COST DRIVERS OF HOUSEHOLD TREATMENT OF PRESUMPTIVE MALARIA IN HOME-BASED MANAGEMENT OF MALARIA IN EJISU-JUABEN MUNICIPALITY

Researchjournalis Journal of Sociology Vol. 2 | No. 2 February | 2014 ISSN 2347-8241

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www.researchjournali.com

Benedicta O. Asante

ZoomLion Ghana LTD, Research and Development

Department

Agyei-Baffour P

Kwame Nkrumah University of Science and

Technology, College of Health Sciences, School of

Medical Sciences, Kumasi, Ghana

Cost Drivers Of

Household Treatment

Of Presumptive

Malaria In Home-Based

Management Of

Malaria In Ejisu-

Juaben Municipality


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Abstract

Home-Based Management of Malaria (HBMM) is one of the key strategies to reduce the burden of malaria

for vulnerable populations in endemic countries. The strategy seeks to allow caregivers to have immediate

health care from some selected and trained community members. The study sought to identify the cost drivers

of presumptive malaria treatment and cost of seeking care from the community medicine distributors

(CMDs). A cross-sectional study was done in the Ejisu-Juaben Municipality in the Ashanti Region. The study

involved randomly selected 400 caregivers, (10) health staff and (90) community-based medicine distributors

(CMDs). Structured questionnaires were employed to collect these data and data was analyzed into

descriptive statistics with SPSS version 17 software. Test for associations were done at 95% confidence

interval. With the assumption that transport cost and food cost were zero (0) in HBMM. The results reveals

that, the cost of treatment of malaria for children between 6-11 months ranged from GHP0.01-1.00 ( 0.19

STD), while children between the ages of 12-24 months ranged from GH1.00-1.50 (0.04 STD) and 36-59

months ranged from GH2.00-3.00 (0.30 STD). Generally cost was described as affordable and drivers of

treatment cost were level of severity of the illness, distance to the homes, time spent in travelling and in the

consumers homes as well as the number of population within the CMDs catchment area. Cost incurred in

accessing HBMM treatment was affordable to caregivers Drivers of treatment cost in HBMM varies from the

caregivers and care seekers.

Keyword: Malaria, Cost, Home-based management, Households, Treatment.

1. Introduction

Malaria, one of the world's most common and serious tropical diseases, cause at least one million deaths

every year. Majority of which occur in the most resource-poor countries which also accommodate more than

half of the world's population and are at risk of acquiring malaria. This proportion increases each year because

of deteriorating health systems, growing drug and insecticide resistance, climate change, natural disasters and

armed conflicts. Overall malaria accounts for 10% of Africas disease burden, and is estimated that malaria

costs the continent more than $12 billion annually. The estimated cost to effectively control malaria in the 82

countries with the highest burden is about $3.2 billion annually. In Ghana however, statistics show that one in

five childhood deaths result from malaria. The cost of treatment of malaria alone is crippling the health

budget in that in 2007 alone the cost of treating malaria amounted to about US $772 million and this equalled

the entire health budget for 2008, and represents 10% of the countrys GDP for 2006.

According to these researchers (Goodman et al., 2000; Akazili, 2002; Hanson et al., 2004) the economic

burden of malaria was not high at the worldwide, but it was seen greatly in the various household and this was


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the barrier to accessing health care as stated earlier. Lots of studies on malaria management are throwing

more light on the importance of wealth position on malaria burden as well as access to treatment and

prevention actions. In other studies, they value and measured economic cost basis on output or income losses

incurred in the household rather than using a general indicator such as average wage rate. Loss of output and

wages accounted for the highest proportion of the economic cost of the patients as well as the households.

Relative to children, more young adults and middle-aged people had `malaria' which also caused greater

economic loss in these age groups. Women tended to care for patients rather than substitute their labour to

cover productive work lost due to illness. Comparing the methods used by other researchers for valuing

economic cost, demonstrating the significant impact that methods of measurement and valuation could have

on the estimation of economic cost, and justify the recommendation for methodological research in this area

(Lipsey, 1994).

Issues influencing cost of home-based management of malaria vary from one point to the other. Factors can

either increase or decrease the size of cost. According to Collette (1994), level of severity of the illness,

distant to the homes, kind of the interventions received (intensive or standard case management), time spent

in travelling and in the consumers homes as well as the size of each of population each CMDs handles could

affect the cost of HBMM. From the article of Joel (2006), there was an ideal that, the length of illness before

getting a treatment was the key factor that determines either a high or low cost of treatment: In that, the longer

the length of illness the higher the cost of treatment hence vice versa. In 1994, Lipsey studies also showed

that, the cost of illness for outpatients who received early diagnosis and prompt treatment was four to seven

times cheaper than the cost of illness for those who were hospitalized. Therefore, people from malaria

endemic sectors should be educated in seeking early treatment from health facilities.

According to some agencies, embedded in the cost involve in the various forms of activities within the

HBMM were the factors which decrease or increase the cost of the programme. These activities included the

administrative support, photo copies, stationery, telephone and supervision, meetings, training, as well as

monitoring, and salaries for facilitators of CMDs. In addition is the cost of purchasing of bicycles, motor

bikes, repairs and maintenance incurred on vehicles, motor bikes, and bicycles, boots, others are raincoats,

torch lights and tool kits (made up of a box, cups and spoons, a torch light, napkins, stop watches, registers,

treatment charts and blister packs of artesunate-amodiaquine, referral and tally cards) for distributors were

also factors which pressurizes the cost of the programme. Again, at home, factors of late reporting of cases,

adult wanting to take medicines when ill, mothers not completing medicines, and mothers refusing referral for

lack of money as well as food and period of recovery, influence cost of home- based management. In


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budgetary, policy and theory formulation as well as service-planning decisions, the above could serve as the

basis.

2. Methodology

2.1 Study Sites And Population

Ejisu-Juaben Municipality considered being one of the 26 political Municipalities of Ashanti Region. Its 2007

population was estimated at 162,256, with a growth rate of 3.4%. The population aged below one year was

4% and pre-school children for 20% of the population. Malaria was the leading cause of outpatient visits and

accounts for 44.3% of OPD visits. Malaria was hyperendemic (Browne et al., 2000). It has 26 health facilities

including 3 hospitals. It has 90 communities with 39 of them having functional village health committees.

There were about 100 community-based medicine distributors (CMDs) who had been trained in home based

management of malaria (HBMM) using pre-packed artesunate-amodiaquine (in the recent HBMM study),

acute respiratory infections (ARI) and diarrhoea case management using ORS. The Municipality has doctor-

patient ratio of 31344:1 and nurse-patient ratio of 4124:1.

The current malaria interventions were case management, home management of malaria, distribution of

insecticides treated nets (ITNs), and intermittent preventive treatment in pregnancy (IPTp). The Municipality

capital, Ejisu was 20 km from Kumasi, the regional capital. It was a predominantly rural Municipality, with

the main of occupation of the people being subsistence farming. A few farmers engaged in commercial

farming, mainly cocoa and oil palm (Source: Population Reference Bureau/ Data Finder - Ghana, 2004). The

study was done within a total population of about 162,256. The study population consisted of caregivers of

children less than five years, health providers and CMDs. They were consented to be part after reading the

informed consent and or the study protocols was interpreted to them in a language best understood by them

and in the presence of a witness (es).

2.2 Sampling Size

A cross-sectional study was done in the Ejisu-Juaben Municipality in the Ashanti Region. The study involved

randomly selected 400 caregivers, (10) health staff and (90) community-based medicine distributors (CMDs).

The main outcome of the study was the proportion of the caregivers whose children presented with fever and

were taken to the community health workers otherwise known as community medicine distributors (CMDs)

for uncomplicated malaria treatment. Based on an unknown parameter, a prevalence figure of 60% was used

to calculate the sample size. With a power of 95% confidence level, 5% significance level, the required error

of 0.002025, design effect of 1, non-respondents of 10%, the sample size was 455 rounded up to 500. This


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was estimated for the survey using the, n=Z2 p (1-p) d/e

2, where Z= (1.96), p=proportion of event of interest,

and e= required error, d=design effect.

2.3 Data Collection Method

Data on the cost of malaria and HBMM were collected as per objectives as follows: Information on

identification of cost drivers were collected from caregivers and health providers including CMDs. These

were done using structured questionnaires. For household cost, the cost of febrile episode receiving prompt

treatment from CMDs, household cost of transport to and from source of care, household time costs of

seeking care were collected. Structured questionnaires were employed to collect these data. Costing of

HBMM was done in three main stages.

Identification stage: This stage involved grouping household costs into cost of care; drugs, food, transport

and time. However, cost of food and transport were valued at zero cost since caregivers never incurred such

costs.

Quantification stage: At this stage, monetary values were assigned to the various items using 2008 prevailing

market prices to value.

Valuation stage: The opportunity costs were estimated by multiplying the time spent in hours by wage rate

per hour. This was done as follows: first all caregivers and CMDs were assumed to be labourers receiving a

minimum wage rate of 1.92 for eight working hours as per the national minimum wage rate of Ghana. It

means that the wage per hour was estimated as GHC 1.92/8 hours which amounted to GHC 0.24. This is

consistent with similar method employed by Asenso-Okyere and Dzator (1997).

Data pertaining to objective three (O3), assessing whether HBMM was sustainable; information was collected

from caregivers and the project office. These were collected using questionnaires. To assess the ability of

CDDs to prescribe medicines in HBMM was collected on participants. These were done using

questionnaires, forms and interview guides. Information on objective five (5), estimating the opportunity costs

of CDD and health providers in HBMM was collected using questionnaires, forms and interview guides.

3. Data Handling And Analysis

Structured questionnaires were employed to collect these data. The data was analysed using descriptive

statistics, summarised and displayed in tables. Frequencies were further analysed using chi-square test to test

for associations between some selected variables. For continuous variables, the estimates were for difference


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in means with 95% confidence levels. Data was entered and analyzed into descriptive statistics with SPSS

version 17 software.Test for associations were done at 95% confidence interval.

3.1 Sensitivity Analysis

Sensitivity analysis was an important feature of economic evaluations in which study results were sensitive to

the values taken by key parameters. (Drummond et al, 2004) Sensitivity shows how the variation in the output

of a mathematical model was apportioned, qualitatively or quantitatively, to different sources of variation in

the input of a mode (Saltell et al, 2008). Sensitivity analysis was done using discount rates of 3% as a

minimum and 5% for the upper ceiling with an estimated change in cost of +/-5%. This analysis indicated the

possible change in cost as a result of change in discount rate. It thus measures the effects of economic

conditions on cost of treatment for malaria.

3.2 Results

This study was done to identify the cost drivers influencing the cost of malaria as well as HBMM. Household

cost as used here were both financial and opportunity cost one incurred whiles seeking treatment for malaria.

The main component of the household cost of malaria were the source of treatment, cost of treatment,

distance and time, days spent among the caregivers, health providers and CMDs. Table 1.1 presents detailed

household cost incurred in seeking malaria treatment in the district.

Table 1.1 Household cost

Variables Indicators Health Consumers Community

Medicine

Distributors

Source of treatment of malaria Home

Chemical sellers

CMDs

100 (25%)

152 (38.0)

148 (37%)

-

-

-

Cost of treatment 0.1-1.00

1.10-2.0

2.10-3.0

27 (6.8%)

328 (82.0%)

45 (11.2%)

47 (100%)

22 (5.5%)

21 (5.25%)

Average cost

Standard deviation

1.0788

0.35140

0.9111

0.67299

Consideration of cost Expensive

Cheap

Very cheap

211 (52.8%)

189 (47.2%)

-

33 (36.7%)

43 (47.8%)

14 (15.6%)


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Bearer of cost of treatment Mother

Father

Other

305 (76.2%)

68 (17.0%)

27 (6.8%)

-

-

-

Distance from the CMDs 0.5km

1km

213 (53.2%)

187 (46.8%)

-

-

Time spent with the CMDs 5

10

15

30

57 (14.2%)

169 (42.2%)

134 (33.5%)

40 (10.0%)

-

-

-

-

Level of satisfaction of treatment Very satisfied

Not satisfied

Not sure

290 (72.5%)

82 (20.5%)

28 (7.0%)

-

-

-

Days spent with the CMDs 1day

2days

3days

85 (21.2%)

274 (68.5%)

41 (10.2%)

-

-

-

Source: Authors Fieldwork, 2009

Household cost of seeking treatment of malaria includes monetary cost incurred, distance, as well as time

spent expressed in monetary cost. The household cost for seeking treatment ranges from 10Gp-30Gp per each

episode of malaria. According to 82% of the health consumers, they pay between 20Gp and 30Gp for

treatment of malaria of their children under five with a mean cost of 1.0788 and standard deviation of

0.35140. Whiles the CMDs also indicate the cost of treatment to be between 10-30Gp with the average cost of

0.9111 and its standard deviation of 0.67299. Again, 53.2% of the caregivers walk a distance of 0.5km, 42.2%

spent 10 minutes and 68.5% spend a maximum of two (2) days with the CMDs. Also for uncomplicated and

severe malaria, 70% and 41% of the CMDs spent 3-20 minutes and 28-39 minutes respectively with children

under five years. However, the costs of the health providers charge for treatment for malaria in their various

hospitals differ and hence expensive as compared to the home based management of malaria. About 40% of

the health providers indicate the cost of treatment to be 6.00GH for uncomplicated malaria with an average

cost of6.4000 and a standard deviation of 1.62959.Also, 40% of the health providers indicates the cost of


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treatment to be 10.0GH for severe malaria with an average cost of 10.1000 and a standard deviation of

1.663333.

Table 1.2: Relationship between cost of treatment and occupational background of health consumers

Occupation Cost of treatment of malaria for children

0.5Gp 10Gp 20Gp p-value <

Frequency (%) Frequency (%) Frequency (%)

Famer 27 (6.75% ) 99 (24.7%) 0 (0%)

0.0001

Trader 0 (0%) 84 (21%) 45 (11.3%)

Artisan 0 (0%) 119 (29.7%) 0 (0%)

civil/public

servant

0 (0%) 26 (6.5%) 0 (0%)

Total 27 328 45


Table 1.2, presents the relationship between occupation of the caregivers and the amount paid for seeking

fever treatment for their children. Twenty-seven of the farmers representing 7% paid 0.5 GP for treatment,

and 99 (25%) paid GHC 1.00 for treatment. Twenty-one and eleven percent of the traders paid GHC 1.00 and

GHC 2.00 respectively for treatment. Just about 30% of the artisans and 7% of the civil or public servants

paid GHC 1.00. There were significant differences between the type of occupation and the cost paid for

treatment, p-value


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the cost Personnel(incentives)

Distance

Type of health facility

Type of treatment

Not sure

-

-

100 (25%)

258 (64.5%)

42 (10.5%)

2 (20.0%)

-

-

-

1 (10%)

43 (47.8%)

-

-

-

-

Factors that

increase

the cost

Suppliers

Personnel(incentives)

Health facility

Not sure

-

-

-

-

3 (30%)

2 (20%)

3 (30%)

2 (20%)

30 (33.3%)

60 (66.7%)

-

-

Reasons for

increase

/decrease of cost

Type of equipment

and drugs availability

Type and number

of personnel

-

-

6 (60%)

2 (20%)

90 (100%)

-

Trend of cost Increasing

Decreasing

-

-

8 (80%)

2 (20%)

-

-

Approaches to

ensure least cost

of treatment

Find additional

source of funds

Outsource for staff

Educate clients to

seek early treatment

-

-

-

2 (20%)

2 (20%)

6 (60%)

-

-

-


Several factors influence the cost of home based management of malaria in the district. There were several

factors either decreasing or increasing the effect of cost in home base management of malaria in the district.

Suppliers, personnel, incentives, distance, type of health facility, type of treatment among others were the

major factors influencing cost of diagnosing and treatment of malaria. For the health consumers, 64.5% of

them indicate the type of health facility they sort treatment from as the cause of change in the cost of

treatment of malaria. Again, 70% of the health providers indicate that the suppliers for the home based

management of malaria influence the cost whiles 60% of CMDs indicates suppliers and the type of health

facility affect the cost of HBMM. Also, 52.2% of the CMDs indicate suppliers as affecting the cost of

treatment and 66.7% of them indicate incentives to the CMDs as another factor.


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Table 1.4: Relationship between the gender and the cost drivers that decrease/increase HBMM

Gender, n=90 P-value

Increase cost of

HBMM

Male Females 1.000

Supplies 14(15.6%) 16(17.8%)

Incentives 28(31.1%) 32(35.6%)

Decreases cost of

HBMM

Male Females 0.030

Supplies 17(18.9%) 30(33.3%)

Incentives 25(27.8%) 18(20.0%)


There were no significant differences between the cost of HBMM and the cost drivers (supplies and

incentives), P-value 1.000 at 95 confidence interval. Based on this, it could be concluded that, largely,

incentives giving to the CMDs has no bearing on the cost of HBMM.There were significant difference

between the cost of HBMM and the cost drivers (supplies and incentives). P-value 0.030 at 95 confidence

interval. Based on this, it could be concluded that, decrease in supplies could reduce the cost of HBMM.

4. Discussions

From the cost of the HBMM and the health providers, it was seen that HBMM was less costly and affordable.

A similar study done by Akazili, (2002) and WHO (2006), found that household cost of malaria treatment

was estimated at 34% and 1% respectively of household income among the poor and the rich. This does not

make the burden of malaria heavy on the household. However, a study done by Goodman et al., (2000);

Akazili, (2002); Hanson et al., (2004) ) showed that, the economic burden of malaria was not high at the

worldwide, but it was seen greatly in the various households and this was the barrier to accessing health care

as stated earlier. Transportation cost is an important cost of seeking care, however in HBMM CMDs are

located within a walking distance of 0.5 to 1km therefore, no transport cost is incurred. Proximity to source of

care (CMDs) meant that time is saved in travelling. It follows that more time would be made available for

economic activities. This confirms a study by Sauerborn et al, (1995) confirms that that reducing time spent in

seeking treatment meant saving money since the time costs of seeking care was far lower than the value of

time lost to care.

According to Guest (1997), researches have uncovered a range of possible influences on rising costs.

Supporting Guests studies, this study uncovered supplies and incentives giving to CMDs as the major factors

influencing the cost of HBMM positively or negatively. Contrary to this study, the study of Collette (1994),


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showed that the level of severity of the illness, distant to the homes, the kind of interventions received (either

intensive or standard case management) time spent in travelling and the size of people each CMDs handles

were the factors which either increase or decrease the cost of HBMM. Table 4.8.3 and 4.8.4 evidently

indicates that suppliers involve in HBMM and incentives given to the CMDs actually influence the cost of

HBMM. Thus the suppliers and monthly incentives could either decrease or increase the cost of HBMM. The

hypothesis that, the cost drivers of integrated diagnostic and treatment package for HBMM does not increase

or decreases the cost of treatment was rejected.

5. Conclusion

From the findings and discussions, cost incurred in accessing HBMM was less as compared to the one sought

from the health facilities. All the caregivers could afford the price range of HBMM; GHC0.5 to GHC2.0.

Supplies and incentives to CMDs were the two key factors influencing cost of HBMM. Other factors such as

transport, distance and cost though expected as important, respondents did not mention them. Cost incurred in

accessing HBMM treatment was affordable to caregivers Drivers of treatment cost in HBMM varies from the

caregivers and care seekers.

Authors Note

This paper was prepared for Department of Community Health as a Masters Degree Thesis by Benedicta O. Asante.

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University of Ghana, Technical Report submitted to the WHO- data Idriss, O (2007) Methodology for Calculating the

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COST DRIVERS OF HOUSEHOLD TREATMENT OF PRESUMPTIVE MALARIA IN HOME-BASED MANAGEMENT OF MALARIA IN EJISU-JUABEN MUNICIPALITY