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 Writing in the Malaria Journal, Otten and colleagues quantify the impact on
 malaria morbidity and mortality of scaled-up prevention and treatment of
 malaria. Full text, with tables and graphics, is at
 Both these countries are bringing down other preventible causes of
 under-five mortality in an effort to reach MDG 4 by 2015. When will we see
 country level studies of the impact on under-five mortality of malaria
 prevention and treatment?
 Good reading.

 Malaria Journal
 Volume 8
 'Initial evidence of reduction of malaria cases and deaths in Rwanda and
 Ethiopia due to rapid scale-up of malaria prevention and treatment'
 Mac Otten1 , Maru Aregawi1 , Wilson Were1 , Corine Karema2 , Ambachew
 Medin1 , Worku Bekele3 , Daddi Jima4 , Khoti Gausi5 , Ryuichi Komatsu6 ,
 Eline Korenromp6 , Daniel Low-Beer6  and Mark Grabowsky6
 1World Health Organization, Global Malaria Program, Geneva, Switzerland
 2Ministry of Health, Kigale, Rwanda
 3World Health Organization, Addis Ababa, Ethiopia
 4Ministry of Health, Addis Ababa, Ethiopia
 5World Health Organization, Harare, Zimbabwe
 6Global Fund for AIDS, TB and Malaria, Geneva, Switzerland
 author email corresponding author email
 Malaria Journal 2009, 8:14doi:10.1186/1475-2875-8-14
 The electronic version of this article is the complete one and can be found
 online at: http://www.malariajournal.com/content/8/1/14
 © 2009 Otten et al; licensee BioMed Central Ltd.
 This is an Open Access article distributed under the terms of the Creative
 Commons Attribution License (http://creativecommons.org/licenses/by/2.0),
 which permits unrestricted use, distribution, and reproduction in any
 medium, provided the original work is properly cited.
 An increasing number of malaria-endemic African countries are rapidly
 scaling up malaria prevention and treatment. To have an initial estimate of
 the impact of these efforts, time trends in health facility records were
 evaluated in selected districts in Ethiopia and Rwanda, where long-lasting
 insecticidal nets (LLIN) and artemisinin-based combination therapy (ACT)
 had been distributed nationwide by 2007.
 In Ethiopia, a stratified convenience sample covered four major regions
 where (moderately) endemic malaria occurs. In Rwanda, two districts were
 sampled in all five provinces, with one rural health centre and one rural
 hospital selected in each district. The main impact indicator was
 percentage change in number of in-patient malaria cases and deaths in
 children < 5 years old prior to (2001–2005/6) and after (2007) nationwide
 implementation of LLIN and ACT.
 In-patient malaria cases and deaths in children < 5 years old in Rwanda
 fell by 55% and 67%, respectively, and in Ethiopia by 73% and 62%. Over
 this same time period, non-malaria cases and deaths generally remained
 stable or increased.
 Initial evidence indicated that the combination of mass distribution of
 LLIN to all children < 5 years or all households and nationwide
 distribution of ACT in the public sector was associated with substantial
 declines of in-patient malaria cases and deaths in Rwanda and Ethiopia.
 Clinic-based data was a useful tool for local monitoring of the impact of
 malaria programmes.
 In 2005, the global malaria community committed itself to the goal of
 reducing the global malaria burden by at least 50% by 2010 [1]. The
 recommended method to achieve this target is  80% coverage of the four
 main malaria control tools: long-lasting insecticide treated bed nets
 (LLIN), indoor residual spraying (IRS), intermittent presumptive treatment
 of pregnant women (IPT), and treatment with effective medicines,
 principally artemisinin-based combination therapy (ACT).
 Starting from a low baseline in 2005 [2], countries are now engaged in
 efforts at rapid scale-up to reach the 2010 coverage targets. This scale-up
 to achieve disease-control targets raises several questions. What is the
 causal relationship between the coverage targets and the disease control
 goals: does 50% disease reduction require 80% coverage of all interventions
 or can partial coverage of selected interventions more efficiently achieve
 the goal ? How rapid is the impact seen after scale-up ? How can impact be
 measured – can one use existing data in the public health system or are
 special surveys required ? To address these questions, the impact of
 malaria control on health facility burdens was assessed in selected areas
 of Rwanda and Ethiopia, two countries that had recently conducted national
 scale-up of the distribution of LLIN and use of ACT.

 Data were collected during two weeks in November–December 2007 by Ministry
 of Health and WHO personnel.
 In Rwanda, the Ministry of Health (MOH) introduced LLIN and ACT nationwide
 within a two-month period, September to October 2006. In September 2006,
 the MOH conducted a mass distribution of 1.96 million LLIN to children < 5
 years, integrated with measles vaccination. (In comparison, Rwanda's
 population was around 9.5 million in 2006.) During a household survey 8
 months after this campaign, LLIN use in children < 5 years old was 60%
 (unpublished MOH Malaria Indicator Survey, 2007). ACT was introduced
 nationwide quickly in October 2006 to public-sector health facilities
 throughout the country.
 In Ethiopia, the MOH conducted continuous mass distribution of LLIN from
 September 2005 to December 2007, aiming to distribute one LLIN per two
 persons at risk. In 2005–2006, 16.3 million LLIN were received, and all
 except 2.2 million were distributed nationwide by end of 2007 [3]. ACT were
 first distributed in the public sector in 2005. Over 2005–6, 10.2 million
 ACT courses were distributed. In comparison, the national population of
 Ethiopia was 81 million in 2006, out of which 55 million people were living
 in areas with (stable or unstable) malaria transmission. Results from a
 Malaria Indicator Survey conducted in October to December 2007 indicated
 that 65% of households in areas < 2000 meters elevation had at least one
 LLIN [3].
 Selection of districts
 To evaluate intervention effect on malaria morbidity and mortality burden
 in both Ethiopia and Rwanda, districts were selected with two main
 objectives: areas with stable Plasmodium falciparum malaria transmission
 and achievement of wide geographical representation.
 In Ethiopia, two districts were conveniently selected from each of four
 major Regions than have areas with moderate malaria: Oromiya; Southern
 Nations, Nationalities, and Peoples (SNNP); Amhara; and Tigray.
 In Rwanda, two districts in all five provinces were sampled, hence covering
 10 of 33 districts.
 Selection of health facilities
 Figures 1 and 2 show the location of selected facilities with complete data
 in both countries. Per selected district, one hospital and one out-patient
 health center were sampled. In Ethiopia, facilities with complete data were
 spread over approximately half the country (Figure 1). In Rwanda,
 facilities with complete data were spread throughout the country (Figure
 Figure 1. Location of hospitals and health centers that were selected for
 data collection and had complete data for 2001–2007, November 2007,
 Figure 2. Location of hospitals and health centers that were selected for
 data collection and had complete data, December 2007, Rwanda.
 In Rwanda, one health center was excluded from analysis because of
 incomplete data, leaving nine out of the total national 39 hospitals, and
 10 out of the 439 national health centers in the sample. All sampled
 facilities performed malaria smears on all suspected malaria cases, and the
 out-patient cases that were analysed represent exclusively
 laboratory-confirmed cases.
 In Ethiopia, health facilities that did not have data going back to 2001
 were excluded. Some health facilities had complete data for some age groups
 (for example, total for all ages, but not < 5 years). Five health
 facilities had complete in-patient data for both cases and deaths for all
 ages (total), four facilities had in-patient case data by age group (< 5
 and ≥ 5 years), and three facilities had in-patient death data by age
 group. Complete out-patient data was available from eight facilities for
 all ages combined, and from seven facilities by age group. For Ethiopia,
 the proportion of out-patient cases that had laboratory examinations was
 not recorded.
 Data and indicators analysed
 Interviewers abstracted data either from health-facility copies of national
 surveillance forms, other health information forms, or from patient
 registers. At least two persons visited each district for at least two
 days. Monthly data between January 2001 and November 2007 were abstracted.
 From hospitals, data on in-patient malaria and all-cause cases and deaths,
 and from out-patient health centers and hospital out-patient departments,
 data on laboratory-confirmed out-patient malaria cases and all-cause
 attendances were collected. For all indicators, data were stratified in two
 age groups, < 5 years and ≥ 5 years, as far as available.
 For facilities which had one or two months of data missing in a year, data
 were imputed as the average of the month prior and month after the month(s)
 of missing data. In Ethiopia, in-patient data was imputed for 15 of 586
 health-facility-months. In Rwanda, out-patient data were imputed for 55 of
 1,595 health-facility-months and in-patient data for 1 of 133
 Evaluation of time trends and intervention impact
 To assess changes in indicators associated with the scale-up of LLIN and
 ACT, numbers of malaria and non-malaria cases and deaths were compared
 between years before LLIN and ACT introduction, and the year thereafter.
 Analyses were limited for all years to January to October because October
 2007 was the last month with complete data in both countries. In Ethiopia,
 2001–2005 as the reference period was used because most LLIN and ACT were
 distributed in September 2005 or later, so these interventions would not
 have much effect on data from January to October 2005. The years 2001–2006
 were used as the reference period for Rwanda, where interventions were
 scaled-up in September–October 2006.
 Two methods were used to estimate the decline in malaria cases and deaths
 after intervention. First, 2007 data were compared with the average of the
 pre-intervention period (2001–2006 for Rwanda and 2001–2005 for Ethiopia;
 Table 1, column 5 for malaria and column 9 for non-malaria).
 Table 1. Percentage change in malaria and non-malaria cases and deaths in
 2007 compared to pre-intervention reference period, in selected health
 facilities, in persons < 5 years and ≥ 5 years, Ethiopia and Rwanda, from
 January to October each year. Positive percentage indicates decline,
 negative percentage indicates an increase.
 The second method accounted for possible time trends in indicators that
 started before the interventions and would thus be unrelated to the
 interventions – such as population growth, improved health facility access
 and attendance. Here, the observed 2007 value for each indicator was
 compared with its corresponding, expected value for that year based on the
 linear trend over 2001 through 2005/6 (using SPSS Inc., version 14.0 for
 linear regressions and 2-tailed Student's T-tests for assessing statistical
 significance of the difference between observation and expectation). In
 this second analysis, any decreases in malaria indicators observed in 2007
 which could be predicted simply from a trend of decline in that indicator
 over preceding years were thus not attributed to the interventions, whereas
 decreases larger than those apparent over previous years and decreases that
 started in 2007 were attributed to the interventions.
 For Rwanda, in addition, the month-to-month trend in malaria slide
 positivity rate was analysed (including all 12 months of all calendar
 years), alongside the month-to-month trends in out-patient malaria
 laboratory-confirmed cases, in-patient malaria cases, and non-malaria
 inpatient cases.
 Time patterns of intervention scale-up and start of health facility impact
 Figures 3 and 4 show the time trends in in-patient malaria and non-malaria
 cases in children < 5 years by calendar year. For both countries, declines
 in malaria indicators were apparent that started within a year after
 scale-up of LLIN and ACT.
 Figure 3. Malaria and non-malaria in- and out-patient cases, children < 5
 years old, January to October 2001–2007, Rwanda. LLIN = long-lasting
 insecticidal nets, ACT = artemisinin-based combination therapy medicines.
 Figure 4. Malaria and non-malaria in- and out-patient cases, children < 5
 years old, January to October 2001–2007, Ethiopia. LLIN = long-lasting
 insecticidal nets, ACT = artemisinin-based combination therapy medicines.
 In Rwanda (Figure 3), the decline observed in 2007 was very sharp and
 distinct, following a trend of moderate increase in malaria cases over
 2001–5. Non-malaria cases increased throughout 2001 to 2007, possibly
 indicating a general trend of improving health facility access and
 attendance – not related to malaria intervention – over this period.
 Data for Ethiopia (Figure 4) also showed a marked decline in malaria cases
 in 2007 (possibly starting in 2006). Here, however, the causal relationship
 with the scale-up of LLIN and ACT was less obvious than in Rwanda, because
 of marked fluctuation in malaria cases over the pre-intervention period
 2001–2005. Non-malaria cases in Ethiopia also fluctuated considerably from
 year to year, with perhaps a slight overall increase over 2001–2007.
 Comparing 2007 against the average of 2001–2006, observed declines in the
 two age groups ranged from 52% to 67% among the three malaria indicators,
 except for in-patient malaria deaths in those ≥ 5 years which declined by
 only 10% (Table 1, column 5). Declines in children < 5 years old were 55%
 for in-patient malaria cases, 67% for in-patient malaria deaths, and 58%
 for out-patient laboratory-confirmed cases.
 Adjusting for pre-intervention trends, the estimated declines in the two
 age groups ranged from 54% to 68% for the three indicators, and these were
 all highly significant (Table 1, column 6). An exception was in-patient
 malaria deaths in those ≥ 5 years, for which the decline was estimated as a
 non-significant 8%. Since most malaria indicators had slightly increased
 between 2001 and 2006, this adjustment slightly increased the estimated
 impact in children < 5 years old, to declines of 64% for in-patient malaria
 cases, 68% for in-patient malaria deaths, and 63% for out-patient
 laboratory confirmed cases.
 Non-malaria out-patient cases and in-patient cases and deaths, in contrast,
 were all higher (by 17% to 75%) in 2007 compared to the average over
 2001–2006 (Table 1, column 9). For none of the non-malaria indicators was
 the observed value in 2007 statistically different from that expected based
 on the trend of increase over 2001–2006 (Table 1, column 10).
 Rwanda was unique because nationwide distribution of LLIN and ACT occurred
 within only 60 days in September–October 2006. As an obvious effect, the
 monthly number of malaria cases showed a sharp immediate decline in
 November and December of that year (Figure 5).
 Figure 5. In-patient malaria cases, out-patient laboratory-confirmed cases,
 and in-patient non-malaria cases, by month, all ages, January 2001 to
 October 2007, Rwanda. LLIN = long-lasting insecticidal nets, ACT =
 artemisinin-based combination therapy medicines.
 As an additional analysis adjusting for month-to-month fluctuations, the
 malaria slide positivity rate, in-patient malaria cases and out-patient
 malaria laboratory-confirmed cases all showed highly significant reductions
 (p < 0.001) in 2007 compared to 2001–6. In contrast, the number of
 non-malaria in-patient cases was not significantly different in 2007
 compared to 2001–6 (p = 0.74), when adjusting for month-to-month
 Comparing 2007 against the average of 2001–2006, observed declines ranged
 from 62% to 85% among the three malaria indicators in the two age groups
 (Table 1, column 5). Observed declines in children < 5 years old were 73%
 for in-patient malaria cases, 62% for in-patient malaria deaths, and 85%
 for out-patient laboratory-confirmed cases.
 Adjusting for pre-intervention trends, the estimated declines in the two
 age groups ranged from 3% to 91% across the eight malaria indicators and
 age groups for which statistical testing was possible, with seven out of
 eight effects being statistically significant (Table 1, column 6). For
 in-patient deaths in children under five years of age, too few data points
 were available to allow statistical testing.
 In comparison, non-malaria out-patient cases and in-patient cases and
 deaths were higher (by 1% to 45%) in 2007 compared to the average of
 2001–2006, except for in-patient deaths which declined by 13% for those <5
 years and 31% for those ≥ 5 years (Table, column 9). After adjustment for
 trends over 2001–2006, non-malaria in-patient cases declined significantly
 by 11–25%, while outpatient cases increased in children under-five, but
 decreased in the age group over five years (Table 1, column 10). For
 in-patient deaths, no significant changes were apparent in 2007, when
 adjusting for prior time trends.
 This study documents for the first time marked reductions in malaria cases
 and deaths in health facilities in two medium- and large-sized countries
 following large-scale distribution of LLIN and ACT. In particular in
 children under-five, declines of malaria cases and deaths were dramatic
 (~50% or higher), and occurred within one year of the scale-up of malaria
 interventions. This suggests that mass distribution of LLIN and nationwide
 roll-out of ACT conducted within months of the start of an expected malaria
 season may reduce cases and deaths during that transmission season.
 In both Rwanda and Ethiopia, declines were of similar magnitude (~50% or
 higher) for in-patient cases and deaths, and out-patient
 laboratory-confirmed malaria cases. In Rwanda, all suspected malaria cases
 had laboratory examination. Since laboratory-confirmed diagnosis has better
 specificity for malaria than clinical diagnosis [4], this similarity
 supports the interpretation that apparent impacts are real, rather than
 being artifacts of changing patterns of diagnostic practices in the sampled
 In Rwanda, the decline of in-patient and out-patient laboratory-confirmed
 malaria cases occurred in the face of increases in in-patient and
 out-patient non-malaria cases over 2001–2005, which probably reflected the
 introduction of health insurance schemes, resolution of civil conflict, and
 improvement of health services. The exceptionally low percentage decline in
 in-patient malaria deaths in Rwandans ≥ 5 years old was due to the doubling
 of reported malaria deaths in a single hospital (Nyanza) in the year 2007
 (51 deaths, compared to < 25 annual malaria deaths during 2001–2006). As
 also non-malaria deaths in Nyanza hospital increased by 54% in 2007
 compared to 2001–5, this sudden increase may relate to non-malaria factors.
 In any case, numbers of malaria deaths analysed (in both Rwanda and
 Ethiopia) were small, resulting in wide uncertainty ranges around mortality
 impact estimates.
 In Ethiopia the strength of evidence was limited, first, by the small
 number of facilities with complete data for both age groups starting in
 2001. Whereas the original sample had covered 13 out-patient and seven
 in-patient health facilities, only eight out-patient and five in-patient
 facilities had complete data over the period 2001–2007 (for at least
 all-ages combined) allowing inclusion in the present analysis. More
 fundamentally, the unstable nature of malaria transmission in Ethiopia [5]
 and the large year-to-year fluctuations in health facility burdens, also
 for non-malaria cases and deaths (Figure 4), make it impossible to draw
 firm conclusions yet regarding the causal relationship between the observed
 malaria declines and LLIN and ACT scale-up. Of note, Ethiopia experienced
 marked epidemics in 2003–4 [6,7], after which declines in malaria
 indicators were expected even in the absence of intervention impact.
 Nevertheless, a repeat of the current analysis with a few additional years
 of post-intervention data could be expected to yield conclusive evidence as
 to the impact of the national LLIN and ACT scale-up.
 The magnitudes of declines found in malaria indicators are in line with
 those reported from similar studies in small-scale areas of Kenya and
 Zanzibar. In three hospitals along the Kenya coast, in-patient pediatric
 malaria cases declined 28–63% as of March 2007, after distributions of ITNs
 and ACT [8]. In District A in Zanzibar, in-patient malaria cases and deaths
 in children declined by 77% and 75%, respectively, within 24 months after
 the introduction of ACT in all 13 health facilities (and prior to
 substantial distribution of LLIN) [9].
 The limitations were the following. Districts and health facilities were
 not randomly selected, but constituted a (stratified) convenience sample,
 selecting those sites where intervention scale-up had been relatively rapid
 and successful and where health facility data were of relatively good
 quality (e.g., in Ethiopia, excluding facilities where records did not go
 back to 2001). Therefore, estimated impacts cannot be extrapolated to the
 countries nation-wide. Also, while these results illustrate the benefits of
 rapid scale-up in the populations sampled, it would be inappropriate to
 extrapolate these findings to other countries with more intense malaria
 transmission, where interventions at similar coverage levels may have lower
 A more general limitation of health facility data is that they cover only
 the cases and deaths of patients who accessed the (public) health care
 system. Especially for ACT, it is possible that their coverage and impact
 is largely limited to the catchment populations of the facilities providing
 these drugs – with population-level impact diminishing by distance from
 health facilities. For this reason, it is difficult to extrapolate the
 observed health facility impacts to effects for the full populations living
 in the districts sampled. Inferences about population-level disease impact
 will typically require triangulation of several sources of data, including
 data from household surveys [10].
 Third, in Ethiopia, indoor residual spraying (IRS) has been a
 well-established vector control intervention for a long period. It is
 applied in a focalized manner by targeting villages at risk for malaria
 epidemics. All districts visited had been applying IRS in a limited way
 during much of 2001–2007. The contribution of IRS to declines could not be
 estimated since IRS activities occurred throughout both pre- and
 post-intervention period.
 Evaluation of impact is an essential part of modern programme management
 practice and will be needed by all high-burden African countries to meet
 the Roll Back Malaria goal of  50% reduction in malaria-related mortality
 by 2010. Health facility data are important for quickly and continuously
 monitoring interventions with high impact at the health facility and
 district level. Facility-based surveillance may become even more important
 in the future, once wide-scale use of LLIN and ACT may change the
 epidemiology of malaria from stable endemic to unstable/epidemic. Areas of
 unstable and highly seasonal malaria will need to rely on continuous,
 timely surveillance to detect and respond to epidemics.
 In conclusion, these initial data suggest that widespread distribution of
 LLIN and use of ACT in the public sector can result in marked reductions in
 the burden of non-severe and severe malaria morbidity and mortality seen in
 public-sector health facilities. International partners should urgently
 collaborate with national governments to ensure that all at-risk persons
 have access to appropriate vector control, including LLIN, and treatment
 with ACT. The magnitude of declines (≥ 50%) found in the studied facilities
 of Rwanda and Ethiopia was similar to that needed – at a population level –
 to reach the RBM target of 50% mortality reduction by 2010. It appears that
 marked reduction in malaria mortality can be achieved quickly and detected
 within a short time of scaling-up interventions, which may enable many
 African countries to make rapid progress towards the child survival targets
 in the Millennium Development Goals.
 Competing interests
 The authors declare that they have no competing interests.
 Authors' contributions
 MO led the conceptual design and writing; MA led field work and
 participating in writing; WW participated in field work and writing; CK and
 KG led the field work in Rwanda and commented on manuscript; AM organized
 field work and help with writing; DJ helped with the field work in
 Ethiopia; WK participated in data collection and reviewing the manuscript;
 RK contributed to the design and writing; EK contributed to the design,
 analysis, and writing; DLB contributed to the design and writing; and MG
 contributed to the design, analysis, and writing.
 We thank Ministry of Health and health staff and data collectors in
 Ethiopia and Rwanda for facilitating the field visits.
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