Early breast cancer intervention is cost-effective for all income groups – model analysis
02 Aug 2025
Such issues
were, in turn, related to funding problems. PhilHealth, with its goals for
Universal Health Care, implemented the so-called Z-Benefits program in 2012 to
provide assistance for breast cancer from stages 0 to IIIA, with a maximum
reimbursement of PHP100,000. But the agency, in its 2017 evaluation, showed Z-Benefits
only lowered financial burden from the time of diagnosis up to one year. The
National Integrated Cancer Control Act (NICCA) provided the Cancer Assistance
Fund (CAF) in 2019, allocating PHP150,000 for in- and outpatient services. Only
20 percent of those seeking assistance were given support due to limited funds.
Moreover, the Philippine Breast Cancer Control Program, which focuses on routine self- and clinical breast examination, has no organized early detection component. Economic evaluation of such programs is lacking in the local setting. Addressing this gap, the Philippine Cancer Society funded the ACT NOW program, a pilot study done in Commonwealth, Quezon City, from February 2023 to August 2024. As related by Chye et al.’s cost-effectiveness study, ACT NOW recruited healthy women, ages 40 to 69, monitored for five years, providing an annual clinical breast examination (CBE) and, for high risk women, a biannual breast ultrasound. [BMJ Glob Health. 2025 Feb 3;10(2):e016402. doi: 10.1136/bmjgh-2024-016402]
The study aimed to detect early cases, and downstage breast cancer to help prevent risk of death and financial burden. The resulting data would be used by Chye et al. for an extended cost-effectiveness analysis to provide the needed information gap for stakeholders. The study would take into account the new PhilHealth Z-package that increased coverage to PHP1.4 million, up to stage-IV breast cancer.
Kimman et al.’s study was used for the ACT NOW study's microsimulation model up to five years, in order to determine costs and outcome. [BMC Med. 2015; 13. doi:10.1186/s12916-015-0433-1] Patients were divided into stages I to II as early, and stages III to IV as late. Diagnosed patients were then classified as cancer death, non-cancer death, or survivor. Early detection and treatment strategies were used for transition probabilities for breast cancer mortality based on low- and upper-middle income countries. Treatment-adherent surviving patients would be expected to account for 35 percent, based on previous Philippine data.
The study's control arm was based on the Philippine population rates on early detection, while the intervention arm was based on the ACT NOW program. Comparison of mortality risk was done among high-, medium-, and low-income groups. Recurrence was not included in the model. Intervention effect based on the study by Devi et al. provided a 52 percent (77 percent down to 37 percent) reduction in late stage diagnosis. [Ann Oncol. 2007;18: 1172–6.doi:10.1093/annonc/mdm105]
The authors’ cost analysis data were obtained from medical centers, PhilHealth Z package information for breast cancer, and studies by Genuino et al. (trastuzumab) and Ngelangel et al. (Philippine Cost in Oncology). Financial catastrophe rates were taken from the ACTION study.
Chye et al. determined the following outcomes: (1) health gains measured by potential breast cancer deaths saved; (2) financial protection measured by reduction of financial catastrophe; and (3) cost effectiveness using Incremental Cost-Effectiveness Ratio (ICER) for cost per disability adjusted life year (DALY) saved. Income stratification was considered in outcomes.
The researchers employed TreeAge pro software for modelled analysis for the collated data per 100,000 sample patients. Scenario analysis included financial coverage from PhilHealth Z package, including CAF, intervention coverage and effectiveness of “downstaging”.
Breast cancer deaths between control (197) and intervention (140) models, according to the authors, showed that many deaths were prevented in the low-income group. Higher-income groups even showed greater percentage of deaths prevented. Overall, 57 cancer deaths per 100,000 women were averted, representing a 29-percent reduction in five years. Financial catastrophe showed a three-percent decrease from one year of diagnosis. Intervention was cost-effective for all income groups, ie, the cost effectiveness for willingness to pay at 1x national GDP per capita were the same for all the groups.
Base scenario ICER would be PHP60,711, which is 0.34 of the Philippine GDP per capita in 2022 (PHP178,751). ICER increased with higher income due to longer survival but there would be no significant difference on incremental costs and DALYs among groups.
For case base scenario analysis, Chye et al. said that the government would spend an average of PHP1,383 per person for early detection. Increasing government financial coverage either on CAF on low-income, or CAF on all incomes would show the same cost effectiveness of ICER at PhP60,711.
If early detection intervention were free only for low-income groups, cost-effectiveness would increase (ICER of PHP50 958 per DALY), decreasing government contribution by PHP266 per person.
“The CAF will avert 42 percent of breast cancer-related financial catastrophe at a government contribution of PHP75 000 and 67 percent at PHP150 000. Restricting the CAF financial benefit to low-income households reduces the cases of financial catastrophe saved to 36 percent at PHP75 000 and 54 percent at PhP150 000, however, also reduces government contributions. Notably, implementing the updated Z-Benefits Package (up to PHP1,400,000) will significantly reduce rates of financial catastrophe by 97 percent.”
Rate of financial catastrophe would be reduced by 97 percent when PhilHealth financial coverage of PHP1.4 million is implemented in the low-income group. Late-stage diagnosis reduction model with a base case of 52 percent reduction, when increased to 75 percent, would improve breast cancer deaths saved, financial catastrophe saved, and cost effectiveness ICER to PHP37,241 per DALY.
When adherence to treatment model was done (35 percent base case), breast cancer deaths saved decreased accordingly as percentage of adherence became 50 and 75 percent due to total absolute death decrease. Financial catastrophe saved also decreased, since patients undergoing treatment incurred more costs.
With time horizon model beyond five years, the authors said that the cost effectiveness of intervention would improve, but with decreasing returns for death and financial catastrophe, as the sample population ages out of screening.
The analysis of the different models, according to Chye et al., showed that early detection intervention is cost-effective, reduces breast cancer mortality, and is fairly effective in reducing financial catastrophe. The improved Z package coverage by PhilHealth would have more favorable effect in minimizing such financial stress.
Moreover, the Philippine Breast Cancer Control Program, which focuses on routine self- and clinical breast examination, has no organized early detection component. Economic evaluation of such programs is lacking in the local setting. Addressing this gap, the Philippine Cancer Society funded the ACT NOW program, a pilot study done in Commonwealth, Quezon City, from February 2023 to August 2024. As related by Chye et al.’s cost-effectiveness study, ACT NOW recruited healthy women, ages 40 to 69, monitored for five years, providing an annual clinical breast examination (CBE) and, for high risk women, a biannual breast ultrasound. [BMJ Glob Health. 2025 Feb 3;10(2):e016402. doi: 10.1136/bmjgh-2024-016402]
The study aimed to detect early cases, and downstage breast cancer to help prevent risk of death and financial burden. The resulting data would be used by Chye et al. for an extended cost-effectiveness analysis to provide the needed information gap for stakeholders. The study would take into account the new PhilHealth Z-package that increased coverage to PHP1.4 million, up to stage-IV breast cancer.
Kimman et al.’s study was used for the ACT NOW study's microsimulation model up to five years, in order to determine costs and outcome. [BMC Med. 2015; 13. doi:10.1186/s12916-015-0433-1] Patients were divided into stages I to II as early, and stages III to IV as late. Diagnosed patients were then classified as cancer death, non-cancer death, or survivor. Early detection and treatment strategies were used for transition probabilities for breast cancer mortality based on low- and upper-middle income countries. Treatment-adherent surviving patients would be expected to account for 35 percent, based on previous Philippine data.
The study's control arm was based on the Philippine population rates on early detection, while the intervention arm was based on the ACT NOW program. Comparison of mortality risk was done among high-, medium-, and low-income groups. Recurrence was not included in the model. Intervention effect based on the study by Devi et al. provided a 52 percent (77 percent down to 37 percent) reduction in late stage diagnosis. [Ann Oncol. 2007;18: 1172–6.doi:10.1093/annonc/mdm105]
The authors’ cost analysis data were obtained from medical centers, PhilHealth Z package information for breast cancer, and studies by Genuino et al. (trastuzumab) and Ngelangel et al. (Philippine Cost in Oncology). Financial catastrophe rates were taken from the ACTION study.
Chye et al. determined the following outcomes: (1) health gains measured by potential breast cancer deaths saved; (2) financial protection measured by reduction of financial catastrophe; and (3) cost effectiveness using Incremental Cost-Effectiveness Ratio (ICER) for cost per disability adjusted life year (DALY) saved. Income stratification was considered in outcomes.
The researchers employed TreeAge pro software for modelled analysis for the collated data per 100,000 sample patients. Scenario analysis included financial coverage from PhilHealth Z package, including CAF, intervention coverage and effectiveness of “downstaging”.
Breast cancer deaths between control (197) and intervention (140) models, according to the authors, showed that many deaths were prevented in the low-income group. Higher-income groups even showed greater percentage of deaths prevented. Overall, 57 cancer deaths per 100,000 women were averted, representing a 29-percent reduction in five years. Financial catastrophe showed a three-percent decrease from one year of diagnosis. Intervention was cost-effective for all income groups, ie, the cost effectiveness for willingness to pay at 1x national GDP per capita were the same for all the groups.
Base scenario ICER would be PHP60,711, which is 0.34 of the Philippine GDP per capita in 2022 (PHP178,751). ICER increased with higher income due to longer survival but there would be no significant difference on incremental costs and DALYs among groups.
For case base scenario analysis, Chye et al. said that the government would spend an average of PHP1,383 per person for early detection. Increasing government financial coverage either on CAF on low-income, or CAF on all incomes would show the same cost effectiveness of ICER at PhP60,711.
If early detection intervention were free only for low-income groups, cost-effectiveness would increase (ICER of PHP50 958 per DALY), decreasing government contribution by PHP266 per person.
“The CAF will avert 42 percent of breast cancer-related financial catastrophe at a government contribution of PHP75 000 and 67 percent at PHP150 000. Restricting the CAF financial benefit to low-income households reduces the cases of financial catastrophe saved to 36 percent at PHP75 000 and 54 percent at PhP150 000, however, also reduces government contributions. Notably, implementing the updated Z-Benefits Package (up to PHP1,400,000) will significantly reduce rates of financial catastrophe by 97 percent.”
Rate of financial catastrophe would be reduced by 97 percent when PhilHealth financial coverage of PHP1.4 million is implemented in the low-income group. Late-stage diagnosis reduction model with a base case of 52 percent reduction, when increased to 75 percent, would improve breast cancer deaths saved, financial catastrophe saved, and cost effectiveness ICER to PHP37,241 per DALY.
When adherence to treatment model was done (35 percent base case), breast cancer deaths saved decreased accordingly as percentage of adherence became 50 and 75 percent due to total absolute death decrease. Financial catastrophe saved also decreased, since patients undergoing treatment incurred more costs.
With time horizon model beyond five years, the authors said that the cost effectiveness of intervention would improve, but with decreasing returns for death and financial catastrophe, as the sample population ages out of screening.
The analysis of the different models, according to Chye et al., showed that early detection intervention is cost-effective, reduces breast cancer mortality, and is fairly effective in reducing financial catastrophe. The improved Z package coverage by PhilHealth would have more favorable effect in minimizing such financial stress.