The direct costs of treating and managing haematological cancers at a tertiary hospital: Payer’s perspective

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The incidence of haematological cancers is increasing in South Africa due to the rise in HIV/AIDS cases, and as cancer incidence rises, so do treatment costs. There is limited knowledge of the direct costs of managing these cancers in the public health care system in South Africa. This study aimed to determine the direct costs associated with the treatment and management of haematological cancers in a tertiary public sector hospital. Methods A descriptive retrospective study was conducted using the data files and medical records of patients treated in the haematology unit 12 months prior to data collection. Using an adapted data collection instrument and a ' time-motion ' method, a micro-costing method was utilised to establish direct medical costs determined from the provider's perspective. The sum of all costs was used to establish the average total cost of care per haematological patient. Results The results are presented as an average per patient with 53 patient files that met the inclusion criteria. Hodgkin’s lymphoma had the highest patient count, accounting for 19 (36%) of the total sample. The average total cost of treatment and management of haematological cancer per patient was R 126385,62 per year, and the average cost of chemotherapy per cycle per patient was R25600,23. The major cost drivers were chemotherapeutic agents, with non-Hodgkin’s lymphoma having the highest average cost and the ABVD regimen being the most prominent. Conclusions A comprehensive quantification of the direct costs of treating and managing haematological cancers was determined. Understanding the costs associated with these cancers will allow public healthcare funders to make effective financial decisions and adequate treatment plans. Cancer Chemotherapy Costs Haematological South Africa 1. INTRODUCTION The prevalence of haematological cancers in South Africa is rising daily, mainly due to lifestyle changes and the growing HIV-positive population. As the incidence of cancer increases, so does the cost of treatment [ 1 ]. Haematological cancers are a significant cause of morbidity and mortality in sub-Saharan Africa. If mortality and morbidity rates continue to increase, haematological cancers will account for 10% of the cancer burden in the region by 2030 [ 2 ]. Cancer drug spending has increased faster than in other healthcare sectors [ 7 ]. In their duty to maximise population health with limited funding, publicly funded healthcare systems face unprecedented obstacles. The clinical health benefits of new cancer regimens are dwindling, and current pricing trends may not be sustainable for drug budgets [ 8 , 9 ]. To protect the already-constrained budgets, the healthcare systems need to use health technology assessment (HTA) to inform decisions about which new drug regimens to fund with limited public resources to protect the already-constrained drug budgets [ 7 , 9 , 10 ]. Several costs go into managing haematological cancers, such as chemotherapy, laboratory tests, administration costs, and supportive care medicines; thus, it is crucial to know the different cost implications of certain health decisions [ 11 ]. In a study conducted in Southern Nigeria, it was determined that the average total cost of care for a patient totalled N232882,95 ( $ 638,04) per patient, with non-Hodgkin’s lymphoma having the highest cost of care of roughly N70000 ( $ 190) per patient per month [ 12 ]. Given the increase in the prevalence and burden of haematological cancers, it is imperative to have an in-depth understanding of the direct costs involved, especially in aligning resources to patient needs and responding to changes in patient requirements as we progress towards Universal Health Coverage (UHC). This will have significant financial implications; thus, identifying the actual burden and cost is necessary for a financially stable National Health Insurance (NHI) fund [ 13 ]. Hence, understanding where the most significant treatment costs lie can be used to decide how funds should be allocated to adequately meet the demands, thus ensuring better access to cancer treatment, equitable healthcare spending, and making the best available resources. 2. METHODOLOGY 2.1 STUDY DESIGN This descriptive retrospective study used data from patient files and medical records. 2.2 STUDY SITE The study was conducted at a public tertiary hospital outpatient haematology clinic unit in Gauteng Province, north of Pretoria, South Africa. This teaching hospital has expert physicians, nurses, and cancer detection and treatment equipment. 2.3 STUDY SAMPLE AND POPULATION All files of adult patients diagnosed with haematological cancers and treated in the Haematology unit 12 months prior to data collection were reviewed (May 2020 ̶- May 2021). A total of 121 patients were treated for haematological conditions at the clinic, but only 53 patients' files met the inclusion criteria. The haematology outpatient clinic registry was used to identify patient file numbers. 2.4 DATA COLLECTION AND INSTRUMENT A data collection instrument adapted from a study by Herbst [ 5 ] in 2017 facilitated information collection regarding demographics, chemotherapy costs, laboratory tests, administration costs (consumables and healthcare workers), and supportive care medication costs. A microcosting approach was used to estimate the inputs and usage separately. A time-motion study was used to quantify the time and expenses of compounding chemotherapy, chemotherapy administration, and doctor consultation. 2.5 DATA ENTRY AND ANALYSIS Data were captured using Microsoft Excel™ and imported into the Statistical Package for the Social Sciences (SPSS®), version 25, for analysis. This study was conducted from the perspective of a healthcare provider, and costs were determined based on the provider's expenditures. Chemotherapy, laboratory tests, consumables, and supportive care medication costs were obtained from the master procurement list, with cost prices from the South African National Department of Health, Uniform Patient Fee Schedule (UPFS), tender single exit price list, and NHLS state price list. The costing model was adapted from a study by Herbst [ 5 ] in 2017, where chemotherapy costs were calculated by determining the cost of each drug per cycle multiplied by the number of cycles in the 12-month review period. Only six patients were observed to determine the number of consumable items each patient used, and from there, the average number of items used for each patient was calculated. Additional necessary information was obtained from the patient files. The price for each item were obtained from the hospital’s Master Procurement List. Direct medical costs were determined by multiplying the total amount of each medical resource by the unit cost, including an adapted ‘time-motion’ method for administration costs. The sum of these costs was used to establish the average total cost of care per patient. The total direct cost of medical care was calculated as the indirect inpatient costs associated with diagnosing and treating haematological cancers (leukaemia, lymphoma, and myeloma), excluding all costs unrelated to malignancy. The bootstrap method was used to increase the validity of the data. The data were adjusted and resampled, and similar results were obtained. Non-parametric analysis was employed due to smaller population sizes and cost data often having gaps that do not fall into specified distributions with estimable parameters; therefore, non-parametric statistics were utilized to evaluate relationships and establish significance. The Kruskal-Wallis test was conducted to assess whether there was a significant difference between the total costs, type of cancer, and each cost variable. Statistical significance was set at p < 0.05. 3. RESULTS 4.1 Demographics A total of 121 patients were registered at the haematology clinic. During data collection, 24 patients' folders were excluded because they were misplaced, and 44 patient files were excluded based on the exclusion criteria. Fifty-three patients' files who were diagnosed with haematological cancer and treated in the haematology unit 12 months prior to the commencement of data collection were enrolled in the study. As shown in Table 1 , the sample (n = 53) represented four types of haematological cancers and different racial groups. Hodgkin’s lymphoma had the highest patient count [36,0%; 19/53], while non-Hodgkin’s lymphoma had the lowest patient count [16,9%; 9/53]. The majority [84,9%; 45/53] of the patients were of African descent, and their ages ranged between 18 and 60 years, with 22% of the patients between the ages of 29 and 39 years. A total of 60,3% (32/53) of patients had comorbidities, with the highest prevalence of 40,7% (22/53), followed by hypertension at 18.9% 10/53). Only 21 patient files indicated the haematological cancer stage, with stage IV having the highest incidence at 26,4% (14/53), and 66,6% of patients with cancer. No statistically significant relationship was found between the type of cancer and demographic and health variables such as gender (P = 0,115), age group (P = 0,426), race, and cancer stage. Statistically insignificant relationships were also found between the total average costs and gender, age, race, and cancer stage. Table 1 Patient demographics (n = 53) Demographic Hodgkin’s lymphoma n (%) Non- Hodgkin’s lymphoma n (%) Multiple myeloma n (%) Leukaemia n (%) Total n (%) Gender Female 14 (26) 3 (5,7) 5 (9,4) 8 (15,0) 30 (56,6) Male 5 (9,4) 6(11,3) 5(9,4) 7(13,2) 23(43,4) Age group 18–29 8 (15,9) - 1(1,9) 2(3,8) 11(20,8) 29–39 7(13,2 2(3,8) 1(1,9) 2(3,8) 12(22,6) 39–49 4(7,5) 3(5,7) - 3(5,7) 10(18,9) 49–59 - 1(1,9) 6(11,3) 3(5,7) 10(18,9) > 60 - 3(5,7) 2(3,8) 5(9,4) 10(18,9) Co-morbidities HIV/AIDS 13(24,5) 4(7,5) - 5(9,4) 22(41,5) Hypertension 11,9) 1(1,9) 3(5,7) 5(9,4) 10(18,9) Race African 18(33,9) 5(9,4) 9(16,9) 13(24,5) 45(84,9) White 1(1,9) 4(7,5) - 2(3,8) 7(13,2) Coloured - - 1(1,9) - 1(1,9) Cancer stage II 2(3,8) - 2(3,8) - 4(7.6) III 2(3,8) - - 1(1,9) 3(5,7) IV 6(11,3) 2(3,8) 1(1,9) 5(9,4) 14(26,4) 4.2 Chemotherapy treatment regimens As shown in Table 2 , the ABVD regimen was the most frequently prescribed chemotherapeutic combination for Hodgkin lymphoma [13:(24,5,4%)]. A few [(4; (7, 5%)] patients did not have specified regimens, as they were prescribed different combinations of individual chemotherapeutic agents or a combination of more than one regimen. The R-FC combination was the most costly of all the regimens used, making it the regimen with the highest average cost per patient per regimen at R1868100,40, as highlighted in Table 2 . There are discrepancies in the number of patients per cancer from the demographics and Table 2 , as certain patients can be on more than one regimen or have changed to a different regimen during the study. Table 2 The total costs per chemotherapy regimen Chemotherapy regimen Number of patients per cancer Total number of Patients per regimen n (%) Total treatment Cost per cycle (R) Total number of cycles per regimen (12 month review period) Total cost (R) Average cost per patient per regimen (R) RICE 2 (NHL): 2 (3,7) 89133,54 10 891335,40 445667,70 BEACOPP 4 (HL) 4 (7,5) 102189,9 18 1839418,2 459854,55 R-CHOP 4 (NHL) 4 (7,5) 230824,6 20 4616492 1154123,00 R-FC 1 (NHL) 5 (Leukaemia) 6 (11,3) 361567,82 31 11208602,42 1868100,40 MEC 1 (Leukaemia) 1 (1,9) 7473,3 5 37366,50 37366,65 ABVD 13 (HL) 1 (NHL) 14 (26,4) 137239,95 71 9744036,5 696002,60 CODOX 1 (NHL) 1 (1,9) 2951,64 1 2951,64 2951,64 IDAC 2 (Leukaemia) 2 (3,7) 3670,9 7 25696,3 12848,15 CVAD 8 (MM) 8 (15,1) 13583,59 45 611216,55 76407,69 IH 3 (Leukaemia) 3 (5,6) 56646,9 20 1132938 377646,00 IMATINIB 2 (Leukaemia) 2 (3,7) 19449 5 97245 48622,50 BRENTUXIMAB 2 (HL) 2 (3,7) 189870,48 12 2278445,76 1139222,88 UNSPECIFIED 1 (NHL) 2 (Leukaemia) 1 (MM) 4 (7,5) 8620,06 8 68960,48 17240,12 TOTAL 53 53 1223220,7 253 32554704,70 6341800,76 The costs per cycle for each patient were determined using the following formula. Drug cost per cycle = [(dose × average body surface area (BSA) or body weight (BW) (number of administrations per cycle) (number of vials/tabs per cycle) (medicine price per vial/tablet)]. As indicated in Table 2 , the total cost of chemotherapeutic medicines per cycle was R1223220.68, with an average of R23079.65 per patient. The relationship between total medication cost per cycle and cancer type was statistically significant (P = 0,047). 4.3 Laboratory tests A total of 1664 laboratory tests were routinely performed during the study review period. The mean number of laboratory tests per patient was 64. Laboratory tests were performed as part of a regular protocol before chemotherapy was administered for each cycle. The average cost of laboratory tests per patient was R2971,60 per cycle. As shown in Table 3 , the main cost drivers of the laboratory tests were urea, electrolytes, and creatinine tests, which were R48369 [(25,67%)]. Hodgkin’s lymphoma had the highest laboratory test costs (R 77873,72 [(40,8%)]; no statistical difference was found between the laboratory tests by type of cancer. Table 3 Cost of laboratory tests for all patients (n = 53) Laboratory test Cost per test (Rands) Hodgkin’s lymphoma (Number of times test was conducted) Non-Hodgkin’s lymphoma (Number of times test was conducted) Total costs for Leukaemia (Number of times test was conducted) Multiple Myeloma (Number of times test was conducted) Total (Rand) CSF Chemistry & Microscopy 183 732 (4) - - - 732 (4) INR & PTT 150 600 (4) 300 (2) 1350 (9) 300 (2) 2550 (17) Urea, electrolytes, creatinine 105,15 16193,10 (154) 11987,10 (114) 14931,30 (142) 5257,50 (50) 48369 (460) Blood gas 54,13 108,26 (2) 108,26 (2) Calcium, Magnesium, Inorganic Phosphate and Uric Acid (CMPU) 91,05 10106,55 (111) 5189,85 (57) 7284 (80) 4006,20 (44) 26586,60 (292) GeneXpert MTB/RIF 191,96 191,96 (1) - - 191,96 (1) 383,92 (2) HIV serology 55,89 391,23 (7) - - - 391,23 (7) HIV viral load 339,39 3393,90 (10) - - - 3393,90 (10) Creatine kinase 48,52 776,32 (16) 194,08 (4) - - 970,40 (20) Full blood count 89,55 10925,1 (122) 8865,45 (99) 16298,10 (182) 5462,55 (61) 41551,2 (464) Liver function tests (LFT) 426 25560 (60) 7242 (17) 6390 (15) 4686 (11) 43878 (103) Glucose 30,35 182,10 (6) 182,10 (6) CD4 181,60 3268,80 (18) 544,80 (3) 181,60 (1) 3087,20 (17) 7082,40 (39) C-reactive protein (CRP) 72,96 510,72 (7) 145,92 (2) - - 656,64 (9) Hepatitis 125,90 377,70 (3) 629,50 (5) 251,80 (2) - 1259 (10) Procalcitonin 259,03 259,03 (1) 518,06 (2) - 777,08 (3) TB culture 75,45 679,05 (9) 75,45 (1) 452,70 (6) 75,45 (1) 1282,65 (17) HIV PCR 380 3800 (10) 1900 (5) 3040 (8) 1140 (3) 9880 (26) Mean, SD 158,89 116,22 4580,81 6943,39 3132,68 3964,73 5036,16 5832,92 2689,65 2143,31 10557,47 16438,08 Total cost 77873,72 (539) 37592,21 (311) 50361,60(451) 24206,86 (190) 190033,79 (1491) 4.4 Administration costs Consumables The safety box used to dispose syringes and needles was the cost of drivers for consumables R3064,46 of the total consumable costs. The average cost for consumables per patient was R265,02 per cycle, as indicated in Table 4 . Table 4 Cost of consumables used for chemotherapy administration. Chemotherapy Item Number of items Cost (Rand) Total cost (Rand) Gloves, non-sterile, single-use 2 0,51 27,03 Surgical face mask 1 49,95 2647,35 Apron impermeable 1 39 2067 Bandage, Adhesive, 3.0 cm, 100/box 1 3,9 206,7 Compress, gauze, sterile and nonsterile, single-use 3 36,07 1911,71 Needles, luer, sterile, single-use (sizes G3) 4 13,65 723,45 Safety box for used syringes/needles 1 57,82 3064,46 Skin-cleaning wipe/swab-pad, alcohol 4 18,03 955,59 Tape, medical, roll (various sizes) 2 22,33 1183,49 Syringes (various capacities) 4 0,68 36,04 IV catheters #22 and #24 2 12,7 673,1 Intravenous catheter (sizes G3) 1 10,38 550,14 Total 26 265,02 14046,06 4.5 Time-motion As indicated in Table 5 , the time-motion was recorded as the average for all patients with cancer. The average time spent by the patients at the clinic was 7 and 22 minutes, respectively. The average cost per patient for chemotherapy was R1591,33. The administration of chemotherapy monitoring averaged 2 hours and 31 minutes, respectively. The length of time varied depending on the type of cancer and treatment regimen used. Administration and monitoring cost R502,83 in terms of nursing staff remuneration. Pharmacist compounding of chemotherapy medication had the highest cost at R658,05. The pharmacist’s time reflected the actual cost incurred from when the pharmacy received the prescription from the clinic to when the compounded chemotherapeutic medicines were received at the clinic. Table 5 Average time and costs Task Average time (hr: min) Salary cost per minute (Rand) Average cost per cycle (Rand) Check-in 00:04 1,51 6,04 Preparation of patient file 00:07 1,51 10,57 Drawing of blood 00:08 3,33 26,61 Labs 02:07 1,77 224,79 Physician visit 00:13 10,19 132,47 Pharmacy 02:03 5,35 658,05 Drip administration and file completion 00:09 3,33 29,97 Administration monitoring 02:31 3,33 502,83 Total 07:22 30,32 1591,33 4.6 Supportive care medication The total cost of supportive care medicine was R150967,05. The most common adverse effect observed in this study was pain, with pain medications prescribed for 11 (20,75%). Nausea and vomiting were the most common adverse events. Three types of nausea and vomiting prevention and treatment medicines (ondansetron, promethazine hydrochloride, and metoclopramide) were prescribed to 36 (67,9%). Multiple myeloma had the highest average cost of supportive care medicines at R3852,93, whereas Hodgkin’s lymphoma had the lowest average cost at R559,93 (Table 6 ). Hydrocortisone was the most prescribed supportive care medication, with 27 (50,9%) patients being prescribed hydrocortisone (Table 6 ). As presented in Table 6 , the major cost driver for supportive care medicines was MESNA at R96968, with multiple myeloma having the highest cost of supportive care medicines at R64861,62. Statistical significance was also found between the average total costs and supportive care medications (P = 0,002). Table 6 Cost (R) of supportive care medications per type of cancer Supportive care medication Indication Hodgkin’s Lymphoma (n) Non-Hodgkin’s Lymphoma (n) Leukaemia (n) Multiple Myeloma (n) Total (R) Cost per patient Ondansetron IV Prevention and symptomatic treatment of chemotherapy induced nausea and vomiting 16180,58 (14) 883,82 (2) 7958,40 (6) 3156,50 (4) 28179,30 (26) 1083,82 Promethazine Hydrochloride IV 938,16 (2) 1997,22 (4) 469,08 (1) - 3404,46 (7) 486,35 Metoclopramide IV/TAB 3540 (2) - 236 (1) - 3776 (3) 1258,67 Hydrocortisone IV Anti-inflammatory 7498,44 (13) 2233,44 (5) 2131,92 (6) 913,68 (3) 1277,48 (27) 47,31 Prednisone TAB 410,4 (5) 357,84 (4) 124,20 (3) 892,44 (12) 74,37 Paracetamol IV/TAB Pain management 3,90 (3) 921,60 (6) 2,1(2) - 927,60 (11) 84,33 Tramadol TAB - - 3432,52(2) - 3432,52 (2) 1716,26 Folic acid TAB Folic acid deficiency and prevention of severe toxicity from certain chemotherapy agents 112,88 (3) 122,40 (1) 401,20(3) - 636,48 (7) 90,92 Calcium Folinate TAB Reduces toxicity and counteract the effects of folate - 1148,40 (2) - - 1148,40 (2) 574,2 MESNA IV Cytoprotectant of the urinary tract 26588 (3) 23460 (5) 9384 (1) 37536 (7) 96968 (16) 6 060,50 Allopurinol TAB Hyperuricaemia 638 (3) - 1518 (6) - 2156 (9) 239,55 Methotrexate TAB Antimetabolite -- 616,61 (3) - - 616,61 (3) 205,53 Lansoprazole TAB Acid reflux 554,49 (3) 694,80 (1) - - 1249,29 (4) 312,32 Zoledronic acid IV Hypercalcemia/reduction of bone metastasis - - - 23131,24 (3) 23131,24 (3) 771,08 Filgrastim Chemotherapy-induced neutropenia - 3773,08(1) - - 3373,08 (1) 3373,08 Total 56464,85 36209,18 25533,22 64861,62 150967,05 Table 7 Total costs (n = 53) Cost Average cost per cycle (R) Average cost per patient Total costs(R) P-value P-value adjusted Chemotherapeutic agents 24190,61 119656,61 6341800,76 0,001 0,000 Laboratory tests 751,12 3585,54 182769,89 0,110 0,107 Consumables 55,51 265,02 14046,06 0,512 0,520 Administration costs 6,28 30,02 402606,49 0 0 Supportive care medication 596,71 2848,43 150967,05 0,002 0,02 Total R25600,23 R126385,62 R7092190 The total cost to the hospital for treating 53 patients in the 12-month review period was R7092190, with chemotherapeutic agents being the cost driver at R6341800 ([76 (89,48%)]). Statistical differences were found among the total costs, chemotherapeutic agents (P = 0,001), and supportive care medications (P = 0,002). All costs included those of all the patients who met the inclusion criteria. The average cost per cycle was R25600,23 (SD = 9539,755) per patient. Chemotherapeutic agents were the major cost drivers at R24190,61 (94,49%), followed by laboratory tests at R751,12(2,93%), with consumables costing the least at R55,51 (0,21%), as indicated in Table 7 . The total number of cycles per patient was 253, translated to R126385,62, for managing and treating haematological cancer per patient over 12 months. No statistically significant differences were found in the average cost per cycle. 4. DISCUSSION Limited information and published literature on the direct costs of managing and treating haematological cancers in South Africa motivated this research. Therefore, this study was conducted to address the gap in the micro-cost of the treatment of haematological cancers in the public sector and provide baseline data that can be used in future cost-effectiveness studies in this population. The study showed that Hodgkin’s lymphoma was the most prevalent haematological cancer in this population. Furthermore, the majority of patients were female, and the most common co-morbidity was HIV. Hodgkin’s lymphoma was the most prevalent haemolytic cancer in this study. The average cost of treating patients per year was R126385,62. Chemotherapy agents at R119656,61 per year (p = 0.001) were found to be the cost driver in treating haematologic cancers, followed by medication used as supportive care. In this study, Hodgkin’s lymphoma had the highest patient count, while non-Hodgkin’s lymphoma had the lowest, contrary to the literature on the global prevalence of haematological cancer trends [ 16 ]. Contrary to our study, a study conducted in South America found that out of 92 cases [56,5% (52/92] ), only 12% (n = 11) were attributed to HL [ 17 ]. According to a study by Ferlay et al. [ 16 ] 2018, NHL was the most common haematological malignancy in South Africa. In the UK, the incidence of HL was found to be more frequent than that of NHL in patients aged between 15 and 39 [ 18 ]. This was similar to the age distribution observed in the present study. These findings indicate that haematological cancers are more frequently observed in young adults with SA. This is also attributed to the high prevalence of HIV/AIDS in younger people in Southern Africa, where the incidence generally peaks between 20 and 24 years for women and 25–29 years for men [ 19 ]. Although there were only a few direct comparisons, this study's overall average cost per patient was higher than in studies conducted in Nigeria [ 12 ] and lower than in the USA [ 20 ]. This might be attributed to differences in population, cancer incidence, socioeconomic level, and healthcare delivery systems. The study conducted in Nigeria estimated the average cost of managing haematological cancer to be $ 638,04 (approximately R10182,48). The study in the USA estimated the average cost per year to be $ 112,000 (approximately R178,942,400). In underdeveloped nations, where economic restrictions sometimes limit access to adequate care, it is especially important to calculate treatment costs. This finding is supported by Ghuza et al. (2020), who asserted that an economically feasible treatment choice without jeopardising patient survival is preferable. According to our findings, chemotherapeutic agents were the major cost drivers (P = 0,001), contrary to Reyes et al. [ 21 ], who showed that hospitalisations contributed 50% of the total cost. Green et al. [ 22 ] found that healthcare expenditures per patient with blood malignancies are twice as high as ordinary cancer costs in Europe owing to extensive hospital stays and complex treatment and diagnostics. Sartorius et al. [ 23 ] stated that cancer medications are expensive but often make up a small portion of the overall cost. Surgery, inpatient care, consultations, and repeated diagnostic and staging procedures exponentially increase expenses. The differences are attributable to the fact that hospitalisation, scans, and imaging were not included in our study, as our study was conducted from the hematology outpatient clinic where patients came weekly or depending on their chemotherapy cycle schedule to receive their medication. In scanning and imaging, inpatient care should be factored into and considered in any new direct cost quantification study to determine the full extent of the cost of the actual need. Chemotherapeutic agents were administered under different regimens. Medications that act via different mechanisms are combined to reduce the probability of developing resistant cancer cells. Once the diverse effects of these medications are combined, each agent can be administered at its optimal dose without causing severe side effects [ 24 ]. The most frequently prescribed regimen, with the highest number of cycles, was the ABVD regimen. This is because ABVD is the standard treatment regimen for Hodgkin’s lymphoma [ 25 ], the most common cancer in our study. The costlier regimen was R-FC. This is attributed mainly to the chemotherapy drug rituximab, an expensive drug that requires a significant number of resources to administer [ 26 ]. This could be reduced by the use of subcutaneous rituximab, which will have shorter visit times as it has a shorter administration time, or the utilisation of a rituximab biosimilar, which would be less costly to acquire but equally effective with faster infusion rates [ 26 ]. Supportive care medications also impacted the total cost, as chemotherapy may cause various side effects. Each patient was prescribed supportive care medications based on the chemotherapeutic agents, combinations, and adverse symptoms [ 27 ]. The number of cycles per patient also affects the prescription of supportive care medications; some cycles do not have supportive care medications, whereas others are prescribed for every cycle [ 27 ]. The coordinated action of supportive treatments can considerably maximise therapeutic outcomes and improve the well-being of patients [ 28 ]. MESNA, a costly supportive care medication, is a cytoprotectant that is usually administered with cyclophosphamide to reduce the risk of bladder inflammation that can cause severe bleeding [ 29 ]. The higher cost of MESNA can be attributed to construction and operation costs, raw materials such as glass vials, active pharmaceutical ingredients, process waste procurement, transportation, and administration [ 30 ]. Another commonly prescribed medication is ondansetron, which prevent and treat chemotherapy-induced nausea and vomiting by blocking the action of serotonin. These natural substances can cause nausea and vomiting [ 31 ]. Hydrocortisone is also commonly used to prevent inflammation [ 32 ]. NHL had the highest average cost per cycle when compared to other studies conducted in the USA by Morrison et al. [ 33 ], which estimated the mean cost for treating NHL to be $ 11,890 (approximately R184560,15) and in the study done by Korubo [ 12 ] in Nigeria found the cost to be $ 1022.46 (approximately R15 859,21). The costs between the two studies differ by a large margin; this contrast can also be attributed to the cost methodology and differences in resource availability, methods of payment, and treatment protocols in developed countries [ 34 ]. Laboratory costs significantly impacted the total cost, but this was expected given that blood was drawn for laboratory tests before each administration of chemotherapy. This is done to assist the physician in determining whether the patient’s blood count is satisfactory for chemotherapy and how well the patient responds to therapy [ 35 ]. This aligns with Wright and Hislop [ 36 ], who found laboratory tests at around a similar price to R2262,18. As expected, the most common comorbidity in the study population was HIV/AIDS, followed by hypertension, as South Africa has the world’s highest prevalence of HIV/AIDS. South Africa accounts for 20% of the global HIV-infected population, and HIV/AIDS is a risk factor for lymphoma [ 37 ]. However, with the introduction of antiretroviral therapy (ART), the likelihood of developing AIDS-defining haematological malignancies has decreased with the introduction of ART [ 37 ]. Therefore, one would expect a lower incidence of these cancers because of ART. Our results agree with those of global studies [ 36 , 38 , 39 ] that have observed a link between HIV/AIDS and HL among HIV-positive individuals. HIV Infection may cause a person to be more prone to haematological cancers, and people infected with HIV are at an increased risk of developing NHL and HL [ 2 ] due to limitations in information found in patient medical records. It is unknown whether the individuals were infected with HIV before or after being diagnosed with hematologic malignancy. Gopal et al. [ 2 ] reiterated the importance of optimising and improving cancer screening and prevention techniques for HIV-infected people to reduce the costs of HIV-associated malignancies and adequately plan for the financial burden that they might impose. Expanding care and research for HIV-associated malignancies in areas where HIV/AIDS is most prevalent, such as East and Southern Africa’s low- and middle-income countries, can lead to significant scientific and humanitarian breakthroughs [ 2 , 40 ]. 5. LIMITATIONS The sample size needed to be larger, making the methods more challenging to reproduce. The patients were not followed up until they were in remission. Therefore, the total costs for the duration of the disease were not determined. Hospitalisation due to the disease was not part of the cost process, thus limiting the representation of the findings. 6. CONCLUSIONS AND RECOMMENDATIONS The direct costs of managing and treating haematological cancers were quantified according to public tertiary haematological cancer management protocols. The main cost drivers were chemotherapeutic agents. This study provides data that can help ensure that decisions and planning for haematological cancer management are evidence-based, thus ensuring that resource allocation is proportional to the predicted burden of disease and associated expenditures. This study projects the cost of haematological cancers in one public hospital. Effective cancer service planning and strategizing will require similar projections for all cancers on a larger scale in different hospital settings to ensure comprehensiveness. Therefore, restructuring the approach in which haematological cancer treatment and management services in the public sector are planned and budgeted for is recommended. Declarations Competing interests The authors declare no competing interests. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Ethics approval and consent to participate This study adhered to the principles of the Declaration of Helsinki and was approved by the Ethics Review Committee of Sefako Makgatho University (SMUREC/P/300/2020:PG). Given the study's retrospective nature, the informed consent requirement was waived. The hospital clinical director obtained the required gatekeeper permission to access records. All patient identifiers were de-linked and anonymised at the data collection point and aggregated thereafter. In this case, we used the hospital approval process governed by the Guidelines for Good Practice in Clinical Trials with Human Participants in South Africa [ 41 ]. Consent for publication Patient consent is not applicable as there is no information or images that could lead to identification of a study participant. Funding sources No funding was received for this research Author Contribution MMM, MM, and MS developed the study concept and completed the data collection. They then performed data analysis and wrote the manuscript. All authors approved the final manuscript. Acknowledgements We acknowledge Mr. Katlego Mokgwabone (statistician), who assisted with data analysis in this paper. We would also like to thank the staff at the haematology unit for their assistance and support. Data Availability Relevant data are included within the manuscript. However, further data will be available on request. Data will be available from Moliehi Matlala email: [email protected] References Hofman K. 2014. Guest Editorial: Non-communicable diseases in South Africa: A challenge to economic development. South African Medical Journal , 104 (10), p.647. Gopal S, Wood WA, Lee SJ, Shea TC, Naresh KN, Kazembe PN, Casper C, Hesseling PB, Mitsuyasu RT. Meeting the challenge of hematologic malignancies in sub-Saharan Africa. Blood J Am Soc Hematol. 2012;119(22):5078–87. Washmuth D. Hematological Malignancies: Types & Causes. Study.com. September 29, 2017. https://study.com/academy/lesson/hematological-malignancies-types-causes.html . Accessed 7 April 2020. Ataguba J. The impact of financing health services on income inequality in an unequal Society: the case of South Africa. Appl Health Econ Health Policy. 2021;19(5):721–33. Herbst CL. 2017. Cost analysis of colorectal cancer chemotherapy treatment in public and private healthcare sectors in South Africa (Doctoral dissertation). McIntyre D, Doherty J, Ataguba J. Health care financing and expenditure: post-1994 progress and remaining challenges. In: Van Rensburg HCJ, editor. Health and health care in South Africa. Pretoria: Van Schaik; 2012. Cressman S, Browman GP, Hoch JS, Kovacic L, Peacock SJ. A time-trend economic analysis of cancer drug trials. Oncologist. 2015;20(7):729–36. Angelis A, Lange A, Kanavos P. Using health technology assessment to assess the value of new medicines: results of a systematic review and expert consultation across eight European countries. Eur J Health Econ. 2018;19(1):123–52. Hollingworth S, Gyansa-Lutterodt M, Dsane-Selby L, Nonvignon J, Lopert R, Gad M, Ruiz F, Tunis S, Chalkidou K. Implementing health technology assessment in Ghana to support universal health coverage: building relationships that focus on people, policy, and process. Int J Technol Assess Health Care. 2020;36(1):8–11. Jonsson B. Bringing in health technology assessment and cost-effectiveness considerations at an early stage of drug development. Mol Oncol. 2015;9(5):1025–33. de Oliveira C, Bremner KE, Pataky R, Gunraj N, Chan K, Peacock S, Krahn MD. Understanding the costs of cancer care before and after diagnosis for the 21 most common cancers in Ontario: a population-based descriptive study. Can Med Association Open Access J. 2013;1(1):E1–8. Korubo KI, Okoye HC, Efobi CC. 2018. The economic burden of malignant and premalignant hematological diseases in Southern Nigeria. Niger J Clin Pract, 21(11), pp.1396 – 402. Finestone E, Wishnia J, Ranchod S. Estimating and projecting the burden of cancer in South Africa. Percept Actuaries and Consultants; 2021. CA 04/2021. Cressman S, Browman GP, Hoch JS, Kovacic L, Peacock SJ. A time-trend economic analysis of cancer drug trials. Oncologist. 2015;20(7):729–36. Rocha-Gonçalves F, Borges M, Redondo P, Laranja-Pontes J. 2016. Health technology assessment and value: the cancer value label (CAVALA) methodology.Ecancermedicalscience, 10. Ferlay J, Ervik M, Lam F, Colombet M, Mery L, Piñeros M, Znaor A, Soerjomataram I, Bray F. 2018. Global Cancer Observatory: Cancer Today. Lyon, France: International Agency for Research on Cancer. Available from: https://gco.iarc.fr/today , accessed 19 April 2020. Baeza Pérez G, Calaf GM, Montalvo Villalba MT, Salgado Prieto K, Burgos C, F. Frequency of hematologic malignancies in the population of Arica. Chile Oncol Lett. 2019;18(5):5637–43. Miller KD, Fidler-Benaoudia M, Keegan TH, Hipp HS, Jemal A, Siegel RL. Cancer statistics for adolescents and young adults, 2020. Cancer J Clin. 2020;70(6):443–59. Risher KA, Cori A, Reniers G, Marston M, Calvert C, Crampin A, Dadirai T, Dube A, Gregson S, Herbst K, Lutalo T. Age patterns of HIV incidence in eastern and southern Africa: a modelling analysis of observational population-based cohort studies. Lancet HIV. 2021;8(7):e429–39. Morrison VA, Bell JA, Hamilton L, Ogbonnaya A, Shih HC, Hennenfent K, Eaddy M, Shou Y, Galaznik A. Economic burden of patients with diffuse large B-cell and follicular lymphoma treated in the USA. Future Oncol. 2018;14(25):2627–42. Green T, Bron D, Chomienne C, de Wit TD, de Haas F, Engert A, Hagenbeek A, Jäger U, MacIntyre E, Muckenthaler MU, Smand C. Costs of haematological disease high and rising. Lancet Haematol. 2016;3(8):e353–4. Sartorius K, Sartorius B, Govender PS, Sharma V, Sheriff A. The future cost of cancer in South Africa: An interdisciplinary cost management strategy. SAMJ: South Afr Med J. 2016;106(10):949–50. Pritchard JR, Lauffenburger DA, Hemann MT. Understanding resistance to combination chemotherapy. Drug Resist Updates. 2012;15(5–6):249–57. Jalali A, Ha FJ, Chong G, Grigg A, Mckendrick J, Schwarer AP, Doig R, Hamid A, Hawkes EA. Hodgkin lymphoma: an Australian experience of ABVD chemotherapy in the modern era. Ann Hematol. 2016;95(5):809–16. Wallace ZS, Harkness T, Blumenthal KG, Choi HK, Stone JH, Walensky RP. Increasing operational capacity and reducing costs of rituximab administration: a costing analysis. ACR open Rheumatol. 2020;2(5):261–8. Neuss MN, Polovich M, McNiff K, Esper P, Gilmore TR, LeFebvre KB, Schulmeister L, Jacobson JO. 2013. 2013 updated American Society of Clinical Oncology/Oncology Nursing Society chemotherapy administration safety standards including standards for the safe administration and management of oral chemotherapy. Journal of Oncology Practice, 9(2S), pp.5s-13s. Mokhtari RB, Homayouni TS, Baluch N, Morgatskaya E, Kumar S, Das B, Yeger H. 2017. Combination therapy in combating cancer. Oncotarget, 8(23), p.38022. Cömert M, Güneş AE, Şahin F, Saydam G. 2013. Quality of life and supportive care in multiple myeloma. Turkish Journal of Hematology, 30(3), p.234. Ogino MH, Tadi P, Cyclophosphamide. 2022. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2022. PMID: 31971727. Gotham D, Barber MJ, Hill AM. Estimation of cost-based prices for injectable medicines in the WHO Essential Medicines List. BMJ open. 2019;9(9):e027780. Griddine A, Bush JS. Ondansetron. 202. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2022 Jan–. PMID: 29763014. National Department of Health, South Africa. 2017. National Cancer Strategic Framework 2017–2022. Available at: http://www.health.gov.za/index.php/2014-08-15-12-53-24?download=3405:national-cancer-strategic-framework-2017-2022-min-pdf . Accessed 9 May 2021. Morrison VA, Bell JA, Hamilton L, Ogbonnaya A, Shih HC, Hennenfent K, Eaddy M, Shou Y, Galaznik A. Economic burden of patients with diffuse large B-cell and follicular lymphoma treated in the USA. Future Oncol. 2018;14(25):2627–42. Horton S, Gauvreau CL. Cancer in low-and middle-income countries: an economic overview. Cancer: disease control priorities. 2015;3:263–80. Neuss MN, Polovich M, McNiff K, Esper P, Gilmore TR, LeFebvre KB, Schulmeister L, Jacobson JO. 2013. 2013 updated American Society of Clinical Oncology/Oncology Nursing Society chemotherapy administration safety standards including standards for the safe administration and management of oral chemotherapy. Journal of Oncology Practice, 9(2S), pp.5s-13s. Wright C, Hislop R. The Price of Cancer: The Public Price of Registered Cancer in New Zealand. Ministry of Health; 2011. Cassim S, Antel K, Chetty DR, Oosthuizen J, Opie J, Mohamed Z, Verburgh E. Diffuse large B-cell lymphoma in a South African cohort with a high HIV prevalence: an analysis by cell-of-origin, Epstein–Barr virus infection and survival. Pathology. 2020;52(4):453–9. Reddy R, Gogia A, Kumar L, Sharma A, Bakhshi S, Sharma MC, Mallick S, Sahoo R. 2016. HIV-associated hematologic malignancies: experience from a tertiary cancer center in India. Indian journal of medical and paediatric oncology: official journal of Indian Society of Medical & Paediatric Oncology, 37(3), p.141. Wiggill TM, Mantina H, Willem P, Perner Y, Stevens WS. Changing pattern of lymphoma subgroups at a tertiary academic complex in a high-prevalence HIV setting: a South African perspective. JAIDS J Acquir Immune Defic Syndr. 2011;56(5):460–6. Idele P, Gillespie A, Porth T, Suzuki C, Mahy M, Kasedde S, Luo C. Epidemiology of HIV and AIDS among adolescents: current status, inequities, and data gaps. JAIDS J Acquir Immune Defic Syndr. 2014;66:S144–53. Department of Health. Guidelines for good practice in the conduct of clinical trials with human participants in South Africa. Pretoria, South Africa: Department of Health; 2006. Additional Declarations No competing interests reported. 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INTRODUCTION","content":"\u003cp\u003eThe prevalence of haematological cancers in South Africa is rising daily, mainly due to lifestyle changes and the growing HIV-positive population. As the incidence of cancer increases, so does the cost of treatment [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Haematological cancers are a significant cause of morbidity and mortality in sub-Saharan Africa. If mortality and morbidity rates continue to increase, haematological cancers will account for 10% of the cancer burden in the region by 2030 [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eCancer drug spending has increased faster than in other healthcare sectors [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In their duty to maximise population health with limited funding, publicly funded healthcare systems face unprecedented obstacles. The clinical health benefits of new cancer regimens are dwindling, and current pricing trends may not be sustainable for drug budgets [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. To protect the already-constrained budgets, the healthcare systems need to use health technology assessment (HTA) to inform decisions about which new drug regimens to fund with limited public resources to protect the already-constrained drug budgets [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSeveral costs go into managing haematological cancers, such as chemotherapy, laboratory tests, administration costs, and supportive care medicines; thus, it is crucial to know the different cost implications of certain health decisions [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In a study conducted in Southern Nigeria, it was determined that the average total cost of care for a patient totalled N232882,95 (\u003cspan\u003e$\u003c/span\u003e638,04) per patient, with non-Hodgkin\u0026rsquo;s lymphoma having the highest cost of care of roughly N70000 (\u003cspan\u003e$\u003c/span\u003e190) per patient per month [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eGiven the increase in the prevalence and burden of haematological cancers, it is imperative to have an in-depth understanding of the direct costs involved, especially in aligning resources to patient needs and responding to changes in patient requirements as we progress towards Universal Health Coverage (UHC). This will have significant financial implications; thus, identifying the actual burden and cost is necessary for a financially stable National Health Insurance (NHI) fund [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Hence, understanding where the most significant treatment costs lie can be used to decide how funds should be allocated to adequately meet the demands, thus ensuring better access to cancer treatment, equitable healthcare spending, and making the best available resources.\u003c/p\u003e"},{"header":"2. METHODOLOGY","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 STUDY DESIGN\u003c/h2\u003e\u003cp\u003eThis descriptive retrospective study used data from patient files and medical records.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 STUDY SITE\u003c/h2\u003e\u003cp\u003eThe study was conducted at a public tertiary hospital outpatient haematology clinic unit in Gauteng Province, north of Pretoria, South Africa. This teaching hospital has expert physicians, nurses, and cancer detection and treatment equipment.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 STUDY SAMPLE AND POPULATION\u003c/h2\u003e\u003cp\u003eAll files of adult patients diagnosed with haematological cancers and treated in the Haematology unit 12 months prior to data collection were reviewed (May 2020 ̶- May 2021). A total of 121 patients were treated for haematological conditions at the clinic, but only 53 patients' files met the inclusion criteria. The haematology outpatient clinic registry was used to identify patient file numbers.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 DATA COLLECTION AND INSTRUMENT\u003c/h2\u003e\u003cp\u003eA data collection instrument adapted from a study by Herbst [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] in 2017 facilitated information collection regarding demographics, chemotherapy costs, laboratory tests, administration costs (consumables and healthcare workers), and supportive care medication costs. A microcosting approach was used to estimate the inputs and usage separately. A time-motion study was used to quantify the time and expenses of compounding chemotherapy, chemotherapy administration, and doctor consultation.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 DATA ENTRY AND ANALYSIS\u003c/h2\u003e\u003cp\u003eData were captured using Microsoft Excel\u0026trade; and imported into the Statistical Package for the Social Sciences (SPSS\u0026reg;), version 25, for analysis.\u003c/p\u003e\u003cp\u003eThis study was conducted from the perspective of a healthcare provider, and costs were determined based on the provider's expenditures. Chemotherapy, laboratory tests, consumables, and supportive care medication costs were obtained from the master procurement list, with cost prices from the South African National Department of Health, Uniform Patient Fee Schedule (UPFS), tender single exit price list, and NHLS state price list. The costing model was adapted from a study by Herbst [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] in 2017, where chemotherapy costs were calculated by determining the cost of each drug per cycle multiplied by the number of cycles in the 12-month review period.\u003c/p\u003e\u003cp\u003eOnly six patients were observed to determine the number of consumable items each patient used, and from there, the average number of items used for each patient was calculated. Additional necessary information was obtained from the patient files. The price for each item were obtained from the hospital\u0026rsquo;s Master Procurement List.\u003c/p\u003e\u003cp\u003eDirect medical costs were determined by multiplying the total amount of each medical resource by the unit cost, including an adapted \u0026lsquo;time-motion\u0026rsquo; method for administration costs. The sum of these costs was used to establish the average total cost of care per patient. The total direct cost of medical care was calculated as the indirect inpatient costs associated with diagnosing and treating haematological cancers (leukaemia, lymphoma, and myeloma), excluding all costs unrelated to malignancy.\u003c/p\u003e\u003cp\u003eThe bootstrap method was used to increase the validity of the data. The data were adjusted and resampled, and similar results were obtained. Non-parametric analysis was employed due to smaller population sizes and cost data often having gaps that do not fall into specified distributions with estimable parameters; therefore, non-parametric statistics were utilized to evaluate relationships and establish significance. The Kruskal-Wallis test was conducted to assess whether there was a significant difference between the total costs, type of cancer, and each cost variable. Statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. RESULTS","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Demographics\u003c/h2\u003e\u003cp\u003eA total of 121 patients were registered at the haematology clinic. During data collection, 24 patients' folders were excluded because they were misplaced, and 44 patient files were excluded based on the exclusion criteria. Fifty-three patients' files who were diagnosed with haematological cancer and treated in the haematology unit 12 months prior to the commencement of data collection were enrolled in the study.\u003c/p\u003e\u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the sample (n\u0026thinsp;=\u0026thinsp;53) represented four types of haematological cancers and different racial groups. Hodgkin\u0026rsquo;s lymphoma had the highest patient count [36,0%; 19/53], while non-Hodgkin\u0026rsquo;s lymphoma had the lowest patient count [16,9%; 9/53]. The majority [84,9%; 45/53] of the patients were of African descent, and their ages ranged between 18 and 60 years, with 22% of the patients between the ages of 29 and 39 years. A total of 60,3% (32/53) of patients had comorbidities, with the highest prevalence of 40,7% (22/53), followed by hypertension at 18.9% 10/53). Only 21 patient files indicated the haematological cancer stage, with stage IV having the highest incidence at 26,4% (14/53), and 66,6% of patients with cancer. No statistically significant relationship was found between the type of cancer and demographic and health variables such as gender (P\u0026thinsp;=\u0026thinsp;0,115), age group (P\u0026thinsp;=\u0026thinsp;0,426), race, and cancer stage. Statistically insignificant relationships were also found between the total average costs and gender, age, race, and cancer stage.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePatient demographics (n\u0026thinsp;=\u0026thinsp;53)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eDemographic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHodgkin\u0026rsquo;s lymphoma\u003c/p\u003e\u003cp\u003en (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNon- Hodgkin\u0026rsquo;s lymphoma\u003c/p\u003e\u003cp\u003en (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMultiple myeloma\u003c/p\u003e\u003cp\u003en (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLeukaemia\u003c/p\u003e\u003cp\u003en (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003cp\u003en (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14 (26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3 (5,7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5 (9,4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8 (15,0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e30 (56,6)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5 (9,4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6(11,3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5(9,4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7(13,2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e23(43,4)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e\u003cb\u003eAge group\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18\u0026ndash;29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8 (15,9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1(1,9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2(3,8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e11(20,8)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e29\u0026ndash;39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7(13,2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2(3,8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1(1,9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2(3,8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e12(22,6)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e39\u0026ndash;49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4(7,5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3(5,7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3(5,7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e10(18,9)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e49\u0026ndash;59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1(1,9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6(11,3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3(5,7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e10(18,9)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3(5,7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2(3,8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5(9,4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e10(18,9)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003eCo-morbidities\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHIV/AIDS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13(24,5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4(7,5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5(9,4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e22(41,5)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHypertension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11,9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1(1,9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3(5,7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5(9,4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e10(18,9)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003eRace\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAfrican\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18(33,9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5(9,4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9(16,9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e13(24,5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e45(84,9)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWhite\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1(1,9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4(7,5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2(3,8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e7(13,2)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eColoured\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1(1,9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e1(1,9)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003eCancer stage\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2(3,8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2(3,8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e4(7.6)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2(3,8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1(1,9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e3(5,7)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6(11,3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2(3,8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1(1,9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5(9,4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e14(26,4)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Chemotherapy treatment regimens\u003c/h2\u003e\u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the ABVD regimen was the most frequently prescribed chemotherapeutic combination for Hodgkin lymphoma [13:(24,5,4%)]. A few [(4; (7, 5%)] patients did not have specified regimens, as they were prescribed different combinations of individual chemotherapeutic agents or a combination of more than one regimen. The R-FC combination was the most costly of all the regimens used, making it the regimen with the highest average cost per patient per regimen at R1868100,40, as highlighted in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. There are discrepancies in the number of patients per cancer from the demographics and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, as certain patients can be on more than one regimen or have changed to a different regimen during the study.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe total costs per chemotherapy regimen\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChemotherapy regimen\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNumber of patients per cancer\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTotal number of Patients per regimen\u003c/p\u003e\u003cp\u003en (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTotal treatment Cost per cycle (R)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTotal number of cycles per regimen (12 month review period)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eTotal cost\u003c/p\u003e\u003cp\u003e(R)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eAverage cost per patient per regimen\u003c/p\u003e\u003cp\u003e(R)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRICE\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2 (NHL):\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 \u003cb\u003e(3,7)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e89133,54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e891335,40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e445667,70\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBEACOPP\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4 (HL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 \u003cb\u003e(7,5)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e102189,9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1839418,2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e459854,55\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eR-CHOP\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4 (NHL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 \u003cb\u003e(7,5)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e230824,6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4616492\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1154123,00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eR-FC\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 (NHL)\u003c/p\u003e\u003cp\u003e5 (Leukaemia)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6 \u003cb\u003e(11,3)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e361567,82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e11208602,42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1868100,40\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMEC\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 (Leukaemia)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 \u003cb\u003e(1,9)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7473,3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e37366,50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e37366,65\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eABVD\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13 (HL)\u003c/p\u003e\u003cp\u003e1 (NHL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14 \u003cb\u003e(26,4)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e137239,95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e9744036,5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e696002,60\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCODOX\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 (NHL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 \u003cb\u003e(1,9)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2951,64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2951,64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2951,64\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eIDAC\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2 (Leukaemia)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 \u003cb\u003e(3,7)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3670,9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e25696,3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e12848,15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCVAD\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8 (MM)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8 \u003cb\u003e(15,1)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e13583,59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e611216,55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e76407,69\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eIH\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3 (Leukaemia)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 \u003cb\u003e(5,6)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e56646,9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1132938\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e377646,00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eIMATINIB\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2 (Leukaemia)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 \u003cb\u003e(3,7)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e19449\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e97245\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e48622,50\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBRENTUXIMAB\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2 (HL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 \u003cb\u003e(3,7)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e189870,48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2278445,76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1139222,88\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eUNSPECIFIED\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 (NHL)\u003c/p\u003e\u003cp\u003e2 (Leukaemia)\u003c/p\u003e\u003cp\u003e1 (MM)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 \u003cb\u003e(7,5)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8620,06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e68960,48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e17240,12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTOTAL\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e53\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e53\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e1223220,7\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e253\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e32554704,70\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e6341800,76\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe costs per cycle for each patient were determined using the following formula.\u003c/p\u003e\u003cp\u003e\u003cb\u003eDrug cost per cycle\u003c/b\u003e = [(dose \u0026times; average body surface area (BSA) or body weight (BW) (number of administrations per cycle) (number of vials/tabs per cycle) (medicine price per vial/tablet)].\u003c/p\u003e\u003cp\u003eAs indicated in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the total cost of chemotherapeutic medicines per cycle was R1223220.68, with an average of R23079.65 per patient. The relationship between total medication cost per cycle and cancer type was statistically significant (P\u0026thinsp;=\u0026thinsp;0,047).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Laboratory tests\u003c/h2\u003e\u003cp\u003eA total of 1664 laboratory tests were routinely performed during the study review period. The mean number of laboratory tests per patient was 64. Laboratory tests were performed as part of a regular protocol before chemotherapy was administered for each cycle. The average cost of laboratory tests per patient was R2971,60 per cycle. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the main cost drivers of the laboratory tests were urea, electrolytes, and creatinine tests, which were R48369 [(25,67%)]. Hodgkin\u0026rsquo;s lymphoma had the highest laboratory test costs (R 77873,72 [(40,8%)]; no statistical difference was found between the laboratory tests by type of cancer.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCost of laboratory tests for all patients (n\u0026thinsp;=\u0026thinsp;53)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLaboratory test\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCost per test (Rands)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHodgkin\u0026rsquo;s lymphoma (Number of times test was conducted)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNon-Hodgkin\u0026rsquo;s lymphoma\u003c/p\u003e\u003cp\u003e(Number of times test was conducted)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTotal costs for Leukaemia\u003c/p\u003e\u003cp\u003e(Number of times test was conducted)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMultiple Myeloma (Number of times test was conducted)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eTotal (Rand)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCSF Chemistry \u0026amp; Microscopy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e183\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e732 \u003cb\u003e(4)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e732 (4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eINR \u0026amp; PTT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e150\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e600 (4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e300 (2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1350 (9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e300 (2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2550 (17)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrea, electrolytes, creatinine\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e105,15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16193,10 (154)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11987,10 (114)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e14931,30 (142)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5257,50 (50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e48369 (460)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlood gas\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e54,13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e108,26 (2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e108,26 (2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCalcium, Magnesium, Inorganic Phosphate and Uric Acid (CMPU)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e91,05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10106,55 (111)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5189,85 (57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7284 (80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4006,20 (44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e26586,60 (292)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGeneXpert MTB/RIF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e191,96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e191,96 (1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e191,96 (1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e383,92 (2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHIV serology\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e55,89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e391,23 (7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e391,23 (7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHIV viral load\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e339,39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3393,90 (10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3393,90 (10)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCreatine kinase\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e48,52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e776,32 (16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e194,08 (4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e970,40 (20)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFull blood count\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e89,55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10925,1 (122)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8865,45 (99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e16298,10 (182)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5462,55 (61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e41551,2 (464)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLiver function tests (LFT)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e426\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25560 (60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7242 (17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6390 (15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4686 (11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e43878 (103)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGlucose\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e30,35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e182,10 (6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e182,10 (6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e181,60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3268,80 (18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e544,80 (3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e181,60 (1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3087,20 (17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e7082,40 (39)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC-reactive protein (CRP)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e72,96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e510,72 (7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e145,92 (2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e656,64 (9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHepatitis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e125,90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e377,70 (3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e629,50 (5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e251,80 (2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1259 (10)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProcalcitonin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e259,03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e259,03 (1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e518,06 (2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e777,08 (3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTB culture\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e75,45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e679,05 (9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e75,45 (1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e452,70 (6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e75,45 (1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1282,65 (17)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHIV PCR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e380\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3800 (10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1900 (5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3040 (8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1140 (3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e9880 (26)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean,\u003c/p\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e158,89\u003c/p\u003e\u003cp\u003e116,22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4580,81\u003c/p\u003e\u003cp\u003e6943,39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3132,68\u003c/p\u003e\u003cp\u003e3964,73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5036,16\u003c/p\u003e\u003cp\u003e5832,92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2689,65\u003c/p\u003e\u003cp\u003e2143,31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e10557,47\u003c/p\u003e\u003cp\u003e16438,08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal cost\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e77873,72 (539)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e37592,21 (311)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e50361,60(451)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e24206,86 (190)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e190033,79 (1491)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e4.4 Administration costs\u003c/h2\u003e\u003cp\u003e\u003cb\u003eConsumables\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe safety box used to dispose syringes and needles was the cost of drivers for consumables R3064,46 of the total consumable costs. The average cost for consumables per patient was R265,02 per cycle, as indicated in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCost of consumables used for chemotherapy administration.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eChemotherapy\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eItem\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNumber of items\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCost (Rand)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTotal cost (Rand)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGloves, non-sterile, single-use\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0,51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e27,03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSurgical face mask\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e49,95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2647,35\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eApron impermeable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2067\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBandage, Adhesive, 3.0 cm, 100/box\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3,9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e206,7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCompress, gauze, sterile and nonsterile, single-use\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e36,07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1911,71\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeedles, luer, sterile, single-use (sizes G3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13,65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e723,45\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSafety box for used syringes/needles\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e57,82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3064,46\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSkin-cleaning wipe/swab-pad, alcohol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e18,03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e955,59\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTape, medical, roll (various sizes)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22,33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1183,49\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSyringes (various capacities)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0,68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e36,04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIV catheters #22 and #24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12,7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e673,1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntravenous catheter (sizes G3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10,38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e550,14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e265,02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e14046,06\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e4.5 Time-motion\u003c/h2\u003e\u003cp\u003eAs indicated in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, the time-motion was recorded as the average for all patients with cancer. The average time spent by the patients at the clinic was 7 and 22 minutes, respectively. The average cost per patient for chemotherapy was R1591,33. The administration of chemotherapy monitoring averaged 2 hours and 31 minutes, respectively. The length of time varied depending on the type of cancer and treatment regimen used. Administration and monitoring cost R502,83 in terms of nursing staff remuneration. Pharmacist compounding of chemotherapy medication had the highest cost at R658,05. The pharmacist\u0026rsquo;s time reflected the actual cost incurred from when the pharmacy received the prescription from the clinic to when the compounded chemotherapeutic medicines were received at the clinic.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAverage time and costs\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTask\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAverage time (hr: min)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSalary cost per minute (Rand)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAverage cost per cycle (Rand)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCheck-in\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e00:04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1,51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6,04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePreparation of patient file\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e00:07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1,51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10,57\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDrawing of blood\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e00:08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3,33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e26,61\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLabs\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e02:07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1,77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e224,79\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePhysician visit\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e00:13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10,19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e132,47\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePharmacy\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e02:03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5,35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e658,05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDrip administration and file completion\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e00:09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3,33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e29,97\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAdministration monitoring\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e02:31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3,33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e502,83\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e07:22\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e30,32\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e1591,33\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e4.6 Supportive care medication\u003c/h2\u003e\u003cp\u003eThe total cost of supportive care medicine was R150967,05. The most common adverse effect observed in this study was pain, with pain medications prescribed for 11 (20,75%). Nausea and vomiting were the most common adverse events. Three types of nausea and vomiting prevention and treatment medicines (ondansetron, promethazine hydrochloride, and metoclopramide) were prescribed to 36 (67,9%). Multiple myeloma had the highest average cost of supportive care medicines at R3852,93, whereas Hodgkin\u0026rsquo;s lymphoma had the lowest average cost at R559,93 (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Hydrocortisone was the most prescribed supportive care medication, with 27 (50,9%) patients being prescribed hydrocortisone (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). As presented in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, the major cost driver for supportive care medicines was MESNA at R96968, with multiple myeloma having the highest cost of supportive care medicines at R64861,62. Statistical significance was also found between the average total costs and supportive care medications (P\u0026thinsp;=\u0026thinsp;0,002).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCost (R) of supportive care medications per type of cancer\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSupportive care medication\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIndication\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHodgkin\u0026rsquo;s Lymphoma\u003c/p\u003e\u003cp\u003e(n)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNon-Hodgkin\u0026rsquo;s Lymphoma\u003c/p\u003e\u003cp\u003e(n)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLeukaemia (n)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMultiple Myeloma\u003c/p\u003e\u003cp\u003e(n)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eTotal (R)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCost per patient\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOndansetron IV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003ePrevention and symptomatic treatment of chemotherapy induced nausea and vomiting\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16180,58 (14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e883,82 (2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7958,40 (6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3156,50 (4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e28179,30 (26)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e1083,82\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePromethazine Hydrochloride IV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e938,16 (2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1997,22 (4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e469,08 (1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e3404,46 (7)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e486,35\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMetoclopramide IV/TAB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3540 (2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e236 (1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e3776 (3)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e1258,67\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHydrocortisone IV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eAnti-inflammatory\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7498,44 (13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2233,44 (5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2131,92 (6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e913,68 (3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e1277,48 (27)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e47,31\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrednisone TAB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e410,4 (5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e357,84 (4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e124,20 (3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e892,44 (12)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e74,37\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParacetamol IV/TAB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ePain management\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3,90 (3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e921,60 (6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2,1(2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e927,60 (11)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e84,33\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTramadol TAB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3432,52(2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e3432,52 (2)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e1716,26\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFolic acid TAB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFolic acid deficiency and prevention of severe toxicity from certain chemotherapy agents\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e112,88 (3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e122,40 (1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e401,20(3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e636,48 (7)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e90,92\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCalcium Folinate TAB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReduces toxicity and counteract the effects of folate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1148,40 (2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e1148,40 (2)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e574,2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMESNA IV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCytoprotectant of the urinary tract\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26588 (3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23460 (5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9384 (1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e37536 (7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e96968 (16)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e6\u0026nbsp;060,50\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAllopurinol TAB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHyperuricaemia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e638 (3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1518 (6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e2156 (9)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e239,55\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMethotrexate TAB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAntimetabolite\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e--\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e616,61 (3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e616,61 (3)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e205,53\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLansoprazole TAB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAcid reflux\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e554,49 (3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e694,80 (1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e1249,29 (4)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e312,32\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eZoledronic acid IV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHypercalcemia/reduction of bone metastasis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e23131,24 (3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e23131,24 (3)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e771,08\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFilgrastim\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eChemotherapy-induced neutropenia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3773,08(1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e3373,08 (1)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e3373,08\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e56464,85\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e36209,18\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e25533,22\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e64861,62\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e150967,05\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eTotal costs (n\u0026thinsp;=\u0026thinsp;53)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCost\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAverage cost per cycle (R)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAverage cost per patient\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTotal costs(R)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP-value adjusted\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eChemotherapeutic agents\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24190,61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e119656,61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6341800,76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0,001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0,000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLaboratory tests\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e751,12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3585,54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e182769,89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0,110\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0,107\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eConsumables\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e55,51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e265,02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14046,06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0,512\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0,520\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAdministration costs\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6,28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30,02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e402606,49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSupportive care medication\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e596,71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2848,43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e150967,05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0,002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0,02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eR25600,23\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eR126385,62\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eR7092190\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe total cost to the hospital for treating 53 patients in the 12-month review period was R7092190, with chemotherapeutic agents being the cost driver at R6341800 ([76 (89,48%)]). Statistical differences were found among the total costs, chemotherapeutic agents (P\u0026thinsp;=\u0026thinsp;0,001), and supportive care medications (P\u0026thinsp;=\u0026thinsp;0,002).\u003c/p\u003e\u003cp\u003eAll costs included those of all the patients who met the inclusion criteria. The average cost per cycle was R25600,23 (SD\u0026thinsp;=\u0026thinsp;9539,755) per patient. Chemotherapeutic agents were the major cost drivers at R24190,61 (94,49%), followed by laboratory tests at R751,12(2,93%), with consumables costing the least at R55,51 (0,21%), as indicated in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. The total number of cycles per patient was 253, translated to R126385,62, for managing and treating haematological cancer per patient over 12 months. No statistically significant differences were found in the average cost per cycle.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. DISCUSSION","content":"\u003cp\u003eLimited information and published literature on the direct costs of managing and treating haematological cancers in South Africa motivated this research. Therefore, this study was conducted to address the gap in the micro-cost of the treatment of haematological cancers in the public sector and provide baseline data that can be used in future cost-effectiveness studies in this population. The study showed that Hodgkin\u0026rsquo;s lymphoma was the most prevalent haematological cancer in this population. Furthermore, the majority of patients were female, and the most common co-morbidity was HIV. Hodgkin\u0026rsquo;s lymphoma was the most prevalent haemolytic cancer in this study. The average cost of treating patients per year was R126385,62. Chemotherapy agents at R119656,61 per year (p\u0026thinsp;=\u0026thinsp;0.001) were found to be the cost driver in treating haematologic cancers, followed by medication used as supportive care.\u003c/p\u003e\u003cp\u003eIn this study, Hodgkin\u0026rsquo;s lymphoma had the highest patient count, while non-Hodgkin\u0026rsquo;s lymphoma had the lowest, contrary to the literature on the global prevalence of haematological cancer trends [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Contrary to our study, a study conducted in South America found that out of 92 cases [56,5% (52/92] ), only 12% (n\u0026thinsp;=\u0026thinsp;11) were attributed to HL [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. According to a study by Ferlay et al. [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] 2018, NHL was the most common haematological malignancy in South Africa. In the UK, the incidence of HL was found to be more frequent than that of NHL in patients aged between 15 and 39 [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. This was similar to the age distribution observed in the present study. These findings indicate that haematological cancers are more frequently observed in young adults with SA. This is also attributed to the high prevalence of HIV/AIDS in younger people in Southern Africa, where the incidence generally peaks between 20 and 24 years for women and 25\u0026ndash;29 years for men [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAlthough there were only a few direct comparisons, this study's overall average cost per patient was higher than in studies conducted in Nigeria [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] and lower than in the USA [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. This might be attributed to differences in population, cancer incidence, socioeconomic level, and healthcare delivery systems. The study conducted in Nigeria estimated the average cost of managing haematological cancer to be \u003cspan\u003e$\u003c/span\u003e638,04 (approximately R10182,48). The study in the USA estimated the average cost per year to be \u003cspan\u003e$\u003c/span\u003e 112,000 (approximately R178,942,400). In underdeveloped nations, where economic restrictions sometimes limit access to adequate care, it is especially important to calculate treatment costs. This finding is supported by Ghuza et al. (2020), who asserted that an economically feasible treatment choice without jeopardising patient survival is preferable.\u003c/p\u003e\u003cp\u003eAccording to our findings, chemotherapeutic agents were the major cost drivers (P\u0026thinsp;=\u0026thinsp;0,001), contrary to Reyes et al. [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], who showed that hospitalisations contributed 50% of the total cost. Green et al. [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] found that healthcare expenditures per patient with blood malignancies are twice as high as ordinary cancer costs in Europe owing to extensive hospital stays and complex treatment and diagnostics. Sartorius et al. [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] stated that cancer medications are expensive but often make up a small portion of the overall cost. Surgery, inpatient care, consultations, and repeated diagnostic and staging procedures exponentially increase expenses. The differences are attributable to the fact that hospitalisation, scans, and imaging were not included in our study, as our study was conducted from the hematology outpatient clinic where patients came weekly or depending on their chemotherapy cycle schedule to receive their medication. In scanning and imaging, inpatient care should be factored into and considered in any new direct cost quantification study to determine the full extent of the cost of the actual need.\u003c/p\u003e\u003cp\u003eChemotherapeutic agents were administered under different regimens. Medications that act via different mechanisms are combined to reduce the probability of developing resistant cancer cells. Once the diverse effects of these medications are combined, each agent can be administered at its optimal dose without causing severe side effects [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The most frequently prescribed regimen, with the highest number of cycles, was the ABVD regimen. This is because ABVD is the standard treatment regimen for Hodgkin\u0026rsquo;s lymphoma [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], the most common cancer in our study. The costlier regimen was R-FC. This is attributed mainly to the chemotherapy drug rituximab, an expensive drug that requires a significant number of resources to administer [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. This could be reduced by the use of subcutaneous rituximab, which will have shorter visit times as it has a shorter administration time, or the utilisation of a rituximab biosimilar, which would be less costly to acquire but equally effective with faster infusion rates [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSupportive care medications also impacted the total cost, as chemotherapy may cause various side effects. Each patient was prescribed supportive care medications based on the chemotherapeutic agents, combinations, and adverse symptoms [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The number of cycles per patient also affects the prescription of supportive care medications; some cycles do not have supportive care medications, whereas others are prescribed for every cycle [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The coordinated action of supportive treatments can considerably maximise therapeutic outcomes and improve the well-being of patients [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. MESNA, a costly supportive care medication, is a cytoprotectant that is usually administered with cyclophosphamide to reduce the risk of bladder inflammation that can cause severe bleeding [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The higher cost of MESNA can be attributed to construction and operation costs, raw materials such as glass vials, active pharmaceutical ingredients, process waste procurement, transportation, and administration [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Another commonly prescribed medication is ondansetron, which prevent and treat chemotherapy-induced nausea and vomiting by blocking the action of serotonin. These natural substances can cause nausea and vomiting [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Hydrocortisone is also commonly used to prevent inflammation [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eNHL had the highest average cost per cycle when compared to other studies conducted in the USA by Morrison et al. [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], which estimated the mean cost for treating NHL to be \u003cspan\u003e$\u003c/span\u003e11,890 (approximately R184560,15) and in the study done by Korubo [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] in Nigeria found the cost to be \u003cspan\u003e$\u003c/span\u003e1022.46 (approximately R15 859,21). The costs between the two studies differ by a large margin; this contrast can also be attributed to the cost methodology and differences in resource availability, methods of payment, and treatment protocols in developed countries [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eLaboratory costs significantly impacted the total cost, but this was expected given that blood was drawn for laboratory tests before each administration of chemotherapy. This is done to assist the physician in determining whether the patient\u0026rsquo;s blood count is satisfactory for chemotherapy and how well the patient responds to therapy [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. This aligns with Wright and Hislop [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], who found laboratory tests at around a similar price to R2262,18.\u003c/p\u003e\u003cp\u003eAs expected, the most common comorbidity in the study population was HIV/AIDS, followed by hypertension, as South Africa has the world\u0026rsquo;s highest prevalence of HIV/AIDS. South Africa accounts for 20% of the global HIV-infected population, and HIV/AIDS is a risk factor for lymphoma [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. However, with the introduction of antiretroviral therapy (ART), the likelihood of developing AIDS-defining haematological malignancies has decreased with the introduction of ART [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Therefore, one would expect a lower incidence of these cancers because of ART. Our results agree with those of global studies [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] that have observed a link between HIV/AIDS and HL among HIV-positive individuals. HIV Infection may cause a person to be more prone to haematological cancers, and people infected with HIV are at an increased risk of developing NHL and HL [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] due to limitations in information found in patient medical records. It is unknown whether the individuals were infected with HIV before or after being diagnosed with hematologic malignancy. Gopal et al. [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] reiterated the importance of optimising and improving cancer screening and prevention techniques for HIV-infected people to reduce the costs of HIV-associated malignancies and adequately plan for the financial burden that they might impose. Expanding care and research for HIV-associated malignancies in areas where HIV/AIDS is most prevalent, such as East and Southern Africa\u0026rsquo;s low- and middle-income countries, can lead to significant scientific and humanitarian breakthroughs [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e"},{"header":"5. LIMITATIONS","content":"\u003cp\u003eThe sample size needed to be larger, making the methods more challenging to reproduce. The patients were not followed up until they were in remission. Therefore, the total costs for the duration of the disease were not determined. Hospitalisation due to the disease was not part of the cost process, thus limiting the representation of the findings.\u003c/p\u003e"},{"header":"6. CONCLUSIONS AND RECOMMENDATIONS","content":"\u003cp\u003eThe direct costs of managing and treating haematological cancers were quantified according to public tertiary haematological cancer management protocols. The main cost drivers were chemotherapeutic agents. This study provides data that can help ensure that decisions and planning for haematological cancer management are evidence-based, thus ensuring that resource allocation is proportional to the predicted burden of disease and associated expenditures. This study projects the cost of haematological cancers in one public hospital. Effective cancer service planning and strategizing will require similar projections for all cancers on a larger scale in different hospital settings to ensure comprehensiveness. Therefore, restructuring the approach in which haematological cancer treatment and management services in the public sector are planned and budgeted for is recommended.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eCompeting interests\u003c/h2\u003e\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\u003c/p\u003e\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003cp\u003e This study adhered to the principles of the Declaration of Helsinki and was approved by the Ethics Review Committee of Sefako Makgatho University (SMUREC/P/300/2020:PG). Given the study's retrospective nature, the informed consent requirement was waived. The hospital clinical director obtained the required gatekeeper permission to access records. All patient identifiers were de-linked and anonymised at the data collection point and aggregated thereafter. In this case, we used the hospital approval process governed by the Guidelines for Good Practice in Clinical Trials with Human Participants in South Africa [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003cp\u003e Patient consent is not applicable as there is no information or images that could lead to identification of a study participant.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding sources\u003c/h2\u003e\u003cp\u003eNo funding was received for this research\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eMMM, MM, and MS developed the study concept and completed the data collection. They then performed data analysis and wrote the manuscript. All authors approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e\u003cp\u003eWe acknowledge Mr. Katlego Mokgwabone (statistician), who assisted with data analysis in this paper. We would also like to thank the staff at the haematology unit for their assistance and support.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eRelevant data are included within the manuscript. However, further data will be available on request. Data will be available from Moliehi Matlala email: [email protected]\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHofman K. 2014. 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JAIDS J Acquir Immune Defic Syndr. 2014;66:S144\u0026ndash;53.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDepartment of Health. Guidelines for good practice in the conduct of clinical trials with human participants in South Africa. Pretoria, South Africa: Department of Health; 2006.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Cancer, Chemotherapy, Costs, Haematological, South Africa","lastPublishedDoi":"10.21203/rs.3.rs-7026606/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7026606/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eHaematological cancers require extensive treatment, which can cause a significant financial burden on the funder and provider of care. The incidence of haematological cancers is increasing in South Africa due to the rise in HIV/AIDS cases, and as cancer incidence rises, so do treatment costs. There is limited knowledge of the direct costs of managing these cancers in the public health care system in South Africa. This study aimed to determine the direct costs associated with the treatment and management of haematological cancers in a tertiary public sector hospital.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eA descriptive retrospective study was conducted using the data files and medical records of patients treated in the haematology unit 12 months prior to data collection. Using an adapted data collection instrument and a ' time-motion ' method, a micro-costing method was utilised to establish direct medical costs determined from the provider's perspective. The sum of all costs was used to establish the average total cost of care per haematological patient.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThe results are presented as an average per patient with 53 patient files that met the inclusion criteria. Hodgkin\u0026rsquo;s lymphoma had the highest patient count, accounting for 19 (36%) of the total sample. The average total cost of treatment and management of haematological cancer per patient was R 126385,62 per year, and the average cost of chemotherapy per cycle per patient was R25600,23. The major cost drivers were chemotherapeutic agents, with non-Hodgkin\u0026rsquo;s lymphoma having the highest average cost and the ABVD regimen being the most prominent.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eA comprehensive quantification of the direct costs of treating and managing haematological cancers was determined. Understanding the costs associated with these cancers will allow public healthcare funders to make effective financial decisions and adequate treatment plans.\u003c/p\u003e","manuscriptTitle":"The direct costs of treating and managing haematological cancers at a tertiary hospital: Payer’s perspective","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-18 06:06:56","doi":"10.21203/rs.3.rs-7026606/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2a8517c2-ad3c-4445-b0cc-8f6530b0bbcd","owner":[],"postedDate":"October 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-11T12:08:44+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-18 06:06:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7026606","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7026606","identity":"rs-7026606","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}