Chart and table of population level and growth rate for the Nairobi, Kenya metro area from 1950 to 2025.

In 2025, *** percent of Kenya’s population live below **** U.S. dollars per day. This meant that over 8.9 million Kenyans were in extreme poverty, most of whom were in rural areas. Over *** million Kenyans in rural communities lived on less than **** U.S. dollars daily, an amount *** times higher than that recorded in urban regions. Nevertheless, the poverty incidence has declined compared to 2020. That year, businesses closed, unemployment increased, and food prices soared due to the coronavirus (COVID-19) pandemic. Consequently, the country witnessed higher levels of impoverishment, although improvements were already visible in 2021. Overall, the poverty rate in Kenya is expected to decline to ** percent by 2025. Poverty triggers food insecurity Reducing poverty in Kenya puts the country on the way to enhancing food security. As of November 2021, *** million Kenyans lacked sufficient food for consumption. That corresponded to **** percent of the country's population. Also, in 2021, over one-quarter of Kenyan children under five years suffered from chronic malnutrition, a growth failure resulting from a lack of adequate nutrients over a long period. Another *** percent of the children were affected by acute malnutrition, which concerns a rapid deterioration in the nutritional status over a short period. A country where prosperity and poverty walk side by side The poverty incidence in Kenya contrasts with the country's economic development. In 2021, Kenya ranked among the ten highest GDPs in Africa, at almost *** billion U.S. dollars. Moreover, its gross national income per capita has increased to ***** U.S. dollars over the last 10 years, a growth of above**** percent. Generally, while poverty decreased in the country during the same period, Kenya still seems to be far from reaching the United Nation's Sustainable Development Goals (SDGs) to eliminate extreme poverty by 2030.

In 2022, over 1.22 million births were registered in Kenya, increasing from 1.20 million births in the previous year. The birth rate in Kenya grew exponentially from 2016, with a slight drop occurring in 2020.

Abstract

The Kenya Demographic and Health Survey (KDHS) was conducted between December 1988 and May 1989 to collect data regarding fertility, family planning and maternal and child health. The survey covered 7,150 women aged 15-49 and a subsample of 1,116 husbands of these women, selected from a sample covering 95 percent of the population. The purpose of the survey was to provide planners and policymakers with data useful in making informed programme decisions.

OBJECTIVES

On March 1, 1988, 'on behalf of the Government of Kenya, the National Council for Population and Development (NCPD) signed an agreement with the Institute for Resource Development (IRD) to carry out the Kenya Demographic and Health Survey (KDHS).

The KDHS is intended to serve as a source of population and health data for policymakers and for the research community. In general, the objectives of the KDHS are to: assess the overall demographic situation in Kenya, assist in the evaluation of the population and health programmes in Kenya, advance survey methodology, and assist the NCPD strengthen and improve its technical skills to conduct demographic and health surveys.

The KDHS was specifically designed to: - provide data on the family planning and fertility behaviour of the Kcnyan population to enable the NCPD to evaluate and enhance the National Family Planning Programme, - measure changes in fertility and contraceptive prevalence and at the same time study the factors which affect these changes, such as marriage patterns, urban/rural residence, availability of contraception, breastfeeding habits and other socioeconomic factors, and - examine the basic indicators of maternal and child health in Kenya.

SUMMARY OF FINDINGS

The survey data can also be used to evaluate Kenya's efforts to reduce fertility and the picture that emerges shows significant strides have been made toward this goal. KDHS data provide the first evidence of a major decline in fertility. If young women continue to have children at current rates, they will have an average of 6.7 births in their lifetime. This is down considerably from the average of 7.5 births for women now at the end of their childbearing years. The fertility rate in 1984 was estimated at 7.7 births per woman.

A major cause of the decline in fertility is increased use of family pIanning. Twenty-seven percent of married women in Kenya are currcntly using a contraceptive method, compared to 17 percent in 1984. Although periodic abstinence continues to he the most common method (8 percent), of interest to programme planners is the fact that two-thirds of marricd women using contraception have chosen a modern method--either the pill (5 percent) or female sterilisation (5 percent). Contraccptive use varies by province, with those closest to Nairobi having the highest levels. Further evidence of the success in promoting family planning is the fact that more than 90 percent of married women know at least one modern method of contraception (and where to obtain it), and 45 percent have used a contraceptive method at some time in their life.

The survey indicates a high level of knowledge, use and approval of family planning by husbands of interviewed women. Ninety-three percent of husbands know a modern method of family planning. Sixty-five percent of husbands have used a method at some time and almost 49 percent are currently using a method, half of which are modern methods. Husbands in Kenya are strongly supportive of family planning. Ninety-one percent of those surveyed approve of family planning use by couples, compared to 88 percent of married women.

If couples are able to realise their childbearing preferences, fertility may continue to decline in the future. One half of married women say that they want no more children; another 26 percent want to wait at least two years before having another child. Husbands report similar views on limiting births--one-half say they want no more children. The desire to limit childbearing appears to be greater in Kenya than in other subSaharan countries. In Botswana and Zimbabwe, for example, only 33 percent of married women want no more children. Another indicator of possible future decline in fertility in Kenya is the decrease in ideal family size. According to the KDHS, the mean ideal family size declined from 5.8 in 1984 to 4.4 in 1989.

The KDHS indicates that in the area of health, government programmes have been effective in providing health services for womcn and children. Eight in ten births benefit from ante-natal care from a doctor, nurse, or midwife and one-half of births are assisted at delivery by a doctor, nurse, or midwife. At least 44 percent of children 12-23 months of age are fully immunised against the major childhood diseases, Almost all children benefit from an extended period of breastfeeding. The average duration of breastfeeding is 19 months and the practice does not appear to be waning among either younger women or urban women. Another encouraging piece of information is the high level of ORT (oral rehydration therapy) use for treating childhood diarrhoea. Among children under five reported to have had an episode of diarrhoea in the two weeks before the survey, half were treated with a homemade solution and almost one-quarter were given a solution prepared from commercially prepared packets.

The survey indicates several areas where there is room for improvement. Although young women are marrying later, many are still having births at young ages. More than 20 percent of teen-age girls have had at least one child and 7 percent were pregnant at the time of the survey. There is also evidence of an unmet need for family planning services. Of the births occurring in the 12 months before the survey, over half were either mistimed or unwanted; one fifth occurred less than 24 months after a previous birth.

Geographic coverage

The 1989 KDHS sample is national in scope, with the exclusion of all three districts in North Eastern Province and four other northern districts (Samburu and Turkana in Rift Valley Province and Isiolo and 4 Marsabit in Eastern Province). Together the excluded areas account for less than 4 percent of Kenya's population.

Analysis unit

• Household
• Women age 15-49
• Men age not specified

Universe

The population covered by the 1989 KDHS is defined as the universe of all women age 15-49 in Kenya and all husband living in the household.

Kind of data

Sample survey data

Sampling procedure

The sample for the KDHS is based on the National Sample Survey and Ewduation Programme (NASSEP) master sample maintained by the CBS. The KDHS sample is national in coverage, with the exclusion of North Eastern Province and four northern districts which together account for only about five percent of Kenya's population. The KDHS sample was designed to produce completed interviews with 7,500 women aged 15-49 and with a subsample of 1,000 husbands of these women.

The NASSEP master sample is a two-stage design, stratified by urban-rural residence, and within the rural stratum, by individual district. In the first stage, 1979 census enumeration areas (EAs) were selected with probability proportional to size. The selected EAs were segmented into the expected number of standard-sized clusters, one of which was selected at random to form the NASSEP cluster. The selected clusters were then mapped and listed by CBS field staff. In rural areas, household listings made betwecn 1984 and 1985 were used to select the KDHS households, while KDHS pretest staff were used to relist households in the selected urban clusters.

Despite the emphasis on obtaining district-level data for phoning purposes, it was decided that reliable estimates could not be produced from the KDHS for all 32 districts in NASSEP, unless the sample were expanded to an unmanageable size. However, it was felt that reliable estimates of certain variables could be produced lbr the rural areas in the 13 districts that have been initially targeted by the NCPD: Kilifi, Machakos, Meru, Nyeri, Murang'a, Kirinyaga, Kericho, Uasin Gishu, South Nyanza, Kisii, Siaya, Kakamega, and Bungoma. Thus, all 24 rural clusters in the NASSEP were selected for inclusion in the KDHS sample in these 13 districts. About 450 rural households were selected in each of these districts, just over 1000 rural households in other districts, and about 3000 households in urban areas, for a total of almost 10,000 households. Sample weights were used to compensate for the unequal probability of selection between strata, and weighted figures are used throughout the remainder of this report.

Mode of data collection

Face-to-face

Research instrument

The KDHS utilised three questionnaires: a household questionnaire, a woman's questionnaire, and a husband's questionnaire. The first two were based on the DHS Programme's Model "B" Questionnaire that was designed for low contraceptive prevalence countries, while the husband's questionnaire was based on similar questionnaires used in the DHS surveys in Ghana and Burundi. A two-day seminar was held in Nyeri in November 1987 to develop the questionnaire design. Participants included representatives from the Central Bureau of Statistics (CBS), the Population Studies Research Institute at the University of Nairobi, the Community Health Department of Kenyatta Hospital, and USAID. The decision to include a survey of husbands was based on the recommendation of the seminar participants. The questionnaires were subsequently translated into eight local languages (Kalenjin, Kamba, Kikuyu, Kisii, Luhya, Luo, Meru and Mijikenda), in addition to Kiswahili.

Cleaning operations

Data

Although vaccination is a cost-effective, equitable, and impactful public health intervention in curbing the spread of infectious disease, low uptake is a significant concern, especially among high-risk population groups. Nearly half of the population is unvaccinated in Nairobi, yet there is a shortage of vaccination information on vulnerable tuberculosis (TB) patients. The interplay of factors influences uptake, and protecting this vulnerable group and the general population from severe disease, hospitalization, and deaths is worthy. The purpose of this study is to determine the prevalence and individual-level enablers and barriers to COVID-19 vaccine uptake among adult TB patients attending selected clinics in Nairobi County, Kenya. This cross-sectional mixed-method study was conducted at TB clinics across six sub-counties in Nairobi County. It included 388 participants sampled from each clinic’s TB register. Quantitative data was collected using a questionnaire, and qualitative data ..., Design and setting The study was an analytical cross-sectional study based on a mixed method (quantitative and qualitative) approach. Research assistants started with collecting quantitative data and later assisted investigators in conducting the interviews. The study was conducted at six sites in Nairobi County, Kenya. Sample and sampling The study targeted adult TB patients receiving treatment between certain months in 2023, EPI logisticians, and TB coordinators. Those eligible consented participants were helped to fill out the electronic questionnaires and focus group discussions (FGD) with different participants later. The EPI logisticians and TB coordinators were the main targets for key informant interviews. The study excluded newly diagnosed TB patients or those initiated on TB treatment on the day of data collection, patients who refused or were unable to state their vaccination status, and patients who were too unwell to participate. The Cochran formula was used to calculate t..., , # Prevalence and individual level enablers and barriers for COVID-19 vaccine uptake among adult tuberculosis patients attending selected clinics in Nairobi County, Kenya

https://doi.org/10.5061/dryad.zcrjdfnms

The main parts of the data

• General information including Questionnaire number, date of interview, name of interviewer, facility name, sub-county name, facility level, and facility type
• Socio-demographic factors like gender, employment status, and level of education
• Vaccination detail (heard of vaccine, trusted source, tested for COVID-19, hospitalized and received any dose, how many doses, type of vaccine, and status of vaccination)
• Individual-level factors and uptake of covid-19 vaccine (fear of side effects, risk perception, protection of others, etc.)
• Health facility level factors and uptake of COVID-19 vaccination (supply chain, accessibility, way government handled control measures)

| Data ...

Abstract

The 1998 Kenya Demographic and Health Survey (KDHS) is a nationally representative survey of 7,881 wo 881 women age 15-49 and 3,407 men age 15-54. The KDHS was implemented by the National Council for Population and Development (NCPD) and the Central Bureau of Statistics (CBS), with significant technical and logistical support provided by the Ministry of Health and various other governmental and nongovernmental organizations in Kenya. Macro International Inc. of Calverton, Maryland (U.S.A.) provided technical assistance throughout the course of the project in the context of the worldwide Demographic and Health Surveys (DHS) programme, while financial assistance was provided by the U.S. Agency for International Development (USAID/Nairobi) and the Department for International Development (DFID/U.K.). Data collection for the KDHS was conducted from February to July 1998. Like the previous KDHS surveys conducted in 1989 and 1993, the 1998 KDHS was designed to provide information on levels and trends in fertility, family planning knowledge and use, infant and child mortality, and other maternal and child health indicators. However, the 1998 KDHS went further to collect more in-depth data on knowledge and behaviours related to AIDS and other sexually transmitted diseases (STDs), detailed “calendar” data that allows estimation of contraceptive discontinuation rates, and information related to the practice of female circumcision. Further, unlike earlier surveys, the 1998 KDHS provides a national estimate of the level of maternal mortality (i.e. related to pregnancy and childbearing).The KDHS data are intended for use by programme managers and policymakers to evaluate and improve health and family planning programmes in Kenya. Fertility. The survey results demonstrate a continuation of the fertility transition in Kenya. At current fertility levels, a Kenyan women will bear 4.7 children in her life, down 30 percent from the 1989 KDHS when the total fertility rate (TFR) was 6.7 children, and 42 percent since the 1977/78 Kenya Fertility Survey (KFS) when the TFR was 8.1 children per woman. A rural woman can expect to have 5.2 children, around two children more than an urban women (3.1 children). Fertility differentials by women's education level are even more remarkable; women with no education will bear an average of 5.8 children, compared to 3.5 children for women with secondary school education. Marriage. The age at which women and men first marry has risen slowly over the past 20 years. Currently, women marry for the first time at an average age of 20 years, compared with 25 years for men. Women with a secondary education marry five years later (22) than women with no education (17).The KDHS data indicate that the practice of polygyny continues to decline in Kenya. Sixteen percent of currently married women are in a polygynous union (i.e., their husband has at least one other wife), compared with 19 percent of women in the 1993 KDHS, 23 percent in the 1989 KDHS, and 30 percent in the 1977/78 KFS. While men first marry an average of 5 years later than women, men become sexual active about onehalf of a year earlier than women; in the youngest age cohort for which estimates are available (age 20-24), first sex occurs at age 16.8 for women and 16.2 for men. Fertility Preferences. Fifty-three percent of women and 46 percent of men in Kenya do not want to have any more children. Another 25 percent of women and 27 percent of men would like to delay their next child for two years or longer. Thus, about three-quarters of women and men either want to limit or to space their births. The survey results show that, of all births in the last three years, 1 in 10 was unwanted and 1 in 3 was mistimed. If all unwanted births were avoided, the fertility rate in Kenya would fall from 4.7 to 3.5 children per woman. Family Planning. Knowledge and use of family planning in Kenya has continued to rise over the last several years. The 1998 KDHS shows that virtually all married women (98 percent) and men (99 percent) were able to cite at least one modern method of contraception. The pill, condoms, injectables, and female sterlisation are the most widely known methods. Overall, 39 percent of currently married women are using a method of contraception. Use of modern methods has increased from 27 in the 1993 KDHS to 32 percent in the 1998 KDHS. Currently, the most widely used methods are contraceptive injectables (12 percent of married women), the pill (9 percent), female sterilisation (6 percent), and periodic abstinence (6 percent). Three percent of married women are using the IUD, while over 1 percent report using the condom and 1 percent use of contraceptive implants (Norplant). The rapid increase in use of injectables (from 7 to 12 percent between 1993 and 1998) to become the predominant method, plus small rises in the use of implants, condoms and female sterilisation have more than offset small decreases in pill and IUD use. Thus, both new acceptance of contraception and method switching have characterised the 1993-1998 intersurvey period. Contraceptive use varies widely among geographic and socioeconomic subgroups. More than half of currently married women in Central Province (61 percent) and Nairobi Province (56 percent) are currently using a method, compared with 28 percent in Nyanza Province and 22 percent in Coast Province. Just 23 percent of women with no education use contraception versus 57 percent of women with at least some secondary education. Government facilities provide contraceptives to 58 percent of users, while 33 percent are supplied by private medical sources, 5 percent through other private sources, and 3 percent through community-based distribution (CBD) agents. This represents a significant shift in sourcing away from public outlets, a decline from 68 percent estimated in the 1993 KDHS. While the government continues to provide about two-thirds of IUD insertions and female sterilisations, the percentage of pills and injectables supplied out of government facilities has dropped from over 70 percent in 1993 to 53 percent for pills and 64 percent for injectables in 1998. Supply of condoms through public sector facilities has also declined: from 37 to 21 percent between 1993 and 1998. The survey results indicate that 24 percent of married women have an unmet need for family planning (either for spacing or limiting births). This group comprises married women who are not using a method of family planning but either want to wait two year or more for their next birth (14 percent) or do not want any more children (10 percent). While encouraging that unmet need at the national level has declined (from 34 to 24 percent) since 1993, there are parts of the country where the need for contraception remains high. For example, the level of unmet need is higher in Western Province (32 percent) and Coast Province (30 province) than elsewhere in Kenya. Early Childhood Mortality. One of the main objectives of the KDHS was to document current levels and trends in mortality among children under age 5. Results from the 1998 KDHS data make clear that childhood mortality conditions have worsened in the early-mid 1990s; this after a period of steadily improving child survival prospects through the mid-to-late 1980s. Under-five mortality, the probability of dying before the fifth birthday, stands at 112 deaths per 1000 live births which represents a 24 percent increase over the last decade. Survival chances during age 1-4 years suffered disproportionately: rising 38 percent over the same period. Survey results show that childhood mortality is especially high when associated with two factors: a short preceding birth interval and a low level of maternal education. The risk of dying in the first year of life is more than doubled when the child is born after an interval of less than 24 months. Children of women with no education experience an under-five mortality rate that is two times higher than children of women who attended secondary school or higher. Provincial differentials in childhood mortality are striking; under-five mortality ranges from a low of 34 deaths per 1000 live births in Central Province to a high of 199 per 1000 in Nyanza Province. Maternal Health. Utilisation of antenatal services is high in Kenya; in the three years before the survey, mothers received antenatal care for 92 percent of births (Note: These data do not speak to the quality of those antenatal services). The median number of antenatal visits per pregnancy was 3.7. Most antenatal care is provided by nurses and trained midwives (64 percent), but the percentage provided by doctors (28 percent) has risen in recent years. Still, over one-third of women who do receive care, start during the third trimester of pregnancy-too late to receive the optimum benefits of antenatal care. Mothers reported receiving at least one tetanus toxoid injection during pregnancy for 90 percent of births in the three years before the survey. Tetanus toxoid is a powerful weapon in the fight against neonatal tetanus, a deadly disease that attacks young infants. Forty-two percent of births take place in health facilities; however, this figure varies from around three-quarters of births in Nairobi to around one-quarter of births in Western Province. It is important for the health of both the mother and child that trained medical personnel are available in cases of prolonged labour or obstructed delivery, which are major causes of maternal morbidity and mortality. The 1998 KDHS collected information that allows estimation of mortality related to pregnancy and childbearing. For the 10-year period before the survey, the maternal mortality ratio was estimated to be 590 deaths per 100,000 live births. Bearing on average 4.7 children, a Kenyan woman has a 1 in 36 chance of dying from maternal causes during her lifetime. Childhood Immunisation. The KDHS

Abstract

Abstract Background: Both hypertension (HTN) and diabetes represent two major risk factors for atherosclerotic cardiovascular diseases (CVD), the number one cause of death globally1 . Despite the clear evidence that lowering blood pressure (BP) and blood sugar through lifestyle changes and drug treatment can greatly reduce the risk of CVD), HTN-control (defined as the proportion of people who reach their target for BP lowering) and diabetes control (defined as HbA1c<7%) is still poor. Objective: aim to develop, implement and evaluate a model that improves BP and blood sugar control for patients and streamlines and partly finances the provision of hypertension and diabetes care for healthcare providers while potentially reducing cost and improving access to quality care Methodology: The core of the model is the interaction between M-TIBA (a mobile health platform for financial inclusion in healthcare that enables people to save, send, receive and pay money for medical treatment through a mobile health wallet on their phone) and mobile phone application (Afya-pap) for patients and a patient tracker for healthcare providers, which will improve access and allow for home-based measuring and monitoring of BP and blood sugar in both low and middle socio economic status populations in Nairobi, Kenya. The project will test the acceptability of the mobile application and feasibility of integrating the mHealth model into clinical care, including the effectiveness in controlling BP and blood sugar respectively when patients receive regular text messages encouraging them to take measurments at home or using a mini-tracker calendar. For the text messaging behavioural intervention patients will be randomized to reminders on blood pressure measurements in three arms (daily, weekly and no messages), and two arms for blood sugar measurments (weekly and no messages). Three surveys (baseline, midline and endline-6 months apart) will be conducted to estimate the proportion of patients with blood pressure and blood sugar controlled. The mini-tracker behavioural intervention will be conducted among patients and evaluated through one-group prepost design. Cost-effectiveness of the intervention will also be evaluated. Duration and budget: The project will be undertaken for 2 years. Interviews will be conducted with healthcare providers and the patients. The total budget will be 151,688 USD Conclusion: The outcomes of this project will inform the integration of the mHealth based service model into the daily routine of the participating clinics. This generation of real-time clinical data, will ensure easy translation into clinical practice, and will facilitate rapid scale-up.

Geographic coverage

National ( Nairobi low and middle income)

Analysis unit

-Hypertension and Diabetes patients -Doctors - Phrmacy staff

Universe

Patients of Hypertension and Diabetes in low and middle socio economic parts of Nairobi

Sampling procedure

This will be a prospective cohort study with a pre-post design. It will be divided into two phases: (i) Pilot phase This will involve recruitment of patients on Afyapap and each patient followed for four months to assess the acceptability of and feasibility of using the service module and home based management of hypertension or diabetes. Within the cohort, we aim to test patient retention in care as primary outcome. However, secondary outcomes include the optimal frequency of home blood-pressure measurements and uptake of the behavioral intervention and patient satisfaction with the use of the applications. This pilot group will receive BP devices plus behavior intervention and measure their BP daily (to be recruited in October 2018) and followed for four months. Depending on the lessons learnt on the feasibility of scaling up, this group could be followed for one year. The lessons learnt from the pilotwill provide insights into the scale up phase. The patients will be screened for eligibility (the inclusion and exclusion criteria is outlined above). The reasons that patients are not eligible will be recorded. If patients are eligible, they will be asked to participate in the study. If patients are eligible and unwilling to participate in the study, they will be asked to fill out a short quantitative questionnaire describing their reasons for not participating, they will however not be forced to fill in the questionnaire. In the pilot phase, 500 eligible patients with hypertension or diabetes will be enrolled consecutively on to AFya pap and will be asked to monitor their BP daily or fasting blood sugar weekly if they have diabetes. This arm also contains a behavioral intervention in the form of reminders to measure their BP prior to each prescribed measurement and is treated as a pilot arm to examine the feasibility of the study procedures in the first 4 months. 11 We additionally propose to pilot a behavioural intervention that entails using a mini-tracker by patients enrolled on Afyapap. The mini-tracker is a card including a small calendar, instructions on how to enter measurements into Afya Pap in the USSD platform, and a goal chart where patients can indicate their BP or blood glucose goal for the end of the month. The calendar will display one month’s worth of days and times for which patients can indicate their measurement results after each reading. Every month new cards will be issued through patient group leaders while cards of their previous months are returned. Patients will also receive various reminders to measure, based on their chosen day and time of measuring during the onboarding phase. To determine the most effective source of messages on measurement adherence, we will provide reminders coming from their preferred clinic, their patient group leader, their chosen physician, or a family member of their choice. These more personalized messages serve to increase the patient’s motivation to continue monitoring their blood pressure and/or blood sugar levels and to engage with the Afya pap platform. Patients will also be assigned a patient group based on their geographic location. These groups exist to provide support to each patient through group discussions of chronic disease management, practice and information on measuring their BP or blood sugar, and social support through other patient members dealing with either HTN or Diabetes. We anticipate including approximately 1,000 Afya Pap users who are not receiving the SMS intervention and each will be followed for 6 months to test these iterations of the tracker. Based on previous research with providing calendars to help new or expectant mothers save for health insurance, we postulate that the tracker will provide a similar function for Afya Pap users and can be accomplished at a reduced cost compared to providing full annual calendars. (ii) Scale up phase After the learning from the pilot phase of 4 months, the subsequent enrolments will be randomized to two armsin the ratio of 1:1. Thepatients will be randomly assigned to 2 groups defined by frequency of measuring BP or blood sugar and the presence or absence of behavioral interventions. The study design is outlined in figures 2 and 3. In arm A, patients with hypertension or diabetes will be asked to monitor their BP or blood sugar weekly using a BP device or glucometer provided to them by the study team and will enter the measurements on afyapap. Patients with diabetes using smartphones will be provided with glucometers that are connected by a device that automatically relays blood sugar readings to the smartphone. Those with non-smartphones (feature phones) will use a USSD code to activate Afyapap and enter blood sugar mearurements manually. Arm A has behavioral intervention in the form of weekly SMS remiders to increase adherence to BP or blood glucose measurements. In arm B devices will also be provided for measurement of BP and blood sugar at home, however the particpants will choose when to take the measurements. There will be no SMS reminders to the participants. 12 Study population The study population will be lower income and middle income adult patients with HTN or diabetes or both, recruited from selected health facilities: divided into the two arms of the intervention. The patients will be selected based on the inclusion and exclusion criteria outlined before. Description of the steps in enrolment and follow up. Following randomization to one of the two study arms, day 0 of the study starts with a baseline study assessment and onboarding of the patient on M-TIBA and in the self-management application. Healthcare providers or a study nurse will assist patients with installing the application on their smart phone, and instruct them in its use. During the patient set-up in the application, patients will complete a personal profile that allows for tailoring of the behavioral intervention. Patients will then receive training in the use of a home based BP or blood sugar monitoring device. Patients will be instructed to perform weekly BP measurements (or more, if national guidelines prescribe more frequent monitoring). Once at home and using the BP monitor, patients will enter their measurement results in the application. We will set up protocols for extreme BP and blood sugar values based on best practice and national guidelines. Study setting The study will take place in health care clinics servicing the low and middle- income populations in Nairobi, Kiambu and Vihiga Counties in Kenya. The choice of this population is based the challenges of affording care and inability to enrol in private health insurance. But the patient population included will be those patients who have some means to afford HTN or diabetes treatment

Kenya recorded a crude birth rate of 27.9 births per 1,000 population in 2019. The estimated number of live births varied among Kenyan counties. Makueni recorded the lowest rate: 19.8 births per 1,000 population, against 49.4 births per 1,000 population in Mandera.

Abstract

The 2014 Kenya Demographic and Health Survey (KDHS) provides information to help monitor and evaluate population and health status in Kenya. The survey, which follows up KDHS surveys conducted in 1989, 1993, 1998, 2003, and 2008-09, is of special importance for several reasons. New indicators not collected in previous KDHS surveys, such as noncommunicable diseases, fistula, and men's experience of domestic violence, are included. Also, it is the first national survey to provide estimates for demographic and health indicators at the county level. Following adoption of a constitution in Kenya in 2010 and devolution of administrative powers to the counties, the new 2014 KDHS data should be valuable to managers and planners. The 2014 KDHS has specifically collected data to estimate fertility, to assess childhood, maternal, and adult mortality, to measure changes in fertility and contraceptive prevalence, to examine basic indicators of maternal and child health, to estimate nutritional status of women and children, to describe patterns of knowledge and behaviour related to the transmission of HIV and other sexually transmitted infections, and to ascertain the extent and pattern of domestic violence and female genital cutting. Unlike the 2003 and 2008-09 KDHS surveys, this survey did not include HIV and AIDS testing. HIV prevalence estimates are available from the 2012 Kenya AIDS Indicator Survey (KAIS), completed prior to the 2014 KDHS. Results from the 2014 KDHS show a continued decline in the total fertility rate (TFR). Fertility decreased from 4.9 births per woman in 2003 to 4.6 in 2008-09 and further to 3.9 in 2014, a one-child decline over the past 10 years and the lowest TFR ever recorded in Kenya. This is corroborated by the marked increase in the contraceptive prevalence rate (CPR) from 46 percent in 2008-09 to 58 percent in the current survey. The decline in fertility accompanies a marked decline in infant and child mortality. All early childhood mortality rates have declined between the 2003 and 2014 KDHS surveys. Total under-5 mortality declined from 115 deaths per 1,000 live births in the 2003 KDHS to 52 deaths per 1,000 live births in the 2014 KDHS. The maternal mortality ratio is 362 maternal deaths per 100,000 live births for the seven-year period preceding the survey; however, this is not statistically different from the ratios reported in the 2003 and 2008-09 KDHS surveys and does not indicate any decline over time. The proportion of mothers who reported receiving antenatal care from a skilled health provider increased from 88 percent to 96 percent between 2003 and 2014. The percentage of births attended by a skilled provider and the percentage of births occurring in health facilities each increased by about 20 percentage points between 2003 and 2014. The percentage of children age 12-23 months who have received all basic vaccines increased slightly from the 77 percent observed in the 2008-09 KDHS to 79 percent in 2014. Six in ten households (59 percent) own at least one insecticide-treated net, and 48 percent of Kenyans have access to one. In malaria endemic areas, 39 percent of women received the recommended dosage of intermittent preventive treatment for malaria during pregnancy. Awareness of AIDS is universal in Kenya; however, only 56 percent of women and 66 percent of men have comprehensive knowledge about HIV and AIDS prevention and transmission. The 2014 KDHS was conducted as a joint effort by many organisations. The Kenya National Bureau of Statistics (KNBS) served as the implementing agency by providing guidance in the overall survey planning, development of survey tools, training of personnel, data collection, processing, analysis, and dissemination of the results. The Bureau would like to acknowledge and appreciate the institutions and agencies for roles they played that resulted in the success of this exercise: Ministry of Health (MOH), National AIDS Control Council (NACC), National Council for Population and Development (NCPD), Kenya Medical Research Institute (KEMRI), Ministry of Labour, Social Security and Services, United States Agency for International Development (USAID/Kenya), ICF International, United Nations Fund for Population Activities (UNFPA), the United Kingdom Department for International Development (DfID), World Bank, Danish International Development Agency (DANIDA), United Nations Children's Fund (UNICEF), German Development Bank (KfW), World Food Programme (WFP), Clinton Health Access Initiative (CHAI), Micronutrient Initiative (MI), US Centers for Disease Control and Prevention (CDC), Japan International Cooperation Agency (JICA), Joint United Nations Programme on HIV/AIDS (UNAIDS), and the World Health Organization (WHO). The management of such a huge undertaking was made possible through the help of a signed memorandum of understanding (MoU) by all the partners and the creation of active Steering and Technical Committees.

Geographic coverage

County, Urban, Rural and National

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for the 2014 KDHS was drawn from a master sampling frame, the Fifth National Sample Survey and Evaluation Programme (NASSEP V). This is a frame that the KNBS currently operates to conduct household-based surveys throughout Kenya. Development of the frame began in 2012, and it contains a total of 5,360 clusters split into four equal subsamples. These clusters were drawn with a stratified probability proportional to size sampling methodology from 96,251 enumeration areas (EAs) in the 2009 Kenya Population and Housing Census. The 2014 KDHS used two subsamples of the NASSEP V frame that were developed in 2013. Approximately half of the clusters in these two subsamples were updated between November 2013 and September 2014. Kenya is divided into 47 counties that serve as devolved units of administration, created in the new constitution of 2010. During the development of the NASSEP V, each of the 47 counties was stratified into urban and rural strata; since Nairobi county and Mombasa county have only urban areas, the resulting total was 92 sampling strata. The 2014 KDHS was designed to produce representative estimates for most of the survey indicators at the national level, for urban and rural areas separately, at the regional (former provincial1) level, and for selected indicators at the county level. In order to meet these objectives, the sample was designed to have 40,300 households from 1,612 clusters spread across the country, with 995 clusters in rural areas and 617 in urban areas. Samples were selected independently in each sampling stratum, using a two-stage sample design. In the first stage, the 1,612 EAs were selected with equal probability from the NASSEP V frame. The households from listing operations served as the sampling frame for the second stage of selection, in which 25 households were selected from each cluster. The interviewers visited only the preselected households, and no replacement of the preselected households was allowed during data collection. The Household Questionnaire and the Woman's Questionnaire were administered in all households, while the Man's Questionnaire was administered in every second household. Because of the non-proportional allocation to the sampling strata and the fixed sample size per cluster, the survey was not self-weighting. The resulting data have, therefore, been weighted to be representative at the national, regional, and county levels.

Sampling deviation

Not available

Mode of data collection

Face-to-face [f2f]

Research instrument

The 2014 KDHS used a household questionnaire, a questionnaire for women age 15-49, and a questionnaire for men age 15-54. These instruments were based on the model questionnaires developed for The DHS Program, the questionnaires used in the previous KDHS surveys, and the current information needs of Kenya. During the development of the questionnaires, input was sought from a variety of organisations that are expected to use the resulting data. A two-day workshop involving key stakeholders was held to discuss the questionnaire design. Producing county-level estimates requires collecting data from a large number of households within each county, resulting in a considerable increase in the sample size from 9,936 households in the 2008-09 KDHS to 40,300 households in 2014. A survey of this magnitude introduces concerns related to data quality and overall management. To address these concerns, reduce the length of fieldwork, and limit interviewer and respondent fatigue, a decision was made to not implement the full questionnaire in every household and, in so doing, to collect only priority indicators at the county level. Stakeholders generated a list of these priority indicators. Short household and woman's questionnaires were then designed based on the full questionnaires; the short questionnaires contain the subset of questions from the full questionnaires required to measure the priority indicators at the county level. Thus, a total of five questionnaires were used in the 2014 KDHS: (1) a full Household Questionnaire, (2) a short Household Questionnaire, (3) a full Woman's Questionnaire, (4) a short Woman's Questionnaire, and (5) a Man's Questionnaire. The 2014 KDHS sample was divided into halves. In one half, households were administered the full Household Questionnaire, the full Woman's Questionnaire, and the Man's Questionnaire. In the other half, households were administered the short Household Questionnaire and the short Woman's Questionnaire. Selection of these subsamples was done at the household level-within a cluster, one in every two

The share of urban population in Kenya increased by 0.5 percentage points (+1.72 percent) in 2023 in comparison to the previous year. With 29.52 percent, the share thereby reached its highest value in the observed period. Notably, the share continuously increased over the last years.The urban population refers to the share of the total population living in urban centers. Each country has their own definition of what constitutes an urban center (based on population size, area, or space between dwellings, among others), therefore international comparisons may be inconsistent.Find more key insights for the share of urban population in countries like Zambia and Madagascar.

Key information about Kenya Monthly Earnings

• Kenya Monthly Earnings stood at 590 USD in Jun 2023, compared with the previous figure of 639 USD in Jun 2022
• Kenya Monthly Earnings data is updated yearly, available from Jun 1997 to Jun 2023, with an average number of 420 USD
• The data reached the an all-time high of 651 USD in Jun 2020 and a record low of 157 USD in Jun 1997

CEIC calculates Monthly Earnings from annual Average Wage Earnings divided by 12 and converts it into USD. The Kenya National Bureau of Statistics provides Average Wage Earnings in local currency. The Central Bank of Kenya average market exchange rate is used for currency conversions. Monthly Earnings are in annual frequency, ending in June of each year. Monthly Earnings prior to 2008 are based on ISIC Rev. 2.


Further information about Kenya Monthly Earnings

• In the latest reports, Kenya Population reached 49 million people in Dec 2021
• Unemployment Rate of Kenya increased to 3 % in Dec 2020
• The country's Labour Force Participation Rate increased to 74 % in Dec 2023

Abstract

This report documents demographic characteristics and health conditions of Nairobi City's slum residents based on a representative sample survey of urban informal settlement residents carried out from February to June 2000. The aims of the "Nairobi Cross-sectional Slums Survey (NCSS)" were to determine the magnitude of the general and health problems facing slum residents, and to compare the demographic and health profiles of slum residents to those of residents of other urban and rural areas as depicted in the 1998 Kenya Demographic and Health Survey (KDHS). The NCSS is probably the first comprehensive survey explicitly designed to provide demographic and health indicators for sub-Saharan city slum residents.

Geographic coverage

Informal settlements in Nairobi county, Kenya: Central, Makadara, Kasarani, Embakasi, Pumwani, Westlands, Dagoretti and Kibera

Analysis unit

Individuals and Households

Universe

The survey covered all women aged 15-49 years and adolescent boys and girls aged 12-24 years resident in the househol

Sampling procedure

Based on census enumeration areas used in the 1999 Kenya National Census, a weighted cross-sectional sample was designed that is representative of households in all slum clusters of Nairobi. A two-stage stratified sample design was used. Sample points or enumeration areas (EAs) were selected at the first stage of sampling while households were selected from sampled EAs at the second stage. To generate a sampling frame, the NCSS used all the household listings for Nairobi province from the 1999 census. This listing contains the name of the division, location, sub-location, enumeration area as well as structure number, structure owner, number of dwelling units and use of structure (dwelling, business, dwelling/business). Processing of listing forms and identification of slum EAs were conducted in close collaboration with Central Bureau of Statistics (CBS) staff from both the headquarters and the different locations throughout Nairobi.

Before processing the data to generate a sampling frame, two important activities were undertaken. First, two of the EAs were selected and CBS maps were used to identify structures that were indicated and the name of the structure owner, and to assess the number of dwelling units in the structure. The objective of this exercise was to determine if field teams would be able to find selected structures and dwelling units using the CBS enumeration lists. The second activity sought to validate the completeness of the sampling frame. In this second activity, a random sample of one percent of the slum EAs were selected and a fresh listing of structures and dwelling units in each was conducted. A comparison of these structures and dwelling units with the original listing provided by the CBS showed a difference of only 0.7 percent.

Once the sampling frame was validated for completeness, a database of structures was generated from the listing forms and then expanded using the numbers of dwelling units in a given structure to create a sampling frame based on dwelling units. The frame consisted of 31 locations, with at least one slum enumeration area (EA), 48 sub-locations, 1,364 EAs, 29,895 structures, and 250,620 dwelling units.

The first stage of the sampling procedure yielded 98 EAs, while the second stage produced 5463 households. Since dwelling units were neither numbered nor was information collected on household headship during the listing exercise, a method was devised for identifying selected dwelling units within structures. After identifying the right structure (using the map, the name of the owner, the number of dwelling units, and any other physical landmarks noted on the map), fieldworkers identified the selected dwelling unit by first identifying all dwelling units and then counting from the left until they reach the selected number. A dwelling unit generally refers to one or more rooms occupied by the same household within one structure. Although this often corresponds to a room, a household may reside in more than one room. Interviewers were instructed to identify households occupying more than one room and then to count these as one dwelling unit before numbering and identifying the selected dwelling unit.

In each selected dwelling unit, a household questionnaire schedule was completed to identify household members and visitors who would be eligible for individual interviews. All female household members and visitors who slept in the house the previous night and are aged 12 to 49 years were eligible for individual female interviews while all male members and visitors aged 12 to 24 years old were eligible for male interviews. A full census of all sampled households was also carried out. In total, the NCSS administered interviews to 4564 households, 3256 women of reproductive age (15-49), and 1683 adolescent boys (Table 1.2). The 1,934 adolecent girls (whose results are compared with those for boys) comprise 316 aged 12-14 and 1,1618 aged 15-24. Details of the sample design are given in Appendix A.

• The household response rate is computed as the number of completed household interviews divided by the number of eligible households. For the NCSS, 90% of the sampled households (4856) were eligible (i.e. sampled households minus households that were vacant, destroyed, and where all members were absent).

Sampling deviation

None

Mode of data collection

Face-to-face [f2f]

Research instrument

The NCSS instruments were modified from KDHS instruments. Core sections of the 1998 KDHS were replicated without revision, but the service delivery exposure questions were modified so that questions were more relevant to the urban context. The similarity with the DHS questionnaires permitted direct comparison to national, urban, rural, and Nairobi-city results derived from the 1998 KDHS. The fact that the NCSS was carried out less than two years following the DHS ensured that findings were timely enough for useful comparison.

Three instruments were used in this survey: The first one was the household schedule, which enabled us to conduct a full household census from which all eligible respondents were identified. This instrument solicited information on background characteristics of households. The second instrument was for individual women age 12-49, and it had modules on their background and mobility, reproduction, contraception, pregnancy, ante-natal and post-natal care, child immunization and health, marriage, fertility preferences, husband's background and the woman's work and livelihood activities. Information on AIDS and other sexually transmitted infections was also sought, as was information on general and health matters.

The third instrument was the adolescent questionnaire for young women and men age 12-24. The adolescent questionnaire was designed to investigate health, livelihood, and social issues pertaining to adolescents in the slum communities.

NB: All questionnaires and modules are provided as external resources.

Cleaning operations

A total of 49 interviewers (37 women and 12 men), 3 office editors and 4 data-entry clerks were trained for two weeks, from February 17 through March 3, 2000. On the last day of training, the instruments were pre-tested and revised before finalizing them for fieldwork. Fieldwork started on March 5, 2000 and ended on June 4, 2000. Fieldworkers were sent to the field in six teams -each with at least one male interviewer, three or four female interviewers, one supervisor, and a field editor. Three trainees were retained as office editors to edit all questionnaires coming from the field before the questionnaires were sent for data entry.

Response rate

Households : 94.0%

Women (15-49) : 97.0%

Adolescents Girls (12-24): 88.1%

Adolescents Boys (12-24): 91.3%

Abstract

The Verbal Autopsy Form is one of the forms administered in the Nairobi Urban Health and Demographic Surveillance System. It was introduced in the first round in 2002 and is ongoing. It is designed to establish probable cause of death using methodologies developed through the International Network of field sites with continuous Demographic Evaluation of Populations and Their Health (INDEPTH Network). Information on circumstances and/or events surrounding deaths among all deceased within the NUHDSS are collected every 4 months. The data contain both symptom level data as the well as the actual cause of death codes. APHRC employs physicians to independently review the symptom level data contained in the completed verbal autopsy forms and generate probable cause of death codes from an abridged ICD-10 list.

Geographic coverage

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

Analysis unit

The unit of analysis is the deceased individual

Universe

All NUHDSS residents that are deceased.

Sampling procedure

The routine verbal autopsy questionnaires collect information on all deceased who were de jure household members (usual residents) in the geographic coverage area.

Mode of data collection

Face-to-face [f2f]

Research instrument

1. Rounds 1 - 7: The questionnaire used was one structured questionnaire, Verbal Autopsy Form. It included: Background Information, Respondent Particulars, Office/Field Check Details, Open History, Neonatal, Post-Neonatal, and Under-12 Deaths, Adolescent/Adult Deaths, Treatment and Records

2. Rounds 8+: There were two questionnaires that were used. Verbal Autopsy Form for People 5 Years and Older, which included: Background Information, Respondent Particulars, Open History, All Deaths, Pregnancy Related Deaths, Treatment and Records, Office/Field Check Details. Verbal Autopsy Form for Children Under-Five Years, which included: Background Information, Respondent Particulars, Open History, Birth and Death Circumstances for all Deaths Under 1 Year, Deaths at Age Less than 28 Days Old, and Deaths at Age Between 28 days and 5 Years, Treatment and Records, Office/Field Check Details.

All questionnaires are provided as external resources.

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.

In 2023, the national gross income per capita in Kenya decreased by 60 U.S. dollars (-2.76 percent) compared to 2022. Nevertheless, the last two years recorded a significantly higher national gross income than the preceding years.Gross national income (GNI) per capita is the total value of money received by a country, from both domestic or foreign sources, divided by the midyear population. The World Bank uses a conversion system known as the Atlas method, which implements a price adjusted, three year moving average, smoothing out fluctuations in exchange rates.Find more key insights for the national gross income per capita in countries like Tanzania and Mozambique.

Abstract

This research of registered businesses with one to four employees was conducted in Kenya between April 2013 and January 2014, at the same time with Kenya Enterprise Survey 2013. Data from 360 establishments was analyzed. Stratified random sampling was used to select the surveyed businesses. The objective of the survey was to obtain feedback from enterprises on the state of the private sector and constraints to its growth.

Micro-Enterprise Survey topics include firm characteristics, gender participation, access to finance, annual sales, costs of inputs/labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, capacity utilization, land and permits, taxation, informality, business-government relations, innovation and technology, and performance measures. Over 90 percent of the questions objectively ascertain characteristics of a country's business environment. The remaining questions assess the survey respondents' opinions on what are the obstacles to firm growth and performance.

Geographic coverage

Central, Nyanza, Mombasa, Nairobi, and Nakuru regions

Analysis unit

The primary sampling unit of the study is an establishment. An establishment is a physical location where business is carried out and where industrial operations take place or services are provided. A firm may be composed of one or more establishments. For example, a brewery may have several bottling plants and several establishments for distribution. For the purposes of this survey an establishment must make its own financial decisions and have its own financial statements separate from those of the firm. An establishment must also have its own management and control over its payroll.

Universe

The whole population, or the universe, covered in the Enterprise Surveys is the non-agricultural private economy. It comprises: all manufacturing sectors according to the ISIC Revision 3.1 group classification (group D), construction sector (group F), services sector (groups G and H), and transport, storage, and communications sector (group I). Note that this population definition excludes the following sectors: financial intermediation (group J), real estate and renting activities (group K, except sub-sector 72, IT, which was added to the population under study), and all public or utilities sectors. Companies with 100% government ownership are not eligible to participate in the Enterprise Surveys.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for Ethiopia was selected using stratified random sampling. Two levels of stratification were used: firm sector and geographic region.

For industry stratification, the universe was divided into four manufacturing industries (food, textiles and garments, chemicals and plastics, other manufacturing) and two service sectors (retail and other services).

Regional stratification was defined in five regions: Central, Nyanza, Mombasa, Nairobi, and Nakuru.

2012 Census of Business Establishments of the Kenya National Bureau of Statistics was used as a sample frame for the survey of micro firms.

The enumerated establishments with less than five employees (micro establishments) were used as sample frame for the Kenya micro survey with the aim of obtaining interviews at 360 establishments.

The quality of the frame was assessed at the onset of the project through visits to a random subset of firms and local contractor knowledge. The sample frame was not immune from the typical problems found in establishment surveys: positive rates of non-eligibility, repetition, non-existent units, etc. In addition, the sample frame contains no telephone/fax numbers so the local contractor had to screen the contacts by visiting them.

Given the impact that non-eligible units included in the sample universe may have on the results, adjustments may be needed when computing the appropriate weights for individual observations. The percentage of confirmed non-eligible units as a proportion of the total number of sampled establishments contacted for the survey was 5.2% (39 out of 756) for micro firms.

Mode of data collection

Face-to-face [f2f]

Research instrument

The following survey instruments are available: - Manufacturing Module Questionnaire - Services Module Questionnaire

The survey is fielded via manufacturing or services questionnaires in order not to ask questions that are irrelevant to specific types of firms, e.g. a question that relates to production and nonproduction workers should not be asked of a retail firm. In addition to questions that are asked across countries, all surveys are customized and contain country-specific questions. An example of customization would be including tourism-related questions that are asked in certain countries when tourism is an existing or potential sector of economic growth.

There is a skip pattern in the Service Module Questionnaire for questions that apply only to retail firms.

Cleaning operations

Data entry and quality controls are implemented by the contractor and data is delivered to the World Bank in batches (typically 10%, 50% and 100%). These data deliveries are checked for logical consistency, out of range values, skip patterns, and duplicate entries. Problems are flagged by the World Bank and corrected by the implementing contractor through data checks, callbacks, and revisiting establishments.

Response rate

Survey non-response must be differentiated from item non-response. The former refers to refusals to participate in the survey altogether whereas the latter refers to the refusals to answer some specific questions. Enterprise Surveys suffer from both problems and different strategies were used to address these issues.

Item non-response was addressed by two strategies: a- For sensitive questions that may generate negative reactions from the respondent, such as corruption or tax evasion, enumerators were instructed to collect "Refusal to respond" (-8) as a different option from "Don't know" (-9). b- Establishments with incomplete information were re-contacted in order to complete this information, whenever necessary.

Survey non-response was addressed by maximizing efforts to contact establishments that were initially selected for interview. Attempts were made to contact the establishment for interview at different times, days of the week before a replacement establishment (with similar strata characteristics) was suggested for interview. Survey non-response did occur but substitutions were made in order to potentially achieve strata-specific goals.