Abstract

Abstract

Background: Adolescent girls in Kenya are disproportionately affected by early and unintended pregnancies, unsafe abortion and HIV infection. The In Their Hands (ITH) programme in Kenya aims to increase adolescents' use of high-quality sexual and reproductive health (SRH) services through targeted interventions. ITH Programme aims to promote use of contraception and testing for sexually transmitted infections (STIs) including HIV or pregnancy, for sexually active adolescent girls, 2) provide information, products and services on the adolescent girl's terms; and 3) promote communities support for girls and boys to access SRH services.

Objectives: The objectives of the evaluation are to assess: a) to what extent and how the new Adolescent Reproductive Health (ARH) partnership model and integrated system of delivery is working to meet its intended objectives and the needs of adolescents; b) adolescent user experiences across key quality dimensions and outcomes; c) how ITH programme has influenced adolescent voice, decision-making autonomy, power dynamics and provider accountability; d) how community support for adolescent reproductive and sexual health initiatives has changed as a result of this programme.

Methodology ITH programme is being implemented in two phases, a formative planning and experimentation in the first year from April 2017 to March 2018, and a national roll out and implementation from April 2018 to March 2020. This second phase is informed by an Annual Programme Review and thorough benchmarking and assessment which informed critical changes to performance and capacity so that ITH is fit for scale. It is expected that ITH will cover approximately 250,000 adolescent girls aged 15-19 in Kenya by April 2020. The programme is implemented by a consortium of Marie Stopes Kenya (MSK), Well Told Story, and Triggerise. ITH's key implementation strategies seek to increase adolescent motivation for service use, create a user-defined ecosystem and platform to provide girls with a network of accessible subsidized and discreet SRH services; and launch and sustain a national discourse campaign around adolescent sexuality and rights. The 3-year study will employ a mixed-methods approach with multiple data sources including secondary data, and qualitative and quantitative primary data with various stakeholders to explore their perceptions and attitudes towards adolescents SRH services. Quantitative data analysis will be done using STATA to provide descriptive statistics and statistical associations / correlations on key variables. All qualitative data will be analyzed using NVIVO software.

Study Duration: 36 months - between 2018 and 2020.

Geographic coverage

Homabay,Kakamega,Nakuru and Nairobi counties

Analysis unit

Private health facilities that provide T-safe services under the In Their Hands(ITH) Program.

Universe

1.Adolescent girls aged 15-19 who enrolled on the T-safe platform and received services and those who enrolled but did not receive services from the ITH facilities. 2.Service providers incharge of provision of T-safe services in the ITH facilities. 3.Mobilisers incharge of adolescent girls aged 15-19 recruitment into the T-safe program.

Sampling procedure

Qualitative Sampling

IDI participants were selected purposively from ITH intervention areas and facilities located in the four ITH intervention counties; Homa Bay, Nakuru, Kakamega and Nairobi respectively which were selected for the midline survey. Study participants were identified from selected intervention facilities. We interviewed one service provider of adolescent friendly ITH services per facility. Additionally, we conducted IDI's with adolescent girls' who were enrolled and using/had used the ITH platform to access reproductive health services or enrolled but may not have accessed the services for other reasons.

Sample coverage We successfully conducted a total of 122 In-depth Interviews with 54 adolescents enrolled on the T-Safe platform, including those who received services and those who were enrolled but did not receive services, 39 IDIS with service providers and 29 IDIs with mobilizers. The distribution per county included 51 IDI's in Nairobi City County (24 with adolescent girls, 17 with service providers and 10 with mobilisers), 15 IDI's in Nakuru County (2 with adolescent girls,8 with service providers and 5 with mobilisers), 34 IDI's in Homa Bay County (18 with adolescent girls,8 with service providers and 8 with mobilisers) and 22 IDI's in Kakamega County (10 with adolescent girls,6 with service providers and another 6 with mobilisers.)

Sampling deviation

N/A

Mode of data collection

Face-to-face [f2f]

Research instrument

The midline evaluation included qualitative in-depth interviews with adolescent T-Safe users, adolescents enrolled in the platform but did not use the services, providers and mobilizers to assess the adolescent user experience and quality of services as well as provider accountability under the T-Safe program. Generally,the aim of the qualitative study was to assess adolescents' T-Safe users experience across quality dimensions as well as provider's experiences and accountability. The dimensions assessed include adolescent's journey with the platforms, experience with the platform, perceptions of quality of services and how the ITH platforms changed provider behavior and accountability.

Adolescent in-depth interview included:Adolescent journey,Barriers to adolescents access to SRH services,Community attitudes towards adolescent use of contraceptives,Decision making,Factors influencing decision to visit a clinic,Motivating factors for girls to join ITH,Notable changes since the introduction of ITH,Parental support ,and Perceptions about T-Safe.

Service providers in-depth interview included;Personal and professional background,Provider's experience with ITH/T-safe platform,Notable changes/influences since the introduction of ITH/T-safe,Influence/Impact on the preference of adolescent service users and health care providers as a result of the program,Impact/influence of ITH on quality of care,Facilitators and barriers for adolescents to access SRH services,Mechanisms to address the barriers,Challenges related to the facility,Feedback about facility from adolescents,Types of support needed to improve SRH services provided to adolescents Scenarios of different clients accessing SRH services,and Free node.

Mobilisers in-depth interview included;Mobilizer responsibilities and designation,Job description,Motivation for joining ITH,Personal and professional background,Training,Mobilizer roles in ITH,Mobilization process ,Experience with ITH platform,Key messages shared with adolescent about ITH/ Tsafe during enrollment,Motivating factors for adolescents to join ITH/Tsafe,Community's attitude towards ITH/Tsafe,Challenges faced by mobilizers when mobilizing adolescents for Tsafe,Adolescents view regarding platform,Addressing the challenges ,andFree node

Cleaning operations

Qualitative interviews were audio-recorded and the audio recordings were transmitted to APHRC study team by uploading the audios to google drive which was only accessible to the team. Related interview notes, participant's description forms and Informed consent forms were transported to APHRC offices in Nairobi at the end of data collection where the data transcription and coding was conducted. Audio recordings from qualitative interviews were transcribed and saved in MS Word format. The transcripts were stored electronically in password protected computers and were only accessible to the evaluation team working on the project. A qualitative software analysis program (NVIVO) was used to assist in coding and analyzing the data. A “thematic analysis” approach was used to organize and analyze the data, and to assist in the development of a codebook and coding scheme. Data was analyzed by first reading the full IDI transcripts, becoming familiar with the data and noting the themes and concepts that emerged. A thematic framework was developed from the identified themes and sub-themes and this was then used to create codes and code the raw data.

Response rate

N/A

Sampling error estimates

N/A

According to a 2021 survey, organizations worldwide anticipate a move towards cloud based data storage as part of their business continuity and disaster recovery plans. While around a ***** of respondents reported using cloud disaster recovery as a service (DRaaS) solutions in 2022, over **** anticipate to be doing so by 2023. This is matched with an anticipated decline in the hosting of secondary data on-site.

Abstract (en): The National Center for Early Development and Learning (NCEDL) combined the data of two major studies in order to understand variations among state-funded pre-kindergarten (pre-k) programs and in turn, how these variations relate to child outcomes at the end of pre-k and in kindergarten. The Multi-State Study of Pre-Kindergarten and the State-Wide Early Education Programs (SWEEP) Study provide detailed information on pre-kindergarten teachers, children, and classrooms in 11 states. By combining data from both studies, information is available from 721 classrooms and 2,982 pre-kindergarten children in these 11 states. Pre-kindergarten data collection for the Multi-State Study of Pre-Kindergarten took place during the 2001-2002 school year in six states: California, Georgia, Illinois, Kentucky, New York, and Ohio. These states were selected from among states that had committed significant resources to pre-k initiatives. States were selected to maximize diversity with regard to geography, program settings (public school or community setting), program intensity (full-day vs. part-day), and educational requirements for teachers. In each state, a stratified random sample of 40 centers/schools was selected from the list of all the school/centers or programs (both contractors and subcontractors) provided to the researchers by each state's department of education. In total, 238 sites participated in the fall and two additional sites joined the study in the spring. Participating teachers helped the data collectors recruit children into the study by sending recruitment packets home with all children enrolled in the classroom. On the first day of data collection, the data collectors determined which of the children were eligible to participate. Eligible children were those who (1) would be old enough for kindergarten in the fall of 2002, (2) did not have an Individualized Education Plan, according to the teacher, and (3) spoke English or Spanish well enough to understand simple instructions, according to the teacher. Pre-kindergarten data collection for the SWEEP Study took place during the 2003-2004 school year in five states: Massachusetts, New Jersey, Texas, Washington, and Wisconsin. These states were selected to complement the states already in the Multi-State Study of Pre-K by including programs with significantly different funding models or modes of service delivery. In each of the five states, 100 randomly selected state-funded pre-kindergarten sites were recruited for participation in the study from a list of all sites provided by the state. In total, 465 sites participated in the fall. Two sites declined to continue participation in the spring, resulting in 463 sites participating in the spring. Participating teachers helped the data collectors recruit children into the study by sending recruitment packets home with all children enrolled in the classroom. On the first day of data collection, the data collectors determined which of the children were eligible to participate. Eligible children were those who (1) would be old enough for kindergarten in the fall of 2004, (2) did not have an Individualized Education Plan, according to the teacher, and (3) spoke English or Spanish well enough to understand simple instructions, according to the teacher. Demographic information collected across both studies includes race, teacher gender, child gender, family income, mother's education level, and teacher education level. The researchers also created a variable for both the child-level data and the class-level data which allows secondary users to subset cases according to either the Multi-State or SWEEP study. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Performed recodes and/or calculated derived variables.. Response Rates: Multi-State: Of the 40 sites per state, 78 percent of eligible sites agreed to participate (fall of pre-k, n = 238). For fall of pre-k (n = 238), 94 percent of the one classroom per site selected agreed to participate. For fall (n = 940) and spring (n = 960) of pre-k, 61 percent of the parents of eligible children consented.; SWEEP: Of the 10...

The research team collected data from statewide datasets on 268 stalking cases including a population of 108 police identified stalking cases across Rhode Island between 2001 and 2005 with a sample of 160 researcher identified stalking incidents (incidents that met statutory criteria for stalking but were cited by police for other domestic violence offenses) during the same period. The secondary data used for this study came from the Rhode Island Supreme Court Domestic Violence Training and Monitoring Unit's (DVU) statewide database of domestic violence incidents reported to Rhode Island law enforcement. Prior criminal history data were obtained from records of all court cases entered into the automated Rhode Island court file, CourtConnect. The data contain a total of 121 variables including suspect characteristics, victim characteristics, incident characteristics, police response characteristics, and prosecutor response characteristics.

Background: Core vocabulary lists provide an evidence-based method for describing the vocabulary of individuals across various age groups, categorised by different parts of speech. Despite its value, there is a paucity of core vocabulary lists in nonmainstream languages. Resource limitations contribute to this paucity; therefore, more efficient methods for developing core vocabulary lists are needed Purpose: This study aimed to compare two sets of previously collected language samples from typically developing five- to six-year-old Afrikaans-speaking children to compare two different elicitation methods for developing a core vocabulary list. We also compared the duration of the language samples to inform the duration required for accurate and representative language samples for the development of core vocabulary lists. Methods: Using secondary data analysis, we compared the core vocabulary lists from two existing data sets in terms of the number of different words (NDW), the frequency of use of each of these words, type-token ratio (TTR), and parts of speech used by typically developing five- to six-year-old Afrikaans-speaking children. Results: The average recording time for Data set A was 60 minutes in a single session. The corresponding value for Data set B was 250 minutes, recorded over a period of one to three days. A perfect positive Spearman correlation was observed between the results for the two data sets for all parts of speech except interjections and enclitics. Code switching formed part of Data set B’s core words but did not appear in Data set A’s core word list. Conclusions: The findings demonstrate that similar core vocabulary lists can be obtained for five- to six-year-old children using a less invasive and time-effective 60-minute elicited method for language samples compared to naturalistic samples collected over one to three days. Proposing a more robust and less time- and resource-intensive method of developing vocabulary lists may further support the development of core word lists across ages and in other languages.

This table contains some of the science results from the Nuclear Spectroscopic Telescope Array (NuSTAR) Serendipitous Survey. The catalog incorporates data taken during the first 40 months of NuSTAR operation, which provide ~20 Ms of effective exposure time over 331 fields, with an areal coverage of 13 deg². The primary catalog (available as the HEASARC NUSTARSSC table) contains 498 sources (the abstract of the reference paper states that there are 497 sources) detected in total over the 3-24 keV energy range. There are 276 sources with spectroscopic redshifts and classifications, largely resulting from the authors' extensive campaign of ground-based spectroscopic follow-up. The authors characterize the overall sample in terms of the X-ray, optical, and infrared source properties. The sample is primarily composed of active galactic nuclei (AGN), detected over a large range in redshift from z = 0.002 to 3.4 (median redshift z of 0.56), but also includes 16 spectroscopically confirmed Galactic sources. There is a large range in X-ray flux, from log (f_3-24_keV) ~ -14 to -11 (in units of erg s^-1 cm^-2), and in rest-frame 10-40 keV luminosity, from log (L_10-40keV) ~ 39 to 46 (in units of erg s^-1), with a median of 44.1. Approximately 79% of the NuSTAR sources have lower-energy (<10 keV) X-ray counterparts from XMM-Newton, Chandra, and Swift XRT observations. The mid-infrared (MIR) analysis, using WISE all-sky survey data, shows that MIR AGN color selections miss a large fraction of the NuSTAR-selected AGN population, from ~15% at the highest luminosities (L_X > 10⁴⁴ erg s^-1) to ~80% at the lowest luminosities (L_X < 10⁴³ erg s^-1). The authors' optical spectroscopic analysis finds that the observed fraction of optically obscured AGN (i.e., the type 2 fraction) is F_Type2 = 53 (+14, -15) per cent, for a well-defined subset of the 8-24 keV selected sample. This is higher, albeit at a low significance level, than the type 2 fraction measured for redshift- and luminosity-matched AGNs selected by < 10 keV X-ray missions. This table contains the Secondary NuSTAR Serendipitous Source Catalog of 64 sources found using wavdetect to search for significant emission peaks in the FPMA and FPMB data separately (see Section 2.1.1 of Alexander et al. 2013, ApJ, 773, 125) and in the combined A+B data. These sources are listed in Table 7 of the reference paper. This method was developed alongside the primary one (Section 2.3 of the reference paper) in order to investigate the optimum source detection methodologies for NuSTAR and to identify sources in regions of the NuSTAR coverage that are automatically excluded in the primary source detection. The authors emphasize that these secondary sources are not used in any of the science analyses presented in their paper. Nevertheless, these secondary sources are robust NuSTAR detections, some of which will be incorporated in future NuSTAR studies, and for many of them (35 out of the 43 sources with spectroscopic identifications) the authors have obtained new spectroscopic redshifts and classifications through their follow-up program. The X-ray photometric parameters for 4 sources are left blank as in these cases the A+B data prohibit reliable photometric constraints. Additional information on these Secondary Catalog sources that the authors obtained using optical spectroscopy is available in Table 8 of the reference paper (q.v.). This table does NOT contain the the 498 sources in the Primary NuSTAR Serendipitous Source Catalog that were found using the source detection procedure described in Section 2.3 of the reference paper, and that are listed in Table 5 (op. cit.). This table was created by the HEASARC in July 2017 based on the machine-readable version of Table 7 from the reference paper, the Secondary NuSTAR Serendipitous Source Catalog, that was obtained from the ApJ web site. This is a service provided by NASA HEASARC .

Abstract

The primary objective of the 2017 Indonesia Dmographic and Health Survey (IDHS) is to provide up-to-date estimates of basic demographic and health indicators. The IDHS provides a comprehensive overview of population and maternal and child health issues in Indonesia. More specifically, the IDHS was designed to: - provide data on fertility, family planning, maternal and child health, and awareness of HIV/AIDS and sexually transmitted infections (STIs) to help program managers, policy makers, and researchers to evaluate and improve existing programs; - measure trends in fertility and contraceptive prevalence rates, and analyze factors that affect such changes, such as residence, education, breastfeeding practices, and knowledge, use, and availability of contraceptive methods; - evaluate the achievement of goals previously set by national health programs, with special focus on maternal and child health; - assess married men’s knowledge of utilization of health services for their family’s health and participation in the health care of their families; - participate in creating an international database to allow cross-country comparisons in the areas of fertility, family planning, and health.

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 age 15-49 years resident in the household, and all men age 15-54 years resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The 2017 IDHS sample covered 1,970 census blocks in urban and rural areas and was expected to obtain responses from 49,250 households. The sampled households were expected to identify about 59,100 women age 15-49 and 24,625 never-married men age 15-24 eligible for individual interview. Eight households were selected in each selected census block to yield 14,193 married men age 15-54 to be interviewed with the Married Man's Questionnaire. The sample frame of the 2017 IDHS is the Master Sample of Census Blocks from the 2010 Population Census. The frame for the household sample selection is the updated list of ordinary households in the selected census blocks. This list does not include institutional households, such as orphanages, police/military barracks, and prisons, or special households (boarding houses with a minimum of 10 people).

The sampling design of the 2017 IDHS used two-stage stratified sampling: Stage 1: Several census blocks were selected with systematic sampling proportional to size, where size is the number of households listed in the 2010 Population Census. In the implicit stratification, the census blocks were stratified by urban and rural areas and ordered by wealth index category.

Stage 2: In each selected census block, 25 ordinary households were selected with systematic sampling from the updated household listing. Eight households were selected systematically to obtain a sample of married men.

For further details on sample design, see Appendix B of the final report.

Mode of data collection

Face-to-face [f2f]

Research instrument

The 2017 IDHS used four questionnaires: the Household Questionnaire, Woman’s Questionnaire, Married Man’s Questionnaire, and Never Married Man’s Questionnaire. Because of the change in survey coverage from ever-married women age 15-49 in the 2007 IDHS to all women age 15-49, the Woman’s Questionnaire had questions added for never married women age 15-24. These questions were part of the 2007 Indonesia Young Adult Reproductive Survey Questionnaire. The Household Questionnaire and the Woman’s Questionnaire are largely based on standard DHS phase 7 questionnaires (2015 version). The model questionnaires were adapted for use in Indonesia. Not all questions in the DHS model were included in the IDHS. Response categories were modified to reflect the local situation.

Cleaning operations

All completed questionnaires, along with the control forms, were returned to the BPS central office in Jakarta for data processing. The questionnaires were logged and edited, and all open-ended questions were coded. Responses were entered in the computer twice for verification, and they were corrected for computer-identified errors. Data processing activities were carried out by a team of 34 editors, 112 data entry operators, 33 compare officers, 19 secondary data editors, and 2 data entry supervisors. The questionnaires were entered twice and the entries were compared to detect and correct keying errors. A computer package program called Census and Survey Processing System (CSPro), which was specifically designed to process DHS-type survey data, was used in the processing of the 2017 IDHS.

Response rate

Of the 49,261 eligible households, 48,216 households were found by the interviewer teams. Among these households, 47,963 households were successfully interviewed, a response rate of almost 100%.

In the interviewed households, 50,730 women were identified as eligible for individual interview and, from these, completed interviews were conducted with 49,627 women, yielding a response rate of 98%. From the selected household sample of married men, 10,440 married men were identified as eligible for interview, of which 10,009 were successfully interviewed, yielding a response rate of 96%. The lower response rate for men was due to the more frequent and longer absence of men from the household. In general, response rates in rural areas were higher than those in urban areas.

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 result from mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding 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 2017 Indonesia Demographic and Health Survey (2017 IDHS) 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 2017 IDHS 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 error is a measure of the variability among 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 2017 IDHS sample is the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex formulas. The computer software used to calculate sampling errors for the 2017 IDHS is a STATA program. This program used the Taylor linearization method for variance estimation for survey estimates that are means or proportions. 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 C of the survey final report.

Data appraisal

Data Quality Tables - Household age distribution - Age distribution of eligible and interviewed women - Age distribution of eligible and interviewed men - Completeness of reporting - Births by calendar year - Reporting of age at death in days - Reporting of age at death in months

See details of the data quality tables in Appendix D of the survey final report.

Abstract

The 1991 Indonesia Demographic and Health Survey (IDHS) is a nationally representative survey of ever-married women age 15-49. It was conducted between May and July 1991. The survey was designed to provide information on levels and trends of fertility, infant and child mortality, family planning and maternal and child health. The IDHS was carried out as collaboration between the Central Bureau of Statistics, the National Family Planning Coordinating Board, and the Ministry of Health. The IDHS is follow-on to the National Indonesia Contraceptive Prevalence Survey conducted in 1987.

The DHS program has four general objectives: - To provide participating countries with data and analysis useful for informed policy choices; - To expand the international population and health database; - To advance survey methodology; and - To help develop in participating countries the technical skills and resources necessary to conduct demographic and health surveys.

In 1987 the National Indonesia Contraceptive Prevalence Survey (NICPS) was conducted in 20 of the 27 provinces in Indonesia, as part of Phase I of the DHS program. This survey did not include questions related to health since the Central Bureau of Statistics (CBS) had collected that information in the 1987 National Socioeconomic Household Survey (SUSENAS). The 1991 Indonesia Demographic and Health Survey (IDHS) was conducted in all 27 provinces of Indonesia as part of Phase II of the DHS program. The IDHS received financial assistance from several sources.

The 1991 IDHS was specifically designed to meet the following objectives: - To provide data concerning fertility, family planning, and maternal and child health that can be used by program managers, policymakers, and researchers to evaluate and improve existing programs; - To measure changes in fertility and contraceptive prevalence rates and at the same time study factors which affect the change, such as marriage patterns, urban/rural residence, education, breastfeeding habits, and the availability of contraception; - To measure the development and achievements of programs related to health policy, particularly those concerning the maternal and child health development program implemented through public health clinics in Indonesia.

Geographic coverage

National

Analysis unit

• Household
• Children under five years
• Women age 15-49
• Men

Kind of data

Sample survey data [ssd]

Sampling procedure

Indonesia is divided into 27 provinces. For the implementation of its family planning program, the National Family Planning Coordinating Board (BKKBN) has divided these provinces into three regions as follows:

• Java-Bali: Jakarta, West Java, Central Java, Yogyakarta, East Java, and Bali
• Outer Java-Bali I: Aceh, North Sumatra, West Sumatra, South Sumatra, Lampung, West Kalimantan, South Kalimantan, North Sulawesi, South Sulawesi, and West Nusa Tenggara
• Outer Java-Bali II: Riau, Jambi, Bengkulu, East Nusa Tenggara, East Timor, Central Kalimantan, East Kalimantan, Central Sulawesi, Southeast Sulawesi, Maluku, and Irian Jaya.

The 1990 Population Census of Indonesia shows that Java-Bali contains about 62 percent of the national population, while Outer Java-Bali I contains 27 percent and Outer Java-Bali II contains 11 percent. The sample for the Indonesia DHS survey was designed to produce reliable estimates of contraceptive prevalence and several other major survey variables for each of the 27 provinces and for urban and rural areas of the three regions.

In order to accomplish this goal, approximately 1500 to 2000 households were selected in each of the provinces in Java-Bali, 1000 households in each of the ten provinces in Outer Java-Bali I, and 500 households in each of the 11 provinces in Outer Java-Bali II for a total of 28,000 households. With an average of 0.8 eligible women (ever-married women age 15-49) per selected household, the 28,000 households were expected to yield approximately 23,000 individual interviews.

Note: See detailed description of sample design in APPENDIX A of the survey report.

Mode of data collection

Face-to-face [f2f]

Research instrument

The DHS model "A" questionnaire and manuals were modified to meet the requirements of measuring family planning and health program attainment, and were translated into Bahasa Indonesia.

Cleaning operations

The first stage of data editing was done by the field editors who checked the completed questionnaires for completeness and accuracy. Field supervisors also checked the questionnaires. They were then sent to the central office in Jakarta where they were edited again and open-ended questions were coded. The data were processed using 11 microcomputers and ISSA (Integrated System for Survey Analysis).

Data entry and editing were initiated almost immediately after the beginning of fieldwork. Simple range and skip errors were corrected at the data entry stage. Secondary machine editing of the data was initiated as soon as sufficient questionnaires had been entered. The objective of the secondary editing was to detect and correct, if possible, inconsistencies in the data. All of the data were entered and edited by September 1991. A brief report containing preliminary survey results was published in November 1991.

Response rate

Of 28,141 households sampled, 27,109 were eligible to be interviewed (excluding those that were absent, vacant, or destroyed), and of these, 26,858 or 99 percent of eligible households were successfully interviewed. In the interviewed households, 23,470 eligible women were found and complete interviews were obtained with 98 percent of these women.

Note: See summarized response rates by place of residence in Table 1.2 of the survey report.

Sampling error estimates

The results from sample surveys are affected by two types of errors, non-sampling error and sampling error. Non-sampling error is due to mistakes made in carrying out field activities, such as failure to locate and interview the correct household, errors in the way the questions are asked, misunderstanding on the part of either the interviewer or the respondent, data entry errors, etc. Although efforts were made during the design and implementation of the IDHS to minimize this type of error, non-sampling errors are impossible to avoid and difficult to evaluate analytically.

Sampling errors, on the other hand, can be measured statistically. The sample of women selected in the IDHS is only one of many samples that could have been selected from the same population, using the same design and expected size. Each one would have yielded results that differed somewhat from the actual sample selected. The sampling error is a measure of the variability between all possible samples; although it is not known exactly, it can be estimated from the survey results. Sampling error is usually measured in terms of standard error of 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 one can reasonably be assured that, apart from non-sampling errors, the true value of the variable for the whole population falls. For example, for any given statistic calculated from a sample survey, the value of that same statistic as measured in 95 percent of all possible samples with the same design (and expected size) will fall within a range of plus or minus two times the standard error of that statistic.

If the sample of women had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the IDHS sample design depended on stratification, stages and clusters. Consequently, it was necessary to utilize more complex formulas. The computer package CLUSTERS, developed by the International Statistical Institute for the World Fertility Survey, was used to assist in computing the sampling errors with the proper statistical methodology.

Note: See detailed estimate of sampling error calculation in APPENDIX B of the survey report.

Data appraisal

Data Quality Tables - Household age distribution - Age distribution of eligible and interviewed women - Completeness of reporting - Births by calendar year since birth - Reporting of age at death in days - Reporting of age at death in months

Note: See detailed tables in APPENDIX C of the survey report.

The purpose of the study was to evaluate the extent to which deterrence or cooperative strategies motivated firms and their facilities to comply with environmental regulations. The project collected administrative data (secondary data) for a sample of publicly owned, United States companies in the pulp and paper, steel, and oil refining industries from 1995 to 2000 to track each firm's economic, environmental, and enforcement compliance history. Company Economic and Size Data (Part 1) from 1993 to 2000 were gathered from the Standard and Poor's Industrial Compustat, Mergent Online, and Securities and Exchange Commission, resulting in 512 company/year observations. Next, the research team used the Directory of Corporate Affiliations, the Environmental Protection Agency's (EPA) Toxic Release Inventory (TRI), and the EPA's Permit Compliance System (PCS) to identify all facilities owned by the sample of firms between 1995 and 2000. Researchers then gathered Facility Ownership Data (Part 2), resulting in 15,408 facility/year observations. The research team gathered various types of PCS data from the EPA for facilities in the sample. Permit Compliance System Facility Data (Part 3) were gathered on the 214 unique major National Pollutant Discharge Elimination System (NPDES) permits issued to facilities in the sample. Although permits were given to facilities, facilities could have one or more discharge points (e.g., pipes) that released polluted water directly into surface waters. Thus, Permit Compliance System Discharge Points (Pipe Layout) Data (Part 4) were also collected on 1,995 pipes. The EPA determined compliance using two methods: inspections and evaluations/assessments. Permit Compliance System Inspections Data (Part 5) were collected on a total of 1,943 inspections. Permit Compliance System Compliance Schedule Data (Part 6) were collected on a total of 3,336 compliance schedule events. Permit Compliance System Compliance Schedule Violation Data (Part 7) were obtained for a total of 246 compliance schedule violations. Permit Compliance System Single Event Violations Data (Part 8) were collected on 75 single event violations. Permit Compliance System Measurement/Effluent and Reporting Violations Data (Part 9) were collected for 396,479 violations. Permit Compliance System Enforcement Actions Data (Part 10) were collected on 1,730 enforcement actions. Occupational Safety and Health Administration Data (Part 11) were collected on a total of 2,243 inspections. The OSHA data were collected by company name and include multiple facilities owned by each company and were not limited to facilities in the Permit Compliance System. Additional information about firm noncompliance was drawn from EPA Docket and CrimDoc systems. Administrative and Judicial Docket Case Data (Part 12) were collected on 40 administrative and civil cases. Administrative and Judicial Docket Case Settlement Data (Part 13) were collected on 36 administrative and civil cases. Criminal Case Data (Part 14) were collected on three criminal cases. For secondary data analysis purposes, the research team created the Yearly Final Report Data (Part 15) and the Quarterly Final Report Data (Part 16). The yearly data contain a total of 378 company/year observations; the quarterly data contain a total of 1,486 company/quarter observations. The research team also conducted a vignette survey of the same set of companies that are in the secondary data to measure compliance and managerial decision-making. Concerning the Vignette Data (Part 17), a factorial survey was developed and administered to company managers tapping into perceptions of the costs and benefits of pro-social and anti-social conduct for themselves and their companies. A total of 114 respondents from 2 of the sampled corporations read and responded to a total of 384 vignettes representing 4 scenario types: technical noncompliance, significant noncompliance, over-compliance, and response to counter-terrorism. Part 1 contains 19 economic and size variables. Part 2 contains a total of eight variables relating to ownership. Part 3 contains 67 variables with regard to facility characteristics. Part 4 contains 31 variables relating to discharge points and pipe layout information. Part 5 contains 13 inspections characteristics variables. Part 6 contains 13 compliance schedule event characteristics variables. Part 7 contains 11 compliance schedule violation characteristics variables. Part 8 contains 10 single event violation characteristics variables. Part 9 contains 79 variables including variables for matching limits and discharge monitoring reports, actual limits (permitted levels) variables, standardized limits variables, statistical base codes variables, reported units on limits variables, units for standardized limits variables, sampling information variables, additional limits information, actual DMR reports for each limit, effluent violations, and variables relating to technical aspects of reporting. Part 10 contains 26 enforcement actions variables. Part 11 contains 24 Occupational Safety and Health Administration inspection variables. Part 12 contains 39 administrative and judicial court case characteristics variables. Part 13 contains 21 court case settlement characteristics variables. Part 14 contains 9 criminal case characteristics variables. Part 15 contains 95 variables created for final report analyses by year. Part 16 contains 46 variables created for final report analyses by quarter. Part 17 contains 157 variables including pro-social variables with security/over-compliance intentions, noncompliance variables with technical/significant noncompliance intentions, vignette characteristics variables, other variables derived from survey questions, environmental norms variables, and demographic characteristics variables.

This document contains text excerpts captured from the literature as secondary data to develop the qualitative system dynamics model as well as two example coding tables. Table 1 shows the final list of research works selected for model development through a systematic paper selection procedure as described in chapter 3 of the thesis. Table 2 shows the initial causal links created based on the identified casual relationships. Table 3 shows an intermediate merging step (3rd iteration), where causal links are combined into more general links. For a detailed explanation of the model development process refer to chapter 3 of the thesis.

Trust-Level Data: Data is broken down by individual NHS Trusts, enabling regional comparisons, benchmarking, and targeted analysis of specific Trusts. Medicine Identification: Medicines in the dataset are identified using Virtual Medicinal Product (VMP) codes from the Dictionary of Medicines and Devices (dm+d): VMP_PRODUCT_NAME: The name of the Virtual Medicinal Product (VMP) as defined by the dm+d, which includes key details about the product. For example: Paracetamol 500mg tablets. VMP_SNOMED_CODE: The code for the Virtual Medicinal Product (VMP), providing a unique identifier for each product. For example: 42109611000001109 represents Paracetamol 500mg tablets. By making this data publicly available, the NHSBSA aims to enhance transparency, accountability, and the effective use of NHS resources. Overview of Service Information about our NHSBSA Prescriptions Data service can be found here - Prescription data | NHSBSA

Secondary data on social indicators and public expenditure on district and regional level in Tanzania (1996-2010), as for example: THINV: Logarithm of deflated public per capita spending on health in the short- and long term (total spending of the current and the last five budget years) SANI: Latrines per 100 pupils INFRA: Percentage of women and men age 15-49 who reported serious problems in accessing health care due to the distance to the next health facility URB: Percentage of people living in urban areas TAINV: Logarithm of deflated public per capita spending on agriculture (current and previous budget year)* BREASTF: Percentage who started breastfeeding within 1 hour of birth, among the last children born in the five years preceding the survey IODINE: Percentage of households with adequate iodine content of salt (15+ ppm) MEDU: Percentage of women age 15-49 who completed grade 6 at the secondary level VACC: Percentage of children age 12-23 months with a vaccination card TWINV: Logarithm of deflated public per capita spending on water in the short- and long term (total spending of the current and the last five budget years)* TEINV: Logarithm of deflated public per capita spending on education in the short- and long term (total spending of the current and the last five budget years)* LABOUR: Percentage of women and men employed in the 12 months preceding the survey LAND: Per capita farmland in ha (including the area under temporary mono/mixed crops, permanent mono/mixed crops and the area under pasture) RAIN: Yearly rainfall in mm etc. Purpose: The uploaded data were the basis for the following PhD-thesis: The optimal allocation of scarce resources for health improvement is a crucial factor to lower the burden of disease and to strengthen the productive capacities of people living in developing countries. This research project aims to devise tools in narrowing the gap between the actual allocation and a more efficient allocation of resources for health in the case of Tanzania. Firstly, the returns from alternative government spending across sectors such as agriculture, water etc. are analysed. Maximisation of the amount of Disability Adjusted Life Years (DALYs) averted per dollar invested is used as criteria. A Simultaneous Equation Model (SEM) is developed to estimate the required elasticities. The results of the quantitative analysis show that the highest returns on DALYs are obtained by investments in improved nutrition and access to safe water sources, followed by spending on sanitation. Secondly, focusing on the health sector itself, scarce resources for health improvement create the incentive to prioritise certain health interventions. Using the example of malaria, the objective of the second stage is to evaluate whether interventions are prioritized in such a way that the marginal dollar goes to where it has the highest effect on averting DALYs. PopMod, a longitudinal population model, is used to estimate the cost-effectiveness of six isolated and combined malaria intervention approaches. The results of the longitudinal population model show that preventive interventions such as insecticide–treated bed nets (ITNs) and intermittent presumptive treatment with Sulphadoxine-Pyrimethamine (SP) during pregnancy had the highest health returns (both US$ 41 per DALY averted). The third part of this dissertation focuses on the political economy aspect of the allocation of scarce resources for health improvement. The objective here is to positively assess how political party competition and the access to mass media directly affect the distribution of district resources for health improvement. Estimates of cross-sectional and panel data regression analysis imply that a one-percentage point smaller difference (the higher the competition is) between the winning party and the second-place party leads to a 0.151 percentage point increase in public health spending, which is significant at the five percent level. In conclusion, we can say that cross-sectoral effects, the cost-effectiveness of health interventions and the political environment are important factors at play in the country’s resource allocation decisions. In absolute terms, current financial resources to lower the burden of disease in Tanzania are substantial. However, there is a huge potential in optimizing the allocation of these resources for a better health return.

The NHS Business Services Authority (NHSBSA) publishes Secondary Care Medicines Data on behalf of NHS England (NHSE). This dataset provides 'Provisional' Secondary Care Medicines data for all NHS Acute, Teaching, Specialist, Mental Health, and Community Trusts in England. It provides information on pharmacy stock control, reflecting processed medicines data. RX Info is responsible for refreshing the Provisional data at the close of each financial year to include backtracking adjustments. The data is 'Finalised' to provide validated and complete figures for each reporting period, incorporating any updates and corrections throughout the year. The Finalised dataset serves as the definitive record for each month and year, offering the most accurate information on medicines issued. While we do not analyse changes, users can compare the finalised data with provisional data to identify any discrepancies. Key Components of the Data Quantities of Medicines Issued: Details the total quantities of medicines stock control via NHS Secondary Care services. Indicative Costs: Actual costs cannot be displayed in the dataset as NHS Hospital pricing contracts and NICE Patient Access Schemes are confidential. The indicative cost of medicines is derived from current medicines pricing data held in NHSBSA data systems (Common Drug Reference and dm+d), calculated to VMP level. Indicative costs are calculated using: Community pharmacy reimbursement prices for generic medicines. List prices for branded medicines. Care should be taken when interpreting and analysing this indicative cost as it does not reflect the net actual cost of NHS Trusts, which will differ due to the application of confidential discounts, rebates, or procurement agreements paid by hospitals when purchasing medicines. Standardisation with SNOMED CT and dm+d: SNOMED CT (Systematised Nomenclature of Medicine - Clinical Terms) is used to enhance the dataset’s compatibility with electronic health record systems and clinical decision support tools. SNOMED CT is a globally recognised coding system that provides precise definitions for clinical terms, ensuring interoperability across healthcare systems. Trust-Level Data: Data is broken down by individual NHS Trusts, enabling regional comparisons, benchmarking, and targeted analysis of specific Trusts. Medicine Identification: Medicines in the dataset are identified using Virtual Medicinal Product (VMP) codes from the Dictionary of Medicines and Devices (dm+d): VMP_PRODUCT_NAME: The name of the Virtual Medicinal Product (VMP) as defined by the dm+d, which includes key details about the product. For example: Paracetamol 500mg tablets. VMP_SNOMED_CODE: The code for the Virtual Medicinal Product (VMP), providing a unique identifier for each product. For example: 42109611000001109 represents Paracetamol 500mg tablets. You can access the finalised files in our Finalised Secondary Care Medicines Data (SCMD) with indicative price dataset. Dataset Details Service Overview Information about our NHSBSA Prescriptions Data service can be found here - Prescription data | NHSBSA The NHS Business Services Authority (NHSBSA) publishes this dataset, provided by RX Info, which contains information about pharmacy stock control in NHS Secondary Care settings across England on behalf of NHS England. It includes data from NHS Trusts and is in a standardised dm+d format (Dictionary of medicines and devices (dm+d) | NHSBSA). For further context about the Secondary Care Medicines Data, you can explore the following resources: Secondary Care Medicines Data Release Guidance v0.5 (Word: 78.3KB) RX Info: RX Info is the provider of the data related to pharmacy stock control medicines issued in NHS Secondary Care settings, which is made available by NHSBSA. Visit RX Info's website for more details. Data Source The data is sourced from NHS Trusts' pharmacy stock control systems which capture detailed records of medicines issued, including quantities. The data is provided to NHSBSA by RX Info, a data provider that supplies records on medicines issued in NHS Secondary Care settings. Data quality controls are in place to exclude transactions flagged as outliers, non-standardised items and zero activity. No personal or patient-identifiable information is included, ensuring compliance with data protection regulations. Rx-Info will provide a complete annual refresh of the data two months after the close of a financial year, planned for the end May, which will then be the fixed data set accounting for backtracking. The data for the finalised view is provided to NHSBSA. Data Collection Data is from NHS England sites only and provided under the agreement entered into by Trusts and Rx-Info (Define) facilitated by NHS England. The data owners and data controllers are the respective NHS Trusts. Time Periods Publication frequency: Data is uploaded on a monthly basis and is published retrospectively with a two-month delay. For example, January data is published in March. Historical Data: Data is available from April 2021 onwards. Geography NHS Trusts in England. Statistical Classification This is not an official statistic. A related official statistic can be found in our Prescribing Costs in Hospitals and the Community publication, which includes Secondary Care Medicines data with actual cost, broken down by British National Formulary (BNF) Section. Caveats Information: Interpreting 'Cost' Data Cost Limitations and Interpretation Indicative Costs: The costs in this dataset are indicative and do not reflect the net actual cost (including discounts and rebates) paid by hospitals when purchasing medicines. Due to confidential procurement agreements, the indicative costs will overestimate total NHS hospital expenditure. These figures are most useful for trend analysis rather than precise cost predictions.

raw LC-MS/MS data (Bruker Daltonics - tims TOF SCP) and related PEAKS search results (.csv) plus corresponding custom database (.fasta). Protein identifications were performed using PEAKS programs.

Data were used to:

extract cleavage preferences of Clarity Ferm AnPEP. The target sample was HEK cell lysate.

These data are related to https://doi.org/10.5281/zenodo.13938580.

Abstract

The primary objective of the 2012 Indonesia Demographic and Health Survey (IDHS) is to provide policymakers and program managers with national- and provincial-level data on representative samples of all women age 15-49 and currently-married men age 15-54.

The 2012 IDHS was specifically designed to meet the following objectives: • Provide data on fertility, family planning, maternal and child health, adult mortality (including maternal mortality), and awareness of AIDS/STIs to program managers, policymakers, and researchers to help them evaluate and improve existing programs; • Measure trends in fertility and contraceptive prevalence rates, and analyze factors that affect such changes, such as marital status and patterns, residence, education, breastfeeding habits, and knowledge, use, and availability of contraception; • Evaluate the achievement of goals previously set by national health programs, with special focus on maternal and child health; • Assess married men’s knowledge of utilization of health services for their family’s health, as well as participation in the health care of their families; • Participate in creating an international database that allows cross-country comparisons that can be used by the program managers, policymakers, and researchers in the areas of family planning, fertility, and health in general

Geographic coverage

National coverage

Analysis unit

• Household
• Women age 15-49
• Ever married men age 15-54
• Never married men age 15-24

Kind of data

Sample survey data [ssd]

Sampling procedure

Indonesia is divided into 33 provinces. Each province is subdivided into districts (regency in areas mostly rural and municipality in urban areas). Districts are subdivided into subdistricts, and each subdistrict is divided into villages. The entire village is classified as urban or rural.

The 2012 IDHS sample is aimed at providing reliable estimates of key characteristics for women age 15-49 and currently-married men age 15-54 in Indonesia as a whole, in urban and rural areas, and in each of the 33 provinces included in the survey. To achieve this objective, a total of 1,840 census blocks (CBs)-874 in urban areas and 966 in rural areas-were selected from the list of CBs in the selected primary sampling units formed during the 2010 population census.

Because the sample was designed to provide reliable indicators for each province, the number of CBs in each province was not allocated in proportion to the population of the province or its urban-rural classification. Therefore, a final weighing adjustment procedure was done to obtain estimates for all domains. A minimum of 43 CBs per province was imposed in the 2012 IDHS design.

Refer to Appendix B in the final report for details of sample design and implementation.

Mode of data collection

Face-to-face [f2f]

Research instrument

The 2012 IDHS used four questionnaires: the Household Questionnaire, the Woman’s Questionnaire, the Currently Married Man’s Questionnaire, and the Never-Married Man’s Questionnaire. Because of the change in survey coverage from ever-married women age 15-49 in the 2007 IDHS to all women age 15-49 in the 2012 IDHS, the Woman’s Questionnaire now has questions for never-married women age 15-24. These questions were part of the 2007 Indonesia Young Adult Reproductive Survey questionnaire.

The Household and Woman’s Questionnaires are largely based on standard DHS phase VI questionnaires (March 2011 version). The model questionnaires were adapted for use in Indonesia. Not all questions in the DHS model were adopted in the IDHS. In addition, the response categories were modified to reflect the local situation.

The Household Questionnaire was used to list all the usual members and visitors who spent the previous night in the selected households. Basic information collected on each person listed includes age, sex, education, marital status, education, and relationship to the head of the household. Information on characteristics of the housing unit, such as the source of drinking water, type of toilet facilities, construction materials used for the floor, roof, and outer walls of the house, and ownership of various durable goods were also recorded in the Household Questionnaire. These items reflect the household’s socioeconomic status and are used to calculate the household wealth index. The main purpose of the Household Questionnaire was to identify women and men who were eligible for an individual interview.

The Woman’s Questionnaire was used to collect information from all women age 15-49. These women were asked questions on the following topics: • Background characteristics (marital status, education, media exposure, etc.) • Reproductive history and fertility preferences • Knowledge and use of family planning methods • Antenatal, delivery, and postnatal care • Breastfeeding and infant and young children feeding practices • Childhood mortality • Vaccinations and childhood illnesses • Marriage and sexual activity • Fertility preferences • Woman’s work and husband’s background characteristics • Awareness and behavior regarding HIV-AIDS and other sexually transmitted infections (STIs) • Sibling mortality, including maternal mortality • Other health issues

Questions asked to never-married women age 15-24 addressed the following: • Additional background characteristics • Knowledge of the human reproduction system • Attitudes toward marriage and children • Role of family, school, the community, and exposure to mass media • Use of tobacco, alcohol, and drugs • Dating and sexual activity

The Man’s Questionnaire was administered to all currently married men age 15-54 living in every third household in the 2012 IDHS sample. This questionnaire includes much of the same information included in the Woman’s Questionnaire, but is shorter because it did not contain questions on reproductive history or maternal and child health. Instead, men were asked about their knowledge of and participation in health-careseeking practices for their children.

The questionnaire for never-married men age 15-24 includes the same questions asked to nevermarried women age 15-24.

Cleaning operations

All completed questionnaires, along with the control forms, were returned to the BPS central office in Jakarta for data processing. The questionnaires were logged and edited, and all open-ended questions were coded. Responses were entered in the computer twice for verification, and they were corrected for computeridentified errors. Data processing activities were carried out by a team of 58 data entry operators, 42 data editors, 14 secondary data editors, and 14 data entry supervisors. A computer package program called Census and Survey Processing System (CSPro), which was specifically designed to process DHS-type survey data, was used in the processing of the 2012 IDHS.

Response rate

The response rates for both the household and individual interviews in the 2012 IDHS are high. A total of 46,024 households were selected in the sample, of which 44,302 were occupied. Of these households, 43,852 were successfully interviewed, yielding a household response rate of 99 percent.

Refer to Table 1.2 in the final report for more detailed summarized results of the of the 2012 IDHS fieldwork for both the household and individual interviews, by urban-rural residence.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: (1) nonsampling errors, and (2) sampling errors. Nonsampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the 2012 Indonesia Demographic and Health Survey (2012 IDHS) 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 2012 IDHS 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 error is 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 2012 IDHS 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 2012 IDHS is a SAS program. This program used the Taylor linearization method

Demographic characteristics of participants who reported the use of opioids with a medical prescription within the last 12 months.

The Fast Response Survey System (FRSS) was established in 1975 by the National Center for Education Statistics (NCES), United States Department of Education. FRSS is designed to collect issue-oriented data within a relatively short time frame. FRSS collects data from state education agencies, local education agencies, public and private elementary and secondary schools, public school teachers, and public libraries. To ensure minimal burden on respondents, the surveys are generally limited to three pages of questions, with a response burden of about 30 minutes per respondent. Sample sizes are relatively small (usually about 1,000 to 1,500 respondents per survey) so that data collection can be completed quickly. Data are weighted to produce national estimates of the sampled education sector. The sample size is large enough to permit limited breakouts by classification variables. However, as the number of categories within the classification variables increases, the sample size within categories decreases, which results in larger sampling errors for the breakouts by classification variables. The Arts Education Surveys of Secondary School Teachers provide national estimates on arts education and arts instructors in public secondary schools during the 2009-10 school year. This data collection contains two surveys that provide information about music specialists and visual arts specialists. These two surveys are part of a set of seven surveys that collected data on arts education during the 2009-10 school year. In addition to these secondary teacher surveys, the set includes a survey of elementary school principals, a survey of secondary school principals, and three elementary teacher-level surveys. A stratified sample design was used to select music specialists and visual arts specialists for the Arts Education Surveys of Secondary School Teachers. Data collection was conducted September 2009 through July 2010. Altogether, 1,065 eligible music specialists and 1,046 eligible visual arts specialists completed the surveys by web, mail, fax, or telephone. The secondary teacher surveys collected data on the availability of curriculum-based arts education activities outside of regular school hours; teaching load of music and visual arts specialists in secondary schools; teacher participation in various professional development activities and the perceived impact of such participation on teaching; and teachers' use of formal methods of assessment of students' progress and achievement in the arts. Furthermore, teachers were also asked to provide administrative information such as school level, school enrollment size, school community type, and percent of students eligible for free or reduced-price lunch.

If this Data Set is useful, and upvote is appreciated. This data approach student achievement in secondary education of two Portuguese schools. The data attributes include student grades, demographic, social and school related features) and it was collected by using school reports and questionnaires. Two datasets are provided regarding the performance in two distinct subjects: Mathematics (mat) and Portuguese language (por). In [Cortez and Silva, 2008], the two datasets were modeled under binary/five-level classification and regression tasks. Important note: the target attribute G3 has a strong correlation with attributes G2 and G1. This occurs because G3 is the final year grade (issued at the 3rd period), while G1 and G2 correspond to the 1st and 2nd-period grades. It is more difficult to predict G3 without G2 and G1, but such prediction is much more useful (see paper source for more details).

The present study investigates primary and secondary sources of organic carbon for Bakersfield, CA, USA as part of the 2010 CalNex study. The method used here involves integrated sampling that is designed to allow for detailed and specific chemical analysis of particulate matter (PM) in the Bakersfield airshed. To achieve this objective, filter samples were taken during thirty-four 23-hr periods between 19 May and 26 June 2010 and analyzed for organic tracers by gas chromatography – mass spectrometry (GC-MS). Contributions to organic carbon (OC) were determined by two organic tracer-based techniques: primary OC by chemical mass balance and secondary OC by a mass fraction method. Radiocarbon (14C) measurements of the total organic carbon were also made to determine the split between the modern and fossil carbon and thereby constrain unknown sources of OC not accounted for by either tracer-based attribution technique. From the analysis, OC contributions from four primary sources and four secondary sources were determined, which comprised three sources of modern carbon and five sources of fossil carbon. The major primary sources of OC were from vegetative detritus (9.8%), diesel (2.3%), gasoline (<1.0%), and lubricating oil impacted motor vehicle exhaust (30%); measured secondary sources resulted from isoprene (1.5%), α-pinene (<1.0%), toluene (<1.0%), and naphthalene (<1.0%, as an upper limit) contributions. The average observed organic carbon (OC) was 6.42 ± 2.33 μgC m-3. The 14C derived apportionment indicated that modern and fossil components were nearly equivalent on average; however, the fossil contribution ranged from 32-66% over the five week campaign. With the fossil primary and secondary sources aggregated, only 25% of the fossil organic carbon could not be attributed. Whereas, nearly 80% of the modern carbon could not be attributed to primary and secondary sources accessible to this analysis, which included tracers of biomass burning, vegetative detritus and secondary biogenic carbon. The results of the current study contributes source-based evaluation of the carbonaceous aerosol at CalNex Bakersfield. This dataset is associated with the following publication: Sheesley, R., P. Dev Nallathamby, J. Surratt, A. Lee, M. Lewandowski, J. Offenberg, M. Jaoui, and T. Kleindienst. Constraints on primary and secondary particulate carbon sources using chemical tracer and 14C methods during CalNex-Bakersfield. ATMOSPHERIC ENVIRONMENT. Elsevier Science Ltd, New York, NY, USA, 166: 204-214, (2017).

Risk factors for opioid use disorder (weighted).