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1Amity Journal of Healthcare Management
Volume 2 Issue 1 2017 AJHM
ADMAA
Amity Journal of Healthcare Management2 (1), (1–20)
©2017 ADMAA
Utilization Pattern and Effectiveness of IRS and ITNs/LLINs in High Endemic Districts in a North Eastern State of India: Issues
and Challenges
V K Tiwari, Sherin Raj T P, Ramesh Gandotra & P D KulkarniNational Institute of Health and Family Welfare, New Delhi, India
Abstract
Mizoram is a North Eastern state of India and is co-endemic for Plasmodium falciparum and P. vivax malaria being the predominant and life threatening infection (>70%). The GFATM Round 9, IMCP-II aimed to scale up effective preventive and curative interventions in high endemic districts in the state. The provision of LLIN has proved to be an effective strategy in preventing spread of drug resistant malaria in the state. The present article assesses effective use of IRS and ITNs/LLINs in the community in the state. A cross-sectional malarial surveys comprising 880 HHs was conducted during July-August 2014 in high endemic blocks (API>2) across the states of Mizoram. In addition, programme activities data available in the website was also studied. It was found that more than 70% respondents were aware about malaria but the awareness in endemic far away districts like Logtlai and Lunglei was low compared to other Districts/Blocks. Data revealed that supply of LLINs were reduced in the year 2014-15, but about 93% LLIN, 81 % ITNs and 87% of ordinary bed nets were in the usable condition. The Aizawl East and Longtlai districts were having less percentage of any type of usable bed nets. About 90% of households confirmed IRS in their houses and it was found that higher percentage of households confirmed IRS in most affected districts like Kolasib, Sahiya Lunglei Aizawl West etc. Malaria mortality reduced from 119 in 2009 to 21 in the year 2013 in the state but again rise to 31 in 2014.There has been considerable decline in the state of Mizoram during 2009 onwards due to effective IRS, distribution of ITNs/LLINs among BPL population in high endemic districts. Due to reduced mortality, tendency of complacency also cropped up in some of the relatively better off districts.
Key Words: Malaria, North Eastern States, Mizoram, Mortality, IRS, LLIN
JEL Classification: I19
Paper Classification: Research Paper
IntroductionMalaria is a deadly parasitic disease caused by infective bite of Anopheles mosquito. Parasites
responsible for malaria are known as Plasmodium viviax (P.vivax), Plasmodium falciparum (P.falciparum), Plasmodium malariae (P.malariae) and Plasmodium ovale (P.ovale). Infection with P.falciparum is reported as the most deadly form of malaria.
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According to the World Malaria Report 2017, out of the 216 million cases of malaria that occurred worldwide in 2016, India accounts for 6 per cent. 7% per cent of all the malaria-related deaths happened in India in 2016. India stands third in the list of 15 countries that contributed to 80 percent of the global malaria burden. India reported 85 percent of vivax malaria cases. It appears that India may not be able to reduce its malaria burden by half by 2020. The WHO Report 2017 also says that malaria mostly affects poor and vulnerable groups in tropical and subtropical areas, where the temperature and rainfall are conducive for development and spread of the causative parasite. Malaria is still endemic in North Eastern part of the country.
The official figures for malaria in India, available at NVBDCP web site indicate 0.84 million confirmed cases, 63.39% were Pf cases and 105 deaths (NVBDCP, 2017). The NVBDCP countrywide data on malaria load (NVBDCP, 2015) shows that the state of Orissa is severely affected due to humid conditions, and contributes to one fourth of the total annual malaria cases in the country, more than two fifth of P. falciparum malaria cases and around quarter deaths due to malaria in India. The other severely affected states were Meghalaya, Mizoram, Maharashtra, Rajasthan, Gujarat, Karnataka, Goa, southern Madhya Pradesh, Chhattisgarh, and Jharkhand (NVBDCP, 2015). A study done by the Kumar et al., 2007 reported that the P. falciparum accounts for 30 to 90% of the infections in the forested areas inhabited by ethnic tribes and <10% of such malaria cases in indo-gangetic plains and northern hilly states, northwestern India, and southern Tamil Nadu. However, malaria is co-endemic for both Plasmodium falciparum and P. vivax malaria in North Eastern States causing high fatality.
The country is unable to achieve good progress like Sri Lanka, Maldives etc as 80% malaria cases exist in just 20% of the population living in tribal, hilly, difficult and inaccessible areas (World malaria Report, 2017). Many researchers found complexity in handling malaria epidemic because of high concentration in tribal population, difficult terrain, high density forest and suitable climatic conditions for its growth and transmission (Dev, Bhattacharyya & Talukdar, 2003). The NVBDCP, 2015 also states that the malaria transmission is complex due to multi-species co-existence and variable species dominance and bionomical characteristics. Many scientists also found that the proportion of P. falciparum and P. vivax, had large variations greatly, inter alia, from one ecotype to another due to climatic conditions and malaria control activities implemented by the states (Joshi et al., 2008).
In spite of hectic vector control activities by the Government of India with support from GFATM, WHO etc., malarial deaths and endemicity are continuously decreasing but malaria still remains major public health concern in India especially in NE States including Assam. For malaria prevention, government of India is also supporting for the low cost, wash-resistant and ready to use factory treated mosquito net (popularly known as LLIN) in the high endemic marginalized population groups living in remote inaccessible/forest areas which is more acceptable over indoor residual sprays (Guillet P et al, 2001). The LLIN is also advocated by WHO as sustainable key intervention for universal coverage against malaria in the programme (MOHFW, 2012-17).
The National Vector Borne Disease Control Programme as on date is facing many challenges including some from supply side and some from demand side viz., (i) multiple insecticide resistance, (ii) emerging multi drug resistance and steadily rising proportions of P. falciparum to nearly 50% of reported cases, (iii) short supply of anti-malarial drugs and insecticides and lack of awareness on preventive measures and seeking prompt treatment (MOHFW, 2012-17). The GFATM Round 9, covered 86 districts in the seven NE (North-East) States aiming for universal use of LLIN so as to reduce malaria morbidity and mortality by 30% by 2015 in the project districts.
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A study was conducted in the high prevalence areas in the state of Mizoram to assess (i) awareness among community about malaria prevention (ii) effective coverage of Indoor Residual Spray (IRS) (iii) utilization of ITNs/LLINs at community level by assessing (iv) household ownership of mosquito bed nets (v) use of bed nets among households, particularly by pregnant women and children under five.
Material and Methods
Sample SizeAssuming 50 percent use of LLINs by the population (Households) at any point of time (in
peak season) and allowable error of 5%, the sample size at 5% level of confidence is calculated as 384 (rounded off to 400). Assuming a design effect of 2 to cover heterogeneity in the population, the sample size doubles up to 800 Households. Next, adjusting for non-response of 10%, the final sample in the study was 880 Households (HHs). An equal number of sample fever cases in last two weeks were considered for the detailed investigations. Hence, the total sample size was 1760 HHs (880 HHs and 880 old fever cases) for the State.
Sampling Design and Sampling TechniqueA two stage sampling technique for selection of blocks and villages within the selected state
was followed in order to give a reasonable spread of the sample across the population and make it representative.
At the first stage, 10 endemic Blocks (Sub-districts) were selected from the list using the PPS sampling technique. In each of the selected Block, all the Sub-centres with API >2 in the last three years (2010-12) were listed alphabetically. Then all the villages under those Sub-centres were listed along with their population and 8 villages were selected by PPS method, giving a total of 8 villages per Block. In the selected village, all the houses in the village (minimum 100 households) were listed using a pre-tested survey form and all same day fever cases were noted, for details to be taken on the next day. The same day fever cases were tested by the local health workers using RDT Kit and medicines were also provided as per the programme guidelines.
A sample of 11 old fever cases during last 14 day was selected by systematic random sampling from the list of old fever cases prepared during house listing. Thus, for old fever cases during 14 days, total number of fever cases interviewed were 11 fever cases per village x 80 villages= 880 fever cases.
For detailed study of utilization of LLIN bed nets/ Ordinary Bed Nets and Indoor Residual Spray, a sample of 11 households was drawn by using systematic random sampling from the list of all households in the village. Thus finally, sample of eight villages per block for total 10 blocks were studied to give a total of 80 villages for study in the State. For utilization of LLINs / Ordinary bed nets the total number of HHs interviewed were 11 HHs per village x 80 villages= 880 HHs.
The primary data was collected from households in endemic districts of Mizoram during peak malaria season in the year 2014-15. The secondary data regarding programme activities were included from the web-site of state health department during the year 2015-16 and 2016-17.
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Tools used for the SurveyThe present survey utilized (i) Household listing schedule a day prior to survey (ii) Interview
schedule for Head of Household / Respondent for use of LLINs and IRS (iii) Fever/chills in last 14 days of visit (iv) Fever/chills on the day of visit. Besides, programme data were also collected from the concerned officials in the State/District. Programme specific information available on the website of state health department was also downloaded and analysed.
Data Collection and Analysis A survey team, consisting of 5 well trained members (1 Supervisor + 4 Field investigators),
was responsible for survey in each village for 2 days. For each selected Block, there were two such teams and each team covered 4 villages in 8 days. Different sets of data were collected from the health functionaries and community members using different sets of pre-tested interview schedules. The data was analyzed using SPSS version 21.0. The study was approved by the IRB of the Institute. Informed consent was obtained from all respondents.
Quality AssuranceThe supervisor of local evaluation/survey team verified at least 10% of the completed
interview schedule of 2 weeks fever cases and interview schedule for utilization of bed nets. In each block, one state level coordinator and one local/ block level coordinator were trained and made responsible for monitoring of survey in villages, quality and completeness of interview schedules. All completed schedules were rigorously checked before data entry.
Study Limitations Due to the heavy rains, landslide and road blockade during data collection in the peak malaria
season, team had to replace 2 inaccessible samples villages in one district in consultation with district health authority.
Findings
Background InformationAs per the details available on the website of Department of Health and Family Welfare,
Government of Mizoram (http://health.mizoram.gov.in/programmes/malaria accessed on 5/1/2015), the State of Mizoram consists of 9 Districts and 925 villages with total population 10,87,160 according to Census 2011. As per the Annual report 2013 of the State Vector Borne
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Disease Control Programme (SVBDCP), there were total 82 Malaria Centers (M./C), 57 PHCs, 12 CHCs, 5 Urban Health Centers, 370 Sub-Centers and 139 Clinics providing malaria treatment. The State posted MPWs (under NVBDCP) in remote areas and involved ASHA workers in malaria surveillance and prompt treatment. The LLINs were distributed in the year 2008, 2009, 2010, 2011 and 2012, thereafter stopped due to lack of supply in the programme.
It was found that during 2013-2014, roughly 200 people out of every 1000 population were tested for malaria parasite under the State Vector Borne Diseases Programme and approximately 10 in 1000 population were found positive. In the year 2009, the total deaths reported from malaria were 119 which further reduced to 31 in 2010, 30 in 2011, 25 in 2012 and 21 in 2013 i.e. almost 84% reduction in deaths due to malaria in 5 years duration.
Data revealed that Monthly Blood Examination Rate (MERB) decreased over years from 33.74% in 2010, 17.41% in 2011, 14.29% in 2012 and 20.9% in 2013-14. Probably due to decreased risk of death, less community was coming forward for voluntary blood examination. The API varied district to district; lowest in Champhai (0.68%) and Highest (35.96%) in Lawngtlai with the state average as 10.67%. The Pf % was lowest (74%) in Aizawl District and highest (95.5%) in the Mamit District with the state average as 88%. The malaria programme data in the year 2012 revealed that in the 0-14 age group, both males and females were equally affected (53% males and 47% females). However, in the adult age group (15 years and above) higher percentage of malaria cases were among males (61%) compared to females (39%). In overall, 12.5% cases were in 0-4 age group, 21.2% were in 5-14 age group and 66.4% were in the age group 15 years and above. Only 0.3% of pregnant women were tested positive for malaria.
Socio-economic and Demographic Profile of RespondentsIn the survey, majority of the households (45%) belonged to the age group 30-39 years, were
males (73%) and literate (87%). The survey population was predominantly Christians (96%) and Schedule Tribes (98%). According to economic status, Non BPL population was 55%. As per the occupational details, 54% were engaged in agriculture and 16% were in government/private job.
Awareness about Malaria in CommunityTable 1 describes that more than 70% respondents were aware about how person gets malaria,
symptoms of malaria fever, how to prevent malaria and availability of ITNs/LLINs from the government. The awareness in endemic far away districts like Logtlai and Lunglei was low compared to other Districts/Blocks.
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Tab
le 1
.Aw
aren
ess
on M
alar
ia a
nd
its
Pre
ven
tion
am
ong
the
Com
mu
nit
y in
the
Sta
te
Dis
tric
tA
izaw
l E
ast
Aiz
awl
Wes
tK
olas
ibM
amit
Ch
amp
hai
Llu
ngl
eiL
awn
gtla
iS
aih
ya
Blo
ck
(Ph
ull
en
n=
88)
Aib
awk
(n
=95
)K
olas
ib
(n=
11)
Bik
haw
thir
(n
=57
)T
hin
gdow
l (n
=11
)Z
amu
ang
(n=
89)
Ngo
pa
(n=
88)
Llu
ngl
ei
(n=
202)
Law
ngt
lai
(n=
176)
Tu
ipan
g (n
=88
)T
otal
(n=
905)
How
a
pers
on
gets
M
alar
ia?
70.5
95.8
100.
096
.590
.992
.183
.069
.360
.275
.076
.9
How
to
kn
ow
Mal
aria
feve
r?69
.380
.081
.880
.772
.791
.088
.650
.055
.783
.069
.7
How
to
pr
even
t M
alar
ia?
67.0
93.7
100.
091
.290
.988
.895
.565
.859
.787
.577
.2
Aw
are
of
avai
labi
lity
of L
LIN
/ IT
N b
ed n
et65
.969
.572
.770
.263
.671
.984
.183
.756
.871
.671
.7
Aw
aren
ess
on M
alar
ia
Cat
egor
ies
SC
(n=
7)S
T(n
=88
6)O
BC
(n
=5)
OT
HE
RS
(n=
7)L
LIN
(n
=71
8)N
on
LL
IN(n
= 1
87)
BP
L
(n=
382)
Non
BP
L(n
=52
3)T
otal
(n
=90
5)
Des
crib
e ho
w a
per
son
gets
Mal
aria
71.4
77.2
80.6
42.9
77.4
74.9
76.2
77.4
76.9
How
yo
u kn
ow
feve
r is
d
ue
to
mal
aria
57.1
70.0
100.
028
.667
.777
.568
.370
.069
.7
Wha
t sh
ould
be
d
one
to
prev
ent
mal
aria
71.4
77.5
80.0
42.9
75.6
83.4
73.8
79.7
77.2
Wer
e yo
u aw
are
of a
vaila
bilit
y of
L
LIN
/ IT
N b
ed n
et57
.172
.260
.028
.671
.074
.367
.574
.871
.7
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The awareness about availability of ITNs/LLINs was also low (57%) in SC community. Awareness on various issues was low in BPL population and also in LLIN villages.
Availability and Utilization of Different Types of Bed Nets in the Community Under the IMCP, government distributed Bed Nets; initially ITNs and later LLINs three to four years ago among BPL Households. It was found that even to BPL population, ITNs/LLINs were not available in sufficient quantity i.e. 1 bed net for 2 persons.
Table 2. Availability and Usable Utilization of Bed Nets among Households in the State
District Aizawl East
Aizawl West
Kolasib Mamit Cham-phai
Llun-glei
Lawngt-lai
Sai-hya
Total
Block Phul-len
Aibawk Kola-sib
Bikhawthir Thing-dowl
Za-muang
Ngopa Llun-glei
Lawngt-lai
Tui-pang
Total plain bed nets
258 170 29 138 19 241 254 572 469 268 2405
Plain bed nest in usable condition (%)
83.72 92.35 100.00 78.99 89.47 93.36 80.31 94.06 74.84 96.27 87.4
Total ITN Bed nets treated in last 6 months
37 69 13 75 11 3 80 161 105 15 569
Total ITN bed nets treated in last 6 months and in usable condition (%)
67.6 73.9 69.2 77.3 72.7 100.0 87.5 91.9 71.4 100 81.2
Total LLIN bed nets
5 66 3 18 7 90 63 253 96 49 650.0
Total LLIN bed nets in usable condition (%)
80.0 100.0 100.0 88.9 85.7 92.2 100.0 92.9 83.3 93.9 92.6
Table 2 describes that about 93% LLIN, 81 % ITNs and 87% of ordinary bed nets were in the usable condition. The Aizawl East and Longtlai districts were having less percentage of any type of usable bed nets.
Use of Bed Nets among Vulnerable GroupsTable 3 indicates that though quite high percentage (78%) children slept under plain bed net
but less than half (47%) slept under ITN/LLIN, 24% under LLIN and 22% under the ITN bed nets. However, more than half (55%) of children were reported ‘usually sleeping’ in any type of bed net (ITN/LLIN/ordinary).
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Tab
le 3
. Use
of
Bed
Net
s am
ong
Ch
ild
ren
, Pre
gnan
t Wom
en a
nd
oth
er F
emal
e an
d M
ale
Com
mu
nit
y M
emb
ers
in th
e S
tate
Dis
tric
tA
izaw
l Ea
stA
izaw
l W
est
Kol
asib
Mam
itC
ham
-ph
aiLl
ungl
eiLa
wng
tlai
Saih
yaTo
tal
Hou
se-
hold
s (N
=
905)
Bloc
kPh
ulle
n (N
= 8
8)A
ibaw
k (N
= 9
5)K
olas
ib
(N =
11)
Bikh
awth
ir
(N =
57)
Thin
gdow
l (N
= 1
1)Za
mua
ng
(N =
89)
Ngo
pa
(N =
88)
Llun
glei
(N
= 2
02)
Law
ngtla
i (N
= 1
76)
Tuip
ang
(N =
88)
Tota
l und
er 5
Chi
ldre
n
(N =
587
)U
nder
5 C
hild
ren
(N =
587
)
2870
738
776
5712
612
256
587
Und
er 5
chi
ldre
n sl
ept u
nder
pla
in b
ed n
ets
(%)
71.4
84.3
71.4
376
.385
.771
.152
.688
.968
.080
.477
.9
Und
er 5
sle
pt u
nder
ITN
bed
net
s w
hich
trea
ted
in la
st 6
mon
ths
(%)
034
.314
.334
.228
.66.
626
.332
.522
.11.
822
.3
Und
er 5
chi
ldre
n sl
ept u
nder
LLI
N b
ed n
ets
(%)
14.3
44.3
14.3
18.4
28.5
32.9
17.5
30.9
11.5
16.1
24.2
Und
er 5
sle
pt u
nder
ITN
/LLI
N b
ed n
ets
(%)
14.3
78.2
28.6
52.6
57.1
39.5
43.9
63.5
35.3
17.9
46.5
Und
er 5
usu
ally
sle
eps
unde
r ITN
/LLN
/Ord
inar
y be
d ne
ts (%
)
78.2
60.0
71.4
63.2
100.
048
.754
.465
.875
.466
.155
.0
Tota
l num
ber o
f pre
gnan
t w
omen
(N
= 6
9)Pr
egna
nt W
omen
(No.
)
98
24
05
149
145
69
Preg
nant
wom
en s
lept
und
er p
lain
bed
net
s (%
)
77.8
75.0
100.
010
0.0
0.0
100.
078
.677
.887
.580
.094
.2
Preg
nant
wom
en s
lept
und
er IT
N b
ed n
ets
whi
ch tr
eate
d in
last
6 m
onth
s (%
)
012
.550
00.
00
5033
.335
.720
.026
.1
Preg
nant
wom
en s
lept
und
er L
LIN
bed
net
s (%
)
11.1
50.0
0.0
0.0
0.0
0.0
28.6
22.2
21.4
0.0
20.3
Preg
nant
wom
en s
lept
und
er IT
N/L
LIN
bed
net
s (%
)
11.1
62.5
50.0
0.0
0.0
0.0
78.6
55.6
57.2
20.0
46.4
Preg
nant
wom
en u
sual
ly s
leep
s un
der I
TN/L
LN/O
rdin
ary
bed
nets
(%)
77.8
50.0
100.
010
0.0
0.0
100.
057
.077
.882
.460
.084
.1
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Tota
l oth
er F
emal
es (n
= 18
28)
Oth
er th
an P
regn
ant W
omen
(No.
)
168
167
1795
1520
521
139
136
219
718
28
Oth
er fe
mal
es w
ho s
lept
und
er p
lain
bed
net
s (%
)
82.7
89.8
100
70.5
100
80.0
81.0
84.7
72.7
79.7
80.9
1
Oth
er fe
mal
es w
ho s
lept
und
er IT
N b
ed n
ets
(No)
10.7
44.9
17.7
37.9
6.7
8.3
27.9
24.0
16.9
2.0
20.1
Oth
er fe
mal
es w
ho s
lept
und
er th
e LL
IN b
ed n
ets
(%)
2.9
44.3
11.8
6.3
13.3
36.1
28.9
29.4
19.6
18.8
24.5
Oth
er fe
mal
es w
ho s
lept
und
er IT
N/L
LIN
/Ord
inar
y be
d ne
ts (N
o)
30.9
53.3
52.9
44.2
60.0
43.4
45.0
40.7
47.2
42.6
43.7
Tota
l oth
er m
ales
(n=1
904)
Oth
er m
ales
(No.
)
183
171
1510
212
216
231
397
355
222
1904
Oth
er m
ales
who
sle
pt u
nder
pla
in b
ed n
ets
(%)
85.3
84.2
86.7
69.6
83.3
74.5
70.6
77.8
69.3
84.7
76.7
Oth
er m
ales
who
sle
pt u
nder
ITN
bed
net
s (%
)
9.8
37.4
40.0
45.1
0.0
6.5
24.2
21.2
20.3
0.5
19.0
Oth
er m
ales
who
sle
pt u
nder
the
LLIN
bed
net
s (%
)
2.7
42.7
0.0
3.9
16.7
33.3
20.4
26.2
17.8
18.5
21.6
Oth
er m
ales
who
sle
pt u
nder
ITN
/LLI
N/o
rdin
ary
bed
nets
(%)
Resp
onde
nt’s
Use
of B
ed
Net
dur
ing
last
nig
ht33
.952
.660
.039
.258
.341
.738
.540
.848
.737
.442
.3
Resp
onde
nt’s
sle
pt u
nder
the
Bed
nets
(%)
72.7
85.3
100
73.7
100
92.1
73.9
80.7
80.7
35.2
75.6
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It was found that high percentage (94%) of pregnant women ‘slept last night’ under any ordinary bed net but less percentage under ITN bed net (26%) and under LLIN bed net (20%). Nearly 76% respondents confirmed use of any type of bed nets in the last night with minimum 35% in Sahiya district and 100% in Kolasib district which is high endemic. It is also evident less non-vulnerable population (about almost half of other than pregnant women and males above 5 years) was usually slept in any type of bed net.
Use of Bed Nets among Community by Cast, Village Type and BPL Status
It was found that on an average 88% plain bed nets were in usable condition which was lower in other than ST community, BPL population and Non LLIN villages (Table 4).
Table 4. Use of Bed Nets among Community by Cast, Village Type and BPL Status in the State
USE OF BED NETS SC ST OBC Others LLIN Villages
Non-LLIN
Villages
BPL Non-BPL
Total
Total plain bed nets 7 2370 11 17 1850 555 881 1524 2405
% Plain Bed Nest in usable condition
71.4 88.2 72.7 76.5 89.1 84.5 86.6 88.9 88.0
Total ITNs treated in last 6 months
0 555 3 0 440 118 221 337 558
% ITN treated Bed nets in usable conditions
0.0 83.2 0.0 0.0 82.0 85.6 91.4 77.2 82.8
Total LLINs available 5 636 1 0 562 80 262 380 642
% LLIN Bed Nets in usable condition
Total LLIN/ITN/Plain Bed nets
100.0 93.6 100.0 0.0 92.3 98.8 95.8 92.4 93.6
12 3561 15 17 2852 753 1364 2241 3605
% Total LLIN/ITN/Plain Bed Nets in usable condition
Total under 5 Children (N=587)
83.3 88.4 60.0 76.5 88.7 86.7 89.2 87.7 88.2
- - - - 469 118 258 329 587
% Total under 5 children slept under LLIN/ITN/Plain bed nets
Total Pregnant Women(N=69) - 73.3 80.0 - 69.5 88.9 56.2 86.9 73.42
- 69 - - 48 21 24 45 69
% Total pregnant women usually sleep under ITN/LLIN/Ordinary bed nets
Total other Females (N=1828) - - - - 83.33 85.71 95.83 77.78 84.05
- - - - 1410 418 722 1106 1828
% Total other females slept under ITN/LLIN/Ordinary bed nets
Total other Males (N=1904) - - - - 35.67 31.34 37.95 32.55 34.68
- - - - 1445 459 741 1163 1904
% Total other males slept under ITN/LLIN/Ordinary bed nets
35.02 30.50 36.9 31.98 33.93
Avg Population per Bed Net Average number of Population per Bed nets in the Blocks
- - - - 10.5 16.6 - - 11.7
Almost 94% LLIN bed nets were in usable condition but less percentage of ITN bed nets (83%) were in usable condition. 74% children and 84% pregnant women usually sleep in any type of bed
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net. The higher percentage of children and pregnant women usually slept in any type of bed nets in Non-LLIN and Non-BPL category. However, even higher percentage (95%) of pregnant women in BPL category usually slept under any type of bed net. However, this percentage in respect of other than pregnant women and males>5 years was relatively far less (33%).
Further, availability of bed nets was also analysed and it was found that on an average one bed net was available per 12 persons in the State and it was one bed net per 11 persons in LLIN villages and one bed net per 17 persons in Non-LLIN villages against norm of 1 bed net per 2.5 persons.
Findings indicate that 78% children slept under plain bed net but less than half (47%) slept under ITN/LLIN, 24% under LLIN and 22% under the ITN bed nets. Even in the situation of non-supply of LLIN in recent years, high percentage (94%) of pregnant women slept last night under any ordinary bed net but less percentage slept under ITN bed net (26%) and under LLIN bed net (20%). However, 84% of pregnant women usually sleep under any type of bed net (Ordinary/LLIN/ITN).
Washing Practices of LLIN/ITN Bed Nets in Households
To assess effectiveness of ITN bed nets households were asked about washing of LLINs/ITNs and findings are presented in Table 5.
Table 5. Frequency of washing Bed Nets in Households
Fre-quency of washing
Aizawl East
Aizawl West
Kolasib Mamit Cham-phai
Llun-glei
Lawngt-lai
Saihya Total (n=905)
Phul-len
(n=88)
Aibawk (n=95)
Ko-lasib
(n=11)
Bikhawthir (n=57)
Thing-dowl (n=11)
Za-muang (n=89)
Ngopa (n=88)
Llun-glei
(n=202)
Lawngt-lai
(n=176)
Tuipang (n=88)
Weekly 2.3 4.2 18.2 7.0 9.1 1.1 2.3 7.4 6.3 0.0 4.6
Monthly 27.3 7.4 18.2 31.6 45.4 3.4 3.4 10.4 18.2 0.0 12.7
Once in 3 months
2.3 25.3 27.3 14.0 36.4 57.3 26.1 18.8 5.7 6.8 18.7
Do not wash at all
19.3 48.4 27.3 28.1 9.1 25.8 55.7 44.6 24.4 31.8 34.9
No Response
48.8 14.7 9.0 19.3 0.0 12.4 12.5 18.8 45.4 61.4 29.1
It is found that majority of households (35%) did not wash but 19% washed quarterly,13% washed monthly and meagre 5% washed weekly. Almost one third (29%) did not respond. Findings reveal that households may not be educated by health workers about proper upkeep, usage and washing requirements of ITNs/LLINs distributed under the programme. Therefore, along with the distributions of ITNs/LLINs, beneficiaries may also be educated about effective usage and right washing practices.
Difficulties in use of Bed Nets in CommunityIn the study, difficulties faced by community members in use of LLINs/ITNs were asked and
findings are presented in the Table 6.
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Tab
le 6
. Dif
ficu
ltie
s in
usi
ng
LL
IN/I
TN
Bed
Net
s am
ong
Hou
seh
old
s
Dis
tric
t/Blo
cks
Aiz
awl
East
Aiz
awl
Wes
tK
olas
ibM
amit
Cha
mph
aiLl
ungl
eiLa
wng
tlai
Saih
yaTo
tal
(n=9
05)
Bloc
ks (P
hulle
n n=
88)
Aib
awk
(n=9
5)K
olas
ib
(n=1
1)Bi
khaw
thir
(n
=57)
Thin
gdow
l (n
=11)
Zam
uang
(n
=89)
Ngo
pa
(n=8
8)Ll
ungl
ei
(n=2
02)
Law
ngtla
i (n
=176
)Tu
ipan
g (n
=88)
Dif
ficu
lties
face
d by
Com
mun
ity in
usi
ng L
LIN
s/IT
Ns
Can
not F
inan
cial
ly
affo
rd to
buy
8.0
67.4
72.7
45.6
81.8
11.4
8.0
33.2
31.3
9.1
33.3
Gov
t. is
sued
less
bed
ne
ts th
an th
e nu
mbe
r of
fam
ily m
embe
rs
14.8
52.6
54.5
59.6
36.4
17.0
14.8
64.4
60.8
30.7
51.9
No
repl
acem
ent o
f the
be
d ne
t by
gove
rnm
ent
wor
kers
10.2
51.6
90.9
63.2
81.8
57.4
10.2
59.9
61.9
31.8
52.2
No
regu
lar t
reat
men
t of
bed
net
s by
go
vern
men
t wor
kers
11.4
52.6
90.9
57.9
81.8
61.9
11.4
57.4
57.4
31.8
50.5
Wor
king
at n
ight
/ou
tsid
e/ fa
mily
in
field
s et
c
9.1
27.4
9.1
31.6
18.2
60.8
9.1
31.2
1723
.921
.0
Slee
ping
out
side
ho
use/
room
8.0
13.7
9.1
12.3
18.2
31.3
8.0
20.3
11.4
2514
.0
Oth
ers
4.50
--
--
2.2
-8.
9-
6.7
7.3
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It was found that almost one third gave financial reason as difficulty and it was more in Kolasib and Aizawl West districts. Almost 50% replied reasons related to programme viz., less supply, no replacement and no regular treatment of ITN bed nets. Unfortunately such responses came from districts like Kolasib and Lawngtlai which are having higher cases of malaria in the State.
Suggestions to improve use of Bed NetsIn view of low utilization of bed-nets, community was enquired about their suggestions for
improving use of bed nets which are presented in Table 7.
Table 7. Suggestions for improving use of LLIN/ ITN by Households
District Aizawl East
Aizawl West
Kolasib Mamit Cham-phai
Llun-glei
Lawngt-lai
Saihya Total (n=905)
Block (Phul-len
n=88)
Aibawk (n=95)
Kolasib (n=11)
Bikhawthir (n=57)
Thing-dowl (n=11)
Za-muang (n=89)
Ngopa (n=88)
Llun-glei
(n=202)
Lawngt-lai
(n=176)
Tui-pang (n=88)
Provide more bed nets
52.3 96.8 100.0 94.7 90.9 98.9 61.4 86.1 76.1 39.8 77.1
More frequent replace-ment of the bed net
50.0 96.8 100.0 87.7 72.7 98.9 62.5 87.1 73.9 38.6 76.1
Ensure regular treat-ment ITNs
47.7 91.6 81.8 87.7 100.0 98.9 62.5 87.1 75.0 38.6 75.6
It is found that more than 70% households suggested to provide more bed nets, frequent replacement of bed nets and to ensure regular treatment of ITN bed nets by the Government workers but interestingly higher percentage of community gave such suggestion from districts like Aizawl West and Kolasib which are relatively better developed and not far away from State HQs. These clearly indicate need to improve availability of Bed Nets in the community but priority must be given to endemic districts.
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Indoor Residual Spray (IRS) under the ProgrammeUnder the programme at least two rounds of indoor residual spray were being done in the
State as per the programme guidelines. About 90% of households confirmed IRS in their houses and it was found that higher percentage of households confirmed IRS in most affected districts like Kolasib, Sahiya, Lunglei Aizawl West etc. Table 8 describes that almost 87% households confirmed spray during April to July which is also peak season for malaria.
Almost 75% respondents informed 2 rounds of spray and about 20% informed three rounds of spray in their houses. Majority of respondents (75%) informed that spray was done by the government staff but almost one fifth (18%) informed it by others like Private agency/NGOs etc. Quite high percentage (83%) replied that they were informed before IRS.
Indoor Residual Spray (IRS) by Cast & other groupsAttempts were made to assess the coverage of insecticide spray which is defined as
“percentage of rooms in the household (excluding kitchen, cattle sheds, and store room) which were sprayed last time during spraying session”. Similarly, the effective coverage is defined as the “total number of rooms in the household (excluding kitchen, cattle sheds, and storeroom) which were sprayed last time during spraying session and where after spray walls were not painted or plastered”. The IRS coverage and effective coverage was assessed and findings are given in table 3.9. The overall coverage was very high (90%) but effective coverage (i.e. wall not painted/plastered) was significantly low (65%). The coverage was lowest (79%) in Longtlai district which is far away from the state HQs and is one of the endemic districts.
In view of the location and other differences due to social groups, economic status analysis is made considering all these aspects and presented in Table 8. It is also found that coverage is less among SC, OBC and other households compared to ST population. The effective coverage in SC and other households were far below than ST households i.e. less than one third which is a matter of concern. About 83% were informed before IRS in their houses but less SC people (43%) were informed before spraying.
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Tab
le 8
- P
erce
nta
ge D
istr
ibu
tion
of
Sp
ray
Act
ivit
ies
by
Hea
lth
Sta
ffs
in th
e S
tate
Dis
tric
tA
izaw
l Ea
stA
izaw
l W
est
Kol
asib
Mam
itC
ham
phai
Llun
glei
Law
ngtla
iSa
ihya
Tota
l (n
=905
)
Bloc
kPh
ulle
n (n
=88)
Aib
awk
(n=9
5)K
olas
ib
(n=1
1)Bi
khaw
thir
(n
=57)
Thin
gdow
l (n
=11)
Zam
uang
(n
=89)
Ngo
pa
(n=8
8)Ll
ungl
ei
(n=2
02)
Law
ngtla
i (n
=176
)Tu
ipan
g (n
=88)
Hou
se v
isite
d by
hea
lth
staf
ffor s
pray
ing
5893
1146
1171
8319
515
886
812
65.9
97.9
100.
080
.710
0.0
79.8
94.3
96.5
89.8
97.7
89.7
Hou
se s
pray
ed w
ith
inse
ctic
ide
6180
943
972
7518
813
665
738
69.3
84.2
81.8
75.4
81.8
80.9
85.2
93.1
77.3
73.9
81.5
How
man
y M
onth
s ag
o H
ouse
s w
ere
Spra
yed
(Ref
per
iod
July
201
4)?
0-1
mon
ths
26.1
57.9
54.5
40.4
36.4
53.9
78.4
70.3
65.3
85.2
61.9
2-3
mon
ths
65.9
6.3
9.1
35.1
36.4
36.0
9.1
27.2
21.0
10.2
25.4
4 &
abo
ve3.
434
.736
.419
.327
.310
.19.
1.5
8.5
3.4
9.9
How
Man
y Ti
mes
Spr
ayed
in la
st 1
2 M
onth
s
One
26.1
8.4
27.3
38.6
-27
.064
.849
.541
.54.
534
.7
Two
10.2
78.9
36.4
10.5
45.5
51.7
18.2
33.7
42.0
75.0
40.8
Thre
e59
.111
.636
.443
.954
.521
.35.
713
.99.
78.
019
.2
4 &
abo
ve-
--
7.0
--
8.0
0.5
2.8
11.4
2.5
Who
Spr
ayed
the
Hou
se?
Gov
t. W
orke
r50
.097
.910
0.0
75.4
100.
087
.686
.474
.367
.065
.975
.4
Pvt.
Age
ncy
17.0
--
14.0
-2.
24.
55.
92.
320
.57.
0
NG
O-
--
--
--
1.5
8.0
12.5
3.1
Oth
ers
2.3
--
--
--
--
-.8
.0
Cou
ldn’
t spe
cifie
d-
--
--
-6.
812
.45.
6-
4.0
Wer
e Yo
u In
form
ed b
efor
e Sp
rayi
ng?
Yes
67.0
96.8
81.8
89.5
100.
088
.895
.581
.265
.397
.782
.9
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Almost 75% respondents informed 2 rounds of spray and about 20% informed three rounds of spray in their houses. Majority of respondents (75%) informed that spray was done by the government staff but almost one fifth (18%) informed it by others like Private agency/NGOs etc. Quite high percentage (83%) replied that they were informed before IRS.
Under the programme at least two times insecticide spray is done in the community. Attempts were made to assess the coverage of insecticide spray which is defined as “percentage of rooms in the household (excluding kitchen, cattle sheds, and store room) which were sprayed last time during spraying session”. Similarly, the effective coverage is defined as the “total number of rooms in the household (excluding kitchen, cattle sheds, and storeroom) which were sprayed last time during spraying session and where after spray walls were not painted or plastered”.
Table 9 - Coverage of Indoor Residual Spraying (IRS) in the State
District Aizawl East
Aizawl West
Kolasib Mamit Cham-phai
Llun-glei
Lawngt-lai
Saihya Total num-ber of rooms
(N =1852)
Block Phul-len (N =167)
Aibawk (N =173)
Kola-sib (N =26)
Bikhawthir (N =98)
Thing-dowl
(N =22)
Za-muang
(N =187)
Ngopa (n=224)
Llun-glei (N =416)
Lawngt-lai (N =319)
Tui-pang (N
=220)
Coverage of Indoor Residual Spraying (IRS)
89.82 95.38 76.92 82.65 100 89.84 95.54 87.50 79.62 96.36 90.80
Effective coverage (IRS)
40.72 80.35 65.38 52.04 54.55 56.68 75.89 68.51 51.72 64.55 65.39
Coverage of Indoor Residual Spray (IRS) by Cast & other groups(N=1852)
SC (n=10)
ST (n=1820)
OBC (n=12)
Others (n=10)
LLIN (N=1448)
Non LLIN (N=404)
BPL (N=723)
Non BPL (N=1129)
Total (N=1852)
40.00 91.41 80.00 42.86 97.7 89.3 92.1 89.4 90.80
Effective Coverage (IRS) (N=1852)
30.00 65.75 70.00 28.57 90.8 67.5 61.3 63.0 65.39
Were you informed before spraying?
42.9 83.5 80.0 42.9 81.9 86.6 87.7 79.3 82.9
The IRS coverage and effective coverage was assessed and findings are given in Table 9. The overall coverage was very high (90%) but effective coverage (i.e. wall not painted/plastered) was significantly low (65%). The coverage was lowest (79%) in Longtlai district which is far away from the state HQs and is also one of the endemic districts.
DiscussionThe State of Mizoram is bound by Assam in the north, Manipur to the north-east, Bangladesh
to the south-west and Myanmar to the east and south. The state topography poses many challenges in implementation of SVBDCP. Areas bordering with Bangladesh had high API and high malarial deaths due to geo-climatic conditions. The programme data (SVBDCP, 2016) is used with findings from community leaders and community surveys to bring out issues and challenges in the programme. Under the State Vector Borne Disease Control Programme (SVBDCP),
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awareness about preventive measures and compulsory blood test and to start treatment for malaria within 24 hours was key strategies (SVBDCP, 2013-14).
Raising awareness is the key to success of all programme. In the Aizawl East District, a total of 38 awareness campaign, 38 infotainment activities, and 139 Miking and 58 hoardings were constructed during last 3 years. In one of most developed Champhai District, there was high awareness about malaria in the community. But in the PHCs/CHCs, supply of RDTs, Slides and Medicines were inadequate. Saiha is one of the farthest and backward districts in the state where inadequate human resources and lack of awareness regarding malaria in the community is major constraint. However, contribution of NGOs in raising awareness is found very useful. Mamit is one of the backward and high prevalence districts where low knowledge and awareness regarding preventive aspects of malaria is the major constraint. However, lot of initiatives have been taken by the health department to combat malaria through awareness generation, distribution of LLINs and DDT spray. The Longtlai is also a faraway district, where lots of IEC activities were done in the year 2013. It included hoardings (28), Awareness campaigns for schools (14), Miking (20), Infotainment (10), Awareness campaigns to NGOs/FBOs (10), Malaria Clinic cum Awareness Campaigns (2) and Dengu Awareness campaign to NGOs (12). These activities were continuing in future years, also. World Vision NGO is very active in the area for raising awareness about malaria prevention activities. Through community surveys, we found high awareness (70%) but in endemic far away districts like Logtlai and Lunglei awareness was low. Besides Chakma migrants are more vulnerable to disease and deaths due to social backwardness, low awareness and poverty.
In the Aizawl West District (Aibawk Block), it was found that number of blood samples collected decreased during 2009 to 2012 may be due to decrease in prevalence rate and decreased participation. It was 1598 during 2009, 1121 in 2010, 1084 in 2011 and 687 in 2012. However, it increased to 2635 in 2013. The number of Pf cases was 18 in 2009 but in 2010, no Pf cases were reported. However, number of Pf cases was 2 in 2011, 4 in 2012 and were 8 in 2013. Because of difficult terrain and landslide, bad road conditions specially during peak malaria season people in backward districts like Mamit faced tremendous difficulty while travelling to PHC or CHC in case of emergency treatment for malaria. In the far away Longtalai district poor communication & transportation facilities, scarcity of human resources, logistics and supply of medicines & test kits always hampered treatment during peak malaria season. Low awareness and poor literacy and communication are constraints in the programme implementation in the district. Lunglei was one of the better performing districts. However, the staff crunch was major hurdle for implementation of activities at community levels. The post of Community Health Officer (CHO), male and female health supervisors’ were empty in many malaria centers.
IRS Operations in the StateAs per the details of the year 2013-14 available on the website of Department of Health and
Family Welfare, Government of Mizoram, two rounds of IRS operations were carried out in the entire 9 districts in the State. Lowest percentage of households (53%) sprayed were in Aizawl West and Saiha District and highest percentage was (81.3%) in Champhai District and the state average was 64%. Similarly, rooms completely sprayed were lowest (25.8%) in Aizawl East and highest (78%) in Champhai. The state average was 43.9%. The population protected through IRS was lowest (49.3%) in Aizawl East District and highest (73.4%) in Champhai District with state average as 59%. During the second round, percentage household remained same as 64%, percentage rooms completely sprayed increased from 44% to 49.9% and percentage population protected increased from 59% to 63%. This clearly shows disliking for IRS in developed districts like Aizawl East and
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Aizawl West (SVBDCP, n.d). It is also due to reduced API in both the districts over years. Our findings indicate that about 90% of households confirmed IRS in their houses and it was found that higher percentage of households confirmed IRS in high endemic districts like Kolasib, Sahiya, Lunglei, Aizawl West etc.
The information on district wise distribution of ITNs/LLINs as available from SVBDCP site is also studied. The Long lasting insecticidal net (LLIN) can be washed many times but still retain bio-efficacy against target disease vector species. In the Aizawl East District approximately 4800 LLINs were distributed during last 3 years but not sufficient to cover all the people in rural areas. Champhai is one of the faraway districts in the state. In this district, LLINs helped to reduce malaria cases. LLINs were distributed during last 4 years (2009, 2010, 2011and 2012) only to BPL population and it did not cover whole population in villages. Moreover, it was informed by the community members that width of LLINs was less, so two persons cannot sleep in 1 LLIN provided. However, other than BPL card holders were also poor and they could not buy even ordinary mosquito net. There was high unmet demand of LLINs in the villages as people still stay in their agricultural field. Kolasib is one of the high prevalence districts in the state. 9891 pieces LLINs, were distributed in 2009, 10408 in 2010, nil in 2011, 10000 in 2012 and nil in 2013. In Mamit District LLINs were distributed in 2011 but thereafter no further distribution took place. The LLINs are not properly used by the community as they informed that holes in LLINs are big so mosquitoes easily enter in the LLIN. In Longtlai no LLINs were distributed in the year 2013 but there is high demand in the community (SVBDCP, 2016). Our survey revealed that very high percentage (92.6%) of LLINs were in usable condition. People who did not get LLINs were also using normal bed nets. We found one fifth households (18.7%) were washing ITNs/LLINs once in three months and one third (34.9%) did not wash at all. It was found that high percentage (94%) of pregnant women ‘slept last night’ under any ordinary bed net but less percentage under ITN bed net (26%) and under LLIN bed net (20%).
The state of Mizoram shares vast international borders with neighbouring countries like Myanmar and Bangladesh. Studies revealed that northeast region is an established route for spread of drug-resistant P. falciparum malaria to rest of the country due to the migration (Shaw et. al, 2013).
We need to learn from success of anti-malaria activities in neighbouring countries like Sri-Lanka where the malaria menace was eliminated during 1999 to 2009. Strategies like indoor residual spraying and distribution of long-lasting insecticide-treated nets have contributed to the low transmission of malaria during this period. A good entomological surveillance was established and maintained for effective action. A strong case detection system was introduced which resulted in prompt treatment and case monitoring (Rabindra et al., 2012). At present, Sri Lanka is the only country in South Asia, which has almost accomplished the elimination of indigenous P. falciparum malaria by year 2012, elimination of indigenous P. vivax malaria by 2014, maintenance of a zero mortality of malaria cases and prevention of re-introduction of malaria into the country (Sri Lanka MOH, 2008-12).
Conclusions and RecommendationsFrom the year 2009 to 2013, almost 84% reduction in deaths due to malaria is recorded. Because
of resistance by the community due to the harmful effect of pesticides, LLINs should be provided in sufficient numbers for personal protection among outreach marginalized population groups living in 5 (out of 9) remote, inaccessible and malaria endemic districts (API>2) viz., Mamit, Kolasib, Lunglei, Lawngtlai and Saiha District reporting most cases and deaths. Government
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may provide subsidized LLINs to Non BPL population in NE states to ensure universal access to population. Along with up-scaling LLIN supply through NGOs and other innovative approaches like social marketing etc, timely and appropriate drug supply also need to be ensured right from ASHA workers to Sub-centers, PHCs, CHCs and District Hospitals to combat the malaria illness.
A well-focused action plan prioritizing preventive, and universal access to malaria treatment and prevention in the high malaria endemic districts is needed. Besides pre-monsoon stocking of anti-malaria drugs and IEC material in remote and inaccessible districts, improved surveillance, strengthening and retaining trained human resources are pre-requisite to meet the GOI Strategy of eliminating Malaria by 2030.
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20 Amity Journal of Healthcare Management
Volume 2 Issue 1 2017AJHM
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Authors’ Profile
V K Tiwari holds a Ph.D in Statistics from University of Allahabad, Allahabad, India. He did Certificate Course in Health Policy, Planning and Health Economics from Nuffield Institute of Health, University of Leeds, UK. He is also honored with the ‘Fellow of the Royal Statistical Society’, UK. He has received six international fellowships/awards, important ones are by East-West Centre, U.S.A; UNFPA; PPD; WHO (SEARO); GTZ, Japanese Foundation of AIDS Prevention and Research, Endeavour Executive Award from Government of Australia etc. He is currently working as Professor & Head, Department of Planning and Evaluation at the National Institute of Health and Family Welfare, New Delhi. He has 25 years of experience in the public health in India and abroad. He has 75 research papers, published in national and international journals in the field of Demography, Public Health, HMIS etc and authored 7 modules for distance learning programmes.
Sherin Raj T P has done his Post Graduation and M.Phil in Demography from University of Kerala, Kerala, India and done his Ph.D from King George Medical Universtiy (KGMU), Lucknow, India. He has an experience of more than 15 years in research, teaching and training. He has more than 50 publications in his account in various National and international journals in the field of Demography, Public Health, HMIS etc. He also has presented more than 20 papers in various conferences and attended several workshops. He is working as Assistant Research Officer in National Institute of Health and Family Welfare, New Delhi, India.
Ramesh Gandotra has done his Post Graduation and M.Phil in Management Science and done his Ph.D from Indira Gandhi National Open University (IGNOU), New Delhi, India. He has an experience of more than 22 years in research, teaching and training. He has more than 15 publications in his account in various national and international journals. He also attended several workshops and conferences. He is working as Assistant Research Officer in National Institute of Health and Family Welfare, New Delhi, India.
P D Kulkarni has done his Post Graduation in Statistics from Aurangabad University, and worked as computer programmer at NIHFW. He has done his Ph.D from King George Medical University (KGMU), Lucknow, India. He has more than 10 publications in his account in various national and international journals. He also attended several workshops and conferences.