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 onequarter of households still need to be reached with IRS. The innovative survey
methodologies provided valuable health and malaria data rapidly.
2
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 malariarelated 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
3
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 number of
a
Namibia Demographic and Health Survey 2006/2007
b
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).
4
organizations, including the MOHSS and the Namibia Red Cross, provided services in
the camps.
In 2001, Caprivi Region had a population of 79,826 people (with a household
population of 78,785 and 16,839 households), representing 4.4% of Namibia’s total
(Population Census 2001). Twenty-eight percent of the population is urban and 72%
rural. The average household size in Caprivi region is 4.7 persons compared to 5.1
persons nationally, and well below the average rural household size of 5.7. Femaleheaded households account for 49% of households, and 46% of households have
orphans or children under the age of 18. The average number of children per women is
3.8 and 13% of the population is under five years. More than 95% of the populations
speak one of several dialects of what is commonly lumped together as Caprivian. The
main dialects are Sifwe, Subiya, Totela, and Yeyi. The 2006-07 Namibia
Demographic and Health Survey showed that Caprivi was one of four regions in
Namibiad that have the highest proportion of the population in the lowest wealth
quintile of the wealth index and only a small proportion in the highest quintile.
Insecticide-treated bed nets (ITNs) are distributed in Caprivi through the MOHSS
routine health services. A pregnant woman is eligible to receive one free bed net during
antenatal care services, and children under age five years that attend a health facility
are also eligible to receive a free LLIN. Over the past few years the MOHSS and the
Namibia Red Cross have distributed LLINs in the settlement camps established for
those affected by the flooding of the Zambezi and the Chobe Rivers through mass
distribution to targeted vulnerable groups (pregnant women, children under age five
years, and elderly women aged 60 years and over). There has only been one regionwide mass campaign in Caprivi, with LLINs distributed in 2008 as part of the Zambezi
d
The other three are Kavango, Ohangwena and Omusati.
5
River Basin Initiative. Since 2009, the Namibia Red Cross (NRC) has been
distributing LLINs in the camps.
In addition to the MOHSS and the NRC, the NGO SMA (Social Marketing Associates)
has been distributing LLINs in villages in some constituencies. SFH (Society for
Family Health) sells LLINs through their social marketing program, and also
distributes LLINs to the elderly when they receive their monthly government pension
benefit.
This malaria indicator survey will serve as a baseline for the next phase of the Namibia
Red Cross project Communities Fighting Malaria that aims to improve the health and
well-being and improve malaria control for persons in four of the constituencies in
Caprivi Region where the Namibia Red Cross works--Kabbe, Katima Mulilo Rural,
Kongola and Linyanti. In this project, the NRC, in collaboration with the village heads,
will recruit 51 community volunteers who will serve as the project’s supervisors in the
villages. Each supervisor will have the responsibility of supervising an average of
about 10 community-based volunteers. A total of more than 500 volunteers will be
trained by mid-2012 by these supervisors. In turn, each community-based RC
volunteer is responsible for about 100 households.
In addition to serving as the baseline for the next phase of the CFM project, this survey
is the endline for the first phase of the project. A previous survey was carried out by
the NRC and IFRC in May 2011. That survey was also carried out using the RAMP
survey methodology. It is expected that the final evaluation will be done in November
2012.
The sampling frame for the May 2011 RAMP malaria survey was not identical to the
2012 RAMP malaria survey because in the May 2011 survey, the Caprivi Region was
devastated by floods, with over 90% of the Kabbe constituency flooded. More than
6
9,000 people were affected by the floods, and a total of 20 temporary camps and two
permanent resettlements camps (Choi in Linyanti and Izwe in Kongola) had been
established (Namibia Red Cross, personal communications). Of the 30 clusters
selected, 12 of them were in the settlement camps.
While the RAMP survey does not collect data on all the key indicators of the
Communities Fighting Malaria project, it does collect data on many of the core
indicators related to bed net ownership and usage, and to the treatment of fever in
under-five year olds and the diagnosis of malaria. These include indicators that are
aligned to the MOHSS Malaria Strategic Plan (2010-2016) and the MOHSS National
Monitoring and Evaluation Plan (2010-2016). In addition, the survey collects data on
indicators that measure universal coverage, a goal that the GRN is moving toward.
The results of this survey can be used to adjust the MFC project and the malaria
prevention and control program, if necessary, and to advocate for needed changes to
support the provision of LLINs, and the provision of services for treating fever in
children under age five years, to communities within the sample constituencies. Other
investigations will be done by the NRC in 2012 to explore in more depth aspects of the
CFM project that are related to behavior change and communications.
The International Federation of the Red Cross (IFRC), with the support of DataDyne,
WHO, and epidemiologists have been working for several years on a “management
survey” concept that uses cell phones for data entry for surveys, primarily on the
survey methodology and operations. In 2011, the management survey concept was
successfully field tested in three African countries: Kenya in January, Namibia in May,
and Nigeria in June. This concept includes use of cell phones and freely-available
software to conduct health surveys rapidly, simply, at low cost, with minimal external
7
technical assistance. The survey sampling method avoids 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 PSU). This
survey approach is now called the RAMP (Rapid Mobile Phone-based) survey. IFRC
will soon publish a technical reference manual and a training manual that will be
readily accessible for those interested in carrying out a RAMP survey.
This RAMP survey in Caprivi 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 the Episurveyor web-based tool to collaboratively design model
malaria questionnaires (including responses and skip patterns) that can be easily
adapted to local surveys 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 review and feedback of data quality
issues to interviewers and team supervisors 8) daily analysis of uploaded data, and 9)
completion of preliminary results bulletin within 24 hours of the last interview.
8
Methods
The survey was conducted during 30 January to 3 February 2012 in the four
constituencies in Caprivi Region where the Namibia Red Cross works--Kabbe, Katima
Mulilo Rural, Kongola and Linyanti. The sampling frame was a list of communal
lands, settlements, and mixed areas, from the 2008 sampling frame provided by the
Namibian Bureau of Statistics, based on the latest census data (2001 national census)
released by the GRN. The 2008 census estimation of population in the area was
46,727. Thirty primary sampling units (PSUs) were selected using probability
proportional to estimated size. Using maps of each PSU that were obtained from the
Namibia Central Bureau of Statistics, the selected PSUs in the settlements and
communal lands were mapped and divided into 2-10 segments using natural
boundaries. Once a segment was selected by PPES, all households were listed or
mapped and 10 households were chosen by simple random sampling. Additional
households were chosen 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.
Three questionnaires were developed online using the Episurveyor web-based
questionnaire design tool (www.episurveyor.org)—household, person roster, and bed
net roster. Questions were modeled after the Roll Back Malaria (RBM) 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. “Access”
to an ITN was defined as the population that could have been covered by ITNs present
in the households at the time of the survey. The ratio of persons that could have been
9
covered per ITN was calculated from the net roster data (1.88 persons/ITN). The crude
estimate of access was the total number of ITNs in the households times 1.88
persons/ITN divided by total population. The ratio of persons to ITN (1.88) for this
calculation was similar to the ratio of 2.0 used by WHO in its World Malaria Report
2010 to calculate access to ITNs.3
We estimated the number of ITNs needed for universal access, the number of ITNs
currently in the whole survey domain, and the gap to be filled. The number of ITNs
needed for universal access was calculated by dividing the sampling frame population
by the average number of persons sleeping under ITNs during the survey (1.88). The
number of ITNs currently present was estimated by multiplying the number of ITNs
found in households by the survey weight (36.42). The total number of LLINs that
need to be distributed in 2012 to achieve universal coverage needs to include nets that
need to be replaced due to age (36 months or old nets).
There are two new MERG indicators of access—by household and for populations.
The indicator by households is the persons of households with sufficient nets to cover
all inhabitants based on a ratio of 2 persons for one ITN. The indicators for
populations is the number of persons with access to an ITN assuming that 2 persons
can use each ITN.
The indicator about protection of households by ITNs or IRS assumed that any ITN
(even just one) in the household protected all inhabitants. In households with many
people, a single ITN may provide only partial protection from malaria. Therefore, this
indicator may over-estimate protection.
10
There was significant "heaping" of responses of the age of nets in months at 12 and 36
months. We counted "12" to be in the 12-23 month category and "36" to be in the 3647 month category.
The questionnaires (see annex for a copy, exported from the EpiSurveyor form to
Word format) on the mobile phones were in English, and the majority of the interviews
were carried out in Silosi, the local language in Caprivi.
Survey operations. The survey operations were led by the Namibia Red Cross.
Training was provided for the 12 interviewers and six team supervisors during four
days (23-27 January 2012). Survey field work took 5 days (30 January to 3 February
2012). Nokia-brand cell phones--Nokia 2730 ($80 USD, no keyboard, no GPS) and
Nokia C-02 ($105 USD, no keyboard, no GPS) --were used to run DataDyne’s freelyavailable cell-phone-based EpiSurveyor software (http://www.datadyne.org/ and
http://www.episurveyor.org/). Survey data were immediately entered into the cellphone database during the interview for the household questionnaire. Immediately
before administering the person roster and the net roster questionnaires, a paper line
list of persons that slept in the household the previous night was created by asking
about each person who slept in the household the previous night so that the person line
number/identifier was available during questions in the person roster, and for questions
in the net roster about who slept under each net. Uploading of data on the cell-phone to
the internet-based database using EpiSurveyor software required a 2G/GPRS cellnetwork connection. The field supervisors uploaded data at the end of the day upon
their return to Katima Mulilo. Interviewers (and cell-phone data entry persons) were
the Namibian Red Cross volunteers who serve as supervisors in the four constituencies
included in the Communities Fighting Malaria project: Kabbe, Katima Rural, Kongola
and Linyanti. The team supervisors were Namibian Red Cross supervisors that were
involved in the Communities Fighting Malaria and other NRC projects.
11
Since the data were sent from the mobile phones each evening to an internet database,
the local consultant and out-of-country consultants were able to perform data cleaning
each evening. A Namibian analyst could not be located, so an out-of-country
consultant analyzed the data each night. 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 distributed to interviewers at an end-of-survey debriefing
meeting within 15 hours of the last interview on 4 February 2012. A preliminary
report was distributed to stakeholders within 72 hours on 6 February 2012.
Report tables and graphs. The tables and graphs of the survey-results bulletin act as
the tables and graphs for this preliminary reporte.
Results
Table 1 on the survey-results bulletin shows key descriptive information about the
survey. The number of persons in the sample frame was 46,727. Data were collected
on 1283 persons of all ages and 175 children <5 years old in 300 households. Ninetysix percent of the nets were permitted by the residents to be observed. Ninety-four
percent of the ITNs were reported hung the night prior to the survey. There were
enough nets to cover 48% of the sleeping places and enough hanging nets to cover
46% of the sleeping places. 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
e
The survey-results bulletin is a companion document to the survey report, and contains information, tables and graphs
that show the survey findings.
12
was 62% (95% confidence interval [CI], 55-69%). The percentage of households with
sufficient ITNs for all household members was 33% and percentage of the population
with access to an ITN was 50%. The average number of persons sleeping under an
ITN was 1.88. The percentage of persons that slept under an ITN last night was 44%
(CI 36-51%) for all persons and was 55% (CI 48-62%) 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 79%. Seventy-two percent of households received
indoor residual spraying with insecticide in the last 12 months. The percentage of
children with fever in the previous two weeks that had a finger or heel stick for blood
was 25%. The percentage of children with fever that received treatment with an ACT
was 29% and the percentage that received an ACT within 24 hours of onset of fever
was 22%. The estimated number of ITNs needed to provide access to all persons
(based on 1.88 persons per ITN) in the entire sampling frame was 24,855. The number
of nets currently in all households in the survey frame (those sampled and not sampled)
was 13,038, 48% (n=24,855) of that needed to achieve universal access. The number
of ITNs needed to fill the gap was 11,816. The percentage of nets that were 36 months
and older was 37%. Therefore, an additional 4,824 ITNs need to be replaced
immediately, bringing the total ITNs needed in 2012 to 16,640 (11,816 + 4,824).
Table 2 shows key ITN indicator point estimates, confidence intervals, and data by
wealth quintile. The ownership of ITNs was nearly the same across wealth quintiles.
Table 3 shows the age of ITNs—15% of the ITNs were <12 months old, 26% were 3
years old, and 37% were ≥36 months old. Table 4 shows that recent home visits and
visits to clinics where malaria was discussed were infrequent (17%). Table 5 shows
additional ITN and IRS information. The greatest source of information about nets
was from health center staff (44%) and radio (36%). Table 6 shows additional
13
information about treatment, including the rare use of chloroquine, quinine, and
sulfadoxine-pyramethamine for treatment. The percentage of children <5 years old
with fever in the previous two weeks was 49%. Twenty-five percent of children with
fever reported to have received a malaria blood test, and, of those, 70% (19 of 27) were
reported to be positive. Twenty-nine percent of children <5 years old with fever
received an ACT. Table 7 shows the width of the 95% confidence interval and the
design effect for 3 key variables. The confidence interval was ±8% for the percentage
of persons using an ITN, ±7% for household ownership of at least one ITN, and ±7%
for children using an ITN. The design effect was <2.0 for two key indicators and was
6.3 for ITN use in all ages. The graph on page 4 shows the age distribution of ITN use.
Those 10-24 years old had lower use than other age groups. The supplemental
analyses on page 4 showed that 97% of nets were LLINs and 20% of nets had 3 or 4
persons sleeping under them.
Two-thirds of household heads were able to identify “mosquito bites” as the cause of
malaria. Sixty-nine percent were able to identify mosquito nets as the main method to
prevent malaria.
Discussion
This malaria survey showed similar ITN coverage as the survey 8 months previous—
the number of ITNs in households needs to be doubled to achieve universal coverage—
16,640 LLINs need to be distributed in 2012 to fill the gap. Approximately half (44%)
of the persons were sleeping under an ITN the previous night. A high percentage of
ITNs were used the night before the survey (94%) so that there is only a small gap in
ITN use given ownership. Therefore, the primary ITN gap is ownership and access,
not ITN use. The age of ITNs may also be a significant issue since 37% of the ITNs
14
were reported to be 3 years old or older. Most of the nets (56%) were reportedly
obtained from the health center and 24% from a mass distribution. About half of the
nets from a mass distribution were received when LLINs were distributed in 2011
(either by the RGN or by NRC or NGOs) to populations that had been relocated due to
flooding and were living in settlement camps. The percentage of children <5 years old
with fever in the previous two weeks was high (49%) compared to 30% found in most
other African countries (however, the percentage was also higher than average in
Malindi, Kenya in January 2011 [40%] in the first pilot survey, and in the Namibia
2009 MIS survey the highest percent of under-fives with fever in the past two weeks
was found to be in the Caprivi region, at 36%). The percent of children with fever
may have been higher in the survey because, in this area, the local term for fever,
“mena”, can also mean a runny nose without fever. Seventy-two percent of households
had received IRS within the previous 12 months. The recommended coverage for IRS
is >80% to have reasonable impact. A recent survey of IRS coverage found that 85%
of the population had been protected by IRS in the latest round of MOHSS spraying in
the Caprivi Region (MOHSS, personal communications). IRS is a primary malaria
control tool in most low-incidence countries in southern Africa.
The survey findings for some of the ITN indicators were similar to those in the 2008
DHS in Caprivi Region where it was reported that 55% of households owned an ITN,
and the average number of ITNs per household was 1.0. While the 2008 DHS survey
reported that 41% of children <5 years old slept under an ITN the night before the
survey, the 2009 MIS survey reported this to be 35%, and the 2012 RAMP survey
reported a value of 54% (up from the RAMP May 2011 finding of 42%). This figure
rises to 79% when reporting ITN use in children under five years of age in households
that own t least one ITN.
15
The survey findings for some of the ITN indicators were also similar to those in the
2009 Malaria Indicator Survey for the Caprivi Region. Fifty-seven percent of
households owned at least one ITN, and the average number of ITNs per household
was 1.0. However, the RAMP survey findings report that 55% of children <5 years old
slept under an ITN the night before the survey, compared to a finding of 35% in the
MIS survey. The 2009 MIS reported that 22% of all people slept under an ITN, while
the 2011 RAMP survey reported this to be 38%, and the 2012 RAMP survey, 44%.
This survey provided an excellent estimate of the number of LLINs that the project
needs to obtain to achieve universal access: 16,640 (gap plus replacement of older
nets). An advantage of this survey is that it provides a reasonably accurate estimate of
the total number of existing ITNs, the gap that needs to be filled, and the number of
ITNs that are ≥36 months old that also need immediate replacement.
The confidence interval of ≤10% 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%.
Daily uploading of data to an internet database allowed an external analyst to complete
most of the analytic tasks within 24 hours and send the results to local survey leaders
to discuss with surveyors and stakeholders.
16
Conducting a health survey with precision of ±10% at low cost with rapid results is
likely to be attractive to non-governmental organizations (NGOs) and ministries of
health (MOHs).
This survey and analysis had several limitations. First, the education and experience
level of interviewers was substantially less than those of other more-expensive surveys
such as national malaria indicator surveys and Demographic and Health Surveys.
Second, the sample size of 300 households was too limited to provide 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 for just females). However, the rapid mobile survey (RAMP) 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.
In conclusion, this survey successfully met its objective of rapidly measuring baseline
malaria intervention coverage prior to the start of a new project cycle at low cost.
17
References
World Health Organization. The EPI coverage survey.
www.who.int/immunization_monitoring/routine/EPI_coverage_survey.pdf. Accessed
30 January 2011.
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.
World Health Organization. World Malaria Report 2010. Geneva, Switzerland.
2006-07 Namibian DHS.
Ministry of Health and Social Services. Republic of Namibia. Malaria Strategic Plan
(2010-2016). November 2010. Windhoek, Namibia: Ministry of Health and Social
Services
Ministry of Health and Social Services. Republic of Namibia. National Malaria
Monitoring and Evaluation Plan (2010-2016). December 2010. Windhoek, Namibia:
Ministry of Health and Social Services
Ministry of Health and Social Services. Republic of Namibia. National Malaria Policy.
Windhoek, Namibia: Ministry of Health and Social Services.
National Planning Commission (NPC), Central Bureau of Statistics, 2002. 2001
Population and Housing Census. Windhoek, Namibia: National Planning Commission.
18
Acknowledgements
The assistance provided by the Namibia Central Bureau of Statistics for the survey
sampling was invaluable. A special thanks goes to Mr. Weerasinghe, CBS, Windhoek
and Ms. Benitha Kwala, GIS analyst, CBS Caprivi branch for their collaboration and
assistance.
The support of the MOHSS in this survey was also greatly appreciated. In Windhoek,
the MOHSS took part in reviewing the survey questionnaires, while in Katimi Mulilo,
the survey benefited from the information and collaboration provided by Mr. Albius
Maswahu, Caprivi Region Health Director. Mr. Jamela Dube and Ms. Agnes Mwilima
served as resource persons during the training of the enumerators and team supervisors,
and we are grateful for their excellent presentations and support provided. The insights
provided by Dr. Dube when the preliminary survey results were presented to the
survey field teams was also of great value.
19
Annex 1. Questionnaires
Survey Name : 1_HHold_Nam_01_2012
No of Questions:39
=======================================================================
1:NAMIBIA RAMP SURVEY HOUSEHOLD QUESTIONNAIRE (label)
2:Consent Obtained? (multi)
Data Field Name : consent
Possible responses:
- Yes
- No
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)
20
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)
21
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
20:HOUSEHOLD ASSET questions follow next (label)
22
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
- No
23
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
Possible responses:
24
- 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
36:What is the principal type of toilet/sanitary facility used by members of your household? (multi)
25
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
26
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)
Survey Name : 2_Person_Nam_01_2012
No of Questions:16
=====================================================================
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
27
- 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
28
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)
Survey Name : 3_Net_Nam_01_2012
No of Questions: 17
================================================================
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
29
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
Possible responses:
30
- 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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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
World Health Organization. World Malaria Report 2010. Geneva, Switzerland.
32