Abstract Public open spaces are key to cities, as a place where society can create and recreate public life. Thus, we aim to investigate the production of these areas in slums through concepts such as “right to the city”, “return to the city”, accessibility, mobility, and the implications of fragmentation and segregation, in promoting the socio-integration into the city. The empirical object is the “Beira Molhada” slum in João Pessoa, Paraíba, because of its inclusion in peri-urban area and of environmental preservation, with large public open spaces. The methodology consists of literature and documentary research, mental maps and geo-referenced analyticals. The results indicate an issue poorly related with city and environmental goods under threat. However, there is great potential for socio-spatial integration of the slum with the city, by encouraging social dynamic that provides accessibility and mobility to the environmental heritage and its fruition, contributing to the establishment of an “urban society” as stated by Lefebvre.

As of 2022, **** percent of the global urban population lived in slums. In Sub-Saharan Africa, the slum population amounted to **** percent of the total population, which is the highest out of all major regions. In Australia and New Zealand, on the other hand, the urban population living in slums was zero in 2022.

Greater Mumbai Municipal Corporation: Revenue Expenditure Fund 22 Slum Clearance

This dataset provides survey responses from 240 people surveyed as part of the "Investigating the Impact of Kenya’s Open Data Initiative on Marginalized Communities: Case Study of Urban Slums and Rural Settlements" project.

The data, collected in mid-2013 looks at issues of where citizens look for data, and how successful they have been in getting government information from different sources, as well as their awareness of the Kenya open data portal, and their interest in getting information through different digital channels in future.

Descriptive statistics have been analysed in the publication "Open Government Data for Effective Public Participation: Findings of a Case Study Research Investigating The Kenya's Open Data Initiative in Urban Slums and Rural Settlements", but no further analysis has yet been carried out.

Data descriptions

The Codebook.csv file lists variable names and the questions asked to elicit each response.

JHC-Data.csv contains the results from the questionnaires collected through structured in-person interview in the three locations. The questionnaires were administered at chiefs centres, community resource centres, constituency development fund office and religious centres). The questionnaires were filled in by every 2nd these centres.

More information

More information on the research project can be found at http://opendataresearch.org/project/2013/jhc

Greater Mumbai Municipal Corporation: Budget Summary (Capital) Fund 23 Slum Improvement

We collected the data presented in this paper in partnership with the slum dwellers in order to overcome the challenges such as validity and efficacy of self-reported data. Our survey of Bangalore slums covered 36 slums across the city. The slums were chosen based on stratification criteria which included the geographical location of the slums, whether the slums were resettled or rehabilitated, slums in planned localities, the size of the slum and the religious profile. This paper describes the relational model of the slum dataset, the variables in the dataset, the variables constructed for analysis and the issues identified with the dataset. The data collected includes around 267,894 data points spread over 242 questions for 1107 households. The dataset can facilitate interdisciplinary research on spatial and temporal dynamics of urban poverty and well-being in the context of rapid urbanization of cities in developing countries.In 2010, an estimated 860 million people were living in slums worldwide with around 60 million added to the slum population between 2000 and 2010. In 2011, 200 million people in urban Indian households were considered to live in slums. To identify the poor is to be able to deliver benefits to them. Unfortunately, there is a paucity of highly granular data at the level of individual slums. We collected the data presented in this paper in partnership with the slum dwellers in order to overcome the challenges such as validity and efficacy of self-reported data. Our survey of Bangalore slums covered 36 slums across the city. The slums were chosen based on stratification criteria which included the geographical location of the slums, whether the slums were resettled or rehabilitated, slums in planned localities, the size of the slum and the religious profile. This paper describes the relational model of the slum dataset, the variables in the dataset, the variables constructed for analysis and the issues identified in the dataset. The data collected includes around 267,894 data points spread over 242 questions for 1107 households. The dataset can facilitate interdisciplinary research on spatial and temporal dynamics of urban poverty and well-being in the context of rapid urbanization of cities in developing countries. The data was captured in paper questionnaires with handwritten responses, with most answers coded into structured replies, in addition to a few open-ended questions. The data collected from this survey underwent cleaning and was stored in a relational database for further analysis. Specifically, the data was vetted by the enumerators and research team by randomly picking households and a site visit with field verification was carried out. Once the data was verified by the surveyors, the filled-in questionnaires were translated to English and then digitized by an independent group. The research team then carried out two rounds of validation, in the first round, the data was checked for consistency and outliers and in the second round, the research team coordinated with the enumerators to validate any discrepancies.

BackgroundLeptospirosis has emerged as an urban health problem as slum settlements have rapidly spread worldwide and created conditions for rat-borne transmission. Prospective studies have not been performed to determine the disease burden, identify risk factors for infection and provide information needed to guide interventions in these marginalized communities.Methodology/Principal FindingsWe enrolled and followed a cohort of 2,003 residents from a slum community in the city of Salvador, Brazil. Baseline and one-year serosurveys were performed to identify primary and secondary Leptospira infections, defined as respectively, seroconversion and four-fold rise in microscopic agglutination titers. We used multinomial logistic regression models to evaluate risk exposures for acquiring primary and secondary infection. A total of 51 Leptospira infections were identified among 1,585 (79%) participants who completed the one-year follow-up protocol. The crude infection rate was 37.8 per 1,000 person-years. The secondary infection rate was 2.3 times higher than that of primary infection rate (71.7 and 31.1 infections per 1,000 person-years, respectively). Male gender (OR 2.88; 95% CI 1.40–5.91) and lower per capita household income (OR 0.54; 95% CI, 0.30–0.98 for an increase of $1 per person per day) were independent risk factors for primary infection. In contrast, the 15–34 year age group (OR 10.82, 95% CI 1.38–85.08), and proximity of residence to an open sewer (OR 0.95; 0.91–0.99 for an increase of 1 m distance) were significant risk factors for secondary infection.Conclusions/SignificanceThis study found that slum residents had high risk (>3% per year) for acquiring a Leptospira infection. Re-infection is a frequent event and occurs in regions of slum settlements that are in proximity to open sewers. Effective prevention of leptospirosis will therefore require interventions that address the infrastructure deficiencies that contribute to repeated exposures among slum inhabitants.

Throughout the developing world, urban centres with sprawling slum settlements are rapidly expanding and invading previously forested ecosystems. Slum communities are characterized by untended refuse, open sewers and overgrown vegetation, which promote rodent infestation. Norway rats (Rattus norvegicus) are reservoirs for epidemic transmission of many zoonotic pathogens of public health importance. Understanding the population ecology of R. norvegicus is essential to formulate effective rodent control strategies, as this knowledge aids estimation of the temporal stability and spatial connectivity of populations. We screened for genetic variation, characterized the population genetic structure and evaluated the extent and patterns of gene flow in the urban landscape using 17 microsatellite loci in 146 rats from nine sites in the city of Salvador, Brazil. These sites were divided between three neighbourhoods within the city spaced an average of 2.7 km apart. Surprisingly, we detected very little relatedness among animals trapped at the same site and found high levels of genetic diversity, as well as structuring across small geographical distances. Most FST comparisons among sites were statistically significant, including sites <400 m apart. Bayesian analyses grouped the samples in three genetic clusters, each associated with distinct sampling sites from different neighbourhoods or valleys within neighbourhoods. These data indicate the existence of complex genetic structure in R. norvegicus in Salvador, linked to the heterogeneous urban landscape. Future rodent control measures need to take into account the spatial and temporal linkage of rat populations in Salvador, as revealed by genetic data, to develop informed eradication strategies.

BackgroundLeptospirosis is an important zoonotic disease that causes considerable morbidity and mortality globally, primarily in residents of urban slums. While contact with contaminated water plays a critical role in the transmission of leptospirosis, little is known about the distribution and abundance of pathogenic Leptospira spp. in soil and the potential contribution of this source to human infection.Methods/Principal findingsWe collected soil samples (n = 70) from three sites within an urban slum community endemic for leptospirosis in Salvador, Brazil. Using qPCR of Leptospira genes lipl32 and 16S rRNA, we quantified the pathogenic Leptospira load in each soil sample. lipl32 qPCR detected pathogenic Leptospira in 22 (31%) of 70 samples, though the median concentration among positive samples was low (median = 6 GEq/g; range: 4–4.31×102 GEq/g). We also observed heterogeneity in the distribution of pathogenic Leptospira at the fine spatial scale. However, when using 16S rRNA qPCR, we detected a higher proportion of Leptospira-positive samples (86%) and higher bacterial concentrations (median: 4.16×102 GEq/g; range: 4–2.58×104 GEq/g). Sequencing of the qPCR amplicons and qPCR analysis with all type Leptospira species revealed that the 16S rRNA qPCR detected not only pathogenic Leptospira but also intermediate species, although both methods excluded saprophytic Leptospira. No significant associations were identified between the presence of pathogenic Leptospira DNA and environmental characteristics (vegetation, rat activity, distance to an open sewer or a house, or soil clay content), though samples with higher soil moisture content showed higher prevalences.Conclusion/SignificanceThis is the first study to successfully quantify the burden of pathogenic Leptospira in soil from an endemic region. Our results support the hypothesis that soil may be an under-recognized environmental reservoir contributing to transmission of pathogenic Leptospira in urban slums. Consequently, the role of soil should be considered when planning interventions aimed to reduce the burden of leptospirosis in these communities.

Greater Mumbai Municipal Corporation Budget B-Fund 22 (Slum Clearance) 2016-17

BackgroundDespite the multitude of healthcare services available in India, health inequalities persist. People in low-resource settings are both disadvantaged and have the greatest need for healthcare. To address these disparities and achieve universal health coverage, healthcare services need to be tailored to the specific needs of this population.ObjectiveThis study aimed to understand health and healthcare perceptions of people in slums and villages in and around Bengaluru, a city in the southern part of India. It was conducted in partnership with Bangalore Baptist Hospital, a charity hospital dedicated to supporting underserved populations in this region.MethodsThe study employed qualitative methods. Twenty-eight open-ended interviews and eight focus groups were conducted with residents of selected slums and villages in and around Bengaluru. The interviews were transcribed verbatim, translated to English and analyzed applying thematic analysis.Results and conclusionThe study finds that participants defined health as the absence of illness, the ability to work, and the result of a good lifestyle. With regards to healthcare expectations, the analysis shows the themes of the “good doctor,” recovering quickly, cost affordability, cleanliness, and emergency services and diagnostic facilities. In addition, stigma related to healthcare, was identified, especially among residents of villages. Participants highlight the importance of good relationships with healthcare providers and accessible healthcare facilities to improve healthcare uptake in Bengaluru's slums and rural areas. This study also shows that achieving universal health coverage requires addressing not only direct costs but also other associated expenses like travel and lost wages, considering healthcare costs as a comprehensive expense tied to patients' living conditions. These results contribute to the growing body of literature on health and healthcare perceptions in low-resource settings, offering insights that may inform future research and context-specific strategies for improving healthcare access and delivery.

No description was included in this Dataset collected from the OSF

BackgroundMore than a third of the world's children are infected with intestinal nematodes. Current control approaches emphasise treatment of school age children, and there is a lack of information on the effects of deworming preschool children.MethodologyWe studied the effects on the heights and weights of 3,935 children, initially 1 to 5 years of age, of five rounds of anthelmintic treatment (400 mg albendazole) administered every 6 months over 2 years. The children lived in 50 areas, each defined by precise government boundaries as urban slums, in Lucknow, North India. All children were offered vitamin A every 6 months, and children in 25 randomly assigned slum areas also received 6-monthly albendazole. Treatments were delivered by the State Integrated Child Development Scheme (ICDS), and height and weight were monitored at baseline and every 6 months for 24 months (trial registration number NCT00396500). p Value calculations are based only on the 50 area-specific mean values, as randomization was by area.FindingsThe ICDS infrastructure proved able to deliver the interventions. 95% (3,712/3,912) of those alive at the end of the study had received all five interventions and had been measured during all four follow-up surveys, and 99% (3,855/3,912) were measured at the last of these surveys. At this final follow up, the albendazole-treated arm exhibited a similar height gain but a 35 (SE 5) % greater weight gain, equivalent to an extra 1 (SE 0.15) kg over 2 years (99% CI 0.6–1.4 kg, p = 10−11).ConclusionsIn such urban slums in the 1990s, five 6-monthly rounds of single dose anthelmintic treatment of malnourished, poor children initially aged 1–5 years results in substantial weight gain. The ICDS system could provide a sustainable, inexpensive approach to the delivery of anthelmintics or micronutrient supplements to such populations. As, however, we do not know the control parasite burden, these results are difficult to generalize.Trial RegistrationClinicalTrials.gov NCT00396500

Abstract

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

Geographic coverage

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

Analysis unit

The unit of analysis is the deceased individual

Universe

All NUHDSS residents that are deceased.

Sampling procedure

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

Mode of data collection

Face-to-face [f2f]

Research instrument

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

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

All questionnaires are provided as external resources.

Cleaning operations

Data editing took place at a number of stages throughout the processing, including:

1. Quality control through back-checks on 10 percent of completed questionnaires and editing of all completed questionnaires by supervisors and project management staff.

2. A quality control officer performed internal consistency checks for all questionnaires and edited all paper questionnaires coming from the field before their submission for data entry with return of incorrectly filled questionnaires to the field for error-resolution.

3. During data entry, any questionnaires that were found to be inconsistent were returned to the field for resolution.

4. Data cleaning and editting was carried out using STATA Version 13 software.

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

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

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

Abstract

The DPHS in Addis Ababa and Dire Dawa was conducted in May and June 2017, with the objective to assess the role of poverty in disaster risk, focusing primarily on urban flooding but also other hazards.

This project was a collaborative effort between Global Facility for Disaster Reduction and Recovery (GFDRR), the Poverty Global Practice and Urban, Disaster Risk Management, Resilience and Land Global Practice (GPURL). Data collection was carried out by UDA Consulting under the supervision of the World Bank.

Geographic coverage

Cities of Addis Ababa and Dire Dawa, Ethiopia.

Analysis unit

• Household
• Individual

Kind of data

Sample survey data [ssd]

Sampling procedure

Satellite images of Addis Adaba and Dire Dawa were used to divide both cities into 100m x 100m grids and among those, 173 and 81 grids in Addis Ababa and Dire Dawa respectively were randomly selected. In each selected grid, a 10 x 10 meters secondary dot grids were created. Then, in each secondary grid, 5 households were randomly assessed for inclusion. If the house corresponded to the characteristics of a residential and “low-income/slum” dwelling, it was included in the sample. While the sampling was carried out in a manner to assure representativeness at the city level, caution should be taken before generalizing results generating from this data for the entire city population. This is because the sample intended to sample slum dwellers and low-income households (based on factors that are detectable in high-resolution satellite imagery and visible from above, such as quality of roofing and dwelling size, size of plot, etc.).

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The survey questionnaire consists of 13 sections that were used to collect the survey data. See the attached questionnaire.

Cleaning operations

The following data editing was done for anonymization purposes: • Precise location data, such as GPS coordinates, and 10 x 10 meters grids were dropped • Personal information, such as names and phone numbers were dropped • The number of religions reported was reduced from 6 to 3 categories, the number of ethnicities from 14 to 4 categories, marital status from 6 to 4 categories • Household size exceeding seven household members was categorized as “above 7 members” • Household member information for 7th member and above was dropped to avoid reconstruction of the household size variable.

For more information on the anonymization process, see the Technical Document.

Abstract

The National Family Health Surveys (NFHS) programme, initiated in the early 1990s, has emerged as a nationally important source of data on population, health, and nutrition for India and its states. The 2005-06 National Family Health Survey (NFHS-3), the third in the series of these national surveys, was preceded by NFHS-1 in 1992-93 and NFHS-2 in 1998-99. Like NFHS-1 and NFHS-2, NFHS-3 was designed to provide estimates of important indicators on family welfare, maternal and child health, and nutrition. In addition, NFHS-3 provides information on several new and emerging issues, including family life education, safe injections, perinatal mortality, adolescent reproductive health, high-risk sexual behaviour, tuberculosis, and malaria. Further, unlike the earlier surveys in which only ever-married women age 15-49 were eligible for individual interviews, NFHS-3 interviewed all women age 15-49 and all men age 15-54. Information on nutritional status, including the prevalence of anaemia, is provided in NFHS3 for women age 15-49, men age 15-54, and young children.

A special feature of NFHS-3 is the inclusion of testing of the adult population for HIV. NFHS-3 is the first nationwide community-based survey in India to provide an estimate of HIV prevalence in the general population. Specifically, NFHS-3 provides estimates of HIV prevalence among women age 15-49 and men age 15-54 for all of India, and separately for Uttar Pradesh and for Andhra Pradesh, Karnataka, Maharashtra, Manipur, and Tamil Nadu, five out of the six states classified by the National AIDS Control Organization (NACO) as high HIV prevalence states. No estimate of HIV prevalence is being provided for Nagaland, the sixth high HIV prevalence state, due to strong local opposition to the collection of blood samples.

NFHS-3 covered all 29 states in India, which comprise more than 99 percent of India's population. NFHS-3 is designed to provide estimates of key indicators for India as a whole and, with the exception of HIV prevalence, for all 29 states by urban-rural residence. Additionally, NFHS-3 provides estimates for the slum and non-slum populations of eight cities, namely Chennai, Delhi, Hyderabad, Indore, Kolkata, Meerut, Mumbai, and Nagpur. NFHS-3 was conducted under the stewardship of the Ministry of Health and Family Welfare (MOHFW), Government of India, and is the result of the collaborative efforts of a large number of organizations. The International Institute for Population Sciences (IIPS), Mumbai, was designated by MOHFW as the nodal agency for the project. Funding for NFHS-3 was provided by the United States Agency for International Development (USAID), DFID, the Bill and Melinda Gates Foundation, UNICEF, UNFPA, and MOHFW. Macro International, USA, provided technical assistance at all stages of the NFHS-3 project. NACO and the National AIDS Research Institute (NARI) provided technical assistance for the HIV component of NFHS-3. Eighteen Research Organizations, including six Population Research Centres, shouldered the responsibility of conducting the survey in the different states of India and producing electronic data files.

The survey used a uniform sample design, questionnaires (translated into 18 Indian languages), field procedures, and procedures for biomarker measurements throughout the country to facilitate comparability across the states and to ensure the highest possible data quality. The contents of the questionnaires were decided through an extensive collaborative process in early 2005. Based on provisional data, two national-level fact sheets and 29 state fact sheets that provide estimates of more than 50 key indicators of population, health, family welfare, and nutrition have already been released. The basic objective of releasing fact sheets within a very short period after the completion of data collection was to provide immediate feedback to planners and programme managers on key process indicators.

Geographic coverage

• National (29 states )
• Regional (for HIV Prevalence : Andhra Pradesh, Karnataka, Maharashtra, Manipur, and Tamil Nadu)
• Local (population and health indicators for slum and non-slum populations for eight cities, namely Chennai, Delhi, Hyderabad, Indore, Kolkata, Meerut, Mumbai, and Nagpur)

Analysis unit

• Household
• Women age 15-49
• Men age 15-59

Universe

The population covered by the 2005 DHS is defined as the universe of all ever-married women age 15-49, NFHS-3 included never married women age 15-49 and both ever-married and never married men age 15-54 as eligible respondents.

Kind of data

Sample survey data

Sampling procedure

SAMPLE SIZE

Since a large number of the key indicators to be estimated from NFHS-3 refer to ever-married women in the reproductive ages of 15-49, the target sample size for each state in NFHS-3 was estimated in terms of the number of ever-married women in the reproductive ages to be interviewed.

The initial target sample size was 4,000 completed interviews with ever-married women in states with a 2001 population of more than 30 million, 3,000 completed interviews with ever-married women in states with a 2001 population between 5 and 30 million, and 1,500 completed interviews with ever-married women in states with a population of less than 5 million. In addition, because of sample-size adjustments required to meet the need for HIV prevalence estimates for the high HIV prevalence states and Uttar Pradesh and for slum and non-slum estimates in eight selected cities, the sample size in some states was higher than that fixed by the above criteria. The target sample was increased for Andhra Pradesh, Karnataka, Maharashtra, Manipur, Nagaland, Tamil Nadu, and Uttar Pradesh to permit the calculation of reliable HIV prevalence estimates for each of these states. The sample size in Andhra Pradesh, Delhi, Maharashtra, Tamil Nadu, Madhya Pradesh, and West Bengal was increased to allow separate estimates for slum and non-slum populations in the cities of Chennai, Delhi, Hyderabad, Indore, Kolkata, Mumbai, Meerut, and Nagpur.

The target sample size for HIV tests was estimated on the basis of the assumed HIV prevalence rate, the design effect of the sample, and the acceptable level of precision. With an assumed level of HIV prevalence of 1.25 percent and a 15 percent relative standard error, the estimated sample size was 6,400 HIV tests each for men and women in each of the high HIV prevalence states. At the national level, the assumed level of HIV prevalence of less than 1 percent (0.92 percent) and less than a 5 percent relative standard error yielded a target of 125,000 HIV tests at the national level.

Blood was collected for HIV testing from all consenting ever-married and never married women age 15-49 and men age 15-54 in all sample households in Andhra Pradesh, Karnataka, Maharashtra, Manipur, Tamil Nadu, and Uttar Pradesh. All women age 15-49 and men age 15-54 in the sample households were eligible for interviewing in all of these states plus Nagaland. In the remaining 22 states, all ever-married and never married women age 15-49 in sample households were eligible to be interviewed. In those 22 states, men age 15-54 were eligible to be interviewed in only a subsample of households. HIV tests for women and men were carried out in only a subsample of the households that were selected for men's interviews in those 22 states. The reason for this sample design is that the required number of HIV tests is determined by the need to calculate HIV prevalence at the national level and for some states, whereas the number of individual interviews is determined by the need to provide state level estimates for attitudinal and behavioural indicators in every state. For statistical reasons, it is not possible to estimate HIV prevalence in every state from NFHS-3 as the number of tests required for estimating HIV prevalence reliably in low HIV prevalence states would have been very large.

SAMPLE DESIGN

The urban and rural samples within each state were drawn separately and, to the extent possible, unless oversampling was required to permit separate estimates for urban slum and non-slum areas, the sample within each state was allocated proportionally to the size of the state's urban and rural populations. A uniform sample design was adopted in all states. In each state, the rural sample was selected in two stages, with the selection of Primary Sampling Units (PSUs), which are villages, with probability proportional to population size (PPS) at the first stage, followed by the random selection of households within each PSU in the second stage. In urban areas, a three-stage procedure was followed. In the first stage, wards were selected with PPS sampling. In the next stage, one census enumeration block (CEB) was randomly selected from each sample ward. In the final stage, households were randomly selected within each selected CEB.

SAMPLE SELECTION IN RURAL AREAS

In rural areas, the 2001 Census list of villages served as the sampling frame. The list was stratified by a number of variables. The first level of stratification was geographic, with districts being subdivided into contiguous regions. Within each of these regions, villages were further stratified using selected variables from the following list: village size, percentage of males working in the nonagricultural sector, percentage of the population belonging to scheduled castes or scheduled tribes, and female literacy. In addition to these variables, an external estimate of HIV prevalence, i.e., 'High', 'Medium' or 'Low', as estimated for all the districts in high HIV prevalence states, was used for stratification in high HIV prevalence states. Female literacy was used for implicit stratification (i.e., villages were

Abstract

The places we live affect our health status and the choices and opportunities we have (or do not have) to lead fulfilling lives. Over the past ten years, the African Population & Health Research Centre (APHRC) has led pioneering work in highlighting some of the major health and livelihood challenges associated with rapid urbanization in sub-Saharan Africa (SSA). In 2002, the Centre established the first longitudinal platform in urban Africa in the city of Nairobi in Kenya. The platform known as the Nairobi Urban Health and Demographic Surveillance System collects data on two informal settlements - Korogocho and Viwandani - in Nairobi City every four months on issues ranging from household dynamics to fertility and mortality, migration and livelihood as well as on causes of death, using a verbal autopsy technique. The dataset provided here contains key demographic and health indicators extracted from the longitudinal database. Researchers interested in accessing the micro-data can look at our data access policy and contact us.

Geographic coverage

The Demographic Surveillance Area (combining Viwandani and Korogocho slum settlements) covers a land area of about 0.97 km2, with the two informal settlements located about 7 km from each other. Korogocho is located 12 km from the Nairobi city center; in Kasarani division (now Kasarani district), while Viwandani is about 7 km from Nairobi city center in Makadara division (now Madaraka district). The DSA covers about seven villages each in Korogocho and Viwandani.

Analysis unit

Individual

Universe

Between 1st January and 31st December,2015 the Nairobi HDSS covered 86,304 individualis living in 30,219 households distributed across two informal settlements(Korogocho and Viwandani) were observed. All persons who sleep in the household prior to the day of the survey are included in the survey, while non-resident household members are excluded from the survey.

The present universe started out through an initial census carried out on 1st August,2002 of the population living in the two Informal settlements (Korogocho and Viwandani). Regular visits have since then been made (3 times a year) to update information on births, deaths and migration that have occurred in the households observed at the initial census. New members join the population through a birth to a registered member, or an in-migration, while existing members leave through a death or out-migration. The DSS adopts the concept of an open cohort that allows new members to join and regular members to leave and return to the system.

Kind of data

Event history data

Frequency of data collection

Three rounds in a year

Sampling procedure

This dataset is related to the whole demographic surveillance area population. The number of respondents has varied over the last 13 years (2002-2015), with variations being observed at both household level and at Individual level. As at 31st December 2015, 66,848 were being observed under the Nairobi HDSS living in 25,812 households distributed across two informal settlements(Korogocho and Viwandani). The variable IndividualId uniquely identifies every respondent observed while the variable LocationId uniquely identifies the room in which the individual was living at any point in time. To identify individuals who were living together at any one point in time (a household) the data can be split on location and observation dates.

Sampling deviation

None

Mode of data collection

Proxy Respondent [proxy]

Research instrument

Questionnaires are printed and administered in Swahili, the country's national language.

The questionnaires for the Nairobi HDSS were structured questionnaires based on the INDEPTH Model Questionnaire and were translated into Swahili with some modifications and additions.After an initial review the questionnaires were translated back into English by an independent translator with no prior knowledge of the survey. The back translation from the Swahili version was independently reviewed and compared to the English original. Differences in translation were reviewed and resolved in collaboration with the original translators. The English and Swahili questionnaires were both piloted as part of the survey pretest.

At baseline, a household questionnaire was administered in each household, which collected various information on household members including sex, age, relationship, and orphanhood status. In later rounds questionnaires to track the migration of the population observed at baseline, and additonal questionnaires to capture demographic and health events happening to the population have been introduced.

Cleaning operations

Data editing took place at a number of stages throughout the processing, including: a) Office editing and coding b) During data entry c) Structure checking and completeness d) Secondary editing e) Structural checking of STATA data files

Where changes were made by the program, a cold deck imputation is preferred; where incorrect values were imputed using existing data from another dataset. If cold deck imputation was found to be insufficient, hot deck imputation was used, In this case, a missing value was imputed from a randomly selected similar record in the same dataset.

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

1. 100% forms filled in by FRAs are rechecked for completeness, ensured that all the necessary event forms are filled in.
2. Spot checks are done on field over data collection by FRAs for reliability of data.
3. FRS instructs revisits wherever required.
4. Forms are checked on sample basis
5. Checks if all the necessary event forms are filled in.
6. Forms with inconsistencies identified at the time of entry are sent back to the field.
7. Creating and managing data entry checks for picking up inconsistencies
8. Monitoring field work: balancing work target and quality.
9. Dealing with data inconsistencies at data level and giving feedbacks to field staff.
10. Conducting training and refresher training wherever required.
11. Data cleaning

Response rate

Over the years the response rate at household level has varied between 95% and 97% with response rate at Individual Level varying between 92% and 95%. Challenges to acheiving a 100% response rate have included: - high population mobility within the study area - high population attrition - respondent fatigue - security in some areas

Sampling error estimates

Not applicable for surveillance data

Data appraisal

CentreId MetricTable QMetric Illegal Legal Total Metric RunDate KE031 MicroDataCleaned Starts 219285 2017-05-16 18:25
KE031 MicroDataCleaned Transitions 825036 825036 0 2017-05-16 18:25
KE031 MicroDataCleaned Ends 219285 2017-05-16 18:25
KE031 MicroDataCleaned SexValues 825036 2017-05-16 18:25
KE031 MicroDataCleaned DoBValues 42 824994 825036 0 2017-05-16 18:25

Goal 11Make cities and human settlements inclusive, safe, resilient and sustainableTarget 11.1: By 2030, ensure access for all to adequate, safe and affordable housing and basic services and upgrade slumsIndicator 11.1.1: Proportion of urban population living in slums, informal settlements or inadequate housingEN_LND_SLUM: Proportion of urban population living in slums (%)Target 11.2: By 2030, provide access to safe, affordable, accessible and sustainable transport systems for all, improving road safety, notably by expanding public transport, with special attention to the needs of those in vulnerable situations, women, children, persons with disabilities and older personsIndicator 11.2.1: Proportion of population that has convenient access to public transport, by sex, age and persons with disabilitiesTarget 11.3: By 2030, enhance inclusive and sustainable urbanization and capacity for participatory, integrated and sustainable human settlement planning and management in all countriesIndicator 11.3.1: Ratio of land consumption rate to population growth rateIndicator 11.3.2: Proportion of cities with a direct participation structure of civil society in urban planning and management that operate regularly and democraticallyTarget 11.4: Strengthen efforts to protect and safeguard the world’s cultural and natural heritageIndicator 11.4.1: Total per capita expenditure on the preservation, protection and conservation of all cultural and natural heritage, by source of funding (public, private), type of heritage (cultural, natural) and level of government (national, regional, and local/municipal)Target 11.5: By 2030, significantly reduce the number of deaths and the number of people affected and substantially decrease the direct economic losses relative to global gross domestic product caused by disasters, including water-related disasters, with a focus on protecting the poor and people in vulnerable situationsIndicator 11.5.1: Number of deaths, missing persons and directly affected persons attributed to disasters per 100,000 populationVC_DSR_MISS: Number of missing persons due to disaster (number)VC_DSR_AFFCT: Number of people affected by disaster (number)VC_DSR_MORT: Number of deaths due to disaster (number)VC_DSR_MTMP: Number of deaths and missing persons attributed to disasters per 100,000 population (number)VC_DSR_MMHN: Number of deaths and missing persons attributed to disasters (number)VC_DSR_DAFF: Number of directly affected persons attributed to disasters per 100,000 population (number)VC_DSR_IJILN: Number of injured or ill people attributed to disasters (number)VC_DSR_PDAN: Number of people whose damaged dwellings were attributed to disasters (number)VC_DSR_PDYN: Number of people whose destroyed dwellings were attributed to disasters (number)VC_DSR_PDLN: Number of people whose livelihoods were disrupted or destroyed, attributed to disasters (number)Indicator 11.5.2: Direct economic loss in relation to global GDP, damage to critical infrastructure and number of disruptions to basic services, attributed to disastersVC_DSR_GDPLS: Direct economic loss attributed to disasters (current United States dollars)VC_DSR_LSGP: Direct economic loss attributed to disasters relative to GDP (%)VC_DSR_AGLH: Direct agriculture loss attributed to disasters (current United States dollars)VC_DSR_HOLH: Direct economic loss in the housing sector attributed to disasters (current United States dollars)VC_DSR_CILN: Direct economic loss resulting from damaged or destroyed critical infrastructure attributed to disasters (current United States dollars)VC_DSR_CHLN: Direct economic loss to cultural heritage damaged or destroyed attributed to disasters (millions of current United States dollars)VC_DSR_CDAN: Number of damaged critical infrastructure attributed to disasters (number)VC_DSR_HFDN: Number of destroyed or damaged health facilities attributed to disasters (number)VC_DSR_EFDN: Number of destroyed or damaged educational facilities attributed to disasters (number)VC_DSR_CDYN: Number of other destroyed or damaged critical infrastructure units and facilities attributed to disasters (number)VC_DSR_BSDN: Number of disruptions to basic services attributed to disasters (number)VC_DSR_ESDN: Number of disruptions to educational services attributed to disasters (number)VC_DSR_HSDN: Number of disruptions to health services attributed to disasters (number)VC_DSR_OBDN: Number of disruptions to other basic services attributed to disasters (number)VC_DSR_DDPA: Direct economic loss to other damaged or destroyed productive assets attributed to disasters (current United States dollars)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 managementIndicator 11.6.1: Proportion of municipal solid waste collected and managed in controlled facilities out of total municipal waste generated, by citiesEN_REF_WASCOL: Municipal Solid Waste collection coverage, by cities (%)Indicator 11.6.2: Annual mean levels of fine particulate matter (e.g. PM2.5 and PM10) in cities (population weighted)EN_ATM_PM25: Annual mean levels of fine particulate matter in cities, urban population (micrograms per cubic meter)Target 11.7: By 2030, provide universal access to safe, inclusive and accessible, green and public spaces, in particular for women and children, older persons and persons with disabilitiesIndicator 11.7.1: Average share of the built-up area of cities that is open space for public use for all, by sex, age and persons with disabilitiesIndicator 11.7.2: Proportion of persons victim of physical or sexual harassment, by sex, age, disability status and place of occurrence, in the previous 12 monthsTarget 11.a: Support positive economic, social and environmental links between urban, peri-urban and rural areas by strengthening national and regional development planningIndicator 11.a.1: Number of countries that have national urban policies or regional development plans that (a) respond to population dynamics; (b) ensure balanced territorial development; and (c) increase local fiscal spaceSD_CPA_UPRDP: Countries that have national urban policies or regional development plans that respond to population dynamics; ensure balanced territorial development; and increase local fiscal space (1 = YES; 0 = NO)Target 11.b: By 2020, substantially increase the number of cities and human settlements adopting and implementing integrated policies and plans towards inclusion, resource efficiency, mitigation and adaptation to climate change, resilience to disasters, and develop and implement, in line with the Sendai Framework for Disaster Risk Reduction 2015–2030, holistic disaster risk management at all levelsIndicator 11.b.1: Number of countries that adopt and implement national disaster risk reduction strategies in line with the Sendai Framework for Disaster Risk Reduction 2015–2030SG_DSR_LGRGSR: Score of adoption and implementation of national DRR strategies in line with the Sendai FrameworkSG_DSR_SFDRR: Number of countries that reported having a National DRR Strategy which is aligned to the Sendai FrameworkIndicator 11.b.2: Proportion of local governments that adopt and implement local disaster risk reduction strategies in line with national disaster risk reduction strategiesSG_DSR_SILS: Proportion of local governments that adopt and implement local disaster risk reduction strategies in line with national disaster risk reduction strategies (%)SG_DSR_SILN: Number of local governments that adopt and implement local DRR strategies in line with national strategies (number)SG_GOV_LOGV: Number of local governments (number)Target 11.c: Support least developed countries, including through financial and technical assistance, in building sustainable and resilient buildings utilizing local materials