Analysis of ‘Monthly provisional counts of deaths by age group and HHS region for select causes of death’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/d061abcf-387a-4240-85d3-9e12b172e966 on 27 January 2022.

--- Dataset description provided by original source is as follows ---

Provisional counts of deaths by the month the deaths occurred, by age group and HHS region, for select underlying causes of death for 2019-2020. The dataset also includes monthly provisional counts of death for COVID-19, coded to ICD-10 code U07.1 as an underlying or multiple cause of death.

--- Original source retains full ownership of the source dataset ---

The CMS Office of Enterprise Data and Analytics has developed CMS Program Statistics, which includes detailed summary statistics on national health care, Medicare populations, utilization, and expenditures, as well as counts for Medicare-certified institutional and non-institutional providers. CMS Program Statistics is organized into sections which can be downloaded and viewed separately. Tables and maps will be posted as they become finalized. CMS Program Statistics is replacing the Medicare and Medicaid Statistical Supplement, which was published annually in electronic form from 2001-2013.

Summary of geographically weighted regression (GWR) analysis of open defecation among households in Ethiopia, 2019.

The Health Insurance Marketplace Public Use Files contain data on health and dental plans offered to individuals and small businesses through the US Health Insurance Marketplace.

Exploration Ideas

To help get you started, here are some data exploration ideas:

• How do plan rates and benefits vary across states?
• How do plan benefits relate to plan rates?
• How do plan rates vary by age?
• How do plans vary across insurance network providers?

See this forum thread for more ideas, and post there if you want to add your own ideas or answer some of the open questions!

Data Description

This data was originally prepared and released by the Centers for Medicare & Medicaid Services (CMS). Please read the CMS Disclaimer-User Agreement before using this data.

Here, we've processed the data to facilitate analytics. This processed version has three components:

1. Original versions of the data

The original versions of the 2014, 2015, 2016 data are available in the "raw" directory of the download and "../input/raw" on Kaggle Scripts. Search for "dictionaries" on this page to find the data dictionaries describing the individual raw files.

2. Combined CSV files that contain

In the top level directory of the download ("../input" on Kaggle Scripts), there are six CSV files that contain the combined at across all years:

• BenefitsCostSharing.csv
• BusinessRules.csv
• Network.csv
• PlanAttributes.csv
• Rate.csv
• ServiceArea.csv

Additionally, there are two CSV files that facilitate joining data across years:

• Crosswalk2015.csv - joining 2014 and 2015 data
• Crosswalk2016.csv - joining 2015 and 2016 data

3. SQLite database

The "database.sqlite" file contains tables corresponding to each of the processed CSV files.

The code to create the processed version of this data is available on GitHub.

Abstract

Protracted and new displacements of large numbers of people as well as complex conflict dynamics continue to be a major issue in Darfur. In 2020, an estimated 2.5 million people were internally displaced and close to 400,000 Darfuris refugees resided in neighbouring countries. The political transition following years of conflict paved the way for the signing of the Juba Peace Agreement (JPA) in 2020. The peace agreement aims to address the root causes of conflict but also establishes durable solutions for displaced populations as a necessity for lasting peace in Darfur. In 2021, the Government furthermore initiated work on a National Strategy on Solutions, which will offer a critical strategic framework and operational roadmap towards solutions for displaced communities in Sudan.

In 2017, the Government of Sudan (GoS) and the international community agreed on the need to collectively support Durable Solutions for IDPs, returnees, and their host communities to end the situation of protracted displacement. The collaboration on Durable Solutions between the GoS and international community resulted in two Durable Solution pilots in respectively El Fasher (North Darfur) and Um Dukhun (Central Darfur).

JIPS provided technical support for the scale-up of the durable solutions analysis across Darfur under the Central Emergency Relief Fund (CERF). Focusing on nine localities, including urban areas, the data collection exercises build directly on the durable solutions analysis approach piloted in El Fasher in 2019. The Durable Solutions Working Group (DSWG) identified a joint evidence base and a collaborative approach as priorities and therefore undertook a joint area-based profiling exercise, focusing on the Abu Shouk and El Salaam IDP camps on the outskirts of El Fasher.

The focus was set on profiling of IDPs (in camp settlements and out of camps), IDP returnees, refugee returnees, and non-displaced. The profiling exercises are aimed at: i.Informing CERF programming and Action Plan development in each state/locality; ii.Provide the baseline of the agreed upon CERF outcome/output indicators (for later measurement of impact); and iii.Inform broader UNHCR programming beyond the Fund.

Geographic coverage

Kebkabiya and Kutum localities within North Darfur State

Analysis unit

Households

Universe

All IDP returnees, refugee returnees, IDPs in camps and out of camps, and non-displaced populations across Kabkabiya and Kutum.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling followed a systematic simple random approach, through which the households were treated as the primary sampling unit. The sample size for each target group was identified proportionately based on the group's population size. The sampling is designed to produce results representative for each target group in the targeted area of the locality. Analysis at the settlement level is not possible.

The selection of settlements included in each locality is based on a prioritization by partner agencies and local partners based on the programmatic scope of the CERF. The data is thus not representative of whole locality, but the specific geographic scope targeted within the locality.

In Kutum, the total sample included: 1442 households, covering IDPs in camps (389 HHs), IDPs out of camps (382 HHs), return IDPs (370 HHs) and non-displaced (301 HHs). In Kebkabiya, the total sample included: IDPs (394 HHs) and non-displaced (382 HHs). Additionally, 66 IDP returnee HHs were included in a nearby village (Bardi) - due to this very limited sample, no statistical analysis is done and the actual numbers are included.

The sample frame of the household survey was based on the population estimates of each target group, that were provided by key informants and validated through fieldwork missions.

Mode of data collection

Face-to-face [f2f]

Cleaning operations

Some households with over 14 members have had individuals removed from their household roster due to anonymization techniques.

Rate of deaths by age/gender (per 100,000 population) for motor vehicle occupants killed in crashes, 2012 & 2014. 2012 Source: Fatality Analysis Reporting System (FARS). 2014 Source: National Highway Traffic Safety Administration's (NHTSA) Fatality Analysis Reporting System (FARS), 2014 Annual Report File Note: Blank cells indicate data are suppressed. Fatality rates based on fewer than 20 deaths are suppressed.

This dataset represents preliminary weekly hospital respiratory data and metrics aggregated to national and state/territory levels reported to CDC’s National Health Safety Network (NHSN) beginning August 2020. This dataset updates weekly on Wednesdays with preliminary data reported to NHSN for the previous reporting week (Sunday – Saturday).

Data for reporting dates through April 30, 2024 represent data reported during a previous mandated reporting period as specified by the HHS Secretary. Data for reporting dates May 1, 2024 – October 31, 2024 represent voluntarily reported data in the absence of a mandate. Data for reporting dates beginning November 1, 2024 represent data reported during a current mandated reporting period. All data and metrics capturing information on respiratory syncytial virus (RSV) were voluntarily reported until November 1, 2024. All data included in this dataset represent aggregated counts, and include metrics capturing information specific to hospital capacity, occupancy, hospitalizations, and new hospital admissions with corresponding metrics indicating reporting coverage for a given reporting week. NHSN monitors national and local trends in healthcare system stress and capacity for all acute care and critical access hospitals in the United States.

For more information on the reporting mandate per the Centers for Medicare and Medicaid Services (CMS) requirements, visit: Updates to the Condition of Participation (CoP) Requirements for Hospitals and Critical Access Hospitals (CAHs) To Report Acute Respiratory Illnesses.

For more information regarding NHSN’s collection of these data, including full reporting guidance, visit: NHSN Hospital Respiratory Data.

For data that is considered final for a given reporting week (Sunday – Saturday), and reflects that which is used in NHSN HRD dashboards for publication each Friday, visit: https://data.cdc.gov/Public-Health-Surveillance/Weekly-Hospital-Respiratory-Data-HRD-Metrics-by-Ju/ua7e-t2fy/about_data.

CDC coordinates weekly forecasts of hospitalization admissions based on this data set. More information about flu forecasting can be found at About Flu Forecasting | FluSight | CDC, and information about COVID-19 forecasting and other modeling analyses for the Respiratory Virus Season are available at CFA's Insights for Respiratory Virus Season | CFA | CDC.

Source: CDC National Healthcare Safety Network (NHSN).

• Data source description (updated November 15, 2024): As of October 9, 2024, Hospital Respiratory Data (HRD; formerly Respiratory Pathogen, Hospital Capacity, and Supply data or 'COVID-19 hospital data') are reported to HHS through CDC's National Healthcare Safety Network (NHSN) based on updated requirements from the Centers for Medicare and Medicaid Services (CMS). These data were voluntarily reported to NHSN May 1, 2024 until November 1, 2024, at which time CMS began requiring acute care and critical access hospitals to electronically report information via NHSN about COVID-19, influenza, and RSV, hospital bed census and capacity. Hospital bed capacity and occupancy data for all patients and for patients with COVID-19 or influenza for collection dates prior to May 1, 2024, represent data reported during a previously mandated reporting period as specified by the HHS Secretary, and data for collection dates May 1, 2024 – October 31, 2024 represent data reported voluntarily to NHSN. All RSV data through October 31, 2024 represent voluntarily reported data; as such, all voluntarily reported data included in this dataset represent reporting hospitals only for a given week and might not be complete or representative of all hospitals during the specified reporting periods.
• NHSN monitors national and local trends in healthcare system stress and capacity for all acute care and critical access hospitals in the United States. Data reported by hospitals to NHSN represent aggregated counts and include metrics capturing information specific to hospital capacity, occupancy, hospitalizations, and admissions. Find more information about reporting to NHSN: https://www.cdc.gov/nhsn/psc/hospital-respiratory-reporting.html.
• Data quality: This dataset represents preliminary weekly hospital respiratory data and metrics aggregated to national and state/territory levels reported to CDC’s National Health Safety Network (NHSN) beginning August 2020, and updates weekly on Wednesdays with preliminary data reported to NHSN for the previous reporting week (Sunday – Saturday). While CDC reviews reported data for completeness and errors and corrects those found, some reporting errors might still exist within the data. CDC and partners work with reporters to correct these errors and update the data in subsequent weeks. Data reported as of December 1, 2020 are subject to thorough, routine data quality review procedures, including identifying and excluding invalid values from metric calculations and application of error correction methodology; data prior to this date may have anomalies that are not yet resolved. Data prior to August 1, 2020, are unavailable. As a result of data quality implementation and submission of any backfilled data, data and metrics might fluctuate or change week-over-week after initial posting.
• Inclusion criteria and metric calculations:
• Facility types and status: Many hospital subtypes, including acute care and critical access hospitals, are included in the metric calculations displayed on this page. Psychiatric, rehabilitation, and religious non-medical hospital types are excluded from calculations. Number of reporting hospitals is determined based on the NHSN unique hospital identifier and not aggregated to the CMS certification number (CCN). Only hospitals indicated as active reporters in NHSN are included.
• For occupancy metrics through week ending October 5, 2024: hospitals that reported those data at least one day during a given week are included in the metric calculation, which are displayed as weekly averages.
• For occupancy metrics beginning week ending October 12, 2024: hospitals that reported those data for Wednesday during a given week are included in the metric calculation, which are displayed as single day (i.e. Wednesday) values.
• For new hospital admissions metrics through week ending October 5, 2024: hospitals that reported those data at least one day during a given week are included in the metric calculation, which are displayed as weekly totals. Under previous reporting requirements, new hospital admissions data were reported daily to NHSN, as the number of new hospital admissions for the previous day.
• For new hospital admissions metrics beginning week ending October 12, 2024: hospitals that reported those data for an entire reporting week are included in the metric calculation, which are displayed as weekly totals. Under current reporting requirements, new admissions data are reported to represent the number of new admissions occurring on a given reporting date (rather than previous day) or during a given reporting week.
• Find full details on NHSN Hospital Respiratory Data (HRD) reporting guidance, including additional information on bed type definitions at https://www.cdc.gov/nhsn/psc/hospital-respiratory-reporting.html.

1. https://data.cdc.gov/Public-Health-Surveillance/Weekly-United-States-COVID-19-Hospitalization-Metr/akn2-qxic/about_data
2. https://data.cdc.gov/Public-Health-Surveillance/Weekly-United-States-COVID-19-Hospitalization-Metr/82ci-krud/about_data
3. https://data.cdc.gov/Public-Health-Surveillance/Respiratory-Virus-Response-RVR-United-States-Hospi/9t9r-e5a3/about_data
4. https://data.cdc.gov/Public-Health-Surveillance/Weekly-United-States-COVID-19-Hospitalization-Metr/7dk4-g6vg/about_data
5. https://data.cdc.gov/Public-Health-Surveillance/United-States-COVID-19-Hospitalization-Metrics-by-/39z2-9zu6/about_data
6. https://healthdata.gov/Hospital/COVID-19-Reported-Patient-Impact-and-Hospital-Capa/g62h-syeh/about_data

1. https://data.cdc.gov/Public-Health-Surveillance/Weekly-United-States-COVID-19-Hospitalization-Metr/akn2-qxic/about_data
2. https://data.cdc.gov/Public-Health-Surveillance/Weekly-United-States-Hospitalization-Metrics-by-Ju/ype6-idgy

Note: December 26, 2024: The following columns were added to this dataset as of December 26th,

Characteristics of included studies in qualitative analysis for magnitude of diabetic emergencies.

Probability and case number of different categories in the latent profile analysis.

Abstract

The Geneal Household Survey is a brainchild of the National Bureau of Statistics (NBS) and is often referred to as Regular survey carried out on quarterly basis by the NBS over the years. In recent times, starting from 2004 to be precise, there is a collaborative effort between the NBS and the CBN in 2004 and 2005 and in 2006, 2007and 2008, the collaboration incorporated Nigerian Communications commission (NCC).

The purpose of the surveys or collaboration include among others: (i) To conduct multipurpose surveys to generate social and economic data series for 2009 and the first quarter of 2010

(ii) To enable NBS/CBN/NCC fulfil their mandate in production of current and credible statistics to monitor and evaluate the State of the economy and the various government programmes such as NEEDS, MDGs and 7 Point Agenda.

The key objectives of the survey include:

i) Collection of relevant statistics to facilitate the production of GDP

ii) Production of data to aid economic analysis on non-oil outputs such as Manufacturing, Agriculture and Services

iii) Production of State and Local Government Finance Statistics, Producer Price Index (PPI), Oil Sector Statistics and Flow of Funds

Collection of current socio-economic statistics in Nigeria to assist in policy formulation and aid the monitoring and evaluation of various government programmes at National and sub-national levels

Geographic coverage

National Zone State Local Government

Analysis unit

Household Analysis

Universe

Household

Kind of data

Sample survey data [ssd]

Sampling procedure

The General Household Survey and the National Agricultural Sample Survey designs derived from NBS 2007/12 NISH sample design. The 2007/12 NISH sample design is a 2-stage, replicated and rotated cluster sample design with Enumeration Areas (EAs) as first stage sampling units or Primary Sampling Units (PSUs) while Households constituted the second stage units (secondary sampling units). The households were the Ultimate Sampling Units for the multi-subject survey.

Generally, the NISH Master Sample in each State is made up of 200 EAs drawn in 20 replicates. A replicate consists of 10 EAs. Replicates 10-15, subsets of the Master Sample were studied for modules of the NISH.

The GHS was implemented as a NISH module. three replicates were studied per State including the FCT, Abuja. With a fixed-take of 15 HHs systematically selected per EA, 450 HHs thus were selected for interview per State including the FCT, Abuja. Hence, nationally, a total of 16,650 HHs were drawn from the 1,110 EAs selected for interview for the GHS. The selected EAs (and hence the HHs) cut across the rural and urban sectors.

Sampling deviation

Variance Estimate (Jackknife Method) Estimating variances using the Jackknife method will require forming replicate from the full sample by randomly eliminating one sample cluster [Enumeration Area (EA) at a time from a state containing k EAs, k replicated estimates are formed by eliminating one of these, at a time, and increasing the weight of the remaining (k-1) EAs by a factor of k/(k-1). This process is repeated for each EA.

For a given state or reporting domain, the estimate of the variance of a rate, r, is given by k Var(r ) = (Se)2 = 1 S (ri - r)2 k(k-1) i=1

where (Se) is the standard error, k is the number of EAs in the state or reporting domain.

r is the weighted estimate calculated from the entire sample of EAs in the state or reporting domain.
ri = kr - (k - 1)r(i), where

r(i) is the re-weighted estimate calculated from the reduced sample of k-1 EAs.

To obtain an estimate of the variance at a higher level, say, at the national level, the process is repeated over all states, with k redefined to refer to the total number of EAs (as opposed to the number in the states).

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire for the GHS is a structured questionnaire based on household characteristics with some modifications and additions. The House project module is a new addition and some new questions on ICT.

The questionnaires were scaned.

This section were divided into eleven parts.

Part A: Identification code, Response status, Housing characteristics/amenities and Information communication Technology (ICT). Part B: Socio-demographic characteristics and Labour force characteristics Part C: Information about the people in the household who were absent during the period of the survey. Part D: Female contraceptive only, and children ever born by mothers aged 15 years and above Part E: Births of children in the last 12 months, and trained birth attendant used during child delivery. Part F: Immunization of children aged 1 year or less and records of their vaccination Part G: Child nutrition, exclusive breast feeding and length of breast feeding. Part H: Deaths in the last 12 months, and causes of such deaths. Part I: Health of all members, of the household and health care providers. Part J: Household enterprises, income and profit made from such activities. Part K: Household expenditure, such as school fees, medical expenses, housing expenses, remittance, cloth expenses, transport expenses and food expenses.

Cleaning operations

The data editing is in 2 phases namely manual editing before the questionnaires were scanned. This involved using editors at the various zones to manually edit and ensure consistency in the information on the questionnaire.

The second editing is the computer editing, this is the cleaning of the already scanned data by the subject mater group. The questionnaires were processed at the zones. On completion, computer editing was also carried out to ensure the integrity of the data. .

Response rate

At National level ,out of the expected 1,110 EAs, all were covered which showed 100% retrieval rate. (by the table 1.12 on page 196 of the report)

At household level, out of the 16,650 expected to be covered, 16,355 were canvassed which showed 98% retrieval.

At sector level (Urban/Rural), 28.4% were recorded for Urban while Rural recorded 71.6%.

Sampling error estimates

No sampling error estimate

Data appraisal

The Quality Control measures were carried out during the survey, essentially to ensure quality of data. There were three levels of supervision involving the supervisors at the first level, CBN staff, NBS State Officers and Zonal Controllers at second level and finally the NBS/NCC Headquarters staff constituting the third level supervision. Field monitoring and quality check exercises were also carried out during the period of data collection as part of the quality control measures

Infection intensity thresholds of STHs with the HH training status of selected districts of Seka Chekorsa woreda, Jimma zone, Southwest Ethiopia.

ObjectiveThis meta-analysis aims to assess the efficacy and safety of platelet-rich plasma (PRP) for osteonecrosis of the femoral head (ONFH).MethodsWe comprehensively searched randomized controlled trials in PubMed, Web of Science, EMBASE, the Cochrane Central Register of Controlled Trials, Chinese National Knowledge Infrastructure, China Science and Technology Journal Database, WanFang, and Chinese BioMedical Literature Database from inception until October 25, 2024. The literature on the clinical efficacy of autologous PRP for ONFH was collated. According to the inclusion and exclusion criteria, the literature was screened, quality evaluated and the data was extracted. Meta-analysis was carried out with the software Review Manager 5.4.1 software and Stata 17.0 software. In addition, potential publication bias was detected by the funnel plot test and Egger’s test. The GRADE system was used to evaluate the quality of evidence for outcome indicators.ResultsFourteen studies involving 909 patients were included in this study. Compared with non-PRP, PRP exhibited significant improvements in the Harris hip score (HHS) at 3 months (MD = 3.58, 95% Cl: 1.59 to 5.58, P = 0.0004), 6 months (MD = 6.19, 95% Cl: 3.96 to 8.41, P < 0.00001), 12 months (MD = 4.73, 95% Cl: 3.24 to 6.22, P < 0.00001), ≥ 24 months (MD = 6.83, 95% Cl: 2.09 to 11.59, P = 0.0003), and the last follow-up (MD = 6.57, 95% Cl: 4.81 to 8.33, P < 0.00001). The PRP also showed improvement in HHS compared to baseline than the non-PRP at 3 months (MD = 3.60, 95% Cl: 1.26 to 5.94, P = 0.003), 6 months (MD = 6.17, 95% Cl: 3.74 to 8.61, P < 0.00001), 12 months (MD = 5.35, 95% Cl: 3.44 to 7.25, P < 0.00001), ≥ 24 months (MD = 8.19, 95% Cl: 3.76 to 12.62, P = 0.0003), and the last follow-up (MD = 6.94, 95% Cl: 5.09 to 8.78, P < 0.00001). The change in visual analog scale (VAS) score 3 months post intervention (MD = -0.33, 95% Cl: -0.52 to -0.13, P = 0.001), 6 months (MD = -0.69, 95% Cl: -0.90 to -0.48, P < 0.00001), 12 months (MD = -0.75, 95% Cl: -1.05 to -0.46, P < 0.00001), ≥ 24 months (MD = -1.05, 95% Cl: -1.20 to -0.89, P < 0.00001), and the last follow-up (MD = -0.75, 95% Cl: -0.97 to -0.54, P < 0.00001). The PRP also showed a decrease in VAS score compared to baseline than the non-PRP at 3 months (MD = -0.29, 95% Cl: -0.41 to -0.17, P = 0.003), 6 months (MD = -0.63, 95% Cl: -0.96 to -0.30, P = 0.0002), 12 months (MD = -0.78, 95% Cl: -1.22 to -0.33, P = 0.0006), ≥ 24 months (MD = -1.11, 95% Cl: -1.27 to -0.96, P < 0.00001), and the last follow-up (MD = -0.74, 95% Cl: -1.05 to -0.43, P < 0.00001). Additionally, it was found that the PRP group had the advantages in the following aspects: collapse rate of the femoral head (RR = 0.33, 95% Cl: 0.17 to 0.62, P = 0.0006), rate of conversion to total hip arthroplasty (RR = 0.37, 95% Cl: 0.18 to 0.74, P = 0.005), and overall complications (RR = 0.33, 95% Cl: 0.13 to 0.83, P = 0.02). The GRADE evidence evaluation showed overall complication as very low quality and other indicators as low quality.ConclusionThere is limited evidence showing benefit of PRP therapy for treatment of ONFH patients, and most of this evidence is of low quality. Caution should therefore be exercised in interpreting these results. It is recommended that future research involve a greater number of high-quality studies to validate the aforementioned conclusions.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/ #recordDetails, CRD42023463031.

Descriptive characteristics of the participants (n = 517).

Risk factor analysis.