Nigeria HIV/AIDS Indicator and Impact Survey 2018, NAIIS 2018
Nigeria, 2018
Federal Ministry of Health (FMOH), National Agency for the Control of AIDS (NACA), Maryland Global Initiative Corporation (MGIC Nigeria)
Identification
IDNO
NGA-FMOH-NAIIS-2018-v1.03
Title
Nigeria HIV/AIDS Indicator and Impact Survey 2018, NAIIS 2018
Subtitle
NAIIS 2018
Country
| Name | Country code |
|---|---|
| Nigeria | NGA |
Abstract
The 2018 Nigeria AIDS Indicator and Impact Survey (NAIIS) is a cross-sectional survey that will assess the prevalence of key human immunodeficiency virus (HIV)-related health indicators. This survey is a two-stage cluster survey of 88,775 randomly-selected households in Nigeria, sampled from among 3,551 nationally-representative sample clusters. The survey is expected to include approximately 168,029 participants, ages 15-64 years and children, ages 0-14 years, from the selected household. The 2018 NAIIS will characterize HIV incidence, prevalence, viral load suppression, CD4 T-cell distribution, and risk behaviors in a household-based, nationally-representative sample of the population of Nigeria, and will describe uptake of key HIV prevention, care, and treatment services. The 2018 NAIIS will also estimate the prevalence of hepatitis B virus (HBV), hepatitis C virus (HCV) infections, and HBV/HIV and HCV/HIV co-infections.
Kind of Data
Sample survey data [ssd]
Version
Version number
Version 1.0 Feb. 2019
Version Date
2019-02-01
Version Notes
This version of the survey document is used for demonstration purposes only.
This document will require approval before publishing as the official reference to the NAIIS.
This document will require approval before publishing as the official reference to the NAIIS.
Scope
Topic Classification
| Topic |
|---|
| HIV/AIDS |
| Population- based HIV Impact Assessment (PHIA) |
Keywords
| Keyword |
|---|
| HIV/AIDS |
| Population- based HIV Impact Assessment (PHIA) |
Coverage
Geographic Coverage
National
l
The Nigeria AIDS Indicator and Impact Survey (NAIIS) 2018 survey covered all the 36 states of the Federal Republic Nigeria and the Federal Capital Teritory (FCT)
l
The Nigeria AIDS Indicator and Impact Survey (NAIIS) 2018 survey covered all the 36 states of the Federal Republic Nigeria and the Federal Capital Teritory (FCT)
Unit of Analysis
Household Health Survey
Universe
1. Women and men aged 15-64 years living in residential households and visitors who slept in the household the night before the survey
2. Children aged 0-14 years living in residential households and child visitors who slept in the household the night before the survey
2. Children aged 0-14 years living in residential households and child visitors who slept in the household the night before the survey
Producers and sponsors
Authoring entity/Primary investigators
| Agency Name | Affiliation |
|---|---|
| Federal Ministry of Health (FMOH) | Government of Nigeria |
| National Agency for the Control of AIDS (NACA) | Government of Nigeria |
| Maryland Global Initiative Corporation (MGIC Nigeria) | University of Maryland Baltiimore (USA) |
Producers
| Name | Affiliation | Role |
|---|---|---|
| National Population Commission | Government of Nigeria | Provided key input on household mapping and sample selection |
| ICF | UMB Subcontractor | Designed CAPI system and primary data editing |
| Maryland Global Initiative Corporation (MGIC Nigeria) | Univerisity of Maryland Baltimore (USA) | Supervised the entire Maintained and finalized survey documentation, Led the consortium that implemented the survey, responsible for report writing. |
Funding Agency/Sponsor
| Name | Abbreviation | Role |
|---|---|---|
| US Centres for Disease Control and Prevention | CDC | funding |
| The Global Fund | GF | funding |
Other Identifications/Acknowledgments
| Name | Affiliation | Role |
|---|---|---|
| NAIIS National Steering & Technical Committee | Federal Government of Nigeria | Participate in protocol development, ethical reviews, survey monitoring, data analysis, reporting writing, and dissemination of results |
| National Bureau of Statistics | Federal Government of Nigeria | Data Collection and Analysis |
Sampling
Sampling Procedure
This cross-sectional, household-based survey will use a two-stage cluster sampling design (enumeration area followed by households). The target population is people 15-64 and children ages 0-14 years. The overall size and distribution of the sample is determined by analysis of existing estimates of national HIV incidence, sub-national HIV prevalence, and the number of HIV-positive cases needed to obtain estimates of VLS among adults 15-64 years for each of the 36 states and the FCT while not unnecessarily inflating the sample size needed.
From a sampling perspective, the three primary objectives of this proposal are based on competing demands, one focused on national incidence and the other on state-level estimates in a large number of states (37). Since the denominator used for estimating VLS is HIV-positive individuals, the required minimum number of blood draws in a stratum is inversely proportional to the expected HIV prevalence rate in that stratum. This objective requires a disproportionate amount of sample to be allocated to states with the lowest prevalence. A review of state-level prevalence estimates for sources in the last 3 to 5 years shows that state-level estimates are often divergent from one source to the next, making it difficult to ascertain the sample size needed to obtain the roughly 100 PLHIV needed to achieve a 95% confidence interval (CI) of +/- 10 for VLS estimates.
An equal-size approach is proposed with a sample size of 3,700 blood specimens in each state. Three-thousand seven hundred specimens will be sufficiently large to obtain robust estimates of HIV prevalence and VLS among HIV-infected individuals in most states. In states with a HIV prevalence above 2.5%, we can anticipate 95% CI of less than +/-10% and relative standard errors (RSEs) of less than 11% for estimates of VLS. In these states, with HIV prevalence above 2.5%, the anticipated 95% CI around prevalence is +/- 0.7% to a high of 1.1-1.3% in states with prevalence above 6%. In states with prevalence between 1.2 and 2.5% HIV prevalence estimates would remain robust with 95% CI of +/- 0.5-0.6% and RSE of less than 20% while 95% CI around VLS would range between 10-15% (and RSE below 15%). With this proposal only a few states, with HIV prevalence below 1.0%, would have less than robust estimates for VLS and HIV prevalence.
The evaluation of this "equal-size" approach to the 37 strata are presented in Table 1 below using the 2016 Spectrum estimates with states sorted by prevalence level from highest to lowest. This "equal-size" approach will ensure sufficiently large sample in each state for comparisons between states and satisfy overall need for national incidence estimate. As a result of the "equal-size" approach and the large number of strata (37) it is anticipated that the RSE for a national incidence estimate will be quite small, at less than 9%, when the survey is complete. It is also anticipated that regional incidence estimates (6 regions) will be possible with RSEs of 30% or less.
From a sampling perspective, the three primary objectives of this proposal are based on competing demands, one focused on national incidence and the other on state-level estimates in a large number of states (37). Since the denominator used for estimating VLS is HIV-positive individuals, the required minimum number of blood draws in a stratum is inversely proportional to the expected HIV prevalence rate in that stratum. This objective requires a disproportionate amount of sample to be allocated to states with the lowest prevalence. A review of state-level prevalence estimates for sources in the last 3 to 5 years shows that state-level estimates are often divergent from one source to the next, making it difficult to ascertain the sample size needed to obtain the roughly 100 PLHIV needed to achieve a 95% confidence interval (CI) of +/- 10 for VLS estimates.
An equal-size approach is proposed with a sample size of 3,700 blood specimens in each state. Three-thousand seven hundred specimens will be sufficiently large to obtain robust estimates of HIV prevalence and VLS among HIV-infected individuals in most states. In states with a HIV prevalence above 2.5%, we can anticipate 95% CI of less than +/-10% and relative standard errors (RSEs) of less than 11% for estimates of VLS. In these states, with HIV prevalence above 2.5%, the anticipated 95% CI around prevalence is +/- 0.7% to a high of 1.1-1.3% in states with prevalence above 6%. In states with prevalence between 1.2 and 2.5% HIV prevalence estimates would remain robust with 95% CI of +/- 0.5-0.6% and RSE of less than 20% while 95% CI around VLS would range between 10-15% (and RSE below 15%). With this proposal only a few states, with HIV prevalence below 1.0%, would have less than robust estimates for VLS and HIV prevalence.
The evaluation of this "equal-size" approach to the 37 strata are presented in Table 1 below using the 2016 Spectrum estimates with states sorted by prevalence level from highest to lowest. This "equal-size" approach will ensure sufficiently large sample in each state for comparisons between states and satisfy overall need for national incidence estimate. As a result of the "equal-size" approach and the large number of strata (37) it is anticipated that the RSE for a national incidence estimate will be quite small, at less than 9%, when the survey is complete. It is also anticipated that regional incidence estimates (6 regions) will be possible with RSEs of 30% or less.
Response Rate
A total of 101,267 households were selected, 89,345 were occupied and 83,909 completed
the household interview .
• For adults aged 15-64 years, interview response rate was 91.6% for women and 88.2% for
men; blood draw response rate was 92.9% for women and 93.6% for men.
• For adolescents aged 10-14 years, interview response rate was 86.8% for women and 86.2%
for men; blood draw response rate was 91.2% for women and 92.3% for men.
• For children aged 0-9 years, blood draw response rate was 68.5% for women and men.
the household interview .
• For adults aged 15-64 years, interview response rate was 91.6% for women and 88.2% for
men; blood draw response rate was 92.9% for women and 93.6% for men.
• For adolescents aged 10-14 years, interview response rate was 86.8% for women and 86.2%
for men; blood draw response rate was 91.2% for women and 92.3% for men.
• For children aged 0-9 years, blood draw response rate was 68.5% for women and men.
Weighting
The following weights have been computed and are included in the data files. Use of these weights will assure that the results produced are representative. Weights are computed at the state level.
1. Household weight
2. Individual Participant weight
3. Adolescent weight
4. Network Scale-up Method (NSUM)
5. Children's biometric
6. Adult biometric
1. Household weight
2. Individual Participant weight
3. Adolescent weight
4. Network Scale-up Method (NSUM)
5. Children's biometric
6. Adult biometric
Data Collection
Dates of Data Collection (YYYY/MM/DD)
| Start date | End date |
|---|---|
| 2018 | 2018 |
Mode of data collection
Face-to-face [f2f]
Supervision
Federal Ministry of Health
Type of Research Instrument
HOUSEHOLD QUESTIONNAIRE
ADULT QUESTIONNAIRE
EARLY ADOLESCENT QUESTIONNAIRE (10-14 YEARS)
ADULT QUESTIONNAIRE
EARLY ADOLESCENT QUESTIONNAIRE (10-14 YEARS)
Data Collectors
| Name | Abbreviation |
|---|---|
| University of Maryland, Baltimore Consortium | UMB Consortium |
Data Processing
Cleaning Operations
During the household data collection, questionnaire and laboratory data were transmitted between tablets via
Bluetooth connection. This facilitated synchronization of household rosters and ensured data collection for each
participant followed the correct pathway. All field data collected in CSPro and the Laboratory Data Management
System (LDMS) were transmitted to a central server using File Transfer Protocol Secure (FTPS) over a 4G or 3G
telecommunication provider at least once a day. Questionnaire data cleaning was conducted using CSPro and
SAS 9.4 (SAS Institute Inc., Cary, North Carolina, United States). Laboratory data were cleaned and merged with
the final questionnaire database using unique specimen barcodes and study identification numbers.
All results presented in the technical report were based on weighted estimates unless otherwise
stated. Analysis weights accounted for sample selection probabilities and adjusted for nonresponse and
noncoverage. Nonresponse adjusted weights were calculated for households, individual interviews and
individual blood draws in a hierarchical form. Adjustment for nonresponse for initial individual and bloodlevel
weights was based on the development of weighting adjustment cells defined by a combination of
variables that were potential predictors of response and HIV status. The nonresponse adjustment cells
were constructed using the Chi-square Automatic Interaction Detector (CHAID) algorithm. The cells were
defined based on data from the household interview for the adjustment of individual-level weights and
from both the household and individual interviews for the adjustment of blood specimen-level weights.
Post-stratification adjustments were implemented to compensate for non-coverage in the sampling
process. This final adjustment calibrated the nonresponse-adjusted individual and blood weights to make
the sum of each set of weights conform to national population totals by sex and five-year age groups.
Descriptive analyses of response rates, characteristics of respondents, HIV prevalence, CD4 count
distribution, HIV testing, self-reported HIV status, self-reported ART, VLS, PMTCT indicators, HBV, HCV and
sexual behavior were conducted using SAS 9.4.
Bluetooth connection. This facilitated synchronization of household rosters and ensured data collection for each
participant followed the correct pathway. All field data collected in CSPro and the Laboratory Data Management
System (LDMS) were transmitted to a central server using File Transfer Protocol Secure (FTPS) over a 4G or 3G
telecommunication provider at least once a day. Questionnaire data cleaning was conducted using CSPro and
SAS 9.4 (SAS Institute Inc., Cary, North Carolina, United States). Laboratory data were cleaned and merged with
the final questionnaire database using unique specimen barcodes and study identification numbers.
All results presented in the technical report were based on weighted estimates unless otherwise
stated. Analysis weights accounted for sample selection probabilities and adjusted for nonresponse and
noncoverage. Nonresponse adjusted weights were calculated for households, individual interviews and
individual blood draws in a hierarchical form. Adjustment for nonresponse for initial individual and bloodlevel
weights was based on the development of weighting adjustment cells defined by a combination of
variables that were potential predictors of response and HIV status. The nonresponse adjustment cells
were constructed using the Chi-square Automatic Interaction Detector (CHAID) algorithm. The cells were
defined based on data from the household interview for the adjustment of individual-level weights and
from both the household and individual interviews for the adjustment of blood specimen-level weights.
Post-stratification adjustments were implemented to compensate for non-coverage in the sampling
process. This final adjustment calibrated the nonresponse-adjusted individual and blood weights to make
the sum of each set of weights conform to national population totals by sex and five-year age groups.
Descriptive analyses of response rates, characteristics of respondents, HIV prevalence, CD4 count
distribution, HIV testing, self-reported HIV status, self-reported ART, VLS, PMTCT indicators, HBV, HCV and
sexual behavior were conducted using SAS 9.4.
Other Processing
Data collection was done using CSPro verion 7.2 for CAPI Android.
Incidence estimates were based on the number of HIV infections identified as recent with the HIV-1 LAg
Avidity plus VL algorithm and ARV algorithm and obtained using the formula recommended by the WHO
Incidence Working Group and Consortium for Evaluation and Performance of Incidence Assays and with
assay performance characteristics of a mean duration of recent infection (MDRI) = 130 days (95% CI: 118,
142), a time cutoff (T) = 1.0 year and percentage false recent (PFR) = 0.00.
Incidence estimates were based on the number of HIV infections identified as recent with the HIV-1 LAg
Avidity plus VL algorithm and ARV algorithm and obtained using the formula recommended by the WHO
Incidence Working Group and Consortium for Evaluation and Performance of Incidence Assays and with
assay performance characteristics of a mean duration of recent infection (MDRI) = 130 days (95% CI: 118,
142), a time cutoff (T) = 1.0 year and percentage false recent (PFR) = 0.00.
Data Appraisal
Estimates of Sampling Error
Estimates from sample surveys are affected by two types of errors: non-sampling errors and sampling
errors. Non-sampling errors result from mistakes made during data collection, e.g., misinterpretation
of an HIV test result and data management errors such as transcription errors during data entry. While
NAIIS implemented numerous quality assurance and control measures to minimize non-sampling errors,
these were impossible to avoid and difficult to evaluate statistically. In contrast, sampling errors can be
evaluated statistically. Sampling errors are a measure of the variability between all possible samples. The
sample of respondents selected for NAIIS was only one of many samples that could have been selected
from the same population, using the same design and expected size. Each of these samples could yield
results that differed somewhat from the results of the actual sample selected. Although the degree of
variability cannot be known exactly, it can be estimated from the survey results.
The standard error, which is the square root of the variance, is the usual measurement of sampling error
for a statistic (e.g., proportion, mean, rate, count). In turn, the standard error can be used to calculate
confidence intervals within which the true value for the population can reasonably be assumed to fall.
For example, for any given statistic calculated from a sample survey, the value of that statistic will fall
within a range of approximately plus or minus two times the standard error of that statistic in 95% of all
possible samples of identical size and design.
NAIIS utilized a multi-stage stratified sample design, which required complex calculations to obtain
sampling errors. The Taylor linearization method of variance estimation was used for survey estimates
that are proportions, e.g., HIV prevalence. The Jackknife repeated replication method was used for
variance estimation of more complex statistics such as rates, e.g., annual HIV incidence and counts such
as the number of people living with HIV.
The Taylor linearization method treats any percentage or average as a ratio estimate, , where y
represents the total sample value for variable y and x represents the total number of cases in the group
or subgroup under consideration. The variance of r is computed using the formula given below, with the
standard error being the square root of the variance:
in which
Where represents the stratum, which varies from 1 to H,
is the total number of clusters selected in the hth stratum,
is the sum of the weighted values of variable y in the ith cluster in the hth stratum,
is the sum of the weighted number of cases in the ith cluster in the hth stratum and,
f is the overall sampling fraction, which is so small that it is ignored.
145
NAIIS 2018 Technical Report
In addition to the standard error, the design effect for each estimate is also calculated. The design effect is
defined as the ratio of the standard error using the given sample design to the standard error that would
result if a simple random sample had been used. A design effect of 1.0 indicates that the sample design is
as efficient as a simple random sample, while a value greater than 1.0 indicates the increase in the sampling
error due to the use of a more complex and less statistically efficient design. Confidence limits for the
estimates, which are calculated as
where t(0.975, K) is the 97.5th percentile of a t-distribution with K degrees of freedom, are also computed.
Sampling errors for selected variables from NAIIS are presented in Tables C.1 through C.9. For most variables,
sampling error tables include the weighted estimate, unweighted denominator, standard error or
design effect and lower- and upper-95% confidence limits.
errors. Non-sampling errors result from mistakes made during data collection, e.g., misinterpretation
of an HIV test result and data management errors such as transcription errors during data entry. While
NAIIS implemented numerous quality assurance and control measures to minimize non-sampling errors,
these were impossible to avoid and difficult to evaluate statistically. In contrast, sampling errors can be
evaluated statistically. Sampling errors are a measure of the variability between all possible samples. The
sample of respondents selected for NAIIS was only one of many samples that could have been selected
from the same population, using the same design and expected size. Each of these samples could yield
results that differed somewhat from the results of the actual sample selected. Although the degree of
variability cannot be known exactly, it can be estimated from the survey results.
The standard error, which is the square root of the variance, is the usual measurement of sampling error
for a statistic (e.g., proportion, mean, rate, count). In turn, the standard error can be used to calculate
confidence intervals within which the true value for the population can reasonably be assumed to fall.
For example, for any given statistic calculated from a sample survey, the value of that statistic will fall
within a range of approximately plus or minus two times the standard error of that statistic in 95% of all
possible samples of identical size and design.
NAIIS utilized a multi-stage stratified sample design, which required complex calculations to obtain
sampling errors. The Taylor linearization method of variance estimation was used for survey estimates
that are proportions, e.g., HIV prevalence. The Jackknife repeated replication method was used for
variance estimation of more complex statistics such as rates, e.g., annual HIV incidence and counts such
as the number of people living with HIV.
The Taylor linearization method treats any percentage or average as a ratio estimate, , where y
represents the total sample value for variable y and x represents the total number of cases in the group
or subgroup under consideration. The variance of r is computed using the formula given below, with the
standard error being the square root of the variance:
in which
Where represents the stratum, which varies from 1 to H,
is the total number of clusters selected in the hth stratum,
is the sum of the weighted values of variable y in the ith cluster in the hth stratum,
is the sum of the weighted number of cases in the ith cluster in the hth stratum and,
f is the overall sampling fraction, which is so small that it is ignored.
145
NAIIS 2018 Technical Report
In addition to the standard error, the design effect for each estimate is also calculated. The design effect is
defined as the ratio of the standard error using the given sample design to the standard error that would
result if a simple random sample had been used. A design effect of 1.0 indicates that the sample design is
as efficient as a simple random sample, while a value greater than 1.0 indicates the increase in the sampling
error due to the use of a more complex and less statistically efficient design. Confidence limits for the
estimates, which are calculated as
where t(0.975, K) is the 97.5th percentile of a t-distribution with K degrees of freedom, are also computed.
Sampling errors for selected variables from NAIIS are presented in Tables C.1 through C.9. For most variables,
sampling error tables include the weighted estimate, unweighted denominator, standard error or
design effect and lower- and upper-95% confidence limits.
Data Appraisal
Remote data quality Check was carried out using data editor
Data access
Contact
| Name | Affiliation |
|---|---|
| Federal Ministry of Health | Federal Government of Nigeria |
Confidentiality Declaration
Confidentiality of the respondent is guranteed by law. Before accessing the data, the user will have to review the confidentiality agreement and agree to the terms and conditions provided in the access policy.
Before being granted access to the dataset, all users have to formally agree:
1. To make no copies of any files or portions of files to which s/he is granted access except those authorized by the data depositor.
2. Not to use any technique in an attempt to learn the identity of any person, establishment, or sampling unit not identified on public use data files.
3. To hold in strictest confidence the identification of any establishment or individual that may be inadvertently revealed in any documents or discussion, or analysis. Such inadvertent identification revealed in her/his analysis will be immediately brought to the attention of the data depositor.
Conditions
Access to the data set is determined by a review committee to be set up by the Federal Ministry of Health. Public use files are anonymized.
Citation requirement
Federal Ministry of Health, Nigeria AIDS Indicator and Impact Survey 2018 (NAIIS), Version 1.0. Data provided through NASCP.
Disclaimer and copyrights
Disclaimer
The Federal Ministry of Health authorizes the distribution of the data. Users of the data are required to provide a copy of their published results in order to maintain record of the citations. However, the FMoH, CDC, UMB and parties associated with the survey reprsenting the Government of Nigeria bear no responsiblity for inferences and interpretations published. Publishers can seek official validation of their results through a peer review process that has been defined by the FMoH.
Copyright
(c) 2021
Contacts
Contact
| Name | Affiliation | |
|---|---|---|
| Dr Adebobola Bashorun | Federal Ministry of Health | bashogee@yahoo.com |
| Akipu Ehoche | University of Maryland (UMB) | aehoche@mgic.umaryland.edu |
Metadata production
Document ID
DDI-NGA-FMOH-NAIIS-2018-v1.03
Producers
| Name | Abbreviation | Affiliation | Role |
|---|---|---|---|
| Maryland Global Initiative Corporation, Nigeria | MGIC | University of Maryland Baltimore, USA | Maintained and finalized survey documentation, Led the consortium that implemented the survey, responsible for report writing. |
| ICF | ICF | Documentation of the survey and producer of the DDI | |
| National Bureau of Statistics | NBS | Government of Nigeria | Archiving of the survey datasets for public access |
| Federal Ministry of Health | FMOH | Government of Nigeria |
Date of Production
2019-02-15
Document version
Version 1.0 Feb. 2019