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    Home / Central Data Catalog / HEALTH_AND_WELL-BEING / APHRC-2018-STEP-UP-V10
Health_and_Well-Being

Strengthening Evidence for Programming on Unintended Pregnancy, Developing and Validating Measures of Unintended Pregnancy and Reasons for Contraceptive Non-use among Married Women in Nairobi’s Informal Settlements

KENYA, 2017
Health and Well-Being (HaW)
African Population and Health Research Center, London School of Hygiene and Tropical Medicine, UK
Last modified October 19, 2021 Page views 336693 Documentation in PDF Metadata DDI/XML JSON
  • Study description
  • Data Description
  • Get Microdata
  • Identification
  • Version
  • Coverage
  • Producers and sponsors
  • Sampling
  • Data Collection
  • Data Processing
  • Data Appraisal
  • Data access
  • Disclaimer and copyrights
  • Metadata production

Identification

IDNO
APHRC-2018-STEP-UP-v10
Title
Strengthening Evidence for Programming on Unintended Pregnancy, Developing and Validating Measures of Unintended Pregnancy and Reasons for Contraceptive Non-use among Married Women in Nairobi’s Informal Settlements
Subtitle
Developing and Validating Measures of Unintended Pregnancy and Reasons for Contraceptive Non-use among Married Women in Nairobi’s Informal Settlements
Country
Name Country code
KENYA KEN
Abstract
Measuring unintended pregnancies is important for demographers and public health workers worldwide. Pregnancy intentions and attendant fertility-related behaviors have significant implications on forecasting fertility rates, designing family planning programs and estimating the unmet need for contraception. However, most current estimates of the levels of unintended pregnancy in developing countries are derived from retrospective reporting on the last pregnancy or childbirth in Demographic and Health Surveys (DHS). An unintended pregnancy in these surveys is classified as one that is reported to have been mistimed (occurred earlier than planned) or unwanted (occurred when no more children were desired). Such measures of pregnancy intentions, that are dichotomous and retrospective, have been shown to be overly simplistic and suffer from reporting bias. Application of measures which capture the multidimensionality of fertility intentions in a prospective longitudinal study have been proposed as being better approaches to capture the complexity of unintended pregnancy. Given the potential advantages of prospective measurements, it is unfortunate that only few studies of this nature have been undertaken in developing countries. The presence of numerous health and demographic surveillance systems (HDSS) in several developing countries offer the opportunity to strengthen the evidence on unintended pregnancy by developing and validating the use of such measures through their longitudinal data collection mechanisms.

The overall objective of this study is to develop and validate new measures of unintended pregnancy and reasons for non-use of contraceptives in developing countries. Such tools would provide an improved understanding of the determinants and dynamics of pregnancy intentions, contraceptive decision-making and use, and the impact of fertility intentions on pregnancy outcomes, especially in settings where fertility intentions may be high or ambiguous and where contraceptive use is low and unmet need high. The study will be carried out in Korogocho and Viwandani in Nairobi, Kenya, where the African Population and Health Research Center (APHRC) has been running the Nairobi Urban and Health Demographic Surveillance System (NUHDSS) since 2002. The study is being implemented in two phases. In the first phase (completed), a conceptual framework and draft module, consisting of a questionnaire and a protocol for its administration was developed through a consultative process and review of the literature. The module was developed in collaboration with the London School of Hygiene and Tropical Medicine, the Population Council, and the International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b). During the second phase, the module will be administered two times to married or cohabiting women aged between 15 and 39 years old living in the demographic surveillance area: at baseline to generate baseline measures of the key variables, and after twelve (12) months. There might be the possibility of a third wave, but the implementation of the third wave will depend on receipt of additional funding.

Data will be collected through face-to-face interviews with eligible women randomly sampled from the NUHDSS. Predictive validity of pregnancy and contraceptive measures will be assessed using factor analysis and multivariate regression analysis to assess the independent net effect of explanatory variables on outcome variables of interest.

Version

Version Date
2018-12-10
Version Notes
No changes were made

Coverage

Geographic Coverage
Two informal settlements (slums) in Nairobi county, Kenya (specifically, Korogocho and Viwandani slums).
Unit of Analysis
All Women aged 15-39 and are married or living with a partner.
Universe
All married (living together with a partner)women aged 15-39 living in the Nairobi DSS(Korogocho and Viwandani).

Producers and sponsors

Authoring entity/Primary investigators
Agency Name Affiliation
African Population and Health Research Center APHRC
London School of Hygiene and Tropical Medicine, UK LSHTM
Producers
Name Affiliation
Clement Oduor African Population and Health Research Center (APHRC)
Prof. John Cleland London School of Hygiene and Tropical Medicine, UK
Prof. John Casterline Ohio State University, USA
Joyce N Mumah African Population and Health Research Center (APHRC)
Caroline W Kabiru African Population and Health Research Center (APHRC)
Dr Kazuyo Machiyama London School of Hygiene and Tropical Medicine, UK
Funding Agency/Sponsor
Name Abbreviation
United Kingdom Aid UKAid
Other Identifications/Acknowledgments
Name Role
Residents of Korogocho and Viwandani Slums Study Subjects
Community leaders - chiefs and village elders Support to field teams

Sampling

Sampling Procedure
We assume both exposure/predictor and outcome variables are dichotomous, with say 20% in the unexposed and 40% in the exposed positive on outcome variable, such as current contraceptive use. Our sample size calculation will be based on the following formulae to be able to detect 20-50% differences in two proportions (Fleiss, Levin, and Paik 2003). We use power of 80% and 90% and significance level of 0.05. As the distribution of exposure is unknown, different ratios of sample size of the exposed to the unexposed (20% vs 80%, 30% vs70%, 80% vs 20%) are used to calculate sample sizes. The calculation for HDSS assumes a simple random sampling in the database.

In addition, the continuity correction factor is applied to the normal approximation of the discrete distribution. A 10% non-response rate is assumed.

The primary interest in the single round survey is women who are in need for family planning, i.e. women who are not currently pregnant, are not in postpartum amenorrhea, and do not want a child soon. Based on the latest KDHS survey, it is estimated that these women account for about 50% of women in union aged 15-39.

In addition, follow-up data collection to measure predictive validity of prospective intentions on reporting of pregnancy or childbirths, contraceptive use-continuation, adoption and unmet need for family planning, and the validity of retrospective fertility preferences are taken into account in the sample size calculations. It is estimated that about 15% of women would report being pregnant or having had a birth at 1-year follow-up and 30% in 2 years among women among the unexposed group at the baseline. The sample sizes were calculated for the prospective study using the same formulae and assumptions used in the single round survey. It is estimated that women who are pregnant or want a child within 2 years accounts for about 30% of women aged 20-39, so the overall sample sizes are calculated by multiplying by 1.3.

According to the calculations, if time and budget allows, it is desirable to recruit 2,600 women in union aged 15-39 to be able to detect at least 30% of differences with 80% of power both in single round and prospective surveys.
Deviations from the Sample Design
na
Response Rate
na
Weighting
na

Data Collection

Dates of Data Collection (YYYY/MM/DD)
Start date End date Cycle
2017-10-12 2017-12-08 Round 2
Mode of data collection
Face-to-face [f2f]
Supervision
Two main teams Korogocho and Viwandani, with 12 and 18 interviewers respectively. The main teams were further broken into 2, each under a field supervisor. Further, a full-time research assistant was attached to the field to directly oversee the data collection and report any challenges to the survey team besides handling field logistics. A team leader who was in charge of the calendar editing and data entry was appointed to work in both study sites. In addition, the study had a full time research officer and data analysts who together looked at the data on a day-to-day basis to help identify and address any data quality related concerns in consultation with the survey's principal investigator (PI).
Type of Research Instrument
Strengthening evidence for programming on unintended pregnancy (step up) developing and validating a measure of unintended pregnancy and reasons for contraceptive non-use form

Data Processing

Cleaning Operations
Data editing took place at a number of stages throughout the processing, including:
1. Quality control through back-checks on 10 percent of completed questionnaires and editing of all completed questionnaires by supervisors and project management staff.
2. A quality control officer performed internal consistency checks for all questionnaires and edited all paper questionnaires coming from the field before their submission for data entry with return of incorrectly filled questionnaires to the field for error-resolution.
3. During data entry, any questionnaires that were found to be inconsistent were returned to the field for resolution.
4. Data cleaning and editting was carried out using STATA Version 13 software.

Detailed documentation of the editing of data can be found in the "Standard Procedures Manual" document provided as an external resource.

Some corrections are made automatically by the program (80%) and the rest by visual control of the questionnaire (20%).

Where changes are made by the program, a cold deck imputation is preferred; where incorrect values are imputed using existing data from another dataset. If cold deck is found to be insufficient, hot deck imputation is used. In this case, a missing value is imputed from a randomly selected similar record in the same dataset.
Other Processing
Data were entered with the use of;
1. Using tablets

Data were captured using in-house software developed with a Visual Basic. Net front-end and a Microsoft Structured Query Language (SQL) Server back-end.

Data Appraisal

Estimates of Sampling Error
na

Data access

Contact
Name Email URI
African Population & Health Research Center info@aphrc.org www.aphrc.org
London School of Hygiene and Tropical Medicine
Population Council
Ohio State University
icddr, b
Conditions
1. The investigators will neither release nor permit others to release the files or data therein to any person (including media and subcontractors) except with written approval of each participating site.

a. A proposal to use raw or pooled data for a Master's or Doctoral thesis or by other researchers including a member of staff in the collaborating institutions will need the approval of all the STEP UP partner institutions (LSHTM, icddr,b, APHRC, Population Council) and the investigator from Ohio State University. All proposals should be submitted to the study PI who will coordinate the approval process.

b. The investigators will neither use nor permit others to use the data in any way other than listed in the original application for access to the dataset.

2. The investigators will ensure that the data are kept in a secure environment and that only authorized users have access to the data. In order to optimize the dissemination of results, for the baseline and each round of follow-up, data derived from this study should be made available in a usable format to all the investigators of the study and the study repository hosted at LSHTM within two months of completion of data collection. However, given the remaining life-span of the STEP UP, it would be useful if the STEP UP partner institutions share their data with all the investigators of the study and the repository as soon as possible after collection in order to maximise the STEP UP's impact.

3. In accordance with DFID's Research Open and Enhanced Access Policy:

a. Datasets (derived or raw) are to be placed in an institutional repository within 12 months of final data collection or on publication of outputs underpinned by that data, whichever is sooner.

b. All published results must include information on how to access original raw datasets.

c. Each institution is required to retain the raw datasets for a minimum of five years after the end of the project, and make them available on request, free, any time after 12 months from final data collection.

d. Documentation should be created that describe the data's provenance and enable its content to be understood. This will include a list of files in the set, how the data was collected, and which parameters have been used
Each of the institutions collecting data will host their data, and the raw data from all the three sites will be hosted at the Population Council's website, within 12 months from the end of the final round of data collection. Researchers requesting the data need to provide study description and justification for use from the institution hosting the data.

4. Every publication or report based on the data must carry an acknowledgement of the form:

"Analysis based on data pooled by the Improving Measurement of Unintended Pregnancy and Unmet Need for Family Planning, as supplied by: the Matlab Health and Demographic Surveillance System, managed by icddr, b, in Bangladesh; Nairobi Urban Health Demographic Surveillance System, managed by APHRC, in Kenya; the Homa-Bay study, managed by Population Council, in Kenya. This work was funded by the Department for International Development (DFID) through STEP UP (Strengthening Evidence for Programming on Unintended Pregnancy) Research Programme Consortium. "
(Analyses using site-specific data should only use acknowledgments from the specific institution managing and hosting the data:
If using data from APHRC:
The Improving Measurement of Unintended Pregnancy and Unmet Need for Family Planning study was funded by the Department for International Development (DFID) through the STEP UP (Strengthening Evidence for Programming on Unintended Pregnancy) project (Grant SR1109D-6). The Nairobi Urban Health and Demographic Surveillance System (NUHDSS) has received support from a number of donors including the Rockefeller Foundation (USA), the Wellcome Trust (UK), the William and Flora Hewlett Foundation (USA), Comic relief (UK), Swedish International Development Cooperation (SIDA) and the Bill and Melinda Gates Foundation (USA). The implementation and management of the NUHDSS would have not been possible without the continuous contribution and support of the data entry and management team, field teams, community leaders and residents of the Korogocho and Viwandani slums.
If using data from icddr,b:
This research study was funded by the Department for International Development (DFID) through STEP UP (Strengthening Evidence for Programming on Unintended Pregnancy) project. icddr,b acknowledges with gratitude the commitment of DFID to its research efforts. icddr,b is also grateful to the Governments of Bangladesh, Canada, Sweden and the UK for providing core/unrestricted support.
If using data from Population Council: This work was funded by the Department for International Development (DFID) through STEP UP (Strengthening Evidence for Programming on Unintended Pregnancy) Research Programme Consortium.

5. All investigators must inform all participating investigators of their intent to conduct specific analyses for the purposes of publication and provide an opportunity for team members to contribute to the planned work.

6. Each of the STEP UP partner institution must be provided with the opportunity to review all publications, which include their individual site data that they have contributed. The institutions will have the opportunity to have one or more co-authors participate (in accordance with the International Commission of Medical Journal Authors) in any publication based on analyses of their individual level data.

7. All the persons designated as authors should qualify for authorship, and all those who qualify should be listed. Authorship will be determined prior to starting work on a publication and confirmed before submission of the final manuscript; all participating authors will agree upon the order of authorship. Authorship will be based on:

1. Substantial contributions to conception and design or analysis and interpretation and

2. Drafting the article or revising it critically for important intellectual content and

3. Final approval of the version to be published.

Authors should meet conditions 1, 2, and 3. Each author should have made a significant intellectual contribution to the work represented by the article to take public responsibility for appropriate portions of the content, including its accuracy. All authors are responsible for the content of the paper and should be able to identify their own contribution.

8. All contributions of formal collaborators, funders, sponsors and all others who significantly assisted the research but who do not quality for authorship should be properly acknowledged.

9. The investigators will not release, or permit others to release, data that identifies statistical units such as people, households, or other micro-level data (up to and including the village/neighbourhood level) directly or indirectly. Data users are expected to respect the confidentiality and privacy of individuals whose records they access; to observe any restrictions that apply to sensitive data; and to abide by applicable laws, policies, procedures and guidelines with respect to access, use, sharing or disclosure of information. The unauthorized storage, disclosure or distribution of the data in any medium or use of any such data for one's own personal gain is strictly prohibited and considered a gross misconduct.

10. The investigators will not attempt to identify by any means whatsoever any individual statistical unit, nor will the investigators claim to have done so.

11. The datasets remain the property of the STEP UP partner institutions which participate in this study and the institutions reserve the right to request the return of the dataset should any of the above conditions be violated.
Citation requirement
"African Population & Health Research Center (APHRC), Step up Table (), Version 1.0 of the licensed public use dataset August 2017), provided by the APHRC. www.aphrc.org"

Disclaimer and copyrights

Disclaimer
The user of the data acknowledges that APHRC and the relevant funding agency bear no responsibility for use of the data or for interpretations or inferences based upon such uses.
Copyright
Copyright © APHRC, 2017

Metadata production

Document ID
APHRC-2018-STEP-UP-v10
Producers
Name Abbreviation Role
AFRICAN POPULATION AND HEALTH RESEARCH CENTER APHRC Data Collection, Processing, and Documentation
Date of Production
2018-12-10
Document version
Version 1.0,DECEMBER 2018.
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