Daily Respiratory Research Analysis

BACKGROUND: Many governments around the world subsidise upgrades to poorly insulated homes, yet the extent to which these energy-efficiency improvements reduce health risks remains unclear. We aimed to provide the first large-scale evidence on whether such retrofits lower the use of respiratory health-care services, particularly for children and other vulnerable individuals. METHODS: We leveraged a large-scale natural experiment in which public housing units across the Netherlands were retrofitted between 2012 and 2021. Upgrades included insulation and mechanical ventilation and were implemented in homes eligible on the basis of poor energy efficiency and construction before the early 1990s. Treatment assignment was based on technical factors and was therefore shown to be unrelated to health outcomes, and opting out was not possible. We followed up 2 million individuals over 10 years, totalling approximately 12 million person-years-a sample size that provided high statistical power (95% CIs narrow enough to detect relative risk changes in medication use as small as 1%). Individual-level medication data were obtained from health insurers. Medication use and other health-care outcomes among 180 000 tenants in retrofitted homes were compared with those in not-yet-retrofitted homes using a staggered difference-in-differences design with individual fixed effects. The primary outcomes were the use of prescription respiratory-system medications: asthma or chronic obstructive pulmonary disease drugs, cough remedies, and antihistamines. FINDINGS: Antihistamine use declined by 1·87% (95% CI 0·19-3·55; p=0·029) after retrofits. Among children younger than 18 years, respiratory medication use fell by 3·76% (1·04-6·48; p=0·0067). Specifically, after 5 years, asthma medication use was reduced by 6·91% (-0·04 to 13·85; p=0·051). No statistically significant effects were found for non-respiratory medication outcomes and health-care costs. INTERPRETATION: Energy-efficiency upgrades led to measurable reductions in respiratory medication use, especially for children. These benefits probably reflect reduced exposure to indoor dampness and bad air quality. Childhood asthma reduction is a crucial co-benefit of energy-efficiency home upgrades. FUNDING: NWO Dutch Research Council, RVO Netherlands Enterprise Agency, and Villum Fonden.

BACKGROUND: Low-dose computed tomography (LDCT) screening reduces lung cancer mortality, the leading cause of cancer deaths globally. Segmentation-free deep learning (DL) models such as Sybil can improve screening efficiency but require extensive validation and possible improvement. RESEARCH QUESTION: Can the integration of deep learning based on LDCT scans and clinical data improve lung cancer risk prediction? STUDY DESIGN AND METHODS: Retrospective cohort data from 4 different screening programs, one used for model training and three for external validation. Data collected between the years 2002 and 2021. The median follow-up period was 7 years. All participants had a history of either current or former smoking, with at least 10 pack-years or who smoked over 20 years. The area under the receiver operating characteristic curve (AUC) was calculated for lung cancer risk within 1 to 6 years, stratified by pulmonary nodule presence and size. Key clinical and epidemiological factors were evaluated for their added predictive value. RESULTS: This analysis uses 52,482 LDCT series from 22,469 participants. Sybil's AUC ranged from 0.93 in year 1 and reduced to 0.79 in year 6 in the independent cohorts. The predictive performance was suboptimal in the absence of documented nodules (AUC=0.64), and for small nodules (AUC=0.61) in year 6. Our new model, Sybil-Epi, trained with baseline scans, achieved higher predictive performance (AUC=0.83, 95% CI 0.81 to 0.85) compared to Sybil (AUC=0.80, 95% CI 0.78 to 0.82) in year 6. The difference is most notable when nodules are absent, with Sybil-Epi AUC of 0.76 (95% CI 0.70 to 0.82) and Sybil AUC of 0.64 (95% CI 0.57 to 0.70). INTERPRETATION: Sybil performs better for short-term lung cancer risk, but the predictive accuracy was suboptimal when nodules were absent. Our integrated Sybil-Epi model with deep learning and clinical-epidemiological factors significantly improved model predictive performance.

RATIONALE: An estimated 14% of Chronic Obstructive Pulmonary Disease (COPD) is caused by occupational exposure to vapor, gas, dust and fumes (VGDF). In collaboration with the Washington State Department of Labor and Industries' Safety and Health Assessment and Research for Prevention Program, we developed a surveillance algorithm for retrospective identification of Occupational COPD using a cohort of Washington workers. METHODS: Potential cases were identified from workers' compensation claims filed in Washington State between 2010-2020 using keywords, disease diagnosis codes, and occupational injury and illness codes. Cases were confirmed for the presence of COPD using post-bronchodilator forced-expiratory-volume-over-1-second/forced-vital-capacity (FEV1/FVC) < 0.7. If spirometry was unavailable, alternate clinical criteria were used. In an iterative process based on review of the first 100 cases and longitudinal studies on Occupational COPD, we developed an algorithm to classify cases as Probable, Possible, Work-Aggravated or Unlikely Occupational COPD. This algorithm incorporated confirmation of COPD diagnosis, total career duration with exposure to medium or high VGDF, and smoking history. Exposure risk was estimated using a COPD-specific, occupation-based Job Exposure Matrix (JEM). RESULTS: Of 508 potential cases identified in the workers' compensation claims data, 494 had had a COPD diagnosis and were included in the final analysis. Of these, 132 (27%) met criteria for Occupational COPD, including 72 Probable, 4 Possible, and 56 Work-Aggravated cases. There were 362 (73%) cases that did not meet criteria and were classified as Unlikely to have Occupational COPD. Overall, 19 cases with Occupational COPD were never-smokers or had smoked < 10 pack years total, including seventeen classified as Probable and two classified as Work-Aggravated. Occupations ascribed through the JEM to have medium or high exposure to VGDF included construction workers, production workers, maintenance workers and drivers, among others, and there was overlap in occupations and hazards between Occupational COPD classifications. CONCLUSIONS: This comprehensive algorithm provides an approach for Occupational COPD surveillance that can be used to inform and prioritize prevention efforts in an effort to reduce disease burden in high-risk occupations.