Abstract

THE CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 100% OF THE ORIGINAL SURVEY DATA COLLECTED BY THE PALESTINIAN CENTRAL BUREAU OF STATISTICS

The Palestinian Central Bureau of Statistics (PCBS) carried out four rounds of the Labor Force Survey 2017 (LFS). The survey rounds covered a total sample of about 23,120 households (5,780 households per quarter).

The main objective of collecting data on the labour force and its components, including employment, unemployment and underemployment, is to provide basic information on the size and structure of the Palestinian labour force. Data collected at different points in time provide a basis for monitoring current trends and changes in the labour market and in the employment situation. These data, supported with information on other aspects of the economy, provide a basis for the evaluation and analysis of macro-economic policies.

The raw survey data provided by the Statistical Agency were cleaned and harmonized by the Economic Research Forum, in the context of a major project that started in 2009. During which extensive efforts have been exerted to acquire, clean, harmonize, preserve and disseminate micro data of existing labor force surveys in several Arab countries.

Geographic coverage

Covering a representative sample on the region level (West Bank, Gaza Strip), the locality type (urban, rural, camp) and the governorates.

Analysis unit

1- Household/family. 2- Individual/person.

Universe

The survey covered all Palestinian households who are a usual residence of the Palestinian Territory.

Kind of data

Sample survey data [ssd]

Sampling procedure

THE CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 100% OF THE ORIGINAL SURVEY DATA COLLECTED BY THE PALESTINIAN CENTRAL BUREAU OF STATISTICS

The methodology was designed according to the context of the survey, international standards, data processing requirements and comparability of outputs with other related surveys.

---> Target Population: It consists of all individuals aged 10 years and Above and there are staying normally with their households in the state of Palestine during 2017.

---> Sampling Frame: The sampling frame consists of the master sample, which was updated in 2011: each enumeration area consists of buildings and housing units with an average of about 124 households. The master sample consists of 596 enumeration areas; we used 494 enumeration areas as a framework for the labor force survey sample in 2017 and these units were used as primary sampling units (PSUs).

---> Sampling Size: The estimated sample size is 5,780 households in each quarter of 2017.

---> Sample Design The sample is two stage stratified cluster sample with two stages : First stage: we select a systematic random sample of 494 enumeration areas for the whole round ,and we excluded the enumeration areas which its sizes less than 40 households. Second stage: we select a systematic random sample of 16 households from each enumeration area selected in the first stage, se we select a systematic random of 16 households of the enumeration areas which its size is 80 household and over and the enumeration areas which its size is less than 80 households we select systematic random of 8 households.

---> Sample strata: The population was divided by: 1- Governorate (16 governorate) 2- Type of Locality (urban, rural, refugee camps).

---> Sample Rotation: Each round of the Labor Force Survey covers all of the 494 master sample enumeration areas. Basically, the areas remain fixed over time, but households in 50% of the EAs were replaced in each round. The same households remain in the sample for two consecutive rounds, left for the next two rounds, then selected for the sample for another two consecutive rounds before being dropped from the sample. An overlap of 50% is then achieved between both consecutive rounds and between consecutive years (making the sample efficient for monitoring purposes).

Mode of data collection

Face-to-face [f2f]

Research instrument

The survey questionnaire was designed according to the International Labour Organization (ILO) recommendations. The questionnaire includes four main parts:

---> 1. Identification Data: The main objective for this part is to record the necessary information to identify the household, such as, cluster code, sector, type of locality, cell, housing number and the cell code.

---> 2. Quality Control: This part involves groups of controlling standards to monitor the field and office operation, to keep in order the sequence of questionnaire stages (data collection, field and office coding, data entry, editing after entry and store the data.

---> 3. Household Roster: This part involves demographic characteristics about the household, like number of persons in the household, date of birth, sex, educational level…etc.

---> 4. Employment Part: This part involves the major research indicators, where one questionnaire had been answered by every 15 years and over household member, to be able to explore their labour force status and recognize their major characteristics toward employment status, economic activity, occupation, place of work, and other employment indicators.

Cleaning operations

---> Raw Data PCBS started collecting data since 1st quarter 2017 using the hand held devices in Palestine excluding Jerusalem in side boarders (J1) and Gaza Strip, the program used in HHD called Sql Server and Microsoft. Net which was developed by General Directorate of Information Systems. Using HHD reduced the data processing stages, the fieldworkers collect data and sending data directly to server then the project manager can withdrawal the data at any time he needs. In order to work in parallel with Gaza Strip and Jerusalem in side boarders (J1), an office program was developed using the same techniques by using the same database for the HHD.

---> Harmonized Data - The SPSS package is used to clean and harmonize the datasets. - The harmonization process starts with a cleaning process for all raw data files received from the Statistical Agency. - All cleaned data files are then merged to produce one data file on the individual level containing all variables subject to harmonization. - A country-specific program is generated for each dataset to generate/ compute/ recode/ rename/ format/ label harmonized variables. - A post-harmonization cleaning process is then conducted on the data. - Harmonized data is saved on the household as well as the individual level, in SPSS and then converted to STATA, to be disseminated.

Response rate

The survey sample consists of about 30,230 households of which 23,120 households completed the interview; whereas 14,682 households from the West Bank and 8,438 households in Gaza Strip. Weights were modified to account for non-response rate. The response rate in the West Bank reached 82.4% while in the Gaza Strip it reached 92.7%.

Sampling error estimates

---> Sampling Errors Data of this survey may be affected by sampling errors due to use of a sample and not a complete enumeration. Therefore, certain differences can be expected in comparison with the real values obtained through censuses. Variances were calculated for the most important indicators: the variance table is attached with the final report. There is no problem in disseminating results at national or governorate level for the West Bank and Gaza Strip.

---> Non-Sampling Errors Non-statistical errors are probable in all stages of the project, during data collection or processing. This is referred to as non-response errors, response errors, interviewing errors, and data entry errors. To avoid errors and reduce their effects, great efforts were made to train the fieldworkers intensively. They were trained on how to carry out the interview, what to discuss and what to avoid, carrying out a pilot survey, as well as practical and theoretical training during the training course. Also data entry staff were trained on the data entry program that was examined before starting the data entry process. To stay in contact with progress of fieldwork activities and to limit obstacles, there was continuous contact with the fieldwork team through regular visits to the field and regular meetings with them during the different field visits. Problems faced by fieldworkers were discussed to clarify any issues. Non-sampling errors can occur at the various stages of survey implementation whether in data collection or in data processing. They are generally difficult to be evaluated statistically.

They cover a wide range of errors, including errors resulting from non-response, sampling frame coverage, coding and classification, data processing, and survey response (both respondent and interviewer-related). The use of effective training and supervision and the careful design of questions have direct bearing on limiting the magnitude of non-sampling errors, and hence enhancing the quality of the resulting data. The implementation of the survey encountered non-response where the case ( household was not present at home ) during the fieldwork visit and the case ( housing unit is vacant) become the high percentage of the non response cases. The total non-response rate reached14.2% which is very low once compared to the household surveys conducted by PCBS , The refusal rate reached 3.0% which is very low percentage compared to the

These data were used to generate the results in the article “Household Food Waste Trending Upwards in the United States: Insights from a National Tracking Survey,” by Ran Li, Yiheng Shu, Kathryn E. Bender & Brian E. Roe, which has been accepted for publication in the Journal of the Agricultural and Applied Economics Association (doi – https://doi.org/10.1002/jaa2.59). The Stata code used to generate results is available from the authors upon request. U.S. residents who participate in consumer panels managed by a commercial vendor were invited by email or text message to participate in a two-part online survey during four waves of data collection: February and March of 2021 (Feb 21 wave, 425 initiated, 361 completed), July and August of 2021 (Jul 21 wave, 606 initiated, 419 completed), December of 2021 and January of 2022 (Dec 21 wave, 760 initiated, 610 completed), and February, March and April of 2022 (Feb 22 wave, 607 initiated, 587 completed), July, August and Septemper of 2022 (Jul 22 wave, 1817 initiated, 1067 completed). We are not able to determine if any respondents participated in multiple waves, i.e., if any of the observations are repeat participants. All participants provided informed consent and received compensation. Inclusion criteria included age 18 years or older and performance of at least half of the household food preparation. No data was collected during major holidays, i.e., the weeks of the Fourth of July (Independence Day), Christmas, or New Years. Recruitment quotas were implemented to ensure sufficient representation by geographical region, race, and age group. Post-hoc sample weights were constructed to reflect population characteristics on age, income and household size. The protocol was approved by the local Internal Review Board. The approach begins with participants completing an initial survey that ends with an announcement that a follow-up survey will arrive in about one week, and that for the next 7 days, participants should pay close attention to the amounts of different foods their household throws away, feeds to animals or composts because the food is past date, spoiled or no longer wanted for other reasons. They are told to exclude items they would normally not eat, such as bones, pits, and shells. Approximately 7 days later they received the follow-up survey, which elicited the amount of waste in up to 24 categories of food and included other questions (see supplemental materials for core survey questions in Li et al. 2023). Waste amounts in each category are reported by selecting from one of several ranges of possible amounts. The gram weight for categories with volumetric ranges (e.g., listed in cups) were derived by assigning an appropriate mass to the midpoint of the selected range consistent with the food category. For the categories with highly variable weight per volume (e.g., a cup of raw asparagus weighs about 7 times more than a cup of raw chopped arugula), we use the profile of items most consumed in the United States to determine the appropriate gram weight. For display purposes, the 24 categories are consolidated into 8 more general categories. Total weekly household food waste is calculated by summing up reported gram amounts across all categories. We divide this total by the number of household members to generate the per person weekly food waste amount.

ObjectiveSurgery remains the primary form of treatment for infective endocarditis (IE). However, it is not clear what type of prosthetic valve provides a better prognosis. We conducted a meta-analysis to compare the prognosis of infective endocarditis treated with biological valves to cases treated with mechanical valves.MethodsPubmed, Embase and Cochrane databases were searched from January 1960 to November 2016.Randomized controlled trials, retrospective cohorts and prospective studies comparing outcomes between biological valve and mechanical valve management for infective endocarditis were analyzed. The Newcastle-Ottawa Scale(NOS) was used to evaluate the quality of the literature and extracted data, and Stata 12.0 software was used for the meta-analysis.ResultsA total of 11 publications were included; 10,754 cases were selected, involving 6776 cases of biological valves and 3,978 cases of mechanical valves. The all-cause mortality risk of the biological valve group was higher than that of the mechanical valve group (HR = 1.22, 95% CI 1.03 to 1.44, P = 0.023), as was early mortality (RR = 1.21, 95% CI 1.02 to 1.43, P = 0.033). The recurrence of endocarditis (HR = 1.75, 95% CI 1.26 to 2.42, P = 0.001), as well as the risk of reoperation (HR = 1.79, 95% CI 1.15 to 2.80, P = 0.010) were more likely to occur in the biological valve group. The incidence of postoperative embolism was less in the biological valve group than in the mechanical valve group, but this difference was not statistically significant (RR = 0.90, 95% CI 0.76 to 1.07, P = 0.245). For patients with prosthetic valve endocarditis (PVE), there was no significant difference in survival rates between the biological valve group and the mechanical valve group (HR = 0.91, 95% CI 0.68 to 1.21, P = 0.520).ConclusionThe results of our meta-analysis suggest that mechanical valves can provide a significantly better prognosis in patients with infective endocarditis. There were significant differences in the clinical features of patients receiving a biological valve compared to patients receiving a mechanical valve. A large, multicenter retrospective study included in our meta-analysis suggested that any mortality risk of the biological valve group was significant higher than that of the mechanical valve group. However, the risk was no different after risk was adjusted. So, we thought the reason for this result may be related to the characteristics of the patient rather than valve dysfunction. It is still necessary to future randomized studies to verify this conclusion.

Description

This dataset contains data from an online experiment designed to test whether economically equivalent penalties—fees (paid before taking) and fines (paid after taking)—influence prosocial behaviour differently. Participants played a modified dictator game in which they could take points from another participant.

The dataset is provided in Excel format (Full-data.xlsx), along with a Stata do-file (submit.do) that reshapes, cleans, and analyses the data.

Data Collection

• Platform: oTree

• Recruitment: Prolific

• Sample size: 201 participants

• Design: Each participant played 20 rounds: 10 in the control condition and 10 in one treatment condition (fee or fine). Order of blocks was randomised.

• Payment: 200 points = £1. One round was randomly selected for payment.

Variables

Identification

• session – Session number

• id – Participant ID

• treatment – Assigned treatment (1 = Fee, 2 = Fine)

• order – Order of blocks (0 = Control first, 1 = Treatment first)

Decision Rounds

For each round, participants made decisions in both control (c) and treatment (t) conditions.

• c1, t1, c2, t2, … – Tokens available and/or allocated across control and treatment rounds.

• takeX – Amount taken from the other participant in case X.

Norm Elicitation

Social norms were elicited after the taking task. Variables include empirical, normative, and responsibility measures at both extensive and intensive margins:

• eyX, etX – Empirical expectations (beliefs about what others do)

• nyX, ntX – Normative expectations (beliefs about what others think is appropriate)

• ryX, rtX – Responsibility measures

• casenormX – Case identifier for norm elicitation

Demographics

From survey responses:

• Sex – Gender

• Ethnicitysimplified – Simplified ethnicity category

• Countryofresidence – Participant’s country of residence

Other

• order, session – Experimental setup metadata

Stata Do-File (analysis.do)

The .do file performs the following steps:

1. Data Preparation

   • Import raw Excel file

   • Reshape from wide to long format (cases per participant)

   • Declare panel data (xtset id)

2. Variable Generation

   • Rename variables for clarity (e.g., take for amount taken)

   • Generate treatment dummies (treat)

   • Construct demographic dummies (gender, race, nationality)

3. Analysis Preparation

   • Create extensive and intensive margin variables

   • Generate expectation and norm measures

4. Output

   • Ready-to-analyse panel dataset for regression and statistical analysis

BackgroundDespite being a preventable disease, pediatric HIV infection continues to be a public health concern due to the morbidity and mortality associated with the disease. Vertical transmission of HIV occurs when a mother living with HIV passes the virus to her baby during pregnancy, childbirth, or breastfeeding. Globally, the vertical transmission rate of HIV is 9% with sub-Saharan Africa accounting for 90% of these infections. In Kenya, the national vertical transmission rates of HIV stood at 11.5% by the end of 2018, with a target to reduce vertical transmission rates to below 5% and 2% in breastfeeding and non-breastfeeding infants respectively, by the end of 2021.ObjectiveTo determine the prognostic factors influencing HIV-free survival among infants enrolled for HIV early infant diagnosis (EID) services in selected hospitals in Nairobi County, Kenya.MethodsA prospective cohort study design was adopted. HIV exposed infants were recruited at six weeks to determine HIV-free survival over 12 months follow up. Simple random sampling was used to select 166 infants and data were collected from the mothers using semi-structured interviewer-administered questionnaires. Log-rank tests were used to test for associations at the bi-variable level while Cox-proportional regression was used to analyze data at the multi-variable level, with the aid of STATA 14 software. Ethical approval was obtained from Kenya Medical Research Institute, Scientific Ethics Review Unit.ResultsThe overall infant HIV incidence rate over one-year follow-up was 9 cases per 100 person-years (95% CI: 5.465–16.290). The failure event was defined as an infant with a positive PCR test during the study period with total failures being 13 (9.41%) over 12 months. Prognostic factors associated with poor infant HIV-free survival were young maternal age (18–24 years) and mothers with a recent HIV diagnosis of ≤ 2 years since a positive HIV diagnosis (HR 5.97 CI: 1.20, 29.58) and (HR 6.97 CI: 1.96, 24.76), respectively.ConclusionMaternal prognostic factors associated with poor infant HIV-free survival were young maternal age (18–24 years) and recent maternal HIV diagnosis of ≤ 2 years since positive HIV diagnosis. The study recommended the development of an intervention package with more rigorous adherence counseling and close monitoring for young mothers, and mothers with recent HIV diagnoses.

The Infant Feeding Survey (IFS) has been carried out every five years since 1975, in order to establish information about infant feeding practices. Government policy in the United Kingdom has consistently supported breastfeeding as the best way of ensuring a healthy start for infants and of promoting women's health. Current guidance on infant feeding is as follows:

• breastmilk is the best form of nutrition for infants;
• exclusive breastfeeding is recommended for around the first six months (26 weeks) of an infant's life;
• infant formula is the only recommended alternative to breastfeeding for babies who are under 12 months old;
• around six months is the recommended age for the introduction of solid foods for infants, whether breastfed or fed on breastmilk substitutes;
• breastfeeding (and/or breastmilk substitutes) should continue beyond the first six months, along with appropriate types and amounts of solid foods;
• mothers who are unable to, or choose not to, follow these recommendations should be supported to optimise their infants' nutrition.

Since the IFS began, the content of the survey has evolved to reflect the prevailing government policy agenda, while recognising the importance of maintaining consistency over time to allow comparison and trend analysis. The first IFS in 1975 took place in England and Wales only. From 1980 the survey covered Scotland, while from 1990 Northern Ireland was also included. The 2005 survey was the first to provide separate estimates for England, Wales, Scotland and Northern Ireland, as well as for the UK as a whole, and to provide estimates of exclusive breast-feeding (where the baby is given only breast milk, no other liquids or solids).

Further information about the IFS series may be found on the Health and Social Care Information Centre website (search for 'Infant Feeding Survey').

The UK Data Archive holds IFS data from 1985 onwards. A separate survey, Infant Feeding in Asian Families, 1994-1996, covering England only, is held under SN 3759.

The 2010 IFS was based on an initial representative sample of mothers who were selected from all UK births registered during August and October 2010. Three stages of data collection were conducted, with Stage 1 being carried out when babies were around 4-10 weeks old, Stage 2 when they were 4-6 months old, and Stage 3 when they were 8-10 months old. A total of 10,768 mothers completed and returned all three questionnaires. For the first time in 2010, additional questions were included alongside the main Stage 2 questionnaire for mothers of multiple births.

Users should note that the UK Data Archive study currently includes questionnaire data from Stages 1, 2 and 3 and the multiple births data, with Excel data tables relating to survey methodology and sampling error.

The main aims of the 2010 survey were broadly similar to previous IFS, and were as follows:

• to establish how infants born in 2010 were being fed and to provide national figures on the incidence, prevalence and duration of breastfeeding and exclusive breastfeeding;
• to examine trends in infant feeding practices over recent years, in particular to compare changes between 2005 and 2010;
• to investigate variations in feeding practices among different socio-demographic groups and the factors associated with mothers' feeding intentions and with the feeding practices adopted in the early weeks;
• to establish the age at which solid foods are introduced and to examine practices associated with introducing solid foods up to 9 months;
• to measure the proportion of mothers who smoke and drink during pregnancy, and to look at the patterns of smoking and drinking behaviour before, during and after the birth; and
• to measure levels of awareness of and registration on the Healthy Start scheme and understand how Healthy Start vouchers are being used. (The Healthy Start scheme provides support for mothers in receipt of certain benefits and tax credits. Vouchers are provided that can be spent on milk, infant formula, fresh fruit or vegetables for pregnant women and children under 4 years old and coupons are also available for free vitamins for pregnant women, mothers and babies.)

For the second edition (July 2013), data and documentation from Stage 3 of the survey were added to the study.

Linking files in Stata - a warning
Stata users should note that the case identifier variable (ID) number structure may differ across datasets for all three stages. The letter prefixing the ID number may be upper case in one dataset and lower case in another. This is related to whether an online, face-to-face, CATI or postal route was used to complete the questionnaire- for example one respondent has the ID number 'E00157' in Stage 1 and Stage 2, but 'e00157' in Stage 3. Apart from the upper/lower case prefix letter, the ID number is exactly the same. However, the Stata command used to link the datasets (the 'merge' function) requires an exact match on the matching variable (ID), so if the prefix letter is lower case in one stage and upper case in another stage, Stata will reject the link and assume those cases are different respondents. At present, 441 cases are affected by this. The original datasets were compiled in SPSS, which does not distinguish between the upper and lower case prefix letters while merging datasets.

Note from the depositor, September 2016:
The depositor has sent the following note to data users: "An error in the Stage 1 dataset has been identified. Ninety-nine mothers stated that it was their first birth (Q3), that they had a total of 1 child (Q4) but then also selected the option to say that they had a multiple birth (Q5). The Stage 2 and Stage 3 data are unaffected and no figures in the published report or tables are affected. Users analysing the Stage 1 dataset should take this anomaly into account when including multiple births data in Stage 1 in their analysis."

Abstract

The high-frequency phone survey of refugees monitors the economic and social impact of and responses to the COVID-19 pandemic on refugees and nationals, by calling a sample of households every four weeks. The main objective is to inform timely and adequate policy and program responses. Since the outbreak of the COVID-19 pandemic in Ethiopia, two rounds of data collection of refugees were completed between September and November 2020. The first round of the joint national and refugee HFPS was implemented between the 24 September and 17 October 2020 and the second round between 20 October and 20 November 2020.

Analysis unit

Household

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample was drawn using a simple random sample without replacement. Expecting a high non-response rate based on experience from the HFPS-HH, we drew a stratified sample of 3,300 refugee households for the first round. More details on sampling methodology are provided in the Survey Methodology Document available for download as Related Materials.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The Ethiopia COVID-19 High Frequency Phone Survey of Refugee questionnaire consists of the following sections:

• Interview Information
• Household Roster
• Camp Information
• Knowledge Regarding the Spread of COVID-19
• Behaviour and Social Distancing - Access to Basic Services
• Employment
• Income Loss
• Coping/Shocks
• Social Relations
• Food Security
• Aid and Support/ Social Safety Nets.

A more detailed description of the questionnaire is provided in Table 1 of the Survey Methodology Document that is provided as Related Materials. Round 1 and 2 questionnaires available for download.

Cleaning operations

DATA CLEANING At the end of data collection, the raw dataset was cleaned by the Research team. This included formatting, and correcting results based on monitoring issues, enumerator feedback and survey changes. Data cleaning carried out is detailed below.

Variable naming and labeling: • Variable names were changed to reflect the lowercase question name in the paper survey copy, and a word or two related to the question. • Variables were labeled with longer descriptions of their contents and the full question text was stored in Notes for each variable. • “Other, specify” variables were named similarly to their related question, with “_other” appended to the name. • Value labels were assigned where relevant, with options shown in English for all variables, unless preloaded from the roster in Amharic.

Variable formatting: • Variables were formatted as their object type (string, integer, decimal, time, date, or datetime). • Multi-select variables were saved both in space-separated single-variables and as multiple binary variables showing the yes/no value of each possible response. • Time and date variables were stored as POSIX timestamp values and formatted to show Gregorian dates. • Location information was left in separate ID and Name variables, following the format of the incoming roster. IDs were formatted to include only the variable level digits, and not the higher-level prefixes (2-3 digits only.)
• Only consented surveys were kept in the dataset, and all personal information and internal survey variables were dropped from the clean dataset. • Roster data is separated from the main data set and kept in long-form but can be merged on the key variable (key can also be used to merge with the raw data). • The variables were arranged in the same order as the paper instrument, with observations arranged according to their submission time.

Backcheck data review: Results of the backcheck survey are compared against the originally captured survey results using the bcstats command in Stata. This function delivers a comparison of variables and identifies any discrepancies. Any discrepancies identified are then examined individually to determine if they are within reason.

Data appraisal

The following data quality checks were completed: • Daily SurveyCTO monitoring: This included outlier checks, skipped questions, a review of “Other, specify”, other text responses, and enumerator comments. Enumerator comments were used to suggest new response options or to highlight situations where existing options should be used instead. Monitoring also included a review of variable relationship logic checks and checks of the logic of answers. Finally, outliers in phone variables such as survey duration or the percentage of time audio was at a conversational level were monitored. A survey duration of close to 15 minutes and a conversation-level audio percentage of around 40% was considered normal. • Dashboard review: This included monitoring individual enumerator performance, such as the number of calls logged, duration of calls, percentage of calls responded to and percentage of non-consents. Non-consent reason rates and attempts per household were monitored as well. Duration analysis using R was used to monitor each module's duration and estimate the time required for subsequent rounds. The dashboard was also used to track overall survey completion and preview the results of key questions. • Daily Data Team reporting: The Field Supervisors and the Data Manager reported daily feedback on call progress, enumerator feedback on the survey, and any suggestions to improve the instrument, such as adding options to multiple choice questions or adjusting translations. • Audio audits: Audio recordings were captured during the consent portion of the interview for all completed interviews, for the enumerators' side of the conversation only. The recordings were reviewed for any surveys flagged by enumerators as having data quality concerns and for an additional random sample of 2% of respondents. A range of lengths were selected to observe edge cases. Most consent readings took around one minute, with some longer recordings due to questions on the survey or holding for the respondent. All reviewed audio recordings were completed satisfactorily. • Back-check survey: Field Supervisors made back-check calls to a random sample of 5% of the households that completed a survey in Round 1. Field Supervisors called these households and administered a short survey, including (i) identifying the same respondent; (ii) determining the respondent's position within the household; (iii) confirming that a member of the the data collection team had completed the interview; and (iv) a few questions from the original survey.

General characterisitcs of selected studies for the prevalence of mortality among mechanically ventilated ICU patients in Ethiopia, 2023.

Abstract

The 2016 Integrated Household Panel Survey (IHPS) was launched in April 2016 as part of the Malawi Fourth Integrated Household Survey fieldwork operation. The IHPS 2016 targeted 1,989 households that were interviewed in the IHPS 2013 and that could be traced back to half of the 204 enumeration areas that were originally sampled as part of the Third Integrated Household Survey (IHS3) 2010/11. The 2019 IHPS was launched in April 2019 as part of the Malawi Fifth Integrated Household Survey fieldwork operations targeting the 2,508 households that were interviewed in 2016. The panel sample expanded each wave through the tracking of split-off individuals and the new households that they formed. Available as part of this project is the IHPS 2019 data, the IHPS 2016 data as well as the rereleased IHPS 2010 & 2013 data including only the subsample of 102 EAs with updated panel weights. Additionally, the IHPS 2016 was the first survey that received complementary financial and technical support from the Living Standards Measurement Study – Plus (LSMS+) initiative, which has been established with grants from the Umbrella Facility for Gender Equality Trust Fund, the World Bank Trust Fund for Statistical Capacity Building, and the International Fund for Agricultural Development, and is implemented by the World Bank Living Standards Measurement Study (LSMS) team, in collaboration with the World Bank Gender Group and partner national statistical offices. The LSMS+ aims to improve the availability and quality of individual-disaggregated household survey data, and is, at start, a direct response to the World Bank IDA18 commitment to support 6 IDA countries in collecting intra-household, sex-disaggregated household survey data on 1) ownership of and rights to selected physical and financial assets, 2) work and employment, and 3) entrepreneurship – following international best practices in questionnaire design and minimizing the use of proxy respondents while collecting personal information. This dataset is included here.

Geographic coverage

National coverage

Analysis unit

• Households
• Individuals
• Children under 5 years
• Consumption expenditure commodities/items
• Communities
• Agricultural household/ Holder/ Crop

Universe

The IHPS 2016 and 2019 attempted to track all IHPS 2013 households stemming from 102 of the original 204 baseline panel enumeration areas as well as individuals that moved away from the 2013 dwellings between 2013 and 2016 as long as they were neither servants nor guests at the time of the IHPS 2013; were projected to be at least 12 years of age and were known to be residing in mainland Malawi but excluding those in Likoma Island and in institutions, including prisons, police compounds, and army barracks.

Kind of data

Sample survey data [ssd]

Sampling procedure

A sub-sample of IHS3 2010 sample enumeration areas (EAs) (i.e. 204 EAs out of 768 EAs) was selected prior to the start of the IHS3 field work with the intention to (i) to track and resurvey these households in 2013 in accordance with the IHS3 fieldwork timeline and as part of the Integrated Household Panel Survey (IHPS 2013) and (ii) visit a total of 3,246 households in these EAs twice to reduce recall associated with different aspects of agricultural data collection. At baseline, the IHPS sample was selected to be representative at the national, regional, urban/rural levels and for each of the following 6 strata: (i) Northern Region - Rural, (ii) Northern Region - Urban, (iii) Central Region - Rural, (iv) Central Region - Urban, (v) Southern Region - Rural, and (vi) Southern Region - Urban. The IHPS 2013 main fieldwork took place during the period of April-October 2013, with residual tracking operations in November-December 2013.

Given budget and resource constraints, for the IHPS 2016 the number of sample EAs in the panel was reduced to 102 out of the 204 EAs. As a result, the domains of analysis are limited to the national, urban and rural areas. Although the results of the IHPS 2016 cannot be tabulated by region, the stratification of the IHPS by region, urban and rural strata was maintained. The IHPS 2019 tracked all individuals 12 years or older from the 2016 households.

Mode of data collection

Computer Assisted Personal Interview [capi]

Cleaning operations

Data Entry Platform To ensure data quality and timely availability of data, the IHPS 2019 was implemented using the World Bank’s Survey Solutions CAPI software. To carry out IHPS 2019, 1 laptop computer and a wireless internet router were assigned to each team supervisor, and each enumerator had an 8–inch GPS-enabled Lenovo tablet computer that the NSO provided. The use of Survey Solutions allowed for the real-time availability of data as the completed data was completed, approved by the Supervisor and synced to the Headquarters server as frequently as possible. While administering the first module of the questionnaire the enumerator(s) also used their tablets to record the GPS coordinates of the dwelling units. Geo-referenced household locations from that tablet complemented the GPS measurements taken by the Garmin eTrex 30 handheld devices and these were linked with publically available geospatial databases to enable the inclusion of a number of geospatial variables - extensive measures of distance (i.e. distance to the nearest market), climatology, soil and terrain, and other environmental factors - in the analysis.

Data Management The IHPS 2019 Survey Solutions CAPI based data entry application was designed to stream-line the data collection process from the field. IHPS 2019 Interviews were mainly collected in “sample” mode (assignments generated from headquarters) and a few in “census” mode (new interviews created by interviewers from a template) for the NSO to have more control over the sample. This hybrid approach was necessary to aid the tracking operations whereby an enumerator could quickly create a tracking assignment considering that they were mostly working in areas with poor network connection and hence could not quickly receive tracking cases from Headquarters.

The range and consistency checks built into the application was informed by the LSMS-ISA experience with the IHS3 2010/11, IHPS 2013 and IHPS 2016. Prior programming of the data entry application allowed for a wide variety of range and consistency checks to be conducted and reported and potential issues investigated and corrected before closing the assigned enumeration area. Headquarters (the NSO management) assigned work to the supervisors based on their regions of coverage. The supervisors then made assignments to the enumerators linked to their supervisor account. The work assignments and syncing of completed interviews took place through a Wi-Fi connection to the IHPS 2019 server. Because the data was available in real time it was monitored closely throughout the entire data collection period and upon receipt of the data at headquarters, data was exported to Stata for other consistency checks, data cleaning, and analysis.

Data Cleaning The data cleaning process was done in several stages over the course of fieldwork and through preliminary analysis. The first stage of data cleaning was conducted in the field by the field-based field teams utilizing error messages generated by the Survey Solutions application when a response did not fit the rules for a particular question. For questions that flagged an error, the enumerators were expected to record a comment within the questionnaire to explain to their supervisor the reason for the error and confirming that they double checked the response with the respondent. The supervisors were expected to sync the enumerator tablets as frequently as possible to avoid having many questionnaires on the tablet, and to enable daily checks of questionnaires. Some supervisors preferred to review completed interviews on the tablets so they would review prior to syncing but still record the notes in the supervisor account and reject questionnaires accordingly. The second stage of data cleaning was also done in the field, and this resulted from the additional error reports generated in Stata, which were in turn sent to the field teams via email or DropBox. The field supervisors collected reports for their assignments and in coordination with the enumerators reviewed, investigated, and collected errors. Due to the quick turn-around in error reporting, it was possible to conduct call-backs while the team was still operating in the EA when required. Corrections to the data were entered in the rejected questionnaires and sent back to headquarters.

The data cleaning process was done in several stages over the course of the fieldwork and through preliminary analyses. The first stage was during the interview itself. Because CAPI software was used, as enumerators asked the questions and recorded information, error messages were provided immediately when the information recorded did not match previously defined rules for that variable. For example, if the education level for a 12 year old respondent was given as post graduate. The second stage occurred during the review of the questionnaire by the Field Supervisor. The Survey Solutions software allows errors to remain in the data if the enumerator does not make a correction. The enumerator can write a comment to explain why the data appears to be incorrect. For example, if the previously mentioned 12 year old was, in fact, a genius who had completed graduate studies. The next stage occurred when the data were transferred to headquarters where the NSO staff would again review the data for errors and verify the comments from the

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

Narok and Homabay counties

Analysis unit

Households

Universe

All adolescent girls aged 15-19 years resident in the household.

Sampling procedure

The sampling of adolescents for the household survey was based on expected changes in adolescent's intention to use contraception in future. According to the Kenya Demographic and Health Survey 2014, 23.8% of adolescents and young women reported not intending to use contraception in future. This was used as a baseline proportion for the intervention as it aimed to increase demand and reduce the proportion of sexually active adolescents who did not intend to use contraception in the future. Assuming that the project was to achieve an impact of at least 2.4 percentage points in the intervention counties (i.e. a reduction by 10%), a design effect of 1.5 and a non- response rate of 10%, a sample size of 1885 was estimated using Cochran's sample size formula for categorical data was adequate to detect this difference between baseline and end line time points. Based on data from the 2009 Kenya census, there were approximately 0.46 adolescents girls per a household, which meant that the study was to include approximately 4876 households from the two counties at both baseline and end line surveys.

We collected data among a representative sample of adolescent girls living in both urban and rural ITH areas to understand adolescents' access to information, use of SRH services and SRH-related decision making autonomy before the implementation of the intervention. Depending on the number of ITH health facilities in the two study counties, Homa Bay and Narok that, we sampled 3 sub-Counties in Homa Bay: West Kasipul, Ndhiwa and Kasipul; and 3 sub-Counties in Narok, Narok Town, Narok South and Narok East purposively. In each of the ITH intervention counties, there were sub-counties that had been prioritized for the project and our data collection focused on these sub-counties selected for intervention. A stratified sampling procedure was used to select wards with in the sub-counties and villages from the wards. Then households were selected from each village after all households in the villages were listed. The purposive selection of sub-counties closer to ITH intervention facilities meant that urban and semi-urban areas were oversampled due to the concentration of health facilities in urban areas.

Qualitative Sampling

Focus Group Discussion participants were recruited from the villages where the ITH adolescent household survey was conducted in both counties. A convenience sample of consenting adults living in the villages were invited to participate in the FGDS. The discussion was conducted in local languages. A facilitator and note-taker trained on how to use the focus group guide, how to facilitate the group to elicit the information sought, and how to take detailed notes. All focus group discussions took place in the local language and were tape-recorded, and the consent process included permission to tape-record the session. Participants were identified only by their first names and participants were asked not to share what was discussed outside of the focus group. Participants were read an informed consent form and asked to give written consent. In-depth interviews were conducted with purposively selected sample of consenting adolescent girls who participated in the adolescent survey. We conducted a total of 45 In-depth interviews with adolescent girls (20 in Homa Bay County and 25 in Narok County respectively). In addition, 8 FGDs (4 each per county) were conducted with mothers of adolescent girls who are usual residents of the villages which had been identified for the interviews and another 4 FGDs (2 each per county) with CHVs.

Sampling deviation

N/A

Mode of data collection

Face-to-face [f2f] for quantitative data collection and Focus Group Discussions and In Depth Interviews for qualitative data collection

Research instrument

The questionnaire covered; socio-demographic and household information, SRH knowledge and sources of information, sexual activity and relationships, family planning knowledge, access, choice and use when needed, exposure to family planning messages and voice and decision making autonomy and quality of care for those who visited health facilities in the 12 months before the survey. The questionnaire was piloted before the data collection and the questions reviewed for appropriateness, comprehension and flow. The questionnaire was piloted among a sample of 42 adolescent girls (two each per field interviewer) 15-19 from a community outside the study counties.

The questionnaire was originally developed in English and later translated into Kiswahili. The questionnaire was programmed using ODK-based Survey CTO platform for data collection and management and was administered through face-to-face interview.

Cleaning operations

The survey tools were programmed using the ODK-based SurveyCTO platform for data collection and management. During programming, consistency checks were in-built into the data capture software which ensured that there were no cases of missing or implausible information/values entered into the database by the field interviewers. For example, the application included controls for variables ranges, skip patterns, duplicated individuals, and intra- and inter-module consistency checks. This reduced or eliminated errors usually introduced at the data capture stage. Once programmed, the survey tools were tested by the programming team who in conjunction with the project team conducted further testing on the application's usability, in-built consistency checks (skips, variable ranges, duplicating individuals etc.), and inter-module consistency checks. Any issues raised were documented and tracked on the Issue Tracker and followed up to full and timely resolution. After internal testing was done, the tools were availed to the project and field teams to perform user acceptance testing (UAT) so as to verify and validate that the electronic platform worked exactly as expected, in terms of usability, questions design, checks and skips etc.

Data cleaning was performed to ensure that data were free of errors and that indicators generated from these data were accurate and consistent. This process begun on the first day of data collection as the first records were uploaded into the database. The data manager used data collected during pilot testing to begin writing scripts in Stata 14 to check the variables in the data in 'real-time'. This ensured the resolutions of any inconsistencies that could be addressed by the data collection teams during the fieldwork activities. The Stata 14 scripts that perform real-time checks and clean data also wrote to a .rtf file that detailed every check performed against each variable, any inconsistencies encountered, and all steps that were taken to address these inconsistencies. The .rtf files also reported when a variable was

Formatted data that was used to do the analysis on STATA.

This study uses panel data on Chinese A-share listed companies in Shanghai and Shenzhen covering 2014 to 2020 selected through the following screening: first, we exclude listed companies in the finance and insurance sectors; second, we exclude listed companies in ST and *ST (Special Treatment); finally, we exclude samples that lack important data. This approach generates 8,658 valid research sample observations. The data are obtained from several official websites, such as those for CSMAR (China Stock Market & Accounting Research Database), CNRDS (Chinese Research Data Services), and the Shanghai and Shenzhen stock exchanges.In this study, the descriptive and relevance of the final data was tested using Stata software, and baseline regression, threshold regression, and robustness and heterogeneity tests were performed. The final data were tested for descriptiveness and correlation using Stata software, and baseline regression, threshold regression, and robustness and heterogeneity tests were performed.

ObjectiveAlterations in leptin expression contributes to the progression of various diseases, including cancers. This meta-analysis investigated the clinical significance of leptin levels in osteoarthritis (OA) patients, with the goal of building a leptin-based diagnostic criterion for OA.MethodMultiple scientific databases in English and Chinese languages, such as the Cochrane Library Database, CINAHL, Chinese Biomedical (CBM), EMBASE, PubMed, and Web of Science, were exhaustively searched, without any language restrictions, to identify high-quality studies relevant to leptin and OA. Version 12.0 STATA software was used for data analysis. We used odds ratios (OR) and 95% confidence intervals (CI) to test the correlation between serum leptin levels and OA progression.ResultsA total of 11 clinical studies were finally selected for their high quality and relevance to the topic in this meta-analysis. The 11 case-control studies contained a combined total of 3,625 subjects. The meta-analysis results showed that leptin expression was significantly increased in OA patients, compared with the controls (SMD = 0.87, 95%CI: 0.72-1.02, P < 0.001), and there was also a strong association between leptin expression levels and gender (SMD = 8.55, 95%CI: 4.74-12.35, P < 0.001). In ethnicity-stratified subgroup analysis, all the study populations, irrespective of ethnicity, showed remarkably high leptin expression levels in females and in OA patients (all P < 0.05), compared to their respective counterparts.ConclusionThe present study revealed that increased leptin expression levels are associated with disease severity in OA patients, especially among the female OA patients. Based on our results, we propose that leptin level may be a useful biomarker for the assessment of the clinical status in OA patients.

Multivariable analysis of factors associated with COVID-19 infection prevention practices among food handlers at food and drink establishments in Dessie City and Kombolcha Town, Northeastern Ethiopia, July–August 2020.

Availability of supplies and its bivariable analysis with infection prevention practices among food handlers at food and drink establishments in Dessie City and Kombolcha Town, Northeastern Ethiopia, July–August 2020.

IntroductionRespiratory distress syndrome is the major cause of neonatal death. However, data on the mortality and predictors related to respiratory distress syndrome were scarce. Hence, this study aimed to assess the incidence and predictors of death among neonates admitted with respiratory distress syndrome in West Oromia Referral Hospitals, Ethiopia, 2022.MethodsA retrospective follow-up study was conducted among 406 neonates admitted with respiratory distress syndrome at five referral hospitals from, 1 January 2019 to, 31 December 2021 in West Oromia, Ethiopia. The data were collected using a structured checklist and participants were selected using simple random sampling technique. The data were entered into Epi data version 4.6.0.2 and exported to STATA version 14 for cleaning, coding and analysis. The Kaplan–Meier curve was used to estimate survival time. The Weibull regression model was fitted to identify the predictors of mortality and variables with a P-value < 0.05 was taken as significant predictors of mortality.ResultFour hundred six neonates with respiratory distress syndrome were included in the analysis. The overall incidence of neonatal mortality was 59.87/1000 neonates-days observations (95%CI: 51.1–70.2) with a proportion of 152 (37.44%) (95% CI: 32.7–42.2). The median time of follow-up was 11 days (95% CI: 10–23). Very low birthweight (AHR = 4.5, 95%CI: 2.0–10.9) and low birth weight (AHR = 3.1, 95%CI: 1.4–6.6), perinatal asphyxia (AHR = 2.7, 95%CI: 1.8–4), Chorioamnionitis (AHR = 2.2, 95%CI: 1.4–3.5) and multiple pregnancies (AHR = 2.2, 95%CI: 1.4–3.4) increased the hazard of death, whereas, antenatal corticosteroid administration (AHR = 0.33, 95%CI: 0.2–0.7) was negatively associated with neonatal mortality.Conclusion and recommendationHigh mortality rate of neonates with respiratory distress syndrome was observed. Chorioamnionitis, perinatal asphyxia, low birth weight and multiple pregnancies increase the, mortality hazard while administering antenatal corticosteroids decreases it. Thus, administering corticosteroids- before giving birth and special emphasis on children with Chorioaminoitis, asphyxia, low birth weight and multiple pregnancies is important for reducing neonatal mortality.

Knowledge and attitude of food handlers and bivariable analysis with infection prevention practices among food handlers at food and drink establishments in Dessie City and Kombolcha Town, Northeastern Ethiopia, July–August 2020.