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Survey of malaria indicators in Cross River State, Nigeria,
using cell phone data entry. Report date: 19 July 2011
Iyam Ugot (1), Ikwo Imoke Bassey (2), Bong Duke (3), Emmanuel Obi (4), Miko
Thomas (5), Toju Maleghemi (6), Marcy Erskine (7), Jenny Cervinskas (8), Karen
Bramhill (9), Melanie Caruso (10), Jason Peat (11), Mac Otten (12)
1. Special Advisor to the Governor for Community Health, Cross River State Government,
Calabar, Nigeria
2. RAMP Survey Tool State Coordinator, Nigeria Red Cross, Calabar, Nigeria
3. Consultant, International Federation of Red Cross and Red Crescent Societies, Nigeria
4. State Support Team Leader, National Malaria Control Programme, Ministry of Health,
Abuja, Nigeria
5. Malaria project manager, International Federation of Red Cross and Red Crescent Societies,
Calabar, Nigeria
6. State Coordinator, Malaria Action Programme for States, Calabar, Nigeria
7. International Federation of Red Cross and Red Crescent Societies, Geneva, Switzerland
8. Consultant, International Federation of Red Cross and Red Crescent Societies, Ottawa,
Canada
9. Operational Research delegate, International Federation of Red Cross and Red Crescent
Societies, Nairobi, Kenya
10. Administrative Coordinator, International Federation of Red Cross and Red Crescent
Societies, Geneva
11. Senior health officer (Malaria), International Federation of Red Cross and Red Crescent
Societies, Geneva, Switzerland
12. Consultant, International Federation of Red Cross and Red Crescent Societies, Atlanta,
USA
1
Executive Summary
Background. The Nigeria Red Cross Society (NRCS), the International Federation
of Red Cross and Red Crescent Societies (IFRC) and the Government of Cross
River State, Nigeria implemented a survey in 10 of 18 local government areas (total
population of 1.7 million) during the rainy season from 27 June through 1 July 2011.
The survey examined important malaria indicators following a mass door-to-door
distribution and hang-up of long-lasting insecticide treated nets (LLINs) for malaria
prevention during January-June 2011. Methods: The survey used probability-proportional-to-estimated-size (PPES)
sampling of primary sampling units (PSUs) and PPES to select one segment from
each PSU. Households were chosen from the segment using simple random
sampling. The sample included 30 PSUs, 10 households per PSU, and 1548 persons.
Cellphones were used by NRCS volunteers to enter the survey data using
EpiSurveyor software in real-time during the interview. Results: The percentage of households with at least one ITN was 87%, of persons
with access to an ITN was 66%, and of persons of all ages that slept under an ITN
was 60%. The percentage of ITNs used during the previous night was 84%. Of
children <5 years old with fever in the two weeks before the survey, 60% received
an ACT and 24% received an ACT within 24 hours. The local cost of the survey
including the cell phones was US$ 43.868. Conclusion: A high percentage of households were reached with LLINs and three
out of five persons were sleeping under LLINs. However, many households had
insufficient LLINs to cover all household members. This level of ITN coverage
should result in substantial reductions in malaria-related morbidity and mortality in
Cross River State. The innovative survey methodology provided valuable health
data rapidly and at low cost.
2
Background The government of Nigeria and partners are striving to reduce the number of cases
and deaths from malaria by 50% by 2010 and by 75% by 2015 in line with the
Millennium Development Goals (MDGs), World Health Organization (WHO), Roll
Back Malaria (RBM), and government of Nigeria goals. The Ministry of Health’s
(MoH) National Malaria Control Programme (NMCP) in Nigeria is now stressing
the importance of universal coverage of persons of all ages (100% of persons using
an LLIN every night) as advocated by the World Health Organization to achieve the
disease-reduction goals. Two important indicators of universal coverage with
LLINs are: 1) the percentage of persons that had access to ITNs in the household
and 2) the percentage of all persons using ITNs the previous night. For treatment,
all persons with malaria are supposed to receive an appropriate treatment within 24
hours of the onset of fever, especially children <5 years old, the highest risk group
for malaria-related mortality. In late 2009, WHO advocated parasite-based testing
of all suspected malaria cases (including the use of rapid diagnostic tests—RDTs) in
all high-burden countries.
In an effort to rapidly scale-up access to and use of LLINs in Cross River State, the
State Government and partners (USAID, Canadian Red Cross, WHO) conducted a
statewide distribution of (LLINs) in December 2008 and January-February 2009 as
part of an integrated campaign composed of different health interventions. This
mass distribution was targeted to children <5 years old. During 2009-2010, WHO
and RBM emphasized the necessity for universal coverage (all ages) with LLINs. In
early 2011, the State Government and partners distributed additional LLINs in 10 of
the 18 LGAs in Cross River State during a mass door-to-door distribution and hang-
up campaign with the objective to achieve universal coverage for persons of all ages.
This report describes the survey that was undertaken to measure the campaign-
related malaria indicators in late June 2011.
3
Previous mass distribution of LLINs in 2008-2009 and post-campaign survey in May
2009. Immediately following the mass LLIN distribution campaign in December
2008 and January-February 2009, Red Cross volunteers attempted to visit all
households to disseminate information about the importance of sleeping under
the nets every night (the “hang-up campaign”) and to physically assist households,
where necessary, with hanging the nets received in the campaign. Over 900
volunteers undertook house-to-house visits during a period of 5 days. The post-campaign survey conducted within 4 months of the end of the mass
campaign (May 2009) showed the following results: LLIN use in all ages was 41%;
LLIN use in children under 5 years was 63%; ratio of LLINs per household was 1.6;
percentage of children under 5 years with fever in the previous two weeks that
received ACTs at any time was 29%; percentage of households that were visited for
dissemination of key malaria messages in the previous six months was 20%; and the
equity ratio (highest to lowest wealth quintile) for LLIN use in those of all ages was
0.92. The sampling frame covered all 18 LGAs in Cross River State. Fill-in campaign: mass distribution of LLIN in 2011. Based on the survey results
following the 2008-2009 LLIN distribution, USAID procured 614,000 LLINs to
distribute in a fill-in campaign. A fill-in campaign provides additional LLINs to
cover all persons of all ages in an attempt to achieve universal coverage. The
number of nets was based on the estimated number of households and existing
coverage from the survey, which indicated that approximately 1 net would be
needed per household (average household size of 5) to reach universal coverage.
The objective of the fill-in campaign was to reach universal coverage (at an access
ratio of 1 LLIN for 2 persons) throughout the State. Between July and August 2010,
a door-to-door household registration exercise took place in 16 out of 18 local
government areas (LGAs); the two remaining LGAs, the urban areas of Calabar
4
Municipal and Calabar South, were excluded in order to determine an appropriate
strategy for the activity given the dense population. The household registration
accounted for the nets distributed in 2008 and 2009 and revealed a total additional
need of 1,461,594 LLINs. Fifty-two percent of the nets distributed in late 2008 and
early 2009 were found in households. Given the shortage of LLINs to meet the
newly calculated need, the State team opted to work towards achievement of
universal coverage in as many LGAs as possible (targeting 9 LGAs first, then
adding a 10th LGA in June 2011 after it was determined that enough LLINs
remained to complete a 10th LGA). From the household registration data, 866,820
LLINs were required for universal access (1 LLIN for 2 persons, with rounding up)
for the 1,596,272 persons in the 10 LGAs; 178,035 existing nets (21% of the total
need) that were obtained by households in 2008 (previous campaign) or later,
therefore, 688,785 LLINs would need to be distributed. However, two factors
reduced the number of LLINs that would need to be distributed. First, a maximum
of 4 LLINs per household would be distributed. Second, LLINs would only be
given if there was enough space in the household to hang them (distribution and
hanging were done during a single visit by the Red Cross volunteers). A mass door-to-door distribution and hang-up campaign of Dawa LLINs to all
households was conducted in 9 LGAs during January to March 2011. The hang-up
activities took place during the same visit as the distribution. The distribution and
hang-up campaign in a 10th LGA was conducted from May to June 2011 once it was
clear that sufficient nets remained after the 9 LGAs were complete. The objective
of the campaign was to ensure the sufficient distribution of LLINs to achieve
universal coverage (defined as 1 LLIN for every 2 persons). During the door-to-
door LLIN distribution and hang-up campaign in January - June 2011, Red Cross
volunteers provided LLINs to each household based on the criteria of the household
registration and adjusted for available space in the household to hang additional
LLINs. A maximum of 4 LLINs were distributed and hung per household (the limit 5
of 4 LLINs was based on average household size from the household registration--
4.86--and the typical physical structure of households). These nets were distributed
in a door-to-door campaign that involved over 1900 Red Cross volunteers. The
number of LLINs was available for distribution and hang-up was 614,000. The
number of LLINs distributed in the first 9 LGAs was 488,090. The estimated
number of LLINs distributed in the 10th LGA in which the distribution was done
several months after the others was 70,778). Therefore, the total distributed was
558,868. During the 2011 universal coverage campaign, Red Cross volunteers were
equipped with hammers, nails and string to distribute and hang LLINs in all
households visited, while also disseminating key messages on malaria prevention
and treatment. It should be noted that the sampling frames for the two surveys (2009 and 2011
surveys) were not the same—the 2009 survey covered all 18 LGAs and the 2011
survey covered the 10 LGAs where distribution and hang-up took place. Treatment and diagnostic testing. ACTs were first distributed in public-sector
health facilities in Cross River State starting in 2005. Funding for subsidizing of
ACTs through the private sector began in 2008 through a grant from The Global
Fund to Fight AIDS, Tuberculosis, and Malaria. Rapid diagnostic tests (RDTs)
were first introduced in 2011 and are still being rolled out to health facilities in the
State. Background on Cross River State. Cross River State is one of 37 Nigerian states
and is located in the South-South region of Nigeria (see Figure 1) and has a
population of about 3.1 million people. Cross River is one of the coastal states. The
state occupies 20,156 square kilometers on the southeastern coast and the Niger
Delta and shares its boundary to the east with Cameroon. The state capital is
Calabar, a seaport. Cross River State has salt-water swamps and dense tropical
forest, and rivers are an important means of inland transport. The climate is tropical
6
and characterized by a rainy season between April and October and a dry season
between November and April.
Figure 1: Cross River State, one of the 36 states in Nigeria
Rapid Mobile Phone-based (RAMP) survey concept. The IFRC, with the support of
DataDyne, WHO, American Red Cross, and CDC epidemiologists at the Global
Immunization Division, have been working on a “RAMP survey” concept that uses
cellphones for survey data entry. This work has been ongoing for several years,
primarily focused on the technical manual and field-testing the concept.
The concept includes use of cellphones and freely-available software to conduct
health surveys rapidly, simply, at low cost, with minimal external technical
assistance, and avoiding the main potential bias of the Expanded Programme on
Immunization (EPI) cluster survey method (initial selection of the first household
using a random direction from the center of the primary sampling unit). This
survey was the third field test of the RAMP survey concept—the first was
successfully completed in January 2011 in Malindi, Kenya and the second in
Caprivi, Namibia in May 2011.
These surveys had the following innovations: 1) 30 clusters (like the EPI cluster
survey)1, 2) total of 300 households in the sample (10 households per cluster), 3) use
of DataDyne’s Episurveyor web-based tool to collaboratively design model malaria
questionnaires (including responses and skip patterns) that can be easily adapted and
translated into local languages, 4) use of cell phones to enter data during the
interview, 5) daily upload of data to an internet-based database, 6) daily data
cleaning of uploaded data, 7) daily feedback of data quality issues to interviewers
and team leaders based on previous day’s data quality, 8) daily analysis of uploaded
data, 9) completion of preliminary results bulletin within 24 hours of the last
interview, and 10) completion of a preliminary report within 72 hours of the last
interview.
Methods The survey was conducted from 27 June through 1 July 2011. The sampling frame
was a list of all 109 wards in 10 (Abi, Bekwarra, Etung, Ikom, Obanliku, Obubra,
Obudu, Ogoja, Yakurr, Yala) of 18 local government areas in Cross River State
targeted for the survey. The total population in the sampling frame (10 LGAs) was
1,699,246. The total population for each ward was determined using 2008 census
data projected for population increase (2.9%). Thirty wards were selected as
primary sampling units (PSUs) using probability proportional to estimated size
(PPES). Within each selected ward, one settlement was selected by PPES using
2011 immunization micro-planning data. Within settlements, a village was selected
by PPES. The selected village was further sub-divided into segments of
approximately 20 households each and a final segment selected by PPES. In the
final segment, all households were listed or mapped and 10 households were chosen
by simple random sampling, with an additional 5 households selected in case
members of selected households could not be reached. Data was collected on all
persons sleeping in the household (“sharing a common cooking pot”) the previous
night. The design resulted in an equal probability survey. The sample weight
8
(1097.70) was calculated by dividing the estimated population in the sampling frame
(1,699,246 persons) by the number of persons in the survey (n=1548). Three questionnaires were developed online using the EpiSurveyor web-based
questionnaire design tool (www.episurveyor.org)—household, person roster, and net
roster. Questions were modeled after RBM’s Malaria Indicator Survey (2005).2
Principal component analysis was used to create the wealth quintiles index for each
household. Analysis was performed in STATA version 11 (College Station, Texas,
USA), taking into account the design of the survey. The main indicators analyzed
were those promoted by WHO and Roll Back Malaria’s Monitoring and Evaluation
Reference Group (MERG), including two new “access” indicators that were
proposed at the MERG Survey and Indicator Guidance Task Force Meeting, on 5-6
April 2011 in New York, New York, USA. These two new access indicators were
1) proportion of population with access to an ITN within their household, and 2)
proportion of households that have at least one net for every two people. The
definition of an ITN was changed to exclude pretreated nets obtained within the past
12 months (as recommended at the meeting) as these types of nets are no longer
produced in most places. The number of LLINs needed for universal access and the number of LLINs
currently in households in the whole survey domain was estimated. The number of
LLINs needed for universal access was calculated by dividing the sampling frame
population by the average number of persons sleeping under ITNs during the survey
(1.94). The number of LLINs currently present was estimated by multiplying the
number of LLINs found in households multiplied by the survey weight (1097.70).
9
Indoor residual spraying (IRS) for malaria is not done in Cross River State by public
health workers so those indicators related to IRS were dropped from the report. The
questionnaires were in English. The population access indicator was estimated by
calculating the number of persons with access to an ITN (using a ratio of 2 persons
per 1 ITN) by household, then summing for all households. Survey operations. The government of Cross River State, the NRCS, and IFRC led
the survey operations. Training was provided for the 13 interviewers and 6 team
leaders during five days (20-24 June 2011). Survey fieldwork was conducted over 5
days (27 June to 1 July 2011). Nokia-brand cell phones--Nokia 2700 series (US$ 80
USD, no keyboard, no GPS)--were used to run DataDyne’s freely-available cell-
phone-based EpiSurveyor software (http://www.datadyne.org/ and
http://www.episurveyor.org/). Survey data were immediately entered into the cell-
phone database during the interview for the household questionnaire. A paper-based
job aid was completed to link the number of the person who slept under each net
with the person roster of those who slept in the household the previous night
(whether they slept under a net or not). Uploading of data on the cellphone to the
internet-based database using EpiSurveyor software required a 2G/GPRS cell-
network connection that was present in nearly every settlement. Uploading of data
took place at the end of the work in each cluster (PSU). Interviewers and team
leaders were NRCS volunteers. Since the data were sent from the mobile phones each evening to an internet
database, a local consultant and an out-of-country epidemiologist were able to
perform data cleaning and analysis each evening. A survey-results bulletin was
produced within 24 hours of the last interview using an Excel-based, 4-page “survey
results bulletin” tool. The preliminary survey results (pages on four Excel
worksheets were combined into a single PDF) were shared with interviewers at an
end-of-survey debriefing meeting within 15 hours of the last interview, on 2 July
10
2011. A preliminary report was completed within 72 hours and was ready for
distribution to stakeholders on 5 July 2011. Report tables and graphs. The tables and graphs of the survey-results bulletin act as
the tables and graphs for this preliminary report1.
Results Table 1 on the survey-results bulletin shows key descriptive information about the
survey. The number of persons in the sample frame was 1,699,246. Data were
collected on 1548 persons of all ages that included 204 children <5 years old in 300
households. During the survey, 579 nets were found in households--59 (10%)
LLINs from the 2008-2009 mass campaign, and 492 (85%) LLINs from the 2011
mass campaign. With permission from the household, 99% of the nets were
observed by interviewers. Eighty-six percent of the ITNs were reported to be hung
the night prior to the survey. There were enough ITNs to cover 66% of the sleeping
spaces and enough hanging ITNs to cover 57% of the sleeping spaces. The average
number of persons sleeping under an ITN was 1.94. The main ITN indicators are
shown in the figure on page one of the survey-results bulletin. The percentage of
households with at least one ITN was 87% (95% confidence interval [CI], 80-94%).
In one cluster (PSU), none of the 10 households surveyed had an LLIN from the
2011 campaign. Among the remaining 290 households, 97% of them had at least
one ITN. The percentage of all persons with access to an ITN was 66%, and 56% of
the households had enough ITNs to cover all residents at a ratio of 1 ITN per 2.0
persons. The percentage of persons that slept under an ITN the previous night was
60% for all persons and was 71% for children <5 years old. In households that
owned at least one ITN, the percentage of children <5 years old that slept under an
ITN increased to 80%. The percentage of children with fever in the previous two
1 The survey-results bulletin is a companion document to the survey report, and contains information, tables and graphs that show the survey findings.
11
weeks that had a finger or heel stick for blood was 16%. The percentage of children
with fever that received treatment with an ACT was 60% and the percentage that
received an ACT within 24 hours from the onset of fever was 24%. The estimated
number of LLINs needed to provide access to all persons (based on 1.94 persons per
ITN) in the entire sampling frame was 875,900. Estimate of the number of ITNs/LLINs in all households in the sampling frame. The
number of nets found in the 300 households was 579 (95% CI = 507-651). Of those,
573 (97%, 95% CI 499-647) were LLINs (557 Dawa LLINs, 1 Permanent LLIN,
and 15 other types of LLINs). No nets were non-LLIN ITNs. Six nets were
classified as non-ITNs (2 non-LLIN nets that were not received or treated within 12
months and 4 nets with unknown brand type). Of the 573 ITNs/LLINs, 492 (86%)
LLINs came from the 2011 mass distribution, 59 (10%) were from the 2008 mass
distribution, and 22 (4%) were from other sources (health facility, market, and
friends). Multiplying the number of ITNs/LLINs found in the sample households
(n=573) by the sample weight (1097.70), the estimate of the number of ITNs/LLINs
currently in all households in the survey frame (those sampled and not sampled) was
estimated at 628,982 (95% CI = 548,228-709,736). The estimated number of ITNs/LLINs currently in all households in the survey
frame (those sampled and not sampled) from the 2011 mass distribution was
540,068 (95% CI = 452,604-627,533). The number of mass campaign LLINs
distributed (n=557,762) was similar to the estimated number of mass-campaign
LLINs in the sampling frame (n=540,068) and was well within the confidence
interval (452,604-627,533). The number of ITNs needed for universal access would be 875,900 if one uses the
average of 1.94 persons sleeping under each ITN from the survey, 950,608 using the
number of sleeping spaces in households reported during the survey, and 954,632 12
using the ratio of 1.78 persons per nets as suggested by Kilian et al.3 Using the
person/ITN ratio of 1.94, the number of ITNs in the sampling frame was 72% of
ITNs needed to achieve universal access. The percentage of ITNs that were 36 months and older was 9%.
Table 2 shows key ITN indicator point estimates, confidence intervals, and data by
wealth quintile. The household ownership of ITNs was the similar by wealth
quintile. Table 3 shows the age of ITNs—85% of the ITNs were <12 months old
and 91% were <36 months old. Table 4 shows that recent home visits and visits to
clinics where malaria was discussed - 72% of households reported receiving a
home visit for malaria discussions within the 6 months prior to the survey. Table 5
shows additional ITN information - the mass campaigns in 2008 and 2011 were the
source of 95% of the nets. Table 6 shows additional information about treatment -
chloroquine is still being reported to be used, accounting for 22% of non-ACT
treatments of malaria. The percentage of children <5 years old with fever in the
previous two weeks was 49%. The percentage of children with fever in the two
weeks prior to the survey that received an ACT was 60% and an ACT within 24
hours of the onset of fever was 24%. Table 7 shows the width of the 95%
confidence interval and the design effect for 3 key variables from the household
summary data. The confidence interval was ±8% for the percentage of persons
using an ITN, ±7% for household ownership of at least one ITN, and ±13% for
children using an ITN. The design effect ranged from 3.3 to 9.7 for these three
indicators. The graph on page 4 shows the age distribution of ITN use. Those <5
years old had the highest ITN use and those 5-14 years old had lowest use compared
to the other age groups. The supplemental analyses on page 4 showed that 99% of
nets were ITNs, 98% of nets were LLINs, and 85% of nets were from the 2011 mass
13
campaign and 10% were from the 2008 campaign. Nineteen percent of ITNs had 3
or 4 persons sleeping under them. Cost. The total local cost of the survey was US$ 43,868. Training accounted for
21%, survey operations accounted for 40% (which was represented by: personnel
41%, transport 59%), phones and accessories for 5%, and other for 34% (local
consultant, local data management, etc.).
Discussion The percentage of households that had at least one ITN was very high (87%), the
highest percentage recorded by any state in Nigeria to date. Excluding the one
village in the sample that was missed by the 2011 mass campaign, 97% of
households had at least one ITN. There were enough ITNs in the households to
cover two-thirds (66%) of the population had all the nets been used at a ratio of 2
persons per ITN. ITN use in persons of all ages was 60%. Appropriately, children
<5 years old had the highest percentage of ITN use (71%) among all age groups. A
high percentage of ITNs were used the night before the survey (84%) demonstrating
only a small gap in ITN use given access or ownership. Therefore, the primary gap
in the percentage of the population using ITNs was access to an ITN. The reasons for the 34% gap (100% minus 66%) in access are unknown. The gap
could occur during the distribution (insufficient LLINs were distributed), or after the
distribution and before the survey (“leakage”). It is unlikely that an inadequate
assessment of need contributed to the gap since the need was calculated during a
household-to-household assessment. The ratio of population to total LLIN needed
(1.84) found during the household-to-household assessment was similar to the ratio
(1.78) calculated by Kilian et al.3 based on studies in other African countries. Some
of the gap could be due to uncertainty in the estimates of population and number of
14
ITNs in households. The population from the census was 1,596,272 and the
population from the sampling frame (from immunization microplan data) was
1,699,246—a difference of 6%. The confidence interval around the estimate of the
number of ITNs in the households was ±13%. It appears that a substantial part of the gap (approximately half) was due to
insufficient distribution of LLINs to households. The estimated population of the 10
LGAs was 1,596,272 and 866,820 ITNs (ratio of 1.84 persons per ITN) were needed
to achieve universal access as assessed by the house-to-house registration. Since
21% of the existing nets had been acquired in 2008 or later as assessed by the
volunteers, 688,785 LLINs were required to be distributed and 558,868 (81%) were
actually distributed. The 19% gap between the number of LLINs needed to be
distributed and the number actually distributed could be due to the two constraining
factors mentioned earlier (insufficient space in the household for hanging additional
LLINs and maximum of 4 LLINs per household) and/or other unknown distribution
factors. The survey data indicated that the policy of a maximum of 4 LLINs per
household could account for a gap of 5%. The other factors that may have contributed to the access gap: 1) insufficient LLINs
were given to households for other unknown reasons, 2) older nets were taken down
and replaced by new LLINs, 3) LLINs were given away as gifts or to other parties
(small percentage of LLIN were found to be lost in this way in other surveys in
Nigeria), and 4) uncertainty and variance in some of the estimates. Six months is
unlikely to be enough time for a high proportion of LLINs to have been destroyed or
to have physically degraded by washing, burning, holes, seam bursting, etc.
Leakage did not appear to be a major issue since the number of LLINs in the
households estimated by the survey (540,068, 95% CI = 452,604-627,533) was
close to the number actually distributed (558,868).
15
During the household-to-household registration, existing nets were 21% of the total
LLINs needed for universal access. During the survey, 14% of the existing nets
were obtained from a source other than the 2011 mass distribution. The similarity
of results indicates the quality of the household registration assessment. The format of the mass campaign was ideal to achieve near 100% access—
household registration and door-to-door distribution. This survey highlights the
need for qualitative investigations to explore reasons for the gaps in LLIN program
performance. Further qualitative investigations are urgently needed to understand if
the factors above or other factors contributed to the gap in access given an excellent
program paradigm (door-to-door distribution) that should yield near 100% access. Despite the gaps, the 2011 campaign produced substantial benefits for Cross River
State residents. The average number of LLINs per household was nearly 2.0 (1.91).
Nearly three-quarters of children and two-thirds of residents were protected from
malaria during the night preceding the survey. This level of ITN coverage should
result in substantial reductions in malaria-related morbidity and mortality in Cross
River State. The age of ITNs is unlikely to be much of a factor in the reduction of malaria by
ITNs since 86% of nets were reported to have come from the 2011 mass campaign
and 85% of the nets were <12 months old. Although the campaign was termed a
“fill-in campaign” there were not very many nets from the 2008-2009 mass
campaign remaining in the households (10%). This may be appropriate since the
survey was 2.5 years after the December 2008 campaign and many nets would have
been physically and/or biochemically compromised. In essence, the 2011 campaign 16
was more of a “replacement” mass campaign than a “fill-in” mass campaign. The
situation in Cross River State highlights the need for LLINs to be produced and
deployed with longer physical duration and longer complete effectiveness of the
insecticides on or within the LLINs. The percentage of children with fever in the two weeks prior to the survey was 49%.
The percentage of children with fever that took an ACT (60%) was high. The
interviewers were trained by a pharmacist to recognize the many different types of
ACTs in the public and private sector in Cross River State. However, it is the
practice of chemists/pharmacists to give medicines to patients in small, unmarked
packages so patients may not know with precision the type of medicines that they
had been given. However, the high percentage of ACTs being given could be true
since 1) the private sector is strong, 2) there have been private-sector ACT subsidies
using The Global Fund grants since 2008, and 3) ACT availability in public health
facilities greatly increased in 2011. Further investigations may be needed to fully
evaluate this issue. The survey analysis provided several innovative analyses of: 1) estimated number of
ITNs that remain in the households after the mass campaign compared to that
distributed, 2) “access” to ITNs according to the new indicator definitions, 3) the
average number of persons using each ITN, 4) the percentage of ITNs that were
used last night, 5) and the age of ITNs. The percentage of existing ITNs that were
used last night has been recommended as a new core indicator by the RMB MERG. The confidence interval of 7-13% for 3 key indicators shows that the sample size
and number of clusters were sufficient for most management decisions. The EPI
cluster survey target precision is ±10%.
17
The local field costs, including the cell phones, of the survey were US$ 43,868. The
local costs in the Malindi, Kenya survey in January 2011 were US$ 22,795 and in
Namibia were US$ 36.454. Conducting health surveys with precision of ±3-13% at
a cost of US$ 20-45,000 is likely to be attractive to non-governmental organizations
(NGOs) and Ministries of Health. Many current malaria surveys are costing from
US$ 300,000 to $1.2 million, although the most expensive of the current surveys
include parasite testing. More frequent surveys of LLIN ownership and use would
provide timely data on the gaps in access and use. The Consensus Statement on
Continuous Distribution Systems for Insecticide Treated Nets from the RBM Vector
Control Working Group (VCWG) Continuous Distribution Workstream on 16 June
2011 stated “a key priority for maintaining universal coverage must be to establish
systems to monitor coverage, and variations in the rate of loss, so that the rate of
input of LLINs can be adjusted to balance this loss.” This survey and analysis had several limitations. First, the high percentage of
children with fever receiving an ACT (60%) triggers questions about validity.
However, as we discussed, access to ACTs may truly be high. Second, the
education and experience level of interviewers was less than those of other more-
expensive surveys such as national Malaria Indicator Surveys and Demographic and
Health Surveys. Third, the sample size of 300 households was too limited to
provide valid estimates on pregnant women (3.5% of the population) and would be
too small to allow high precision for extensively disaggregated analyses (for
example, ITN use in children by wealth quintile by LGA). However, the RAMP
survey tools and methods are not limited to a single survey domain of
30 clusters and 300 households. NGOs and MOHs can use any sample size and
number of clusters that they feel is appropriate. The inclusion of one village in the
18
sample that was entirely missed by the 2011 campaign indicates good sampling
procedures. In conclusion, this survey successfully met the objectives of the RAMP survey
concept—that is, collection of valuable health data with rapid analysis and feedback
at low cost. Enabling local analysis of data still remains to be done, as well as
demonstration that the complete design-to-feedback-of-results cycle can be done in
multiple countries with minimal external technical support while maintaining
adequate data quality.
19
Figure 2. LGAs in the state of Cross River
20
RAMP SURVEY—QUESTIONNAIRE ① HOUSEHOLD QUESTIONNAIRE
No. Variable Response Scale 1 RAMP SURVEY
2 Consent obtained? Yes
No (Skip to Q.38) 3 CLUSTER and HOUSEHOLD questions follow next 4 Cluster number
5 Household number
6 Name of head of household
7 Household in a rural or urban area? (Urban defined as a
town with >=5000 persons)
8 How many kilometers is your household from the nearest government, NGO, or mission health facility or hospital? (98=do not know). If less than 1 km, put “1”.
9 How many Minutes does it take to walk to the nearest health facility?
10 BEDNET questions follow next 11 Number of people of all ages who slept in this household
last night? (do NOT include usual members of this household that slept somewhere else last night)
12 Last night, how many sleeping spaces were there (both inside and outside if someone slept outside)? (Sleeping space defined as a place where people sleep that could be covered by a single net).
13 Has anyone visited this household in the last 6 months to talk about malaria or mosquito nets?
Rural Urban Yes No Do not know
14 Has anyone in your household visited the health facility in
the last 6 months? 15 Has anyone in this household talked with people at the
clinic or hospital about malaria or mosquito nets in the last 6 months?
Yes No (Skip to Q16) Do not know Yes No Do not know
16 What is your greatest source of information on the use of
mosquito nets? 17 Indoor Residual Spraying (IRS) question follows next
Radio Health centre staff Community based volunteer Community leader Neighbor Relative Other
No information
21
18 At any time in the past 12 months, has anyone sprayed the interior walls of your dwelling against mosquitoes?
Yes No Do not know
19 HOUSEHOLD ASSET questions follow next 20 Does your household have electricity? Yes
No
21 Radio? Yes No
22 Television? Yes No
23 Refrigerator? Yes No
24 Electric iron? Yes No
25 Electric fan? Yes No
26 Bicycle? Yes No
27 Motorcycle or scooter? Yes No
28 Car or truck? Yes No
29 Cow, goat, or sheep? ? Yes No
30 Canoe or boat Yes No
31 Phone? Yes No
32 Domestic worker (unrelated to head of household)? Yes No
33 Do members of this household work on agricultural land belonging to themselves or their family?
Yes No
34 What is the principal household source of drinking water? Piped water into residence Protected well in residence Unprotected well in residence Open well in yard Protected well in yard Unprotected public well Protected public well Tap in yard Tanker truck Bottled water
22
Car
Public tap Rain water Surface water (e.g., river, lake) Spring
35 What is the principal type of toilet/sanitary facility used by members of your household?
Own flush toilet Shared flush toilet Own pit latrine Own improved pit latrine Shared pit latrine Bush or field Other
36 What is the principal type of flooring in your house (interviewer may choose to observe)?
Dirt or sand Dung /wood / palm/ bamboo Cement including vinyl Cement including parquet Tile (e.g., ceramic, marble) Carpeted
¡ Other
37 What is the principal type of cooking fuel in your house? Wood or dung Kerosene Charcoal
¡ Electricity ¡ LPG gas
38 This portion of the interview is complete. Close this questionnaire by clicking the option "Finish for now" on the next screen. If consent was NOT obtained, proceed to the next household. If consent was obtained, please proceed to the 'Person Roster ' questionnaire.
② PERSON ROSTER AND TREATMENT/TESTING OF CHILDREN
No. Variable Response Scale 1 ROSTER OF PERSONS. Ask about persons slept here last night, including those NOT family
members. Do NOT include usual household members if they DID NOT sleep here last night. Keep your paper job aid handy.
2 Cluster number (same as in Household questionnaire)
3 Household number (same as in Household questionnaire)
4 Name of the person 5 Line Number of the person in the household (Obtain this
from paper Person Roster, column 1)
6 Gender Male
Female
23
- -
7 Age in YEARS—Mark zero(0) if less than 12 months old. (Estimate if they do not know, especially for adults) (IF ≥5 years skip to Q.16)
8 Did the child <5 years old have a fever in the last two
weeks? Yes No (skip to Q. 19) Do not know (skip to Q. 19)
9 What was done for the child that had fever? Nothing Treated at Home Taken to a health facility Taken to church Taken to a chemist (PMVs) Taken to a native doctor
10 Did the child with fever receive ANY malaria drugs for the fever?
11 Did the child receive the malaria drugs within 24 hours of having a fever?
12 Did the child with fever receive ACT for the treatment of fever?
13 Did the child with fever receive ACT within 24 hours of onset of the fever?
14 If the child with fever received some malarial drug but not ACT, what was the other malaria drug?
15 Did the child with fever receive a finger or heel stick for blood for testing for malaria?
Yes No (skip to Q. 15) Do not know
Yes No Do not know
Yes No (skip to Q. 14) Do not know
Yes No Do not know
Chloroquine SP_Fansidar Quinine Other Do not know
Yes (skip to Q. 19) No (skip to Q. 19) Do not know (skip to Q. 19)
16 Pregnant: If this person is female from 15 to 49 years is she
pregnant? Yes No or do not know (skip to Q.19)
17 Has this woman started her Ante Natal Care (ANC) at the health facility?
Yes No or do not know (skip to Q.19)
18 Has this woman received any malarial drugs for the prevention of malaria during her ANC visit?
Yes No
19 IF there IS another person slept here last night click “Add New Record” on the next screen. IF there are NO MORE people, close this questionnaire by clicking option” Finish for now” on the next screen. Then, proceed to “Net Roster” questionnaire.
24
LL
Ma
③ NET ROSTER No. Variable Response Scale 1 ROSTER OF NETS. I would like to ask you about each mosquito bednet that you have in the
household (includes all nets that were owned and present in the household last night—Interviewer must enter a new record for each net)
2 Cluster number (same as in Household questionnaire) 3 Household number (same as in Household questionnaire)
4 INTERVIEWER ONLY: What net are you collecting
information about? If the first net PUT number 1, if the second net PUT number 2, etc. (use consecutive numbers)
5 INTERVIEWER ONLY: Ask if you can see this net. Did you observe the net?
6 Was this net hung last night? (Look for evidence of hanging and observe or ask if the net was hanging)
7 How many months ago did your household obtain the
mosquito net? (RECORD IN MONTHS. Put "36" for 3 yrs, "48" for 4 yrs, and "60" for >=5yrs. 98=NOT SURE)
Yes No
Yes No Do not know
8 LLIN (long-lasting insecticidal net)à is a factory treated net that does not require any further
treatment. Pretreatedà is a net that has been pretreated with an insecticide and requires further treatment after 6- 12 months
9 From where did you obtain this net? Door-to-door campaign 2011 Mass campaign 2008
Market/Retail shop Health facility Pharmacy Friend/Relative Other
10 Brand of the net? (Observe or ask for the brand of mosquito net. If the brand is unknown, and you cannot observe the net, show pictures of typical net types/brands to respondent)
11 When you got the net, was it already factory-treated with an
insecticide to kill or repel mosquitoes?
LLIN-Dawa (skip to Q.14) LLIN- Permanet (skip to Q.14) Other LLIN (skip to Q.14)
¡ Pre-treated or treated net
Other Do not know brand Yes No Not sure
12 Since you got the mosquito net, was it ever soaked or
dipped in a liquid to repel mosquitoes or bugs? Yes No (skip to Q.14) Not sure
13 How many months ago was the net last soaked or dipped in
a liquid to repel mosquitoes or bugs? (RECORD IN MONTHS. IF< 1 MONTH AGO, PUT 0 months, PUT "36" for 3 y, "48" for 4 y, and "60" for >=5y. 98=NOT SURE)
25
14 Did anyone sleep under this mosquito net last night? Yes No (skip to Q.20) Not sure
15 Line number of the first person that slept under this net. (Get this from the paper job aid “Person Roster”)
16 Line number of the second person that slept under this net.
(Get this from the paper job aid “Person Roster”)
17 Line number of the third person that slept under this net. (Get this from the paper job aid “Person Roster”)
18 Line number of the fourth person that slept under this net. (Get this from the paper job aid “Person Roster”)
19 Line number of the fifth person that slept under this net. (Get this from the paper job aid “Person Roster”)
20 IF there is another bednet in the household click “Add New Record” on the next screen. IF there are NO MORE bednets, close this questionnaire by clicking "Finish for now". Proceed to the next household.
26
References
1 World Health Organization. The EPI coverage survey.
www.who.int/immunization_monitoring/routine/EPI_coverage_survey.pdf.
Accessed 30 January 2011. 2 Roll Back Malaria. Malaria Indicator Survey: Basic Documentation for Survey
Design and Implementation.
http://www.rbm.who.int/partnership/wg/wg_monitoring/docs/mis2005/cc1.pdf.
Accessed 30 January 2011. 3 Kilian A, Boulay M, Koenker H, Lynch M. How many mosquito nets are needed
to achieve universal coverage? Recommendations for the quantification and
allocation of long-lasting insecticidal nets for mass campaigns. Malaria Journal
2010, 9:330. doi:10.1186/1475-2875-9-330.
27
Malaria Survey, 10 Local Government Areas, Cross River State, Nigeria, June 2011 Survey conducted from 27 June to 1 July 2011; bulletin date: 2 July 2011
Table 1. Survey description and key information
n % n %
Sample domain (pop) 1,699,246
No. sleeping places 866
of sleeping
No. clusters 30 No. nets 579 67% places
No. HH per cluster 10 No. nets observed 576 99% of nets
No. households 300 No. ITNs 573 99% of nets
No. persons (all ages) 1548 No. ITNs hanging 491 86% of ITNs
Average HH size 5.16 ITNs/sleeping places 66% No. children <5 y 204 13% Hanging ITNs/sleeping places 57%
ITNs/household 1.91 Sample weight 1097.70 No. persons per sleeping place 1.79 Rural 87% Av. # persons slept under ITN* 1.94
Within 5 km of facility 86% * data from the net roster
Total ITNs needed for universal coverage (based on 1.94
persons/ITN from this survey)
875,900
Estimated number of ITNs in the whole survey domain 628,982 72% ITN gap 246,918 28%
Figure: Summary of malaria indicators
Percentage
100
90 87 84
80 80
71 70 66
60 60 60 56
50
40
30 24
20 16
10
0
Access: % HH Access: % pop. Access: % HH ITN use, all
ITN use,
ITN use in
% of ITNs used
Treated with
Treated with
Blood taken
≥1 ITN with access to ITN**
with sufficient ITNs***
persons children <5 years
children <5y, given at least 1
ITN in HH
last night ACT, children with fever
ACT within 24 hr, children with
fever
(testing), children with
fever
** calculated for each household, then summed. *** % of households with sufficient ITNs for ≥1.0 ITN per 2.0 persons
Table 2: ITN indicators by wealth quintile
Most
Wealth quintiles
Survey report: page 2
Ratio highest/
HH Ownership
All CI poor More poor Middle Less Poor Least Poor lowest*
All 87 79-‐-‐-‐94 88 80 87 88 90 1.0 Rural 88 82-‐-‐-‐94 Urban 76 47-‐-‐-‐100
Hanging
% of all ITNs that were hanging
86 82-‐-‐-‐90 89 85 82 88 85 1.0 Use of ITNs
All persons 60 52-‐-‐-‐68 69 59 57 59 60 0.9 Children <5 years 71 57-‐-‐-‐84 81 56 71 72 73 0.9
Urban
48
22-‐-‐-‐73 Rural 62 55-‐-‐-‐69
Use of any net All persons 61 53-‐-‐-‐69 70 60 57 59 60 0.9
* interpret with caution: limited sample size in each wealth quintile Table 3: Age of ITNs (years)
Category, years <1 1 2 3 4 5+ % 85 1 5 6 1.5 1.5 cum. % 85 86 91 97 98.5 100
Table 4: Impact of household (HH) and health facility visits on ITN indicators
% %
No. HH with home visit in last 6 months about malaria/ITN 72
No. HH with malaria/ITN discussed at health clinic in last 6 months 47
Table 5: ITN source and information, IRS, and ITN+IRS protection
Survey report: page 3
ITNs: Source of ITNs
State/ Mass Market Clinic Other
% 95 3 1 0 Greatest source of information about ITN use
Health centre
staff
Commun-‐-‐-‐ ity-‐-‐-‐based volunteer
Commun-‐-‐-‐ ity leader Radio
Neighb
or Other
% 16 23 1 6 1 53 Table 6: Children Tested and Treated for Malaria
% Types of non-‐-‐-‐ACT antimalarial
Children with fever in the last 2 weeks 49 %
% who received a heel or finger stick 16 Chloroquine 22
% who received ACT 60 Quinine 2
% who received ACT within 24 hours of fever 24 SP 1
% who received any antimalarial 75 Other 8
Do not know 66 Table 7: Precision and design effect of key indicators
Key indicators CI, ± Design effect
ITN use, all ages 8 9.7
ITN use, children <5 years old 13 4.0 Household ownership, ≥1 ITN 7 3.3
<1y 1
2-4
5-9
10-1
4
15-2
4
24-4
4
45-5
9
60+
Additional ITN analyses Figure 2: % ITN use by age group and gender
Survey report: page 4
Male Female Both genders
100
90
80
70
60
50
40
30
20
10
0
Age groups (years) Table 8. Percentage type of net Table 9. Percentage of persons
sleeping under ITNs last night LLIN 2008/2009 10 1 person 33 LLIN 2011 86 2 persons 45 Other LLIN 3 3 persons 18 Non-LLIN ITN 0 4+ persons 1 Non-ITN 1 Mean persons/ITN = 1.94