Seasonal speading of malaria

Volume : 3 | Issue : 1 | Jan 2014 ISSN - 2250-1991
156 X PARIPEX - INDIAN JOURNAL OF RESEARCH
Research Paper
Seasonal Variation in Spread, Morbidity and
Mortality of Malaria in an Endemic Area of
Bangladesh
* Dr Shahjada Selim
Medical Science
t* Registrar, Department of Medicine, Shaheed Suhrawardy Medical College, Dhaka-1207, Bangladesh
Keywords : Malaria, Cox’s Bazaar, Rain fall, humidity
ABSTRACT
A retrospective study was conducted in Cox’s Bazar district of Bangladesh collecting all the records of malaria cases from the
UHCs (Upazila Health Complex) and district hospital during January 2008 to December 2011. The records of temperature,
rainfall and humidity of the corresponding months were collected from the Department of Meteorology, Bangladesh.
Humidity and rain fall showed significant association between incidences of all three types of malaria. Linear regression
models were consistent in reporting the association. Pearson correlation matrix between weather parameters and malaria
during January 2008 – December 2011 showed temporal trends in malaria occurrence.
INTRODUCTION
Malaria is the most widespread parasitic disease in the
world today and a major health burden in many tropical and
sub-tropical regions of Africa, the Americas, Eurasia, and
Oceania. It is endemic in 106 countries, putting half of the
world’s population (3.3 billion people) at risk (WHO 2010). In
2009, an estimated 225 million cases of malaria worldwide
accounted for approximately 781,000 deaths (WHO 2010). In
Bangladesh, malaria is endemic in 13 of the 64 administrative
districts (WHO 2010).
In temperate regions, seasonal interruptions and temper-
ature fluctuations provide a perennial source of instability.
Climate change is likely to affect the patterns and spread
of malaria transmission in different countries, other driv-
ers of the disease include, social economic status of the
country, type of vectors available, population immigration
and vector dispersal. Africa will continue to carry the great-
est burden of the disease and in particular Eastern and
Southern Africa (Garnham 1948). In all malaria endemic
countries, temporal variation in spreading, morbidity and mor-
tality of malaria should be exhibited. This is also pertinent for
Bangladesh. The study was conducted with the aim to deter-
mine the pick season of malaria infection along with rain fall,
temperature and humidity and period of lower spreading of
malaria in Bangladesh.
MATERIALS and METHODS
All the records of malaria cases of January 2008 to June
2012 were collected from the district hospital and from all
8 upazila hospitals (UHC) of Cox’s Bazar district. The data
were also collected from the NGO offices operating malaria
health care services. Care was taken to prevent duplication
of cases from different centre. All cases were confirmed as
uncomplicated malaria presumptive (UMP), uncomplicated
malaria confirmed (UMC), as well as SM (Severe Malaria)
and VM (Vivax Malaria). Neither microscopy nor rapid di-
agnosis tests (RDT) were performed for UMP, but for the
others either microscopy or RDT was used for diagnosis of
malaria. All collected data were checked and verified thor-
oughly to reduce any inconsistency. Then were edited into
computer, processed, and were tabulated to get a master
sheet.
RESULTS
During the preceding four years of the study (2008-2011),
530 malaria patients of different types were diagnosed and
treated from all the UHCs, NGO offices and district hospital
of Cox’s Bazaar. Month wise reports of temperature (both
maximum and minimum), humidity, rain falls were collected
from Bangladesh Meteorological Department. The number of
malaria cases was then analyzed with temperature, humidity
and rain fall.
Table 1: Distribution of the malaria cases in upazilas of
Cox’s Bazar
Statistics UMC S.M V.M
Chokoria Mean 97.8 19.7 9.2
Median 89.0 15.0 6.5
Sadar Mean 21.1 0.0 3.5
Median 16.5 0.0 2.0
Maheshkhali Mean 4.8 1.9 1.1
Median 5.0 1.0 0.0
Ramu Mean 133.0 20.0 20.7
Median 115.0 13.5 13.0
Teknaf Mean 66.8 0.1 1.2
Median 70.5 0.0 0.0
Ukhia Mean 49.1 4.3 8.4
Median 35.5 3.0 6.0
Pekua Mean 55.3 0.2 11.8
Median 47.0 0.0 9.0
District Hospital Mean 427.9 46.3 55.9
Median 399.5 35.5 40.5
Table 1 shows the summary distribution of malaria cases
in upazilas of Cox’s Bazaar district. In Cox’s Bazaar district
mean monthly reported UMC was 428, MS was 46.3 and VM
was around 56.

Table 2: Correlation between weather parameter and ma-
laria occurrence
UMC SM. VM
r P
Value r P
Value r P
Value
Humidity .400**
.005 .475**
.001 .360*
.012
Max Temperature -.221 .131 -.107 .469 -.029 .844
Min Temperature .254 .082 .331*
.022 .278 .056
Rain .490**
.000 .362*
.012 .463**
.001
**. Correlation is significant at the 0.01 level .
*. Correlation is significant at the 0.05 level (2-tai).
Table 3: Correlation between weather parameter and ma-
laria occurrence between January 2008 – December 2011.
Year
r
Humidity Tmax Min_T Rain
p r p R p r p
2008
UMC .641*
.025 -.141 .662 .452 .140 .869**
.000
SM. -.133 .679 -.372 .234 -.290 .361 -.286 .368
VM .748**
.005 .249 .435 .744**
.006 .864**
.000
2009
UMC .220 .493 -.807**
.002 -.177 .583 .414 .181
SM. .810**
.001 -.114 .723 .591*
.043 .836**
.001
VM -.007 .983 -.278 .382 -.131 .684 .153 .634
2010
UMC .275 .387 -.353 .260 .206 .521 .557 .060
SM. .686*
.014 -.100 .757 .413 .182 .809**
.001
VM .712**
.009 .015 .964 .616*
.033 .640*
.025
2011
UMC .548 .065 .000 .999 .454 .138 .287 .367
SM. .656*
.020 .171 .595 .642*
.025 .504 .095
VM .580*
.048 -.170 .598 .468 .125 .518 .085
**. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).
Pearson correlation was drawn through correlation ma-
trix between weather parameters and malaria incidences
separately in four years from January 2008 – December
2011 for assessing temporal trends. Humidity and rain fall
showed statistically significant association between inci-
dences of all three disease parameters.
Table 4: Regression model for Malaria occurrence based
on climatic parameters
Regression model for UMC
Model
B
Unstandardized
Coefficients
Standardized
Coefficients
t p
Std.
Error Beta
(Constant) 5.661 3.323 1.704 .096
LgHum .785 1.177 .210 .666 .509
LgTmax -3.375 1.553 -.481 -2.174 .035
LgTmin .481 .540 .335 .890 .379
LgRain .003 .028 .021 .097 .923
Regression model for SM
Model
B
Unstandardized
Coefficients
Standardized
Coefficients
t P
Std.
Error Beta
(Constant) -8.198 9.783 -.838 .407
LgHum 7.489 3.466 .652 2.161 .036
LgTmax -1.433 4.572 -.067 -.313 .755
LgTmin -1.914 1.591 -.434 -1.203 .236
LgRain .093 .083 .233 1.128 .266
Regression model for SM
M l
B
Unstandardized
Coefficients
Standardized
Coefficients
t p
Std.
Error Beta
1
(Constant) 1.895 5.925 .320 .751
LgHum 2.173 2.099 .340 1.035 .306
LgTmax -3.258 2.769 -.272 -1.177 .246
LgTmin .522 .964 .213 .542 .591
LgRain -.014 .050 -.062 -.274 .786
a. Dependent Variable: LgVM
Figure 3: Scatter plot showing correlation between hu-
midity and UMC.
Figure 4: Scatter plot showing correlation between hu-
midity and SM.
DISCUSSION
Humidity and rain fall showed statistically significant as-
sociation between incidences of all three types of malaria.
Linear regression models were consistent in reporting the

association. Pearson correlation matrix between weather
parameters and malaria during 2008 – 2011 showed tempo-
ral trends in malaria occurrence. The relationship between
climate parameter and malaria occurrences were examined
in this study. This study showed that the highest geometric
mean parasite density was observed in midyear to august.
The month prior to these is known as a period of sudden
mosquito population outburst due to frequent rainfall with fair-
ly long periods of sunshine which increase the opportunity
for mosquitoes’ prolific breeding. Findings of this study are
valuable contributions to an already existing pool of baseline
data. It could guide in designing of control programs that are
locally adapted and technically/financially feasible such as
Malaria Early Warnings Systems which is extremely relevant
where malaria transmission is unstable. Indicators such as
temperature, humidity and rainfall, Pearson correlation matrix
between weather parameters and malaria during 2008 – 2011
showed temporal trends in malaria occurrence. Humidity and
rain fall showed statistically significant association between
incidences of all three disease parameters adjusted got other
related parameters. Linear regression models were consist-
ent in reporting the association.
The outcome of this study agrees with the results of the ear-
lier studies by Thomson and Ayanlade that the seasonality of
climate greatly influences the seasonality of malaria transmis-
sion (Thomson et al., 2005). This study has further confirmed
that malaria parasitemia in the Sahel varies with a clear sea-
sonal pattern in climate. This is in agreement with Pull (Pull
et al., 1976) and Molta et al (1995) that the relatively dry area
demonstrates strong seasonality in malaria transmission.
Oguche (2001) further demonstrated this strong seasonality
in a study with cerebral malaria of the pattern of childhood
cerebral malaria in northeastern Nigeria. Ninety-five percent
of the patients presented between June and November had
malaria with a peak in October. In Africa studies reported that
that seasonal fluctuations in rainfall affects the occurrence
of malaria. Molta (1995) observed that the monthly figures
of malaria among in-patients in the Sahel showed seasonal
fluctuations and that low values are characteristic of the dry
season and high values are of the rainy season.
Malaria remains the world’s most important tropical parasitic
disease, and one of the major public health challenges in the
poorest countries of the world, particularly in sub-Saharan Af-
rica. With the new move towards malaria eradication and the
scaling-up of malaria control interventions, there is a renewed
energy and drive to maximize the impact of control tools in
each epidemiological context. Where malaria transmission
is seasonal, optimal timing of control becomes particularly
important. Most malaria endemic settings have “seasonal
peaks” of malaria cases, which are usually described in terms
of the duration and timing of the rains during a given study
period. However, this may vary from year-to-year, giving a va-
riety of subjective descriptions of seasonality for a single site.
To date, several attempts have been made to describe the
seasonality of malaria endemic areas. More recently, a dif-
ferent approach to define seasonality was carried out by
Mabaso and others who aimed to predict seasonality from
environmental covariates. They defined a seasonality con-
centration index to model the relationship between environ-
mental covariates and seasonality in malaria incidence and
EIR data (Mabaso et al., 2007). The authors reported that
sites tended to show stronger seasonality of clinical malaria in
all-year round transmission settings than in areas with short-
er duration of malaria transmission, but no investigation was
made on variations between years to look at consistency of
findings (Mabaso et al., 2005). To develop robust definitions,
several years of data from each place are needed as there
are annual variations, both in rainy seasons and in the inten-
sity and timing of peaks in malaria.
Few discrepancies found between the concentrated period
of malaria and the rainy season corresponded to those sites
reporting two peaks of rain. A better fit was usually observed
between the concentrated period of malaria and the rainy
season in sites where the source of information on the rainy
season was the paper that reported the monthly malaria data.
This is likely to be due to year-to-year variation in the onset
of the rainy season, resulting in a better fit when the onset of
the rainy season is matched to the period of data collection.
With the rapid scaling up of malaria control interventions,
continued surveillance is needed to monitor changes in trans-
mission intensity levels and in the burden of malaria disease.
Several authors have already reported a drop in hospital ma-
laria admissions after analyzing several years of surveillance
data (Ceesay et al., 2008). Further work is needed however
to assess whether changes in seasonality may occur with de-
clining transmission intensity in areas of perennial transmis-
sion. Although results from these analyses are encouraging
for assessing seasonal variation, in practice there is little reli-
able monthly data on clinical and severe malaria, questioning
its utility on a wide scale.
ACKNOWLEDGEMENT
The study was funded by the National Malaria Control Pro-
gram (NMCP) of Ministry of Health and Family Welfare of the
Government of the people’s Republic of Bangladesh. The au-
thor is grateful to Prof Dr Md Be-Nazir Ahmed, Line Director,
Communicable Disease Control Unit, Directorate General of
Health Services, Bangladesh and Dr Md Nazmul Karim of
WHO office, Dhaka, for their support. The author also thanks
the Civil Surgeon and Upazila Health & Family Planning Of-
ficers of Cox’s Bazaar district. He also thanks the anonymous
referees of the journal for their thoughtful comments that have
improved the presentation of the manuscript.
Linear regression models were generated to assess possi-
ble relation of uncomplicated malaria confirmed (UMC), as
well as SM and VM (vivax malaria) with weather parameters.
Maximum temperature is found to be associated with UMC
incidence and, Humidity is found to be associated with Hu-
midity.
Figure 1: seasonal pattern of weather parameters.
Figure shows the distribution of average monthly weather
parameters of Cox’s Bazaar district from the Department of
Meteorology from January 2008 – December 2011.
Figure 2: seasonal pattern of Malaria cases.
Figure shows the distribution of average monthly reporting of
UMC, SM and VM of Cox’s Bazar district from the department
of meteorology from January 2008 – December 2011.

Figure shows the distribution of average monthly reporting of
UMC, SM and VM of Cox’s Bazar district from the department
of meteorology from January 2008 – December 2011.
REFERENCES
Ceesay SJ, Casals-Pascual C, Erskine J, Anya SE, Duah NO, Fulford AJC, Sesay SSS et al.,: Changes in malaria indices between 1999 and 2007 in The Gambia: a
retrospective analysis. Lancet 2008, 372:1545-1554. | Garnham PCC. The incidence of malaria at high altitudes. J Mal Soc 1948, 7: 275–78 | Mabaso ML, Craig M,
Ross A, Smith T: Environmental predictors of the seasonality of malaria transmission in Africa: the challenge. Am J Trop Med Hyg 2007, 76:33-38. | Mabaso ML, Craig
M, Vounatsou P, Smith T: Towards empirical description of malaria seasonality in southern Africa: the example of Zimbabwe. Trop Med Int Health 2005, 10:909-918. |
Molta, N. B., Watila, I. M., & Gadzama, N. M.. Malaria–related hospital attendance, admissions and deaths in five selected surveillance centre in north eastern Nigeria.
Research Journal of Science 1995, 1(1), 102-110. http://dx.doi.org/10.1289/ehp.95103458. | Oguche, S., Samdi, L. M., Molta, N. B., et al. A four-year (2001-2004) hospi-
tal based retrospective study on morbidity and mortality due to malaria in the Sahel North Eastern Nigeri. J Mal Afri Trop 2006, 2 (2), 56-68. | Pull, J. H., & Gramiccia, G.
Research in malaria control in Africa. WHO Chronicle 1976, 30, 286-289. | Thomson MC, Mason SJ, Phindela T, Connor SJ. Use of rainfall and sea surface temperature
monitoring for malaria early warning in Botswana. Am J Trop Med Hyg 2005, 73 (1): 214–221. | WHO: World malaria report. World Health Organization, Geneva, 2010.

Seasonal speading of malaria

More Related Content

What's hot (13)

Similar to Seasonal speading of malaria (20)

More from Bangabandhu Sheikh Mujib Medical University (20)

Recently uploaded (20)

Seasonal speading of malaria