Among the states in India, Kerala had the highest literary rate with 94 percent in 2011. Chandigarh, Himachal Pradesh and the capital territory of Delhi followed Kerala with above average literacy rates. Notably, all the leading states in the country had more literate males than females at the time of the census.

As of March 2020, around 4.46 million Mexicans over 15 years of age were unable to write or read, approximately 4.74 percent. Mexico State, the federal entity with the highest share of the population, also registered the highest number of literate people with over 12 million.

The statistic depicts the literacy rate in Mexico from 2008 to 2020. The literacy rate measures the percentage of people ages 15 and above who can read and write. In 2020, Mexico's literacy rate was around 95.25 percent. The source does not provide data for 2019.Education in MexicoThe literacy rate is commonly defined as the share of people in a country who are older than 15 years and are able to read and write. In Mexico, a state with more than 115 million inhabitants, the literacy rate is above 90 percent, making it significantly higher than the global average. More than 70 percent of Mexico’s population is older than 15 years, a figure than has been quite consistent over the last ten years. Mexico’s compulsory education comprises grades 1 to 9, with an optional secondary education up to grade 12. Literacy is considered basic education. The lowest literacy rates can be found in African countries, the highest in Europe. Additionally, the literacy rate is one of the factors that determines a country’s ranking on the Human Development Index of the United Nations, which ranks the overall well-being of a country’s population. Apart from literacy, it also includes factors such as per-capita income, health and life expectancy and others. Mexico is currently not among the countries with the highest Human Development Index value.

United States - Literacy Rate, Adult Total for the Arab World was 73.36777 % of People Ages 15 and Above in January of 2020, according to the United States Federal Reserve. Historically, United States - Literacy Rate, Adult Total for the Arab World reached a record high of 75.76682 in January of 2017 and a record low of 44.90297 in January of 1977. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Literacy Rate, Adult Total for the Arab World - last updated from the United States Federal Reserve on March of 2025.

In 2020, China had a youth literacy rate of about 99.8 percent. In contrast, Afghanistan's youth literacy rate stood at 55.9 percent in 2021.Indicators for the literacy rateAn indicator which can be seen to affect the literacy rate is the governmental effort in investing in education. The amount of funds invested into the education sector is a factor which can determine a country’s state of education, as the amount of money being spent on education would have an impact on resources, learning environment, and teaching quality. Singapore’s student-teacher ratio in primary education is significantly lower than that of South Asian countries. For instance, Nepal had 20.3 students for every teacher in 2019. Meanwhile, Singaporean teachers only had around 14.5 students on average as of 2020. Notably, South Asia, together with sub-Saharan Africa, had a much higher illiteracy rate compared to the East Asian and Pacific region in 2022. The importance of literacyThe literacy rate indicates the percentage of people within a population who can read and write. This enables them to identify, understand and interpret materials with various contexts. Ensuring literacy for all pupils is a part of the Sustainable Development Goals (SDG) for quality education. This in turn stimulates economic and societal growth for the future.

Literacy Rate: Tamil Nadu data was reported at 80.100 % in 12-01-2011. This records an increase from the previous number of 73.450 % for 12-01-2001. Literacy Rate: Tamil Nadu data is updated decadal, averaging 58.525 % from Dec 1961 (Median) to 12-01-2011, with 6 observations. The data reached an all-time high of 80.100 % in 12-01-2011 and a record low of 36.390 % in 12-01-1961. Literacy Rate: Tamil Nadu data remains active status in CEIC and is reported by Office of the Registrar General & Census Commissioner, India. The data is categorized under India Premium Database’s Education Sector – Table IN.EDA001: Literacy Rate.

The statistic displays the main states and union territories in India with the highest number of illiterate people in 2011. In that year, Uttar Pradesh was at the top of the list, with more than 85 million illiterate people, followed by the state of Bihar with over 51 million people.

The highest literacy rates in Nigeria were registered in the southern regions of the country. In the South West, 89 percent of males and 80.6 percent of females were literate as of 2018. Also, the south zones showed the lowest percentage differences between male and female literacy. Female literacy rate in Nigeria is among the highest in West Africa. The highest female literacy rates were registered in Cabo Verde and Ghana, while Nigeria ranked third.

The National Center for Education Statistics surveyed 12,330 U.S. adults ages 16 to 74 living in households from 2012 to 2017 for the Program for the International Assessment of Adult Competencies (PIAAC), an international study involving over 35 countries. Using small area estimation models (SAE), indirect estimates of literacy and numeracy proficiency have been produced for all U.S. states and counties. By using PIAAC survey data in conjunction with data from the American Community Survey, the Skills Map data provides reliable estimates of adult literacy and numeracy skills in all 50 states, all 3,141 counties, and the District of Columbia.

SAE is a model-dependent approach that produces indirect estimates for areas where survey data is inadequate for direct estimation. SAE models assume that counties with similar demographics would have similar estimates of skills. An estimate for a county then “borrows strength” across related small areas through auxiliary information to produce reliable indirect estimates for small areas. The models rely on covariates available at the small areas, and PIAAC survey data. In the absence of any other proficiency assessment data for individual states and counties, the estimates provide a general picture of proficiency for all states and counties. In addition to the indirect estimates, this website provides precision estimates and facilitates statistical comparisons among states and counties. For technical details on the SAE approach applied to PIAAC, see section 5 of the State and County Estimation Methodology Report.

The U.S. county indirect estimates reported in this data are not directly comparable with the direct estimates for PIAAC countries that are reported by the Organization for Economic Cooperation and Development (OECD). Specifically, the U.S. county indirect estimates (1) represent modeled estimates for adults ages 16-74 whereas the OECD’s direct estimates for participating countries represent estimates for adults ages 16-65, (2) include data for “literacy-related nonresponse” (i.e., adults whose English language skills were too low to participate in the study) whereas the OECD’s direct estimates for countries exclude these data, and (3) are based on three combined data collections (2012/2014/2017) whereas OECD’s direct estimates are based on a single data collection.

Please visit the Skills Map to learn more about this data.

In 2023, the illiteracy rate among adults aged 15 years and older was almost 32 percent in Sub-Saharan Africa. In South Asia, the illiteracy rate was 25 percent. Adult illiteracy rate is defined as the percentage of the population aged 15 and older who can not read or write. Even though illiteracy continues to persist around the world, illiteracy levels have been reduced significantly over the past decades.

Literacy in India has been increasing as more and more people receive a better education, but it is still far from all-encompassing. In 2022, the degree of literacy in India was about 76.32 percent, with the majority of literate Indians being men. It is estimated that the global literacy rate for people aged 15 and above is about 86 percent. How to read a literacy rateIn order to identify potential for intellectual and educational progress, the literacy rate of a country covers the level of education and skills acquired by a country’s inhabitants. Literacy is an important indicator of a country’s economic progress and the standard of living – it shows how many people have access to education. However, the standards to measure literacy cannot be universally applied. Measures to identify and define illiterate and literate inhabitants vary from country to country: In some, illiteracy is equated with no schooling at all, for example. Writings on the wallGlobally speaking, more men are able to read and write than women, and this disparity is also reflected in the literacy rate in India – with scarcity of schools and education in rural areas being one factor, and poverty another. Especially in rural areas, women and girls are often not given proper access to formal education, and even if they are, many drop out. Today, India is already being surpassed in this area by other emerging economies, like Brazil, China, and even by most other countries in the Asia-Pacific region. To catch up, India now has to offer more educational programs to its rural population, not only on how to read and write, but also on traditional gender roles and rights.

Graph and download economic data for Expenditures: Reading by Highest Education: Less Than College Graduate: High School Graduate (CXUREADINGLB1404M) from 2012 to 2023 about book, no college, secondary schooling, secondary, expenditures, education, and USA.

Goal 4Ensure inclusive and equitable quality education and promote lifelong learning opportunities for allTarget 4.1: By 2030, ensure that all girls and boys complete free, equitable and quality primary and secondary education leading to relevant and effective learning outcomesIndicator 4.1.1: Proportion of children and young people (a) in grades 2/3; (b) at the end of primary; and (c) at the end of lower secondary achieving at least a minimum proficiency level in (i) reading and (ii) mathematics, by sexSE_TOT_PRFL: Proportion of children and young people achieving a minimum proficiency level in reading and mathematics (%)Indicator 4.1.2: Completion rate (primary education, lower secondary education, upper secondary education)SE_TOT_CPLR: Completion rate, by sex, location, wealth quintile and education level (%)Target 4.2: By 2030, ensure that all girls and boys have access to quality early childhood development, care and pre-primary education so that they are ready for primary educationIndicator 4.2.1: Proportion of children aged 24-59 months who are developmentally on track in health, learning and psychosocial well-being, by sexiSE_DEV_ONTRK: Proportion of children aged 36−59 months who are developmentally on track in at least three of the following domains: literacy-numeracy, physical development, social-emotional development, and learning (% of children aged 36-59 months)Indicator 4.2.2: Participation rate in organized learning (one year before the official primary entry age), by sexSE_PRE_PARTN: Participation rate in organized learning (one year before the official primary entry age), by sex (%)Target 4.3: By 2030, ensure equal access for all women and men to affordable and quality technical, vocational and tertiary education, including universityIndicator 4.3.1: Participation rate of youth and adults in formal and non-formal education and training in the previous 12 months, by sexSE_ADT_EDUCTRN: Participation rate in formal and non-formal education and training, by sex (%)Target 4.4: By 2030, substantially increase the number of youth and adults who have relevant skills, including technical and vocational skills, for employment, decent jobs and entrepreneurshipIndicator 4.4.1: Proportion of youth and adults with information and communications technology (ICT) skills, by type of skillSE_ADT_ACTS: Proportion of youth and adults with information and communications technology (ICT) skills, by sex and type of skill (%)Target 4.5: By 2030, eliminate gender disparities in education and ensure equal access to all levels of education and vocational training for the vulnerable, including persons with disabilities, indigenous peoples and children in vulnerable situationsIndicator 4.5.1: Parity indices (female/male, rural/urban, bottom/top wealth quintile and others such as disability status, indigenous peoples and conflict-affected, as data become available) for all education indicators on this list that can be disaggregatedSE_GPI_PTNPRE: Gender parity index for participation rate in organized learning (one year before the official primary entry age), (ratio)SE_GPI_TCAQ: Gender parity index of trained teachers, by education level (ratio)SE_GPI_PART: Gender parity index for participation rate in formal and non-formal education and training (ratio)SE_GPI_ICTS: Gender parity index for youth/adults with information and communications technology (ICT) skills, by type of skill (ratio)SE_IMP_FPOF: Immigration status parity index for achieving at least a fixed level of proficiency in functional skills, by numeracy/literacy skills (ratio)SE_NAP_ACHI: Native parity index for achievement (ratio)SE_LGP_ACHI: Language test parity index for achievement (ratio)SE_TOT_GPI: Gender parity index for achievement (ratio)SE_TOT_SESPI: Low to high socio-economic parity status index for achievement (ratio)SE_TOT_RUPI: Rural to urban parity index for achievement (ratio)SE_ALP_CPLR: Adjusted location parity index for completion rate, by sex, location, wealth quintile and education levelSE_AWP_CPRA: Adjusted wealth parity index for completion rate, by sex, location, wealth quintile and education levelSE_AGP_CPRA: Adjusted gender parity index for completion rate, by sex, location, wealth quintile and education levelTarget 4.6: By 2030, ensure that all youth and a substantial proportion of adults, both men and women, achieve literacy and numeracyIndicator 4.6.1: Proportion of population in a given age group achieving at least a fixed level of proficiency in functional (a) literacy and (b) numeracy skills, by sexSE_ADT_FUNS: Proportion of population achieving at least a fixed level of proficiency in functional skills, by sex, age and type of skill (%)Target 4.7: By 2030, ensure that all learners acquire the knowledge and skills needed to promote sustainable development, including, among others, through education for sustainable development and sustainable lifestyles, human rights, gender equality, promotion of a culture of peace and non-violence, global citizenship and appreciation of cultural diversity and of culture’s contribution to sustainable developmentIndicator 4.7.1: Extent to which (i) global citizenship education and (ii) education for sustainable development are mainstreamed in (a) national education policies; (b) curricula; (c) teacher education; and (d) student assessmentTarget 4.a: Build and upgrade education facilities that are child, disability and gender sensitive and provide safe, non-violent, inclusive and effective learning environments for allIndicator 4.a.1: Proportion of schools offering basic services, by type of serviceSE_ACS_CMPTR: Schools with access to computers for pedagogical purposes, by education level (%)SE_ACS_H2O: Schools with access to basic drinking water, by education level (%)SE_ACS_ELECT: Schools with access to electricity, by education level (%)SE_ACC_HNDWSH: Schools with basic handwashing facilities, by education level (%)SE_ACS_INTNT: Schools with access to the internet for pedagogical purposes, by education level (%)SE_ACS_SANIT: Schools with access to access to single-sex basic sanitation, by education level (%)SE_INF_DSBL: Proportion of schools with access to adapted infrastructure and materials for students with disabilities, by education level (%)Target 4.b: By 2020, substantially expand globally the number of scholarships available to developing countries, in particular least developed countries, small island developing States and African countries, for enrolment in higher education, including vocational training and information and communications technology, technical, engineering and scientific programmes, in developed countries and other developing countriesIndicator 4.b.1: Volume of official development assistance flows for scholarships by sector and type of studyDC_TOF_SCHIPSL: Total official flows for scholarships, by recipient countries (millions of constant 2018 United States dollars)Target 4.c: By 2030, substantially increase the supply of qualified teachers, including through international cooperation for teacher training in developing countries, especially least developed countries and small island developing StatesIndicator 4.c.1: Proportion of teachers with the minimum required qualifications, by education leveliSE_TRA_GRDL: Proportion of teachers who have received at least the minimum organized teacher training (e.g. pedagogical training) pre-service or in-service required for teaching at the relevant level in a given country, by sex and education level (%)

Abstract

Economy of Papua New Guinea had been in a state of recession since the mid-1990s. The fiscal situation had been compromised by large deficits. Pertinent questions about how effectively social spending was translating into the actual delivery of services had been raised.

The Public Expenditure and Service Delivery Survey (PESD) was conducted in February-August 2002 to study resources flow in education and health sectors. The PESD was launched by the World Bank as part of the Bank's analytical work on poverty in Papua New Guinea, in close cooperation with the country's government and the Australian Agency for International Development.

The main focus of the project was on expenditure in education. The health facility survey was not intended to be a full service delivery survey in order to keep the field operations and costs within manageable limits. It was added as a rider to the school survey. Health facilities that could be reached within 20 minutes from the sample schools were covered. Against a sample of 214 schools, the survey covered 117 health facilities. A short instrument collected information on how often the facilities were open, the presence of staff, and the availability of key medicines.

The PESD education sector survey covered 214 schools in 19 districts across 8 provinces (out of 20), with two provinces selected in each of the four main regions.

Geographic coverage

Regions: Gulf, National Capital District (NCD), Enga, Eastern Highlands, West Sepik (Sandaun), Morobe, West New Britain and East New Britain.

Analysis unit

• Health facilities

Kind of data

Sample survey data [ssd]

Sampling procedure

Health facilities that could be reached within 20 minutes from the sample schools were covered. Against a sample of 214 schools, 117 health facilities were selected.

Below is the discription of how the schools sample was selected:

1) Following regions were covered: Gulf, National Capital District (NCD), Enga, Eastern Highlands, West Sepik (Sandaun), Morobe, West New Britain, East New Britain. These provinces cover a wide spectrum both in terms of poverty levels and educational development. They range from the relatively rich (NCD and Gulf with headcounts of 19 and 28%) to the poor Sandaun (headcount of over 60%), from the well-educated (NCD and East New Britain with adult literacy rates of 84 and 74%) to poorly-educated (Enga and Eastern Highlands with adult literacy rates of 26 and 38%), from those with high primary enrolment (NCD and ENB) to those with low enrolment (Enga, Gulf and Sandaun), from those with high grade 1-8 retention rates (NCD with 79%) to those with low retention rates (Eastern Highlands and Sandaun with just above 20%).

2) Three districts were randomly selected within provinces with probability proportional to the number of schools in the district. In two of the provinces, Gulf and West New Britain, that only had two districts, both were selected. Ten schools were then selected randomly within each district. In NCD, which does not have districts but is organized by wards/census enumeration areas, 30 schools were randomly selected.

3) The original sample included 220 schools. Many of the schools in the original sample could not be covered for a variety of reasons. In these cases, replacement schools (randomly selected from the same district) were used. A special effort was made to ensure coverage of remote schools. In particular, some sites were revisited later to cover schools that could not be surveyed during the first attempt due to logistical difficulties. The final sample included 214 schools.

4) The PESD schools were further classified by the level of poverty and remoteness. The level of poverty was measured by the estimated poverty rate for the LLG where the school was located, and the remoteness index was based on a composite measure of distance and travel time from the school to a range of facilities. The PESD sample of schools was well distributed across the remoteness and poverty spectrum.

Mode of data collection

Face-to-face [f2f]

In the past five decades, the global literacy rate among adults has grown from 67 percent in 1976 to 87.36 percent in 2023. In 1976, males had a literacy rate of 76 percent, compared to a rate of 58 percent among females. This difference of over 17 percent in 1976 has fallen to just seven percent in 2020. Although gaps in literacy rates have fallen across all regions in recent decades, significant disparities remain across much of South Asia and Africa, while the difference is below one percent in Europe and the Americas. Reasons for these differences are rooted in economic and cultural differences across the globe. In poorer societies, families with limited means are often more likely to invest in their sons' education, while their daughters take up a more domestic role. Varieties do exist on national levels, however, and female literacy levels can sometimes exceed the male rate even in impoverished nations, such as Lesotho (where the difference was over 17 percent in 2014); nonetheless, these are exceptions to the norm.

The National Science Foundation (NSF) Surveys of Public Attitudes monitored the general public's attitudes toward and interest in science and technology. In addition, the survey assessed levels of literacy and understanding of scientific and environmental concepts and constructs, how scientific knowledge and information were acquired, attentiveness to public policy issues, and computer access and usage. Since 1979, the survey was administered at regular intervals (occurring every two or three years), producing 11 cross-sectional surveys through 2001. Data for Part 1 (Survey of Public Attitudes Multiple Wave Data) were comprised of the survey questionnaire items asked most often throughout the 22-year survey series and account for approximately 70 percent of the original questions asked. Data for Part 2, General Social Survey Subsample Data, combine the 1983-1999 Survey of Public Attitudes data with a subsample from the 2002 General Social Survey (GSS) (GENERAL SOCIAL SURVEYS, 1972-2002: [CUMULATIVE FILE] [ICPSR 3728]) and focus solely on levels of education and computer access and usage. Variables for Part 1 include the respondents' interest in new scientific or medical discoveries and inventions, space exploration, military and defense policies, whether they voted in a recent election, if they had ever contacted an elected or public official about topics regarding science, energy, defense, civil rights, foreign policy, or general economics, and how they felt about government spending on scientific research. Respondents were asked how they received information concerning science or news (e.g., via newspapers, magazines, or television), what types of television programming they watched, and what kind of magazines they read. Respondents were asked a series of questions to assess their understanding of scientific concepts like DNA, probability, and experimental methods. Respondents were also asked if they agreed with statements concerning science and technology and how they affect everyday living. Respondents were further asked a series of true and false questions regarding science-based statements (e.g., the center of the Earth is hot, all radioactivity is manmade, electrons are smaller than atoms, the Earth moves around the sun, humans and dinosaurs co-existed, and human beings developed from earlier species of animals). Variables for Part 2 include highest level of math attained in high school, whether the respondent had a postsecondary degree, field of highest degree, number of science-based college courses taken, major in college, household ownership of a computer, access to the World Wide Web, number of hours spent on a computer at home or at work, and topics searched for via the Internet. Demographic variables for Parts 1 and 2 include gender, race, age, marital status, number of people in household, level of education, and occupation.

Abstract

The National Family Health Survey 2019-21 (NFHS-5), the fifth in the NFHS series, provides information on population, health, and nutrition for India, each state/union territory (UT), and for 707 districts.

The primary objective of the 2019-21 round of National Family Health Surveys is to provide essential data on health and family welfare, as well as data on emerging issues in these areas, such as levels of fertility, infant and child mortality, maternal and child health, and other health and family welfare indicators by background characteristics at the national and state levels. Similar to NFHS-4, NFHS-5 also provides information on several emerging issues including perinatal mortality, high-risk sexual behaviour, safe injections, tuberculosis, noncommunicable diseases, and the use of emergency contraception.

The information collected through NFHS-5 is intended to assist policymakers and programme managers in setting benchmarks and examining progress over time in India’s health sector. Besides providing evidence on the effectiveness of ongoing programmes, NFHS-5 data will help to identify the need for new programmes in specific health areas.

The clinical, anthropometric, and biochemical (CAB) component of NFHS-5 is designed to provide vital estimates of the prevalence of malnutrition, anaemia, hypertension, high blood glucose levels, and waist and hip circumference, Vitamin D3, HbA1c, and malaria parasites through a series of biomarker tests and measurements.

Geographic coverage

National coverage

Analysis unit

• Household
• Individual
• Children age 0-5
• Woman age 15-49
• Man age 15 to 54

Universe

The survey covered all de jure household members (usual residents), all women aged 15-49, all men age 15-54, and all children aged 0-5 resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

A uniform sample design, which is representative at the national, state/union territory, and district level, was adopted in each round of the survey. Each district is stratified into urban and rural areas. Each rural stratum is sub-stratified into smaller substrata which are created considering the village population and the percentage of the population belonging to scheduled castes and scheduled tribes (SC/ST). Within each explicit rural sampling stratum, a sample of villages was selected as Primary Sampling Units (PSUs); before the PSU selection, PSUs were sorted according to the literacy rate of women age 6+ years. Within each urban sampling stratum, a sample of Census Enumeration Blocks (CEBs) was selected as PSUs. Before the PSU selection, PSUs were sorted according to the percentage of SC/ST population. In the second stage of selection, a fixed number of 22 households per cluster was selected with an equal probability systematic selection from a newly created list of households in the selected PSUs. The list of households was created as a result of the mapping and household listing operation conducted in each selected PSU before the household selection in the second stage. In all, 30,456 Primary Sampling Units (PSUs) were selected across the country in NFHS-5 drawn from 707 districts as on March 31st 2017, of which fieldwork was completed in 30,198 PSUs.

For further details on sample design, see Section 1.2 of the final report.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Four survey schedules/questionnaires: Household, Woman, Man, and Biomarker were canvassed in 18 local languages using Computer Assisted Personal Interviewing (CAPI).

Cleaning operations

Electronic data collected in the 2019-21 National Family Health Survey were received on a daily basis via the SyncCloud system at the International Institute for Population Sciences, where the data were stored on a password-protected computer. Secondary editing of the data, which required resolution of computer-identified inconsistencies and coding of open-ended questions, was conducted in the field by the Field Agencies and at the Field Agencies central office, and IIPS checked the secondary edits before the dataset was finalized.

Field-check tables were produced by IIPS and the Field Agencies on a regular basis to identify certain types of errors that might have occurred in eliciting information and recording question responses. Information from the field-check tables on the performance of each fieldwork team and individual investigator was promptly shared with the Field Agencies during the fieldwork so that the performance of the teams could be improved, if required.

Response rate

A total of 664,972 households were selected for the sample, of which 653,144 were occupied. Among the occupied households, 636,699 were successfully interviewed, for a response rate of 98 percent.

In the interviewed households, 747,176 eligible women age 15-49 were identified for individual women’s interviews. Interviews were completed with 724,115 women, for a response rate of 97 percent. In all, there were 111,179 eligible men age 15-54 in households selected for the state module. Interviews were completed with 101,839 men, for a response rate of 92 percent.

Objective: To test whether there is an association between abortion legislation and maternal mortality outcomes after controlling for other factors thought to influence maternal health. Design: Population-based natural experiment. Setting and data sources: Official maternal mortality data from 32 federal states of Mexico between 2002 and 2011. Main outcomes: Maternal mortality ratio (MMR), MMR with any abortive outcome (MMRAO) and induced abortion mortality ratio (iAMR). Independent variables: Abortion legislation grouped as less (n=18) or more permissive (n=14); constitutional amendment protecting the unborn (n=17); skilled attendance at birth; all-abortion hospitalisation ratio; low birth weight rate; contraceptive use; total fertility rates (TFR); clean water; sanitation; female literacy rate and intimate-partner violence. Main results: Over the 10-year period, states with less permissive abortion legislation exhibited lower MMR (38.3 vs 49.6; p<0.001), MMRAO (2.7 vs 3.7; p<0.001) and iAMR (0.9 vs 1.7; p<0.001) than more permissive states. Multivariate regression models estimating effect sizes (β-coefficients) for mortality outcomes showed independent associations (p values between 0.001 and 0.055) with female literacy (β=−0.061 to −1.100), skilled attendance at birth (β=−0.032 to −0.427), low birth weight (β=0.149 to 2.166), all-abortion hospitalisation ratio (β=−0.566 to −0.962), clean water (β=−0.048 to −0.730), sanitation (β=−0.052 to −0.758) and intimate-partner violence (β=0.085 to 0.755). TFR showed an inverse association with MMR (β=−14.329) and MMRAO (β=−1.750) and a direct association with iAMR (β=1.383). Altogether, these factors accounted for (R2) 51–88% of the variance among states in overall mortality rates. No statistically independent effect was observed for abortion legislation, constitutional amendment or other covariates. Conclusions: Although less permissive states exhibited consistently lower maternal mortality rates, this finding was not explained by abortion legislation itself. Rather, these differences were explained by other independent factors, which appeared to have a more favourable distribution in these states.

BackgroundDigital literacy refers to the capacity to critically assess digital content, use digital tools in professional settings, and operate digital devices with proficiency. The healthcare sector has rapidly digitized in the last few decades. This systematic review and meta-analysis aimed to assess the digital literacy level of health professionals in the Ethiopian health sector and identify associated factors. The study reviewed relevant literature and analyzed the data to provide a comprehensive understanding of the current state of digital literacy among health professionals in Ethiopia.MethodsThe study was examined by using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) criteria. Evidence was gathered from the databases of Google Scholar, Pub Med, Cochrane Library, Hinari, CINAHL, and Global Health. Consequently, five articles met the eligible criteria for inclusion. The analysis was carried out using STATA version 11. The heterogeneity was evaluated using the I2 test, while the funnel plot and Egger’s regression test statistic were used to examine for potential publication bias. The pooled effect size of each trial is evaluated using a random effect model meta-analysis, which provides a 95% confidence interval.ResultA total of five articles were included in this meta-analysis and the overall pooled prevalence of this study was 49.85% (95% CI: 37.22–62.47). six variables, Monthly incomes AOR = 3.89 (95% CI: 1.03–14.66), computer literacy 2.93 (95% CI: 1.27–6.74), perceived usefulness 1.68 (95% CI: 1.59–4.52), educational status 2.56 (95% CI: 1.59–4.13), attitude 2.23 (95% CI: 1.49–3.35), perceived ease of use 2.22 (95% CI: 1.52–3.23) were significantly associated with the outcome variable.ConclusionThe findings of the study revealed that the overall digital literacy level among health professionals in Ethiopia was relatively low. The study highlights the importance of addressing the digital literacy gap among health professionals in Ethiopia. It suggests the need for targeted interventions, such as increasing monthly incomes, giving computer training, creating a positive attitude, and educational initiatives, to enhance digital literacy skills among health professionals. By improving digital literacy, health professionals can effectively utilize digital technologies and contribute to the advancement of healthcare services in Ethiopia.

ObjectiveIndividuals with Limited English Proficiency (LEP), including Spanish-preferred patients, face healthcare challenges due to language barriers. Despite the potential of digital health technologies to improve access and outcomes, there is a “digital divide” with underutilization among vulnerable populations, including Spanish-speaking LEP individuals, highlighting a need for increased understanding and equitable digital health solutions.Materials and methodsA multi-mode, multi-language cross-sectional survey was built based on the Technology Acceptance Model and deployed from a multi-state healthcare practice. Measures included patient-reported comfort level with reading and speaking English, internet and computer access and satisfaction, ability to perform healthcare-related online tasks, and the eHEALS scale of digital health literacy.ResultsA total of 212 Spanish-preferred patients completed the survey (response rate, 212/2,726 = 7.8%), of which 73.6% indicated lack of comfort in reading or writing in English (LEP n = 156). Spanish-speaking individuals with LEP reported higher rates of needing help when learning how to use new technology or devices, reporting difficulty in the evaluation of health information on the internet and being able to differentiate high-quality information from low-quality online health resources, feeling confident in using health information found online to make health decisions, and having lower access to health-related online services than Spanish-speaking individuals without LEP.DiscussionImproving equitable accessibility to digital tools for individuals with LEP seeking healthcare can help to improve their engagement with their providers and promote self-efficacy in their care. Opportunities exist with emerging technologies to develop language-concordant healthcare resources that will improve outcomes for Spanish-preferred patients.