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Impact of Essential Medicine Stock Outs on Cancer Therapy Delivery in a Resource-Limited Setting

Impact of Essential Medicine Stock Outs on Cancer Therapy Delivery in a Resource-Limited Setting original report abstract Impact of Essential Medicine Stock Outs on Cancer Therapy Delivery in a Resource-Limited Setting 1 1,2 1 2 3 Yehoda M. Martei, MD, MSCE ; Surbhi Grover, MD, MPH ; Warren B. Bilker, PhD ; Barati Monare, RNM, MPH ; Dipho I. Setlhako, MD ; 3 3 1 1 Tlotlo B. Ralefala, MD ; Patrick Manshimba, MD ; Robert Gross, MD, MSCE ; Lawrence N. Shulman, MD ;and Angela DeMichele, MD, MSCE PURPOSE Essential cancer medicine stock outs are occurring at an increasing frequency worldwide and represent a potential barrier to delivery of standard therapy in patients with cancer in low- and middle-income countries. The objective of this study was to measure the impact of cancer medicine stock outs on delivery of optimal therapy in Botswana. METHODS We conducted a retrospective analysis of patients with common solid tumor malignancies who received systemic cancer therapy in 2016 at Princess Marina Hospital, Gaborone, Botswana. Primary exposure was the duration of cancer medicine stock out during a treatment cycle interval, when the cancer therapy was intended to be administered. Mixed-effects univariable and multivariable logistic regression analyses were used to calculate the association of the primary exposure, with the primary outcome, suboptimal therapy delivery, defined as any dose reduction, dose delay, missed cycle, or switch in intended therapy. RESULTS A total of 378 patients met diagnostic criteria and received systemic chemotherapy in 2016. Of these, 76% received standard regimens consisting of 1,452 cycle intervals and were included in this analysis. Paclitaxel stock out affected the highest proportion of patients. In multivariable mixed-effects logistic regression, each week of any medicine stock out (odds ratio, 1.9; 95% CI, 1.7 to 2.13; P , .001) was independently associated with an increased risk of a suboptimal therapy delivery event. CONCLUSION Each week of cancer therapy stock out poses a substantial barrier to receipt of high-quality cancer therapy in low- and middle-income countries. A concerted effort between policymakers and cancer specialists is needed to design implementation strategies to build sustainable systems promoting a reliable supply of cancer medicines. J Global Oncol. © 2019 by American Society of Clinical Oncology Creative Commons Attribution Non-Commercial No Derivatives 4.0 License 8,12 INTRODUCTION stock outs. Previous studies have reported ex- tended stock-out duration for essential medicines Recent updates to the WHO Essential Medicines List ranging from a mean of 1 month for cancer medicines, (EML) have included an expansion of essential cancer 6 months for antipneumonia and antimalarial therapy, medicines in an effort to increase access to can- and up to 76 days for combination antiretroviral cer medicines, especially in low- and middle-income 8,10,13 therapies (ARTs). Among HIV-infected patients ASSOCIATED countries (LMICs). Despite these efforts, essential studied in Cote d’Ivoire, ART stock outs that resulted in CONTENT cancer medicine stock outs are occurring at a high treatment discontinuation were independently asso- Appendix frequency worldwide and represent a complex global ciated with a significantly higher risk of interruption in Author affiliations 2-8 issue. The Millennium Development Goal Gap Task and support care or death ; in a small cross-sectional study in Force report in 2015 called attention to low access to information (if Nigeria, they were associated with significantly higher applicable) appear at essential health products in LMICs, with on average 14 rates of drug resistance mutations. Furthermore, the end of this 58.1% of generic medicines available in public sector drug stock outs also resulted in an increase in the article. and 66.6% available in private sector facilities. financial burden of care in a recent study in Tanzania Accepted on February showing that stock outs resulted in a 21% increase in 19, 2019 and Research in sub-Saharan Africa (SSA) has identified published at the cost of care for malaria when compared with weak infrastructure along the supply chain, includ- ascopubs.org/journal/ periods without stock outs. ing procurement and distribution, inadequate jgo on April 10, 2019: 10,11 drug supply and lack of trained personnel, and In Botswana and other LMICs where cancer incidence DOI https://doi.org/10. 1200/JGO.18.00230 inaccurate demand forecasting as mechanisms for and mortality are increasing, chemotherapy stock 1 Martei et al outs potentially present a significant barrier to standard obtained from drug stock and availability data prospectively therapy delivery, leading to adverse disease outcomes reported by the Central Medical Stores (CMS) in Botswana, (including cancer recurrence and survival), thereby im- a semiautonomous agency responsible for tendering, peding efforts to address the global cancer epidemic. Al- procurement, and distribution of all medicines in the public though stock outs of essential medicines for cancer are sector. Stock-out duration was calculated by counting the prevalent in SSA, the impact of these stock outs on the days from the date the drug was out of stock to the date it adequacy of therapy delivery and subsequent disease was recorded as being back in stock. Complete chemo- outcomes in patients with cancer in the region has not been therapy administration data, including dates and doses studied. A prior study showed that more than 80% of the administered, were obtained from patient records and drugs included in the proposed 2015 WHO EML for cancer a pharmacy log book with data on all chemotherapy ad- were also included in the Botswana national EML, and 40% ministered in the hospital in 2016. The exposure window of these drugs were out of stock for a median of 30 days. was calculated as the duration of chemotherapy stock out The objective of this study was to assess the impact of within a given chemotherapy cycle interval. We calculated cancer medicine stock outs on delivery of optimal therapy the duration of a stock out by generating a code for six for patients with cancer in Botswana. different permutations of possible patterns of chemother- apy stock out in association with a given cycle (Fig 2). METHODS Pattern 1 represents a stock out occurring after cycle 1 (C1) Study Design and Population and before C2. Patterns 2 and 4, occurring before C1, were We conducted a retrospective cohort study of patients not captured as part of our exposure, because we were diagnosed with any of the 10 most commonly diagnosed unable to determine whether therapy was initiated on time and treatable solid tumor malignancies in Botswana: cer- or delayed; therefore, our analysis was limited to the ex- vical, breast, prostate, esophageal, lung, uterine, ovarian, posure window once therapy was initiated. Patterns 3 and 6 colorectal, and head and neck cancers and Kaposi sar- represent periods where CMS drug stock outs are reported; coma. Patients were included in the study if they were age however, the local pharmacy may still have some supply in 18 years or older and had been diagnosed with any of these stock, and therefore, patient care may not be affected solid malignancies, regardless of the date of diagnosis, and despite the central stock outs. Pattern 5 was not included in received at least one dose of systemic chemotherapy from our exposure for C2, day 1 (C2D1), because it occurs after January 1, 2016, to December 31, 2016, at the Princess C2D1 has been administered. Delineating these patterns Marina Hospital (PMH) in Gaborone, Botswana. This site was designed to ensure that the duration of stock out was was selected because it is the largest cancer care provider calculated specific to a cycle interval to more accurately in the country. Institutional review and ethics boards at the assess association between the duration of stock out and University of Pennsylvania and Botswana Ministry of Health the therapy delivery event within that cycle. The codes were approved this study. executed in STATA software (STATA, College Station, TX) to generate the number of days of cancer medicine stock out Measures and Definitions per given cycle interval (Fig 2). If more than one medicine The measured exposure was chemotherapy stock out, stock out occurred during a cycle, the greater number of quantified as the duration of chemotherapy stock out within stock-out days was assigned as the exposure. a cycle interval (Figs 1 and 2). Dates of stock outs were The primary outcome, suboptimal therapy delivery, was defined as any of the following events: any dose reduction, Diagnosis at least 1-week delay in receipt of therapy, any missed dose, Initiation of and any switch in intended therapy. We used the National therapy Comprehensive Cancer Network Clinical Practice Guide- lines in Oncology and the WHO supplemental guidelines to Covariates 17,18 Age define standard chemotherapy regimens. Sex HIV status We assessed several covariates, including patient de- Disease Stage mographics (age, sex), medical comorbidities (HIV, di- Risk of febrile neutropenia Exposure Outcome Intent of therapy abetes, hypertension, tuberculosis, cardiac disease), and Chemotherapy Suboptimal cancer characteristics (primary diagnosis, stage at di- stock out therapy delivery agnosis, molecular phenotype [estrogen receptor, pro- gesterone receptor, and human epidermal growth factor Disease-free survival receptor 2 status for patients with breast cancer]), which Overall survival were extracted from patient paper and electronic medical records where available, and indication for current therapy FIG 1. Study schematic highlighting exposure, measured covariates, (adjuvant v metastatic setting). Neutropenia, a serious and suboptimal therapy delivery, along the treatment pathway from diagnosis to survival outcomes. complication of systemic chemotherapy that can lead to 2 © 2019 by American Society of Clinical Oncology Impact of Drug Stock Out on Cancer Therapy Delivery in Botswana Cycle interval C1D1 C2D1 Date (treatment out of stock) Date (treatment in stock) 2 3 4 5 Stock-out duration FIG 2. Suboptimal therapy delivery and stock-out metrics. C1D1 represents cycle 1 day 1, and C2D2 represents cycle 2 day 2 of a given regimen. C1D1 to C2D1 represents the cycle interval between cycles 1 and 2. The numbered scenarios represent different ways in which stock out can occur during a given cycle: (1) midinterval stock out; (2) stock out occurring before and extending during the cycle interval; (3) stock out occurring during the cycle and extending post cycle interval; (4) stock out occurring prior to the cycle; (5) stock out occurring after the cycle; and (6) stock out affecting the entire duration of the cycle interval. clinically indicated delays in treatment, was not routinely 2016, and December 31, 2016. Of these, 286 (76%) were documented in the patient records. Therefore, we imple- administered therapy on a standard regimen consisting of mented a surrogate measure of this potential confounding 1,452 cycle intervals and were included in our receipt of factor using the National Comprehensive Cancer Network optimal therapy analysis (Table 1; Appendix Table A1). The Clinical Practice Guidelines in Oncology to identify regi- median age at diagnosis for our sample was 51.8 years. mens with high risk for febrile neutropenia (. 20%). More than 70% of our patients were younger than 65 years. Additionally, almost half of the patients included in our Statistical Analysis analysis had a diagnosis of breast cancer. A majority of the Descriptive statistics were used to summarize baseline patients with stage information had either stage III or IV characteristics and covariates for all patients. Two-sample disease. Of patients with known intent of treatment, 51% t test and analysis of variance were used to test the dif- were receiving curative regimens and 49% were receiving ference in mean stock out per cycle for the overall group noncurative regimens. Of those who had information re- and stratified by the selected covariates. Mixed-effects garding HIV status (57% of patients in our analysis), 51% logistic regression was used to analyze the association were HIV positive. The patient medical records had limited between duration of specific cancer medicine stock outs, data on other medical comorbid illnesses. covariates, and risk of suboptimal therapy. The covariates associated with both exposure and outcome in the uni- Cancer Medicine Stock-Out Analysis variable analyses with a P value of less than .1 were in- Thirty-nine percent of patients had no cancer therapy stock cluded in the mixed-effects multivariable logistic regression out of their cancer regimen drugs during the course of their model to adjust for possible confounding. We developed treatment (Fig 3). Capecitabine, carboplatin, cisplatin, the multivariable modeling using a forward regression docetaxel, doxorubicin, gemcitabine, fluorouracil, metho- analysis. Age was included in our regression analysis as trexate, and trastuzumab stock outs affected patients re- a dichotomous variable using the age groups of younger ceiving therapy in 2016 (Fig 3). Paclitaxel was out of stock than 65 years and 65 years or older, because age 65 years during the treatment cycle for 37% of patients for whom it or older has been listed in prior studies as a predictor of low was prescribed. Of all patients who experienced a medicine dose-intensity cancer therapy. The regimens were coded stock out during treatment, 64% had a diagnosis of breast based on whether they were associated with a high risk for cancer. Of adjuvant chemotherapy cycle intervals that febrile neutropenia and included as a covariate in our resulted in a suboptimal therapy delivery event, 42% oc- analysis. curred when there was a cancer medicine stock out, RESULTS compared with 41% in the metastatic setting. The median We identified 378 patients who met diagnostic and age and mean durations of stock out per treatment cycle in- criteria for our study and who had received at least one dose terval for cancer medicines that were out of stock were 16 of systemic chemotherapy at PMH between January 1, and 18 days, respectively (standard deviation, 13 days), Journal of Global Oncology 3 Martei et al TABLE 1. Baseline Demographic and Clinical Characteristics of All Patients Included in the Study (N = 286) No. (%) Any Suboptimal Therapy Event Characteristic No Yes Total Age, years Mean 52.8 51.5 52 SD 14 , 65 68 (31) 149 (69) 217 (76) ≥ 65 22 (36) 39 (64) 61 (21) Unknown 2 (25) 6 (75) 8 (3) Sex Male 25 (32) 52 (68) 77 (27) Female 51 (28) 129 (72) 180 (63) Unknown 16 (55) 13 (45) 29 (10) Cancer diagnosis Breast 37 (27) 102 (73) 139 (49) Kaposi sarcoma 24 (41) 35 (59) 59 (21) Cervical 3 (43) 4 (57) 7 (2) Colon 4 (20) 16 (80) 20 (7) Prostate 5 (42) 7 (58) 12 (4) Rectal 0 (0) 5 (100) 5 (2) Anal 0 (0) 3 (100) 3 (1) Esophageal 1 (100) 0 (0) 1 (0.4) Head and neck 6 (55) 5 (45) 11 (4) Nasopharyngeal 4 (67) 2 (33) 6 (2) Ovarian 2 (18) 9 (88) 11 (4) Uterine 3 (60) 2 (40) 5 (2) Lung 3 (43) 4 (57) 7 (2) Cancer stage (TNM) I 0 (0) 1 (100) 1 (0.4) II 6 (23) 20 (77) 26 (9) III 11 (18) 49 (72) 60 (21) IV 19 (35) 35 (65) 54 (19) Unknown 56 (39) 89 (61) 145 (51) Intent of treatment Noncurative 39 (39) 62 (61) 101 (35) Curative 19 (18) 86 (82) 105 (37) Unknown 34 (43) 46 (57) 80 (28.1) HIV status Positive 26 (33) 54 (67) 80 (28) Negative 19 (24) 61 (76) 80 (28) Unknown 47 (37) 79 (63) 126 (44) Abbreviation: SD, standard deviation. with a range of 1 to 122 days. In stratified analyses of stock- setting, and those receiving non–high-risk febrile neu- out duration by measured covariates, women, older pa- tropenia regimens were affected by medicines that had tients, those receiving regimens used in the metastatic significantly longer durations of stock out (Table 2). 4 © 2019 by American Society of Clinical Oncology Impact of Drug Stock Out on Cancer Therapy Delivery in Botswana Type of Cancer No stock out Doxorubicin Paclitaxel Trastuzumab Carboplatin Gemcitabine Cisplatin Methotrexate Fluorouracil Capecitabine Docetaxel FIG 3. Proportions of patients with cancer affected by specific cancer medicine stock outs. Impact on Therapy Delivery stock outs. We found that a majority of patients experienced a cancer medicine stock out during their therapy, and each In unadjusted analyses, each week of stock out (odds ratio week of chemotherapy stock out conferred a 1.9-fold in- [OR], 1.81; 95% CI, 1.62 to 2.02) was strongly associated creased risk of experiencing suboptimal cancer treatment. with a suboptimal therapy delivery event. Additionally, risk Adjusting for risk of febrile neutropenia and type of cancer of febrile neutropenia and cancer type were associated with had no impact on the strong risk of experiencing a sub- a suboptimal therapy event at P , .1 and were included in optimal treatment event. our adjusted analysis. In contrast, there was no significant association between age, sex, stage, HIV status, intent of Similar findings from SSA among HIV patients have iden- therapy, and suboptimal therapy delivery event. tified ART stock outs as a barrier to initiation of therapy and retention in care among patients treated in Tanzania and As summarized in Table 3, after adjustment for covariates, Cote d’Ivoire. In cancer treatment as well, given that most stock-out duration remained independently associated with cancer regimens are dosed every 2, 3, or 4 weeks, in- a higher risk of a suboptimal therapy delivery event during terruptions in cancer medicine supplies leading to erratic the course of prescribed treatment. Every week of stock-out stock outs lead to significant gaps in adequate therapy duration was associated with an almost two-fold increased delivery, as demonstrated by the results of our study. risk of a suboptimal therapy delivery event (OR, 1.9; 95% Outside the context of LMICs, other studies based on CI, 1.7 to 2.13; P , .001). Our model also suggested that provider perspectives in the United States have reported patients receiving treatment regimens for colon (OR, 6.34; that cancer drug shortages resulted in delays in chemo- 95% CI, 3.11 to 12.9; P , .001) or rectal cancer (OR, 7.07; therapy administration or changes in regimens for patients 95% CI, 1.83 to 27.3; P = .004) were at the highest risk of seen in their respective institutions and also affected an event after adjusting for stock out, whereas those with the conduct of clinical trials at 44% of the institutions prostate cancer were less likely than their counterparts to surveyed. experience a suboptimal therapy delivery event (adjusted OR, 0.24; 95% CI, 0.08 to 0.79; P = .019; Table 3). Paclitaxel stock outs adversely affected therapy delivery for a significant proportion of patients with cancer, most of DISCUSSION whom had breast cancer. Paclitaxel stock outs also confer Our study identified a high rate of cancer medicine stock potentially high financial costs to the health care system. A outs affecting standard regimens for commonly treated study from a New York City university hospital comparing cancers in Botswana. Those with breast cancer were the periods of low drug shortages in 2010 with high drug highest proportion of patients affected by cancer medicine shortages in 2011 showed a 69% significant decrease in Journal of Global Oncology 5 Breast Cervical Kaposi sarcoma Prostate Esophageal Colon Rectal Anal Ovarian Uterine Nasopharyngeal Head and neck Lung Total Patients With Cancer (%) Martei et al TABLE 2. Association Between Baseline Characteristics and Mean Duration of Stock-Out Days per Cycle No. of Cycle Intervals Mean Stock-Out Days Characteristic (N = 1,452) per Cycle Interval (95% CI) P Sex .0017 Male 376 3.5 (2.6 to 4.5) Female 983 5.6 (4.9 to 6.3) Age, years .0118 , 65 1127 4.6 (4 to 5.23) ≥ 65 296 6.4 (5.1 to 7.7) Cancer diagnosis, No. (%) , .001 Breast 821 5.41 (10) Kaposi sarcoma 294 2.8 (8.8) Cervical 20 10.8 (15) Colon 105 1.9 (9.1) Prostate 60 5.7 (10.21) Rectal 28 5.7 (11.7) Anal 9 5.3 (16) Esophageal 5 8 (12.0) Head and neck 19 10.8 (12.6) Nasopharyngeal 22 10 (12.9) Ovarian 46 6.3 (19.9) Uterine 6 17 (13.8) Lung 21 8.3 (13.4) HIV status .393 Positive 431 4.4 (3.5 to 5.4) Negative 472 5 (4 to 6.1) Cancer stage (TNM), No. (%) , .001 I 7 1.14 (3) II 163 4.6 (10) III 386 5.96 (10.8) IV 271 6 (11.2) Intent of treatment .08 Noncurative 484 4.3 (3.3 to 5.3) Curative 623 5.4 (4.6 to 6.2) Neutropenic fever* , .001 Not high risk 1218 5.6 (5 to 6.2) High risk 234 1.7 (0.9 to 2.5) *High risk of febrile neutropenia, . 20%. paclitaxel use and an 80% increase in docetaxel use required for some colon cancer regimens, compared with resulting from stock outs (P = .009 and .024, respectively), patients with prostate cancer receiving androgen-deprivation which resulted in an estimated 1,704% increase in cost, therapy, who receive doses every 1 or 3 months depending from $47.49 for paclitaxel to $858.39 for docetaxel sub- on the luteinizing hormone-releasing hormone agonist pre- stitution per patient for a complete regimen. scribed. However, not many patients in these groups were included in our analysis (7%, 2%, and 4% for colon, rectal, Regimens for colon and rectal cancers were independently and prostate cancers, respectively), limiting our ability to draw associated with an increased risk of suboptimal therapy, any firm conclusions about this association. whereas the converse was noted for regimens for prostate cancer. This association might be explained by the increased Our analysis had several limitations. First, we present data dosing frequency and once-every-two-weeks infusion visits on risk of suboptimal treatment per cycle, which does not 6 © 2019 by American Society of Clinical Oncology Impact of Drug Stock Out on Cancer Therapy Delivery in Botswana TABLE 3. Adjusted Estimates for the Association Between Stock-Out Duration and Suboptimal Therapy Delivery Event Univariable Multivariable Covariate OR 95% CI P OR 95% CI P Stock-out duration (per 1-wk increase) 1.81 1.62 2.02 , .001 1.9 1.7 2.13 , .001 Cancer diagnosis Breast 1 1 Kaposi sarcoma 0.82 0.52 1.3 .397 1.17 0.71 1.95 .535 Cervical 1.2 0.32 4.41 .8 0.77 0.17 3.4 .735 Colon 3.5 1.82 6.81 , .001 6.34 3.11 12.9 , .001 Prostate 0.29 0.09 0.9 .024 0.24 0.08 0.79 .019 Rectal 4.75 1.32 17.05 .024 7.07 1.83 27.3 .004 Anal 4.06 0.65 25.2 .133 0.78 39.8 .087 Esophageal* 5.58 Head and neck 1.9 0.56 6.33 .311 1.23 0.32 4.73 .758 Nasopharyngeal 0.69 0.16 2.86 .605 0.49 0.1 2.35 .374 Ovarian 1.15 0.44 3.02 .774 1.38 0.49 3.93 .542 Uterine 2.9 0.41 21.16 .287 2.32 0.21 25.8 .494 Lung 0.7 0.17 2.85 .616 0.45 0.09 2.18 .32 Myelosuppression Not high risk 1 High risk 0.7 0.48 1.05 .084 1.36 0.88 2.1 .170 Age, years ≥ 65 1 , 65 1.4 0.87 2.24 .166 Sex Female 1 Male 1 0.66 1.53 .974 Stage I1 II 1.73 0.07 42.6 .736 III 2.77 0.12 65.22 .529 IV 2.53 0.11 60.4 .567 Intent of treatment Noncurative 1 Curative 1.21 0.82 1.79 .335 HIV status Negative 1 Positive 1.1 0.7 1.75 .674 Abbreviation: OR, odds ratio. *Insufficient data. convey the overall clinical impact per patient. However, our Second, the data on chemotherapy stock outs are based on study builds upon previous work highlighting that these stockdataavailable at thecountry’s CMS. If a drug reported out events lead to reduced relative dose-intensity and worse of stock at CMS were still available at PMH pharmacy, we would 20,25-28 survival outcomes. Our results highlight that patients have misclassified the interval as having a stock-out exposure. with breast cancer comprise a majority of patients receiving In general, this misclassification bias would result in bias toward systemic therapy at PMH and the highest proportion of the null, which means the actual risk for suboptimal treatment patients affected by stock outs. may have been even greater than we found in our analysis. Journal of Global Oncology 7 Martei et al Third, the population of patients studied only reflects those interval. These data were also critical in estimating that receiving systemic chemotherapy at PMH. Therefore, al- 1-week duration of cancer medicine stock out may be though cervical cancer is the most common cancer di- deemed clinically meaningful and is correlated with a sig- agnosed among women in Botswana, these patients are nificantly high risk of inadequate therapy delivery. underrepresented in our study, because a majority of these The findings reported in our study are likely generalizable to patients are referred to a private facility for concurrent other countries in SSA where breast cancer is either the chemoradiotherapy. Therefore, our data do not fully rep- most common or second most common cancer diagnosed resent the impact of stock outs for patients diagnosed with among women and represents a vulnerable population cervical cancer in Botswana. Furthermore, our data are most affected by stock outs. Specifically, our study may be reflective only of patients who engage in therapy and generalizable to other countries where cancer medicines therefore represent only a proportion of the disease prev- are on respective national EMLs and provided free of alence of these cancers. charge to patients through the public sector but frequently Finally, our analysis is limited by the inability to sufficiently experience stock outs. In settings where the cost of cancer adjust for certain covariates, such as sex, HIV status, and treatment is out of pocket, cost may be a more significant intent of therapy, because of the collinearity between fe- barrier to care, or the effect may be multiplicative. For male sex and breast and cervical cancers and male sex and instance, studies have shown that stock outs not only pose prostate cancer; this is similar for palliative intent and HIV- a barrier to care but also subsequently increase the cost of positive status and Kaposi sarcoma, because all treatment care for patients seeking malaria treatment. regimens for Kaposi sarcoma are palliative. To address the Our analysis of the impact of essential cancer medicine issue of multicollinearity among covariates, only one of stock outs in an understudied population provides critical these variables was included in our adjusted analysis. For data and an essential framework for evaluating barriers to instance, sex and HIV status were not included in our receipt of timely and high-quality cancer treatment, es- multivariable analysis but were included the primary cancer pecially in LMICs, where current efforts to scale up access diagnoses. As a result of multicollinearity, we could po- to cancer medicines are under way. Our results show that tentially have missed additional independent predictors of stock outs undermine investments in health for cancer suboptimal therapy delivery, such as HIV status, in our treatment and have potentially adverse effects on clinical analysis. outcomes. An analysis of how cancer medicine stock outs Our study has several strengths. Although some studies affect survival outcomes will be performed in future studies. have highlighted the magnitude of the essential medicine Our research adds to the current literature on the magni- shortages in developing countries, most of these have tude and impact of cancer medicine stock outs in LMICs been conducted among patients with communicable dis- and raises awareness of similar challenges faced in the eases. Additionally, prior studies on stock outs and cancer continuum of care for patients with cancer. Current systems therapy delivery globally have been survey-based per- and innovative interventions in LMICs that have shown spectives of medical providers and oncologists in de- success in minimizing stock outs in other disease areas veloped countries and have not quantified individual should be scaled up to address stock outs of cancer patient risk per treatment cycle when there is a specific medicines. However, given the complexity of chemother- cancer medicine stock out. In contrast, our analysis con- apy ordering for different cancer types, a concerted effort sidered different types of stock out that may or may not have between policymakers and cancer specialists is needed to affected therapy delivery by analyzing the specific pattern design implementation strategies to build sustainable of cancer medicine stock out in relation to a given cycle systems promoting a reliable supply of cancer medicines. AUTHOR CONTRIBUTIONS AFFILIATIONS Conception and design: Yehoda M. Martei, Surbhi Grover, Warren B. University of Pennsylvania, Philadelphia, PA Bilker, Barati Monare, Robert Gross, Lawrence N. Shulman, Angela Botswana University of Pennsylvania Partnership, Gaborone, Botswana DeMichele Princess Marina Hospital, Gaborone, Botswana Financial support: Lawrence N. Shulman Administrative support: Surbhi Grover, Barati Monare, Lawrence N. CORRESPONDING AUTHOR Shulman Yehoda M. Martei, MD, MSCE, Perelman Center for Advanced Medicine, Provision of study material or patients: Surbhi Grover, Barati Monare, Dipho 3400 Civic Center Blvd, SPE Suite 12-160, Philadelphia, PA 19104; I. Setlhako e-mail: yehoda.martei@uphs.upenn.edu. Collection and assembly of data: Yehoda M. Martei, Surbhi Grover, Barati Monare, Dipho I. Setlhako, Patrick Manshimba, Angela DeMichele EQUAL CONTRIBUTION Data analysis and interpretation: Yehoda M. Martei, Warren B. Bilker, L.N.S. and A.D. contributed equally to this work. Tlotlo B. Ralefala, Robert Gross, Lawrence N. Shulman, Angela DeMichele 8 © 2019 by American Society of Clinical Oncology Impact of Drug Stock Out on Cancer Therapy Delivery in Botswana Manuscript writing: All authors Tlotlo B. Ralefala Final approval of manuscript: All authors Travel, Accommodations, Expenses: Roche Accountable for all aspects of the work: All authors Robert Gross Consulting or Advisory Role: Pfizer AUTHORS' DISCLOSURES OF POTENTIAL CONFLICTS OF INTEREST Angela DeMichele Impact of Essential Medicine Stock Outs on Cancer Therapy Delivery in Honoraria: Pfizer a Resource-Limited Setting Consulting or Advisory Role: Calithera Biosciences, Novartis, Context The following represents disclosure information provided by authors of Therapeutics, Pfizer (I) this manuscript. All relationships are considered compensated. Research Funding: Pfizer (Inst), Genentech (Inst), Incyte (Inst), Relationships are self-held unless noted. I = Immediate Family Member, Millennium Pharmaceuticals (Inst), Bayer HealthCare Pharmaceuticals Inst = My Institution. Relationships may not relate to the subject matter of (Inst), Veridex (Inst), Calithera Biosciences (Inst), GlaxoSmithKline this manuscript. For more information about ASCO's conflict of interest (Inst), Wyeth (Inst) policy, please refer to www.asco.org/rwc or ascopubs.org/jco/site/ifc. Travel, Accommodations, Expenses: Pfizer, Calithera Biosciences, Yehoda Martei Novartis, Pfizer Research Funding: Celgene (Inst) No other potential conflicts of interest were reported. Warren B. Bilker Consulting or Advisory Role: Genentech REFERENCES 1. Shulman LN, Wagner CM, Barr R, et al: Proposing essential medicines to treat cancer: methodologies, processes, and outcomes. J Clin Oncol 34:69-75, 2016 2. Gatesman ML, Smith TJ: The shortage of essential chemotherapy drugs in the United States. N Engl J Med 365:1653-1655, 2011 3. Gray A, Manasse HR, Jr.: Shortages of medicines: A complex global challenge. Bull World Health Organ 90:158-158A, 2012 4. Fox ER, Sweet B V., Jensen V: Drug shortages: A complex health care crisis. Mayo Clin Proc 89:361-373, 2014 5. Gehrett BK: A prescription for drug shortages. JAMA 307:153-154, 2012 6. Link MP, Hagerty K, Kantarjian HM: Chemotherapy drug shortages in the United States: Genesis and potential solutions. J Clin Oncol 30:692-694, 2012 7. Tirelli U, Berretta M, Spina M, et al: Oncologic drug shortages also in Italy. Eur Rev Med Pharmacol Sci 16:138-139, 2012 8. Martei YM, Chiyapo S, Grover S, et al: Availability of WHO essential medicines for cancer treatment in Botswana. J Glob Oncol 4:1-8, 2018 9. UN Economic Analysis & Policy Division: MDG Gap Task Force Report 2015: Taking Stock of the Global Partnership for Development. http://www.un.org/en/ development/desa/policy/mdg_gap 10. Lufesi NN, Andrew M, Aursnes I: Deficient supplies of drugs for life threatening diseases in an African community. BMC Health Serv Res 7:86, 2007 11. Mikkelsen-Lopez I, Shango W, Barrington J, et al: The challenge to avoid anti-malarial medicine stock-outs in an era of funding partners: The case of Tanzania. Malar J 13:181, 2014 12. Leung N-HZ, Chen A, Yadav P, et al: The impact of inventory management on stock-outs of essential drugs in sub-Saharan Africa: Secondary analysis ofa field experiment in Zambia. PLoS One 11:e0156026, 2016 13. Pasquet A, Messou E, Gabillard D, et al: Impact of drug stock-outs on death and retention to care among HIV-infected patients on combination antiretroviral therapy in Abidjan, Cote d’Ivoire. PLoS One 5:e13414, 2010 14. Meloni ST, Chaplin B, Idoko J, et al: Drug resistance patterns following pharmacy stock shortage in Nigerian Antiretroviral Treatment Program. AIDS Res Ther 14:58, 2017 15. Mikkelsen-Lopez I, Tediosi F, Abdallah G, et al: Beyond antimalarial stock-outs: implications of health provider compliance on out-of-pocket expenditure during care-seeking for fever in South East Tanzania. BMC Health Serv Res 13:444, 2013 16. Ferlay J, Soerjomataram I, Ervik M, et al: GLOBOCAN 2012: Estimated Cancer Incidence, Mortality, and Prevalence Worldwide in 2012 v1.0. IARC Press, Lyon, France. 2013 17. National Comprehensive Cancer Network: NCCN evidence-based cancer guidelines, oncology drug compendium, oncology continuing medical education. https://www.nccn.org 18. World Health Organization: The Selection and Use of Essential Medicines: Report of the WHO Expert Committee, 2015 (including the 19th WHO Model List of Essential Medicines and the 5th WHO Model List of Essential Medicines for Children). http://apps.who.int/medicinedocs/documents/s22190en/s22190en.pdf 19. Breslow NE, Clayton DG: Approximate inference in generalized linear mixed models. J Am Stat Assoc 88:9-25, 1993 20. Lyman GH, Dale DC, Crawford J: Incidence and predictors of low dose-intensity in adjuvant breast cancer chemotherapy: A nationwide study of community practices. J Clin Oncol 21:4524-4531, 2003 21. National Comprehensive Cancer Network: NCCN clinical practice guidelines in oncology: Myeloid growth factors. https://www.nccn.org 22. Layer EH, Kennedy CE, Beckham SW, et al: Multi-level factors affecting entry into and engagement in the HIV continuum of care in Iringa, Tanzania. PLoS One 9:e104961, 2014 23. McBride A, Holle LM, Westendorf C, et al: National survey on the effect of oncology drug shortages on cancer care. Am J Health Syst Pharm 70:609-617, 2013 24. Becker DJ, Talwar S, Levy BP, et al: Impact of oncology drug shortages on patient therapy: Unplanned treatment changes. J Oncol Pract 9:e122-e128, 2013 25. Hanna RK, Poniewierski MS, Laskey RA, et al: Predictors of reduced relative dose intensity and its relationship to mortality in women receiving multi-agent chemotherapy for epithelial ovarian cancer. Gynecol Oncol 129:74-80, 2013 26. Lyman GH: Impact of chemotherapy dose intensity on cancer patient outcomes. J Natl Compr Canc Netw 7:99-108, 2009 27. Chirivella I, Bermejo B, Insa A, et al: Optimal delivery of anthracycline-based chemotherapy in the adjuvant setting improves outcome of breast cancer patients. Breast Cancer Res Treat 114:479-484, 2009 28. Budman DR, Berry DA, Cirrincione CT, et al: Dose and dose intensity as determinants of outcome in the adjuvant treatment of breast cancer. J Natl Cancer Inst 90:1205-1211, 1998 29. Yifru S, Muluye D: Childhood cancer in Gondar University Hospital, Northwest Ethiopia. BMC Res Notes 8:474, 2015 nn n Journal of Global Oncology 9 Martei et al APPENDIX TABLE A1. Intended Chemotherapy Regimen by Diagnosis No. (%) Noncurative or Diagnosis and Intended Regimen Curative Regimen Palliative Regimen Unspecified Breast cancer (n = 155) Doxorubicin + cyclophosphamide once every 3weeks → paclitaxel 76 (49) once every 3 weeks Doxorubicin + cyclophosphamide once every 3 weeks→ paclitaxel + 12 (7.7) trastuzumab once every 3 weeks Cyclophosphamide, methotrexate, and fluorouracil once every 10 (6.5) 3 weeks Docetaxel, carboplatin, and herceptin once every 3 weeks 1 (0.7) Paclitaxel 21 (13.5) Gemcitabine + cisplatin 3 (2) Gemcitabine + paclitaxel 1 (0.6) Gemcitabine + carboplatin 1 (0.6) Carboplatin + paclitaxel 5 (3.2) Trastuzumab 7 (4.5) 10 (6.5) Trastuzumab + paclitaxel 1 (0.6) 2 (1.3) Docetaxel 3 (2) Gemcitabine 1 (0.6) Kaposi sarcoma (n = 89) Doxorubicin, bleomycin, and vincristine 57 (64) Bleomycin + vincristine 15 (17) Paclitaxel 12 (13.5) Etoposide 3 (3) Indeterminate 2 (2) Cervical cancer (n = 12) Carboplatin + paclitaxel 5 (41.7) Paclitaxel 2 (16.7) Gemcitabine + cisplatin 2 (16.7) Cisplatin + paclitaxel 2 (16.7) Fluorouracil + carboplatin 1 (8.3) Prostate cancer (n = 13) Androgen-deprivation therapy 6 (46) Docetaxel 7 (53.9) Colon cancer (n = 24) FOLFOX 1 (4.2) Capecitabine + oxaliplatin 6 (25) 13 (54) Capecitabine 1 (4.2) Fluorouracil 2 (8.3) Rectal cancer (n = 7) Capecitabine + oxaliplatin 7 (100) (Continued on following page) 10 © 2019 by American Society of Clinical Oncology Impact of Drug Stock Out on Cancer Therapy Delivery in Botswana TABLE A1. Intended Chemotherapy Regimen by Diagnosis (Continued) No. (%) Noncurative or Diagnosis and Intended Regimen Curative Regimen Palliative Regimen Unspecified Anal cancer (n = 4) Carboplatin + paclitaxel 3 (75) Cisplatin + fluorouracil 1 (25) Ovarian cancer (n = 14) Carboplatin + paclitaxel 11 (78.6) Carboplatin 1 (7.1) Gemcitabine 2 (14.3) Esophageal cancer (n = 1) Carboplatin + paclitaxel 1 (100) Head and neck cancer (n = 21) Carboplatin + paclitaxel 1 (4.8) 15 (71.4) Carboplatin 1 (4.8) Cisplatin + paclitaxel 2 (9.5) Gemcitabine + paclitaxel 1 (4.8) Paclitaxel 1 (4.8) Nasopharyngeal Carboplatin + paclitaxel 1 (14.3) 1 (14.3) Cisplatin + gemcitabine 1 (14.3) Cisplatin + paclitaxel 1 (14.3) Gemcitabine + carboplatin 1 (14.3) Gemcitabine + cisplatin 1 (14.3) Gemcitabine 1 (14.3) Ovarian cancer (n = 14) Carboplatin + paclitaxel 11 (78.6) Carboplatin 1 (7.1) Gemcitabine 2 (14.3) Uterine cancer (n = 6) Doxorubicin + cisplatin 1 (16.7) Carboplatin + paclitaxel 4 (66.7) Doxorubicin 1 (16.7) Lung cancer (n = 11) Carboplatin + paclitaxel 6 (54.5) Carboplatin + etoposide 1 (9) Doxorubicin 1 (9) Gemcitabine + carboplatin 1 (9) Gemcitabine 1 (9) Paclitaxel 1 (9) Abbreviation: FOLFOX, fluorouracil, leucovorin, and oxaliplatin. 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Abstract

original report abstract Impact of Essential Medicine Stock Outs on Cancer Therapy Delivery in a Resource-Limited Setting 1 1,2 1 2 3 Yehoda M. Martei, MD, MSCE ; Surbhi Grover, MD, MPH ; Warren B. Bilker, PhD ; Barati Monare, RNM, MPH ; Dipho I. Setlhako, MD ; 3 3 1 1 Tlotlo B. Ralefala, MD ; Patrick Manshimba, MD ; Robert Gross, MD, MSCE ; Lawrence N. Shulman, MD ;and Angela DeMichele, MD, MSCE PURPOSE Essential cancer medicine stock outs are occurring at an increasing frequency worldwide and represent a potential barrier to delivery of standard therapy in patients with cancer in low- and middle-income countries. The objective of this study was to measure the impact of cancer medicine stock outs on delivery of optimal therapy in Botswana. METHODS We conducted a retrospective analysis of patients with common solid tumor malignancies who received systemic cancer therapy in 2016 at Princess Marina Hospital, Gaborone, Botswana. Primary exposure was the duration of cancer medicine stock out during a treatment cycle interval, when the cancer therapy was intended to be administered. Mixed-effects univariable and multivariable logistic regression analyses were used to calculate the association of the primary exposure, with the primary outcome, suboptimal therapy delivery, defined as any dose reduction, dose delay, missed cycle, or switch in intended therapy. RESULTS A total of 378 patients met diagnostic criteria and received systemic chemotherapy in 2016. Of these, 76% received standard regimens consisting of 1,452 cycle intervals and were included in this analysis. Paclitaxel stock out affected the highest proportion of patients. In multivariable mixed-effects logistic regression, each week of any medicine stock out (odds ratio, 1.9; 95% CI, 1.7 to 2.13; P , .001) was independently associated with an increased risk of a suboptimal therapy delivery event. CONCLUSION Each week of cancer therapy stock out poses a substantial barrier to receipt of high-quality cancer therapy in low- and middle-income countries. A concerted effort between policymakers and cancer specialists is needed to design implementation strategies to build sustainable systems promoting a reliable supply of cancer medicines. J Global Oncol. © 2019 by American Society of Clinical Oncology Creative Commons Attribution Non-Commercial No Derivatives 4.0 License 8,12 INTRODUCTION stock outs. Previous studies have reported ex- tended stock-out duration for essential medicines Recent updates to the WHO Essential Medicines List ranging from a mean of 1 month for cancer medicines, (EML) have included an expansion of essential cancer 6 months for antipneumonia and antimalarial therapy, medicines in an effort to increase access to can- and up to 76 days for combination antiretroviral cer medicines, especially in low- and middle-income 8,10,13 therapies (ARTs). Among HIV-infected patients ASSOCIATED countries (LMICs). Despite these efforts, essential studied in Cote d’Ivoire, ART stock outs that resulted in CONTENT cancer medicine stock outs are occurring at a high treatment discontinuation were independently asso- Appendix frequency worldwide and represent a complex global ciated with a significantly higher risk of interruption in Author affiliations 2-8 issue. The Millennium Development Goal Gap Task and support care or death ; in a small cross-sectional study in Force report in 2015 called attention to low access to information (if Nigeria, they were associated with significantly higher applicable) appear at essential health products in LMICs, with on average 14 rates of drug resistance mutations. Furthermore, the end of this 58.1% of generic medicines available in public sector drug stock outs also resulted in an increase in the article. and 66.6% available in private sector facilities. financial burden of care in a recent study in Tanzania Accepted on February showing that stock outs resulted in a 21% increase in 19, 2019 and Research in sub-Saharan Africa (SSA) has identified published at the cost of care for malaria when compared with weak infrastructure along the supply chain, includ- ascopubs.org/journal/ periods without stock outs. ing procurement and distribution, inadequate jgo on April 10, 2019: 10,11 drug supply and lack of trained personnel, and In Botswana and other LMICs where cancer incidence DOI https://doi.org/10. 1200/JGO.18.00230 inaccurate demand forecasting as mechanisms for and mortality are increasing, chemotherapy stock 1 Martei et al outs potentially present a significant barrier to standard obtained from drug stock and availability data prospectively therapy delivery, leading to adverse disease outcomes reported by the Central Medical Stores (CMS) in Botswana, (including cancer recurrence and survival), thereby im- a semiautonomous agency responsible for tendering, peding efforts to address the global cancer epidemic. Al- procurement, and distribution of all medicines in the public though stock outs of essential medicines for cancer are sector. Stock-out duration was calculated by counting the prevalent in SSA, the impact of these stock outs on the days from the date the drug was out of stock to the date it adequacy of therapy delivery and subsequent disease was recorded as being back in stock. Complete chemo- outcomes in patients with cancer in the region has not been therapy administration data, including dates and doses studied. A prior study showed that more than 80% of the administered, were obtained from patient records and drugs included in the proposed 2015 WHO EML for cancer a pharmacy log book with data on all chemotherapy ad- were also included in the Botswana national EML, and 40% ministered in the hospital in 2016. The exposure window of these drugs were out of stock for a median of 30 days. was calculated as the duration of chemotherapy stock out The objective of this study was to assess the impact of within a given chemotherapy cycle interval. We calculated cancer medicine stock outs on delivery of optimal therapy the duration of a stock out by generating a code for six for patients with cancer in Botswana. different permutations of possible patterns of chemother- apy stock out in association with a given cycle (Fig 2). METHODS Pattern 1 represents a stock out occurring after cycle 1 (C1) Study Design and Population and before C2. Patterns 2 and 4, occurring before C1, were We conducted a retrospective cohort study of patients not captured as part of our exposure, because we were diagnosed with any of the 10 most commonly diagnosed unable to determine whether therapy was initiated on time and treatable solid tumor malignancies in Botswana: cer- or delayed; therefore, our analysis was limited to the ex- vical, breast, prostate, esophageal, lung, uterine, ovarian, posure window once therapy was initiated. Patterns 3 and 6 colorectal, and head and neck cancers and Kaposi sar- represent periods where CMS drug stock outs are reported; coma. Patients were included in the study if they were age however, the local pharmacy may still have some supply in 18 years or older and had been diagnosed with any of these stock, and therefore, patient care may not be affected solid malignancies, regardless of the date of diagnosis, and despite the central stock outs. Pattern 5 was not included in received at least one dose of systemic chemotherapy from our exposure for C2, day 1 (C2D1), because it occurs after January 1, 2016, to December 31, 2016, at the Princess C2D1 has been administered. Delineating these patterns Marina Hospital (PMH) in Gaborone, Botswana. This site was designed to ensure that the duration of stock out was was selected because it is the largest cancer care provider calculated specific to a cycle interval to more accurately in the country. Institutional review and ethics boards at the assess association between the duration of stock out and University of Pennsylvania and Botswana Ministry of Health the therapy delivery event within that cycle. The codes were approved this study. executed in STATA software (STATA, College Station, TX) to generate the number of days of cancer medicine stock out Measures and Definitions per given cycle interval (Fig 2). If more than one medicine The measured exposure was chemotherapy stock out, stock out occurred during a cycle, the greater number of quantified as the duration of chemotherapy stock out within stock-out days was assigned as the exposure. a cycle interval (Figs 1 and 2). Dates of stock outs were The primary outcome, suboptimal therapy delivery, was defined as any of the following events: any dose reduction, Diagnosis at least 1-week delay in receipt of therapy, any missed dose, Initiation of and any switch in intended therapy. We used the National therapy Comprehensive Cancer Network Clinical Practice Guide- lines in Oncology and the WHO supplemental guidelines to Covariates 17,18 Age define standard chemotherapy regimens. Sex HIV status We assessed several covariates, including patient de- Disease Stage mographics (age, sex), medical comorbidities (HIV, di- Risk of febrile neutropenia Exposure Outcome Intent of therapy abetes, hypertension, tuberculosis, cardiac disease), and Chemotherapy Suboptimal cancer characteristics (primary diagnosis, stage at di- stock out therapy delivery agnosis, molecular phenotype [estrogen receptor, pro- gesterone receptor, and human epidermal growth factor Disease-free survival receptor 2 status for patients with breast cancer]), which Overall survival were extracted from patient paper and electronic medical records where available, and indication for current therapy FIG 1. Study schematic highlighting exposure, measured covariates, (adjuvant v metastatic setting). Neutropenia, a serious and suboptimal therapy delivery, along the treatment pathway from diagnosis to survival outcomes. complication of systemic chemotherapy that can lead to 2 © 2019 by American Society of Clinical Oncology Impact of Drug Stock Out on Cancer Therapy Delivery in Botswana Cycle interval C1D1 C2D1 Date (treatment out of stock) Date (treatment in stock) 2 3 4 5 Stock-out duration FIG 2. Suboptimal therapy delivery and stock-out metrics. C1D1 represents cycle 1 day 1, and C2D2 represents cycle 2 day 2 of a given regimen. C1D1 to C2D1 represents the cycle interval between cycles 1 and 2. The numbered scenarios represent different ways in which stock out can occur during a given cycle: (1) midinterval stock out; (2) stock out occurring before and extending during the cycle interval; (3) stock out occurring during the cycle and extending post cycle interval; (4) stock out occurring prior to the cycle; (5) stock out occurring after the cycle; and (6) stock out affecting the entire duration of the cycle interval. clinically indicated delays in treatment, was not routinely 2016, and December 31, 2016. Of these, 286 (76%) were documented in the patient records. Therefore, we imple- administered therapy on a standard regimen consisting of mented a surrogate measure of this potential confounding 1,452 cycle intervals and were included in our receipt of factor using the National Comprehensive Cancer Network optimal therapy analysis (Table 1; Appendix Table A1). The Clinical Practice Guidelines in Oncology to identify regi- median age at diagnosis for our sample was 51.8 years. mens with high risk for febrile neutropenia (. 20%). More than 70% of our patients were younger than 65 years. Additionally, almost half of the patients included in our Statistical Analysis analysis had a diagnosis of breast cancer. A majority of the Descriptive statistics were used to summarize baseline patients with stage information had either stage III or IV characteristics and covariates for all patients. Two-sample disease. Of patients with known intent of treatment, 51% t test and analysis of variance were used to test the dif- were receiving curative regimens and 49% were receiving ference in mean stock out per cycle for the overall group noncurative regimens. Of those who had information re- and stratified by the selected covariates. Mixed-effects garding HIV status (57% of patients in our analysis), 51% logistic regression was used to analyze the association were HIV positive. The patient medical records had limited between duration of specific cancer medicine stock outs, data on other medical comorbid illnesses. covariates, and risk of suboptimal therapy. The covariates associated with both exposure and outcome in the uni- Cancer Medicine Stock-Out Analysis variable analyses with a P value of less than .1 were in- Thirty-nine percent of patients had no cancer therapy stock cluded in the mixed-effects multivariable logistic regression out of their cancer regimen drugs during the course of their model to adjust for possible confounding. We developed treatment (Fig 3). Capecitabine, carboplatin, cisplatin, the multivariable modeling using a forward regression docetaxel, doxorubicin, gemcitabine, fluorouracil, metho- analysis. Age was included in our regression analysis as trexate, and trastuzumab stock outs affected patients re- a dichotomous variable using the age groups of younger ceiving therapy in 2016 (Fig 3). Paclitaxel was out of stock than 65 years and 65 years or older, because age 65 years during the treatment cycle for 37% of patients for whom it or older has been listed in prior studies as a predictor of low was prescribed. Of all patients who experienced a medicine dose-intensity cancer therapy. The regimens were coded stock out during treatment, 64% had a diagnosis of breast based on whether they were associated with a high risk for cancer. Of adjuvant chemotherapy cycle intervals that febrile neutropenia and included as a covariate in our resulted in a suboptimal therapy delivery event, 42% oc- analysis. curred when there was a cancer medicine stock out, RESULTS compared with 41% in the metastatic setting. The median We identified 378 patients who met diagnostic and age and mean durations of stock out per treatment cycle in- criteria for our study and who had received at least one dose terval for cancer medicines that were out of stock were 16 of systemic chemotherapy at PMH between January 1, and 18 days, respectively (standard deviation, 13 days), Journal of Global Oncology 3 Martei et al TABLE 1. Baseline Demographic and Clinical Characteristics of All Patients Included in the Study (N = 286) No. (%) Any Suboptimal Therapy Event Characteristic No Yes Total Age, years Mean 52.8 51.5 52 SD 14 , 65 68 (31) 149 (69) 217 (76) ≥ 65 22 (36) 39 (64) 61 (21) Unknown 2 (25) 6 (75) 8 (3) Sex Male 25 (32) 52 (68) 77 (27) Female 51 (28) 129 (72) 180 (63) Unknown 16 (55) 13 (45) 29 (10) Cancer diagnosis Breast 37 (27) 102 (73) 139 (49) Kaposi sarcoma 24 (41) 35 (59) 59 (21) Cervical 3 (43) 4 (57) 7 (2) Colon 4 (20) 16 (80) 20 (7) Prostate 5 (42) 7 (58) 12 (4) Rectal 0 (0) 5 (100) 5 (2) Anal 0 (0) 3 (100) 3 (1) Esophageal 1 (100) 0 (0) 1 (0.4) Head and neck 6 (55) 5 (45) 11 (4) Nasopharyngeal 4 (67) 2 (33) 6 (2) Ovarian 2 (18) 9 (88) 11 (4) Uterine 3 (60) 2 (40) 5 (2) Lung 3 (43) 4 (57) 7 (2) Cancer stage (TNM) I 0 (0) 1 (100) 1 (0.4) II 6 (23) 20 (77) 26 (9) III 11 (18) 49 (72) 60 (21) IV 19 (35) 35 (65) 54 (19) Unknown 56 (39) 89 (61) 145 (51) Intent of treatment Noncurative 39 (39) 62 (61) 101 (35) Curative 19 (18) 86 (82) 105 (37) Unknown 34 (43) 46 (57) 80 (28.1) HIV status Positive 26 (33) 54 (67) 80 (28) Negative 19 (24) 61 (76) 80 (28) Unknown 47 (37) 79 (63) 126 (44) Abbreviation: SD, standard deviation. with a range of 1 to 122 days. In stratified analyses of stock- setting, and those receiving non–high-risk febrile neu- out duration by measured covariates, women, older pa- tropenia regimens were affected by medicines that had tients, those receiving regimens used in the metastatic significantly longer durations of stock out (Table 2). 4 © 2019 by American Society of Clinical Oncology Impact of Drug Stock Out on Cancer Therapy Delivery in Botswana Type of Cancer No stock out Doxorubicin Paclitaxel Trastuzumab Carboplatin Gemcitabine Cisplatin Methotrexate Fluorouracil Capecitabine Docetaxel FIG 3. Proportions of patients with cancer affected by specific cancer medicine stock outs. Impact on Therapy Delivery stock outs. We found that a majority of patients experienced a cancer medicine stock out during their therapy, and each In unadjusted analyses, each week of stock out (odds ratio week of chemotherapy stock out conferred a 1.9-fold in- [OR], 1.81; 95% CI, 1.62 to 2.02) was strongly associated creased risk of experiencing suboptimal cancer treatment. with a suboptimal therapy delivery event. Additionally, risk Adjusting for risk of febrile neutropenia and type of cancer of febrile neutropenia and cancer type were associated with had no impact on the strong risk of experiencing a sub- a suboptimal therapy event at P , .1 and were included in optimal treatment event. our adjusted analysis. In contrast, there was no significant association between age, sex, stage, HIV status, intent of Similar findings from SSA among HIV patients have iden- therapy, and suboptimal therapy delivery event. tified ART stock outs as a barrier to initiation of therapy and retention in care among patients treated in Tanzania and As summarized in Table 3, after adjustment for covariates, Cote d’Ivoire. In cancer treatment as well, given that most stock-out duration remained independently associated with cancer regimens are dosed every 2, 3, or 4 weeks, in- a higher risk of a suboptimal therapy delivery event during terruptions in cancer medicine supplies leading to erratic the course of prescribed treatment. Every week of stock-out stock outs lead to significant gaps in adequate therapy duration was associated with an almost two-fold increased delivery, as demonstrated by the results of our study. risk of a suboptimal therapy delivery event (OR, 1.9; 95% Outside the context of LMICs, other studies based on CI, 1.7 to 2.13; P , .001). Our model also suggested that provider perspectives in the United States have reported patients receiving treatment regimens for colon (OR, 6.34; that cancer drug shortages resulted in delays in chemo- 95% CI, 3.11 to 12.9; P , .001) or rectal cancer (OR, 7.07; therapy administration or changes in regimens for patients 95% CI, 1.83 to 27.3; P = .004) were at the highest risk of seen in their respective institutions and also affected an event after adjusting for stock out, whereas those with the conduct of clinical trials at 44% of the institutions prostate cancer were less likely than their counterparts to surveyed. experience a suboptimal therapy delivery event (adjusted OR, 0.24; 95% CI, 0.08 to 0.79; P = .019; Table 3). Paclitaxel stock outs adversely affected therapy delivery for a significant proportion of patients with cancer, most of DISCUSSION whom had breast cancer. Paclitaxel stock outs also confer Our study identified a high rate of cancer medicine stock potentially high financial costs to the health care system. A outs affecting standard regimens for commonly treated study from a New York City university hospital comparing cancers in Botswana. Those with breast cancer were the periods of low drug shortages in 2010 with high drug highest proportion of patients affected by cancer medicine shortages in 2011 showed a 69% significant decrease in Journal of Global Oncology 5 Breast Cervical Kaposi sarcoma Prostate Esophageal Colon Rectal Anal Ovarian Uterine Nasopharyngeal Head and neck Lung Total Patients With Cancer (%) Martei et al TABLE 2. Association Between Baseline Characteristics and Mean Duration of Stock-Out Days per Cycle No. of Cycle Intervals Mean Stock-Out Days Characteristic (N = 1,452) per Cycle Interval (95% CI) P Sex .0017 Male 376 3.5 (2.6 to 4.5) Female 983 5.6 (4.9 to 6.3) Age, years .0118 , 65 1127 4.6 (4 to 5.23) ≥ 65 296 6.4 (5.1 to 7.7) Cancer diagnosis, No. (%) , .001 Breast 821 5.41 (10) Kaposi sarcoma 294 2.8 (8.8) Cervical 20 10.8 (15) Colon 105 1.9 (9.1) Prostate 60 5.7 (10.21) Rectal 28 5.7 (11.7) Anal 9 5.3 (16) Esophageal 5 8 (12.0) Head and neck 19 10.8 (12.6) Nasopharyngeal 22 10 (12.9) Ovarian 46 6.3 (19.9) Uterine 6 17 (13.8) Lung 21 8.3 (13.4) HIV status .393 Positive 431 4.4 (3.5 to 5.4) Negative 472 5 (4 to 6.1) Cancer stage (TNM), No. (%) , .001 I 7 1.14 (3) II 163 4.6 (10) III 386 5.96 (10.8) IV 271 6 (11.2) Intent of treatment .08 Noncurative 484 4.3 (3.3 to 5.3) Curative 623 5.4 (4.6 to 6.2) Neutropenic fever* , .001 Not high risk 1218 5.6 (5 to 6.2) High risk 234 1.7 (0.9 to 2.5) *High risk of febrile neutropenia, . 20%. paclitaxel use and an 80% increase in docetaxel use required for some colon cancer regimens, compared with resulting from stock outs (P = .009 and .024, respectively), patients with prostate cancer receiving androgen-deprivation which resulted in an estimated 1,704% increase in cost, therapy, who receive doses every 1 or 3 months depending from $47.49 for paclitaxel to $858.39 for docetaxel sub- on the luteinizing hormone-releasing hormone agonist pre- stitution per patient for a complete regimen. scribed. However, not many patients in these groups were included in our analysis (7%, 2%, and 4% for colon, rectal, Regimens for colon and rectal cancers were independently and prostate cancers, respectively), limiting our ability to draw associated with an increased risk of suboptimal therapy, any firm conclusions about this association. whereas the converse was noted for regimens for prostate cancer. This association might be explained by the increased Our analysis had several limitations. First, we present data dosing frequency and once-every-two-weeks infusion visits on risk of suboptimal treatment per cycle, which does not 6 © 2019 by American Society of Clinical Oncology Impact of Drug Stock Out on Cancer Therapy Delivery in Botswana TABLE 3. Adjusted Estimates for the Association Between Stock-Out Duration and Suboptimal Therapy Delivery Event Univariable Multivariable Covariate OR 95% CI P OR 95% CI P Stock-out duration (per 1-wk increase) 1.81 1.62 2.02 , .001 1.9 1.7 2.13 , .001 Cancer diagnosis Breast 1 1 Kaposi sarcoma 0.82 0.52 1.3 .397 1.17 0.71 1.95 .535 Cervical 1.2 0.32 4.41 .8 0.77 0.17 3.4 .735 Colon 3.5 1.82 6.81 , .001 6.34 3.11 12.9 , .001 Prostate 0.29 0.09 0.9 .024 0.24 0.08 0.79 .019 Rectal 4.75 1.32 17.05 .024 7.07 1.83 27.3 .004 Anal 4.06 0.65 25.2 .133 0.78 39.8 .087 Esophageal* 5.58 Head and neck 1.9 0.56 6.33 .311 1.23 0.32 4.73 .758 Nasopharyngeal 0.69 0.16 2.86 .605 0.49 0.1 2.35 .374 Ovarian 1.15 0.44 3.02 .774 1.38 0.49 3.93 .542 Uterine 2.9 0.41 21.16 .287 2.32 0.21 25.8 .494 Lung 0.7 0.17 2.85 .616 0.45 0.09 2.18 .32 Myelosuppression Not high risk 1 High risk 0.7 0.48 1.05 .084 1.36 0.88 2.1 .170 Age, years ≥ 65 1 , 65 1.4 0.87 2.24 .166 Sex Female 1 Male 1 0.66 1.53 .974 Stage I1 II 1.73 0.07 42.6 .736 III 2.77 0.12 65.22 .529 IV 2.53 0.11 60.4 .567 Intent of treatment Noncurative 1 Curative 1.21 0.82 1.79 .335 HIV status Negative 1 Positive 1.1 0.7 1.75 .674 Abbreviation: OR, odds ratio. *Insufficient data. convey the overall clinical impact per patient. However, our Second, the data on chemotherapy stock outs are based on study builds upon previous work highlighting that these stockdataavailable at thecountry’s CMS. If a drug reported out events lead to reduced relative dose-intensity and worse of stock at CMS were still available at PMH pharmacy, we would 20,25-28 survival outcomes. Our results highlight that patients have misclassified the interval as having a stock-out exposure. with breast cancer comprise a majority of patients receiving In general, this misclassification bias would result in bias toward systemic therapy at PMH and the highest proportion of the null, which means the actual risk for suboptimal treatment patients affected by stock outs. may have been even greater than we found in our analysis. Journal of Global Oncology 7 Martei et al Third, the population of patients studied only reflects those interval. These data were also critical in estimating that receiving systemic chemotherapy at PMH. Therefore, al- 1-week duration of cancer medicine stock out may be though cervical cancer is the most common cancer di- deemed clinically meaningful and is correlated with a sig- agnosed among women in Botswana, these patients are nificantly high risk of inadequate therapy delivery. underrepresented in our study, because a majority of these The findings reported in our study are likely generalizable to patients are referred to a private facility for concurrent other countries in SSA where breast cancer is either the chemoradiotherapy. Therefore, our data do not fully rep- most common or second most common cancer diagnosed resent the impact of stock outs for patients diagnosed with among women and represents a vulnerable population cervical cancer in Botswana. Furthermore, our data are most affected by stock outs. Specifically, our study may be reflective only of patients who engage in therapy and generalizable to other countries where cancer medicines therefore represent only a proportion of the disease prev- are on respective national EMLs and provided free of alence of these cancers. charge to patients through the public sector but frequently Finally, our analysis is limited by the inability to sufficiently experience stock outs. In settings where the cost of cancer adjust for certain covariates, such as sex, HIV status, and treatment is out of pocket, cost may be a more significant intent of therapy, because of the collinearity between fe- barrier to care, or the effect may be multiplicative. For male sex and breast and cervical cancers and male sex and instance, studies have shown that stock outs not only pose prostate cancer; this is similar for palliative intent and HIV- a barrier to care but also subsequently increase the cost of positive status and Kaposi sarcoma, because all treatment care for patients seeking malaria treatment. regimens for Kaposi sarcoma are palliative. To address the Our analysis of the impact of essential cancer medicine issue of multicollinearity among covariates, only one of stock outs in an understudied population provides critical these variables was included in our adjusted analysis. For data and an essential framework for evaluating barriers to instance, sex and HIV status were not included in our receipt of timely and high-quality cancer treatment, es- multivariable analysis but were included the primary cancer pecially in LMICs, where current efforts to scale up access diagnoses. As a result of multicollinearity, we could po- to cancer medicines are under way. Our results show that tentially have missed additional independent predictors of stock outs undermine investments in health for cancer suboptimal therapy delivery, such as HIV status, in our treatment and have potentially adverse effects on clinical analysis. outcomes. An analysis of how cancer medicine stock outs Our study has several strengths. Although some studies affect survival outcomes will be performed in future studies. have highlighted the magnitude of the essential medicine Our research adds to the current literature on the magni- shortages in developing countries, most of these have tude and impact of cancer medicine stock outs in LMICs been conducted among patients with communicable dis- and raises awareness of similar challenges faced in the eases. Additionally, prior studies on stock outs and cancer continuum of care for patients with cancer. Current systems therapy delivery globally have been survey-based per- and innovative interventions in LMICs that have shown spectives of medical providers and oncologists in de- success in minimizing stock outs in other disease areas veloped countries and have not quantified individual should be scaled up to address stock outs of cancer patient risk per treatment cycle when there is a specific medicines. However, given the complexity of chemother- cancer medicine stock out. In contrast, our analysis con- apy ordering for different cancer types, a concerted effort sidered different types of stock out that may or may not have between policymakers and cancer specialists is needed to affected therapy delivery by analyzing the specific pattern design implementation strategies to build sustainable of cancer medicine stock out in relation to a given cycle systems promoting a reliable supply of cancer medicines. AUTHOR CONTRIBUTIONS AFFILIATIONS Conception and design: Yehoda M. Martei, Surbhi Grover, Warren B. University of Pennsylvania, Philadelphia, PA Bilker, Barati Monare, Robert Gross, Lawrence N. Shulman, Angela Botswana University of Pennsylvania Partnership, Gaborone, Botswana DeMichele Princess Marina Hospital, Gaborone, Botswana Financial support: Lawrence N. Shulman Administrative support: Surbhi Grover, Barati Monare, Lawrence N. CORRESPONDING AUTHOR Shulman Yehoda M. Martei, MD, MSCE, Perelman Center for Advanced Medicine, Provision of study material or patients: Surbhi Grover, Barati Monare, Dipho 3400 Civic Center Blvd, SPE Suite 12-160, Philadelphia, PA 19104; I. Setlhako e-mail: yehoda.martei@uphs.upenn.edu. Collection and assembly of data: Yehoda M. Martei, Surbhi Grover, Barati Monare, Dipho I. Setlhako, Patrick Manshimba, Angela DeMichele EQUAL CONTRIBUTION Data analysis and interpretation: Yehoda M. Martei, Warren B. Bilker, L.N.S. and A.D. contributed equally to this work. Tlotlo B. Ralefala, Robert Gross, Lawrence N. Shulman, Angela DeMichele 8 © 2019 by American Society of Clinical Oncology Impact of Drug Stock Out on Cancer Therapy Delivery in Botswana Manuscript writing: All authors Tlotlo B. Ralefala Final approval of manuscript: All authors Travel, Accommodations, Expenses: Roche Accountable for all aspects of the work: All authors Robert Gross Consulting or Advisory Role: Pfizer AUTHORS' DISCLOSURES OF POTENTIAL CONFLICTS OF INTEREST Angela DeMichele Impact of Essential Medicine Stock Outs on Cancer Therapy Delivery in Honoraria: Pfizer a Resource-Limited Setting Consulting or Advisory Role: Calithera Biosciences, Novartis, Context The following represents disclosure information provided by authors of Therapeutics, Pfizer (I) this manuscript. All relationships are considered compensated. Research Funding: Pfizer (Inst), Genentech (Inst), Incyte (Inst), Relationships are self-held unless noted. I = Immediate Family Member, Millennium Pharmaceuticals (Inst), Bayer HealthCare Pharmaceuticals Inst = My Institution. Relationships may not relate to the subject matter of (Inst), Veridex (Inst), Calithera Biosciences (Inst), GlaxoSmithKline this manuscript. For more information about ASCO's conflict of interest (Inst), Wyeth (Inst) policy, please refer to www.asco.org/rwc or ascopubs.org/jco/site/ifc. Travel, Accommodations, Expenses: Pfizer, Calithera Biosciences, Yehoda Martei Novartis, Pfizer Research Funding: Celgene (Inst) No other potential conflicts of interest were reported. Warren B. Bilker Consulting or Advisory Role: Genentech REFERENCES 1. Shulman LN, Wagner CM, Barr R, et al: Proposing essential medicines to treat cancer: methodologies, processes, and outcomes. J Clin Oncol 34:69-75, 2016 2. Gatesman ML, Smith TJ: The shortage of essential chemotherapy drugs in the United States. N Engl J Med 365:1653-1655, 2011 3. Gray A, Manasse HR, Jr.: Shortages of medicines: A complex global challenge. Bull World Health Organ 90:158-158A, 2012 4. Fox ER, Sweet B V., Jensen V: Drug shortages: A complex health care crisis. Mayo Clin Proc 89:361-373, 2014 5. Gehrett BK: A prescription for drug shortages. JAMA 307:153-154, 2012 6. Link MP, Hagerty K, Kantarjian HM: Chemotherapy drug shortages in the United States: Genesis and potential solutions. J Clin Oncol 30:692-694, 2012 7. Tirelli U, Berretta M, Spina M, et al: Oncologic drug shortages also in Italy. Eur Rev Med Pharmacol Sci 16:138-139, 2012 8. Martei YM, Chiyapo S, Grover S, et al: Availability of WHO essential medicines for cancer treatment in Botswana. J Glob Oncol 4:1-8, 2018 9. UN Economic Analysis & Policy Division: MDG Gap Task Force Report 2015: Taking Stock of the Global Partnership for Development. http://www.un.org/en/ development/desa/policy/mdg_gap 10. Lufesi NN, Andrew M, Aursnes I: Deficient supplies of drugs for life threatening diseases in an African community. BMC Health Serv Res 7:86, 2007 11. Mikkelsen-Lopez I, Shango W, Barrington J, et al: The challenge to avoid anti-malarial medicine stock-outs in an era of funding partners: The case of Tanzania. Malar J 13:181, 2014 12. Leung N-HZ, Chen A, Yadav P, et al: The impact of inventory management on stock-outs of essential drugs in sub-Saharan Africa: Secondary analysis ofa field experiment in Zambia. PLoS One 11:e0156026, 2016 13. Pasquet A, Messou E, Gabillard D, et al: Impact of drug stock-outs on death and retention to care among HIV-infected patients on combination antiretroviral therapy in Abidjan, Cote d’Ivoire. PLoS One 5:e13414, 2010 14. Meloni ST, Chaplin B, Idoko J, et al: Drug resistance patterns following pharmacy stock shortage in Nigerian Antiretroviral Treatment Program. AIDS Res Ther 14:58, 2017 15. Mikkelsen-Lopez I, Tediosi F, Abdallah G, et al: Beyond antimalarial stock-outs: implications of health provider compliance on out-of-pocket expenditure during care-seeking for fever in South East Tanzania. BMC Health Serv Res 13:444, 2013 16. Ferlay J, Soerjomataram I, Ervik M, et al: GLOBOCAN 2012: Estimated Cancer Incidence, Mortality, and Prevalence Worldwide in 2012 v1.0. IARC Press, Lyon, France. 2013 17. National Comprehensive Cancer Network: NCCN evidence-based cancer guidelines, oncology drug compendium, oncology continuing medical education. https://www.nccn.org 18. World Health Organization: The Selection and Use of Essential Medicines: Report of the WHO Expert Committee, 2015 (including the 19th WHO Model List of Essential Medicines and the 5th WHO Model List of Essential Medicines for Children). http://apps.who.int/medicinedocs/documents/s22190en/s22190en.pdf 19. Breslow NE, Clayton DG: Approximate inference in generalized linear mixed models. J Am Stat Assoc 88:9-25, 1993 20. Lyman GH, Dale DC, Crawford J: Incidence and predictors of low dose-intensity in adjuvant breast cancer chemotherapy: A nationwide study of community practices. J Clin Oncol 21:4524-4531, 2003 21. National Comprehensive Cancer Network: NCCN clinical practice guidelines in oncology: Myeloid growth factors. https://www.nccn.org 22. Layer EH, Kennedy CE, Beckham SW, et al: Multi-level factors affecting entry into and engagement in the HIV continuum of care in Iringa, Tanzania. PLoS One 9:e104961, 2014 23. McBride A, Holle LM, Westendorf C, et al: National survey on the effect of oncology drug shortages on cancer care. Am J Health Syst Pharm 70:609-617, 2013 24. Becker DJ, Talwar S, Levy BP, et al: Impact of oncology drug shortages on patient therapy: Unplanned treatment changes. J Oncol Pract 9:e122-e128, 2013 25. Hanna RK, Poniewierski MS, Laskey RA, et al: Predictors of reduced relative dose intensity and its relationship to mortality in women receiving multi-agent chemotherapy for epithelial ovarian cancer. Gynecol Oncol 129:74-80, 2013 26. Lyman GH: Impact of chemotherapy dose intensity on cancer patient outcomes. J Natl Compr Canc Netw 7:99-108, 2009 27. Chirivella I, Bermejo B, Insa A, et al: Optimal delivery of anthracycline-based chemotherapy in the adjuvant setting improves outcome of breast cancer patients. Breast Cancer Res Treat 114:479-484, 2009 28. Budman DR, Berry DA, Cirrincione CT, et al: Dose and dose intensity as determinants of outcome in the adjuvant treatment of breast cancer. J Natl Cancer Inst 90:1205-1211, 1998 29. Yifru S, Muluye D: Childhood cancer in Gondar University Hospital, Northwest Ethiopia. BMC Res Notes 8:474, 2015 nn n Journal of Global Oncology 9 Martei et al APPENDIX TABLE A1. Intended Chemotherapy Regimen by Diagnosis No. (%) Noncurative or Diagnosis and Intended Regimen Curative Regimen Palliative Regimen Unspecified Breast cancer (n = 155) Doxorubicin + cyclophosphamide once every 3weeks → paclitaxel 76 (49) once every 3 weeks Doxorubicin + cyclophosphamide once every 3 weeks→ paclitaxel + 12 (7.7) trastuzumab once every 3 weeks Cyclophosphamide, methotrexate, and fluorouracil once every 10 (6.5) 3 weeks Docetaxel, carboplatin, and herceptin once every 3 weeks 1 (0.7) Paclitaxel 21 (13.5) Gemcitabine + cisplatin 3 (2) Gemcitabine + paclitaxel 1 (0.6) Gemcitabine + carboplatin 1 (0.6) Carboplatin + paclitaxel 5 (3.2) Trastuzumab 7 (4.5) 10 (6.5) Trastuzumab + paclitaxel 1 (0.6) 2 (1.3) Docetaxel 3 (2) Gemcitabine 1 (0.6) Kaposi sarcoma (n = 89) Doxorubicin, bleomycin, and vincristine 57 (64) Bleomycin + vincristine 15 (17) Paclitaxel 12 (13.5) Etoposide 3 (3) Indeterminate 2 (2) Cervical cancer (n = 12) Carboplatin + paclitaxel 5 (41.7) Paclitaxel 2 (16.7) Gemcitabine + cisplatin 2 (16.7) Cisplatin + paclitaxel 2 (16.7) Fluorouracil + carboplatin 1 (8.3) Prostate cancer (n = 13) Androgen-deprivation therapy 6 (46) Docetaxel 7 (53.9) Colon cancer (n = 24) FOLFOX 1 (4.2) Capecitabine + oxaliplatin 6 (25) 13 (54) Capecitabine 1 (4.2) Fluorouracil 2 (8.3) Rectal cancer (n = 7) Capecitabine + oxaliplatin 7 (100) (Continued on following page) 10 © 2019 by American Society of Clinical Oncology Impact of Drug Stock Out on Cancer Therapy Delivery in Botswana TABLE A1. Intended Chemotherapy Regimen by Diagnosis (Continued) No. (%) Noncurative or Diagnosis and Intended Regimen Curative Regimen Palliative Regimen Unspecified Anal cancer (n = 4) Carboplatin + paclitaxel 3 (75) Cisplatin + fluorouracil 1 (25) Ovarian cancer (n = 14) Carboplatin + paclitaxel 11 (78.6) Carboplatin 1 (7.1) Gemcitabine 2 (14.3) Esophageal cancer (n = 1) Carboplatin + paclitaxel 1 (100) Head and neck cancer (n = 21) Carboplatin + paclitaxel 1 (4.8) 15 (71.4) Carboplatin 1 (4.8) Cisplatin + paclitaxel 2 (9.5) Gemcitabine + paclitaxel 1 (4.8) Paclitaxel 1 (4.8) Nasopharyngeal Carboplatin + paclitaxel 1 (14.3) 1 (14.3) Cisplatin + gemcitabine 1 (14.3) Cisplatin + paclitaxel 1 (14.3) Gemcitabine + carboplatin 1 (14.3) Gemcitabine + cisplatin 1 (14.3) Gemcitabine 1 (14.3) Ovarian cancer (n = 14) Carboplatin + paclitaxel 11 (78.6) Carboplatin 1 (7.1) Gemcitabine 2 (14.3) Uterine cancer (n = 6) Doxorubicin + cisplatin 1 (16.7) Carboplatin + paclitaxel 4 (66.7) Doxorubicin 1 (16.7) Lung cancer (n = 11) Carboplatin + paclitaxel 6 (54.5) Carboplatin + etoposide 1 (9) Doxorubicin 1 (9) Gemcitabine + carboplatin 1 (9) Gemcitabine 1 (9) Paclitaxel 1 (9) Abbreviation: FOLFOX, fluorouracil, leucovorin, and oxaliplatin. Journal of Global Oncology 11

Journal

Journal of Global OncologyWolters Kluwer Health

Published: Apr 10, 2019

Keywords: CALR, AGRP

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