Improving identification of symptomatic cancer at primary care clinics: A predictive modeling analysis in Botswana.

In resource-limited settings, augmenting primary care provider (PCP)-based referrals with data-derived algorithms could direct scarce resources towards those patients most likely to have a cancer diagnosis and benefit from early treatment. Using data from Botswana, we compared accuracy of predictions of probable cancer using different approaches for identifying symptomatic cancer at primary clinics. We followed cancer suspects until they entered specialized care for cancer treatment (following pathologically confirmed diagnosis), exited from the study following noncancer diagnosis, or died. Routine symptom and demographic data included baseline cancer probability assessed by the primary care provider (low, intermediate, high), age, sex, performance status, baseline cancer probability by study physician, predominant symptom (lump, bleeding, pain or other) and HIV status. Logistic regression with 10-fold cross-validation was used to evaluate classification by different sets of predictors: (1) PCPs, (2) Algorithm-only, (3) External specialist physician review and (4) Primary clinician augmented by algorithm. Classification accuracy was assessed using c-statistics, sensitivity and specificity. Six hundred and twenty-three adult cancer suspects with complete data were retained, of whom 166 (27%) were diagnosed with cancer. Models using PCP augmented by algorithm (c-statistic: 77.2%, 95% CI: 73.4%, 81.0%) and external study physician assessment (77.6%, 95% CI: 73.6%, 81.7%) performed better than algorithm-only (74.9%, 95% CI: 71.0%, 78.9%) and PCP initial assessment (62.8%, 95% CI: 57.9%, 67.7%) in correctly classifying suspected cancer patients. Sensitivity and specificity statistics from models combining PCP classifications and routine data were comparable to physicians, suggesting that incorporating data-driven algorithms into referral systems could improve efficiency.

A Step Toward Timely Referral and Early Diagnosis of Cancer: Implementation and Impact on Knowledge of a Primary Care-Based Training Program in Botswana.

INTRODUCTION: Health system delays in diagnosis of cancer contribute to the glaring disparities in cancer mortality between high-income countries and low- and middle-income countries. In Botswana, approximately 70% of cancers are diagnosed at late stage and median time from first health facility visit for cancer-related symptoms to specialty cancer care was 160 days (IQR 59-653). We describe the implementation and early outcomes of training targeting primary care providers, which is a part of a multi-component implementation study in Kweneng-East district aiming to enhance timely diagnosis of cancers. METHODS: Health-care providers from all public facilities within the district were invited to participate in an 8-h intensive short-course program developed by a multidisciplinary team and adapted to the Botswana health system context. Participants' performance was assessed using a 25-multiple choice question tool, with pre- and post-assessments paired by anonymous identifier. Statistical analysis with Wilcoxon signed-rank test to compare performance at the two time points across eight sub-domains (pathophysiology, epidemiology, social context, symptoms, evaluation, treatment, documentation, follow-up). Linear regression and negative binomial modeling were used to determine change in performance. Participants' satisfaction with the program was measured on a separate survey using a 5-point Likert scale. RESULTS: 176 participants attended the training over 5 days in April 2016. Pooled linear regression controlling for test version showed an overall performance increase of 16.8% after participation (95% CI 15.2-18.4). Statistically significant improvement was observed for seven out of eight subdomains on test A and all eight subdomains on test B. Overall, 71 (40.3%) trainees achieved a score greater than 70% on the pretest, and 161 (91.5%) did so on the posttest. Participants reported a high degree of satisfaction with the training program's content and its relevance to their daily work. CONCLUSION: We describe a successfully implemented primary health care provider-focused training component of an innovative intervention aiming to reduce health systems delays in cancer diagnosis in sub-Saharan Africa. The training achieved district-wide participation, and improvement in the knowledge of primary health-care providers in this setting. CLINICAL TRIAL REGISTRATION: www.ClinicalTrials.gov, identifier NCT02752061.