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.

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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.

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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

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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

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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

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(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).

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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).

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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%.

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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