Study design and settings
A descriptive, cross-sectional study design was employed. There are total 23 (18 public and four private sector tertiary care) hospitals in Punjab province of Pakistan which provide services to cancer patients. Out of these 23 hospitals, seven were specialized cancer-care hospitals. One hospital was excluded from the survey because it provides services solely to the pediatrics. Survey was carried out in 22 cancer-care hospitals and 44 private pharmacies in Punjab, a province of Pakistan. Data were collected from the pharmacies and cancer patients attending selected hospitals and evaluated according to the objectives of study.
Study population and sample size
The population under study was cancer patients aged ≥18 years, who visited the selected cancer-care hospitals for routine examinations. According to the latest Pakistani census, the population of the surveyed province consisted of 101,391,000 individuals [20]. The minimum sample size was 4147 as calculated by the Raosoft sample size calculator [21] based on cancer prevalence in Pakistan. With contingency of 5% for non-response and inappropriate responses, the final sample was calculated to be 4400.
Data collection and outcome variables
A total of 4913 cancer patients were approached over a six month period (1st January, 2017 to 30th June, 2017), 4400 patients consented to participate (response rate = 89.6%). Data was collected at different intervals from the selected cancer-care hospitals.
A data collection form was designed for this study which consisted of three main parts: (1) socio-demographic characteristics, (2) diagnosis and (3) recommended medicines. The reliability of the survey tool was assessed by conducting a pilot study. Piloting was undertaken using data from 100 patients. After piloting, the data collection form was restructured.
Measurements
Socio-demographic characteristics
Socio-demographic characteristics given in Table 1 were recorded for each participant. Those participants who were retired (taking pension) or running a business were classified as employed and housewives were considered as unemployed. The data was obtained through face to face questioning of patients. To avoid biasness, the data regarding employment status and income level of the participants was validated by using online tax payer verification system of Federal Board of Revenue (FBR) [22].
Diagnosis and prescribing pattern
The type of cancer and all the medicines present in each prescription were noted on a pre-designed performa sheet. Anticancer medicines having more than one active ingredient were not evaluated. The most commonly prescribed anticancer medicines were categorized according to the prescribing trend; low (prescribed to <5% of the selected patients), medium (prescribed to ≥5% of the selected patients but <10%) and high (prescribed to >10% of the selected patients).
Availability of anticancer medicines and their per month cost
Forty anticancer medicines were chosen for the survey. These anticancer medicines were selected on the basis of, (a) pilot study in which local needs and cancer burden was assessed, (b) literature review, and (c) the opinions of various experts. During the survey, if medicines were present at the pharmacy settings then they considered as available.
The availability of anticancer medicines was evaluated in public hospitals, private hospitals, and private pharmacies. For the assessment of prices associated with these medicines, Pharmaguide 2016, was consulted [23]. The process of data collection was done by trained pharmacy students under the supervision of survey manager and principal investigator. Principal investigator checked the collected and completed Performa’s on weekly basis. If any information was found missing then a follow up visit to the respective setting was conducted. Before initiation of the process of data collection, medical superintendents/directors were contacted by the principal investigator. In this way a good cooperation was established between the team of investigators and the staff members of the selected settings. To avoid report biasness (e.g. up coding, less availability of medicine to gain attention for budget increase, etc.), the drugs were said to be available if they were present in the settings and the patients could avail them on prescription. Also, the formulary list and purchase records were assessed for data validation. For each medicine, data were collected on the basis of per unit price, and availability of OBs and LPGs. On the basis of standard guidelines and the recommended treatment, per unit price of anticancer agents were transformed into per month cost.
Furthermore, the following criteria were used to describe the availability of medicines:
Absent: 0% of facilities: these medicines were not found in any facility surveyed;
Low: <50% of facilities: these medicines were hard to find;
Fairly high: 50–74% of facilities: these medicines were available in many facilities;
High: >75% of facilities: good availability.
Affordability of anticancer medicines
According to the WHO and Health Action International (HAI) methodology, for the assessment of affordability we have to calculate that “the income of how many days is required to purchase the medicines for 30 days (in case of chronic condition e.g. cancer)”. Generally, if the total cost of therapy for 1 month is equal to or less than the wage of 1 day then it is said to be affordable.
A study published by Rasha Khatib et al. [24] defined it as; “if the combined cost of therapy is <20% of household capacity-to-pay then it can be considered as affordable.” In this study this concept modified and affordability was measured for each prescribed medicine by low, middle, and high income class of patients through this formula;
$$ Affordability=\frac{\%\ast \mathrm{of}\ \mathrm{household}\ \mathrm{capacity}\ \mathrm{to}\ \mathrm{pay}}{Per\ month cost of the medicine}\times 100 $$
* If 1 medicine was prescribed it was 20%, if 2 medicines were prescribed it was 10%, if 3 medicines were prescribed it was 6.7% and if 4 medicines were prescribed it was 5% of household capacity to pay.
Statistical analysis
Statistical Package for Social Sciences (IBM, SPSS Statistics for Windows, version 21.0. Armonk, NY: IBM Corp.) was used for data analysis. Descriptive statistics such as frequencies, percentages, and mean were used to present the data.