Abstract
Highlights
What are the main findings?
Malaria is an endemic disease throughout Nigeria, which is a developing country with high poverty levels of citizens, and patients’ inappropriate practices in combating malaria were reported as one of the causes.
Material resources, health workers, and patient-related factors were implicated as reasons for such practices.
What is the implication of the main finding?
This study will provide a comprehensive picture of the cost implications of treating the disease of patients and its projected economic impacts on the state.
It might be useful in advising the health policymakers, as well as the healthcare professionals and patients, towards necessary attention and interventions.
Abstract
Objectives: This study aims at evaluating the costs incurred by patients in Primary Healthcare facilities of Plateau State, Nigeria, due to uncomplicated malaria management. Methods: Patients’ information on resources used and absence from the labour market due to uncomplicated malaria illness were collected using the self-reported cost of illness instruments across 24 selected Primary Health Care (PHC) facilities in Plateau State. The collated data were used to estimate the direct medical and non-medical costs incurred by patients through the summation of the various costs paid out of pocket for the services; while the indirect cost was estimated using the human capital theory. All analyses were conducted through Microsoft Excel and IBM Statistical Package for Social Sciences (SPSS®) version 23 software. Results: The average direct cost per episode of uncomplicated malaria was estimated at NGN 2808.37/USD 7.39, while the indirect average money equivalence of the time lost due to the ailment was estimated at NGN 2717/USD 7.55, giving an average cost of treating uncomplicated malaria borne by patients in Plateau State per episode to be NGN 5525.37/USD 14.94. The projected annual cost of the disease was NGN 9, 921,671,307.22 (USD 27, 560,198.08). Conclusions: The study showed substantial financial costs borne by patients due to uncomplicated malaria in Plateau State, comprising 50.83% of direct cost and 49.17% of the indirect cost of medications.
Keywords: direct costs, human capital approach, indirect costs, primary health care facilities, uncomplicated malaria
1. Introduction
Malaria is a vector-borne disease caused by a parasitic protozoan of the genus plasmodium and is transmitted to humans through infected female mosquitoes known as Anopheles gambiae [1]. It is a worldwide public health problem, especially in Sub-Saharan Africa [2]. The impacts of the disease on populations include direct health impacts, in which the affected persons suffer from both physical and psychological pains, which may result in either morbidity or mortality; loss and/or decrease in economic activities of such patients and their caregivers, thereby leading to significant reductions in outputs [3,4,5]. Nigeria was reported as one of the countries that shared the largest burdens of the disease in Africa, with 25% of Africa’s burdens coming from the country [4,5,6].
Despite the huge amount of the country’s annual health budget for controlling the disease, in addition to various aids from external aids/development partners for controlling the disease, the benefits are still less felt by people, especially the rural dwellers, because it has not significantly reduced the disease incidence in the country [7]. This might be due to the reported correlations between malaria and poverty, whereby poverty sustains the conditions for malaria to thrive and malaria impedes economic growth, thereby keeping the population in poverty [8,9,10]. Lutala et al. [4] reported that people most affected by malaria are those living below the poverty line and Nigeria was shown to be one of the poorest countries in the world [6]. Due to the endemic nature of the disease in Nigeria, malaria affects the productivity levels of the people because such sick persons will not be able to work, or even if they can work, their inputs will be low compared to those of healthy persons; and there will also be time spent by both the patient and the caregivers during treatment, which cumulatively leads to a reduction in their earnings that would have been useful for the family needs. In addition, the little incomes earned by such populations are mostly spent on treating malaria-infected persons [11]. The combination of all these issues has a great impact on the patients and the country’s overall economic growth and productivity as a nation [3,5,12].
In order to ascertain the extent to which patients spent their resources during malaria management, there is every need to evaluate such management and services rendered in terms of their costs and consequences [13]. The cost of illness (COI) approach, also known as burdens of illness studies, is a partial economics evaluation method that is useful for the estimation of both the financial (direct and indirect) and intangible implications of the disease on the patients and society at large [13,14].
This includes the identification and estimation of various aspects of a disease’s impact, for example, money paid in consultation fees, laboratory tests, and cost of drugs or bed fees if admitted, and the non-healthcare direct cost including expenses incurred on transportation, food and others household expenditures related to the ailment [13,14]. Additionally included are the indirect costs, also known as productivity losses or productivity costs due to the ailment, which are borne by the individual patient or family, society, or employer [15], and can be best assessed using the human capital theory [16]. It is an important analytical method in the identification and estimation of the overall economic impact of ailments; with the advantage of defining the disease and its epidemiology in a particular population [17]. The analysis helps in knowing how much individuals/societies are spending on a particular disease and, by implication, the amount that would be saved if the disease were abolished.
Although some studies on the financial cost burdens of malaria on patients were conducted in some parts of Nigeria with varying outcomes, the majority of such studies were carried out in the South-Eastern region of the country [18,19,20,21,22]. The purpose of this study was to estimate the economic burdens of uncomplicated malaria management on patients receiving treatment in primary healthcare facilities of Plateau State, north-central Nigeria.
2. Methods
2.1. Study Design and Location
The prevalence-based bottom-up approach was used to assess the cost of uncomplicated malaria through patients’ perspectives in 24 PHC facilities across Plateau State, north-central Nigeria. The state has seventeen (17) Local Government Areas with a landmark of 30,913 km2 and population of 5,178,712 [23].
2.2. Sample Population, Size and Sampling Technique
The study was carried out on populations attending public PHC facilities across Plateau State for uncomplicated malaria management, including both genders and age groups.
Multistage probability sampling techniques including stratified and simple random approaches were used in the selection of participants. The sample size for the study was calculated in accordance with the World Health Organisation (WHO) manual on how to investigate drug use in health facilities [24,25]. Twenty-four (24) public PHC facilities were selected across the state and fifty (50) patients per PHC facility receiving treatment for uncomplicated malaria were recruited to participate in the study.
2.3. Ethical Approval
Approval for the study was granted by the Joint Research Review and Ethics Committee, Research Management Centre (RMC), MAHSA University, Malaysia (Ref. number: RMC/EC01/2016). Permission was also given by Plateau State Ministry of Health, Jos, Nigeria to access the PHC facilities, and the various directors of PHCs of the selected Local Government Areas (LGAs) were informed through memos prior to data collection.
2.4. Study Instruments
The COI instrument adapted from Salihu and Sanni [26] was used to estimate the cost impacts of uncomplicated malaria incurred by patients in PHC facilities. The instrument was divided into two subsections: subsection one consisted of relevant variables such as patients’ frequency of PHC facility visits for treatment, number of days spent on admission and cost (if admitted), means and cost of transportation, amount spent on registration, laboratory test, drugs and food per PHC visit, number of working hours when not sick and when sick, and national health insurance scheme (NHIS) registration status were used to estimate the cost of treating the disease using the patients’ perspectives approach.
2.5. Data Collection
Nine hundred and fifty-six (956) (79.67%) of the target populations receiving treatment for uncomplicated malaria who were available during the survey in the selected PHC facilities consented to participate in the study, who filled the self-reported instruments, and in situations where caregivers accompanied the patients, they were also given the instrument to fill for proper capturing of the cost incurred due to the ailment. The data were collected during the rainy season of Nigeria, between February and July 2017. Due to flooding and formation of water puddles that served as the breeding ground for mosquitoes, there was increased prevalence of malaria. To ensure that the correct information related to the various cost components paid by patients in accessing health services such as registration cost, laboratory and drug cost, and time spent waiting for medication among others were captured, the researcher only distributed the instrument to them to fill out at the point of drug dispensing, which was their last point of exit from the facility.
2.6. Economic Burden Analyses
The cost incurred by patients due to uncomplicated malaria in PHC facilities of Plateau State, Nigeria, was evaluated and presented.
Direct costs
Descriptive statistics of all the related variables used in extracting information that were used in estimating the direct and indirect cost burdens of uncomplicated malaria on patients were presented as frequency and percentage distributions for categorical and continuous variables, in addition to the mean, standard deviations, and median and interquartile range (IQR) values for continuous variables. The direct and indirect cost burdens of the disease on patients per episode were estimated through the bottom-up approach, in naira (NGN) (Nigerian currency) and converted to United State Dollars (USD) based on 2017 exchange rate of NGN 360 to 1 USD when the study was conducted. All the data were managed and analysed through Microsoft Excel (version 2007) and the IBM-SPSS®) version 23 software.
The direct financial cost variables included patients registrations, laboratory diagnosis, and cost of drugs (medical costs) and costs of transportation, food, and accommodation if admitted (non-medical costs) since some of the patients were kept in the facilities under observation for some hours before discharged to continue with their medications at home.
In summary, the estimated direct financial cost of managing the disease was calculated as follows:
| Direct cost (DC) = Medical cost (MC) + Non-medical cost (NMC) | (1) |
| Medical cost (MC) = Patients registrations cost (PRC) + Laboratory diagnosis cost (LDC) + Cost of drugs (CoD) | (2) |
| Non-medical cost (NMC) = Transportation cost (TC) + Feeding cost (FC) + Accommodation cost (AC) | (3) |
-
ii.
Indirect costs (opportunity costs)
The indirect monetary cost (opportunity cost due to loss of productive time during the ailment) was estimated using human capital theory (HCT) approach [14,15]. HCT is a valuation method for the estimation of indirect costs of ailments. This approach values potential loss in productivity in terms of potential lost earnings, unlike the friction cost method, which values the loss in productivity over the length of time a particular organisation takes to return to its initial production and cost levels. The HCT approach is widely used in studying productivity losses due to illnesses or injuries of workers.
In the valuation of financial losses by patients due to malaria illness, the average time loss (days) due to the disease was first calculated, including time lost by caregivers for children and other patients who were taken to healthcare facilities by family members or friends, which was then followed by estimating its economic values using opportunity cost approach.
The opportunity cost (Z) due to uncomplicated malaria is the value of the output that is lost as a result of the sick person or caregiver’s inability to work either permanently or partially due to malaria-related morbidity and premature mortality; this was calculated based on Equation (4) [27,28].
| Indirect cost (Z) = μ (Z1 + Z2 + Z3 … + Zn) | (4) |
where:
Z1 = waiting time at the PHC facilities;
Z2 = decreased in working time per day due to the ailment;
Z3 = time lost due to incapacitation;
Zn = other opportunity cost due to malaria ailment;
µ = market wages rate to represent the opportunity cost of lost time (for workers), or for self-employed and retired personnel, the average daily agricultural wage rate was used in the estimation of opportunity cost of lost time considering the large involvement of the study population in agriculture as main source of income.
2.7. Assumptions Made during the Calculations
The following assumptions were made during the calculations:
-
i.
Government working hours start from 8 am to 4 pm (8 h) per day;
-
ii.
Five working days per week, totalling 20 working days per month;
-
iii.
A minimum wage of NGN 18,000.00 (naira) per month for salary earners = NGN 900 per day;
-
iv.
Exchange rate: NGN 360.00 = 1 USD (based on Nigerian central bank foreign exchange rate of 2017);
-
v.
Average minimum monthly income for farmers in Nigeria = ?
For the self-employed categories of patients, average daily agricultural wage rate was used in calculating their opportunity cost of lost time because majority of them were farmers. There were no fixed daily agriculture wages in Plateau State, the amount ranges from NGN 500 to NGN 1500 per person depending on the location, since the study covers the whole state, and an average of NGN 1000 was used as the daily agriculture wage rate for the non-salary earners, while a daily wage of NGN 900 based on NGN 18,000 minimum wage per month was used for the salary earners.
Therefore, average daily wage for patients
| (5) |
Therefore, average daily wage of NGN 950 was used to estimate the average money equivalence of the time loss of patients due to uncomplicated malaria illness.
-
iii.
Economic burden due to uncomplicated malaria on patients in Plateau State
Therefore, the total economic burdens on patients due to uncomplicated malaria treatment in PHC facilities of Plateau State was estimated by the summation of direct cost (medical and non-medical) and the indirect costs as follows:
| The financial cost of illness = Direct costs + Indirect costs. |
-
iv.
Projected annual economic burden due to malaria on patients in Plateau State
In order to estimate the annual economic burden due to uncomplicated malari borne by patients in Plateau State, malaria prevalence rate baseline of 0.56 was used in the analysis. This was estimated from two sources: retrospective study aspect of the present study across PHC facilities of the state, which estimated the rate as 0.63 and similar prevalence studies conducted recently in Plateau State, which found the rate to be 0.4 [29]. The basis of using the average rate was to ensure wider coverage across the state since Nanvyat et al.’s [29] study was conducted only in the southern and northern parts of the state, while the present study covered the three zones (northern, central, and southern) of the state.
Based on the last census conducted in Nigeria, the population of Plateau State was 3,206,531 [30]. This was used when estimating the annual financial cost of malaria borne by patients in the state.
| Projected economic burden = Mean cost × population × prevalence rate | (6) |
3. Results
A total of nine hundred and fifty-six (956) patients participated in the study and all of them attempted all the questions, which indicated their acceptability to participate in the survey. Five hundred and twenty-three (523) (54.7%) of them were females, while 433 (45.3%) were males. Four hundred and seventy-five (475) (49.69%) of the respondents were salary earners, while 481 (50.31%) were non-salary earners. Regarding their responses to the individual items, the majority of them (75.29%) had visited a PHC facility about 1–2 times for malaria-related ailments in the last 30 days, with the least (0.58%) indicating ≥ 5 times of visits to the facilities for the same purpose. Many respondents (50.73%) travelled to the PHC facilities by motorbike, popularly known as ‘going’, followed by 25.00% that preferred trekking and 10.46% that travelled by commercial cars. With a mean (±SD) of 3.12 (±3.90) hours’ time spent at PHC facilities by patients being attended to by healthcare workers per visit, most of them (42.68%) spent between 3–4 h, while 32.32% spent between 1.5 and 2.5 h, and only about 8.89% of the population spent ≥ 5 h in the PHC facilities, including those that were admitted whereby some spent not less than 48 h depending on the seriousness of their conditions. With the overall mean (±SD) working hours of 6.38 (± 3.21) per day before being sick, many worked for between 4 and 6 (46.23%) and 7 and 8 (29.39%) hours per day before they became sick but during post-diagnosis, the overall mean (±SD) daily working hours of the respondents reduced to 2.3 (±2.62), with many working for ≤3 (42.57%) and 4–6 h (37.34%) per day.
Apart from the indirect cost incurred by patients in terms of average time lost due to malaria ailment, various amounts of money (mean (±SD)) (in Naira) were spent by patients to gain access to treatment in PHC facilities. These included the cost of transportation to PHC facilities (NGN 178.28 (±214.20)), registration (NGN 138.34 (±426.78)), laboratory services (NGN 377.17 (±250.30)), purchase of anti-malarial drugs and co-medications (NGN 779.17 (±459.37)), food (NGN 168.55 (±270.34)), and accommodation (NGN 1166.86 (±1859.59)) for those who were admitted, which constituted the direct medical and non-medical cost burdens on the patients. About 4.08% of the study population were beneficiaries of health-related insurance policies/schemes. The details of payment distribution among the participants are presented in Table 1.
Table 1.
Frequency distribution of patients’ cost variables for uncomplicated malaria (N = 956).
| Variables | n (%) | Descriptive Statistics |
|---|---|---|
| Number of visits to PHC facilities in past 30 days (times) | ||
| Mean (±SD) | 2.53 (±1.93) | |
| Median (IQR) | 2 (0) | |
| Minimum–Maximum | 1–6 | |
| Have never | 88 (9.23) | |
| 1–2 times | 720 (75.29) | |
| 3–4 times | 142 (14.90) | |
| ≥5 times | 6 (0.58) | |
| Number of days on admission in PHC facility (mean (± SD)) | 0.03 (±0.20) | |
| <0.5 | 61 (6.38) | |
| 0.5–1 | 9 (0.94) | |
| Means of travel to PHC facilities | ||
| Trekking | 239 (25.00) | |
| Bicycle | 82 (8.58) | |
| Motorbike (Going) | 485 (50.73) | |
| Commercial car | 100 (10.46) | |
| Other means | 50 (5.23) | |
| Total time spent in PHC facilities per visit (hours) | ||
| Mean (±SD) | 3.12 (±3.90) | |
| Median (IQR) | 3 (1) | |
| Minimum–Maximum | 0–48 | |
| ≤1 | 154 (16.11) | |
| 1.5–2.5 | 309 (32.32) | |
| 3–4 | 408 (42.68) | |
| ≥5 | 85 (8.89) | |
| Transportation/visit cost to PHC facilities (NGN) | ||
| Mean (±SD) | 178.28 (±214.2) | |
| Median (IQR) | 200 (200) | |
| Minimum–Maximum | 10–2000 | |
| ≤NGN 100 | 266 (27.82) | |
| NGN150–250 | 272 (28.45) | |
| ≥NGN 300 | 70 (7.32) | |
| Registration cost before accessing PHC facilities (NGN) | ||
| Mean (±SD) | 138.34 (±426.78) | |
| Median (IQR) | 50 (450) | |
| Minimum–Maximum | 20–1000 | |
| ≤NGN 50 | 357 (37.34) | |
| NGN 60–NGN 200 | 236 (24.69) | |
| ≥NGN 250 | 86 (9.00) | |
| Blood taken for laboratory test | ||
| Yes | 861 (90.06) | |
| No | 95 (9.94) | |
| Laboratory test cost (NGN) | ||
| Mean (±SD) | 377.17 (±250.30) | |
| Median (IQR) | 350 (300) | |
| Minimum–Maximum | 100–2500 | |
| NGN 100 | 178 (18.62) | |
| NGN 150–350 | 291 (30.44) | |
| NGN 400–650 | 316 (33.05) | |
| ≥NGN 700 | 76 (7.95) | |
| Cost of prescribed drugs (NGN) | ||
| Mean (±SD) | 779.17 (±459.37) | |
| Median (IQR) | 700 (1000) | |
| Minimum–Maximum | 80–5000 | |
| ≤NGN 400 | 132 (13.81) | |
| NGN 450–800 | 483 (50.52) | |
| NGN 850–1250 | 200 (20.92) | |
| ≥NGN 1300 | 111 (11.61) | |
| Food cost during visit to PHC facilities (NGN) | ||
| Mean (±SD) | 168.55 (±270.34) | |
| Median (IQR) | 100 (100) | |
| Minimum–Maximum | 20–900 | |
| ≤NGN 100 | 210 (21.97) | |
| NGN 150–500 | 117 (12.24) | |
| ≥NGN 550 | 22 (2.30) | |
| Accommodation cost in PHC facilities (if admitted) (NGN) | ||
| Mean (±SD) | 1166.86 (±1859.59) | |
| Median (IQR) | 500 (600) | |
| Minimum–Maximum | 10–10,000 | |
| ≤NGN 500 | 36 (3.77) | |
| NGN 600–1000 | 19 (1.99) | |
| ≥NGN 1200 | 15 (1.57) | |
| Number of hours worked per day before sick (hours) | ||
| Mean (±SD) | 6.38 (±3.21) | |
| Median (IQR) | 6 (6) | |
| Minimum–Maximum | 0–24 | |
| ≤3 | 124 (12.97) | |
| 4–6 | 442 (46.23) | |
| 7–8 | 281 (29.39) | |
| >8 | 109 (11.40) | |
| Number of hours worked per day when sick (hours) | ||
| Mean (±SD) | 2.3 (±2.62) | |
| Median (IQR) | 3 (6) | |
| Minimum–Maximum | 2–12 | |
| ≤3 | 407 (42.57) | |
| 4–6 | 357 (37.34) | |
| 7–8 | 102 (10.67) | |
| >8 | 90 (9.41) | |
| Registered with any health-related insurance policy/scheme | ||
| Yes | 39 (4.08) | |
| No | 917 (95.92) | |
| If registered, it covered: | ||
| Healthcare costs | 16 (1.67) | |
| Income protection | 20 (2.09) | |
| Any others | 3 (0.31) |
3.1. Direct Cost
3.1.1. Direct Medical Cost for Patients
With a total registration cost of 93,932.86 Naira (NGN) (Nigeria currency) paid by the study population, 27.54% of them paid an average of NGN 138.34/USD 0.38 as a patient registration cost (PRC) before receiving treatment in the PHC facilities.
The average cost for laboratory services (blood microscopy and RDTs) paid by each patient amounted to NGN 377.17/USD 1.05. Similarly, the majority of patients (37.55%) paid an average of NGN 779.17/USD 2.16 per drug prescription, including antimalarial drugs and co-medications (Table 2).
Table 2.
Average direct medical costs per case of uncomplicated malaria in Plateau State (in Naira (NGN) (Nigeria currency/USD) (N = 956).
| Total Costs for Study Population | |||||
|---|---|---|---|---|---|
| Mean Cost | Standard Deviation | ||||
| Variables | Frequency | Percentage | NGN/USD | NGN/USD | NGN/USD |
| 426.78/ | |||||
| Registration | 679 | 27.54 | 93,932.86/260.92 | 138.34/0.38 | 1.19 |
| Laboratory | 861 | 34.91 | 324,743.37/902.06 | 377.17/1.05 | 250.3/0.7 |
| 459.37/ | |||||
| Drugs | 926 | 37.55 | 721,511.42/20,004.2 | 779.17/2.16 | 1.28 |
| 1294.68/ | |||||
| Total | 2466 | 100 | 1,140,187.65/3167.18 | 3.59 | 323.89/0.9 |
3.1.2. Direct Non-Medical Costs
Out of the 956 respondents that participated in the study, 59.2% of them use public transport as their means of visiting the PHC facilities for medication, and each of them spent approximately NGN 178.28/USD 0.5 on transportation. On the other hand, about 40.8% of the patients and their caregivers visited the PHC facilities through various means such as private cars, motorcycles and by trekking.
An average of NGN 168.55/USD 0.47 was spent on food by 33.98% of patients who cared to eat during the visit. Furthermore, the majority (93.18%) of the patients were treated as outpatients, with only a total of NGN 71,386/USD 198.1 spent on accommodation by the 6.82% of them that were admitted, at a mean total accommodation cost of NGN 1166.86/USD 2.83 naira per patient for the entire number of days admitted (Table 3).
Table 3.
Average direct non-medical costs per case of uncomplicated malaria in Plateau State (in Naira) (NGN) (Nigeria currency/USD) (N = 956).
| Total Costs for Study Population | Standard | ||||
|---|---|---|---|---|---|
| Mean Cost | Deviation | ||||
| Variables | Frequency | Percentage | NGN/USD | NGN/USD | NGN/USD |
| Transportation | 608 | 59.2 | 108,406.4/304 | 178.28/0.5 | 214.2/0.6 |
| 168.55/ | 270.34/ | ||||
| Food | 349 | 33.98 | 58,841.4/164.03 | 0.47 | 0.75 |
| 1166.86/ | 1859.59/ | ||||
| Accommodation | 70 | 6.82 | 71,386/198.1 | 2.83 | 5.17 |
| 1513.69/ | 573.59/ | ||||
| Total | 1027 | 100 | 238,633.8/666.13 | 3.8 | 1.35 |
3.1.3. Total Direct Cost Incurred per Patient in Treating Uncomplicated Malaria
The average financial cost of treating uncomplicated malaria paid (direct medical and non-medical costs) by patients in the selected PHC facilities in Plateau State per episode was estimated to be NGN 2808.37/USD 7.39, the total cost of treating the disease among the study population per episode amounted to NGN 1,378,821.45/USD 3833.31. Out of this, 70.6% constituted direct medical costs, while 29.4% constituted the direct non-medical cost incurred by the patients during the management of the ailment (Table 4).
Table 4.
Direct average and total costs per case of uncomplicated malaria on respondents in Plateau State (in Naira) (NGN) (Nigeria currency/USD) (N = 956).
| Total Costs for Study | |||||
|---|---|---|---|---|---|
| Population | |||||
| Mean Cost | Standard Deviation | ||||
| Variables | Frequency | Percentage | NGN/USD | NGN/USD | NGN/USD |
| Direct | |||||
| medical cost | 2466 | 70.6 | 1,140,187.65/3167.18 | 1294.68/3.59 | 323.89/0.9 |
| Direct non- | |||||
| medical costs | 1027 | 29.4 | 238,633.8/666.13 | 1513.69/3.8 | 573.59/1.35 |
| Total | 3493 | 100 | 1,378,821.45/3833.31 | 2808.37/7.39 | 418.51/1.03 |
3.2. Indirect Costs of Uncomplicated Malaria on Patients
The average time lost by patients due to malaria ailment and the financial cost equivalence borne by the patients is presented in Table 5.
Table 5.
Average time lost (days) and money equivalence by patients due to uncomplicated malaria in Plateau State (in Naira) (NGN) (Nigeria currency/USD) (N = 956).
| Mean Time | Total Money | Mean Money | Standard | |
|---|---|---|---|---|
| Variables | Lost (±SD) | Equivalence | Equivalence | Deviation |
| Days | NGN/USD | NGN/USD | NGN/USD | |
| Waiting time at PHC | ||||
| (days) | 0.13 (±0.16) | 118,066/327.96 | 123.5/0.34 | 152/0.42 |
| Decrease in | ||||
| working time (days) | 0.17 (±0.11) | 154,394/428.87 | 161.5/0.45 | 104.5/0.29 |
| Time lost due to | ||||
| incapacitation (days) | 2.56 (±1.93) | 2,324,992/6458.31 | 2432/6.76 | 1833.5/5.09 |
| Total | 2.86 (±1.39) | 2,597,452/7215.14 | 2717/7.55 | 1320.5/3.67 |
3.3. Economic Burden due to Uncomplicated Malaria on Patients in Plateau State
The average direct cost of treating uncomplicated malaria borne by patients in the selected PHC facilities in Plateau State per episode was estimated to be NGN 2808.37/USD 7.39, which was almost the same as the indirect cost due to the disease (NGN 2717/USD 7.55), as summarised in Table 6.
Table 6.
Economic burden per case of uncomplicated malaria per patient and study population in Plateau state (in Naira) (NGN) (Nigeria currency/USD) (N = 956).
| Variables | Total Costs for Study Population | Means Cost | Standard Deviation |
|---|---|---|---|
| NGN/USD | NGN/USD | NGN/USD | |
| Direct cost | 1,378,821.45/3833.31 | 2808.37/7.8 | 418.51/1.03 |
| Indirect cost | 2,597,452/7215.14 | 2717/7.55 | 1320.5/3.67 |
| Total cost | 3,976,273.45/11,048.45 | 5525.37/15.35 | 64.61/0.11 |
3.4. Projected Annual Economic Burden Due to Malaria on Patients in Plateau State
The total annual financial cost was estimated at NGN 9,921,671,307.22 (USD 27, 560,198.08), comprising 50.83% direct cost and 49.17% indirect cost borne by patients (Table 7).
Table 7.
Projected annual economic burden due to uncomplicated malaria on patients in Plateau State (in Naira) (NGN) (Nigeria currency/USD) (N = 956).
| Variables | NGN | USD |
|---|---|---|
| Direct cost | 5,042,870,260.1 | 14,007,972.94 |
| Indirect cost | 4,878,801,047.12 | 13,552,225.13 |
| Total cost | 9,921,671,307.22 | 27,560,198.07 |
4. Discussion
Cost of illness (COI) studies are an important analytical method for the identification and estimation of the overall economic impact of disease [17,31]. The present study followed the prevalence-based and ‘‘bottom up’’ approaches to estimate the financial cost impact of uncomplicated malaria disease in Plateau State, Nigeria. The outcome showed substantial financial burdens of the disease on the study population.
The average financial cost of treating uncomplicated malaria borne by patients in Plateau State per episode was estimated at NGN 5525.37/USD 15.35. In total, 50.81% (NGN 2808.37/USD 7.8) of the cost was due to the direct cost of the medications (direct medical and direct non-medical), which was slightly higher than the indirect cost due to the disease, which constituted about 49.19% (NGN 2717/USD 7.55) (Table 6). The result was in contrast to the outcome of a study conducted in a tertiary hospital in Benin [20], although, it was similar to the result of a household study carried out in Yenagoa metropolis (state capital of Bayelsa), which indicated that patients spent more on the direct cost of medications compared to the indirect cost components [32] but higher than similar studies conducted through the health system perspective in Enugu State, Nigeria [21]. It was similarly higher than the average cost of treating malaria episodes reported in Ghana [33] and south-central Ethiopia [34].
Detailed analyses on the average direct medical cost of the disease per episode indicated about 37.55% of the cost was due to drugs, followed by the cost of laboratory diagnostic tests (34.91%), and registration (27.54%) (Table 2). The observed poly-pharmacy practices in the management of the disease (Table 2) might be contributing factors to the high cost of drugs, although, the cost was lower than the average cost of USD 3.64 paid by patients in Enugu, south-eastern Nigeria for ACTs and co-medications per malaria episodes, as reported by Ezenduka et al. [35]. Furthermore, despite the fact that laboratory diagnoses were reported by Uzochukwu et al. [22] to be cost-effective, the observed amount paid for such services in the PHC facilities in the present study was considered high (Table 2), as most of the study populations were from rural settings who were living below the World Bank reported poverty line [36], and Omorogiuwa et al. [37] had earlier reported relationships between malaria and poverty. These cumulative costs of drugs and laboratory tests coupled with the high mean direct non-medical cost (Table 3) contributed to the high cost of medications in healthcare facilities (Table 4) and might be possible reasons why most patients preferred alternative means of accessing care when ill, especially patent medicine vendors where they are given drugs, mostly monotherapy, which is cheaper, without necessarily conducting laboratory investigations. This might have negative effects on the patients’ outcome of treatment, including resistance and treatment failure, with subsequent financial implications.
The projected estimated annual financial cost due to uncomplicated malaria borne by patients in Plateau State, based on a malaria prevalence rate of 0.56 was estimated as NGN 9,921,671,307.22 (USD 27,560,198.08) (Table 7), and this might have direct and indirect effects on the economy of the citizens and the state. A similar high cost of the disease was reported in other parts of Nigeria and some African countries [13].
5. Limitations
The use of minimum wage and agricultural wage rates in calculating daily wage equivalents for salary earners and non-salary earners could be over- or underestimated for some people, especially for the high salary earners, and this could imply fewer values of their time equivalent.
For respondents whose information was filled in by their caregivers due to their prevailing conditions, there could be some variations in information, especially regarding time spent at work and absenteeism; the amount paid for direct medical services was extracted from patients’ treatment cards and was not a problem since they were asked to fill the questionnaire after they had attended their last point of contact before exit (drug sections).
The resources involved in registration costs were mainly the opening of a folder for each patient for documentation of medication records and the registration cost paid by each patient before such folders were opened varies between NGN 20 to ≥NGN 1000 across the 24 selected PHC facilities. For the laboratory diagnosis, only malaria laboratory investigation was documented; patients’ paid between NGN 100 and NGN 2500, depending on the PHC facilities for such services to be rendered to each patient.
Though it may be an underestimation for those attending school, the time of absence from school could not be used since the main focus of the study was not on the impact of the disease on education.
Bias in findings due to the “hawthorne” effect was possible because some of the respondents might perform and answer better when they knew that their practices were under evaluation. Recall bias might also be a limiting factor to the study because when respondents were questioned about the resources consumed because of the illness, not all of them could exactly recall what they knew.
6. Conclusions
The study revealed a high burden of malaria disease on the Plateau State population, especially those living in the rural areas of the state, which might be causing significant financial hardships for them and, by extension, on the state economy. There is a need for urgent approaches to tackle this poverty-related ailment by ensuring that appropriate resources, both human and material-related, are provided in the rural health settings for case management of the disease, with regular awareness creation among both healthcare workers and the communities ensuring rational practices during medications, in addition to strategising toward poverty eradication.
Acknowledgments
We sincerely thank all respondents that participated in this study.
Author Contributions
Conceptualisation: N.E.I. and N.S.J.; Data curation: N.E.I.; Formal analysis: N.S.J. and K.W.G.; Funding acquisition: K.W.G. and L.C.M.; Investigation: N.E.I. and N.S.J.; Methodology: N.E.I. and N.S.J.; Project administration: N.S.J.; Software: K.W.G. and L.C.M.; Supervision: N.E.I.; Validation: N.E.I.; Writing—original draft: N.S.J.; Writing—review and editing: C.S.T., N.E.I. and L.C.M. All authors have read and agreed to the published version of the manuscript.
Institutional Review Board Statement
The study was conducted according to the guidelines of the Declaration of Helsinki and was approved by the Joint Research Review and Ethics Committee, Research Management Centre (RMC), MAHSA University, Malaysia (Ref. number: RMC/EC01/2016) before conducting the study. Permission was also given by the Plateau State Ministry of Health, Jos, Nigeria to access the PHC facilities, and the various directors of PHCs of the selected Local Government Areas (LGAs) were informed through memos prior to data collection.
Informed consent statement
All of the study participants gave their informed consent.
Data availability statement
Data will be shared upon request and are subject to the applicable and relevant personal data protection laws and regulations.
Conflicts of Interest
The authors declare no conflict of interest.
Funding Statement
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Footnotes
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
References
- 1.Bannister L.H., Sherman I.W. Encyclopedia of Life Sciences. JohnWiley & Sons, Ltd.; Chichester, UK: 2009. Plasmodium. [Google Scholar]
- 2.World Health Organization . World Malaria Report 2016. World Health Organization; Geneva, Switzerland: 2016. [Google Scholar]
- 3.FMOH and RBM . National Malaria Strategic Plan 2014–2020. Federal Ministry of Health; Abuja, Nigeria: 2014. [Google Scholar]
- 4.Lutala P.M., Kasereka C.M., Kasagila E.K., Inipavudu J.B., Toranke S.I. Quality assurance of malaria case management in an urban and in sub-rural health centres in Goma, Congo. Afr. J. PHC Fam. Med. 2011;3:8. doi: 10.4102/phcfm.v3i1.225. [DOI] [Google Scholar]
- 5.World Bank . The World Bank Annual Report 2016. World Bank; Washington, DC, USA: 2016. [(accessed on 11 November 2022)]. Available online: https://openknowledge.worldbank.org/handle/10986/24985. [Google Scholar]
- 6.Muhammed T.U., Aminu C., Abdulgafar O.J. Compliance of Primary Health Care Providers to Recommendation of Artemesinin-Based Combination Therapy in the Treatment of Uncomplicated Malaria in Selected Primary Health Care Centres in Sokoto, North-Western Nigeria. Int. J. Trop. Med. 2011;6:70–72. [Google Scholar]
- 7.Obuh P. Vanguard Newspaper. Jul 6, 2015. N480bn spent annually on malaria treatment-Vanguard News. [Google Scholar]
- 8.Sharma V.P. Malaria and poverty in India. Curr. Sci. 2003;84:513–515. [Google Scholar]
- 9.Teklehaimanot A., Mejia P. Malaria and poverty. Ann. N. Y. Acad. Sci. 2008;1136:32–37. doi: 10.1196/annals.1425.037. [DOI] [PubMed] [Google Scholar]
- 10.World Health Organization . Malaria Eradication: Benefits, Future Scenarios and Feasibility: A Report of the Strategic Advisory Group on Malaria Eradication. World Health Organization; Geneva, Switzerland: 2020. [Google Scholar]
- 11.Yusuf O.B., Adeoye B.W., Oladepo O.O., Peters D.H., Bishai D. Poverty and fever vulnerability in Nigeria: A multilevel analysis. Malar. J. 2010;9:235. doi: 10.1186/1475-2875-9-235. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Olayemi I.K., Ande A.T., Odeyemi M.O., Ibemesi G., Emmanuel R. Temporal ecologic adaptability of the principal vector of malaria, Anopheles gambiae s.l. (Diptera: Culicidae), in north-central Nigeria. Appl. Sci. Rep. 2014;5:110–117. doi: 10.15192/PSCP.ASR.2014.1.3.110117. [DOI] [Google Scholar]
- 13.Okorosobo T., Okorosobo F., Mwabu G., Orem N.J., Kirigia M.J. Economic burden of malaria in six countries of Africa. Eur. J. Bus. Manag. 2011;3:42–63. [Google Scholar]
- 14.Hendriks M.E., Kundu P., Boers A.C., Bolarinwa O.A., Pas M.J., Akande T.M., Tan S.S. Step-by-step guideline for disease-specific costing studies in low- and middle-income countries: A mixed methodology. Glob. Health Action. 2014;7:23573. doi: 10.3402/gha.v7.23573. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Sam K.G., Kuriachan M., Philip S. Pharmacoeconomics: Cost of Illness Studies. Hygeia. 2009;1:46–49. [Google Scholar]
- 16.Dae-Bong K. The 3rd OECD World Forum on “Statistics, Knowledge and Policy” Charting Progress, Building Vision, Improving Life. OECD; Busan, Republic of Korea: 2009. Human capital and its measurement; pp. 1–11. [Google Scholar]
- 17.Cardell L.O., Olsson P., Andersson M., Welin K.O., Svensson J., Tennvall G.R., Hellgren J. Total high cost of allergic rhinitis—A national Swedish population-based questionnaire study. NPJ Prim. Care Respir. Med. 2016;26:15082. doi: 10.1038/npjpcrm.2015.82. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Alaba O.A., Alaba O.B. Malaria in Rural Nigeria: Implications for the Millennium Development Goals. 2015. [(accessed on 15 June 2018)]. Available online: www.saga.cornell.edu/saga/aercconf/alaba.
- 19.Ezenduka C.C., Falleiros D.R., Godman B.B. Evaluating the treatment costs for uncomplicated malaria at a public healthcare facility in Nigeria and the implications. Pharm. Open. 2017;1:185–194. doi: 10.1007/s41669-017-0021-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Obieche O.A., Odili V.U. Evaluation of cost of treatment of malaria in adults in Benin City, Nigeria: Patients’ perspective. Malar. World J. 2016;7:12. doi: 10.5281/zenodo.10818212. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Onwujekwe O., Uguru N., Etiaba E., Chikezie I., Uzochukwu B., Adjagba A. The economic burden of malaria on households and the health system in Enugu state southeast Nigeria. PLoS ONE. 2013;8:e78362. doi: 10.1371/journal.pone.0078362. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Uzochukwu B.S.C., Chiegboka L.O., Enwereuzo C., Nwosu U., Okorafor D., Onwujekwe O.E., Uguru N.P., Sibeudu F.T., Ezeoke O.P. Examining appropriate diagnosis and treatment of malaria: Availability and use of rapid diagnostic tests and artemisinin-based combination therapy in public and private health facilities in south east Nigeria. BMC Public Health. 2010;10:486. doi: 10.1186/1471-2458-10-486. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.National Population Commission (NPC) National Malaria Control Programme (NMCP) & ICF International: 2010 Nigeria malaria Indicator Survey 2012. [(accessed on 21 October 2017)]. Available online: http://dhsprogram.com/what-we-do/survey/survey-display-392.cfm.
- 24.Elmannan A.A.A., Elmardi K.A., Idris Y.A., Spector J.M., Ali N.A., Malik E.M. Anti-malarial prescribing practices in Sudan eight years after introduction of artemisinin-based combination therapies and implications for development of drug resistance. BMC Pharm. Toxicol. 2015;16:3. doi: 10.1186/s40360-015-0002-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.World Health Organization: How to Investigate Drug Use in Health Facilities. 1993. [(accessed on 12 November 2017)]. Available online: http://apps.who.int/medicinedocs/pdf/s2289e/s2289e.pdf.
- 26.Gyllensten H., Rehnberg C., Jönsson A.K., Petzold M., Carlsten A., Sundell K.A. Cost of illness of patient-reported adverse drug events: A population-based cross-sectional survey. BMJ Open. 2013;3:2574. doi: 10.1136/bmjopen-2013-002574. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Mogasale V., Kar S.K., Kim J.H., Mogasale V.V., Kerketta A.S., Patnaik B., Sah B. An estimation of private household costs to receive free oral cholera vaccine in Odisha, India. PLoS Negl. Trop. Dis. 2015;9:e0004072. doi: 10.1371/journal.pntd.0004072. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Anumudu C.I., Adepoju A., Adediran M., Adeoye O., Kassim A., Oyewole I., Nwuba R. Malaria prevalence and treatment seeking behaviour of Young Nigerian Adults. Ann. Afr. Med. 2006;5:82–88. [Google Scholar]
- 29.Nanvyat N., Mulambalah C.S., Ajiji J.A., Dakul D.A., Tsingalia M.H. Prevalence of human malaria infection and its transmission pattern in the highlands and lowlands of Plateau State, Nigeria. Indian J. Sci. Technol. 2017;10:1–9. doi: 10.17485/ijst/2017/v10i32/113622. [DOI] [Google Scholar]
- 30.National Population Commission PHC Priority Table. [(accessed on 21 November 2017)];2006 Available online: population.gov.ng.
- 31.Lensberg B.R., Drummond M.F., Danchenko N., Despiégel N., François C. Challenges in measuring and valuing productivity costs, and their relevance in mood disorders. Clin. Outcomes Res. CEOR. 2013;5:565–573. doi: 10.2147/CEOR.S44866. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Ebenezer , Amawulu , Dorothy A.A. The cost implications of malaria burden on house hold expenditure among socio-economic classes in Bayelsa State. J. Trop. Dis. 2017;5:239. doi: 10.4172/2329-891X.1000239. [DOI] [Google Scholar]
- 33.Akazili J.M.A., Aikins M., Binka F.N. Malaria treatment in Northern Ghana: What is the treatment cost per case to households? Afr. J. Health Sci. 2007;14:70–79. doi: 10.4314/ajhs.v14i1.30849. [DOI] [Google Scholar]
- 34.Hailu A., Lindtjørn B., Deressa W., Gari T., Loha E., Robberstad B. Economic burden of malaria and predictors of cost variability to rural households in south-central Ethiopia. PLoS ONE. 2017;12:e0185315. doi: 10.1371/journal.pone.0185315. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Ezenduka C.C., Ogbonna B.O., Obinna E.I., Mathew J.O., Esimone C.O. Drugs use pattern for uncomplicated malaria in medicine retail outlets in Enugu urban, southeast Nigeria: Implications for malaria treatment policy. Malar. J. 2014;13:243. doi: 10.1186/1475-2875-13-243. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.World Bank . World Development Indicators 2014 Group. World Bank; Washington, DC, USA: 2014. [DOI] [Google Scholar]
- 37.Omorogiuwa O., Zivkovic J., Ademoh F. The Role of Agriculture in the Economic Development of Nigeria. Eur. Sci. J. 2014;10:1857–7881. [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
Data will be shared upon request and are subject to the applicable and relevant personal data protection laws and regulations.