[A new approach to health care evaluation: Assessing the PopGrouper for matching and morbidity measurement using the disease management programs for CHD and breast cancer].
1/5 보강
PICO 자동 추출 (휴리스틱, conf 3/4)
유사 논문P · Population 대상 환자/모집단
환자: active breast cancer treatment in the IG than in the CG
I · Intervention 중재 / 시술
no CHD treatment, had no health care use/disease, or were classified as high-cost cases
C · Comparison 대조 / 비교
추출되지 않음
O · Outcome 결과 / 결론
As an outcome measure, the PopGrouper's multidimensional morbidity mapping offers opportunities for exploratory analyses, including process quality, and can be combined with established endpoints such as mortality. The PopGrouper could contribute to a higher level of standardization in healthcare evaluations.
[BACKGROUND] The PopGrouper is a population-based classification system that was created using data from a large German statutory health insurance fund.
- 표본수 (n) 29,680
APA
Hengel P, Tsatsaronis C, et al. (2026). [A new approach to health care evaluation: Assessing the PopGrouper for matching and morbidity measurement using the disease management programs for CHD and breast cancer].. Zeitschrift fur Evidenz, Fortbildung und Qualitat im Gesundheitswesen. https://doi.org/10.1016/j.zefq.2026.01.002
MLA
Hengel P, et al.. "[A new approach to health care evaluation: Assessing the PopGrouper for matching and morbidity measurement using the disease management programs for CHD and breast cancer].." Zeitschrift fur Evidenz, Fortbildung und Qualitat im Gesundheitswesen, 2026.
PMID
41735094
Abstract
[BACKGROUND] The PopGrouper is a population-based classification system that was created using data from a large German statutory health insurance fund. It annually assigns each individual to one mutually exclusive, clinically meaningful, and cost-homogeneous group at different levels of aggregation. This study tests the PopGrouper for matching and as a morbidity outcome measure in healthcare evaluations.
[METHODS] Disease management programs (DMPs) for coronary heart disease (CHD) and breast cancer were analyzed in a quasi-experimental design (matching: 2021, enrolment: 2022, follow-up: 2023). Insured persons were assigned 1:1 to the intervention (IG: DMP) or to the control group (CG: usual care) using a standard propensity score matching (benchmark), a pure and an extended PopGroup matching including disease-specific variables. Morbidity was assessed by comparing PopGroup distributions between IG and CG before and after intervention.
[RESULTS] In the CHD sample (CG pool n = 29,680; IG n = 7,557) and breast cancer sample (n = 111,509; n = 3,321), sample sizes were reduced by up to 3% after matching. Both benchmark and extended PopGroup matching achieved good covariate balance. Follow-up showed a higher proportion of patients with active breast cancer treatment in the IG than in the CG. CHD patients in the CG more often received no CHD treatment, had no health care use/disease, or were classified as high-cost cases.
[DISCUSSION] Extended PopGroup matching achieved results that are comparable to the benchmark with substantially less effort. As an outcome measure, the PopGrouper's multidimensional morbidity mapping offers opportunities for exploratory analyses, including process quality, and can be combined with established endpoints such as mortality. The PopGrouper could contribute to a higher level of standardization in healthcare evaluations.
[METHODS] Disease management programs (DMPs) for coronary heart disease (CHD) and breast cancer were analyzed in a quasi-experimental design (matching: 2021, enrolment: 2022, follow-up: 2023). Insured persons were assigned 1:1 to the intervention (IG: DMP) or to the control group (CG: usual care) using a standard propensity score matching (benchmark), a pure and an extended PopGroup matching including disease-specific variables. Morbidity was assessed by comparing PopGroup distributions between IG and CG before and after intervention.
[RESULTS] In the CHD sample (CG pool n = 29,680; IG n = 7,557) and breast cancer sample (n = 111,509; n = 3,321), sample sizes were reduced by up to 3% after matching. Both benchmark and extended PopGroup matching achieved good covariate balance. Follow-up showed a higher proportion of patients with active breast cancer treatment in the IG than in the CG. CHD patients in the CG more often received no CHD treatment, had no health care use/disease, or were classified as high-cost cases.
[DISCUSSION] Extended PopGroup matching achieved results that are comparable to the benchmark with substantially less effort. As an outcome measure, the PopGrouper's multidimensional morbidity mapping offers opportunities for exploratory analyses, including process quality, and can be combined with established endpoints such as mortality. The PopGrouper could contribute to a higher level of standardization in healthcare evaluations.