Survey of malaria indicators in Caprivi Regional Red Cross Society area, Namibia, using cell phone data entry — Preliminary report, 7 February 2012 RAMP (Rapid Mobile Phone-based) Survey Naemi Heita (1), Laimi Onesmus (1), Hilma Kambundu (1), Polly Helmut (2), David Simataa (2), Michael Charles (3), Bong Duke (4), Jenny Cervinskas (5), Jason Peat (5), Mac Otten (6) 1. Namibia Red Cross Society, Windhoek Namibia 2. Namibia Red Cross Society, Caprivi Region, Namibia 3. International Federation of the Red Cross, Johannesburg, South Africa 4. Consultant, International Federation of the Red Cross, Canada 5. Consultant, International Federation of the Red Cross, Nigeria
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Survey of malaria indicators in Caprivi Regional Red Cross
Society area, Namibia, using cell phone data entry —
Preliminary report, 7 February 2012
RAMP (Rapid Mobile Phone-based) Survey
Naemi Heita (1), Laimi Onesmus (1), Hilma Kambundu (1), Polly Helmut (2), David
Simataa (2), Michael Charles (3), Bong Duke (4), Jenny Cervinskas (5), Jason Peat (5),
Mac Otten (6)
1. Namibia Red Cross Society, Windhoek Namibia
2. Namibia Red Cross Society, Caprivi Region, Namibia
3. International Federation of the Red Cross, Johannesburg, South Africa
4. Consultant, International Federation of the Red Cross, Canada
5. Consultant, International Federation of the Red Cross, Nigeria
6. International Federation of the Red Cross, Geneva, Switzerland
7. Consultant, International Federation of the Red Cross, Atlanta, USA
Executive Summary
Background. The Namibia Red Cross sponsored a baseline survey for the second
phase of a multi-year project among a population of 46,727 that lived in the Caprivi
Region during 30 January to 3 February 2012 to examine important malaria indicators
using several innovative data collection methods.
Methods: The survey used probability-proportional-to-estimated-size (PPES) sampling
of primary sampling units (PSUs) and PPES to select one segment from unequal-sized
segments. Households were chosen from the segment using simple random sampling.
The sample included 30 PSUs, 10 households per PSU, and 1283 persons. Cell phones
were used by Namibia Red Cross volunteers to conduct the household interviews and
enter survey data using EpiSurveyor software in real-time.
Results: The gap in the number of LLINs needed to achieve universal coverage was
11,816 (48% of sleeping spaces). Since an estimated 4,824 ITNs were 36 month old
and need to be replaced, the total ITNs needed to be distributed in 2012 is 16,640. An
estimated 44% (95% confidence interval [CI] 36-51%) of persons of all ages slept
under an ITN during the night before the survey and 94% of nets were used the
previous night. Of children <5 years old with fever in the two weeks before the
survey, 29% received an ACT and 22% received an ACT within 24 hours. Seventy-
two percent of households received IRS in the previous year.
Conclusion: The number of ITNs available to households needs to be doubled to
achieve universal coverage (all persons sleeping under an ITN). Approximately one-
quarter of households still need to be reached with IRS. The innovative survey
methodologies provided valuable health and malaria data rapidly.
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Background
The government of the Republic of Namibia (GRN) 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 Millennium Development Goals, World Health Assembly, Roll Back Malaria
partners, and GRN goals. The Ministry of Health and Social Services (MOHSS) in
Namibia is now stressing the importance of universal coverage of persons of all ages
(100% of persons using an insecticide-treated bed net in endemic areas) as advocated
by the World Health Organization (WHO) to achieve the disease-reduction goals. The
two most important indicators of universal coverage with ITNs are: 1) the percentage
of persons that had access to ITNs in the household (assuming that 1 ITN covers two
persons), 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, 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), even in high-burden
African countries.
Malaria continues to be a major public health problem in Namibia. The disease was the
leading cause of illness and death from 1999 to 2002 and still remains one of the top
five diseases of public health concern in the country. Malaria is endemic in Caprivi,
Kavango, Kunene, Ohangwena, Omusati, Oshana, Oshikoto, and part of Otjozondjupa
and Omaheke regions, where 65 percent of the Namibian population live and are at risk
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of malariaa. The prevalence of malaria is highest between September to December.
Preliminary MOHSS data shows that malaria cases reported from health facilities in
the Caprivi Region declined from approximately 4000 cases in 2006 to 300 cases in
2010.
This survey was carried out in Caprivi regionb, in the northeast of Namibia. Divided
into the Kongola, Linyanti, Sibanda, Katima Mulilo Urban and Rural, and Kabbe
constituenciesc, the region covers a total area of 14.528 km and accounts for 1.8% of
the total land area of Namibia. It shares borders with four countries, being Angola and
Zambia in the north, Botswana in the south, and Zimbabwe to the east. The
administrative centre of the region is Katima Mulilo, the only town in the region. Six
settlement areas serve as local administrative centers. These are Bukoalo,
Chinchimani, Linyanti, Mafuta, Ngoma, and Omega III. Eastern Caprivi is subject to
seasonal flooding and the Kabbe constituency comprises the eastern flood plains of the
region, and is subject to seasonal flooding. There is an annual flood season with
numerous flood-prone villages. In 2011, the flooding was widespread, and more than
1000 people had been “relocated permanently” from 32 flood-prone villages as the
region prepared to meet a Zambezi river swollen to record-size for this time of the year
as a result of torrential rains upstream (ref: www.irinnews.org/report.aspx?
reportID=91770, accessed March 3, 2011). In 2011, more than 20 settlement camps
were established on higher ground, with entire villages being relocated to these camps.
a Namibia Demographic and Health Survey 2006/2007b Namibia is divided into 13 regions, and each one is further subdivided into electoral constituencies. The latest Namibian Census of Population and Housing for which the results were published was carried out in 2001. A national census was carried out in August 2011 and the results are expected to be released soon.
c A constituency refers to a group or area from which voters in an election are drawn. The number and size of each constituency varies with the size and population of each region. There are a total of 102 constituencies in the country, and overall, the country is divided into 4002 enumeration areas (EAs) (Population Census 2001).
3:CLUSTER and HOUSEHOLD NUMBER questions follow next (label)
4:Cluster number (number)
Data Field Name : hh_clusternumber
5:Household number (number)Data Field Name : hh_hhnumber
6:Name of head of household (text)
Data Field Name : hh_hhname
7:Household in a rural or urban area? (Urban defined as a town with >=5000 persons) (multi)
Data Field Name : rural_urban
Possible responses:
- Rural
- Urban
8:How many kilometers is your household from the nearest health facility or hospital? (98=do not know). If less than 1 km, put "1". (number)
Data Field Name : num_km_to_facility
9:How many minutes does it take to walk to the nearest health facility? (98=do not know). If less than 1 min, put "1". (number)
Data Field Name : min_to_health_facility
10:BEDNET questions follow next (label)
11:Number of people of all ages who slept in this household last night? (do NOT include usual members of this household who slept somewhere else last night) (number)
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Data Field Name : totalpersons
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). (number)
Data Field Name : numhhsleepingspaces
13:Has anyone visited this household in the last 6 months to talk about malaria or bednets? (multi)
Data Field Name : visithomemalaria
Possible responses:
- Yes
- No
- Do not know
14:Has anyone in this household visited a health facility where they discussed malaria or bednets in the last 6 months? (multi)
Data Field Name : visitclinicmalaria
Possible responses:
- Yes
- No
- Do not know
15:In your opinion, what is the main cause of malaria? (multi)
Data Field Name : cause_of_malaria
Possible responses:
- Mosquito bites
- Eating immature sugarcane
- Eating watermelon
- Eating other dirt food
- Drinking dirty water
- Getting soaked with rain
- Cold or changing weather
- Witchcraft
- Others
- Do not Know
16:In your household, what is the main method used to protect against getting malaria? (multi)
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Data Field Name : protection_against_malaria
Possible responses:
- Sleep under a mosquito net
- Sleep under an insecticide-treated mosquito net
- Use mosquito repellent
- Take preventive medication
- Spray houe with insecicide
- Keep house surroundings clean
- Other
- Do not know
17:What is your main source of information about the use of bednets? (multi)
Data Field Name : itn_netinfosource
Possible responses:
- Radio
- Health facility
- Community-based volunteer
- Community leader
- Neighbor
- Relative
- Television
- Other
- No information
18:Indoor Residual Spraying (IRS) question follow next (label)
19:At any time in the past 12 months, has anyone sprayed the interior walls of your dwelling against mosquitoes? (multi)
Data Field Name : irsyn
Possible responses:
- Yes
- No
- Do not know
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20:HOUSEHOLD ASSET questions follow next (label)
21:Does your household have electricity? (multi)
Data Field Name : wlth1
Possible responses:
- Yes
- No
22:Radio? (multi)
Data Field Name : wlth2
Possible responses:
- Yes
- No
23:Television? (multi)
Data Field Name : wlth3
Possible responses:
- Yes
- No
24:Refrigerator? (multi)
Data Field Name : wlth4
Possible responses:
- Yes
- No
25:Electric iron? (multi)
Data Field Name : wlth5
Possible responses:
- Yes
- No
26:Electric fan? (multi)
Data Field Name : wlth6
Possible responses:
- Yes
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- No
27:Bicycle? (multi)
Data Field Name : wlth7
Possible responses:
- Yes
- No
28:Motorcycle or scooter? (multi)
Data Field Name : wlth8
Possible responses:
- Yes
- No
29:Car or truck? (multi)
Data Field Name : wlth9
Possible responses:
- Yes
No
30:Cow, goat, or sheep? (multi)
Data Field Name : wlth10
Possible responses:
- Yes
- No
31:Canoe or boat? (multi)
Data Field Name : wlth11
Possible responses:
- Yes
- No
32:Phone? (multi)
Data Field Name : wlth12
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Possible responses:
- Yes- No
33:Domestic worker (unrelated to head of household)? (multi)
Data Field Name : wlth_domesticworker
Possible responses:
- Yes
- No
34:Do members of this household work on agricultural land OWNED BY themselves or their family? (multi)
Data Field Name : wlth_hhagland
Possible responses:
- Yes
- No
35:What is the principal household source of drinking water? (multi)
Data Field Name : wlth_hhwater
Possible responses:
- Tap 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
- Public tap
- Rain water
- Surface water (eg. River Lake Stream)
- Spring
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36:What is the principal type of toilet/sanitary facility used by members of your household? (multi)
Data Field Name : wlth_hhtoilet
Possible responses:
- Own flush toilet
- Shared flush toilet
- Own pit latrine
- Own improved pit latrine
- Shared pit latrine
- Shared improved pit latrine
- Bush or field
- Other
37:What is the principal type of flooring in your house (interviewer may choose to observe)? (multi)
Data Field Name : wlth_hhfloor
Possible responses:
- Dirt or sand
- Dung/wood/palm/bamboo
- Cement including vinyl
- Cement including parquet
- Tile (e.g. ceramic/mable)
- Carpeted
- Other
38:What is the principal type of cooking fuel in your house? (multi)
Data Field Name : wlth_hhcook
Possible responses:
- Wood or dung
- Kerosene
- Charcoal
- Electricity
- Gas
- Solar Energy
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39: This portion of the interview is complete. Close this questionnaire by clicking ``Finish for now`` on next screen. If consent was NOT obtained, proceed to next HH. If consent was obtained, proceed to the ``Person Roster Questionnaire. (label)
1:PERSONS ROSTER. Ask about persons slept here last night including non-family members. Start with head of HH or oldest person. Do NOT include usual members of HH if they DID NOT sleep here last night (label)
2:Cluster Number (same as in Household questionnaire) (number)
Data Field Name : rp_clusternumber
3:Household number (same as in Household questionnaire) (number)
Data Field Name : rp_hhnumber
4:Name of the person (text)
Data Field Name : rp_name
5:Line Number of the person in the household (Obtain this from paper Person Roster, column 1, Person Number) (number)
Data Field Name : rp_hhpersonnumber
6:Gender (multi)
Data Field Name : rp_gender
Possible responses:
- Male
- Female
7:Age in YEARS—Mark zero(0) if less than 12 months old. (Estimate if they do not know, especially for adults) (number)
Data Field Name : rp_age
8:Did the child <5 years old have a fever in the last two weeks? (multi)
Data Field Name : childfever
Possible responses:
- Yes
- No
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- Do not know
9:What was done for the child that had fever? (multi)
Data Field Name : childfever_typetreat
Possible responses:
- No treatment
- Treated at home
- Taken to a health facility
- Taken to a chemist or private practitioner
- Taken to traditional medicine person
10:Did the child with fever receive a finger or heel stick for blood testing for malaria? (multi)
Data Field Name : childfever_blood
Possible responses:
- Yes
- No
- Do not know
11:Did the child test positive for malaria? (multi)
Data Field Name : Child_malaria_result
Possible responses:
- Yes
- No
- Do not know
12:Did the child with fever receive ANY malaria drugs for the fever? (multi)
Data Field Name : childfever_anyam
Possible responses:
- Yes
- No
- Do not know
13:Did the child with fever receive ACT for the treatment of fever? (multi)
Data Field Name : childfever_act
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Possible responses:
- Yes <<>>
- No <<>>
- Do not know <<>>
14:Did the child with fever receive ACT within 24 hours of having a fever? (multi)
Data Field Name : childfever_act24
Possible responses:
- Yes
- No
- Do not know
15:If the child with fever received some malarial drug but not ACT, what was the other malaria drug? (multi)
Data Field Name : childfever_otheram
Possible responses:
- Chloroquine
- SP_Fansidar
- Quinine
- Others
- Do not know
16:IF there IS another person who slept here last night click ``Add New Record`` on the next screen. IF NO MORE people, close this questionnaire by clicking option `` Finish for now`` on the next screen. Then proceed to ``Net Roster`` questionnaire. (label)
1:ROSTER OF NETS. I would like to ask you about each 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) (label)
2:Cluster number (same as in Household questionnaire) (number)
Data Field Name : rn_clusternumber
3:Household number (same as in Household questionnaire) (number)
Data Field Name : rn_hhnumber
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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) (number)
Data Field Name : Interviewer_net_info
5:INTERVIEWER ONLY: Ask if you can see this net. Did you observe the net? (multi)
Data Field Name : RN_OBSERVENET
Possible responses:
- Yes
- No
6:Was this net hung last night? (Look for evidence of hanging and observe or ask if the net was hanging) (multi)
Data Field Name : rn_hangingnet
Possible responses:
- Yes
- No
- Do not know
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) (number)
Data Field Name : re_netagemonths
8:LLIN (Long Lasting Insecticidal Net): is a factory treated net that does not require any further treatment. (label)
9:From where did you obtain this net? (multi)
Data Field Name : rn_srce
Possible responses:
- Mass distribution in a settlement camp in 2011
- Other Mass Distribution
- Market/Retail shop
- Health facility
- Pharmacy
- Friend/Relative
- Other
10:Brand of the net? (Observe or ask for the brand of net. If the brand is unknown, and you cannot observe the net, show pictures of typical net types/brands to respondent) (multi)
Data Field Name : rn_brandnet
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Possible responses:
- Dawa
- Permanet
- Tana
- SuperNet Plus
- Other LLIN
- Other Non-LLIN
- Do not know brand
11:Did anyone sleep under this net last night? (multi)
Data Field Name : rn_sleeplastnightyn
Possible responses:
- Yes
- No
- Not sure
12:Line number of the first person that slept under this net. (Get this from the paper job aid) (number)
Data Field Name : rn_personone
13:Line number of the second person that slept under this net. (Get this from the paper job aid) (number)
Data Field Name : rn_persontwo
14:Line number of the third person that slept under this net. (Get this from the paper job aid) (number)
Data Field Name : rn_personthree
15:Line number of the fourth person that slept under this net. (Get this from the paper job aid) (number)
Data Field Name : rn_personfour
16:Line number of the fifth person that slept under this net. (Get this from the paper job aid) (number)
Data Field Name : rn_personfive
17: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. (label)
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World Health Organization. The EPI coverage survey.