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CHILD SURVIVAL UPDATE 57/2009: MALARIA CASES AND DEATHS IN RWANDA AND ETHIOPIA
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
http://www.malariajournal.com/content/8/1/14
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.
BD
Malaria Journal
Volume 8
Research
'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.
Abstract
Background
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.
Methods
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.
Results
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.
Conclusion
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.
Background
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.
Methods
Data were collected during two weeks in November–December 2007 by Ministry
of Health and WHO personnel.
Interventions
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
2).
Figure 1. Location of hospitals and health centers that were selected for
data collection and had complete data for 2001–2007, November 2007,
Ethiopia.
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
facility-years.
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.
Results
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.
Rwanda
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
fluctuations.
Ethiopia
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.
Discussion
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
facilities.
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
impact.
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.
Acknowledgements
We thank Ministry of Health and health staff and data collectors in
Ethiopia and Rwanda for facilitating the field visits.
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