the outpatient pooling scheme in China.DOCX

ObjectiveThe implementation of the outpatient pooling scheme in China has substantially elevated the compensation levels for outpatient expenses. This study aims to assess whether socioeconomically disadvantaged enrollees benefit proportionally compared to their non-disadvantaged counterparts.MethodA cohort comprising 14,581 Urban and Rural Resident Basic Medical Insurance (URRBMI) enrollees and 830 Urban Employee Basic Medical Insurance (UEBMI) enrollees was derived from the China Health and Retirement Longitudinal Study 2018. Outpatient pooling scheme benefits were evaluated based on two metrics: the probability of obtaining benefits and the magnitude of benefits (reimbursement amounts and ratios). Two-part models were employed to adjust outpatient benefits for healthcare needs. Inequality in benefit distribution was assessed using the concentration curve and concentration index (CI).ResultsFollowing adjustments for healthcare needs, the CI for the probability of receiving outpatient benefits for URRBMI and UEBMI enrollees were − 0.0760 and − 0.0514, respectively, indicating an evident pro-poor pattern under the outpatient pooling scheme. However, the CIs of reimbursement amounts (0.0708) and ratio (0.0761) for URRBMI recipients were positive, signifying a discernible pro-rich inequality in the degree of benefits. Conversely, socioeconomically disadvantaged UEBMI enrollees received higher reimbursement amounts and ratios.ConclusionDespite a higher likelihood of socioeconomically disadvantaged groups receiving outpatient benefits, a pro-rich inequality persists in the degree of benefits under the outpatient pooling scheme in China. Comprehensive strategies, including expanding outpatient financial benefits, adopting distinct reimbursement standards, and enhancing the accessibility of outpatient care, need to be implemented to achieve equity in benefits distribution.</p

Forest plot of effect sizes for the incidence of women with spontaneous menstruation.

<p>Forest plot of effect sizes for the incidence of women with spontaneous menstruation.</p

Funnel plot and Egger’s test of effect sizes for the included studies.

<p>Funnel plot and Egger’s test of effect sizes for the included studies.</p

The Assessment Of Study Quality.

<p>The Assessment Of Study Quality.</p

Description of Included Trials.

<p>Note: CMF = cyclophosphamide, methotrexate, and 5-fluorouracil regimen; FAC = 5-fluorouracil, doxorubicin, and cyclophosphamide regimen; FSH =  follicle-stimulating hormone; GnRH = gonadotropin-relea sing hormone; NA =  information not available; POF = premature ovarian failure; ^aThis study has four arms: arm A received only chemotherapy ± GnRH analogue; arm B received chemotherapy+ tamoxifen ± GnRH analogue.</p

The combined effects of rs2046210 and rs4784227.

a<p>Adjusted by age, BMI, age at menarche, age at first live birth, menopausal status and family history breast cancer where appropriate.</p>b<p>The risk allele included rs2046210-A and rs4784227-T.</p><p>The combined effects of rs2046210 and rs4784227.</p

Distributions of selected characteristics in breast cancer cases and healthy-control cases.

a<p>Unnatural menopause include hysterectomy operation and other status.</p><p>Distributions of selected characteristics in breast cancer cases and healthy-control cases.</p

Stratified analyses on associations among rs2046210 and rs4784227 and risk of breast cancer.

a<p>Counting genotypes as ranking variables.</p>b<p>Derived from additive model with an adjustment by age, BMI, age at menarche, age at first live birth, menopausal status and family history breast cancer where appropriate.</p>c<p><i>P</i> for heterogeneity test.</p><p>Stratified analyses on associations among rs2046210 and rs4784227 and risk of breast cancer.</p

Logistic regression analyses on associations among rs2046210, rs4784227 and risk of breast cancer.

a<p>Reference allele/risk allele.</p>b<p>Adjusted by age, BMI, age at menarche, age at first live birth, menopausal status and family history of breast cancer where appropriate.</p>c<p><i>P</i> trend for genotypes between cases and controls.</p>d<p>Two-sided χ² test for differences in frequency distribution of alleles between cases and controls.</p>e<p>Two-sided χ² test for differences in frequency distribution of combined genotypes(dominant model) between cases and controls.</p><p>Logistic regression analyses on associations among rs2046210, rs4784227 and risk of breast cancer.</p

Additional file 2 of High leukocyte mitochondrial DNA copy number contributes to poor prognosis in breast cancer patients

Supplementary Material