Descriptive statistics for evaluation participants aged 17 + at service entry.

This research study considers one such urban technology, namely utilising digital twins in cities. Digital twin city (DTC) technology is investigated to identify the gap in soft infrastructure data inclusion in DTC development. Soft infrastructure data considers the social and economic systems of a city, which leads to the identification of socio-economic security (SES) as the metric of investigation. The study also investigated how GIS mapping of the SES system in the specific context of Hatfield informs a soft infrastructure understanding that contributes to DTC readiness. This research study collected desk-researched secondary data and field-researched primary data in GIS using ArcGIS PRO and the Esri Online Platform using ArcGIS software. To form conclusions, grounded theory qualitative analysis and descriptive statistics analysis of the spatial GIS data schema data sets were performed.

Primary and secondary data summary for systematic review titled "Medical therapies for pediatric lymphatic malformations: a systematic review"

BackgroundThe COVID-19 pandemic has highlighted the importance of a well-equipped and supported healthcare workforce, and Bangladesh still faces challenges in providing adequate and well-equipped healthcare services. Therefore, the study aims to assess the level of working conditions of the clinical health workers in Bangladesh and their relative importance in delivering quality healthcare services.MethodsThe study followed a cross-sectional study design and collected primary data adopting a quantitative method. A total of 319 clinical workforces from four districts and eight sub-districts were randomly selected using a multi-stage sampling technique. A 26-component questionnaire used to assess various components of working conditions. Descriptive statistics, and bivariate analysis were used to analyze the data.ResultsThe study found that the working conditions of clinical health workers in primary and secondary healthcare facilities in Bangladesh were quite poor (3.40), with almost two-thirds of respondents showing negative views in 23 out of 26 indicators. The results also showed that working conditions were significantly (p ≤ 0.05) higher in primary compared to secondary level facilities. Moreover, men, younger workforce, and workforce with shorter length of service were more likely to report poor working conditions than their counterparts. Lastly, receiving monthly salary in due time was top-ranked (99.15) in terms of importance for delivering quality healthcare, followed by availability of medicines (98.04), and medical and surgical requisites (97.57), and adequate mentoring and support to perform duties (97.50).ConclusionThe study highlights the poor working conditions of clinical health workers in public health facilities in Bangladesh. It recommends that policymakers should prioritize improving working conditions by addressing the factors that are crucial for delivering quality healthcare. Improving working conditions will have a positive impact on the retention and motivation of workers, which will ultimately lead to better health outcomes for the population.

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

With data on school locations, categories, and contact information, analysts can explore various aspects of public school distribution, accessibility, and resource allocation. The geographical data allows for mapping and spatial analysis, which can help identify areas with higher concentrations of schools or regions that may lack adequate public education facilities. This dataset's uniform structure makes it suitable for integration with other demographic or socioeconomic datasets, enabling more nuanced analysis of educational accessibility and equity. Several analyses can be performed using this dataset: - Descriptive Statistics: To provide a summary of the dataset, including the number of schools by category, average number of schools per ZIP code, and other basic statistics. - Cluster Analysis: To group schools based on similar characteristics such as location, school type (high, middle, elementary), and size to identify patterns in school distribution. - Accessibility Analysis: To evaluate the ease of access to public schools for students in different areas, considering factors such as distance to schools and availability of public transportation. - Demographic and Socioeconomic Impact Analysis: To understand how demographic and socioeconomic factors influence the distribution and accessibility of public schools.

The dataset presents results of a descriptive cross-sectional study based on a survey, delivered through an online self-reported questionnaire. The questionnaire was distributed to managers of hospitals and other health care organisations in a purposive sample of participants to the Exchange Programmes of the European Hospital and Health Care Federation (HOPE) eliciting information on the actual use of performance data in hospitals and other healthcare organisations in Europe in 2019. Data collected through the online questionnaire was analysed using univariate descriptive statistics. Analyses were conducted using the R statistical program version 3.6.1. Respondents were, for certain parts of the analysis, sub-grouped by their reported managerial position and experience, as well as the type of organisation they work for. Analysis was done on a full sample of respondents, including the primary, 2019 HOPE Exchange Programme participants, and the secondary study population, 2015-2018 Exchange Programme alumni and local hosts.

BackgroundAlthough studies throughout the world have investigated potential factors involved in the occurrence of molar incisor hypomineralization (MIH), the findings are varied and inconclusive.ObjectiveThe aim of the present study was to evaluate the prevalence of MIH and identify associated prenatal, perinatal and postnatal factors among Brazilian schoolchildren aged 8 and 9 years.MethodsA cross-sectional study was conducted with a randomly selected population-based sample of 1181 schoolchildren. Information on demographic and socioeconomic characteristics as well as prenatal, perinatal and postnatal aspects was obtained through questionnaires. The clinical examination included the investigation of MIH based on the criteria of the European Academy of Paediatric Dentistry. Dental caries in the permanent dentition and developmental defects of enamel (DDE) on the primary second molars were also recorded. Data analysis involved descriptive statistics, bivariate tests and Poisson regression with robust variance.ResultsThe prevalence of MIH was 20.4%. MIH was more frequent among children with dental caries in the permanent dentition (PR: 2.67; 95% CI: 1.98–3.61), those with DDE on the primary second molars (PR: 2.54; 95% CI: 1.87–3.45) and those who experienced asthma/bronchitis in the first four years of life (PR: 1.93; 95% CI: 1.45–2.56).ConclusionsThe prevalence of MIH was high and was associated with dental caries, the presence of DDE on primary second molars and the experience of asthma/bronchitis in early life. These findings could be useful in the identification of children in need of shorter recall intervals to prevent the consequences of MIH, such as enamel breakdown dental caries.

A Compilation of Motor Vehicle Traffic Crash Data, the National Highway Traffic Safety Administration (NHTSA) presents descriptive statistics about traffic crashes of all severities, from those that result in property damage to those that result in the loss of human life. Information from three of NHTSA’s primary data systems has been combined to create a single source for motor vehicle traffic crash statistics. The first data system, the Fatality Analysis Reporting System (FARS), is probably the better known of the three sources. Established in 1975, FARS contains data on the most severe traffic crashes, those in which someone was killed. The second source is the National Automotive Sampling System General Estimates System (NASS GES), which began operation in 1988. NASS GES contains data from a nationally representative sample of police-reported crashes of all severities, including those that result in death, injury, or property damage. The third source is the new Crash Report Sampling System (CRSS), which replaced NASS GES in 2016. CRSS is the redesigned nationally representative sample of police-reported traffic crashes. Note that 2018 and earlier year FARS data are final and generally not subject to change. However, minor revisions were made to the 2017 and 2018 FARS Final files. For more information refer to "About This Report" in the Introduction section. Although the 2023 data file is a full year's worth of data, it is subject to change when it is finalized. The current version of the 2023 FARS data file is referred to as the Annual Report File (ARF). The additional time between the Annual Report file and the Final file provides the opportunity for submission of important variable data requiring outside sources, which may lead to changes in the final counts. The updated final counts for 2023 will be reflected with the release of the 2024 Annual Report File. Notes: NASS GES was discontinued in 2016 and replaced with a new system called CRSS. The 2016 data year was the first data collection year of CRSS. However, the 2016 and later year estimates from CRSS are not comparable to 2015 and earlier year estimates from NASS GES.GES and CRSS data are included here, FARS data are at https://www.datalumos.org/datalumos/project/239071/version/V1/view

The SHMI methodology uses 144 statistical models, each corresponding to a different diagnosis group. Each of these models is constructed using a 3 year dataset and includes the following risk-adjustment variables: age, gender, method and month of admission, Charlson comorbidity index, year index and birthweight (for perinatal diagnosis groups only). The adjustment for the Charlson comorbidity index is not included for the diagnosis group containing activity with an invalid primary diagnosis. Statistics relating to the model fit for each of the 144 statistical models (model fit statistics) and model coefficients (model predict statistics) are published for the purposes of transparency. Details of the statistics provided in each of the files are available in the 'SHMI statistical model data definitions' file. Notes: 1. Further information on data quality can be found in the SHMI background quality report, which can be downloaded from the 'Resources' section of the publication page.

analyze the survey of consumer finances (scf) with r the survey of consumer finances (scf) tracks the wealth of american families. every three years, more than five thousand households answer a battery of questions about income, net worth, credit card debt, pensions, mortgages, even the lease on their cars. plenty of surveys collect annual income, only the survey of consumer finances captures such detailed asset data. responses are at the primary economic unit-level (peu) - the economically dominant, financially interdependent family members within a sampled household. norc at the university of chicago administers the data collection, but the board of governors of the federal reserve pay the bills and therefore call the shots. if you were so brazen as to open up the microdata and run a simple weighted median, you'd get the wrong answer. the five to six thousand respondents actually gobble up twenty-five to thirty thousand records in the final pub lic use files. why oh why? well, those tables contain not one, not two, but five records for each peu. wherever missing, these data are multiply-imputed, meaning answers to the same question for the same household might vary across implicates. each analysis must account for all that, lest your confidence intervals be too tight. to calculate the correct statistics, you'll need to break the single file into five, necessarily complicating your life. this can be accomplished with the meanit sas macro buried in the 2004 scf codebook (search for meanit - you'll need the sas iml add-on). or you might blow the dust off this website referred to in the 2010 codebook as the home of an alternative multiple imputation technique, but all i found were broken links. perhaps it's time for plan c, and by c, i mean free. read the imputation section of the latest codebook (search for imputation), then give these scripts a whirl. they've got that new r smell. the lion's share of the respondents in the survey of consumer finances get drawn from a pretty standard sample of american dwellings - no nursing homes, no active-duty military. then there's this secondary sample of richer households to even out the statistical noise at the higher end of the i ncome and assets spectrum. you can read more if you like, but at the end of the day the weights just generalize to civilian, non-institutional american households. one last thing before you start your engine: read everything you always wanted to know about the scf. my favorite part of that title is the word always. this new github repository contains t hree scripts: 1989-2010 download all microdata.R initiate a function to download and import any survey of consumer finances zipped stata file (.dta) loop through each year specified by the user (starting at the 1989 re-vamp) to download the main, extract, and replicate weight files, then import each into r break the main file into five implicates (each containing one record per peu) and merge the appropriate extract data onto each implicate save the five implicates and replicate weights to an r data file (.rda) for rapid future loading 2010 analysis examples.R prepare two survey of consumer finances-flavored multiply-imputed survey analysis functions load the r data files (.rda) necessary to create a multiply-imputed, replicate-weighted survey design demonstrate how to access the properties of a multiply-imput ed survey design object cook up some descriptive statistics and export examples, calculated with scf-centric variance quirks run a quick t-test and regression, but only because you asked nicely replicate FRB SAS output.R reproduce each and every statistic pr ovided by the friendly folks at the federal reserve create a multiply-imputed, replicate-weighted survey design object re-reproduce (and yes, i said/meant what i meant/said) each of those statistics, now using the multiply-imputed survey design object to highlight the statistically-theoretically-irrelevant differences click here to view these three scripts for more detail about the survey of consumer finances (scf), visit: the federal reserve board of governors' survey of consumer finances homepage the latest scf chartbook, to browse what's possible. (spoiler alert: everything.) the survey of consumer finances wikipedia entry the official frequently asked questions notes: nationally-representative statistics on the financial health, wealth, and assets of american hous eholds might not be monopolized by the survey of consumer finances, but there isn't much competition aside from the assets topical module of the survey of income and program participation (sipp). on one hand, the scf interview questions contain more detail than sipp. on the other hand, scf's smaller sample precludes analyses of acute subpopulations. and for any three-handed martians in the audience, ther e's also a few biases between these two data sources that you ought to consider. the survey methodologists at the federal reserve take their job...

This report shows monthly numbers of NHS Hospital and Community Health Service (HCHS) staff working in NHS Trusts and CCGs in England (excluding primary care staff). Data is available as headcount and full-time equivalents and are available every month for 30 September 2009 onwards. This data is an accurate summary of the validated data extracted from the NHS HR and Payroll system. Additional statistics on staff in NHS Trusts and CCGs and information for NHS Support Organisations and Central Bodies are published each: September (showing June statistics) December (showing September statistics) March (showing December statistics) June (showing March statistics) Quarterly NHS Staff Earnings and monthly NHS Staff Sickness Absence reports and data relating to the General Practice workforce and the Independent Healthcare Provider workforce are also available via the Related Links below. Changes to the publication, described in the November 2020 (August 2020 data) edition, have been implemented in this report for January 2021 (October 2020 data onwards). Two of these changes are related to improvements in processing and data quality routines that will feed through into the data presented. An additional resource is also published ahead of current timescales, featuring data relating to the main headline staff groups and the Nurses staff group. We welcome feedback on the methodology and tables within this publication. Please email us with your comments and suggestions, clearly stating Monthly HCHS Workforce as the subject heading, via enquiries@nhsdigital.nhs.uk or 0300 303 5678.

This publication was updated on 9th August 2024. Data for Royal Surrey County Hospital NHS Foundation Trust (trust code RA2) has been suppressed from publication. This trust had submitted in error a high percentage of records with no secondary care diagnosis codes, this has made their SHMI values highly misleading. They have corrected the data at source, and this is expected to be reflected in the November SHMI publication. The SHMI methodology uses 144 statistical models, each corresponding to a different diagnosis group. Each of these models is constructed using a 3 year dataset and includes the following risk-adjustment variables: age, gender, method and month of admission, Charlson comorbidity index, year index and birthweight (for perinatal diagnosis groups only). The adjustment for the Charlson comorbidity index is not included for the diagnosis group containing activity with an invalid primary diagnosis. Statistics relating to the model fit for each of the 144 statistical models (model fit statistics) and model coefficients (model predict statistics) are published for the purposes of transparency. Details of the statistics provided in each of the files are available in the 'SHMI statistical model data definitions' file. Notes: 1. Data for Royal Surrey County Hospital NHS Foundation Trust (trust code RA2) has been suppressed from publication. This trust had submitted in error a high percentage of records with no secondary care diagnosis codes, this has made their SHMI values highly misleading. 2. Further information on data quality can be found in the SHMI background quality report, which can be downloaded from the 'Resources' section of the publication page.

IntroductionSoil-transmitted helminth (STH) and schistosomiasis (SCH) infections remain some of the most prevalent neglected tropical diseases (NTDs), causing significant morbidity in most of sub-Saharan Africa (SSA), including Rwanda. With dwindling international funding for NTD services and recent commitments focused on other diseases considered easier to eliminate as a public health problem, it is essential to assess domestic financing sources’ scale, efficiency, and effectiveness. The study aims to strengthen domestic efforts towards sustainable financing for neglected tropical disease programs in Africa, particularly in Rwanda.MethodUp to 235 patients from 24 health centers in four districts of Rwanda were sampled for this survey. The districts selected had the highest number of STH and SCH based on routine data from June 2021 to December 2022, which is the window period of the study. We estimated affordability using the lowest-paid government worker (LPGW) and then compared this with household income and expenditure obtained from patients participating in the survey. Data was collected from August to September 2023. Limited secondary data were collected to complement primary data. Descriptive statistical analysis was used to present the findings.Results and ConclusionsThe most available drugs were mebendazole, with 100% of facilities reporting no stockout. Praziquantel (PZQ) was the most unavailable drug, reporting 92% stockout at the time of the survey, mainly due to delays in getting supplies from MDA-implementing health facilities. Diagnostics for SCH are the most inaccessible lab services. On average, the total cost (both direct and opportunity cost) to access and utilize STH and SCH services was USD 0.72 (RWF 861.92) and USD 0.96 (RWF 1136.41) for male and female patients, respectively. Although the assessment revealed that treatment for STH and SCH was affordable for the LPGW, women pay a 33% higher cost than men to access NTD services. While services are generally satisfactory, the reimbursement processes are slow, hindering timely access and utilization of SCH and STH services at the health facilities in Rwanda.While the access and utilization of STH and SCH services in health centers are generally promising, the findings underscore the potential for improvement. By addressing the efficiency in the supply of praziquantel drugs and enhancing reimbursement timelines, we can ensure the continuity and effectiveness of these services, offering hope for a brighter future in the fight against neglected tropical diseases.

To assess the knowledge, awareness, and practice (KAP) of the Dentists to combat the pandemic which could help shape future guidelines and policies to be implemented in dental settings. The cross-sectional descriptive study was conducted solely online with series of multiple-choice questionnaires collecting responses till the determined sample size was reached. The ethical approval for the study was obtained from Nepal Health Research Council. Electronic informed consent was provided on the initial page of the survey. The positive response was considered as implied consent. The NMC registered Dentists who responded to all the questions of the survey were included while incomplete responses were excluded from the survey. An online structured survey composed of 26 questions created using the free-access Google Forms application. Pretesting of the survey instrument was done and refinements were made as required to facilitate better comprehension and to organize the questions before the final survey instrument was formed. The questionnaire was pretested among ten dental surgeons in Bharatpur. Inter responder reliability was tested among five responders and was found to have high reliability. It was sent to dental practitioners with the link to the online survey sent through an anonymous mailing and social media messaging list to currently practicing dentists across the country regardless of their place of work either in private clinics, hospitals, or health centers. Confidentiality was maintained throughout the study by making participants' information anonymous and not asking to enter their personal details at any point. Eligible dentists’ participation in this survey was completely voluntary. Filled pro forma was collected via e-survey and after completion of the assigned sample size of participants; it was recorded in spreadsheet software as a master chart for statistical analysis. Statistical analysis was performed by the primary and secondary authors themselves using SPSS version 20 for mac OS. Descriptive statistics was performed, and frequencies of responses were reported in proportion. Nonparametric tests of independence and Chi-square test was performed to see the association between categorical variables. Around 84% of the respondents accurately answered mode of transmission, 68% inquired about the travel history while only 49% measured the body temperature. Also, only 42% were receiving salary. A statistically significant difference concerning impact and practice during the COVID-19 was observed between general practitioner and specialist working at different workplaces.

IntroductionSoil-transmitted helminth (STH) and schistosomiasis (SCH) infections remain some of the most prevalent neglected tropical diseases (NTDs), causing significant morbidity in most of sub-Saharan Africa (SSA), including Rwanda. With dwindling international funding for NTD services and recent commitments focused on other diseases considered easier to eliminate as a public health problem, it is essential to assess domestic financing sources’ scale, efficiency, and effectiveness. The study aims to strengthen domestic efforts towards sustainable financing for neglected tropical disease programs in Africa, particularly in Rwanda.MethodUp to 235 patients from 24 health centers in four districts of Rwanda were sampled for this survey. The districts selected had the highest number of STH and SCH based on routine data from June 2021 to December 2022, which is the window period of the study. We estimated affordability using the lowest-paid government worker (LPGW) and then compared this with household income and expenditure obtained from patients participating in the survey. Data was collected from August to September 2023. Limited secondary data were collected to complement primary data. Descriptive statistical analysis was used to present the findings.Results and ConclusionsThe most available drugs were mebendazole, with 100% of facilities reporting no stockout. Praziquantel (PZQ) was the most unavailable drug, reporting 92% stockout at the time of the survey, mainly due to delays in getting supplies from MDA-implementing health facilities. Diagnostics for SCH are the most inaccessible lab services. On average, the total cost (both direct and opportunity cost) to access and utilize STH and SCH services was USD 0.72 (RWF 861.92) and USD 0.96 (RWF 1136.41) for male and female patients, respectively. Although the assessment revealed that treatment for STH and SCH was affordable for the LPGW, women pay a 33% higher cost than men to access NTD services. While services are generally satisfactory, the reimbursement processes are slow, hindering timely access and utilization of SCH and STH services at the health facilities in Rwanda.While the access and utilization of STH and SCH services in health centers are generally promising, the findings underscore the potential for improvement. By addressing the efficiency in the supply of praziquantel drugs and enhancing reimbursement timelines, we can ensure the continuity and effectiveness of these services, offering hope for a brighter future in the fight against neglected tropical diseases.

Diabetes is a major cause of morbidity and mortality worldwide yet preventable. Complications of undetected and untreated diabetes result in serious human suffering and disability. It negatively impacts on individual’s social economic status threatening economic prosperity. There is a scarcity of data on health system diabetes service readiness and availability in Kenya which necessitated an investigation into the specific availability and readiness of diabetes services. A cross sectional descriptive study was carried out using the Kenya service availability and readiness mapping tool in 598 randomly selected public health facilities in 12 purposively selected counties. Ethical standards outlined in the 1964 Declaration of Helsinki and its later amendments were upheld throughout the study. Health facilities were classified into primary and secondary level facilities prior to statistical analysis using IBM SPSS version 25. Exploratory data analysis techniques were employed to uncover the distribution structure of continuous study variables. For categorical variables, descriptive statistics in terms of proportions, frequency distributions and percentages were used. Of the 598 facilities visited, 83.3% were classified as primary while 16.6% as secondary. A variation in specific diabetes service availability and readiness was depicted in the 12 counties and between primary and secondary level facilities. Human resource for health reported a low mean availability (46%; 95% CI 44%-48%) with any NCDs specialist and nutritionist the least carder available. Basic equipment and diagnostic capacity reported a fairly high mean readiness (73%; 95% CI 71%-75%) and (64%; 95%CI 60%-68%) respectively. Generally, primary health facilities had low diabetic specific service availability and readiness compared to secondary facilities: capacity to cope with diabetes increased as the level of care ascended to higher levels. Significant gaps were identified in overall availability and readiness in both primary and secondary levels facilities particularly in terms of human resource for health specifically nutrition and laboratory profession.

Abstract

The evaluation considers a range of outcomes of the ADA program, including production and profitability, investment and technology adoption, employment and wages, and access to credit and markets. Though it was originally designed as a rigorous impact evaluation that incorporated a randomized design, the evaluation was not able to undertake a rigorous statistical analysis of the program on these outcomes for a number of reasons, including the small overall size of the program, changes during implementation that compromised the original evaluation design, and the timing of the evaluation. Instead, the evaluation uses a mixed methods approach combining qualitative data with descriptive quantitative analysis to assess the impact of the project.

Qualitative data collection included focus group discussions and in-depth interviews that collected detailed information from a total of 69 respondents. Respondents were recruited from among those who responded to the ADA survey and were grouped together by type of grantee (PP, VA/VCI, and FSC as separate groups) and by characteristics of interest based on responses to the ADA survey (those that reported an increase in income, those that didn't respond to income questions, those that closed their businesses, exporters, and machinery ring grantees).These interviews and focus groups were transcribed and analyzed using the specialized software package NVivo to systematically categorize responses and identify commonalities. Themes of interest to the evaluation were identified and then coded in all of the transcriptions. Summaries of responses by code and respondent type were completed and interesting cases were highlighted, providing some concrete examples of project results and/or feedback that also served in helping interpret the quantitative data.

Geographic coverage

The program was implemented nationally.

Analysis unit

Small, medium and large agribusinesses (MCG grantees and non-grantees)

Universe

Applicants to the ADA program from all application rounds (9 in total) in Georgia.

Kind of data

Sample survey data [ssd]

Sampling procedure

Round 1: The frame for the survey is the list of all applicants. It was supplied by CNFA, the program implementer, along with the scores from the initial evaluation, various statuses assigned by CNFA, and various items of information taken from the applications.

Each of the four applicant types were considered as separate strata, that is, primary producers (PPs), farm service centers (FSCs), value adders (VAs) and value chain enterprises (VCHs).

For PPs, one comparison case was selected for each new treatment case. A propensity score matching (PSM) methodology was used to select the comparison cases, using binary logistic regression. The dependent variable was the event of being a treatment case. The independent variables, all available from data supplied by CNFA on the frame, were: * the amount of matching contribution the applicant proposed to make * the current turnover of the business when it made its application * the number of employees of the business when it made its application * whether the business was able to secure credit * the year in which the business was established * whether the business was located in a village or larger town * the type of activity the business was proposing to be engaged in * the round in which the applicant applied

For each PP treatment case, the comparison case with the closest PSM score was selected for inclusion in the survey sample, as long as it had not been selected for interview previously.

For the other applicant types (FSCs, VAs and VCHs), stratified random sampling was used to select comparison cases. Because the populations were relatively small, two comparison cases were selected for each treatment case. Selection of comparison cases was to be made within the same strata in which the treatment cases occurred. The strata were defined in terms of the current turnover of the business when it made its application and the year in which the business was established. Type of activity was also used to define the strata for VAs and VCHs.

Round 2: The following sampling rules were applied: 1. Include all businesses that had been interviewed in Round 1 from ADA application waves 1 to 7. a) Interviewees from ADA application waves 8 and 9 were excluded because those interviews had been conducted too recently to expect significant change to have taken place in the meantime. b) Selections were made in terms of "businesses" rather than "applications" because some businesses had applied several times. Where a selected business had made multiple applications, the most recent application was nominally selected for inclusion in the survey, regardless of whether that application or an earlier one was the basis of interview in ADA application waves 1 to 7. The most recent one was chosen because it would have the most up-to-date contact information. c) 199 applications were selected on this basis.

1. Include treatments from any ADA application wave that had not yet been interviewed in Round 1. Some of these were previously non-response and some appeared to have wrongly claimed to have been previously interviewed on the basis of another application. 29 applications were selected on this basis.

2. Include applicants that scored 70+ (passing score) in ADA application waves 1-7, that have not yet been interviewed, but that are not previous nonresponse. Most appear to have wrongly claimed to have been previously interviewed on the basis of another application. 8 applications were selected on this basis.

3. PPs and VAs were not fully enumerated in Round 1, and the process used to randomly select applicants with a score less than 70 has not enabled the probability of selection to be derived. Therefore, for Round 2, select a random sample of 100 PPs and 25 VAs applications, where (i) neither they nor any related application was interviewed in ADA application waves 8 or 9, and (ii) neither they nor any related application received a score of 70+. If the selected application has not already been selected under condition 1 above, include in the Round 2 Survey. a) 78 PP applications were selected on this basis, that is, 22 of the 100 were already selected under condition 1 above. b) 18 VA applications were initially selected on this basis, that is, 7 of the 25 were already selected under condition 1 above.

However, as there were only 20 eligible VAs to be chosen under this condition, all 20 were included and so the VAs became fully enumerated.

In total there were 334 applications selected for inclusion in the survey.

The frame and summary information about the selections are included in the external resource "Followup frame and selections.xlsx".

Round 3: The sample frame was created by NORC and included all cases that were part of the sample in Round 1 and all the cases that were part of the sample in Round 2. The sample comprised of treatment and control groups with three main types of businesses in each group. Overall 600 face-to-face interviews were planned to be conducted for Round 3. This sample frame was then put through a re-listing exercise to update it since the list of business status and contact information included many incorrect telephone numbers and addresses, there was turnover in owners/managers of agribusinesses, and some had shut down.

For the relisting exercise, ACT first tried calling the phone numbers, then conducted field visits to the listed addresses. If still unable to locate the business, ACT regional coordinators contacted local authorities/representatives. Upon contacting the business, updated information about the business status, location, and contact information was collected for use during the main data collection. This updated list was the sample used for data collection.

Sampling deviation

Round 1 It should be noted that the model for PPs was re-estimated many times and some comparison cases were selected on the basis of the PSM scores generated in each of those runs. First of all, it had to be re-estimated for each wave of the survey, as new applicants appeared in the frame and new treatment cases were chosen by CNFA. Secondly, many applicants did not have values for all the independent variables, and therefore the model was re-estimated a number of times with varying reduced sets of independent variables.

In practice, the strata were defined with too much detail and comparison cases often could not be found in the same strata as treatment cases. Therefore strata had to be combined. This was done in an ad hoc way, with the result that the probability of selection is not available and corresponding sampling weights cannot be calculated.

By wave 4, it was also found that the pool of comparison cases was so small for FSCs and VCHs that all cases had to be included in the sample, that is, these categories are fully enumerated. This then applies to wave 5 also.

Selection of comparison cases was on a quota basis, that is, there was substitution for non-responding selections and for selections that no longer existed as separate entities. This occurred because some green-field proposals never commenced operations, because some businesses ceased operations, and because some businesses merged with or had always operated jointly with other applicants that had already been interviewed.

Round 2 During the course of the survey, two notable changes were made to the frame. First, it was discovered that one applicant had not been included in the CNFA Masterlist. This was a VCI applicant and it was therefore added to the survey. Second, it was discovered during interview (and subsequently confirmed) that applicant #318 should have been

Total direct and indirect costs as a % of household income.