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Many low-income adults who smoke also have unmet social needs, such as food insecurity, which can serve as a barrier to smoking cessation. We developed a novel intervention to jointly address smoking cessation and food insecurity and assessed its feasibility, acceptability, and preliminary outcomes. We enrolled participants who screened for food insecurity, reported smoking daily, and were ready to quit. All participants received 3 months of resources navigation from a community health worker through monthly telephone calls for referrals and check-ins for smoking cessation and food access resources. Participants randomized to the intervention group received an economic intervention equivalent to the cost of 1 week of groceries/month for 3 months. We randomized 55 participants who were smoking on average 13 cigarettes/day. The trial was feasible and acceptable based on 3-month retention rates (80%) and end-of-study qualitative feedback (91% would recommend the study to others). At 3 months, participants in the intervention versus control group reported a longer length of abstinence from smoking and had a higher proportion of serious quit attempts. Results from this pilot study suggest the importance of attending to social needs, particularly food insecurity, as a strategy to promote smoking cessation among low-income adults who smoke. [ABSTRACT FROM AUTHOR]

Background: The long-term prognosis and risk factors for quality of life and disability after anterior cruciate ligament (ACL) reconstruction remain unknown. Hypothesis/Purpose: Our objective was to identify patient-reported outcomes and patient-specific risk factors from a large prospective cohort at a minimum 10-year follow-up after ACL reconstruction. We hypothesized that meniscus and articular cartilage injuries, revision ACL reconstruction, subsequent knee surgery, and certain demographic characteristics would be significant risk factors for inferior outcomes at 10 years. Study Design: Therapeutic study; Level of evidence, 2. Methods: Unilateral ACL reconstruction procedures were identified and prospectively enrolled between 2002 and 2004 from 7 sites in the Multicenter Orthopaedic Outcomes Network (MOON). Patients preoperatively completed a series of validated outcome instruments, including the International Knee Documentation Committee (IKDC), Knee injury and Osteoarthritis Outcome Score (KOOS), and Marx activity rating scale. At the time of surgery, physicians documented all intra-articular abnormalities, treatment, and surgical techniques utilized. Patients were followed at 2, 6, and 10 years postoperatively and asked to complete the same outcome instruments that they completed at baseline. The incidence and details of any subsequent knee surgeries were also obtained. Multivariable regression analysis was used to identify significant predictors of the outcome. Results: A total of 1592 patients were enrolled (57% male; median age, 24 years). Ten-year follow-up was obtained on 83% (n = 1320) of the cohort. Both IKDC and KOOS scores significantly improved at 2 years and were maintained at 6 and 10 years. Conversely, Marx scores dropped markedly over time, from a median score of 12 points at baseline to 9 points at 2 years, 7 points at 6 years, and 6 points at 10 years. The patient-specific risk factors for inferior 10-year outcomes were lower baseline scores; higher body mass index; being a smoker at baseline; having a medial or lateral meniscus procedure performed before index ACL reconstruction; undergoing revision ACL reconstruction; undergoing lateral meniscectomy; grade 3 to 4 articular cartilage lesions in the medial, lateral, or patellofemoral compartments; and undergoing any subsequent ipsilateral knee surgery after index ACL reconstruction. Conclusion: Patients were able to perform sports-related functions and maintain a relatively high knee-related quality of life 10 years after ACL reconstruction, although activity levels significantly declined over time. Multivariable analysis identified several key modifiable risk factors that significantly influence the outcome. [ABSTRACT FROM AUTHOR]

Electronic health records (EHRs) present a wealth of data that are vital for improving patient-centered outcomes, although the data can present significant statistical challenges. In particular, EHR data contains substantial missing information that if left unaddressed could reduce the validity of conclusions drawn. Properly addressing the missing data issue in EHR data is complicated by the fact that it is sometimes difficult to differentiate between missing data and a negative value. For example, a patient without a documented history of heart failure may truly not have disease or the clinician may have simply not documented the condition. Approaches for reducing missing data in EHR systems come from multiple angles, including: increasing structured data documentation, reducing data input errors, and utilization of text parsing / natural language processing. This paper focuses on the analytical approaches for handling missing data, primarily multiple imputation. The broad range of variables available in typical EHR systems provide a wealth of information for mitigating potential biases caused by missing data. The probability of missing data may be linked to disease severity and healthcare utilization since unhealthier patients are more likely to have comorbidities and each interaction with the health care system provides an opportunity for documentation. Therefore, any imputation routine should include predictor variables that assess overall health status (e.g. Charlson Comorbidity Index) and healthcare utilization (e.g. number of encounters) even when these comorbidities and patient encounters are unrelated to the disease of interest. Linking the EHR data with other sources of information (e.g. National Death Index and census data) can also provide less biased variables for imputation. Additional methodological research with EHR data and improved epidemiological training of clinical investigators is warranted.