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Low-Income Population

The low-income population refers to individuals and families with limited financial resources, often living below the poverty line.
This group faces unique challenges in accessing healthcare, education, and other essential services.
Researchers studying the low-income population aim to identify barriers, develop targeted interventions, and improve overall well-being and quality of life.
Pubcompare.ai's AI-driven platform can help streamline this research by efortlessly locating and comparing relevant protocols from literature, preprints, and patents.
Our intelligent tool leverages machine learning to maximize the impact of low-income population studies and drive meaningful change.

Most cited protocols related to «Low-Income Population»

1
Promoting Health Among Mexican-American Youth
Cited 59 times
The sample consisted of 598 seventh-grade adolescents and their Mexican American parents from 5 junior high schools that served primarily low-income populations (80% of students were eligible for free lunches) in a large southwestern metropolitan area with a substantial proportion of Mexican American and European American families and a relatively smaller proportion of families from other ethnic/racial groups. Family incomes ranged from $1,000 per year to $150,000 per year, with a mean of $36,310 per year. The original study aimed to recruit Mexican-origin families into a program designed to prevent high school dropout and mental and behavioral health disorders in youth. Sixty-two percent of the 955 eligible families enrolled and completed the first wave of assessments. In addition, the project required that both parents and youth be able to participate in the assessments and the intervention sessions in the same language; 6% of the families were ineligible because of this requirement. The current investigation uses data from the assessments that occurred prior to exposure to the intervention.
Of the 598 adolescents, 303 (50.6%) were female, 295 (49.2%) were male, 112 (18.7%) were born in Mexico, and 447 (74.7%) were born in the United States. Adolescents ranged in age from 11 to 14 years, with a mean age of 12.3 years. Three hundred and nineteen adolescents (53.4%) were interviewed in Spanish and 278 in English (46.6%). Of the parents, 573 mothers and 331 fathers participated in the interviews. Among the mothers, 347 (60.6%) were born in Mexico, 222 (38.7%) were born in the United States (4 mothers did not report their birthplace), 314 (54.8%) were interviewed in Spanish and 259 (45.2%) were interviewed in English. Among the fathers, 227 (68.6%) were born in Mexico, 104 (31.4%) were born in the United States, 200 (60.4%) were interviewed in Spanish and 131 (39.6%) were interviewed in English.
In-home interviews were conducted by trained interviewers using laptop computers. Interviewers were trained to conduct the parent and child surveys in separate rooms and/or out of hearing of other family members. Interviewers read each survey question and possible responses aloud in either Spanish or English to reduce problems associated with variations in literacy. All measures were translated and back-translated to ensure equivalence of all content (Behling & Law, 2000 ). Family members received $30 for participating, for a total of $60 for one-parent and $90 for two-parent families.
Knight G.P., Gonzales N.A., Saenz D.S., Bonds D.D., Germán M., Deardorff J., Roosa M.W, & Updegraff K.A. (2010). The Mexican American Cultural Values scales for Adolescents and Adults. The Journal of early adolescence, 30(3), 444-481.
Publication 2009
Adolescent Behavior Disorders Child Childbirth Europeans Family Member Fathers Hispanic or Latino Interviewers Low-Income Population Males Mexican Americans Mothers Parent Racial Groups Student Student Dropouts Woman Youth
2
Patient Navigation for Cancer Care
Cited 34 times
The working definition of patient navigation was provided by the NCI’s CRHCD in their request for applications. 13 In this definition, patient navigation refers to support and guidance offered to persons with abnormal cancer screening or a new cancer diagnosis in accessing the cancer care system, overcoming barriers, and facilitating timely, quality care provided in a culturally sensitive manner. Patient navigation is intended to target those who are most at risk for delays in care, including racial and ethnic minorities and those from low income populations. Furthermore, patient navigation targets specific time points in the cancer care continuum; we operationally define patient navigation as starting at the time of an abnormal screening result and ending at the determination that the screening test was a false positive or, for those individuals with a new cancer diagnosis, continuing through the completion of cancer treatment. The goal of patient navigation is to facilitate timely access to quality cancer care that meets cultural needs and standards of care for all patients.
Examples of navigation services include: arranging various forms of financial support, arranging for transportation to and childcare during scheduled appointments, identifying and scheduling appointments with culturally sensitive caregivers, coordinating care among providers, arranging for interpreter services, ensuring coordination of services among medical personnel, ensuring that medical records are available at each scheduled appointment, and providing other services to overcome access barriers encountered during the cancer care process including linkage to community resources. Navigators work to address health literacy and to train patients to advocate for themselves in the health care system. They are also trained to provide emotional support to patients during this stressful period. Navigators may also identify systems issues that serve as barriers to many patients, and work towards reduce the complexity to the patient of the multidisciplinary approach to care.
The concept of patient navigation is based upon the care management or case management model, which has four components.14 The first is case identification, which is a systematic approach to the identification of those individuals with abnormal cancer screening in need of follow-up care or incident cancers. The second is identifying individual barriers to receiving care. Navigators contact patients and elicit information about the barriers to completion of recommended care. The third is developing an individualized plan to address the barriers that are identified. The fourth is tracking, which is a systematic method of following each case through resolution of the problem. In the case of cancer navigation, this is to resolution of a diagnostic evaluation when a benign condition is diagnosed or follow-up to completion of primary therapy when a cancer or pre-malignant condition is diagnosed.
The navigator will focus on assisting patients and coordinating care of the patients among providers, community, and the patients and their families. Given that patient navigators are working primarily with racial/ethnic minority and low-income patients, cultural competence is a key feature. Cultural and linguistic competence is a set of congruent behaviors, attitudes, and policies that enable effective work in cross-cultural situations.15
PNRP sites vary in the prior training, skill sets, and educational background of navigators and include lay community peers, health educators and advocates, medical assistants, social workers, and nurses. The study has set a minimum requirement of a high school diploma or General Education Diploma. In an effort to achieve a core set of knowledge, skills, and competencies across navigators, a standardized training has been developed. The curriculum focuses on basic information about cancer and its diagnosis and treatment, professionalism, understanding barriers to care, communication skills, cultural competency, ethical conduct of human subjects research, and developing a local network of resources to support patients.16
Freund K.M., Battaglia T.A., Calhoun E., Dudley D.J., Fiscella K., Paskett E., Raich P.C, & Roetzheim R.G. (2008). The NCI Patient Navigation Research Program Methods, Protocol and Measures. Cancer, 113(12), 3391-3399.
Publication 2008
Cancer Screening Case Management Continuity of Patient Care Diagnosis Emotions Ethnic Minorities Follow-Up Care Health Educators Health Literacy Health Personnel Homo sapiens Low-Income Population Malignant Neoplasms Nurses Patient Navigation Patient Navigators Patients Quality of Health Care Racial Minorities
3
Longitudinal Birth Cohort Study of Environmental Exposures
Cited 28 times
The CHAMA-COS (Center for the Health Assessment of Mothers and Children of Salinas) project, a component of the Center for Children’s Environmental Health Research at the University of California, Berkeley, is a longitudinal birth cohort study of the effects of pesticides and other environmental exposures on the health of pregnant women and their children living in the Salinas Valley. Pregnant women entering prenatal care at Natividad Medical Center, a county hospital located in the town of Salinas, or at one of five centers of Clinica de Salud del Valle de Salinas (located in Castroville, Salinas, Soledad, and Greenfield) were screened for eligibility over 1 year between October 1999 and October 2000. Clinica de Salud del Valle de Salinas is a network of community clinics located throughout the Salinas Valley and serving a low-income population, many of whom are farm workers.
Eligible women were ≥ 18 years of age, < 20 weeks gestation at enrollment, English or Spanish speaking, Medi-Cal eligible, and planning to deliver at the Natividad Medical Center. Of 1,130 eligible women, 601 (53.2%) agreed to participate in this multiyear study. Women who declined to participate were similar to study subjects in age and parity but were more likely to be English speaking and born in the United States and less likely to be living with agricultural field workers. After losses due to miscarriage, moving, or dropping from the study before delivery, birth weight information was available for 538 women. We excluded from these analyses women with gestational or preexisting diabetes (n = 26), hypertension (n = 15), twin births (n = 5), or stillbirths (n = 3). We also excluded one woman for whom birth weight information was out of range (< 500 g). Eleven infants diagnosed with congenital anomalies at birth [International Classification of Diseases, 9th Revision (ICD-9; 1989 ) codes 740–759] were included in the final sample because their exclusion did not materially affect the results. The final sample size was 488. Written informed consent was obtained from all participants, and the study was approved by the institutional review boards.
Eskenazi B., Harley K., Bradman A., Weltzien E., Jewell N.P., Barr D.B., Furlong C.E, & Holland N.T. (2004). Association of in Utero Organophosphate Pesticide Exposure and Fetal Growth and Length of Gestation in an Agricultural Population. Environmental Health Perspectives, 112(10), 1116-1124.
Publication 2004
Birth Weight Care, Prenatal Child Childbirth Congenital Abnormality Diabetes Mellitus Eligibility Determination Environmental Exposure Ethics Committees, Research Farmers High Blood Pressures Hispanic or Latino Infant Low-Income Population Mothers Obstetric Delivery Pesticides Pregnancy Pregnant Women Spontaneous Abortion Twins Woman
4
Opioid Use for Non-Cancer Pain in Integrated Health Systems
Cited 16 times
CONSORT was initiated to study use of opioids for non-cancer pain among adults age 18+ in Group Health Cooperative (located in Washington State) and Kaiser Permanente of Northern California over a ten year period (1997–2006). The two health plans serve about four million persons--over one-percent of the U.S. population. CONSORT research plans were reviewed and approved by the Institutional Review Boards of both health plans.
The demographics of Group Health and Kaiser Permanente are similar to those of their respective regions [14 ,15 ]. There are fewer African-Americans in Western Washington and Northern California than in the U.S. population as a whole, but more Asians and Pacific Islanders. Kaiser Permanente’s membership includes 6% African-Americans, 12% Hispanics, and 17% Asian/Pacific Islanders, whereas Group Health’s includes 3% African-Americans, 2% Hispanics, and 4% Asian/Pacific Islanders. The populations of both health plans are similar in terms of educational attainment and household income to their regional populations. Both health plans serve older populations enrolled in Medicare and lower income persons insured by Medicaid and State health insurance programs for low income populations.
Von Korff M., Saunders K., Ray G.T., Boudreau D., Campbell C., Merrill J., Sullivan M.D., Rutter C., Silverberg M., Banta-Green C, & Weisner C. (2008). Defacto Long-term Opioid Therapy for Non-Cancer Pain. The Clinical Journal of Pain, 24(6), 521-527.
Publication 2008
Adult African American Asian Americans Cancer Pain Ethics Committees, Research Health Insurance Health Planning Hispanics Households Infantile Neuroaxonal Dystrophy Low-Income Population Opioids Pacific Islander Americans Population Group West African People
5
Qualitative Synthesis of Menstrual Experiences
Cited 13 times
The search strategy was designed to capture studies reporting on women’s and girls’ experiences of menstruation. Searches were undertaken in 11 databases (Applied Social Science Index and Abstracts, Cumulative Index of Nursing and Allied Health Literature, ProQuest Dissertation and Theses, Embase, Global Health, Medline, Open Grey, Popline, PsycINFO, Sociological Abstracts, WHO Global Health Library) using a prespecified, piloted strategy reported in Table 1. Searches were completed in January 2019 with no language of publication or date restrictions applied. Comprehensive grey literature searching and hand searching were undertaken. Organisations attending to menstrual health were identified through participation in reports [7 , 21 ], stakeholder meetings [2 (link)], and online searches. Websites (see list in S1 Text) were searched using relevant terms (e.g., ‘menstrual’, ‘menstruation’). Citations of included studies and reference lists of large menstrual health reports were searched [7 , 21 ]. Results were exported into EPPI-Reviewer 4 (EPPI-Centre; https://eppi.ioe.ac.uk/cms/Default.aspx?tabid=2914). Two authors (JH, AS) independently screened titles and abstracts, followed by full-text screening to determine eligibility (JH).
Studies were eligible if they reported qualitative analysis of the menstrual experiences of women and girls residing in low- or middle-income countries (LMICs) as defined by the World Bank [24 ]. Studies that included women from LMICs now residing in high-income countries, or that combined populations from LMICs with those in high-income settings, were excluded. While these experiences also deserve increased attention, this review sought to synthesise the large set of studies situated in LMICs to inform evolving policy and practice in these regions. Studies exclusively concerning the acceptability of menstrual suppression were excluded. Studies focussed on puberty more broadly, or the use of sanitation infrastructure, were only included when they reported on experiences of menstruation. For example, studies that included lists of puberty education needs that referenced menstruation but did not report on women’s or girls’ lived menstrual experiences were not included. Similarly, studies focussed on menopause, premenstrual syndrome, or polycystic ovary syndrome were not eligible for inclusion. Studies capturing the menstrual experiences of populations with menstrual disorders (e.g., dysmenorrhea, endometriosis) were eligible. Menstruating women and girls were the target population; thus, studies were excluded if they focused exclusively on girls’ premenarche, key informants, or males. Where key informant interviews were analysed alongside women’s and girls’ experiences, studies were included, but analysis focused on the experience of the target population. Qualitative and mixed-methods studies reported in peer-reviewed or grey literature were eligible for inclusion. Studies were excluded if they did not report any qualitative analysis or results (e.g., qualitative responses were back-coded for quantitative description).
Following full-text screening, study research questions were extracted and iteratively grouped. Three groupings emerged: studies broadly focused on menstrual experiences, studies of experiences of menstruation for those with dysmenorrhea or disorders, and studies of experiences of menstrual interventions or products. Because the review aimed to provide a synthesis of menstrual experience and advance problem theory rather than explore the role of interventions, the third grouping was excluded from the present review but was retained for analyses reported elsewhere.
Hennegan J., Shannon A.K., Rubli J., Schwab K.J, & Melendez-Torres G.J. (2019). Women’s and girls’ experiences of menstruation in low- and middle-income countries: A systematic review and qualitative metasynthesis. PLoS Medicine, 16(5), e1002803.
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Publication 2019
Anabolism Attention cDNA Library Dysmenorrhea Eligibility Determination Endometriosis Low-Income Population Males Menopause Menstruation Menstruation Disturbances Polycystic Ovary Syndrome Puberty Syndrome, Premenstrual Target Population Woman

Most recents protocols related to «Low-Income Population»

1
Rigorous QC for UK Biobank Ocular Imaging
The most densely populated well-mixed population was selected for using principal component analysis applied to the genotype data. PCA was applied to a subset of SNPs following pruning based on linkage disequilibrium with a window size of 50kb, a step size of 1 and an r2 threshold of 0.8, using PLINK [16 (link)]. Further a minor allele frequency (MAF) threshold of 0.1 was applied. Individuals within a defined euclidean distance (1.45 × 10-3) of the mean of the selected population (CEU, Utah residents with Northern and Western European ancestry, and TSI, Toscana in Italia), as identified by comparison to the HapMap Phase III study [17 (link)] in PC1-PC2 space (PC1 = 7.52 × 10-4, PC2 = -4.66 × 10-4) were selected. This subset largely aligns with those that self-identify as White British in the UK Biobank ethnicity field. Individuals were further excluded if they were related to third degree or more [18 (link)]. Participants who were recommended for exclusion from genetic studies by the UK Biobank were removed from the dataset (Fig 2).
In addition to the genotypic quality control, rigorous quality control was applied to the phenotypic data. Exclusion/inclusion criteria were applied to the OCT images and the quantitative measures derived from them utilising methods previously implemented in Patel et al. (2016) [19 (link)]. In line with this method all participants with an OCT image quality score <45 were removed from the study. Further, individuals with values within the poorest 20% of the population in each of the OCT segmentation indicators were removed. These segmentation indicators include: Inner limiting membrane (ILM) indicator, a measure of the minimum localised edge strength around the ILM boundary across the entire OCT scan. ILM indicator is indicative of blinks, severe signal fading, and segmentation errors; Valid count, used to identify significant clipping in the z-axis of the OCT scan; Minimum motion correlation, maximum motion delta and maximum motion factor, all of which utilise the nerve fibre layer and total retinal thickness to calculate Pearson correlation and absolute differences between the thickness values from each set of consecutive B-scans. The lowest correlation and highest absolute difference in a scan define the resulting indicator values. These values identify blinks, eye motion artefacts and segmentation failures. It should be noted that the image quality score and segmentation indicators are often correlated with one another. Finally individuals with outlier values of refractive error were removed from the study. Outlier refractive error scores were defined as values lying outside one standard deviation of 1.5 times the inter-quartile range from the median. The final dataset included 31,135 individuals.
Currant H., Fitzgerald T.W., Patel P.J., Khawaja A.P., Webster A.R., Mahroo O.A, & Birney E. (2023). Sub-cellular level resolution of common genetic variation in the photoreceptor layer identifies continuum between rare disease and common variation. PLOS Genetics, 19(2), e1010587.
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Publication 2023
Blinking Epistropheus Ethnicity Europeans Genotype HapMap Low-Income Population Muscle Rigidity Nerve Fibers Phenotype Radionuclide Imaging Refractive Errors Retina Single Nucleotide Polymorphism Tissue, Membrane
2
Telehealth Utilization in Chronic Conditions
This retrospective cohort study used electronic medical records data from seven ambulatory-care-based federally qualified health centers (FQHCs). These FQHCs serve a large population of low-income and racially, ethnically, and socioeconomically diverse patients in the Memphis, TN, Metropolitan Statistical Area (MSA), a large, urban metropolitan area in the Southern region of the United States. The study included adults (≥18 years) with ASCSs. Patients with ASCSs were defined using the primary diagnoses of any following conditions: diabetes, hypertension, chronic heart failure, coronary artery disease, chronic obstructive pulmonary disease, and asthma.18 (link) All patients were receiving healthcare for their respective ASCSs diagnoses.
We included adult outpatients treated for an ASCS from March 5, 2020, through December 31, 2020, to assess the association of sociodemographic and neighborhood-level factors with the telehealth service utilization rate during the first year of the COVID-19 pandemic. The starting date was selected based on the date of the first COVID-19 case in the state. We excluded those individuals with missing information on race and other patient-level factors. The information was missing for less than 5% of the total sample. We also excluded census tracks that were outside of Memphis MSA.
Chumbler N.R., Chen M., Harrison A, & Surbhi S. (2023). Racial and Socioeconomic Characteristics Associated with the use of Telehealth Services Among Adults With Ambulatory Sensitive Conditions. Health Services Research and Managerial Epidemiology, 10, 23333928231154334.
Publication 2023
Adult Asthma Care, Ambulatory Chronic Obstructive Airway Disease Congestive Heart Failure Coronary Artery Disease COVID 19 Diabetes Mellitus Diagnosis High Blood Pressures Low-Income Population Outpatients Patients STS protein, human Telemedicine
3
Factors Influencing Telehealth Utilization
The outcome variable, outpatient telehealth service utilization, was defined based on published literature.7 (link),21 (link),22 (link) The telehealth visits were billable and were defined by outpatient Current Procedural Terminology (CPT)/Healthcare Common Procedural Coding System (HCPCS) procedural codes. The outpatient telehealth visits included virtual check-in encounter codes (G2010, G2012), telephone evaluation and management visit codes (99441, 99443), FQHC distant site telehealth code (G2025), and if the reason for visit was recorded as a telehealth visit.
Individual patient-level factors may be associated with telehealth service utilization and the following baseline patient characteristics were captured at the time of each telehealth visit utilization. Patient race and ethnicity was classified into four categories: (1) Hispanic, (2) Non-Hispanic Black, (3) Non-Hispanic White, and (4) Other race. Other race category predominantly included other race (95%) followed by Asian, American Indian, Native Hawaiian or Pacific Islander, and Vietnamese. Patient age (in years) and gender (male, female) were included. Clinical factors included the presence of a mental disorder (yes/no), and the Charlson comorbidity index (CCI). Mental health disorders included depression, anxiety (ICD-10 codes: F32.3, F33.3, F43.21, F32.9, F41.9, F32.9, F33.9, F43.21, F32.9), bipolar disorder, schizophrenia, or other psychotic disorders (F20.89, F22, F32.3, F33.3, F06.2, F06.0, F30.10, F31.10, F31.30, F31.60, F31.9, F39). The CCI is a longstanding assessment tool of a patient's unique clinical situation that has been found to predict long-term mortality in different clinical populations with excellent reliability, concurrent validity, sensitivity, and predictive validity.23 (link)The neighborhood-level characteristics were assessed at zip code and census track levels by linking patient residence zip codes and addresses with zip code- and census track-level neighborhood factors, respectively. These factors included residence in a low-income area or a HPSA measured at the zip code level. Patient residence ZIP codes were linked to the CMS database of HPSA ZIP codes to identify patients residing in low-income areas or HPSAs.24 These are regions with a lack of primary care providers based on need for care.17 (link) Census track level factors included income defined as percent of population in the census track below 100% of the Federal Poverty Level. The percent population below the federally designated poverty level was classified along the three categories: (1) less than 20%, (2) 20% to 30%, and (3) more than 30%. The other census track level neighborhood-level characteristics that are indicators of socioeconomic status included the percentage of the population who commute more than 30 minutes to a health facility (an indicator of geographic access), percentage of the population who had at least a college education, the percentage who rented a living place, the vacancy rate (i.e., the percentages of all available units in a rental property like an apartment complex that is unoccupied, with higher rates being indicative of lower income in the area), and the percentage of households without internet access. The study was reviewed and approved by the University of Tennessee Health Science Center Institutional Review Board.
Chumbler N.R., Chen M., Harrison A, & Surbhi S. (2023). Racial and Socioeconomic Characteristics Associated with the use of Telehealth Services Among Adults With Ambulatory Sensitive Conditions. Health Services Research and Managerial Epidemiology, 10, 23333928231154334.
Publication 2023
American Indians Anxiety Asian Persons Bipolar Disorder Ethics Committees, Research Ethnicity Gender Health Services, Outpatient Hispanics Households Hypersensitivity Low-Income Population Males Mental Disorders Native Hawaiians Outpatients Pacific Islander Americans Patients Primary Health Care Psychotic Disorders Schizophrenia Telehealth Vietnamese Woman
4
Socioeconomic Factors Influencing COVID-19 Transmission

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Publication 2023
Asian Persons COVID 19 Head Health Insurance Hispanics Households Infection Labor Force Low-Income Population Ovarian Failure, Premature Population Health Transmission, Communicable Disease
5
Mental Health Burden in Impoverished Zimbabwean Community
Zimbabwe is a lower-middle-income country with a population of 15 million. Between 34 and 49% of the population live in extreme poverty, defined as living on <$1.90 per day.17 Zimbabwe has one of the highest HIV prevalences in Sub-Saharan Africa, at 12.9%.18 We analysed pre-existing data from a cross-sectional survey that had taken place at a large primary healthcare centre in Mbare. The original survey had been conducted to validate the Patient Health Questionnaire-9 for depression and the Shona Symptom Questionnaire for common mental disorders.19 (link)Mbare is the oldest high-density suburb in the southern district of the capital city of Harare. The predominant language of its population is Shona, followed by Ndebele and English. Mbare is characterised by high levels of deprivation, unemployment, mobility and crime.20 Many households lack adequate electric, water and sanitation services. The major determinants of poverty in high-density suburbs in Harare are large family size, low education level of the household head, lack of income from permanent employment, low cash transfers and short length of residence in the suburb.21 The primary healthcare centre caters to a catchment population of 200 000. It has an average attendance of 140 patients per day and provides a range of services, including acute primary care, chronic disease out-patients, family planning, maternity, and services for the prevention and treatment of HIV and tuberculosis.
Abas M.A., Müller M., Gibson L.J., Derveeuw S., Dissanayake N., Smith P., Verhey R., Danese A, & Chibanda D. (2023). Prevalence of post-traumatic stress disorder and validity of the Impact of Events Scale – Revised in primary care in Zimbabwe, a non-war-affected African country. BJPsych Open, 9(2), e37.
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Publication 2023
Crime Disease, Chronic Electricity Head of Household Households Low-Income Population Mental Disorders Outpatients Patients Primary Health Care Range of Motion, Articular Tuberculosis

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More about "Low-Income Population"

The low-income population, also known as the disadvantaged or underprivileged community, encompasses individuals and families with limited financial resources, often living below the poverty line.
This vulnerable group faces unique challenges in accessing essential services like healthcare, education, and social welfare.
Researchers studying the low-income population aim to identify barriers, develop targeted interventions, and improve overall well-being and quality of life.
Tools like Stata 13, Vitek 2C, STATA version 11, Seca 416 Infantometer, Statistica 12 for Windows, SAS 9.4, SAS v9.4, and SPSS version 19.0 can be leveraged to analyze data and inform decision-making for this population.
PubCompare.ai's AI-driven platform, for example, can help streamline research by effortlessly locating and comparing relevant protocols from literature, preprints, and patents.
This intelligent tool leverages machine learning to maximize the impact of low-income population studies and drive meaningful change.
Key subtopics in this area include access to healthcare, educational attainment, employment opportunities, housing security, and social support systems.
Researchers may also explore the intersections of low-income status with other demographic factors, such as race, ethnicity, gender, and age, to better understand the multifaceted challenges faced by this population.
By addressing the unique needs and barriers of the low-income community, researchers and policymakers can work towards a more equitable and inclusive society.