population density for a five mile radius around Bacons Bridge within Dorchester county

BackgroundAssessing exposure to infections in early childhood is of interest in many epidemiological investigations. Because exposure to infections is difficult to measure directly, epidemiological studies have used surrogate measures available from routine data such as birth order and population density. However, the association between population density and exposure to infections is unclear. We assessed whether neighbourhood child population density is associated with respiratory infections in infants.MethodsWith the Basel-Bern lung infant development study (BILD), a prospective Swiss cohort study of healthy neonates, respiratory symptoms and infections were assessed by weekly telephone interviews with the mother throughout the first year of life. Using population census data, we calculated neighbourhood child density as the number of children < 16 years of age living within a 250 m radius around the residence of each child. We used negative binomial regression models to assess associations between neighbourhood child density and the number of weeks with respiratory infections and adjusted for potential confounders including the number of older siblings, day-care attendance and duration of breastfeeding. We investigated possible interactions between neighbourhood child population density and older siblings assuming that older siblings mix with other children in the neighbourhood.ResultsThe analyses included 487 infants. We found no evidence of an association between quintiles of neighbourhood child density and number of respiratory symptoms (p = 0.59, incidence rate ratios comparing highest to lowest quintile: 1.15, 95%-confidence interval: 0.90–1.47). There was no evidence of interaction with older siblings (p = 0.44). Results were similar in crude and in fully adjusted models.ConclusionsOur study suggests that in Switzerland neighbourhood child density is a poor proxy for exposure to infections in infancy.

population density for a five mile radius around Bacons Bridge within Dorchester county

These data were compiled to help understand how climate change may impact dryland pinyon-juniper ecosystems in coming decades, and how resource management might be able to minimize those impacts. Objective(s) of our study were to model the demographic rates of PJ woodlands to estimate the areas that may decline in the future vs. those that will be stable. We quantified populations growth rates across broad geographic areas, and identified the relative roles of recruitment and mortality in driving potential future changes in population viability in 5 tree species that are major components of these dry forests. We used this demographic model to project pinyon-juniper population stability under future climate conditions, assess how robust these projected changes are, and to identify where on the landscape management strategies that decrease tree competition would effectively resist population decline. These data represent estimated recruitment, mortality and population growth across the distribution of five common pinyon-juniper species across the US Southwest. These data were collected by the US Forest service in their monitoring program, which is a systematic survey of forested regions across the entire US. Our data is from western US states, including AZ, CA, CO, ID, MT, NM, ND, NV, OR, SD, TX, UT, and was collected between 2000-2007, depending on state census collection times. These data were collected by the Forest Inventory and Analysis program of the USDA US Forest Service. Within each established plot, all adult trees greater than 12.7 cm (5 in.) diameter at breast height (DBH) are assigned unique tags and tracked within four, 7.32 m (24 ft.) radius subplots. All saplings <12.7 cm & > 2.54 cm (1 in.) DBH are assigned unique tags and tracked within four, 2.07 m (6.8 ft.) radius microplots within the larger adult plots. Finally, seedlings <2.54 cm DBH are counted within the same microplots as the saplings. Two censuses were conducted 10 years apart in each plot. These data can be used to inform how tree species have unique responses to changing climate conditions and how management actions, like tree density reduction, may effectively resist transformation away from pinyon-juniper woodland to other ecosystem types.

This map shows the population density and total population in the United States in 2010. This is shown by state, county, tract, and block group. The color shows the population per square mile (population density), while the size of each feature shows the total population living there. This is a valuable way to represent population by understanding the quantity and density of the people living there. Areas with high population density are more tightly packed, while low population density means the population is more spread out.The map shows this pattern for states, counties, tracts, and block groups. There is increasing geographic detail as you zoom in, and only one geography is configured to show at any time. The data source is the US Census Bureau, and the vintage is 2010. The original service and data metadata can be found here.

Polygons in this layer represent low food access areas: areas of the District of Columbia which are estimated to be more than a 10-minute walk from the nearest full-service grocery store. These have been merged with Census poverty data to estimate how much of the population within these areas is food insecure (below 185% of the federal poverty line in addition to living in a low food access area).Office of Planning GIS followed several steps to create this layer, including: transit analysis, to eliminate areas of the District within a 10-minute walk of a grocery store; non-residential analysis, to eliminate areas of the District which do not contain residents and cannot classify as low food access areas (such as parks and the National Mall); and Census tract division, to estimate population and poverty rates within the newly created polygon boundaries.Fields contained in this layer include:Intermediary calculation fields for the aforementioned analysis, and:PartPop2: The total population estimated to live within the low food access area polygon (derived from Census tract population, assuming even distribution across the polygon after removing non-residential areas, followed by the removal of population living within a grocery store radius.)PrtOver185: The portion of PartPop2 which is estimated to have household income above 185% of the federal poverty line (the food secure population)PrtUnd185: The portion of PartPop2 which is estimated to have household income below 185% of the federal poverty line (the food insecure population)PercentUnd185: A calculated field showing PrtUnd185 as a percent of PartPop2. This is the percent of the population in the polygon which is food insecure (both living in a low food access area and below 185% of the federal poverty line).Note that the polygon representing Joint Base Anacostia-Bolling was removed from this analysis. While technically classifying as a low food access area based on the OP Grocery Stores layer (since the JBAB Commissary, which only serves military members, is not included in that layer), it is recognized that those who do live on the base have access to the commissary for grocery needs.Last updated November 2017.

Geographic proximity to service centres and population centres is an important determinant of socio-economic and health outcomes. Consequently, it is a relevant dimension in the analysis and delivery of policies and programs. To measure this dimension, Statistics Canada developed an Index of Remoteness of communities. For each populated community (census subdivision), the index is determined by its distance to all the population centres defined by Statistics Canada in a given travel radius, as well as their population size.

This is Census 2020 block data specifically formatted for use by the Environmental Protection Agency (EPA) in-development Environmental Justice Analysis Multisite (EJAM) tool, which uses R code to find which block centroids are within X miles of each specified point (e.g., regulated facility), and to find those distances. The datasets have latitude and longitude of each block's internal point, as provided by Census Bureau, and the FIPS code of the block and its parent block group. The datasets also include a weight for each block, representing this block's Census 2020 population count as a fraction of the count for the parent block group overall, for use in estimating how much of a given block group is within X miles of a specified point or inside a polygon of interest. The datasets also have an effective radius of each block, which is what the radius would be in miles if the block covered the same area in square miles but were circular. The datasets also have coordinates in units that facilitate building a quadtree index of locations. They are in R data.table format, saved as .rda or .arrow files to be read by R code.

Risk factors for respiratory symptoms.

Small planets (Rp<=4R_{Earth}) are divided into rocky super-Earths and gaseous sub-Neptunes separated by a radius gap, but the mechanisms that produce these distinct planet populations remain unclear. Binary stars are the only main-sequence systems with an observable record of the protoplanetary disk lifetime and mass reservoir, and the demographics of planets in binaries may provide insights into planet formation and evolution. To investigate the radius distribution of planets in binary star systems, we observed 207 binary systems hosting 283 confirmed and candidate transiting planets detected by the Kepler mission, then recharacterized the planets while accounting for the observational biases introduced by the secondary star. We found that the population of planets in close binaries ({rho}300au) or single stars. In contrast to planets around single stars, planets in close binaries appear to have a unimodal radius distribution with a peak near the expected super-Earth peak of Rp ~1.3R{Earth}_ and a suppressed population of sub-Neptunes. We conclude that we are observing the direct impact of a reduced disk lifetime, smaller mass reservoir, and possible altered distribution of solids reducing the sub-Neptune formation efficiency. Our results demonstrate the power of binary stars as a laboratory for exploring planet formation and as a controlled experiment of the impact of varied initial conditions on mature planet populations.

Abstract

The Sahel Women Empowerment and Demographic Dividend (P150080) project is a regional project aiming to accelerate the demographic transition by addressing both supply- and demand-side constraints to family planning and reproductive and sexual health. To achieve its objective, the project targets adolescent girls and young women mainly between the ages of 8 and 24, who are vulnerable to early marriage, teenage pregnancy, and early school drop-out. The project targeted 9 countries of the Sahel and Western Africa (Benin, Burkina Faso, Cameroon, Chad, Côte d’Ivoire, Guinea, Mali, Mauritania, and Niger) and is expanding in other African countries. The SWEDD is structured into three main components: component 1 seeks to generate demand for reproductive, maternal, neonatal, child health and nutrition products and services; component 2 seeks to improve supply of these products and qualified personnel; and component 3 seeks to strengthen national capacity and policy dialogue.

The World Bank Africa Gender Innovation Lab and its partners are conducting rigorous impact evaluations of key interventions under component 1 to assess their effects on child marriage, fertility, and adolescent girls and young women’s empowerment. The interventions were a set of activities targeting adolescent girls and their communities, designed in collaboration with the government of Côte d’Ivoire. These were (i) safe spaces to empower girls through the provision of life skills and SRH education; (ii) support to income-generating activities (IGA) with the provision of grants and entrepreneurship training; (iii) husbands’ and future husbands’ clubs, providing boys of the community with life skills and SRH education; and finally (iv) community sensitization by religious and village leaders. The latter two have the objective to change restrictive social norms and create an enabling environment for girls’ empowerment.

These data represent the first round of data collection (baseline) for the impact evaluation.

Geographic coverage

Mali, Regions of Kayes, Ségou and Sikasso

Analysis unit

Households, individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

The baseline sample comprises 8776 households and 7463 girls living in the regions of Kayes, Sikasso and Ségou in Mali. To define the sample, we partnered with INSTAT Mali. At first, INSTAT conducted a census of the population living in the areas around the 49 schools selected by the education focal point that will all benefit from the SWEDD program. Therefore, census activities were concentrated in 287 villages located within a radius of 10/15km around these schools. Eventually, 10 villages had to be dropped due to security reasons. Keeping with the eligibility criteria of surveying villages where there were at least 10 households with a girl aged between 12 and 24 years old, 270 villages were eventually sampled. Households were surveyed before randomization into groups assigned to receive the SWEDD program.

The objective of the baseline survey was to build a comprehensive dataset, which would serve as a reference point for the entire sample, before treatment and control assignment and program implementation.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The questionnaire administrated to girls contains the following sections: 1. Education 2. Marriage and children 3. Aspirations 4. Health and family planning 5. Knowledge of HIV/AIDS 6. Women's empowerment 7. Gender-based violence 8. Income-generating activities 9. Savings and credit 10. Personal relationships and social networks 11. Committee members and community participation

The household questionnaire was administered to the head of the household or to an authorized person capable of answering questions about all individuals in the household. The adolescent questionnaire was administered to an eligible pre-selected girl within the household. Considering the modules of the adolescent questionnaire, it was only administered by female enumerators. The questionnaires were written in French, translated into Bambara, and programmed on tablets in French using the CAPI program.

Census Information By Radio
This dataset falls under the category Traffic Generating Parameters Population.
It contains the following data: Census information of the City, disaggregated by radius.
This dataset was scouted on 2022-02-20 as part of a data sourcing project conducted by TUMI. License information might be outdated: Check original source for current licensing. The data can be accessed using the following URL / API Endpoint: https://data.buenosaires.gob.ar/dataset/informacion-censal-por-radio

This method returns Crystal Roof’s proprietary crime rate map overlays. These overlays are taken directly from our main Crime Rates map.

The overlays are circular PNG images, available in 1,000, 1,500, or 2,000-meter radii.

You can request overlays showing either total crime rates or crime rates for a specific crime type (controlled by the variant parameter).

About Crystal Roof’s Crime Rates Map

• Crime rates are calculated for small geographic areas known as Lower Layer Super Output Areas (LSOAs).
• Rates are calculated per 1,000 residents, using population data from the 2021 Census.
• Crime levels are grouped into 10 categories using our proprietary algorithm, which balances both the distribution of crime values and the number of areas with similar rates. These categories are not standard deciles.
• All figures represent annual data (covering the most recent 12 months).
• The dataset is updated monthly, with a three-month lag between the current date and the most recent available data.

Integration examples

• Google Maps integration example
• Leaflet integration example

Interaction between child density and number of siblings as predictor of respiratory symptoms.

Characteristics of included infants from the BILD cohort.

Experimental public transit transport performance statistics by 200 metre grids for a subset of urban centres in France, with the following fields (Note: These data are experimental, please see the Methods and Known Limitations/Caveats Sections for more details).AttributeDescriptionidUnique IdentifierpopulationGlobal Human Settlement Layer population estimate downsampled to 200 metre (represents the total population across adjacent 100 metre cells)access_popThe total population that can reach the destination cell within 45 minutes using the public transit network (origins within 11.25 kilometres of the destination cell)proxim_popThe total population within an 11.25 kilometre radius of the destination celltrans_perfThe transport performance of the 200 metre cell. The percentage ratio of accessible to proximal populationcity_nmName of the urban centrecountry_nmName of the country that the urban centre belongs toMethods: For more information please visit: · Python Package: https://github.com/datasciencecampus/transport-network-performance · Docker Image: https://github.com/datasciencecampus/transport-performance-docker Known Limitations/Caveats: These data are experimental – see the ONS guidance on experimental statistics for more details. They are being published at this early stage to involve potential users and stakeholders in assessing their quality and suitability. The known caveats and limitations of these experimental statistics are summarised below. Urban Centre and Population Estimates: · Population estimates are derived from data using a hybrid method of satellite imagery and national censuses. The alignment of national census boundaries to gridded estimates introduce measurement errors, particularly in newer housing and built-up developments. See section 2.5 of the GHSL technical report release 2023A for more details. Public Transit Schedule Data (GTFS): · Does not include effects due to delays (such as congestion and diversions). · Common GTFS issues are resolved during preprocessing where possible, including removing trips with unrealistic fast travel between stops, cleaning IDs, cleaning arrival/departure times, route name deduplication, dropping stops with no stop times, removing undefined parent stations, and dropping trips, shapes, and routes with no stops. Certain GTFS cleaning steps were not possible in all instances, and in those cases the impacted steps were skipped. Additional work is required to further support GTFS validation and cleaning. Transport Network Routing: · “Trapped” centroids: the centroid of destination cells on very rare occasions falls on a private road/pathway. Routing to these cells cannot be performed. This greatly decreases the transport performance in comparison with the neighbouring cells. Potential solutions include interpolation based on neighbouring cells or snapping to the nearest public OSM node (and adjusting the travel time accordingly). Further development to adapt the method for this consideration is necessary. Please also visit the Python package and Docker Image GitHub issues pages for more details. How to Contribute: We hope that the public, other public sector organisations, and National Statistics Institutions can collaborate and build on these data, to help improve the international comparability of statistics and enable higher frequency and more timely comparisons. We welcome feedback and contribution either through GitHub or by contacting datacampus@ons.gov.uk.

Experimental public transit transport performance statistics by 200 metre grids for a subset of urban centres in Great Britain, with the following fields (Note: These data are experimental, please see the Methods and Known Limitations/Caveats Sections for more details).AttributeDescriptionidUnique IdentifierpopulationGlobal Human Settlement Layer population estimate downsampled to 200 metre (represents the total population across adjacent 100 metre cells)access_popThe total population that can reach the destination cell within 45 minutes using the public transit network (origins within 11.25 kilometres of the destination cell)proxim_popThe total population within an 11.25 kilometre radius of the destination celltrans_perfThe transport performance of the 200 metre cell. The percentage ratio of accessible to proximal populationcity_nmName of the urban centrecountry_nmName of the country that the urban centre belongs toMethods: For more information please visit: · Python Package: https://github.com/datasciencecampus/transport-network-performance · Docker Image: https://github.com/datasciencecampus/transport-performance-docker Known Limitations/Caveats: These data are experimental – see the ONS guidance on experimental statistics for more details. They are being published at this early stage to involve potential users and stakeholders in assessing their quality and suitability. The known caveats and limitations of these experimental statistics are summarised below. Urban Centre and Population Estimates: · Population estimates are derived from data using a hybrid method of satellite imagery and national censuses. The alignment of national census boundaries to gridded estimates introduce measurement errors, particularly in newer housing and built-up developments. See section 2.5 of the GHSL technical report release 2023A for more details. Public Transit Schedule Data (GTFS): · Does not include effects due to delays (such as congestion and diversions). · Common GTFS issues are resolved during preprocessing where possible, including removing trips with unrealistic fast travel between stops, cleaning IDs, cleaning arrival/departure times, route name deduplication, dropping stops with no stop times, removing undefined parent stations, and dropping trips, shapes, and routes with no stops. Certain GTFS cleaning steps were not possible in all instances, and in those cases the impacted steps were skipped. Additional work is required to further support GTFS validation and cleaning. Transport Network Routing: · “Trapped” centroids: the centroid of destination cells on very rare occasions falls on a private road/pathway. Routing to these cells cannot be performed. This greatly decreases the transport performance in comparison with the neighbouring cells. Potential solutions include interpolation based on neighbouring cells or snapping to the nearest public OSM node (and adjusting the travel time accordingly). Further development to adapt the method for this consideration is necessary. Please also visit the Python package and Docker Image GitHub issues pages for more details. How to Contribute: We hope that the public, other public sector organisations, and National Statistics Institutions can collaborate and build on these data, to help improve the international comparability of statistics and enable higher frequency and more timely comparisons. We welcome feedback and contribution either through GitHub or by contacting datacampus@ons.gov.uk.

1) Data Introduction • The Asteroseismology of ~16000 Kepler red giants Dataset contains asteroseismic analysis data for approximately 16,000 red giants observed by the Kepler Space Telescope. It includes stellar evolutionary phase (RGB/HeB), oscillation characteristics (such as maximum oscillation frequency, mean frequency separation, and amplitude), as well as temperature, gravity, metallicity, mass, radius, and other astronomical and physical parameters. This dataset enables the study of the structure and evolutionary properties of red giants.

2) Data Utilization (1) Characteristics of the Asteroseismology of ~16000 Kepler red giants Dataset: • With large-scale asteroseismic observations and evolutionary phase (RGB/HeB) classification, this dataset is well-suited for research on stellar evolution, internal structure, and analysis of red giant populations using precise astrophysical parameters.

(2) Applications of the Asteroseismology of ~16000 Kepler red giants Dataset: • Stellar Evolutionary Phase Classification: Astronomical observation data can be used to develop machine learning models for classifying and predicting the evolutionary phase (RGB/HeB) of red giants. • Physical Properties Analysis: By comprehensively analyzing various oscillation and physical parameters, the dataset can be widely applied to studies of stellar internal structure, mass and radius estimation, and population statistics in astrophysical research.

Distal Radius Fracture System Market Outlook

The global distal radius fracture system market size was valued at approximately USD 1.2 billion in 2023 and is expected to reach USD 2.3 billion by 2032, growing at a compound annual growth rate (CAGR) of 7.5% over the forecast period. Significant advancements in medical technology, coupled with the increasing incidence of orthopedic injuries, are driving this market's growth. Factors such as an aging population prone to osteoporosis, rising sports-related injuries, and improved healthcare infrastructure are fueling the demand for distal radius fracture systems globally.

One of the primary growth factors for the distal radius fracture system market is the aging global population. As people age, their bones tend to lose density and strength, making them more susceptible to fractures. According to the World Health Organization (WHO), the global population aged 60 years and over is expected to reach 2.1 billion by 2050. This demographic shift significantly increases the need for effective fracture management solutions, thereby propelling the market for distal radius fracture systems. Additionally, the growing prevalence of osteoporosis, a condition that weakens bones, further accentuates the demand for these systems.

Technological advancements in medical devices also play a crucial role in the market's growth. Innovations such as bioabsorbable materials, 3D-printed implants, and minimally invasive surgical techniques have revolutionized fracture treatment. These advancements not only improve the efficacy of fracture management but also reduce recovery times, making them highly attractive to both patients and healthcare providers. The continuous research and development activities in this field are expected to bring forth even more advanced solutions, thus fueling market growth.

Another significant driver for the distal radius fracture system market is the increasing number of sports-related injuries. With the rise in recreational and professional sporting activities, the incidence of sports injuries, including distal radius fractures, has seen a notable increase. According to the American Academy of Orthopaedic Surgeons (AAOS), wrist fractures are among the most common types of sports injuries. The need for effective and quick recovery solutions in athletes is driving the demand for advanced fracture systems, thereby boosting market growth.

The Distal Fibula Plating System is gaining attention as an essential component in the management of fractures, particularly in the lower extremities. This system is designed to provide stability and support for fractures of the distal fibula, a common site of injury in both athletic and elderly populations. The use of anatomically contoured plates and screws in the distal fibula plating system allows for precise fixation, promoting optimal healing and alignment. As with distal radius fracture systems, advancements in materials and surgical techniques are enhancing the effectiveness of distal fibula plating systems, making them a preferred choice for orthopedic surgeons. The integration of minimally invasive approaches with these systems is further improving patient outcomes by reducing recovery times and minimizing surgical trauma. The growing demand for comprehensive fracture management solutions underscores the importance of distal fibula plating systems in modern orthopedic practice.

Geographically, North America holds the largest share of the distal radius fracture system market, owing to its well-established healthcare infrastructure and high adoption of advanced medical technologies. However, the Asia Pacific region is expected to witness the fastest growth during the forecast period. Factors such as improving healthcare facilities, increasing healthcare expenditure, and a growing aging population are contributing to this rapid growth. Additionally, the rising awareness about advanced fracture treatment options in emerging economies like India and China is further propelling the market in this region.

Product Type Analysis

In the distal radius fracture system market, product types play a crucial role in determining the appropriate treatment for various fracture types. Plates and screws are among the most commonly used products for distal radius fractures. These devices offer robust fixation and stability, facilitating the proper alignment and healing of fractures. Technological advancements have led to the development of anatomically contoured

Abstract

Malawi Conditional Cash Transfer Program (CCT) is a randomized cash transfer intervention targeting young women in Zomba region. The program provides incentives to current schoolgirls and recent dropouts to stay in or return to school. The incentives include average payment of US$10 a month conditional on satisfactory school attendance and direct payment of secondary school fees.

The CCT program started at the beginning of the Malawian school year in January 2008 and continued until November 2009. The impact evaluation study was designed to evaluate the impact of the program on various demographic and health outcomes of its target population, such as nutritional health, sexual behavior, fertility, and subsequent HIV risk.

Baseline data collection was administered from September 2007 to January 2008. The research targeted girls and young women, between the ages of 13 and 22, who were never married. Overall, 3,810 girls and young women were surveyed in the first round. The follow-up survey was carried out from October 2008 to February 2009. The third round was conducted between March and September 2010, after Malawi Conditional Cash Transfer Program was completed. The fourth round started in April 2012 and will continue until September 2012.

Datasets from the baseline round are documented here.

Enumeration Areas (EAs) in the study district of Zomba were selected from the universe of EAs produced by the National Statistics Office of Malawi from the 1998 Census. 176 enumeration areas were randomly sampled out of a total of 550 EAs using three strata: urban areas, rural areas near Zomba Town, and rural areas far from Zomba Town.

Baseline schoolgirls in treatment enumeration areas were randomly assigned to receive either conditional or unconditional transfers, or no transfers at all. A multi-topic questionnaire was administered to the heads of households, where the selected sample respondents resided, as well as to girls and young women.

Geographic coverage

Zomba district.

Zomba district in the Southern region was chosen as the site for this study for several reasons. First, it has a large enough population within a small enough geographic area rendering field work logistics easier and keeping transport costs lower. Zomba is a highly populated district, but distances from the district capital (Zomba Town) are relatively small. Second, characteristic of Southern Malawi, Zomba has a high rate of school dropouts and low educational attainment. Third, unlike many other districts, Zomba has the advantage of having a true urban center as well as rural areas. As the study sample was stratified to get representative samples from urban areas (Zomba town), rural areas near Zomba town, and distant rural areas in the district, we can analyze the heterogeneity of the impacts by urban/rural areas. Finally, while Southern Malawi, which includes Zomba, is poorer, has lower levels of education, and higher rates of HIV than Central and Northern Malawi, these differences are relative considering that Malawi is one of the poorest countries in the world with one of the highest rates of HIV prevalence.

Analysis unit

• Households;
• Girls and young women.

Universe

The survey covers never married girls and young women between the ages of 13 and 22 in Zomba district.

Kind of data

Sample survey data [ssd]

Sampling procedure

First, 176 enumeration areas (EA) were randomly sampled out of a total of 550 EAs using three strata in the study district of Zomba. Each of these 176 EAs were then randomly assigned treatment or control status. The three strata are urban, rural areas near Zomba Town, and rural areas far from Zomba Town. Rural areas were defined as being near if they were within a 16-kilometer radius of Zomba Town. Researchers did not sample any EAs in TA Mbiza due to safety concerns (112 EAs).

Enumeration areas (EAs) in Zomba were selected from the universe of EAs produced by the National Statistics Office of Malawi from the 1998 Census. The sample of EAs was stratified by distance to the nearest township or trading centre. Of the 550 EAs in Zomba, 50 are in Zomba town and an additional 30 are classified as urban (township or trading center), while the remaining 470 are rural (population areas, or PAs). The stratified random sample of 176 EAs consisted of 29 EAs in Zomba town, eight trading centers in Zomba rural, 111 population areas within 16 kilometers of Zomba town, and 28 EAs more than 16 kilometers from Zomba town.

After selecting sample EAs, all households were listed in the 176 sample EAs using a short two-stage listing procedure. The first form, Form A, asked each household the following question: "Are there any never-married girls in this household who are between the ages of 13 and 22?" This form allowed the field teams to quickly identify households with members fitting into the sampling frame, thus significantly reducing the costs of listing. If the answer received on Form A was a "yes", then Form B was filled to list members of the household to collect data on age, marital status, current schooling status, etc.

From this researchers could categorize the target population into two main groups: those who were out of school at baseline (baseline dropouts) and those who were in school at baseline (baseline schoolgirls). These two groups comprise the basis of our sampling frame. In each EA, enumerators sampled all eligible dropouts and 75%-100% of all eligible school girls, where the percentage depended on the age of the baseline schoolgirl. This sampling procedure led to a total sample size of 3,810 (in the first round, and 3,805 in follow-up rounds) with an average of 5.1 dropouts and 16.7 schoolgirls per EA.

Mode of data collection

Face-to-face [f2f]

Research instrument

The annual household survey consists of a multi-topic questionnaire administered to the households in which the selected sample respondents reside. The survey consists of two parts: one that is administered to the head of the household and another that is administered to the core respondent - the sampled girl from the target population. The former collects information on the household roster, dwelling characteristics, household assets and durables, shocks and consumption. The core respondent survey provides information about her family background, her education and labor market participation, her health, her dating patterns, sexual behavior, marital expectations, knowledge of HIV/AIDS, her social networks, as well as her own consumption of girl-specific goods (such as soaps, mobile phone airtime, clothing, braids, sodas and alcoholic drinks, etc.).