Malaria preventive measures, health care seeking behaviour and malaria burden in different epidemiological settings in Sudan

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1 Tropical Medicine and International Health doi: /j x volume 14 no 12 pp december 2009 Malaria preventive measures, health care seeking behaviour and malaria burden in different epidemiological settings in Sudan H. S. Mustafa 1, E. M. Malik 2, H. T. Tuok 2, A. A. Mohamed 2, A. I. Julla 3 and A. Bassili 4 1 Faculty of Medicine, University of Khartoum, Khartoum, Sudan 2 National Malaria Control Programme (NMCP), FMO, Sudan 3 State Malaria Control Programme, Upper Nile, Sudan 4 Focal point, Tropical Disease Research, WHO EMRO, Cairo Summary objectives To provide information about preventive measures and treatment seeking behaviour as well as an estimate of the malaria burden in different epidemiological settings for effective monitoring and evaluation of the ongoing efforts. methods Cross-sectional survey carried out in four areas representing different levels of transmission to explore the use of preventive measures, care-seeking behaviour and accessibility in addition to point prevalence was followed by a follow-up phase in which the health workers registered and reported all fever cases including malaria. The relation between the reported malaria incidence, the product of symptomatic asymptomatic ratio and the prevalence of confirmed malaria cases was used to develop the equation that could predict the true malaria incidence. results Thousand households and 3628 individuals were surveyed. The presence of any net varied between 6.6% and 40%; the percentage of people who reportedly slept under mosquito nets in the previous night varied between 35 and 80. Prompt use of medications ranged between 14 and 48% with a delay of more than 24 h noticed in different areas. The mean number of individuals per household who reported use of anti-malarial drugs in the last 2 weeks ranged between 0.6 (SD = 0.92) and 1.2 (SD = 1.1), with variable cost per treatment and affordability. The prevalence of asymptomatic parasitaemia, fever and confirmed malaria at time of the survey differed by area. The incidence of malaria during the follow-up period was estimated to be 8.5, 178.6, 23.7 and 10.3 episodes per 1000 population in Malakal, Elrank, Elhosh and El Matama, respectively. Based on this, a prediction equation was developed. conclusion We found suboptimal health care seeking behaviour, coverage and use of preventive measures with a high malaria burden. We developed a model for future estimation of malaria episodes. keywords Sudan, malaria burden, preventive measures, health care seeking behaviour Introduction Malaria is endemic with varying intensities in 109 countries and territories (WHO 2008). One of the major challenges for effective monitoring and evaluation is inaccuracy of information (Breman 2001) due to many reasons (Breman 2001; Erhart et al. 2007). In fact, there is a consensus that cases reported and recorded in national health information systems capture far less than the full burden of malaria particularly in malaria endemic countries. Another main problem is widespread self-medication (Erhart et al. 2007). In Ghana (Ageyepon & Kayonda 2004) for every case of fever seen in the health facilities, there were approximately four to five cases in the community. In Ethiopia (Kloos et al. 1987), only one-third of all illnesses were treated by modern services. Selftreatment was practiced by 41.2% and 23.9% in rural and urban areas respectively in Sudan (Abdel-Hameed 2000). Thus an estimation of malaria burden is needed to set the baseline for future assessment of interventions and for proper allocation of resources. Therefore, there is an urgent need for developing estimation models that consider the limitations mentioned above. This study aimed to estimate the current malaria burden in various epidemiological settings (hypo-to-mesoendemic and hyperendemic areas) in order to build a sensitive model for future estimation of the malaria burden in Sudan considering preventive measures and care-seeking behaviour ª 2009 Blackwell Publishing Ltd

2 Materials and methods Study design A cross-sectional follow-up study was carried out. The cross-sectional phase was conducted in January 2006 in both hyper-endemic areas with perennial transmission in Malakal and Elrank; in December 2005 in Elhosh area (an irrigated area) and in February 2006 in Elmatama area coinciding with transmission season. This phase consisted of parasitological survey and questionnaire interviewing. The second phase was the follow-up phase (cohort study). During this phase, health workers were trained in proper and adequate registration of all patients including malaria cases. The research team tightly supervised these during the follow-up phase and collected data from the registers on monthly basis. The population living in the catchments areas of these health facilities sought care at these facilities for any febrile illness. Special emphasis was given to individuals living in households who were enrolled during the cross-sectional phase. Study areas The study was conducted in two randomly selected hyperendemic areas and two randomly selected hypo- to mesoendemic areas during The two areas selected from the hyper-endemic strata in Sudan were Malakal and Rank areas in Upper Nile State (south of Sudan). The two sites are located along the White Nile River. The rainy season starts in May and continues up to November. The malaria patients in the area seek treatment for malaria from NGO clinics in addition to public sector facilities such as a major hospital in each city. The total population in Malakal area was and in Rank it was in The two areas selected from the hypo-to-mesoendemic strata in Sudan were Elmatama and Elhosh areas in River Nile (desert-fringe along Nile) and Gazera (irrigated) states respectively. The rainy season starts in July and continues to September. Malaria patients in the area seek treatment from rural hospitals in the area. The total population in Elmatama area was and in Elhosh area in year Sample size With reference to Bruce-Chwatt (Warrell 2002), for hyperendemic areas, the sample size of household members was calculated based on the reported prevalence (50 70% at 95% CI) to be 100 households ( individuals) in each area. A total of 522 and 421 household members in Malakal area and in Rank area, respectively, were involved in the cross-sectional survey and named as target subjects for follow-up phase. For hypo-to-meso-endemic areas, the sample size of household members was calculated based on the reported prevalence (10 50%) at 95% CI to be 500 and 300 households for hypo-endemic and meso-endemic, respectively. A total of 1636 and 1049 household members in Elhosh and in Elmatama area, respectively, were involved in the cross-sectional survey and named as target subjects for follow-up phase. Sampling technique Two districts (one in each area) were selected for the study. In each selected district the list of administrative units constitute the sampling frame from which one administrative unit was selected randomly. Within the selected administrative units, villages urban blocks were grouped (cluster) around the available health facility (within the catchment area). Then after, one group was selected randomly. Households were selected within the selected group using systematic random sampling. Further in each cluster (selected village or block), the starting point was the household located at the North-east corner and then selecting the households using the calculated interval moving from north to south to north. The follow-up phase was directed towards reporting all cases presenting to outpatient clinics at health facilities in the catchment area. Individuals living within the catchment area in each stratum were asked to report to certain health facilities in case of any febrile illness. This message was conveyed by the study team, volunteers and community leaders. Health workers at health facilities were asked to register the number of episodes occurring over the study period. Data collection A pre-tested closed ended structure questionnaire was used to interview the household members or guardians in case of children to collect data about: socio-demographic characteristics of the family members, health-seeking behaviour, health care accessibility, duration of illness, use of preventive measures. Peripheral Blood samples were taken to estimate the asymptomatic parasitaemia at household and from febrile patients attending health facilities. Slides were Giemsa stained, examined at facility level and re-examined at the national malaria control programme referral laboratory. A standard data collection form was used at the facility-level to collect information about the febrile patient characteristics and the result of the blood film. For the purpose of this ª 2009 Blackwell Publishing Ltd 1489

3 study, the box (1) below showed the definition used for various terms and indices. Data analysis Data collected during the cross-sectional survey and follow-up phase were entered and analyzed using spss for windows version Frequencies and rates were calculated with 95% interval. Multiple logistic regression models were used to study the determinants of malaria episodes. The model was then used to estimate the number of malaria episodes in each epidemiological setting. Box 1 Definitions used for the purpose of this study Study definitions: Prevalence of confirmed malaria: Frequency of cases that have fever (clinical) in addition to positive blood film for the parasite during the survey; those who were taking anti-malarials were considered confirmed cases. Prevalence of fever: Frequency of cases of febrile illness exiting in a population at any given time. Prevalence of asymptomatic parasitaemia: Proportion of the sample of the population showing malaria parasite in their blood during any given time (eg time of the cross-sectional survey). Incidence of malaria: Number of confirmed malaria cases reported to health facilities during follow-up period, per 1000 population. Symptomatic-asymptomatic ratio (SA ratio): Ratio of symptomatic to asymptomatic infection in each epidemiological category of each country. Results Households characteristics A total of 1000 households were surveyed: 100 in each of Malakal and Rank, 300 in Elhosh and 500 in Elmatama. Their characteristics regarding malaria preventive measures, anti-malarial drug use and health seeking behaviour are shown in Table 1. Regular indoor residual spraying seemed to be limited and the presence of any net ranged between 6.6% (in Elmatama) and 40% (in Malakal). The mean number of individuals per household that reported the use of anti-malarial drugs in the last 2 weeks ranged between 0.6 (SD = 0.92) and 1.2 (SD = 1.1). The direct cost to treat one episode of malaria was reported to be $6.4 (SD = 6.4), 6.9 (SD = 6), 7.1 (SD = 6.5) and 12.4 (SD = 15.6) with 48%, 65%, 53% and 59.4% of households declaring they were able to afford treatment in ElRank, Malakal, Elhosh and ElMatama respectively. Individuals characteristics During the cross-sectional phase in hyper-endemic areas, 522 and 421 individuals were interviewed in Malakal and Rank, respectively. In hypo-to-meso-endemic areas, 1049 and 1636 individuals were interviewed in Elhosh and Elmatama, respectively. The characteristics of the household members related to malaria preventive measures, malaria prevalence and health seeking behaviour in the hyper- endemic areas are reflected in Table 2. The prevalence of asymptomatic parasitaemia, fever and confirmed malaria at time of the survey was 3.3% (95% CI: ), 21.6% (95% CI: ) and 13.0% (95% CI: ) in Malakal area and 0.95% (95% CI: ), 16.2% (95% CI: ) and 5.0% (95% CI: ) in Elrank area. The prevalence of asymptomatic parasitaemia, fever and confirmed malaria at time of the survey was 1.9% (95% CI: ), 13.3% (95% CI: ) and 3.8% (95% CI: ) in Elhosh area and 0.1% (95% CI: ), 6.0% (95% CI: ) and 2.8% (95% CI: ) in Elmatama area. Among the members of the communities surveyed, the percentage of people reporting having mosquito nets varied between 3.7% in Elmatama and 49.6% in Malakal. The proportion of people who reported sleeping under a mosquito net during the previous night was 63.0%, 79.5%, 35.8% and 42.6% in ElRank, Malakal, Elhosh and ElMatama respectively. Prompt use of medication (within 24 h of fever onset) among those who were febrile during the survey ranged between 14% and 48%, with a delay of more than 3 days among 48.4%, 30.6%, 27.7% and 56.1% in ElRank, Malakal, Elhosh and ElMatama, respectively. During the follow-up phase, a total of 980 (29.7%) tested positive for malaria among 3298 that visited the outpatient clinics in Malakal area while in Elrank area a total of 3526 (53.6%) of 6579 were positive. The incidence of malaria episodes during the follow-up period was 8.5 ( ) and ( ) per 1000 population in Malakal (11 months) and Elrank (5 months) areas, respectively (Table 3). In Elhosh area, 2970 (67.8%) were positive among 4382 that visited the outpatient clinics while in Elmatama area 482 (31.2%) of 1543 were positive. The incidence of malaria episodes during the follow-up period was 23.7 ( ) and 10.3 ( ) per 1000 population in Elhosh (10 months) and Elmatama (7 months) areas respectively (Table 3) ª 2009 Blackwell Publishing Ltd

4 Table 1 Households characteristics related to malaria preventive measures, antimalarial use and health seeking behaviour per area, Sudan Characteristic ElRank (Jan) n = 100 Malakal (Jan) n = 100 Elhosh (Dec) n = 300 Elmatama (Feb) n = 500 P-value No of individuals per HH (Mean (SD)) 7.3 (2.8) 9.2 (4.8) 7.3 (2.8) 5.7 (2.7) 0.53 Range (2 15) (2 40) (2 18) (1 20) No of children under 5 years per HH (Mean (SD)) 1.6 (1.1) 2.2 (1.5) 1.1 (1.1) 0.76 (0.92) 0.12 Range Members use AM drugs in the last 2 weeks Mean (SD)) 1.2 (1.1) 1.1 (1.5) 0.98 (1.1) 0.60 (0.92) 0.00 Range Cost to treat one episode of malaria ($) Mean (SD) 6.4 (6.4) 6.9 (6) 7.1 (6.5) 12.4 (15.6) 0.00 Range Affordability of AM treatment 48 (48%) 65 (65%) 159 (53.0%) 297 (59.4%) 0.3 Type of building Cement & red block 00 (00%) 00 (00%) 132 (44.0%) 093 (18.6%) 0.00 Mud 22 (22%) 28 (28%) 077 (25.7%) 329 (65.8%) 0.00 Grass 21 (21%) 05 (05%) 004 (01.3%) 003 (00.6%) 0.00 Mixed 57 (57%) 67 (67%) 087 (29.0%) 075 (15.0%) 0.00 Socioeconomic status High 02 (02%) 04 (04%) 012 (04.0%) 011 (02.2%) 0.41 Medium 43 (43%) 53 (53%) 207 (69.0%) 392 (78.4%) 0.00 Intermediate 55 (55%) 43 (43%) 081 (27.0%) 097 (19.4%) 0.00 Family income Savings 01 (01%) 08 (08%) 031 (10.3%) 032 (06.4%) 0.01 Income=expenses 31 (31%) 34 (34%) 199 (66.3%) 357 (71.4%) 0.00 In debt < the need 68 (68%) 58 (58%) 077 (23.3%) 111 (22.2%) 0.00 Regular indoor residual spraying 5 (5%) 11 (11%) 69 (23.0%) 124 (24.8%) 0.00 Presence of any net 27 (27%) 40 (40%) 50 (16.7%) 33 (06.6%) 0.00 Presence of any treated net 21 (77.7%) 29 (72%) 30 (60.0%) 18 (54.5%) 0.00 Presence of adequately treated net 4 (19%) 15 (51.4%) 30 (100.0%) 18 (100.0%) 0.00 Type of impregnation LLINs (5 years) 1 (25%) 06 (40%) 05 (1.7%) 01 (00.2%) 0.00 Temporary (1 year) 3 (75%) 09 (60%) 25 (8.3%) 17 (03.4%) 0.01 Use of health services Public 81 (81%) 62 (62%) 297 (99.0%) 474 (94.8%) 0.00 Private 10 (10%) 31 (31%) 001 (00.3%) 020 (01.2%) 0.00 Self 06 (06%) 06 (06%) 002 (00.7%) 006 (04.0%) 0.00 Others 03 (03%) 01 (01%) 000 (00.0%) 000 (00.0%) 0.00 HH distance to the nearest health facility <1 km 19 (19%) 07 (07%) 046 (15.3%) 145 (29.0%) to <2 km 30 (30%) 33 (33%) 072 (24.0%) 140 (29.0%) to <3 km 26 (26%) 42 (42%) 010 (03.3%) 134 (26.8%) (25%) 18 (18%) 172 (57.3%) 081 (16.2%) 0.00 Time needed to reach the health facility < half an hour 62 (62%) 62 (62%) 256 (85.3%) 309 (61.8%) 0.00 More than 1 2 an hour 21 (21%) 37 (37%) 043 (14.3%) 156 (31.2%) 0.00 More than one hour 17 (17%) 01 (01%) 001 (00.3%) 038 (07.0%) 0.00 Type of transportation used to reach the facility On foot 42 (42%) 76 (76%) 269 (89.7%) 326 (65.2%) 0.00 Public 33 (33%) 19 (19%) 010 (03.3%) 108 (21.6%) 0.00 Donkeys and others 24 (24%) 00 (00%) 021 (07.0%) 066 (13.2%) 0.00 Private car 1 (01%) 05 (05%) Presence of AM drug at home 4 (4%) 12 (12%) 30 (10.0%) 42 (08.4%) 0.18 Distance from the nearest breeding site <1 km 20 (20%) 55 (55%) 280 (93.3%) 175 (35.0%) to <2 km 25 (25%) 04 (04%) 020 (07.6%) 052 (10.4%) to <3 km 23 (23%) 04 (04%) 000 (00.0%) 068 (13.6%) km 32 (32%) 37 (37%) 000 (00.0%) 000 (00.0%) 0.00 I. Cross-sectional household survey of the study areas. ª 2009 Blackwell Publishing Ltd 1491

5 Table 2 Characteristics of the household members related to malaria preventive measures, malaria prevalence and health seeking behaviour per area, Sudan Characteristic ElRank (Jan) n = 421 Malakal (Jan) n = 522 Elhosh (Dec) n = 1049 Elmatama (Feb) n = 1636 P-value Socio-demographic characteristics Age Mean (SD) 16.3 (14.2) 19 (15.5) 24.8 (18.5) 25.4 (19.3) 0.01 Range Sex being female 152 (36.1%) 187 (35.8%) 683 (65.1%) 1075 (65.7%) 0.00 Education University and above 002 (0.5%) 012 (2.3%) 067 (6.4%) 99 (6.1%) 0.00 Primary to secondary 214 (50.8%) 286 (54.8%) 636 (60.6%) 975 (59.6%) 0.00 Illiterate 205 (48.7%) 224 (42.9%) 346 (33.0%) 562 (34.3%) 0.00 Occupation Housewife 90 (21.4%) 77 (14.8%) 319 (30.4%) 520 (31.8%) 0.00 Labour 11 (2.6%) 25 (4.8%) 40 (3.8%) 59 (3.6%) 0.36 Farmer 04 (1.0%) 4 (0.8%) 54 (5.1%) 76 (4.6%) 0.00 Others 316 (75.1%) 416 (79.7%) 636 (60.6%) 981 (60.0%) 0.00 Bed net use Having net 73 (17.3%) 259 (49.6%) 176 (16.8%) 61 (3.7%) 0.00 Impregnated net 09 (12.3%) 118 (45.5%) 61 (34.7%) 39 (63.9%) 0.00 Sleeping under net (last night) 46 (63.0%) 206 (79.5%) 63 (35.8%) 26 (42.6%) 0.00 Satisfaction with net 398 (94.5%) 509 (97.5%) 1040 (99.1%) 1609 (98.3%) 0.00 Malaria episodes and health seeking behaviour Febrile illness during the survey 68 (16.2%) 113 (21.6%) 139 (13.3%) 98 (6%) 0.00 (prevalence of fever) Febrile illness during the survey 21 (5.0%) 68 (13.0%) 40 (3.8%) 45 (2.8%) 0.00 attributed to malaria (confirmed malaria) Duration of illness >=24 h 62 (91.2%) 94 (83.2%) 103 (74.1%) 49 (50%) 0.00 Use of any treatment 31 (45.6%) 49 (43.4%) 83 (59.7%) 66 (67.3%) 0.00 Prompt use of medications Within 24 h 09 (29.0%) 7 (14.3%) 40 (48.2%) 22 (33.3%) h 06 (19.4%) 27 (55.1%) 20 (24.1%) 7 (10.6%) 0.00 >72 h 15 (48.4%) 15 (30.6%) 23 (27.7%) 37 (56.1%) 0.38 Source of medications Public 23 (74.2%) 20 (40.5%) 57 (68.7%) 60 (90.9%) 0.09 Private 01 (3.2%) 13 (26.5%) 4 (4.8%) 1 (1.5%) 0.00 Self treatment 04 (12.9%) 13 (26.5%) 17 (20.5%) 2 (3.0%) 0.00 Others 03 (9.7%) 3 (6.1%) 5 (6.0%) 3 (4.6%) 0.31 HH members on treatment with AM drugs 16 (3.8%) 27 (5.2%) 40 (3.8%) 45 (2.8%) 0.06 HH members with history of malaria 21 (5.0%) 27 (5.2%) 40 (3.8%) 45 (2.8%) 0.00 Positive BF results during the survey 04 (0.95%) 17 (3.3%) 20 (1.9%) 2 (0.1%) 0.00 (asymptomatic parasitaemia) Parasite density: Mean (SD) 2,000 (979.8) 8,529 (8,800) 6,200 (2,487) 4,500 (424) 0.01 Median 1,800 6,000 6,000 Range 1,200 3, ,000 2,400 12,000 4,200 4,800 Burden estimate Using the prediction equation (Y = a + bx), the relation between malaria incidence and the product of symptomatic asymptomatic (S AS) ratio and prevalence of confirmed malaria cases was studied in different areas (Table 4; Figure 1). The analysis showed that the best fit for the model was the exponential fit. This explained 82% of the variability in the incidence (R 2 = 82%). The product of prevalence and S AS Ratio was used based on the fact that the prevalence of confirmed symptomatic malaria cannot alone explain the truly 1492 ª 2009 Blackwell Publishing Ltd

6 Table 3 Malaria episodes recorded in the health centres during a 5, 11, 10 and 7 months follow-up periods in ElRank, Malakal, Elhosh and Elmatama respectively Characteristic ElRank n = 6579 Malakal n = 3298 Elhosh n = 4382 Elmatama n = 1543 P-value Age Mean (SD) 17 (16) 17.4 (16.5) 22.9 (19.5) 29.1 (17.2) 0.2 range <1 82 <1 81 (<1 88) (<1 82) Sex Male 2608 (39.6%) 1400 (42.4%) 1794 (40.9%) 616 (39.9%) 0.05 Female 3971 (60.3%) 1898 (57.6%) 2588 (59.1%) 927 (60.1%) 0.05 Individuals seeking care Febrile cases 5954 (90.5%) 2797 (84.8%) 4078 (93.1%) 655 (42.4%) 0.00 Confirmed malaria 3526 (53.6%) 980 (29.7%) 2970 (67.8%) 482 (31.2%) 0.00 Incidence of malaria episodes Population of the area 47, , ,430 80,000 Malaria incidence 1000 pop per year during follow-up period {=(confirmed cases*1000) population} Malaria episodes 1,000 pop per year II- Results of the prospective surveillance of the population in the study areas. infected carriers in the community. The actual infected people consist of the symptomatic and asymptomatic individuals. Obtaining the ratio between symptomatic to asymptomatic indicates the factor by which the reported prevalence should be multiplied in order to obtain the true prevalence of infection. The true prevalence of infection is the main determinant of future malaria episodes in the area. In fact, the product of the S AS ratio and prevalence explained 82% of the variability in the incidence and no other variables reported to have a significant association with the incidence using multivariate logistic regression analysis. Based on the above, the equation predicting malaria incidence (episodes 1000 population) is ðincidence ¼ intercept ðexp(slope ðxþþþþ: where the slope b = and the intercept = , and X is the product of prevalence and S AS ratio. Nationwide referring to the survey done in 2005 (T. A. Mohamed, personal communication), the prevalence of confirmed symptomatic malaria was 5.4% and the prevalence of asymptomatic parasitaemia was 2%. Based on this the S AS ratio calculated to be 2.7%. The product between prevalence of symptomatic parasitaemia and S AS ratio is 14.7(5.4*2.7). Therefore, the incidence of malaria episodes was estimated to be 35.8 ( ) per 1000 population. With a population of this equals episodes per year in Sudan. Discussion The number of malaria episodes in Sudan was estimated to be 7.5, 9, and 5.2 million during years 2000, 2002, and 2007, respectively. The 2000 estimate was based on the Federal Ministry of Health annual statistical report only. In 2002, Abdalla et al. (2007) consolidated data obtained from the annual statistical report and surveys. The 2006 estimate was made by the national malaria control programme and malaria programme at WHO EMR based on the malaria indicator survey conducted in 2005 and covering 10 states out of 15 targeted (T. A. Mohamed, personal communication). Likewise, malaria deaths were estimated to be and in 2000 and 2002 respectively. Abdalla et al. (2007) estimated that DALYs were lost in 2002 due to malaria mortality, episodes, anaemia and neurological sequelae. The main limitation of this estimation is its dependence, partially or totally, on routinely collected data with known shortcomings such as low coverage (in place and time), many malaria cases self-treated at home, reports not including NGOs and private facilities and much doubt about data quality. Though many efforts were carried out in Sudan to compact malaria (Malik et al. 2006a), still there is a need for more. This study reflected the low coverage with indoor residual spraying and mosquito nets in all surveyed areas. This further aggravated by reported health seeking behaviour which ended with 40% of the fever cases seeking care after 72 h from the onset of fever confirming the findings reported before (Malik et al. 2006b). The findings ª 2009 Blackwell Publishing Ltd 1493

7 Table 4 Estimation of the malaria burden using results of the cross-sectional survey (prevalence) and prospective surveillance follow-up phase (incidence) in 4 areas representing different strata in Sudan Elrank (Rural Hyperendemic Malakal (Urban Hyper- endemic) Elhosh (Mesoendemic) Elmatama (Hypoendemic) Population Follow-up duration in months Incidence of episodes (per 1000 population) Prevalence of confirmed malaria ie using 5.0% 5.2% 3.8% 2.8% antimalarial drugs (symptomatic=s) Prevalence of asymptomatic parasitaemia 0.95% 3.3% 1.9% 0.1% (asymptomatic=as) Ratio between symptomatic asymptomatic (S AS ratio) III- Estimation using prevalence and incidence y = e x regression Expon. (regression) Figure 1 The relation between malaria incidence and the product of S AS ratio and prevalence. related to socio-economic status, preventive measures and health care seeking behaviour were considered in developing the suggested model for burden estimates. The need for country and sub-national level estimates for malaria burden has been highlighted (Cibulskis et al. 2007). The method to be used for estimation is still under discussion. Snow et al. (2005) had estimated the number of clinical events caused by Plasmodium falciparum malaria using a combination of epidemiological, geographical and demographic data. Cibulskis et al. (2007) thought that combining sentinel surveillance with occasional household surveys and other population-based methods of surveillance may provide better estimate of malaria trends. Climatic-based estimates are useful and simple in unstable transmission areas (Gomez-Elipe et al. 2007), however, climatic-based approaches need to consider non-climatic determinants of Plasmodium falciparum transmission (Omumbo et al. 2004). The burden of the disease is reflected in morbidity and mortality which usually detected at clinics level (private or public). Based on this, Breman and Holloway (2007) considered the clinical settings as starting point to follow disease trends periodically. The data provided by health information systems including disease surveillance is lagging behind, as seeking treatment from sources other than health facilities is common (Ageyepon & Kayonda 2004; Malik et al. 2006b). In the absence of universal, complete national health reporting systems, Snow and Hay (2006) recommended the use of informed epidemiological approaches to estimating disease burdens worldwide. Previous estimates of malaria cases and deaths in Sudan lacked accuracy and there were growing concerns about their usefulness in reflecting the current situation of malaria in Sudan. Bearing in mind the current ongoing efforts to control malaria in Sudan (Malik et al. 2006a), there is a need to develop more accurate estimates. This study is the first attempt to estimate malaria incidence based on a longitudinal follow-up study design. Previous studies were based on cross-sectional seroprevalence surveys to report on the prevalence of malaria but none of these studies addressed the incidence of the disease. The study tried to estimate malaria episodes using combination of reported incidence, i.e. passive surveillance and prevalence survey. It applied the system in four epidemiological settings: hypo-, meso-, urban hyper- and rural hyperendemic areas. This study has also reported the feasibility of establishing a good surveillance system based on passive case detection following community mobilization to seek care at the local health facilities. An obvious limitation of the study that is common in all longitudinal studies- is the difficulty in ensuring total 1494 ª 2009 Blackwell Publishing Ltd

8 coverage of all the catchment population during the longitudinal phase. Conclusion and recommendation The study reported the high malaria burden in Sudan and the suboptimal health care seeking behaviour of the community and their underused of preventive measures. These findings highlight the need to raise community awareness about the preventive measures and improve their timely utilization of health services. The study was able to develop a model for future estimation of malaria episodes using the results of prevalence surveys. Similar models could be adapted for other epidemiological settings using the same methodology. It is recommended to sustain the surveillance system established in the selected sites and to expand it so as to develop sentinel sites for estimating malaria episodes in different epidemiological settings. Acknowledgements This investigation received technical and financial support from the joint WHO Eastern Mediterranean Region (EMRO), Division of communicable Diseases (DCD) and the WHO Special Programme for Research and Training in Tropical Diseases (TDR; and the EMRO DCD TDR Small Grants Scheme for Operational Research in Tropical and Communicable Diseases. References Abdalla SI, Malik EM & Ali KM (2007) The burden of malaria in Sudan: incidence, mortality and disability-adjusted-life-years. Malaria Journal 6, 97. Abdel-Hameed AA (2000) Malaria case management at the community level in Gazira, Sudan. African Journal Medicine and Science 30(Suppl.), Ageyepon IAA & Kayonda JK (2004) Providing practical estimates of malaria burden for health planners in resource-poor countries. The American Journal of Tropical Medicine and Hygiene 71(Suppl 2), Breman JG (2001) The ears of Hippopotamus: manifestations, determinants and estimates of the malaria burden. The American Journal of Tropical Medicine and Hygiene 64, Breman JG & Holloway CN (2007) Malaria surveillance counts. The American Journal of Tropical Medicine and Hygiene 77(6 Suppl), Cibulskis RE, Bell D, Christophel EM et al. (2007) Estimating trends in the burden of malaria at country level. The American Journal of Tropical Medicine and Hygiene 77(6 Suppl), Erhart A, Thang ND & Xa NX et al. (2007) Accuracy of the health information system on malaria surveillance in Vietnam. Transactions of the Royal Society of Tropical Medicine and Hygiene 101, Gomez-Elipe A, Otero A, van Herp M & Aguirre-Jaime A (2007) Forecasting malaria incidence based on monthly case reports and environmental factors in Karuzi, Burundi, Malaria Journal 6, 129. Kloos H et al. (1987) Illness and health behaviour in Addis Ababa and rural central Ethiopia. Social Science and Medicine 25, Malik EM, Ali EM, Mohamed TA et al. (2006a) Efforts to Control Malaria in Sudan case study of the National Malaria Control Programme, Giornale Italiano di Medicina Tropicale 11, Malik EM, Hanafi K, Ali SH, Ahmed ES & Mohamed KA (2006b) Treatment-seeking behaviour for malaria in children under five years of age: implication for home management in rural areas with high seasonal transmission in Sudan. Malaria Journal 5, 60. Omumbo JA, Hay SI, Guerr CA & Snow RW (2004) The relationship between the Plasmodium falciparum parasite ratio in childhood and climate estimates of malaria transmission in Kenya. Malaria Journal 3, 17. Corresponding Author Elfatih M. Malik, Federal Ministry of Health, P.O. Box: 303, Sudan. Tel.: ; fatihmmalik@gmail.com ª 2009 Blackwell Publishing Ltd 1495