Using community health advisors to increase lung cancer screening awareness in the Black Belt: A pilot study.

2021 ◽  
Vol 39 (28_suppl) ◽  
pp. 117-117
Author(s):  
Soumya J. Niranjan ◽  
William Opoku-Agyeman ◽  
Tara Bowman ◽  
Claudia M. Hardy ◽  
Monica L. Baskin ◽  
...  

117 Background: Disease stage at the time of diagnosis is the most important determinant of prognosis for lung cancer. Despite demonstrated effectiveness of lung cancer screening (LCS) in reducing lung cancer mortality, early detection continues to elude populations with the highest risk for lung cancer death. Consistent with the national rate, current screening rate in Alabama is dismal at 4.2%. While public awareness of LCS may be a likely cause there are no studies that have thoroughly evaluated current knowledge of LCS within the Deep South. Therefore, we measured (LCS) knowledge before and after receiving education delivered by Community Health Advisors (CHAs) among high-risk individuals living in medically-underserved communities of Alabama and to determine impact of psychological, demographic, health status and cognitive factors on rate of lung cancer screening participation. Methods: Participants were recruited from one urban county and six rural Black Belt counties (characterized by poverty, rurality, unemployment, low educational attainment and disproportionate lack of access to health services).100 individuals (i) aged between 55 to 80 years (ii) Currently smoke or have quit within the past 15 years. (iii) Have at least a total of 30-pack-year smoking history were recruited. Knowledge scores to assess lung cancer knowledge were calculated. Paired t-test was used to assess pre and post knowledge score improvement. Screening for lung cancer was modeled as a function of predisposed factors (age, gender, insurance, education, fatalism, smoking status, and history of family lung cancer). Results: Average age was 62.94(SD = 6.28), mostly female (54%); mostly current smokers (53% ). Most participants (80.85%) reported no family history of cancer. Fatalism was low, with a majority of the participants disagreeing that a cancer diagnosis is pre-destined (67.7%) and that there are no treatments for lung cancer (88.66%). Overall, lung cancer knowledge increased significantly from baseline of 4.64(SD = 2.37) to 7.61(SD = 2.26). Of the 100 participants, only 23 underwent screening due to lack of access to primary care providers and reluctance of PCPs to provide referral to LCS. 65% of those who were screened reported family history of lung cancer. Regression analysis revealed no significant association between risk factors and the decision to get screened by participants. Conclusions: Our study demonstrates that while CHA delivered education initiatives increases lung cancer screening knowledge, there are significant structural barriers that prohibit effective utilization of LCS which needs to be addressed.

2021 ◽  
Author(s):  
Maisha R. Huq ◽  
Xin He ◽  
Nathaniel Woodard ◽  
Chang Chen ◽  
Cheryl L Knott

Abstract Purpose: Community health advisors (CHAs) play a key role in promoting health in medically underserved communities, including in addressing cancer disparities. There is a need to expand the research on what criteria makes for an effective CHA. We examined the relationship between CHAs’ personal and family history of cancer, and implementation and efficacy outcomes in a cancer control intervention trial.Methods: Twenty-eight trained CHAs implemented a series of three cancer educational group workshops for N=375 workshop participants across 14 churches. Implementation was operationalized as participant attendance at the educational workshops and efficacy as workshop participants’ cancer knowledge scores at 12-month follow-up, controlling for baseline scores. Results: CHA’s personal history of cancer was not significantly associated with implementation, nor knowledge outcomes. However, CHAs with family history of cancer had significantly greater participant attendance at the workshops than CHAs without family history of cancer (p=.03). In addition, there was a significant, positive association between male CHAs’ family history of cancer and male workshop participants’ prostate cancer knowledge scores at 12 months (estimated beta coefficient=0.49, p<.01) after adjusting for workshop participant baseline knowledge scores, the CHAs’ competence score, and the CHAs’ education levels. Conclusions: Findings suggest CHAs with family history of cancer may be particularly suitable for cancer peer education, though further research is needed to confirm this and identify other factors conducive to CHAs’ success.


2020 ◽  
Vol 9 (12) ◽  
pp. 3860
Author(s):  
J. Luis Espinoza ◽  
Le Thanh Dong

Nearly one-quarter of all cancer deaths worldwide are due to lung cancer, making this disease the leading cause of cancer death among both men and women. The most important determinant of survival in lung cancer is the disease stage at diagnosis, thus developing an effective screening method for early diagnosis has been a long-term goal in lung cancer care. In the last decade, and based on the results of large clinical trials, lung cancer screening programs using low-dose computer tomography (LDCT) in high-risk individuals have been implemented in some clinical settings, however, this method has various limitations, especially a high false-positive rate which eventually results in a number of unnecessary diagnostic and therapeutic interventions among the screened subjects. By using complex algorithms and software, artificial intelligence (AI) is capable to emulate human cognition in the analysis, interpretation, and comprehension of complicated data and currently, it is being successfully applied in various healthcare settings. Taking advantage of the ability of AI to quantify information from images, and its superior capability in recognizing complex patterns in images compared to humans, AI has the potential to aid clinicians in the interpretation of LDCT images obtained in the setting of lung cancer screening. In the last decade, several AI models aimed to improve lung cancer detection have been reported. Some algorithms performed equal or even outperformed experienced radiologists in distinguishing benign from malign lung nodules and some of those models improved diagnostic accuracy and decreased the false-positive rate. Here, we discuss recent publications in which AI algorithms are utilized to assess chest computer tomography (CT) scans imaging obtaining in the setting of lung cancer screening.


2018 ◽  
Vol 7 (3) ◽  
pp. 894-902 ◽  
Author(s):  
Sanja Percac-Lima ◽  
Jeffrey M. Ashburner ◽  
Nancy A. Rigotti ◽  
Elyse R. Park ◽  
Yuchiao Chang ◽  
...  

2015 ◽  
Vol 50 (2) ◽  
pp. 72-81 ◽  
Author(s):  
Patricia M. de Groot ◽  
Brett W. Carter ◽  
Myrna C.B. Godoy ◽  
Reginald F. Munden

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. 1506-1506 ◽  
Author(s):  
Sanja Percac-Lima ◽  
Jeffrey M Ashburner ◽  
Nancy Rigotti ◽  
Elyse R. Park ◽  
Yuchiao Chang ◽  
...  

1506 Background: Annual chest computed tomography (CT) can decrease lung cancer mortality in high risk individuals. Patient navigation (PN) has been shown to improve cancer screening rates in underserved populations. We evaluated the impact of PN on lung cancer screening (LCS) in current smokers in community health centers (CHC). Methods: Current smokers aged 55-77 receiving care in five CHC affiliated with an academic medical center were randomized to intervention (n = 400) or control (n = 800) groups. In the intervention arm, patient navigators (PNs) determined eligibility for LCS, provided brief smoking cessation counseling, introduced shared decision making about LCS, scheduled appointments with the primary care provider (PCP), reminded patients about appointments and PCPs to order CTs, and helped patients attend testing and follow-up any abnormal results. Control patients received usual care. The primary outcome was the proportion of patients in each group who had any chest CT during the study period. Secondary outcomes included proportion of patients receiving lung screening CTs and the number of lung cancers diagnosed in each group. Results: Baseline patient characteristics were similar between randomized groups. From March 2016-January 2017, PNs contacted 332 (83%) of intervention patients; 76 refused further participation. Of participating patients, 130 (51%) were eligible for LCS. Exclusions included insufficient smoking history (n = 117), competing comorbidities (n = 5), moved (n = 2), and died (n = 2). In intention-to-treat analyses, 124 intervention patients (31%) had chest CT vs. 138 control patients (17.3%, p < 0.01). Lung cancer screening CTs were performed in 94 intervention patients (23.5%) vs. 69 control patients (8.6%, p < 0.01). Eight lung cancers were diagnosed in intervention patients (2%) vs. 4 in controls (0.5%). Conclusions: A patient navigation program implemented in community health centers significantly increased lung cancer screening among current smokers. PNs may help underserved low-income current smokers complete LCS and improve equity in care while decreasing lung cancer mortality. Clinical trial information: 2015P002239.


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