The STAMINA study examined the nutritional risks of low-income peri-urban mothers, infants and young children (IYC), and households in Peru during the COVID-19 pandemic. The study was designed to capture information through three, repeated cross-sectional surveys at approximately 6 month intervals over an 18 month period, starting in December 2020. The surveys were carried out by telephone in November-December 2020, July-August 2021 and in February-April 2022. The third survey took place over a longer period to allow for a household visit after the telephone interview.The study areas were Manchay (Lima) and Huánuco district in the Andean highlands (~ 1900m above sea level).In each study area, we purposively selected the principal health centre and one subsidiary health centre. Peri-urban communities under the jurisdiction of these health centres were then selected to participate. Systematic random sampling was employed with quotas for IYC age (6-11, 12-17 and 18-23 months) to recruit a target sample size of 250 mother-infant pairs for each survey.Data collected included: household socio-demographic characteristics; infant and young child feeding practices (IYCF), child and maternal qualitative 24-hour dietary recalls/7 day food frequency questionnaires, household food insecurity experience measured using the validated Food Insecurity Experience Scale (FIES) survey module (Cafiero, Viviani, & Nord, 2018), and maternal mental health.In addition, questions that assessed the impact of COVID-19 on households including changes in employment status, adaptations to finance, sources of financial support, household food insecurity experience as well as access to, and uptake of, well-child clinics and vaccination health services were included.This folder includes the questionnaire for survey 3 in both English and Spanish languages.The corresponding dataset and dictionary of variables for survey 3 are available at 10.17028/rd.lboro.21741014

Cervical cancer screening rates in Haiti are concerningly low. Access to health-related information and health literacy may be important determinants of engagement in cervical cancer screening. This study explored the relationship between sociodemographics,literacy, and sources of information on cervical cancer screening among Haitian women. A secondary data analysis was conducted using USAID Demographics and Health Survey Haiti household data from 2016–2017. Univariate logistic regressions identified significant predictor covariates measuring sociodemographics and sources of information in cervical cancer screening uptake.Two multivariate logistic regression models with adjusted odds ratios were developed using the significant predictor variables from the univariate analysis. N = 610 women responded to questions pertaining to cervical cancer screening. The first multivariate model evaluating sociodemographics demonstrated an economic background of poorer (aOR = 4.06, 95% CI [1.16,14.27]) and richest (aOR = 19.10 , 95% CI[2.58,141.57]), higher education levels (aOR 7.58 , 95% CI [1.64,34.97]), and having insurance (aOR = 16.40, [95% CI 2.65, 101.42]) were significant predictors of cervical cancer screening. The second model evaluating literacy and sources of information indicated that access to a television (aOR = 4.28, 95% CI [1.21,9.34]), mobile phone ownership (aOR = 4.44, 95% CI [1.00,5.59]), and reading the newspaper (aOR = 3.57, [95% CI 1.10,11.59]) were significant predictors of cervical cancer screening. Diverse health communication initiatives that are adapted for literacy level and that incorporate multimedia components may effective in raising women’s cervical cancer knowledge and awareness , and increasing intention and uptake of cervical cancer screening in Haiti.

Socio demographic characteristics of children and their mother’s/care giver’s among IDP at Debark refugee camp, 2022.

Madagascar has experienced significant environmental change since 1960, particularly through forest clearing for agricultural expansion. Climatic patterns are undergoing change in Madagascar as well, with increasing temperatures, droughts, and cyclonic activity. The impact of these environmental and climatic changes will pose threats to food availability, income generation, and local ecosystems, with significant potential effects on the spatial and temporal distribution of disease burden. This study seeks to describe the health status of a large sample of geographically and socially diverse Malagasy communities through multiple clinical measurements, detailed social surveys, and paired data on regional variation in local ecologies. With an increased understanding of the current patterns of variation in human health and nutrition, future studies will be better able to identify associations with climate and anticipate and mitigate the burdens expected from larger, longer-term changes. Our mixed-method approach included an observational cross-sectional study. Research subjects were men, women, and children from 1,125 households evenly distributed across 24 communities in four ecologically and socio-demographically distinct regions of Madagascar. For these 1,125 households, all persons of both sexes and all ages therein (for a total of 6,292 individuals) were recruited into the research study and a total of 5,882 individuals were enrolled. Through repeated social survey recalls and focus group meetings, we obtained social and demographic data, including broad categories of seasonal movements, and characterized the fluctuation of income generation, food production and dietary consumption. Through collection of clinical and biological samples for both point-of-care diagnoses and laboratory analyses, we obtained detailed occurrence (and importantly co-occurrence) data on micronutrient nutritional, infectious disease, and non-communicable disease status. Our research highlights the highly variable social, cultural, and environmental contexts of health conditions in Madagascar, and the tremendous inter-regional, inter-community, and intra-community variation in nutritional and disease status. More than 30% of the surveyed population was afflicted by anemia and 14% of the population had a current malaria infection. This type of rich metadata associated with a suite of biological samples and nutritional and disease outcome data should allow disentangling some of the underlying drivers of ill health across the changing landscapes of Madagascar.

Abstract

The main objectives of the 2018/19 NLSS are: i) to provide critical information for production of a wide range of socio-economic and demographic indicators, including for benchmarking and monitoring of SDGs; ii) to monitor progress in population’s welfare; iii) to provide statistical evidence and measure the impact on households of current and anticipated government policies. In addition, the 2018/19 NLSS could be utilized to improve other non-survey statistical information, e.g. to determine and calibrate the contribution of final consumption expenditures of households to GDP; to update the weights and determine the basket for the national Consumer Price Index (CPI); to improve the methodology and dissemination of micro-economic and welfare statistics in Nigeria.

The 2018/19 NLSS collected a comprehensive and diverse set of socio-economic and demographic data pertaining to the basic needs and conditions under which households live on a day to day basis. The 2018/19 NLSS questionnaire includes wide-ranging modules, covering demographic indicators, education, health, labour, expenditures on food and non-food goods, non-farm enterprises, household assets and durables, access to safety nets, housing conditions, economic shocks, exposure to crime and farm production indicators.

Geographic coverage

National coverage

Analysis unit

• Households
• Individuals
• Communities

Universe

The survey covered all de jure households excluding prisons, hospitals, military barracks, and school dormitories.

Kind of data

Sample survey data [ssd]

Sampling procedure

The 2018/19 NLSS sample is designed to provide representative estimates for the 36 states and the Federal Capital Territory (FCT), Abuja. By extension. The sample is also representative at the national and zonal levels. Although the sample is not explicitly stratified by urban and rural areas, it is possible to obtain urban and rural estimates from the NLSS data at the national level. At all stages, the relative proportion of urban and rural EAs as has been maintained.

Before designing the sample for the 2018/19 NLSS, the results from the 2009/10 HNLSS were analysed to extract the sampling properties (variance, design effect, etc.) and estimate the required sample size to reach a desired precision for poverty estimates in the 2018/19 NLSS.

EA SELECTION: The sampling frame for the 2018/19 NLSS was based on the national master sample developed by the NBS, referred to as the NISH2 (Nigeria Integrated Survey of Households 2). This master sample was based on the enumeration areas (EAs) defined for the 2006 Nigeria Census Housing and Population conducted by National Population Commission (NPopC). The NISH2 was developed by the NBS to use as a frame for surveys with state-level domains. NISH2 EAs were drawn from another master sample that NBS developed for surveys with LGA-level domains (referred to as the “LGA master sample”). The NISH2 contains 200 EAs per state composed of 20 replicates of 10 sample EAs for each state, selected systematically from the full LGA master sample. Since the 2018/19 NLSS required domains at the state-level, the NISH2 served as the sampling frame for the survey.

Since the NISH2 is composed of state-level replicates of 10 sample EAs, a total of 6 replicates were selected from the NISH2 for each state to provide a total sample of 60 EAs per state. The 6 replicates selected for the 2018/19 NLSS in each state were selected using random systematic sampling. This sampling procedure provides a similar distribution of the sample EAs within each state as if one systematic sample of 60 EAs had been selected directly from the census frame of EAs.

A fresh listing of households was conducted in the EAs selected for the 2018/19 NLSS. Throughout the course of the listing, 139 of the selected EAs (or about 6%) were not able to be listed by the field teams. The primary reason the teams were not able to conduct the listing in these EAs was due to security issues in the country. The fieldwork period of the 2018/19 NLSS saw events related to the insurgency in the north east of the country, clashes between farmers and herdsman, and roving groups of bandits. These events made it impossible for the interviewers to visit the EAs in the villages and areas affected by these conflict events. In addition to security issues, some EAs had been demolished or abandoned since the 2006 census was conducted. In order to not compromise the sample size and thus the statistical power of the estimates, it was decided to replace these 139 EAs. Additional EAs from the same state and sector were randomly selected from the remaining NISH2 EAs to replace each EA that could not be listed by the field teams. This necessary exclusion of conflict affected areas implies that the sample is representative of areas of Nigeria that were accessible during the 2018/19 NLSS fieldwork period. The sample will not reflect conditions in areas that were undergoing conflict at that time. This compromise was necessary to ensure the safety of interviewers.

HOUSEHOLD SELECTION: Following the listing, the 10 households to be interviewed were selected from the listed households. These households were selected systemically after sorting by the order in which the households were listed. This systematic sampling helped to ensure that the selected households were well dispersed across the EA and thereby limit the potential for clustering of the selected households within an EA.

Occasionally, interviewers would encounter selected households that were not able to be interviewed (e.g. due to migration, refusal, etc.). In order to preserve the sample size and statistical power, households that could not be interviewed were replaced with an additional randomly selected household from the EA. Replacement households had to be requested by the field teams on a case-by-case basis and the replacement household was sent by the CAPI managers from NBS headquarters. Interviewers were required to submit a record for each household that was replaced, and justification given for their replacement. These replaced households are included in the disseminated data. However, replacements were relatively rare with only 2% of sampled households not able to be interviewed and replaced.

Sampling deviation

Although a sample was initially drawn for Borno state, the ongoing insurgency in the state presented severe challenges in conducting the survey there. The situation in the state made it impossible for the field teams to reach large areas of the state without compromising their safety. Given this limitation it was clear that a representative sample for Borno was not possible. However, it was decided to proceed with conducting the survey in areas that the teams could access in order to collect some information on the parts of the state that were accessible.

The limited area that field staff could safely operate in in Borno necessitated an alternative sample selection process from the other states. The EA selection occurred in several stages. Initially, an attempt was made to limit the frame to selected LGAs that were considered accessible. However, after selection of the EAs from the identified LGAs, it was reported by the NBS listing teams that a large share of the selected EAs were not safe for them to visit. Therefore, an alternative approach was adopted that would better ensure the safety of the field team but compromise further the representativeness of the sample. First, the list of 788 EAs in the LGA master sample for Borno were reviewed by NBS staff in Borno and the EAs they deemed accessible were identified. The team identified 359 EAs (46%) that were accessible. These 359 EAs served as the frame for the Borno sample and 60 EAs were randomly selected from this frame. However, throughout the course of the NLSS fieldwork, additional insurgency related events occurred which resulted in 7 of the 60 EAs being inaccessible when they were to be visited. Unlike for the main sample, these EAs were not replaced. Therefore, 53 EAs were ultimately covered from the Borno sample. The listing and household selection process that followed was the same as for the rest of the states.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Two sets of questionnaires – household and community – were used to collect information in the NLSS2018/19. The Household Questionnaire was administered to all households in the sample. The Community Questionnaire was administered to the community to collect information on the socio-economic indicators of the enumeration areas where the sample households reside.

Household Questionnaire: The Household Questionnaire provides information on demographics; education; health; labour; food and non-food expenditure; household nonfarm income-generating activities; food security and shocks; safety nets; housing conditions; assets; information and communication technology; agriculture and land tenure; and other sources of household income.

Community Questionnaire: The Community Questionnaire solicits information on access to transported and infrastructure; community organizations; resource management; changes in the community; key events; community needs, actions and achievements; and local retail price information.

Cleaning operations

CAPI: The 2018/19 NLSS was conducted using the Survey Solutions Computer Assisted Person Interview (CAPI) platform. The Survey Solutions software was developed and maintained by the Development Economics Data Group (DECDG) at the World Bank. Each interviewer and supervisor was given a tablet

Abstract (en): This data collection represents a loose collaboration between Georgetown University's Center for Democracy and Civil Society (CDACS) and the European Social Survey (ESS). The data in Part 1 are from the United States Citizenship, Involvement, Democracy (CID) Survey, which was conducted between mid-May and mid-July of 2005, and consists of in-person interviews with a representative sample of 1,001 Americans who responded to an 80-minute questionnaire. The CID survey is a study of American civic engagement, social capital, and democracy in comparative perspective, and it provides perspective on citizen participation in both the public and private realms. The CID survey is integrated with several elements of a module from the 2002 version of the ESS, which was administered in 22 European countries. In addition to the replicated questions from the ESS, the CID survey includes questions related to the themes of social capital, activities in formal clubs and organizations, informal social networks and activities, personal networks (strong and weak ties), the composition and diversity of ties and associations, trust (in other people, the community, institutions, and politicians), local democracy and participation, democratic values, political citizenship, social citizenship, views on immigration and diversity, political identification, ideology, mobilization and action, and tolerance (concerning views and attitudes, least-liked groups, and racial sterotypes). In order to facilitate and encourage the common use of several key variables, and to help individual users to avoid having to create certain scales and indices, the data in Part 1, Citizenship, Involvement, Democracy Survey Data (US Only), also include the following constructed variables: generalized trust, political action, party identification, participation in voluntary organizations, citizenship norms, the diversity of social networks, racial prejudice/negative stereotypes, national pride, attitudes toward immigrants, and demographic factors. The data in Part 2, 2002 European Social Survey (ESS) Data Integrated with US Data, comprise the responses from the 2002 ESS merged with the responses from the US CID, but only contains the questions common to both the US CID and the 2002 ESS (without any constructed variables). The central aim of the ESS is to measure and explain how people's social values, cultural norms, and behavior patterns are distributed, the way in which they differ within and between nations, and the direction and speed at which they are changing. Data collection for the ESS takes place every two years, by means of face-to-face interviews of around an hour in duration. Demographic variables for Part 1 and Part 2 include race, gender, age, marital status, income, religious preference, and highest level of education. Although the sample used in Part 1 was designed with an equal probability of selection method sampling, some variations exist (e.g., variations in primary stratum size) resulting in the need for some minor weighting adjustments to achieve equal representation across the sample. Complete weighting information for Part 1 is located in the appendix of the codebook for Part 1. Additional information about the weights used in Part 2 may be obtained via the ESS Web site. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Checked for undocumented or out-of-range codes.. Response Rates: The overall response rate was 40.03 percent. Adults, ages 18 and over, living in the contiguous United States. The study used a classic cluster sample design with an equal probability of selection method of sampling. Eligible respondents were household members, males or females, aged 18 years and older. Respondents were selected using the most recent birthday method. There was no substitution of respondents within households, and there was no substitution across households. The objective of this design was to provide an approximate self-weighting, or epsem, sample of households across the continental United States. The sample was designed specifically to represent the adult population residing in occupied residential housing unit...

Abstract

The 2022 Kenya Demographic and Health Survey (2022 KDHS) was implemented by the Kenya National Bureau of Statistics (KNBS) in collaboration with the Ministry of Health (MoH) and other stakeholders. The survey is the 7th KDHS implemented in the country.

The primary objective of the 2022 KDHS is to provide up-to-date estimates of basic sociodemographic, nutrition and health indicators. Specifically, the 2022 KDHS collected information on: • Fertility levels and contraceptive prevalence • Childhood mortality • Maternal and child health • Early Childhood Development Index (ECDI) • Anthropometric measures for children, women, and men • Children’s nutrition • Woman’s dietary diversity • Knowledge and behaviour related to the transmission of HIV and other sexually transmitted diseases • Noncommunicable diseases and other health issues • Extent and pattern of gender-based violence • Female genital mutilation.

The information collected in the 2022 KDHS will assist policymakers and programme managers in monitoring, evaluating, and designing programmes and strategies for improving the health of Kenya’s population. The 2022 KDHS also provides indicators relevant to monitoring the Sustainable Development Goals (SDGs) for Kenya, as well as indicators relevant for monitoring national and subnational development agendas such as the Kenya Vision 2030, Medium Term Plans (MTPs), and County Integrated Development Plans (CIDPs).

Geographic coverage

National coverage

Analysis unit

• Household
• Individual
• Children age 0-5
• Woman age 15-49
• Man age 15-54

Universe

The survey covered all de jure household members (usual residents), all women aged 15-49, men ageed 15-54, and all children aged 0-4 resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for the 2022 KDHS was drawn from the Kenya Household Master Sample Frame (K-HMSF). This is the frame that KNBS currently uses to conduct household-based sample surveys in Kenya. The frame is based on the 2019 Kenya Population and Housing Census (KPHC) data, in which a total of 129,067 enumeration areas (EAs) were developed. Of these EAs, 10,000 were selected with probability proportional to size to create the K-HMSF. The 10,000 EAs were randomised into four equal subsamples. A survey can utilise a subsample or a combination of subsamples based on the sample size requirements. The 2022 KDHS sample was drawn from subsample one of the K-HMSF. The EAs were developed into clusters through a process of household listing and geo-referencing. The Constitution of Kenya 2010 established a devolved system of government in which Kenya is divided into 47 counties. To design the frame, each of the 47 counties in Kenya was stratified into rural and urban strata, which resulted in 92 strata since Nairobi City and Mombasa counties are purely urban.

The 2022 KDHS was designed to provide estimates at the national level, for rural and urban areas separately, and, for some indicators, at the county level. The sample size was computed at 42,300 households, with 25 households selected per cluster, which resulted in 1,692 clusters spread across the country, 1,026 clusters in rural areas, and 666 in urban areas. The sample was allocated to the different sampling strata using power allocation to enable comparability of county estimates.

The 2022 KDHS employed a two-stage stratified sample design where in the first stage, 1,692 clusters were selected from the K-HMSF using the Equal Probability Selection Method (EPSEM). The clusters were selected independently in each sampling stratum. Household listing was carried out in all the selected clusters, and the resulting list of households served as a sampling frame for the second stage of selection, where 25 households were selected from each cluster. However, after the household listing procedure, it was found that some clusters had fewer than 25 households; therefore, all households from these clusters were selected into the sample. This resulted in 42,022 households being sampled for the 2022 KDHS. Interviews were conducted only in the pre-selected households and clusters; no replacement of the preselected units was allowed during the survey data collection stages.

For further details on sample design, see APPENDIX A of the survey report.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Four questionnaires were used in the 2022 KDHS: Household Questionnaire, Woman’s Questionnaire, Man’s Questionnaire, and the Biomarker Questionnaire. The questionnaires, based on The DHS Program’s model questionnaires, were adapted to reflect the population and health issues relevant to Kenya. In addition, a self-administered Fieldworker Questionnaire was used to collect information about the survey’s fieldworkers.

Cleaning operations

CAPI was used during data collection. The devices used for CAPI were Android-based computer tablets programmed with a mobile version of CSPro. The CSPro software was developed jointly by the U.S. Census Bureau, Serpro S.A., and The DHS Program. Programming of questionnaires into the Android application was done by ICF, while configuration of tablets was completed by KNBS in collaboration with ICF. All fieldwork personnel were assigned usernames, and devices were password protected to ensure the integrity of the data.

Work was assigned by supervisors and shared via Bluetooth® to interviewers’ tablets. After completion, assigned work was shared with supervisors, who conducted initial data consistency checks and edits and then submitted data to the central servers hosted at KNBS via SyncCloud. Data were downloaded from the central servers and checked against the inventory of expected returns to account for all data collected in the field. SyncCloud was also used to generate field check tables to monitor progress and identify any errors, which were communicated back to the field teams for correction.

Secondary editing was done by members of the KNBS and ICF central office team, who resolved any errors that were not corrected by field teams during data collection. A CSPro batch editing tool was used for cleaning and tabulation during data analysis.

Response rate

A total of 42,022 households were selected for the survey, of which 38,731 (92%) were found to be occupied. Among the occupied households, 37,911 were successfully interviewed, yielding a response rate of 98%. The response rates for urban and rural households were 96% and 99%, respectively. In the interviewed households, 33,879 women age 15-49 were identified as eligible for individual interviews. Of these, 32,156 women were interviewed, yielding a response rate of 95%. The response rates among women selected for the full and short questionnaires were similar (95%). In the households selected for the men’s survey, 16,552 men age 15-54 were identified as eligible for individual interviews and 14,453 were successfully interviewed, yielding a response rate of 87%.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: (1) non-sampling errors, and (2) sampling errors. Non-sampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the 2022 Kenya Demographic and Health Survey (2022 KDHS) to minimise this type of error, non-sampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2022 KDHS is only one of many samples that could have been selected from the same population, using the same design and identical size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability between all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

A sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall within a range of plus or minus two times the standard error of that statistic in 95 percent of all possible samples of identical size and design.

If the sample of respondents had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the 2022 KDHS sample is the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex formulae. The computer software used to calculate sampling errors for the 2022 KDHS is a SAS program. This program used the Taylor linearisation method for variance estimation for survey estimates that are means, proportions or ratios. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.

A more detailed description of estimates of sampling errors are presented in APPENDIX B of the survey report.

Data

Food-related choices have an important impact on health. Food preparation methods may be linked to diet and health benefits. However, the factors influencing people’s food choices, and how they are shaped by food preparation experiences, are still not fully understood. We aimed to study home food preparation practices, experiences and perceptions amongst adults in North East England. A matrix was used to purposively sample participants with diverse socio-demographic characteristics. Participants developed photographic food diaries that were used as prompts during semi-structured interviews. Data were analysed using the Framework Method. Interviews were conducted with 18 adults (five men and 13 women), aged approximately 20 to 80 years, to reach data saturation. Participants’ practices varied widely, from reliance on pre-prepared foods, to preparing complex meals entirely from basic ingredients. Key themes emerged regarding the cook (identity), the task (process of cooking), and the context (situational drivers). Resources, in terms of time, money and facilities, were also underpinning influences on food preparation. Participants’ practices were determined by both personal motivations to cook, and the influence of others, and generally reflected compromises between varied competing demands and challenges in life. Most people appeared to be overall content with their food preparation behaviour, though ideally aspired to cook more frequently, using basic ingredients. This often seemed to be driven by social desirability. Home food preparation is complex, with heterogeneous practices, experiences and perceptions both between individuals and within the same individual over time, according to shifting priorities and circumstances. Generalisability of these findings may be limited by the regional participant sample; however the results support and build upon previous research. Focussing interventions on life transition points at which priorities and circumstances change, with careful targeting to stimulate personal motivation and social norms, may prove effective in encouraging home food preparation.

A household survey (cross sectional study) was conducted to establish the consumption of fish, fish products and other food items at household level (N=714). The role of fish and fish products in the diets of urban poor households, and how fish consumption is distributed within the household between women, children and men. Women and children in the first 1,000 days of life were specifically targeted. Children aged 24 – 59 months from participating households were also enrolled in the study. Lusaka district in Lusaka Province was purposively selected as the study area for the following reasons: it is an urban area within Lusaka Province with the highest number of high density settlement townships where the majority of the urban poor live in Zambia. The study targeted low-income settlement localities as the people living in these areas are most vulnerable to food and nutrition insecurity. To derive the sample size, the formula was applied; n is the minimum required sample size, Z is the Z score for the desired level of confidence (assumed to be 95% or = 0.05), is the population proportion of interest estimated to be 11%, the prevalence of stunted growth among children in Lusaka (27) and d is the margin of error (assumed to be 5%). The calculated sample size was further adjusted for the design effect and non-response rate (predicted to be 5%), to obtain the optimal sample size of 714 households. A sampling frame was developed from the 2010 Population Census and Housing report, in consultation with the local authorities and the Central Statistics Office (CSO). The sampling process involved, firstly, purposively selecting the three constituencies (Kanyama, Matero and Munali) from Lusaka district. From each constituency, one ward was randomly selected to participate in the study. In each reporting domain, study households were selected using a three-stage randomized cluster approach, with the first two stages using the Ward and Standard Enumeration Area (SEA) sampling frame from the 2010 CSO. A total of 36 SEAs (clusters) were identified and from each, 20 households were selected. Using a determined sampling interval, systematic random sampling was used in the final sampling stage. Primary data collection was carried out through a tablet-based questionnaire and by the use of the KoBo Toolkit, a platform to customise the survey to collect specific data, in this study: a) Demographic and socio-economic characteristics, including employment and income generating activities, water and sanitation, and household assets; b) Dietary diversity questionnaires were developed and used to collect dietary data for children, women and men. Guidelines on food groups to be included in the questionnaire as provided by FAO 2013 were used in developing the questionnaire for women, men and for household level data collection. The WHO 2010 guidelines were used in developing the questionnaire for collecting dietary data for children 6–23 months of age. Dietary diversity is a proxy for adequate micronutrient-density of foods. A 24 hour recall collected data that was used to estimate food intake for two adults within the household (one male and one female), infants aged 6 – 23 months and one child aged 2 – 5 years. Development of the 24 hr recall was based on the methods described by Gibson and Ferguson (2008). In addition, a dietary diversity questionnaire (FFQ) was used collect data on various food groups women, children and men consumed in the last 24 hours prior to the study. With focus on fish in the diet of young children, information was collected on the use of fish in the initiation of complementary feeding, the age at which fish is fed to children, the perceptions of mother and fathers of the importance of fish for growth and development of the young child. c) Anthropometric measurements such as weight and length/height were taken on the children and mothers/caregivers. This was done to enable determine the nutritional status of children 6 -23 months; 24- 59 months and women aged 19 – 49 years. The weights of children were taken using the SECA electronic scale and for those children, who were unable to stand, the parents/guardians were asked to carry them and their weights were subtracted from the mothers’ weight. The children’s weights were taken to the nearest 0.1 kg with minimal clothes on them. Length/height boards were used to take the length/height to the nearest 0.1 cm. Children’s age was verified using the clinic card. The mothers’ weight and height were also taken using the SECA scales. The measurements were used to determine mothers’ BMI.

Abstract

By the middle of the 1990s, Indonesia had enjoyed over three decades of remarkable social, economic, and demographic change and was on the cusp of joining the middle-income countries. Per capita income had risen more than fifteenfold since the early 1960s, from around US$50 to more than US$800. Increases in educational attainment and decreases in fertility and infant mortality over the same period reflected impressive investments in infrastructure.

In the late 1990s the economic outlook began to change as Indonesia was gripped by the economic crisis that affected much of Asia. In 1998 the rupiah collapsed, the economy went into a tailspin, and gross domestic product contracted by an estimated 12-15%-a decline rivaling the magnitude of the Great Depression.

The general trend of several decades of economic progress followed by a few years of economic downturn masks considerable variation across the archipelago in the degree both of economic development and of economic setbacks related to the crisis. In part this heterogeneity reflects the great cultural and ethnic diversity of Indonesia, which in turn makes it a rich laboratory for research on a number of individual- and household-level behaviors and outcomes that interest social scientists.

The Indonesia Family Life Survey is designed to provide data for studying behaviors and outcomes. The survey contains a wealth of information collected at the individual and household levels, including multiple indicators of economic and non-economic well-being: consumption, income, assets, education, migration, labor market outcomes, marriage, fertility, contraceptive use, health status, use of health care and health insurance, relationships among co-resident and non- resident family members, processes underlying household decision-making, transfers among family members and participation in community activities. In addition to individual- and household-level information, the IFLS provides detailed information from the communities in which IFLS households are located and from the facilities that serve residents of those communities. These data cover aspects of the physical and social environment, infrastructure, employment opportunities, food prices, access to health and educational facilities, and the quality and prices of services available at those facilities. By linking data from IFLS households to data from their communities, users can address many important questions regarding the impact of policies on the lives of the respondents, as well as document the effects of social, economic, and environmental change on the population.

The Indonesia Family Life Survey complements and extends the existing survey data available for Indonesia, and for developing countries in general, in a number of ways.

First, relatively few large-scale longitudinal surveys are available for developing countries. IFLS is the only large-scale longitudinal survey available for Indonesia. Because data are available for the same individuals from multiple points in time, IFLS affords an opportunity to understand the dynamics of behavior, at the individual, household and family and community levels. In IFLS1 7,224 households were interviewed, and detailed individual-level data were collected from over 22,000 individuals. In IFLS2, 94.4% of IFLS1 households were re-contacted (interviewed or died). In IFLS3 the re-contact rate was 95.3% of IFLS1 households. Indeed nearly 91% of IFLS1 households are complete panel households in that they were interviewed in all three waves, IFLS1, 2 and 3. These re-contact rates are as high as or higher than most longitudinal surveys in the United States and Europe. High re-interview rates were obtained in part because we were committed to tracking and interviewing individuals who had moved or split off from the origin IFLS1 households. High re-interview rates contribute significantly to data quality in a longitudinal survey because they lessen the risk of bias due to nonrandom attrition in studies using the data.

Second, the multipurpose nature of IFLS instruments means that the data support analyses of interrelated issues not possible with single-purpose surveys. For example, the availability of data on household consumption together with detailed individual data on labor market outcomes, health outcomes and on health program availability and quality at the community level means that one can examine the impact of income on health outcomes, but also whether health in turn affects incomes.

Third, IFLS collected both current and retrospective information on most topics. With data from multiple points of time on current status and an extensive array of retrospective information about the lives of respondents, analysts can relate dynamics to events that occurred in the past. For example, changes in labor outcomes in recent years can be explored as a function of earlier decisions about schooling and work.

Fourth, IFLS collected extensive measures of health status, including self-reported measures of general health status, morbidity experience, and physical assessments conducted by a nurse (height, weight, head circumference, blood pressure, pulse, waist and hip circumference, hemoglobin level, lung capacity, and time required to repeatedly rise from a sitting position). These data provide a much richer picture of health status than is typically available in household surveys. For example, the data can be used to explore relationships between socioeconomic status and an array of health outcomes.

Fifth, in all waves of the survey, detailed data were collected about respondents¹ communities and public and private facilities available for their health care and schooling. The facility data can be combined with household and individual data to examine the relationship between, for example, access to health services (or changes in access) and various aspects of health care use and health status.

Sixth, because the waves of IFLS span the period from several years before the economic crisis hit Indonesia, to just prior to it hitting, to one year and then three years after, extensive research can be carried out regarding the living conditions of Indonesian households during this very tumultuous period. In sum, the breadth and depth of the longitudinal information on individuals, households, communities, and facilities make IFLS data a unique resource for scholars and policymakers interested in the processes of economic development.

Geographic coverage

National coverage

Analysis unit

• Communities
• Facilities
• Households
• Individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

Because it is a longitudinal survey, the IFLS3 drew its sample from IFLS1, IFLS2, IFLS2+. The IFLS1 sampling scheme stratified on provinces and urban/rural location, then randomly sampled within these strata (see Frankenberg and Karoly, 1995, for a detailed description). Provinces were selected to maximize representation of the population, capture the cultural and socioeconomic diversity of Indonesia, and be cost-effective to survey given the size and terrain of the country. For mainly costeffectiveness reasons, 14 of the then existing 27 provinces were excluded. The resulting sample included 13 of Indonesia's 27 provinces containing 83% of the population: four provinces on Sumatra (North Sumatra, West Sumatra, South Sumatra, and Lampung), all five of the Javanese provinces (DKI Jakarta, West Java, Central Java, DI Yogyakarta, and East Java), and four provinces covering the remaining major island groups (Bali, West Nusa Tenggara, South Kalimantan, and South Sulawesi).

Household Survey:

Within each of the 13 provinces, enumeration areas (EAs) were randomly chosen from a nationally representative sample frame used in the 1993 SUSENAS, a socioeconomic survey of about 60,000 households. The IFLS randomly selected 321 enumeration areas in the 13 provinces, over-sampling urban EAs and EAs in smaller provinces to facilitate urban-rural and Javanese-non-Javanese comparisons.

Within a selected EA, households were randomly selected based upon 1993 SUSENAS listings obtained from regional BPS office. A household was defined as a group of people whose members reside in the same dwelling and share food from the same cooking pot (the standard BPS definition). Twenty households were selected from each urban EA, and 30 households were selected from each rural EA.This strategy minimized expensive travel between rural EAs while balancing the costs of correlations among households. For IFLS1 a total of 7,730 households were sampled to obtain a final sample size goal of 7,000 completed households. This strategy was based on BPS experience of about 90% completion rates. In fact, IFLS1 exceeded that target and interviews were conducted with 7,224 households in late 1993 and early 1994.

IFLS3 Re-Contact Protocols The sampling approach in IFLS3 was to re-contact all original IFLS1 households having living members the last time they had been contacted, plus split-off households from both IFLS2 and IFLS2+, so-called target households (8,347 households-as shown in Table 2.1*) Main field work for IFLS3 went on from June through November, 2000. A total of 10,574 households were contacted in 2000; meaning that they were interviewed, had all members died since the last time they were contacted, or had joined another IFLS household which had been previously interviewed (Table 2.1*). Of these, 7,928 were IFLS3 target households and 2,646 were new split-off households. A 95.0% re-contact rate was thus achieved of all IFLS3 "target" households. The re-contacted households included 6,800 original 1993 households, or 95.3% of those. Of IFLS1 households, somewhat lower re-contact rates were achieved in Jakarta, 84.5%, and North Sumatra,

This data was collected through the HealthyFoodAfrica Project (https://healthyfoodafrica.eu/), a research and innovation project which aims at improving nutrition in Africa by strengthening the diversity, sustainability, resilience, and connectivity of food systems by reconnecting food production and food consumption in effective ways. The project was implemented in 10 cities in 6 African Countries. The objective was to assess the food consumption patterns and their determinants among households in the context of Kisumu informal settlements, providing an understanding of the types of foods consumed in the households (focusing on the consumption of women and children) to inform interventions aimed at improving diet quality in vulnerable urban populations. Methodology:The data was collected as part of HealthyFoodAfrica Project’s baseline conducted in February 2022 in 4 urban informal settlements in Kisumu, Kenya. The 4 informal settlements were Manyatta A, Manyatta B, Obunga and Bandani. We conducted a cross-sectional survey of randomly selected households. The study focused on women of reproductive age (WRA, 15–49 years) and children (6–23 months), the population groups most vulnerable to malnutrition. To determine the number of villages (clusters) to be included in the survey, we first applied a proportionate-to-population-size (PPS) approach based on population estimates for each settlement. Community health promoters provided village names and household lists, which we filtered to retain only households that included our eligible sampling frame of WRA and children aged 6–23 months. Using the PPS method, a total of 42 villages were sampled, distributed across the four settlements as follows: 17 villages each in Manyatta A and Manyatta B, and four villages each in Obunga and Bandani. These were randomly selected from the full list of villages available in each settlement. We determined a minimum sample size of 372 households based on the Charan and Biswas (2013) method. The sample size was increased to 504 households to account for potential non-response attrition (such as refusals or unavailable participants), ensure balanced representation across all four settlements, and improve precision in subgroup analyses. Households were then randomly selected from the eligible list. Because population size had already been considered, we uniformly selected 12 households per village for the survey. In the end, 510 households were surveyed. The slight increase beyond the planned sample size was due to the inclusion of additional eligible woman-child pairs encountered during data collection. We used a semi-structured questionnaire developed in English and uploaded in the KoboToolbox platform to capture a range of household data, including socio-demographic characteristics; nutrition knowledge, attitudes, and practices (KAP); main sources of food; distance to the main food market, and food shopping behavior (frequency of shopping and preferred time of shopping). We assessed individual dietary intakes for both mothers and children using a multiple pass 24-hour quantitative dietary intake recall on two non-consecutive days for at least half of the participants. The tool adhered to Food and Agriculture Organization (FAO) and World Health Organization (WHO) guidelines for measuring dietary diversity in women of reproductive age, and infants and young children, respectively (FAO, 2021; WHO and UNICEF, 2021). Participants reported all foods and beverages consumed in the 24 hours preceding the interview.

Abstract

The datasets include a household survey, a non-farm business survey and a community survey which were collected between November 2017 and January 2018 to document the welfare and economic impacts of the Lesotho Child Grants Programme (CGP) and the Sustainable Poverty Reduction through Income, Nutrition and access to Government Services project (SPRINGS) on direct beneficiaries and the spillover effects in the local economies. The data look specifically at several CGP and SPRINGS outcome and output indicators, related to the following areas: consumption and poverty, dietary diversity and food security, income, agricultural inputs and assets, children well-being, financial inclusion, gardening and operational efficiency of both programmes.

Geographic coverage

Village Coverage.

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Sampling procedure

The impact evaluation design of the CGP and SPRINGS programmes consists of a post-intervention only non-equivalent control group study. Since neither randomization nor regression discontinuity were possible, a propensity score matching (PSM) approach was the only feasible option for the evaluation. To improve the comparability between the households groups, the evaluation team used the National Information System for Social Assistance (NISSA) registry data to match households with and without CGP based on their socio-demographic characteristics.

Before implementing this procedure, the evaluation team took the following decisions concerning the list of households in NISSA to be included in the PSM analysis: 1. Including only households having at least one household member below 18 years of age 2. Including households residing in one of the six districts of Berea, Butha-Buthe, Leribe, Mafeteng, Maseru, Mohale's Hoek. 3. For the comparison group they considered only households living in villages without either CGP or SPRINGS 4. Excluding households living in community councils where CGP had been implemented for more than seven years and less than four years.

The objective of the first condition was to target the same typology of households, i.e. those eligible for the CGP. The second condition aimed to limit the extent of the fieldwork to similar agro-ecological areas, while the third condition was needed to minimize the extent of spillovers, which could lead to bias in impact estimates. Finally, the fourth condition aimed to make households as comparable as possible in terms of CGP receipt at community level.

Overall, the service provider surveyed 2014 households, 1550 of whom were eligible for the CGP (8212 individuals), while 464 were not (2106 individuals). The former group is used to analyze the impacts of CGP and SPRINGS on programme beneficiaries, while the full set of 2014 households is used for a spillover analysis. Among the eligible households interviewed by the service provider, 1343 were targeted by the PSM analysis, while the remaining 207 households were on the list of potential substitutes provided among those with similar propensity scores (13.35 percent replacement rate).

Mode of data collection

Computer Assisted Personal Interview [capi]

Cleaning operations

Data was cleaned and edited by the data provider. The datasets were made anonymous, by removing sensitive fields, such as names and surnames, GPS coordinates, village names, to avoid identification of respondents.

Abstract

The 2022 Ghana Demographic and Health Survey (2022 GDHS) is the seventh in the series of DHS surveys conducted by the Ghana Statistical Service (GSS) in collaboration with the Ministry of Health/Ghana Health Service (MoH/GHS) and other stakeholders, with funding from the United States Agency for International Development (USAID) and other partners.

The primary objective of the 2022 GDHS is to provide up-to-date estimates of basic demographic and health indicators. Specifically, the GDHS collected information on: - Fertility levels and preferences, contraceptive use, antenatal and delivery care, maternal and child health, childhood mortality, childhood immunisation, breastfeeding and young child feeding practices, women’s dietary diversity, violence against women, gender, nutritional status of adults and children, awareness regarding HIV/AIDS and other sexually transmitted infections, tobacco use, and other indicators relevant for the Sustainable Development Goals - Haemoglobin levels of women and children - Prevalence of malaria parasitaemia (rapid diagnostic testing and thick slides for malaria parasitaemia in the field and microscopy in the lab) among children age 6–59 months - Use of treated mosquito nets - Use of antimalarial drugs for treatment of fever among children under age 5

The information collected through the 2022 GDHS is intended to assist policymakers and programme managers in designing and evaluating programmes and strategies for improving the health of the country’s population.

Geographic coverage

National coverage

Analysis unit

• Household
• Individual
• Children age 0-5
• Woman age 15-49
• Man age 15-59

Universe

The survey covered all de jure household members (usual residents), all women aged 15-49, men aged 15-59, and all children aged 0-4 resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

To achieve the objectives of the 2022 GDHS, a stratified representative sample of 18,450 households was selected in 618 clusters, which resulted in 15,014 interviewed women age 15–49 and 7,044 interviewed men age 15–59 (in one of every two households selected).

The sampling frame used for the 2022 GDHS is the updated frame prepared by the GSS based on the 2021 Population and Housing Census.1 The sampling procedure used in the 2022 GDHS was stratified two-stage cluster sampling, designed to yield representative results at the national level, for urban and rural areas, and for each of the country’s 16 regions for most DHS indicators. In the first stage, 618 target clusters were selected from the sampling frame using a probability proportional to size strategy for urban and rural areas in each region. Then the number of targeted clusters were selected with equal probability systematic random sampling of the clusters selected in the first phase for urban and rural areas. In the second stage, after selection of the clusters, a household listing and map updating operation was carried out in all of the selected clusters to develop a list of households for each cluster. This list served as a sampling frame for selection of the household sample. The GSS organized a 5-day training course on listing procedures for listers and mappers with support from ICF. The listers and mappers were organized into 25 teams consisting of one lister and one mapper per team. The teams spent 2 months completing the listing operation. In addition to listing the households, the listers collected the geographical coordinates of each household using GPS dongles provided by ICF and in accordance with the instructions in the DHS listing manual. The household listing was carried out using tablet computers, with software provided by The DHS Program. A fixed number of 30 households in each cluster were randomly selected from the list for interviews.

For further details on sample design, see APPENDIX A of the final report.

Mode of data collection

Face-to-face computer-assisted interviews [capi]

Research instrument

Four questionnaires were used in the 2022 GDHS: the Household Questionnaire, the Woman’s Questionnaire, the Man’s Questionnaire, and the Biomarker Questionnaire. The questionnaires, based on The DHS Program’s model questionnaires, were adapted to reflect the population and health issues relevant to Ghana. In addition, a self-administered Fieldworker Questionnaire collected information about the survey’s fieldworkers.

The GSS organized a questionnaire design workshop with support from ICF and obtained input from government and development partners expected to use the resulting data. The DHS Program optional modules on domestic violence, malaria, and social and behavior change communication were incorporated into the Woman’s Questionnaire. ICF provided technical assistance in adapting the modules to the questionnaires.

Cleaning operations

DHS staff installed all central office programmes, data structure checks, secondary editing, and field check tables from 17–20 October 2022. Central office training was implemented using the practice data to test the central office system and field check tables. Seven GSS staff members (four male and three female) were trained on the functionality of the central office menu, including accepting clusters from the field, data editing procedures, and producing reports to monitor fieldwork.

From 27 February to 17 March, DHS staff visited the Ghana Statistical Service office in Accra to work with the GSS central office staff on finishing the secondary editing and to clean and finalize all data received from the 618 clusters.

Response rate

A total of 18,540 households were selected for the GDHS sample, of which 18,065 were found to be occupied. Of the occupied households, 17,933 were successfully interviewed, yielding a response rate of 99%. In the interviewed households, 15,317 women age 15–49 were identified as eligible for individual interviews. Interviews were completed with 15,014 women, yielding a response rate of 98%. In the subsample of households selected for the male survey, 7,263 men age 15–59 were identified as eligible for individual interviews and 7,044 were successfully interviewed.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: (1) nonsampling errors and (2) sampling errors. Nonsampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the 2022 Ghana Demographic and Health Survey (2022 GDHS) to minimize this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2022 GDHS is only one of many samples that could have been selected from the same population, using the same design and identical size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability between all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results. A sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall within a range of plus or minus two times the standard error of that statistic in 95% of all possible samples of identical size and design.

If the sample of respondents had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the 2022 GDHS sample was the result of a multistage stratified design, and, consequently, it was necessary to use more complex formulas. The computer software used to calculate sampling errors for the GDHS 2022 is an SAS program. This program used the Taylor linearization method to estimate variances for survey estimates that are means, proportions, or ratios. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.

A more detailed description of estimates of sampling errors are presented in APPENDIX B of the survey report.

Data appraisal

Data Quality Tables

• Age distribution of eligible and interviewed women
• Age distribution of eligible and interviewed men
• Age displacement at age 14/15
• Age displacement at age 49/50
• Pregnancy outcomes by years preceding the survey
• Completeness of reporting
• Standardisation exercise results from anthropometry training
• Height and weight data completeness and quality for children
• Height measurements from random subsample of measured children
• Interference in height and weight measurements of children
• Interference in height and weight measurements of women and men
• Heaping in anthropometric measurements for children (digit preference)
• Observation of mosquito nets
• Observation of handwashing facility
• School attendance by single year of age
• Vaccination cards photographed
• Number of

The data addresses the dynamics of coexistence and conflict in increasingly diverse cities from a human-centred perspective. It was collected as part of the EU-funded project Coexistence and Conflict in the Age of Complexity (EmergentCommunity) in nine European cities in Finland, France, and Sweden. The dataset comprises of two parts: EmergentCommunityEthno (qualitative data) and EmergentCommunityVR (quantitative and qualitative data) that were collected during the project. In addition to these, desk research was conducted and these files have been included in the metadata description.

EmergentCommunityEthno (dataset 1):

Across the nine cities, participants consisted of people above 15-years of age, living in the studied urban neighbourhoods or using their public spaces. In Finland, data were collected in the neighbourhoods of Peltolammi and Multisilta in Tampere, in Malmi in Helsinki, and in Martti and Paavola in Hyvinkää. In Tampere, part of the data (n=31 interviews) was collected in collaboration with the EKOS research project (this part of the data is described and archived in the Finnish Social Science Data Archive, DoI: https://doi.org/10.60686/t-fsd3816). The second part of the data was collected in Sweden. The data collection sites there were the neighborhoods of Möllevången and Nydala in Malmö, Farsta and Rågsved in Stockholm, and Fröslunda and Årby in Eskilstuna. The French data were collected in the La Plaine area in Marseille; in La-Chapelle-Saint-Luc, Saint-Andre-Les-Vergers and Les Chartreux in Troyes; and in Guillotière in Lyon.

Across these sites shared methods were used in data collection, consisting of thematic interviews, walking interviews, and observations. The dataset emphasizes the diversity of experiences and the manifestations of distinctions in diverse urban environments and examines the ways in which people form bonds in relation to each other, their neighborhoods, and the broader society.

The first set of participants were located through social media groups (Facebook), from the premises of associations organizing community activities in the areas, libraries, cafes, community events, and youth centers. After this, snowball sampling was used, in addition to which targeted recruitment was applied if a population group represented in the area was completely missing from the dataset. Ethnographic observations were conducted in public spaces, community centres, cafés, stations, and shopping centres that were selected as potentially interesting places based on extant scholarship on living with difference and urban encounters. Here, attention was paid at how people used these sites, who were there and who were absent, as well as how people moved in and across the sites. Notes were made of what kinds of encounters, patterns of behaviour, cooperations, and conflicts occurred. These observations were made at various times of the day, to capture potential temporal changes. This resulted in a rich collection of fieldnotes, sketches, photographs, and movement maps.

Relevant files: 1) EmergentCommunity ethnographic matrix.pdf, 2) EmergentCommunityEthno interview questions.docx, 3) EmergentCommunity_metadata public.xlsx (contains all metadata from the project), 4) EmergentCommunityEthno_metadata.csv (contains metadata only on desk research, ethnographic interviews and fieldnotes).

EmergentCommunityVR (dataset 2):

Data collection was conducted in Helsinki, Marseille, and Malmö. The data was collected using 360-degree videos based on the aforementioned ethnographic data as stimuli to which participants were exposed. A separate video was created for each city, using specifically the data collected therein. We put together a mobile laboratory set-up that travelled to each city and collaborated with local NGOs whose premises were used as our laboratory space. The equipment and software used are explained in the document "EmergentCommunity mobile laboratory.pdf".

The inclusion criteria for participation were: being a major, healthy, not having hearing or vision impairments, being a resident in the city that the video depicted, and knowledge of the local language in which the video was executed. During the viewing of the video stimulus, participants' physiological responses were measured and their eye movements were tracked. VR eye tracking was used as it enables the precise analysis of gaze behaviour – such as fixations and saccades – within immersive, ecologically valid environments. Regarding physiological signals, the focus was on the electrical activity of the heart using electrocardiography (ECG), the electrical activity of the facial muscles using facial electromyography (fEMG), and the electrical conductivity of the skin using galvanic skin response (GSR). To complement the physiological data, a multimodal setup was established to assess the affective content of the stimulus in terms of arousal/valence, avoidance/approach, and unpredictability. After viewing, the participants were asked to evaluate the intensity of their emotional experience and to name the emotional reactions elicited by the video using a questionnaire carried out with Gorilla Experiment Builder. The questionnaire also contained background questions, from basic participant information, such as age and gender, to aspects that relate to diversity and inequality in contemporary societies: language, income, housing, education, political activity, participation, as well as political opinions and social values. After completing the measurements and the questionnaire, participants were interviewed about their experience and the thoughts it provoked, and they were asked to share information regarding their daily lives.

The purpose of the dataset was to help understand the formation of emotional experiences and the significance and functioning of emotions in the everyday life of increasingly diverse and unequal cities. The call for participation was distributed in several thematic Facebook groups (related to e.g., urban development, multiculturalism, neighborhood, local NGOs and minority communities) and via Instagram, as well as through flyers/posters in libraries, local associations, shopping centers, cafes, and on the project's Facebook page and Instagram profile. In the case of Marseille and Malmö, local assistants were used to spread the invitation within their networks and distribute participation invitation leaflets on the streets. In each city, it was possible for already registered participants to invite additional participants as well. Overall, the goal was to ensure the representativeness of the data in terms of age, gender, and minority status.

Relevant files: 1) EmergentCommunity video stimuli.pdf, 2) EmergentCommunityVR interview questions.pdf, 3) EmergentCommunityVR Gorilla questionnaires.pdf, 4) EmergentCommunity mobile laboratory.pdf, 5) EmergentCommunity_metadata public.xlsx (contains all metadata from the project), 6) EmergentCommunityVR interviews.csv (contains metadata on interviews done after watching the 360-degree video), 7) EmergentCommunityVR physio.csv (contains metadata on physiological measuring and questionnaires).

Purpose of the data

The EmergentCommunity project aimed at producing knowledge about what community means and how it is formed in increasingly diverse societies, as well as the conflicts and tensions that everyday life brings out. The project empirically examined the concrete challenges that societal changes produce for cities and coexistence. The aim was to identify how peaceful coexistence could be supported and population relations promoted in urban everyday life. The project emphasized that community relations and everyday coexistence are affective, social, and spatial phenomena, which is why a wide range of research methods from ethnography and observation to psychophysiological measurements and interviews were applied. These approaches were brought into dialogue through virtual reality by utilizing ethnography-based 360-degree videos depicting everyday life in the latter part of the project (EmergentCommunityVR). Thus, the project created new understanding of emotions formed in everyday life and produced unique knowledge in the fields of psychological and sociological emotion research. Bringing these areas together enabled a critical examination of the concept of community and the identification of the practices and ways in which communities are produced in the everyday life of diverse and unequal cities (see CORDIS database for public description, results, and reporting).

Throughout the data collection, the research focused on everyday life and the forms, practices, and interpretations of everyday coexistence in public urban spaces in the selected research neighbourhoods. Participants were also asked to share their experiences, interpretations, and views on societal change and how the change has been visible in their own neighborhoods and what thoughts and feelings it evokes in them. The data was formed through non-probability sampling (self-formed sample).

The research sites were selected by examining statistics, policy reports, and available data on demographic changes and diversity, income inequality, trends of residential and ethnic segregation in different countries and cities (desk research). We chose the countries and cities so that they would complement each other and that changes were observable in each selected context, although their forms, emphases, and manifestations might vary. After this extensive background review, we focused on the city level, complementing the available statistical data with news articles and reports and analyses related to urban areas and development. This allowed us to identify pockets of diversity and inequality within each city. Finally, study neighborhoods were selected based on them having undergone urban development projects, being targeted with anti-segregation measures, their residents' socio-economic

Clinical, feeding and anthropometric characteristics of IDP children at Debark refugee camp, 2022.

The dataset contained in this data article comprises several sections. In all the sections, data were first presented for the full/pooled sample. Moreover, data were disaggregated by gender, Zone and Woreda.

Data on socio-demographic characteristics were presented in Table 1 (for the full sample, male and female youth) and Figure 1 (for the two Zones and four Woredas). Data on youth employment and participation in the labour market for the full sample, male and female youth categories are given in Table 2. The Zonal and Woreda level data were presented in Figure 2. The engagement of youth in on-farm and non-/off-farm income-generating activities (IGAs) was given in Table 3 (for the pooled sample, male and female youth) and Figure 3 (for Zonal and Woreda comparisons). Concerning data on agricultural production, income and food security status, Table 4 presents a summary statistic on various aspects of livelihood activities and outcomes for the pooled sample and male and female youth categories. Whereas Figure 4 depicts the Zonal and Woreda level data on experience in farming, land holding size and livestock possessions, Figure 5 presents the status of land registration in the study Zones and Woredas. Likewise, Figure 6 depicts Zonal and Woreda level comparative data on expenditures for productive assets and farm income. Regarding food security, data on household dietary diversity score (HDDS) and food consumption score (FCS) were presented in Figure 7.

Youth perception on whether agriculture can be a basic means of livelihood (Table 5 and Figure 8); whether agriculture can be a viable profession with a reasonable financial return (Table 6 and Figure 9); and level of satisfaction with current (agricultural) job (Table 7 and Figure 10) were all included herein. Data on youth access to basic services, infrastructure and facilities were given in Figure 11. Youth ownership of asset, control over use of income, and decision about credit is presented in Table 8 and Figure 12.

Data on youth participation in public extension and advisory services, including farmer field schools (FFSs), farmer training centers (FTCs), and pastoral training centers (PTCs) is presented in Table 9 and Figure 13. Similarly, data on youth participation in microfinance institutions (MFI) and small and medium enterprise (SME) promotion activities is depicted in Table 10 and Figure 14. Concerning the participation of youth in community-based organizations (CBOs), networks and groups, data were presented in Table 11, Figure 15 and Figure 16. Furthermore, data on youth participation in the productive safety net program (PSNP) and cooperatives is given in Table 12 and Figure 17. The last part of this data article presents data on status of youth access to and participation in the activities of various NGOs operating in their vicinity (Table 13, Figure 18 and Figure 19).

The Bulgarian School Leaver Survey 2014 (BSLS 2014) has been implemented as part of the bi-national research project “Social disparities and regional differences in school-to-work transitions in Bulgaria” (2011-2015) that includes lead experts from the University of Basel (Switzerland) and the Bulgarian Academy of Sciences in Sofia (Bulgaria).

The transformation of the Bulgarian labour market from state socialism to market capitalism has had a strong impact on the school-to-work transition of young adults. Young people’s passages from education to employment have become uncertain. Today, many graduates risk not gaining ground in the labour market and facing social exclusion in Bulgaria. In particular, little is known how regional labour and educational opportunities impact ethnic and gender disparities in school-to-work transitions.

The research project aims at better understanding

• school-to-work transition in contemporary Bulgaria

• the role of social inequalities in those transitions

• and the mechanisms behind educational and employment (un)success.

A nationally representative school-leaver survey for Bulgaria was developed and implemented in 2014. 2100 young people aged 15-34 participated in the survey.

Survey and data collection:

More than 170 questions were included in this retrospective school-leavers and labour market entry survey following Kogan's et al. (2011) study on school-to-work transitions in 10 Central and Eastern European countries. Retrospective data on school-to-work transitions as collected using using occupational history calendars. The questionnaire provides detailed information on the respondents’ educational background and their employment situation. With the analyses of the survey data and additional datasets young people’s passages from education to employment in Bulgaria can be described in the aftermath of the big recession in 2008/09.

This survey identified young people's pathways along different educational tracks to different positions in the labour market. Particular attention was paid to the operationalization of educational variables in the Bulgarian context, in consultation with the Ministry of Education. Similarly labour market outcomes variables and relevant socio-demographic characteristics were carefully drafted to suit the local contexts and diverse pathways of respondents.

The field work for the survey occurred from August 2013 to October 2014, including the preparatory phase, pilot survey, adjustment of questionnaire and methodology, and two phases of the main survey data collection starting January 26, 2014 through October 12, 2014.

The survey target group of school leavers was defined as those who were residents of Bulgaria; were 15 to 34 years old; and had completed or stopped their education for the first time and for at least one year in the last 5 years.

A total of three samples were drawn: a main sample representative at national level (N=1500); a booster sample representative for the North-West region of Bulgaria (N=300); and a booster sample representative for the South-West region (N=300). A two-stage cluster sample structured by regional planning units (NUTS2) and size of the settlement was employed both for the main and for the booster samples. We achieved a response rate of 81 percent.

The project was co-financed by a grant from Switzerland through the Swiss Contribution to the enlarged European Union.

Geographic coverage

West Bank and Gaza

Analysis unit

Household

Universe

It consists of all Palestinian households and individuals who are staying normally in the state of Palestine during 2013

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample is two stage stratified cluster (pps) sample: First stage: selection a stratified sample of 300 EA with (pps) method. Second stage: selection a random area sample of 25 responded households from each enumeration area selected in the first stage, the selection starts from a random point in the enumeration area ( building number).

Mode of data collection

Face-to-face [f2f]

Research instrument

The Questionnaire Represents the main tool for the data collection, and so it must achieve the technical specifications for all phases of the survey, and the questionnaire consists of several sections: · Cover Page: Contains the identification data for the family, the date of the visit, data on the team work of the field, office and data entry. · The Roaster: Which contains demographic, social and economic data for the family members selected. · Housing Characteristics: It includes data on the type of dwelling, tenure, number of rooms, housing unit connection to public networks (water, electricity), the method of waste disposal, the main source of energy used in the housing unit, durable goods available to the family as well as data on the confiscation / Isolation Lands of the family by the Israeli occupation and land area. · Agriculture: The family ownership of agricultural land and land area, and sources of irrigation of agricultural crops, livestock and their numbers and data on the number of workers in agriculture from family members. · Assistances and Coping Strategy: Contains data about the family receiving of all kinds of assistances (food, cash, employment, school feeding), and source of assistance, and satisfaction for assistance and the reason for the dissatisfaction for assistance. And It contains data on the length of time in which the family can survive financially in the future, and the difficulties faced by the family and the actions carried out by the family to cope with difficulties. · Consumption/Expenditures: This section contains data on household expenditure in terms of increase or decrease, as well as the average household expenditure during the past six months, the rate of household expenditure on food and water during the past six months ... etc.. · Dietary Diversity and Facing Food Shortages: Includes data about how many days the family consume some food during the past week and the origin and source of such food. · Income: This section contains data on the sources of family income and the value of the family's monthly income over the past month and the value of annual income, and the percentage of annual income from agriculture. Freedom of Movement: The data includes all restrictions on the movement of the family during the past six months, and the problems prevent any family member from access to work, land, school or university and health facilities

Cleaning operations

Both data entry and tabulation were performed using the Access and SPSS software programs. Data entry was organized corresponding to the main parts of the questionnaire.
A data entry template was designed to reflect an exact image of the questionnaire, and included various electronic checks: logical check, range checks, consisting checks and cross-validation. Complete manual inspection of results after data entry was performed, and questionnaires containing field-related errors were sent back to the field for corrections.

Response rate

Response rate was 83.6%

Sampling error estimates

Data of this survey affected by sampling errors due to use of the sample and not a complete enumeration. Therefore, certain differences are expected in comparison with the real values obtained through censuses. Variance were calculated for the most important indicators, the variance table is attached with the final report. There is no problem to disseminate results at the national level and regional level (west bank , gaza strip).

Data appraisal

Non-sampling errors are probable in all stages of the project, during data collection or processing. This is referred to as non-response errors, response errors, interviewing errors, and data entry errors. To avoid errors and reduce their effects, great efforts were made to train the fieldworkers intensively. They were trained in how to carry out the interview, what to discuss and what to avoid, carrying out a pilot survey and practical and theoretical training during the training course.

Also data entry staff was trained on the entry program that was examined before starting the data entry process. Continuous contacts with the fieldwork team were maintained through regular visits to the field and regular meetings during the different field visits. Problems faced by fieldworkers were discussed to clarify issues and provide relevant instructions.

Prevalence of IDA from anemic IDP children at debark refugee camp (n = 119), 2022.

IntroductionThe challenge of achieving maternal and neonatal health-related goals in developing countries is significantly impacted by high fertility rates, which are partly attributed to limited access to family planning and access to the healthcare systems. The most widely used indicator to monitor family planning coverage is the proportion of women in reproductive age using contraception (CPR). However, this metric does not accurately reflect the true family planning coverage, as it fails to account for the diverse needs of women in reproductive age. Not all women in this category require contraception, including those who are pregnant, wish to become pregnant, sexually inactive, or infertile. To effectively address the contraceptive needs of those who require it, this study aims to estimate family planning coverage among this specific group. Further, we aimed to explore the geographical variation and factors influencing contraceptive uptake of contraceptive use among those who need.MethodWe used data from the Performance Monitoring for Action Ethiopia (PMA Ethiopia) survey of women of reproductive age and the service delivery point (SDP) survey conducted in 2019. A total of 4,390 women who need contraception were considered as the analytical sample. To account for the study design, sampling weights were considered to compute the coverage of modern contraceptive use disaggregated by socio-demographic factors. Bayesian geostatistical modeling was employed to identify potential factors associated with the uptake of modern contraception and produce spatial prediction to unsampled locations.ResultThe overall weighted prevalence of modern contraception use among women who need it was 44.2% (with 95% CI: 42.4%—45.9%). Across regions of Ethiopia, contraceptive use coverage varies from nearly 0% in Somali region to 52.3% in Addis Ababa. The average nearest distance from a woman’s home to the nearest SDP was high in the Afar and Somali regions. The spatial mapping shows that contraceptive coverage was lower in the eastern part of the country. At zonal administrative level, relatively high (above 55%) proportion of modern contraception use coverage were observed in Adama Liyu Zone, Ilu Ababor, Misrak Shewa, and Kefa zone and the coverage were null in majority of Afar and Somali region zones. Among modern contraceptive users, use of the injectable dominated the method-mix. The modeling result reveals that, living closer to a SDP, having discussions about family planning with the partner, following a Christian religion, no pregnancy intention, being ever pregnant and being young increases the likelihood of using modern contraceptive methods.ConclusionAreas with low contraceptive coverage and lower access to contraception because of distance should be prioritized by the government and other supporting agencies. Women who discussed family planning with their partner were more likely to use modern contraceptives unlike those without such discussion. Thus, to improve the coverage of contraceptive use, it is very important to encourage/advocate women to have discussions with their partner and establish movable health systems for the nomadic community.