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Age, morbidity, or something else? A residual approach using microdata to measure the impact of technological progress on health care expenditure.

ABSTRACT: This study measures the increment of health care expenditure (HCE) that can be attributed to technological progress and change in medical practice by using a residual approach and microdata. We examine repeated cross-sections of individuals experiencing an initial health shock at different point in time over a 10-year window and capture the impact of unobservable technology and medical practice to which they are exposed after allowing for differences in health and socioeconomic characteristics. We decompose the residual increment in the part that is due to the effect of delaying time to death, that is, individuals surviving longer after a health shock and thus contributing longer to the demand of care, and the part that is due to increasing intensity of resource use, that is, the basket of services becoming more expensive to allow for the cost of innovation. We use data from the Danish National Health System that offers universal coverage and is free of charge at the point of access. We find that technological progress and change in medical practice can explain about 60% of the increment of HCE, in line with macroeconomic studies that traditionally investigate this subject.

SUBMITTER: Laudicella M

PROVIDER: S-EPMC9314678 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature

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Age, morbidity, or something else? A residual approach using microdata to measure the impact of technological progress on health care expenditure.

Laudicella Mauro M Li Donni Paolo P Olsen Kim Rose KR Gyrd-Hansen Dorte D

Health economics 20220331 6

This study measures the increment of health care expenditure (HCE) that can be attributed to technological progress and change in medical practice by using a residual approach and microdata. We examine repeated cross-sections of individuals experiencing an initial health shock at different point in time over a 10-year window and capture the impact of unobservable technology and medical practice to which they are exposed after allowing for differences in health and socioeconomic characteristics. ...[more]

PMID: 35362244

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Project description:ObjectiveTo assess whether application of a standard algorithm to hospitalizations in the prenatal and postpartum (42 days) periods increases identification of severe maternal morbidity (SMM) beyond analysis of only the delivery event.MethodsWe performed a retrospective cohort study using data from the PELL (Pregnancy to Early Life Longitudinal) database, a Massachusetts population-based data system that links records from birth certificates to delivery hospital discharge records and nonbirth hospital records for all birthing individuals. We included deliveries from January 1, 2009, to December 31, 2018, distinguishing between International Classification of Diseases Ninth (ICD-9) and Tenth Revision (ICD-10) coding. We applied the modified Centers for Disease Control and Prevention algorithm for SMM used by the Alliance for Innovation on Maternal Health to hospitalizations across the antenatal period through 42 days postpartum. Morbidity was examined both with and without blood transfusion.ResultsOverall, 594,056 deliveries were included in the analysis, and 3,947 deliveries met criteria for SMM at delivery without transfusion and 9,593 with transfusion for aggregate rates of 150.1 (95% CI 146.7-153.5) using ICD-9 codes and 196.6 (95% CI 189.5-203.7) using ICD-10 codes per 10,000 deliveries. Severe maternal morbidity at birth increased steadily across both ICD-9 and ICD-10 from 129.4 in 2009 (95% CI 126.2-132.6) using ICD-9 to 214.3 per 10,000 (95% CI 206.9-221.8) in 2018 using ICD-10. Adding prenatal and postpartum hospitalizations increased cases by 21.9% under both ICD-9 and ICD-10, resulting in a 2018 rate of 258.7 per 10,000 (95% CI 250.5-266.9). The largest increase in detected morbidity in the prenatal or postpartum time period was attributed to sepsis cases.ConclusionInclusion of prenatal and postpartum hospitalizations in the identification of SMM resulted in increased ascertainment of morbid events. These results suggest a need to ensure surveillance of care quality activities beyond the birth event.

Project description:BackgroundLong-term survival after premature birth is significantly determined by development of morbidities, primarily affecting the cardio-respiratory or central nervous system. Existing studies are limited to pairwise morbidity associations, thereby lacking a holistic understanding of morbidity co-occurrence and respective risk profiles.MethodsOur study, for the first time, aimed at delineating and characterizing morbidity profiles at near-term age and investigated the most prevalent morbidities in preterm infants: bronchopulmonary dysplasia (BPD), pulmonary hypertension (PH), mild cardiac defects, perinatal brain pathology and retinopathy of prematurity (ROP). For analysis, we employed two independent, prospective cohorts, comprising a total of 530 very preterm infants: AIRR ("Attention to Infants at Respiratory Risks") and NEuroSIS ("Neonatal European Study of Inhaled Steroids"). Using a data-driven strategy, we successfully characterized morbidity profiles of preterm infants in a stepwise approach and (1) quantified pairwise morbidity correlations, (2) assessed the discriminatory power of BPD (complemented by imaging-based structural and functional lung phenotyping) in relation to these morbidities, (3) investigated collective co-occurrence patterns, and (4) identified infant subgroups who share similar morbidity profiles using machine learning techniques.ResultsFirst, we showed that, in line with pathophysiologic understanding, BPD and ROP have the highest pairwise correlation, followed by BPD and PH as well as BPD and mild cardiac defects. Second, we revealed that BPD exhibits only limited capacity in discriminating morbidity occurrence, despite its prevalence and clinical indication as a driver of comorbidities. Further, we demonstrated that structural and functional lung phenotyping did not exhibit higher association with morbidity severity than BPD. Lastly, we identified patient clusters that share similar morbidity patterns using machine learning in AIRR (n=6 clusters) and NEuroSIS (n=8 clusters).ConclusionsBy capturing correlations as well as more complex morbidity relations, we provided a comprehensive characterization of morbidity profiles at discharge, linked to shared disease pathophysiology. Future studies could benefit from identifying risk profiles to thereby develop personalized monitoring strategies.Trial registrationAIRR: DRKS.de, DRKS00004600, 28/01/2013. NEuroSIS: ClinicalTrials.gov, NCT01035190, 18/12/2009.

Dataset Information

Age, morbidity, or something else? A residual approach using microdata to measure the impact of technological progress on health care expenditure.

Publications

Age, morbidity, or something else? A residual approach using microdata to measure the impact of technological progress on health care expenditure.

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