A starter data kit for Kenya

Kenya E-Commerce Transactions: Value: Computers Electronics & Technology: Programming & Developer Software data was reported at 28.151 USD in 18 Nov 2023. This records a decrease from the previous number of 29.780 USD for 17 Nov 2023. Kenya E-Commerce Transactions: Value: Computers Electronics & Technology: Programming & Developer Software data is updated daily, averaging 50.507 USD from Feb 2019 (Median) to 18 Nov 2023, with 136 observations. The data reached an all-time high of 586.923 USD in 30 Jul 2020 and a record low of 0.515 USD in 08 Dec 2021. Kenya E-Commerce Transactions: Value: Computers Electronics & Technology: Programming & Developer Software data remains active status in CEIC and is reported by Grips Intelligence Inc.. The data is categorized under Global Database’s Kenya – Table KE.GI.EC: E-Commerce Transactions: by Category.

Kenya E-Commerce Transactions: AOV: Computers Electronics & Technology: Programming & Developer Software data was reported at 14.075 USD in 18 Nov 2023. This records a decrease from the previous number of 29.780 USD for 17 Nov 2023. Kenya E-Commerce Transactions: AOV: Computers Electronics & Technology: Programming & Developer Software data is updated daily, averaging 42.616 USD from Feb 2019 (Median) to 18 Nov 2023, with 136 observations. The data reached an all-time high of 358.876 USD in 17 Dec 2019 and a record low of 0.515 USD in 08 Dec 2021. Kenya E-Commerce Transactions: AOV: Computers Electronics & Technology: Programming & Developer Software data remains active status in CEIC and is reported by Grips Intelligence Inc.. The data is categorized under Global Database’s Kenya – Table KE.GI.EC: E-Commerce Transactions: by Category.

The Population Council is the research partner to DREAMS— a global partnership to reduce HIV infections among adolescent girls and young women (AGYW) in 10 sub-Saharan African countries. DREAMS aims to reduce HIV infections among AGYW. This dataset is Round 1 data collection with young women (ages 15–24 years) from Kisumu county, Kenya from a Population Council-led implementation science study to assess the reach and effectiveness of DREAMS programming in two catchment areas in Kisumu county, Kenya. This cross-sectional data includes data from AGYW enrolled in DREAMS programming and AGYW who lived in the catchment area but were not enrolled in DREAMS.

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.

Geographic coverage

Two informal settlements (slums) in Nairobi county, Kenya (specifically, Korogocho and Viwandani slums).

Analysis unit

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).

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.

Sampling deviation

na

Mode of data collection

Face-to-face [f2f]

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

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.

Response rate

na

Sampling error estimates

na

Parenting programs have primarily focused on supporting mothers with knowledge and practice of responsive caregiving. However, the role of non-primary caregivers such as grandparents, aunts, and older siblings has not been adequately addressed in programming. Using ChildFund International’s programmatic data from Kenya and Uganda, this cross-sectional study examined the extent to which children aged 0–5 years were left in the care of non-primary caregivers in the household and whether the primary caregivers’ absence was associated with engagement by non-primary caregivers. We found that a considerable proportion of children aged 0–5 years in Kenya and Uganda were entrusted in the care of non-primary caregivers for at least 3 days or more during the week. Yet, the primary caregivers’ absence was not consistently associated with greater engagement by fathers and other members of the household. Our findings call for parenting programs to consider adopting a holistic and contextualized approach where all involved caregivers are intentionally targeted rather than focusing just on the identified primary caregiver alone.