ODS / Goals and targets (from the 2030 Agenda for Sustainable Development) / Goal 3. Ensure healthy lives and promote well-being for all at all ages / Target 3.a. Strengthen the implementation of the World Health Organization Framework Convention on Tobacco Control in all countries, as appropriate. / Indicator 3.a.1. Age-standardized prevalence of current tobacco use among persons aged 15 years and older

Abstract This article analyzes a sanitation universalization program in precarious urban settlements, implemented by the municipal government of Medellin, Colombia. Based on the theory of Political Ecology, it discusses aspects that enable or restrict the expansion of sanitation services to the poor population, and analyzes the experience of Bello Oriente, a settlement not served by the program, where the population, with the support of a local NGO, has managed to improve water supply conditions and mitigate geotechnical risks. The article shows that the solution developed by the community is much closer to a concept of socioenvironmental unit than the municipal urban policy, which proposes a development based on ecological urbanism, but is subject to the neoliberal model of commodification of the city.

ODS / Goals and targets (from the 2030 Agenda for Sustainable Development) / Goal 11. Make cities and human settlements inclusive, safe, resilient and sustainable / Target 11.6. By 2030, reduce the adverse per capita environmental impact of cities, including by paying special attention to air quality and municipal and other waste management / Indicator 11.6.2. Annual mean levels of fine particulate matter (e.g. PM2.5 and PM10) in cities (population weighted)

This feature layer represents the proportion of households that have a water supply. The layer has been developed as a proxy to represent SDG 6.1.1. ‘Proportion of Population Using Safely Managed Drinking Water Services' for Ireland. Census 2016 data produced by the Central Statistics Office (CSO) and Administrative County boundary data produced by Tailte Éireann were used to create this feature layer.In 2015 UN countries adopted a set of 17 goals to end poverty, protect the planet and ensure prosperity for all as part of a new sustainable development agenda. Each goal has specific targets to help achieve the goals set out in the agenda by 2030. Governments are committed to establishing national frameworks for the achievement of the 17 Goals and to review progress using accessible quality data. With these goals in mind the CSO and Tailte Éireann are working together to link geography and statistics to produce indicators that help communicate and monitor Ireland’s performance in relation to achieving the 17 sustainable development goals.The indicator displayed supports the efforts to achieve goal number 6 which aims to ensure availability and sustainable management of water and sanitation for all.

This feature layer represents the proportion of households that have a sewerage facility. Attributes include a breakdown of sewerage facility by type. The layer has been developed as a proxy to represent SDG 6.2.1 'Proportion of Population Using Safely Managed Sanitation Services, Including a Hand-washing Facility with Soap and Water' for Ireland. Census 2016 data produced by the Central Statistics Office (CSO) and Electoral Division boundary data produced by Tailte Éireann were used to create this feature layer.In 2015 UN countries adopted a set of 17 goals to end poverty, protect the planet and ensure prosperity for all as part of a new sustainable development agenda. Each goal has specific targets to help achieve the goals set out in the agenda by 2030. Governments are committed to establishing national frameworks for the achievement of the 17 Goals and to review progress using accessible quality data. With these goals in mind the CSO and Tailte Éireann are working together to link geography and statistics to produce indicators that help communicate and monitor Ireland’s performance in relation to achieving the 17 sustainable development goals.The indicator displayed supports the efforts to achieve goal number 6 which aims to ensure availability and sustainable management of water and sanitation for all.

This feature layer represents the proportion of households that have a water supply. The layer has been developed as a proxy to represent SDG 6.1.1. ‘Proportion of Population Using Safely Managed Drinking Water Services' for Ireland. Census 2016 data produced by the Central Statistics Office (CSO) and Electoral Division boundary data produced by Tailte Éireann were used to create this feature layer. In 2015 UN countries adopted a set of 17 goals to end poverty, protect the planet and ensure prosperity for all as part of a new sustainable development agenda. Each goal has specific targets to help achieve the goals set out in the agenda by 2030. Governments are committed to establishing national frameworks for the achievement of the 17 Goals and to review progress using accessible quality data. With these goals in mind the CSO and Tailte Éireann are working together to link geography and statistics to produce indicators that help communicate and monitor Ireland’s performance in relation to achieving the 17 sustainable development goals.The indicator displayed supports the efforts to achieve goal number 6 which aims to ensure availability and sustainable management of water and sanitation for all.

Abstract

The primary objective of the 2022 Tanzania Demographic and Health Survey and Malaria Indicator Survey (2022 TDHSMIS) is to provide current and reliable information on population and health issues. Specifically, the 2022 TDHS-MIS collected information on marriage and sexual activity, fertility and fertility preferences, family planning, infant and child mortality, maternal health care, disability among the household population, child health, nutrition of children and women, malaria prevalence, knowledge, and communication, women’s empowerment, women’s experience of domestic violence, adult maternal mortality via sisterhood method, awareness and behaviour regarding HIV/AIDS and other sexually transmitted infections (STIs), female genital cutting, and early childhood development. Other information collected on health-related issues included smoking, blood pressure, anaemia, malaria, and iodine testing, height and weight, and micronutrients.

The information collected through the 2022 TDHS-MIS is intended to assist policymakers and programme managers in evaluating and designing programmes and strategies for improving the health of Tanzania’s population. The 2022 TDHS-MIS also provides indicators to monitor and evaluate international, regional, and national programmes, such as the Global Agenda 2030 on Sustainable Development Goals (2030 SDGs), Tanzania Development Vision 2025, the Third National Five-Year Development Plan (FYDP III 2021/22–2025/26), East Africa Community Vision 2050 (EAC 2050), and Africa Development Agenda 2063 (ADA 2063).

Geographic coverage

National coverage

Analysis unit

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

Universe

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

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample design for the 2022 TDHS-MIS was carried out in two stages and was intended to provide estimates for the entire country, for urban and rural areas in Tanzania Mainland, and for Zanzibar. For specific indicators such as contraceptive use, the sample design allows for estimation of indicators for each of the 31 regions—26 regions in Tanzania Mainland and 5 regions in Zanzibar.

The sampling frame excluded institutional populations, such as persons in hospitals, hotels, barracks, camps, hostels, and prisons. The 2022 TDHS-MIS followed a stratified two-stage sample design. The first stage involved selection of sampling points (clusters) consisting of enumeration areas (EAs) delineated for the 2012 Tanzania Population and Housing Census (2012 PHC). The EAs were selected with a probability proportional to their size within each sampling stratum. A total of 629 clusters were selected. Among the 629 EAs, 211 were from urban areas and 418 were from rural areas.

In the second stage, 26 households were selected systematically from each cluster, for a total anticipated sample size of 16,354 households for the 2022 TDHS-MIS. A household listing operation was carried out in all the selected EAs before the main survey. During the household listing operation, field staff visited each of the selected EAs to draw location maps and detailed sketch maps and to list all residential households found in each EA with addresses and the names of the heads of the households. The resulting list of households served as a sampling frame for the selection of households in the second stage. During the listing operation, field teams collected global positioning system (GPS) data—latitude, longitude, and altitude readings—to produce one GPS point per EA. To estimate geographic differentials for certain demographic indicators, Tanzania was divided into nine geographic zones. Although these zones are not official administrative areas, this classification system is also used by the Reproductive and Child Health Section of the Ministry of Health. Grouping of regions into zones allows for larger denominators and smaller sampling errors for indicators at the zonal level.

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

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Five questionnaires were used for the 2022 TDHS-MIS: the Household Questionnaire, the Woman’s Questionnaire, the Man’s Questionnaire, the Biomarker Questionnaire, and the Micronutrient Questionnaire. The questionnaires, based on The DHS Program’s Model Questionnaires, were adapted to reflect the population and health issues relevant to Tanzania. In addition, a self-administered Fieldworker’s Questionnaire collected information about the survey’s fieldworkers.

Cleaning operations

In the 2022 TDHS-MIS survey, CAPI was used during data collection. The devices used for CAPI were Android-based computer tablets programmed using a mobile version of CSPro. Programming of questionnaires into the android application was done by ICF, while configuration of tablets was done by NBS and OCGS in collaboration with ICF. All fieldwork personnel were assigned usernames, and devices were password protected to ensure the integrity of the data collected. Selected households were assigned to CAPI supervisors, whereas households were assigned to interviewers’ tablets via Bluetooth. The data for all interviewed households were sent back to CAPI supervisors, who were responsible for initial data consistency and editing, before being sent to the central servers hosted at NBS Headquarters via Syncloud.

The data processing of the 2022 TDHS-MIS ran concurrently with the data collection exercise. The electronic data files from each completed cluster were transferred via Syncloud to the NBS central office server in Dodoma. The data files were registered and checked for inconsistencies, incompleteness, and outliers. Errors and inconsistencies were communicated to the field teams for review and correction. Secondary central data editing was done by NBS and OCGS survey staff at the central office. A CSPro batch editing tool was used for cleaning data and included coding of open-ended questions and resolving inconsistencies.

The Biomarker paper questionnaires were collected by field supervisors and compared with the electronic data files to check for any inconsistencies that may have occurred during data entry. The concurrent data collection and processing offered an advantage because it maximised the likelihood of having error-free data. Timely generation of field check tables allowed effective monitoring. The secondary data editing exercise was completed in October 2022.

Response rate

A total of 16,312 households were selected for the 2022 TDHS-MIS sample. This number is slightly less than the targeted sample size of 16,354 because one EA could not be reached due to security reasons, while a few EAs had less than the targeted 26 households. Of the 16,312 households selected, 15,907 were found to be occupied. Of the occupied households, 15,705 were successfully interviewed, yielding a response rate of 99%. In the interviewed households, 15,699 women age 15–49 were identified as eligible for individual interviews. Interviews were completed with 15,254 women, yielding a response rate of 97%. In the subsample (50% of households) of households selected for the male questionnaire, 6,367 men age 15–49 were identified as eligible for individual interviews, and 5,763 were successfully interviewed, yielding a response rate of 91%.

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 in 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 Tanzania Demographic and Health Survey and Malaria Indicator Survey (2022 TDHS-MIS) 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 TDHS-MIS 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 TDHS-MIS sample was the result of a multistage stratified design, and,

IntroductionDue to the current climatic situation of the planet and the increase in concern for the environment, the Universitat Politècnica de València (UPV) aims to be a model for the university community in terms of the preservation of the ecosystem and prevention of the environmental impact caused by daily tasks; thus, aligning itself with the goals of the 2030 Agenda. For this reason, a project has been launched to carry out the green transformation of the UPV toward a university that prioritizes sustainability in all its areas.MethodsAs part of this project, a survey was conducted using anonymous online questionnaires for the student population and employees. The study aimed to gauge the perception of sustainability and campus food supply and included items related to waste management and public awareness. A total of 800 students and 100 employees from the three UPV campuses (Vera, Alcoy, and Gandía) participated, ranging from 17 to 66 years old.ResultsAfter the statistical analysis of the results, significant differences were identified in most of the questions of the different thematic blocks and, in some cases, in terms of gender and age group. In general, good knowledge about sustainability was observed, although participation in initiatives organized by the university was low in both population groups. On the other hand, as the age of the participants increased, a greater adoption of sustainable behaviors was observed, especially in buying and recycling habits. Regarding the food supply on university campuses, the need to improve it to promote healthier and more sustainable options is highlighted. This work investigates ways to improve the menu choices offered in university settings to promote healthier and more sustainable habits. Additionally, the study aims to identify potential obstacles within the university environment that may hinder these efforts, raise awareness, and encourage more environmentally friendly behaviors.DiscussionThe proposed improvements include: (i) increasing the variety of plant-based options, (ii) sourcing food locally to reduce its carbon footprint, and (iii) implementing a waste management system that encourages recycling.

This feature layer represents the proportion of households that have a sewerage facility. Attributes include a breakdown of sewerage facility by type. The layer has been developed as a proxy to represent SDG 6.2.1 'Proportion of Population Using Safely Managed Sanitation Services, Including a Hand-washing Facility with Soap and Water' for Ireland. Census 2016 data produced by the Central Statistics Office (CSO) and Administrative County boundary data produced by Tailte Éireann were used to create this feature layer.In 2015 UN countries adopted a set of 17 goals to end poverty, protect the planet and ensure prosperity for all as part of a new sustainable development agenda. Each goal has specific targets to help achieve the goals set out in the agenda by 2030. Governments are committed to establishing national frameworks for the achievement of the 17 Goals and to review progress using accessible quality data. With these goals in mind the CSO and Tailte Éireann are working together to link geography and statistics to produce indicators that help communicate and monitor Ireland’s performance in relation to achieving the 17 sustainable development goals.The indicator displayed supports the efforts to achieve goal number 6 which aims to ensure availability and sustainable management of water and sanitation for all.

This feature layer represents the proportion of households that have a sewerage facility. Attributes include a breakdown of sewerage facility by type. The layer has been developed as a proxy to represent SDG 6.2.1 'Proportion of Population Using Safely Managed Sanitation Services, Including a Hand-washing Facility with Soap and Water' for Ireland. Census 2016 data produced by the Central Statistics Office (CSO) and NUTS 3 boundary data produced by Tailte Éireann were used to create this feature layer.In 2015 UN countries adopted a set of 17 goals to end poverty, protect the planet and ensure prosperity for all as part of a new sustainable development agenda. Each goal has specific targets to help achieve the goals set out in the agenda by 2030. Governments are committed to establishing national frameworks for the achievement of the 17 Goals and to review progress using accessible quality data. With these goals in mind the CSO and Tailte Éireann are working together to link geography and statistics to produce indicators that help communicate and monitor Ireland’s performance in relation to achieving the 17 sustainable development goals.The indicator displayed supports the efforts to achieve goal number 6 which aims to ensure availability and sustainable management of water and sanitation for all.

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

This feature layer represents the proportion of the female population at work in a management, director or senior official role. The layer has been developed as a proxy to represent SDG 5.5.2. ‘Proportion of Women in Managerial Positions' for Ireland. Census 2016 data produced by the Central Statistics Office (CSO) and NUTS 3 boundary data produced by Tailte Éireann were used to create this feature layer.In 2015 UN countries adopted a set of 17 goals to end poverty, protect the planet and ensure prosperity for all as part of a new sustainable development agenda. Each goal has specific targets to help achieve the goals set out in the agenda by 2030. Governments are committed to establishing national frameworks for the achievement of the 17 Goals and to review progress using accessible quality data. With these goals in mind the CSO and Tailte Éireann are working together to link geography and statistics to produce indicators that help communicate and monitor Ireland’s performance in relation to achieving the 17 sustainable development goals.The indicator displayed supports the efforts to achieve goal number 5 which aims to achieve gender equality and empower all women and girls.

This feature layer represents the proportion of the female population at work in a management, director or senior official role. The layer has been developed as a proxy to represent SDG 5.5.2. ‘Proportion of Women in Managerial Positions' for Ireland. Census 2016 data produced by the Central Statistics Office (CSO) and Administrative County boundary data produced by Tailte Éireann were used to create this feature layer.In 2015 UN countries adopted a set of 17 goals to end poverty, protect the planet and ensure prosperity for all as part of a new sustainable development agenda. Each goal has specific targets to help achieve the goals set out in the agenda by 2030. Governments are committed to establishing national frameworks for the achievement of the 17 Goals and to review progress using accessible quality data. With these goals in mind the CSO and Tailte Éireann are working together to link geography and statistics to produce indicators that help communicate and monitor Ireland’s performance in relation to achieving the 17 sustainable development goals.The indicator displayed supports the efforts to achieve goal number 5 which aims to achieve gender equality and empower all women and girls.