Health Seeking Behaviour and Cost of Fever Treatment to Households in a Malaria Endemic

bioRxiv preprint doi: https://doi.org/10.1101/486183; this version posted December 3, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

1 Health seeking behaviour and cost of fever treatment to households in a malaria endemic

2 setting of northern Ghana: A cross sectional study

4 Maxwell Ayindenaba Dalaba1*, Patricia Akweongo2, Philip Baba Adongo2, Philip Ayizem

5 Dalinjong1, Samuel Chatio1, and Abraham Oduro1

8 1 Navrongo Health Research Centre, P.O Box 114, Navrongo, Ghana

9 2 University of Ghana, School of Public Health, Accra, Ghana

16 Corresponding Author

17 Maxwell Ayindenaba Dalaba, Navrongo Health Research Centre, P.O Box 114, Navrongo,

18 Ghana

19 Email: [email protected]

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24 Abstract

25 Background: This study examined the health seeking behaviours and cost of treatment of malaria

26 to households in the Kassena-Nankana district of Ghana.

27 Methods: A cross-sectional household survey was conducted between July and September 2015.

28 Individuals who had an episode of fever or malaria in the past two weeks identified during routine

29 Health and Demographic Surveillance System data collection were selected for the study. Socio-

30 demographic characteristics, treatment seeking behaviours and cost of treatment of malaria were

31 obtained from the patient perspective.

32 Results: Out of the 1,845 households visited, 21.3% (393/1,845) reported to have had an episode

33 of fever or malaria in the past two weeks. Of the 393 people with malaria, 66.9% (263/393)

34 reported taking an antimalarial. About 53.6% (141/263) of the antimalarials were obtained from

35 formal healthy facilities. About 36.1% (95/263) reported to have taken Dihydroartemisinin-

36 piperaquine, 35.4 % (93/263) took Artesunate–Amodiaquine and 21.7% (57/263) took

37 Artemether-Lumefantrine. Only 49.6%% (195/393) of the study participants had their blood

38 sample taken for illness (microscopic or Rapid Diagnostic Test). Only 23.6% (62/263) took

39 antimalarial within 24 hours of the onset of illness. The overall average costs (direct and indirect

40 cost) incurred by households per malaria treatment was GH¢27.82/US$7.32 (range: GH¢0.2/

41 US$0.05 - GH¢200/ US$52.63). The average cost incurred in the treatment of malaria by those

42 who were enrolled into the National Health Insurance Scheme (NHIS) was GH¢24.75/US$6.51

43 and those not enrolled was GH¢43.95/US$11.57.

44 Conclusions: Prompt treatment such as treatment within 24 hours of onset of malaria was low.

45 The average costs to households per malaria treatment was GH¢27.82/US$7.32 and the preferred

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46 antimalarial was Dihydroartemisinin-piperaquine. There was a positive effect of NHIS enrolment

47 on cost of treatment as the insured incurred less cost (US$5 less) in treatment than the uninsured.

48 Keywords: Malaria, fever, treatment-seeking behaviour, cost, Ghana.

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69 Introduction

70 According to the WHO report, there are an estimated 198 million malaria cases and 584 000

71 malaria deaths worldwide in 2013. The burden of malaria is more severe in sub-Saharan Africa

72 where about 90% of all malaria deaths occur [1]. It is a major cause of poverty and slows economic

73 growth by up to 1·3% per year in endemic countries[2].

74 Malaria is a significant public health problem in Ghana where it accounts for about 33% of all

75 outpatient attendances and 49% of under five years admissions[3]. Malaria is a preventable,

76 diagnosable and treatable illness. The main methods for the prevention of malaria include Long-

77 Lasting Insecticidal Nets (LLINs), Indoor Residual Spraying (IRS), Intermittent Preventive

78 Treatment for pregnant women (IPTp) and vector control methods such as larviciding [1,4]. The

79 WHO has recommended that all persons from endemic areas with suspected malaria should be

80 examined for evidence of infections with malaria parasites by Rapid Diagnostic Test (RDT) or

81 microscopy. In addition, WHO recommended that uncomplicated malaria should be treated with

82 antimalarial such as Artemisinin-based combination therapies (ACTs), particularly in areas where

83 malaria is endemic and persons should have access to ACTs within 24 hours of onset of malaria

84 [1].The recommended ACT combinations are artemether-lumefantrine (AL), artesunate-

85 amodiaquine (AS+AQ), artesunate-mefloquine (AS+MQ), dihydroartemisinin-piperaquine (DP),

86 and artesunate-sulfadoxine-pyrimethamine (AS+SP). The choice of ACT in a country or region is

87 based on the level of resistance of the partner medicine in the combination [5,6].

88 Following WHO recommendation, in 2004 Ghana changed her antimalarial drug policy choosing

89 Artesunate-Amodiaquine combination as the first line drug for the treatment of uncomplicated

90 malaria to replace chloroquine. However, the implementation process was confronted with some

91 challenges which relates to adverse drug reactions, safety concerns and absence of other treatment

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92 options. It therefore became necessary to review the new policy to address all recognized concerns.

93 A task force reviewed the existing policy and selected additional ACT drugs and dosage forms to

94 accommodate for those who could not bear the Artesunate- Amodiaquine combination. Two

95 additional first line ACTs namely; Artemether-Lumefantrine and Dihydroartemisinin-Piperaquine

96 were selected thus making Ghana to have three official concurrent first line antimalarial treatments

97 [7].

98 To date, however, the reaction of the health system to multiple alternative first line malaria therapy

99 has not been comprehensively studied. Little is known about access to effective and prompt

100 treatment and preferences for antimalarial drugs for treatment of fever/malaria as well as cost of

101 malaria treatment in the face of multiple first line treatment policy. This study therefore examined

102 the health seeking behaviours as well cost of treatment of malaria to households in the Kassena-

103 Nankana district of Ghana in the face of multiple first line antimalarial drugs.

104

105 Methods

106 Study area

107 The study was carried out in the Kassena-Nankana East and West Districts of the Upper East

108 Region of Ghana. The Kassena-Nankana East is home to the Navrongo Health Research Centre

109 (NHRC) and staff of the Centre conducted the study. The NHRC operates Health and Demographic

110 Surveillance System (HDSS) and has a data base of all individuals and households in the Kassena-

111 Nankana East and West Districts. For the purpose of research, the NHRC refers to the two districts

112 by their former name - the Kassena-Nankana District (KND).

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113 The KND covers an area of about 1,674 square kilometres of land [8]. The area is characterized

114 by a short rainy season and a prolonged dry season from October to March. The mean annual

115 rainfall is about 850mm, with the heaviest usually occurring in August. The total population

116 currently under surveillance is about 152 000 residing in about 32,000 households [8]. Malaria

117 transmission in the KND occurs all year round but there is a distinct seasonal pattern with the peak

118 of transmission coinciding with the period of the major rains (August) and the dry season seeing

119 very low rates of malaria infection[9].

120 The districts have one district referral hospital located in Navrongo town and 8 health centres

121 strategically located across the district which provide secondary curative and preventive health

122 care. There are 28 Community Based Health Planning and Services (CHPS) compounds/clinics

123 located in various communities and providing primary health care treatment for minor ailments

124 and also carrying out childhood immunizations and antenatal services. There are three private

125 clinics, three pharmacies and over 50 licenced chemical shops in the area.

126 Study design

127 A cross-sectional household survey design was used, and data was collected using quantitative

128 approach. The study was conducted within the INDEPTH-network effectiveness and safety studies

129 (INESS) Phase IV research platform that aimed to assess the effectiveness of new malaria

130 treatments and its determinants in real life health systems[10,11].

131

132 Sample size and data collection

133 A standardized, rolling, nested, household survey was used, by employing two-week recall period.

134 We assumed conservatively that at least 6% of the population would have had fever event two

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135 weeks prior to the interview date and that about 50% of the population with fever gained access to

136 an official point of ACT provision within 24 hours. We accounted for clustering within households

137 by using a design effect of 1.5. We further allowed a 10% drop out rate to achieve a sample size

138 of 1, 845 households.

139

140 The data for this study were collected as an additional module within the framework of the

141 Navrongo Health and Demographic Surveillance System round updates. Field workers visited the

142 sampled households between July and September 2015. All members in the selected households

143 with a history of fever in the prior two weeks were interviewed using a structured questionnaire.

144 For children who were less than 18 years of age their adult caretakers were interviewed.

145 Information on socio-demographic characteristics, treatment seeking behaviours and cost of

146 treatment were obtained. Cost of treatment was collected from the patient perspective.

147 Those who treated with antimalarial drugs were asked the name of the drug and a physical

148 observation of the drugs if it was still in use or if the package was kept. Field workers carried photo

149 images of the variety of antimalarial packages (brand and generic names) available from private

150 and public providers in the district to facilitate identification of treatments received. Types of

151 antimalarial drugs were to be captured on the questionnaire by their generic names. For instance,

152 brands such as Camosunate, Amonate, Coarsucam, to be captured as Artesunate-Amodiaquine;

153 Malar-2, Lonart , lumartem, Coartem, Co-Malagon, Lumether as Artermether-Lumefantrine; and,

154 P-Alaxin, Eurartesin, Duo-Cotecxin, as Dihydroartemisinin-piperaquine.

155

156

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157 Data processing and analysis

158 Data were double entered into fox pro and transferred to Stata 11.0© for analysis. We used

159 descriptive statistics showing proportions, means and numbers.

160

161 Ages of respondents were captured as continuous variables but during analysis were grouped into:

162 under 5 years, 5-14 years (representing children), 15-49 years (representing reproductive age), and

163 49 years and above.

164 Insurance status of the patient was defined as having a valid national health insurance card on the

165 day of interview. The types of health care providers that patients sought care from were grouped

166 into three categories: formal health facilities (comprising hospital, clinics, health centres and

167 CHPS), pharmacy shops/licenced chemical shops, and home-based care (left over drugs and

168 traditional care).

169

170 The wealth of the household was measured in terms of household assets and possessions using

171 principle component analysis (PCA). We did not collect data on household assets and possessions

172 in our study, but rather used data collected by the Navrongo Health and Demographic Health

173 Surveillance System (NHDSS). The NHDSS database has information on all households in the

174 study district[8]. The study respondent’s perm IDs were linked to the NHDSS data to generate the

175 household wealth index using PCA technique.

176

177 A total of 53 households could not be linked with the Navrongo HDSS household assets database

178 and therefore could not be classified and included in the analysis. Thus, household wealth of 340

179 households were generated. The assets and possessions used for the construction of the wealth

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180 index comprised of the type of material used for the wall of the household, roofing material,

181 cooking utensils, toilet facility, source of drinking water and cooking fuel. Household possessions

182 included bicycle, motorbike, car, radio, bed, sewing machine, tape player, television, Digital

183 Versatile Disc (DVD), mobile phone, refrigerator, cattle, sheep, goat, pig and donkey. Households

184 were then assigned to five quintiles: least poor, less poor, poor, very poor, and poorest.

185

186 The total cost of fever/malaria per household was calculated by summing the direct and indirect

187 costs incurred by households. The direct cost included both direct medical cost and non-medical

188 costs. The direct medical costs covered all out-of-pocket payments (OOP) for registration card,

189 consultation, diagnosis, medicines and medical supplies on the patient; and direct non-medical

190 costs included all out-of-pocket payments for transportation to and from health facilities (patient

191 and caregiver) and special foods for patients.

192

193 Indirect cost (productivity lost) was estimated by multiplying the number of days lost due to

194 malaria by the daily wage for year 2015 (GH¢6/US$1.6). This was calculated for both the patient

195 and the caretaker who were actively engaged in an income-generating activity prior to the illness.

196 In-patient or admission was defined as those who were detained in a health facility for malaria for

197 longer than 12 hours. In-patient costs were therefore the sum of medical costs, laboratory costs,

198 and bed costs during the admission. All costs were collected in Ghana cedis (GH¢) and results

199 presented in both Ghana cedis and US$. The conversion of Ghana cedis to US$ was based on the

200 average exchange rate for 2015 at US$1=GH¢3.8.

201

202

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203 Ethical consideration

204 Ethical approval for the study was obtained from the Navrongo Health Research Centre

205 Institutional Review Board and the Ghana Health Service Ethics Committee. Written informed

206 consent was obtained from all adult participants and caregivers/parents of children before

207 interviews were conducted. In addition, assent was sought from participants who were between

208 12-17 years.

209

210 Results

211 Background characteristics

212 Table 1 shows the background characteristics of respondents. Out of the 1,845 households visited,

213 21.3% (393/1,845) reported to have had an episode of fever or malaria in the past two weeks. The

214 average age of fever patients was 25years and majority of the patients 45% (177/393) were above

215 49 years old and 26% were under-five years of age. Majority of the fever cases were females 51.7%

216 (203/393) compared to 48.4% (190/393) males. About 83.2% (327/393) of patients had valid

217 national health insurance scheme (NHIS) cards at the time of illness. About 32.3% (127/393) of

218 patients were actively engaged in income generation activity. Out of those who were actively

219 engaged in income generation activity, majority 40.2% (51/127) were farmers, 37.8% (48/127)

220 were artisans, and 22.1% (28/127) were public servants. Most patients 42.8% (168/393) were from

221 poor households (lowest two quintiles).

222

223

224

225

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226 Table 1: Background characteristics of patients who reported fever/Malaria Characteristics Categories Number Percent (%) (N=393) Under-five 102 26.0 5 -14 years 65 16.5 Age 15-49 49 12.47 Above 49 177 45.02 Male 190 48.35 Sex Female 203 51.65 Insured Insurance status 327 83.21 Uninsured 66 16.79 Actively engaged in Yes 127 32.32 income generating No 266 67.68 activity Occupation of those Farmer 51 40.15 actively engaged in Public service 28 22.05 income generating Artisan activity 48 37.8 Poorest 82 20.9 Very poor 86 21.9 Poor/middle 46 11.7 Household wealth Less poor 52 13.2 Least poor 74 18.8 Missing 53 13.5 227 228 229

230 Malaria diagnoses and treatment seeking behaviours

231 Out of the 393 fever patients interviewed, 66.9% (263/393) reported taking antimalarial (ACT).

232 Most of those who used antimalarials 53.6% (141/263) obtained it from formal health facilities

233 (hospitals, Health centres, clinics and CHPS), 41.4 % (109/263) from pharmacy or chemical shops

234 and 5% (13/263) at home. With regards to the type of antimalarial patients used, 36.1% (95/263)

235 reported to have taken Dihydroartemisinin-piperaquine, 35.4% (93/263) took Artesunate–

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236 Amodiaquine, 21.7% (57/263) took Artemether-Lumefantrine. Only 49% (195/393) of the study

237 participants had their blood sample taken for illness (microscopic or RDT) and subsequently

238 73.3% (143/195) were confirmed to have malaria (Table 2).

239 Table 2: Malaria diagnoses and treatment Variables Categories Frequency Percent (n=393) (%) Antimalarial 263 66.9 Type of treatment Other treatments 130 33.1 Yes 195 49.2 Blood sample taken No 198 50.8 Yes 143 73.3 Confirmed malaria No 52 26.7 Health facility 141 53.6 Place where ACT Licenced chemical shop 109 41.4 was obtained Home 13 5 Artesunate–Amodiaquine 93 35.4 Artemether-Lumefantrine 57 21.7 Type of Antimalarial used Dihydroartemisinin-Piperaquine 95 36.1 Other Antimalarials (single dose) 18 6.8 240

241 With regards to prompt treatment, majority of patients did not take antimalarial within 24 hours.

242 For instance, among those who took antimalarial, only 23.6% (62/263) took antimalarial within 24

243 hours, whiles 44.5% (117/263) took antimalarial between 25 to 48 hours and 31.9% (84/117) took

244 antimalarial after 48 hours of onset of malaria (Table 3).

245

246

247

248

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249 Table 3: Time spent between onset of fever and start of antimalarial treatment 250 Time spent before start of antimalarial Frequency (n) Percentage (%) Within 24 hrs 62 23.6 Between 25 to 48 hrs 117 44.5 more than 48 hrs 84 31.9 Total 263 100.0 251 252 253

254 Cost of treating malaria

255 Table 4 presents direct and indirect costs of treating malaria. Out of the 393 patients, 25.5%

256 (140/393) patients incurred direct medical costs during malaria treatment. The cost was calculated

257 for only those who incurred cost (OOP)in treating the malaria. The average direct medical cost

258 was GH¢11.91 (US$3.13) per treatment. Out of the 140 patients that incurred direct medical costs,

259 74.3% (104/140) were insured and 25.7% (36/140) were not insured. The average direct medical

260 cost by the uninsured was higher (GH¢14.91/US$3.92) than the insured (GH¢10.87/US$2.86).

261 Out of the total of 393 patients, 82.2% (323/393) spent on direct non-medical costs during the

262 malaria treatment. The direct non-medical coats included transportation and other costs such as

263 special food, water and lodging. About 77.4% (304/393) of respondents incurred transportation

264 cost. The average transportation cost to and from health care providers was estimated at GH¢10.40

265 (US$2.74). The average direct non-medical costs (transportation and other costs such as special

266 food, water and lodging) was GH¢11.69 (US$3.08). The average direct cost representing medical

267 and non-medical cost was calculated as GH¢14.87/US$3.91 (range: GH¢0.2/US$0.05 -

268 GH¢166/US$43.68). Average indirect cost which relates to productivity lost was estimated at

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269 GH¢25.04 (US$6.59). The overall average costs (direct and indirect cost) incurred by households

270 per malaria treatment was GH¢27.82/US$7.32 (range GH¢0.2/US$0.05 - GH¢200/US$52.63).

271 Table 4: Direct and indirect costs of malaria outpatient care (GH¢) Cost Sub cost Number Sum total Mean Min Max category Category that GH¢(US$) GH¢(US$) GH¢(US$) GH¢(US$) paid OOP Direct cost Non-medical cost 323 3775.19(993.47) 11.69(3.08) 0.2(0.05) 108(28.42) Medical cost 140 1667.2(438.74) 11.91(3.13) 0.5(0.13) 140(36.84) Total direct Non-medical cost plus medical cost 366 5442.39(1432.21) 14.87(3.91) 0.2(0.05) 166(43.68) Indirect Indirect cost cost 207 5184(1364.21) 25.04(6.59) 6(0.16) 114(30) Overall Direct plus cost indirect cost 382 10626.39(2796.42) 27.82(7.32) 0.2(0.05) 200(52.63) 272

273

274

275 As presented in Table 5, the average cost incurred in the treatment of malaria by the uninsured was

276 greater (GH¢43.95/US$11.57) than their insured counterparts (GH¢24.75/US$6.51).

277 Table 5: Cost by insurance status

Insurance Number Sum Mean Min Max status that paid GH¢(US$) GH¢(US$) GH¢(US$) GH¢(US$) OOP Insured 321 7945.3(2090.87) 24.75(6.51) 0.2(0.05) 196.5(51.71) Uninsured 61 2681.09(705.55) 43.95(11.57) 0.5(0.13) 200(52.63) Total 382 10626.39(2796.42) 27.82(7.32) 0.2(0.05) 200(52.63)

278

279

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280 Hospitalization

281 Out of the 393 patients interviewed, 13.2% (52/393) were admitted and incurred cost. The average

282 number of days spent by patients that were admitted was 4 days (range 2- 7days). The average

283 costs incurred by those who were admitted was GH¢18.94 /US$4.98(range: GH¢0.5/US$0.13 -

284 GH¢150/US$39.47). Surprisingly, the insured paid more hospitalization cost

285 (GH¢20.41/US$5.37) than the uninsured (GH¢14.55/US$3.83).

286

287 Number of days lost due to malaria

288 The average number of days a malaria episode prevented patients from working was about 5 days

289 for patients and 4 days for the caregivers. Controlling for the patients actively engaged in an

290 income generating activities, the number of days lost still remained at 5 days. The number of school

291 days lost by patients attending school due to malaria was 3 days (range: 1- 7 days).

292

293

294

295 Discussion

296

297 The main aim of the study was to examine the health seeking behaviours as well as cost of

298 treatment of malaria to households in the Kassena-Nankana district of Ghana in the face of multiple

299 first line antimalarial drugs.

300 This study revealed that a large proportion of malaria patients treated with ACTs (66.9%) and a

301 little more than 50% obtained the ACTs from formal health facilities (hospitals, Health centres,

302 clinics and CHPS). High utilization of formal health facilities for malaria treatment has been

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303 reported in a previous studies[11–15]. The high use of formal health facilities in our study could

304 be attributed to NHIS as about 83% of patients were enrolled into the scheme. The positive effects

305 of health insurance enrolment on the utilization of formal health facilities has been widely

306 reported[12,16–19]. Although in our study, there was high utilization of formal health facilities for

307 ACT, buying of ACTs at the pharmacy/chemicals shops was also quite high (41.4%%) and

308 worrisome given that most of the pharmacy/licenced chemical shops do not carry out any

309 diagnostics test before treatment. In other studies conducted in Ghana, however, most people

310 reported visiting pharmacies and chemical shops to treat malaria than the formal health facilities

311 [20–22]. Some of the possible reasons for patient’s preference for chemical shops could be due to

312 waiting time at the health facilities and proximity[20].

313

314 Confirmation of malaria infection directs care to those in need. WHO recommends that all persons

315 from endemic areas with suspected malaria should be examined for evidence of infections with

316 malaria parasites by RDT or microscopy[1]. In this regard, the Roll Back Malaria (RBM)

317 partnership had set up a target that at least 80% of malaria patients are diagnosed and treated with

318 effective antimalarial medicines within 24 hours of the onset of illness[23]. Prompt diagnosis and

319 timely treatment can ease illness progression to severe stages and consequently decrease

320 mortality[24]. Given that in our study less than half of patients (49%) had diagnostic tests (Rapid

321 Diagnostic Tests or microscopy) to confirm malaria and only 23.6% took antimalarial within 24

322 hours of onset of illness is not encouraging and suggests that more efforts are still needed to

323 achieve the RBM target. Jima et al also reported a significant low percentage of respondents who

324 sought prompt care in Ethiopia[25]. People may delay before seeking care because of financial

325 constraint or because they want to be sure they are experiencing symptoms of malaria before they

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326 seek care. A possible reason for the low diagnostic test before treatment could be due to limited

327 supply of RDTs at the health facilities[26] where majority of the study participants (53.6%) sought

328 care from.

329

330 Some of the reasons for the treatment failure of chloroquine included non-compliance with

331 duration of treatment regimen and misdiagnosis [27,28]. Therefore, diagnosis and rational use of

332 ACTs is crucial in the efforts to reduce malaria burden and to restrain the emergence of ACTs

333 resistance and treatment failure in the near future.

334

335 It is interesting to know that despite the fact that Artesunate-Amodiaquine (AA) and Artemether-

336 Lumefantrine (AL) are the most stocked ACTs in the market[29], this study revealed that the most

337 used ACT by patients in the Kassena Nankana District was Dihydroartemisinin-piperaquine

338 (36.1%) followed by Artesunate-Amodiaquine (35.4%) and Artemether-Lumefantrine (21.7%). In

339 fact, in terms of cost of antimalarials, Dihydroartemisinin-piperaquine is the most expensive ACT

340 sold in both the health facilities and the chemical shops in the district. For instance, whereas

341 Artesunate-Amodiaquine(AA) cost about GH¢5/US$1.3 in the pharmacies/chemical shops, P-

342 Alaxin (Dihydroartemisinin-piperaquine) cost about GH¢10/US$2.6, which is twice the price of

343 AA. Contrary to our findings, a previous qualitative study conducted in the same study area

344 reported that respondents preferred artemether-lumefantrine (AL) to other ACTs because AL was

345 perceived to be more effective and has minimal side effects as compared to the other ACTs

346 especially Artesunate-Amodiaquine[30,31]. In our study, a possible reason for the high reported

347 usage of Dihydroartemisinin-Piperaquine could be that patients were interested in trying a new

348 drug and also avoiding the side effects of AA. It also suggests that patients preferred

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349 Dihydroartemisinin-Piperaquine because it has the best efficacy and profile in treating

350 uncomplicated malaria as observed in other studies [32]. Our study finding therefore supports the

351 importance of treatment options to take care of those who cannot bear AA and AL.

352

353 The overall average cost of treating malaria in the Kassena Nankana District was estimated at US

354 GH¢27.83/US$7.2. Though the cost of treatment is lower than malaria treatment cost estimated

355 in other similar studies conducted in other parts of Ghana [11,33], this amount is still relatively

356 high given that most of the respondents were from poor households. High treatment costs can

357 prevent the poor from seeking care when they have a fever episode as well as impoverish

358 households who seek care when they have an episode of fever [11,34].

359

360 The NHIS covers the treatment of about 95% of cases in Ghana including malaria. The benefit

361 package covers both inpatient and outpatient care, emergency and transfer services [35] and the

362 insured are generally not supposed to incur direct medical cost in treating malaria at any formal

363 health facility. In this study, the uninsured incurred more direct cost (GH¢14.91/US$3.9) than the

364 insured (GH¢10.87/US$2.9). Overall, the average cost incurred in the treatment of malaria by the

365 uninsured was greater (GH¢43.95/US$11.57) than their insured counterparts

366 (GH¢24.75/US$6.51). Thus, the uninsured paid US$5 more than the insured, which is consistent

367 with many findings where the uninsured paid more than the insured in treating malaria [12,15,36].

368 Though the insured were not supposed to incur direct medical costs, a possible reason for the cost

369 incurred by the insured could be due to co-payments for drugs, laboratory test, ward dues and other

370 informal payments. It could also be that antimalarial drugs were out of stock at the health facilities

371 and patient bought drugs outside the health facilities.

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372

373 The average indirect cost (GH¢25.04/US$6.59) was 1.7 times higher than the direct cost

374 (GH¢14.87/US$3.91). This is consistent with many findings where the indirect costs of malaria

375 treatment were higher than the direct costs of treatment [11,15,33,37,38]. Therefore, efforts to

376 improve access to health care and reduction of financial burden to households in the treatment of

377 malaria should not only be directed to direct medical costs but also indirect costs.

378

379 Limitations

380 Recall bias is a possible limitation as respondents were asked to recall expenditure over a two

381 weeks period. The reported expenditure could either be overestimated or underestimated especially

382 in cases that respondents did not show receipts on expenditure but reported expenditure verbally.

383 However, because the recall period was short, we do not expect much recall issues to adversely

384 affect the study results.

385

386 In the study, we used self-reported fever or malaria to indicate malaria and we acknowledge that

387 as a limitation. For instance, not all fevers are malaria and also it is possible that some reported

388 malaria cases were really not malaria cases. Nevertheless, given that malaria is endemic in the

389 study area and mostly fever is associated with malaria, we assumed high knowledge about malaria

390 signs and symptoms by respondents would not affect the study findings much.

391

392

393

394

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395 Conclusions

396 Though access to antimalarial medicine was quite good, prompt treatment such as treatment within

397 24 hours of onset of malaria as well as diagnostic testing before treatment of suspected fevers is

398 not encouraging when compared to the 80% target set by the Roll Back Malaria programme. There

399 was a positive effect of national health scheme enrolment on cost of treatment as the insured

400 incurred less cost (US$5 less) in treatment than the uninsured. Health facilities need to be

401 adequately equipped with malaria diagnostic equipment such as Rapid Diagnostic Tests (RDTs)

402 or microscopes and be encouraged to use them so as to direct limited and subsidized antimalarial

403 medicines to patients with malaria parasite infections. Also, more efforts are needed to improve

404 NHIS enrolment in order to reduce cost of treatment and accelerate universal health coverage.

405

406 List of abbreviations

407 AA Artesunate–Amodiaquine

408 ACTs Artemisinin-based Combination Therapies

409 AL Artemether-Lumefantrine

410 CHPS Community Based Health Planning and Services

411 DP Dihydroartemisinin-Piperaquine

412 DVD Digital Versatile Disc

413 HDSS Health and Demographic Surveillance System

414 INESS INDEPTH-Network Effectiveness and Safety Studies

415 IPTp Intermittent Preventive Treatment for pregnant women

416 IRS Indoor Residual Spraying

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417 KND Kassena-Nankana District

418 LLINs long-lasting insecticidal nets

419 NHIS National Health Insurance Scheme

420 NHRC Navrongo Health Research Centre

421 OOP Out-Of-Pocket payment

422 PCA Principle Component Analysis

423 RBM Roll Back Malaria

424 RDT Rapid Diagnostic Test

425 WHO World Health Organization

426

427 Competing interests

428 The authors declare that there are no competing interests.

429 Authors’ contribution

430 MAD PA PDA PAD SC TA and AO: contributed in the conception, design, data collection,

431 analysis, interpretation, and drafting of the manuscript. All authors read and approved the final

432 manuscript.

433

434 Consent for publication

435 Not applicable

436 Availability of data and materials

437 Relevant data based on which conclusions were made are included in the document. The

438 questionnaire is included as supplementary material.

21 bioRxiv preprint doi: https://doi.org/10.1101/486183; this version posted December 3, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

439 Funding

440 This research was funded by the Bill and Melinda Gate foundation and facilitated by the INDEPTH

441 Network

442

443 Acknowledgement

444 We wish to express our gratitude to the Navrongo Health Research Centre for the institutional

445 support to undertake the study. We are grateful to the study respondents, data collectors, data

446 managers and the data entry clerks for the various support they provided to this study.

447

448

449

450

451

452

453

454

455

456

457

458

459

460

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