Utilization Pattern and Effectiveness of IRS and ITNs ... 1.pdf · 14 days of visit (iv) Fever/chills on the day of visit. Besides, programme data were also collected from the concerned

1Amity Journal of Healthcare Management

Volume 2 Issue 1 2017 AJHM

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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.

2 Amity Journal of Healthcare Management

Volume 2 Issue 1 2017AJHM

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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


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


Aizawl West


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


Aizawl West


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),



ADMAA

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.

ReferencesDev, V., Bhattacharyya, PC., Talukdar, R .(2003). Transmission of malaria and its control in the Northeastern

Region of India. Journal of the Association of Physicians of India, 51, 1073-6.

Guillet, P., Alnwick, D., Cham, MK., & Neira, M.et al.,(2001). Long-lasting treated mosquito nets: A Breakthrough in Malaria Prevention. Bull. World Health Organisation , 79(10), 998.

GFATM Round 9, India Country Proposal Intensified Malaria Control Project-II, to Global Fund to Fight AIDS, Tuberculosis and Malaria (2009). Retrieved from http://nvbdcp.gov.in/Round-9/IMCP-II-Round-9-proposed.pdf.

Joshi, H., Prajapati, SK., Verma, A., Kang, S., Carlton, JM. (2008). Plasmodium vivax in India. Trends Parasitol, 24, 228-35.

Kumar, A., Valecha, N., Jain, T., Dash, A P. (2007). Burden of Malaria in India: Retrospective and Prospective View. American Journal of Tropical Medicine and Hygiene,77(6_Suppl),69-78.

National Vector Borne Disease Control Programme, Malaria Situation in India (2015). Government of India, Ministry of Health & Family Welfare.

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ADMAA

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.

Documents

Utilization Pattern and Effectiveness of IRS and ITNs ... 1.pdf · 14 days of visit (iv) Fever/chills on the day of visit. Besides, programme data were also collected from the concerned