THIS DATA CONTAINS % OF MEN AND WOMEN ABOVE AGE 15 YRS WHO ARE DIAGNOSED WITH ** ||||||||||||| MILD | MODERATE|ELEVATED||||||||||||||

** May 17th is observed as World Hypertension Day every year with the aim to raise awareness about prevention, detection, and control of high blood pressure. The theme for this year is “Know Your Numbers” which is to raise awareness about the importance of knowing your blood pressure readings. Hypertension or high blood pressure is one of the key controllable risk factors of chronic health problems such as heart disease and stroke. According to the National Family Health Survey in 2017, one in eight Indians suffer from hypertension which translates to 207 million people (men 112 million, women 95 million). In India, high blood pressure is one of the leading causes of premature deaths. The Global Burden of Diseases study reported that hypertension led to 1.63 million deaths in India in 2016[1]. It is directly responsible for 57% stroke and 24% of coronary heart disease deaths in India

BackgroundHypertension is a very common comorbidity and major risk factor for cardiovascular complications, especially in people with Type 2 Diabetes (T2D). Nevertheless, studies in the past have shown that blood pressure is often insufficiently controlled in medical practice. For the DIAB-CARE study, we used longitudinal data based on the German DIAB-CORE Consortium to assess whether health care regarding hypertension has improved during the last decade in our participants.MethodsData of the three regional population-based studies CARLA (baseline 2002-2006 and follow-up 2007-2010), KORA (baseline 1999-2001 and follow-up 2006-2008) and SHIP (baseline 1997-2001 and follow-up 2002-2006) were pooled. Stratified by T2D status we analysed changes in frequencies, degrees of awareness, treatment and control. Linear mixed models were conducted to assess the influence of sex, age, study, and T2D status on changes of systolic blood pressure between the baseline and follow-up examinations (mean observation time 5.7 years). We included 4,683 participants aged 45 to 74 years with complete data and accounted for 1,256 participants who were lost to follow-up by inverse probability weighting.ResultsMean systolic blood pressure decreased in all groups from baseline to follow-up (e.g. – 8.5 mmHg in those with incident T2D). Pulse pressure (PP) was markedly higher in persons with T2D than in persons without T2D (64.14 mmHg in prevalent T2D compared to 52.87 mmHg in non-T2D at baseline) and did not change much between the two examinations. Awareness, treatment and control increased considerably in all subgroups however, the percentage of those with insufficiently controlled hypertension remained high (at about 50% of those with hypertension) especially in prevalent T2D. Particularly elderly people with T2D often had both, high blood pressure ≥140/90 mmHg and a PP of ≥60 mmHg. Blood pressure in men had improved more than in women at follow-up, however, men still had higher mean SBP than women at follow-up.ConclusionBlood pressure management has developed positively during past years in Germany. While hypertension prevalence, awareness and treatment were substantially higher in participants with T2D than in those without T2D at follow-up, hypertension control was achieved only in about half the number of people in each T2D group leaving much room for further improvement.

This dataset presents the footprint of the age-standardised percentage of adults who have high blood pressure. High blood pressure (or hypertension), is defined as including any of the following; systolic blood pressure greater than or equal to 140 mmHg, or; diastolic blood pressure greater than or equal to 90 mmHg, or; receiving medication for high blood pressure. As an indication of the accuracy of estimates, 95% confidence intervals were produced. These were calculated by the Australian Bureau of Statistics (ABS) using standard error estimates of the proportion. The data spans the financial year of 2014-2015 and is aggregated to 2015 Department of Health Primary Health Network (PHN) areas, based on the 2011 Australian Statistical Geography Standard (ASGS). Health risk factors are attributes, characteristics or exposures that increase the likelihood of a person developing a disease or health disorder. Examples of health risk factors include risky alcohol consumption, physical inactivity and high blood pressure. High-quality information on health risk factors is important in providing an evidence base to inform health policy, program and service delivery. For further information about this dataset, visit the data source: Australian Institute of Health and Welfare - Health Risk Factors in 2014-2015 Data Tables. Please note: AURIN has spatially enabled the original data using the Department of Health - PHN Areas. Age-standardisation is a method of removing the influence of age when comparing populations with different age structures - either different populations at the same time or the same population at different times. For this data the Australian estimated resident population of people aged 18 and over as at 30 June 2001 has been used as the standard population. Adults are defined as persons aged 18 years and over. Values assigned to "n.p." in the original data have been removed from the data.

The share of individuals suffering from hypertension in Italy increased between 2016 and 2022. According to the data, in 2016 the percentage of people affected by high blood pressure was equal to **** percent, while in 2022 it reached **** percent. This statistic displays the share of individuals suffering from hypertension in Italy from 2016 to 2022.

Objective: High systolic blood pressure (HSBP) remains the leading risk factor for mortality worldwide; however, limited data have revealed all-cause and cause-specific burdens attributed to HSBP at global and regional levels. This study aimed to estimate the global burden and priority diseases attributable to HSBP by region, sex, and age.Methods: Based on data and evaluation methods from the Global Burden of Diseases, Injuries, and Risk Factors Study 2019, we estimated trends of age-standardized mortality rate (ASMR), the age-standardized rate of disability-adjusted life years (ASDRs), and the age-standardized rate of years lived with disability (ASYRs) attributable to HSBP during 1990-2019. Further, we analyzed cause-specific burdens attributable to HSBP by sex, age, year, and region.Results: Globally, a significant downtrend was found in the ASMR attributed to HSBP while ASYRs did not change substantially during 1990-2019. The majority of HSBP burden has shifted from high-middle sociodemographic index (SDI) regions to lower SDI regions. All-cause and most cause-specific burdens related to HSBP were improved in high SDI regions but the downtrends have stagnated in recent years. Although many cause-specific deaths associated with HSBP declined, chronic kidney disease (CKD) and endocarditis associated deaths were aggravated globally and ischemic heart disease (IHD), atrial fibrillation and flutter, aortic aneurysm (AA), and peripheral artery disease (PAD) associated deaths were on the rise in low/low-middle/middle SDI regions. Additionally, males had higher disease burdens than females. Middle-aged people with CVDs composed the major subgroup affected by HSBP while older people had the highest ASMRs associated with HSBP.Conclusions: This study revealed the global burden and priority diseases attributable to HSBP with wide variation by region, sex, and age, calling for effective and targeted strategies to reduce the prevalence and mortality of HSBP, especially in low/low-middle/middle SDI regions.

Abstract Background: Arterial hypertension is a multisystem disease that increases the risk of fatal cardiac events. Objectives: This study aims to determine the prevalence of increased blood pressure levels of pre-adolescents and adolescents and correlate these pressure levels with the presence of overweight or obesity and family history of hypertension. Methods: In an observational, cross-sectional study, a sample of 157 students from the city of Petropolis aged from ten to nineteen was randomly selected. The study included four public schools and one private school. The persons responsible for each student answered a questionnaire on pre-existing conditions, family history of hypertension and previous blood pressure measurements. A thorough physical examination, anthropometric evaluation and two blood pressure readings were taken at intervals of at least ten minutes, on three different occasions, totaling six measurements. Results: Blood pressure levels have shown to be abnormal in 17 (10.8% / IC95% 5.9-15.7) studied individuals. Statistical significance was found between the change in blood pressure and the presence of overweight and obesity (p < 0.001), as well as with the presence of family history of hypertension (p < 0.05). A portion of 32.5% of the subjects had never had their blood pressure measured, and over the twelve months prior to the study, 45.5% of the sample had not measured it either. Conclusions: This study demonstrated that a significant percentage of students in the city of Petrópolis, in the state of Rio de Janeiro, has high blood pressure with a statistically significant correlation with overweight or obesity and a family history of hypertension.

More than one-quarter (27%) of adults have ever been diagnosed with high blood pressure. This percentage is higher among Japanese (41%) and Vietnamese (33%) adults than among Asian Indian (7%), Chinese (15%), and other Asian/Pacific Islander (17%) adults.

This dataset reports the percentage of patients registered with a general practice who have a recorded diagnosis of established hypertension. The data is derived from the Quality and Outcomes Framework (QOF) provided by NHS Digital. It offers a snapshot of the burden of hypertension within primary care populations and supports efforts to monitor and manage this key cardiovascular risk factor.

Rationale Hypertension is a leading risk factor for cardiovascular disease, stroke, and kidney disease. Reducing its prevalence is a public health priority. This indicator helps identify the scale of the issue within GP-registered populations and supports targeted interventions to improve detection, treatment, and control of high blood pressure.

Numerator The numerator includes all patients with a diagnosis of established hypertension, as recorded on their GP practice's disease register. This information is collected through the Quality and Outcomes Framework (QOF), which incentivizes accurate clinical coding and record-keeping.

Denominator The denominator is the total number of patients registered at the GP practice, also sourced from the QOF. This allows for the calculation of the proportion of patients affected by hypertension.

Caveats There are no specific caveats noted for this indicator. However, the accuracy of prevalence estimates depends on consistent diagnosis and recording practices across GP practices. Underdiagnosis or variations in coding may affect comparability between practices or regions.

External References Further information and related indicators can be found on the Fingertips Public Health Profiles website.

Localities ExplainedThis dataset contains data based on either the resident locality or registered locality of the patient, a distinction is made between resident locality and registered locality populations:Resident Locality refers to individuals who live within the defined geographic boundaries of the locality. These boundaries are aligned with official administrative areas such as wards and Lower Layer Super Output Areas (LSOAs).Registered Locality refers to individuals who are registered with GP practices that are assigned to a locality based on the Primary Care Network (PCN) they belong to. These assignments are approximate—PCNs are mapped to a locality based on the location of most of their GP surgeries. As a result, locality-registered patients may live outside the locality, sometimes even in different towns or cities.This distinction is important because some health indicators are only available at GP practice level, without information on where patients actually reside. In such cases, data is attributed to the locality based on GP registration, not residential address.

Click here to explore more from the Birmingham and Solihull Integrated Care Partnerships Outcome Framework.

The prevalence of multiple age-related cardiovascular disease (CVD) risk factors is high among individuals living in low- and middle-income countries. We described receipt of healthcare services for and management of hypertension and diabetes among individuals living with these conditions using individual-level data from 55 nationally representative population-based surveys (2009–2019) with measured blood pressure (BP) and diabetes biomarker. We restricted our analysis to non-pregnant individuals aged 40–69 years and defined three mutually exclusive groups (i.e., hypertension only, diabetes only, and both hypertension-diabetes) to compare individuals living with concurrent hypertension and diabetes to individuals with each condition separately. We included 90,086 individuals who lived with hypertension only, 11,975 with diabetes only, and 16,228 with hypertension-diabetes. We estimated the percentage of individuals who were aware of their diagnosis, used pharmacological therapy, or achieved appropriate hypertension and diabetes management. A greater percentage of individuals with hypertension-diabetes were fully diagnosed (64.1% [95% CI: 61.8–66.4]) than those with hypertension only (47.4% [45.3–49.6]) or diabetes only (46.7% [44.1–49.2]). Among the hypertension-diabetes group, pharmacological treatment was higher for individual conditions (38.3% [95% CI: 34.8–41.8] using antihypertensive and 42.3% [95% CI: 39.4–45.2] using glucose-lowering medications) than for both conditions jointly (24.6% [95% CI: 22.1–27.2]).The percentage of individuals achieving appropriate management was highest in the hypertension group (17.6% [16.4–18.8]), followed by diabetes (13.3% [10.7–15.8]) and hypertension-diabetes (6.6% [5.4–7.8]) groups. Although health systems in LMICs are reaching a larger share of individuals living with both hypertension and diabetes than those living with just one of these conditions, only seven percent achieved both BP and blood glucose treatment targets. Implementation of cost-effective population-level interventions that shift clinical care paradigm from disease-specific to comprehensive CVD care are urgently needed for all three groups, especially for those with multiple CVD risk factors.

Hypertension is an important and modifiable risk factor for cardiovascular disease and mortality. Over the last decade, national-levels of controlled hypertension have increased, but little information on hypertension prevalence and trends in hypertension treatment and control exists at the county-level. We estimate trends in prevalence, awareness, treatment, and control of hypertension in US counties using data from the National Health and Nutrition Examination Survey (NHANES) in five two-year waves from 1999–2008 including 26,349 adults aged 30 years and older and from the Behavioral Risk Factor Surveillance System (BRFSS) from 1997–2009 including 1,283,722 adults aged 30 years and older. Hypertension was defined as systolic blood pressure (BP) of at least 140 mm Hg, self-reported use of antihypertensive treatment, or both. Hypertension control was defined as systolic BP less than 140 mm Hg. The median prevalence of total hypertension in 2009 was estimated at 37.6% (range: 26.5 to 54.4%) in men and 40.1% (range: 28.5 to 57.9%) in women. Within-state differences in the county prevalence of uncontrolled hypertension were as high as 7.8 percentage points in 2009. Awareness, treatment, and control was highest in the southeastern US, and increased between 2001 and 2009 on average. The median county-level control in men was 57.7% (range: 43.4 to 65.9%) and in women was 57.1% (range: 43.0 to 65.46%) in 2009, with highest rates in white men and black women. While control of hypertension is on the rise, prevalence of total hypertension continues to increase in the US. Concurrent increases in treatment and control of hypertension are promising, but efforts to decrease the prevalence of hypertension are needed.

In 2023, almost 46 percent of adults in Alabama suffered from hypertension. This statistic depicts the rate of adults suffering from hypertension in the United States in 2023, sorted by state.

This dataset presents the footprint of the age-standardised percentage of adults who are overweight. Uncontrolled high blood pressure (measured high blood pressure) is defined as including any of …Show full descriptionThis dataset presents the footprint of the age-standardised percentage of adults who are overweight. Uncontrolled high blood pressure (measured high blood pressure) is defined as including any of the following; measured systolic blood pressure of 140 mmHg or more, or; diastolic blood pressure of 90 mmHg or more, and; irrespective of the use of blood pressure medication. As an indication of the accuracy of estimates, 95% confidence intervals were produced. These were calculated by the Australian Bureau of Statistics (ABS) using standard error estimates of the proportion. The data spans the financial year of 2014-2015 and is aggregated to 2015 Department of Health Primary Health Network (PHN) areas, based on the 2011 Australian Statistical Geography Standard (ASGS). Health risk factors are attributes, characteristics or exposures that increase the likelihood of a person developing a disease or health disorder. Examples of health risk factors include risky alcohol consumption, physical inactivity and high blood pressure. High-quality information on health risk factors is important in providing an evidence base to inform health policy, program and service delivery. For further information about this dataset, visit the data source: Australian Institute of Health and Welfare - Health Risk Factors in 2014-2015 Data Tables. Please note: AURIN has spatially enabled the original data using the Department of Health - PHN Areas. Age-standardisation is a method of removing the influence of age when comparing populations with different age structures - either different populations at the same time or the same population at different times. For this data the Australian estimated resident population of people aged 18 and over as at 30 June 2001 has been used as the standard population. Adults are defined as persons aged 18 years and over. Data for PHN701 (Northern Territory) should be interpreted with caution as the National Health Survey excluded discrete Aboriginal and Torres Strait Islander communities and very remote areas, which comprise around 28% of the estimated resident population of the Northern Territory living in private dwellings. Copyright attribution: Government of the Commonwealth of Australia - Australian Institute of Health and Welfare, (2017): ; accessed from AURIN on 12/3/2020. Licence type: Creative Commons Attribution 3.0 Australia (CC BY 3.0 AU)

Create maps of U.S. blood pressure medication nonadherence among Medicare Part D beneficiaries aged 65 and older, by county. Data can be stratified by race/ethnicity and blood pressure medication class. Blood pressure medication nonadherence is defined as a proportion of days a beneficiary was covered with blood pressure medication of <80%.Visit the CDC/DHDSP Atlas of Heart Disease and Stroke for additional data and maps. Atlas of Heart Disease and StrokeData Source and MethodologyAntihypertensive nonadherence, defined as a proportion of days a beneficiary was covered with antihypertensives of <80%, was assessed using prescription drug claims data among Medicare Advantage or Medicare fee-for-service beneficiaries aged ≥65 years with Medicare Part D coverage. Administrative data and prescription drug event data were accessed via the Centers for Medicare and Medicaid Services Chronic Conditions Data Warehouse. Analyses were stratified by antihypertensive class, beneficiaries’ state and county of residence, type of prescription drug plan, and treatment and demographic characteristics. Visit the Vital Signs Morbidity and Mortality Weekly Report for more detailed information.Data DictionaryData for counties with small populations are not displayed when a reliable rate could not be generated. These counties are represented in the data with values of '-1.' CDC/DHDSP excludes these values when classifying the data on a map, indicating those counties as 'Insufficient Data.'Data field names and descriptions  stcty_fips: state FIPS code + county FIPS code  county: county name  Blood pressure medication nonadherence percentage for Medicare Part D Beneficiaries aged 65 and older    htnadh_all: All beneficiaries    htnadh_aian: American Indian and Alaska Native, non-Hispanic beneficiaries    htnadh_api: Asian and Pacific Islander, non-Hispanic beneficiaries    htnadh_black: Black, non-Hispanic beneficiaries    htnadh_hisp: Hispanic beneficiaries    htnadh_white: White, non-Hispanic beneficiaries    diuradh: Diuretic nonadherence    rasadh: Renin-Angiotensin System nonadherenceMore Questions?Interactive Atlas of Heart Disease and StrokeData SourcesStatistical Methods

This dataset presents the footprint of the crude percentage of adults who have uncontrolled high blood pressure. Uncontrolled high blood pressure (measured high blood pressure) is defined as including any of the following; measured systolic blood pressure of 140 mmHg or more, or; diastolic blood pressure of 90 mmHg or more, and; irrespective of the use of blood pressure medication. As an indication of the accuracy of estimates, 95% confidence intervals were produced. These were calculated by the Australian Bureau of Statistics (ABS) using standard error estimates of the proportion. The data spans the financial year of 2014-2015 and is aggregated to 2015 Department of Health Primary Health Network (PHN) areas, based on the 2011 Australian Statistical Geography Standard (ASGS).

Health risk factors are attributes, characteristics or exposures that increase the likelihood of a person developing a disease or health disorder. Examples of health risk factors include risky alcohol consumption, physical inactivity and high blood pressure. High-quality information on health risk factors is important in providing an evidence base to inform health policy, program and service delivery.

For further information about this dataset, visit the data source: Australian Institute of Health and Welfare - Health Risk Factors in 2014-2015 Data Tables.

Please note:

• AURIN has spatially enabled the original data using the Department of Health - PHN Areas.

• The health risks factors reported are known to vary with age and the different PHN area populations are known to have a range of age structures. As such, comparisons of results between the PHN areas should be made with caution because the crude rates presented do not account for these age differences.

• Adults are defined as persons aged 18 years and over.

• Values assigned to "n.p." in the original data have been removed from the data.

• Data for PHN701 (Northern Territory) should be interpreted with caution as the National Health Survey excluded discrete Aboriginal and Torres Strait Islander communities and very remote areas, which comprise around 28% of the estimated resident population of the Northern Territory living in private dwellings.

Exploring Risk Factors for Cardiovascular Disease in Adults

Examining Age, Gender, Height, Weight and Health Metrics

By Kuzak Dempsy [source]

About this dataset

This dataset contains detailed information on the risk factors for cardiovascular disease. It includes information on age, gender, height, weight, blood pressure values, cholesterol levels, glucose levels, smoking habits and alcohol consumption of over 70 thousand individuals. Additionally it outlines if the person is active or not and if he or she has any cardiovascular diseases. This dataset provides a great resource for researchers to apply modern machine learning techniques to explore the potential relations between risk factors and cardiovascular disease that can ultimately lead to improved understanding of this serious health issue and design better preventive measures

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How to use the dataset

This dataset can be used to explore the risk factors of cardiovascular disease in adults. The aim is to understand how certain demographic factors, health behaviors and biological markers affect the development of heart disease.

To start, look through the columns of data and familiarize yourself with each one. Understand what each field means and how it relates to heart health: - Age: Age of participant (integer) - Gender: Gender of participant (male/female). - Height: Height measured in centimeters (integer) - Weight: Weight measured in kilograms (integer) - Ap_hi: Systolic blood pressure reading taken from patient (integer) - Ap_lo : Diastolic blood pressure reading taken from patient (integer) - Cholesterol : Total cholesterol level read as mg/dl on a scale 0 - 5+ units( integer). Each unit denoting increase/decrease by 20 mg/dL respectively.
‐ Gluc : Glucose level read as mmol/l on a scale 0 - 16+ units( integer). Each unit denoting increase Decreaseby 1 mmol/L respectively. ‐ Smoke : Whether person smokes or not(binary; 0= No , 1=Yes). ‐ Alco ​ : Whether person drinks alcohol or not(binary; 0 =No ,1 =Yes ). • Active : whether person physically active or not( Binary ;0 =No,1 = Yes ). . Cardio : whether person suffers from cardiovascular diseases or not(Binary ;0 – no , 1 ‑yes ).Identify any trends between the different values for each attribute and the developmetn for cardiovascular disease among individuals represented by this dataset . Age, gender, weight, lifestyle practices like smoking & drinking alcohol are all key influences when analyzing this problem set. You can always modify pieces of your analysis until you're able to find patterns that will enable you make conclusions based on your understanding & exploration. You can further enrich your understanding using couple mopdeling technique like Regressions & Classification models over this dataset alongwith latest Deep Learning approach! Have Fun!

Research Ideas

• Analyzing the effect of lifestyle and environmental factors on the risk of cardiovascular disease.
• Predicting the risks of different age groups based on their demographic characteristics such as gender, height, weight and smoking status.
• Detecting patterns between levels of physical activity, blood pressure and cholesterol levels with likelihood of developing cardiovascular disease among individuals

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: heart_data.csv | Column name | Description | |:----------------|:---------------------------------------------------------| | age | Age of the individual. (Integer) | | gender | Gender of the individual. (String) | | height | Height of the individual in centimeters. (Integer) | | weight | Weight of the individual in kilograms. (Integer) | | ap_hi | Systolic blood pressure reading. (Integer) | | ap_lo | Diastolic blood pressure reading. (Integer) | | cholesterol | Cholesterol level of the individual. (Integer) | | gluc |...

This indicator measures how many people with specific long-term conditions, which should not normally require hospitalisation, are admitted to hospital in an emergency. These conditions include, for example, diabetes, epilepsy and high blood pressure. Purpose This outcome is concerned with how successfully the NHS manages to reduce emergency admissions for all long-term conditions where optimum management can be achieved in the community. Current version updated: Feb-17 Next version due: Feb-18

BackgroundHypertension is a major cause of death and disability worldwide. Enhancing health literacy (HL) may help to alleviate the risk of hypertension and its burden. However, evidence on the association between HL and hypertension and potential mechanisms remain to be explored.ObjectivesThis study examined the association between HL and hypertension; and explored whether body mass index (BMI), body fat percentage (PBF), and visceral fat (VF) were mediators of this association in people who resided in rural and suburban areas in Vietnam.MethodsA cross-sectional survey was conducted from 1st July to 31st December 2019, involving 1655 residents and exploring participants' sociodemographic characteristics, HL, health-related behaviors, comorbidities, body composition, and blood pressure (BP). People with systolic BP ≥ 140 mmHg or diastolic BP ≥ 90 mmHg or using antihypertensive medication were classified as having hypertension. Multiple logistic regression and mediation analyses were used to explore associations.ResultsThe hypertension prevalence was 41.9% (694/1,655). In adjusted models, a higher HL score was associated with a lower hypertension likelihood (OR = 0.96; 95%CI = 0.95–0.97; p < 0.001). Factors associated with a higher odd of hypertension were overweight/obese (OR = 1.69; 95%CI = 1.24–2.29; p = 0.001), high PBF (OR = 2.35; 95%CI = 1.85–2.99; p < 0.001), and high VF (OR = 2.27; 95%CI = 1.63–3.16; p < 0.001). Notably, PBF significantly mediated the association between HL and hypertension (indirect effect, OR = 0.99; 95%CI = 0.98–0.99; p = 0.009; percent mediated = 8.56%). The mediating roles of BMI and VF were not found.ConclusionThe prevalence of hypertension was relatively high. People with better HL were less likely to have hypertension. The association between HL and hypertension was partially explained by PBF. Strategic approaches are required to improve people's HL and body fat which further help to manage hypertension in rural and suburban areas.

SUMMARY This table contains data about women, ages 15 to 50, pregnant people, infants, children, and youths, up to age 24. It contains information about a wide range of health topics, including medical conditions, nutrition, dehydration, oral health, mental health, safety, access to health care, and basic needs, like housing. Local, county-level prevalence rates, time trends, and health disparities about national public health priorities, including preterm birth, infant death, childhood obesity, adolescent depression and substance use, and high blood pressure, diabetes, and kidney disease in young adults.

The population data is from the 2023-2024 San Francisco Maternal Child and Adolescent Health needs assessment and is published on the Open Data Portal to share with community partners, plan services, and promote health.

For more information see: