Access to Routine Health Care and its Association with Hypertension Control

Abstract coming soon. 

Many studies have explored the relationship between awareness of care and hypertension. One systematic review in Vietnam found that approximately 30% of household heads did not know about health insurance, which is often needed to cover the cost of hypertension medication (Meiqari et al., 2019). Further, people with chronic conditions spend approximately 19% more on out-patient services than those with other health conditions due to out-of-pocket expenses, showcasing a need for enhanced resource awareness and availability in Vietnamese communities (Meiqari et al., 2019). Another study performed in India found that, in adults aged 45 years and older, a little over half of hypertensive individuals had been formally diagnosed, 38.9% of those individuals took medication for their blood pressure, and only 31.7% achieved controlled blood pressure (Lee et al., 2022). Additionally, Lee et al. (2022) found that access to healthcare and health insurance was associated with enhanced awareness and management of the disease, and access to a public health center that provided free or low-cost medications was especially important for economically disadvantaged populations to receive care. In both articles, aspects of health care access are discussed and measured, including the availability of health insurance and low-cost pharmaceutical providers. However, both studies fail to account for access to a routine place of care as one of the determinants of hypertension outcomes. 

In addition to global studies, similar research within the United States has established the link between hypertension and health care access. One recent study used data from NHANES (National Health and Nutrition Examination Survey, 2011–2018) to investigate the potential association between access to routine health care and the awareness status of hypertension among adults in the United States, finding that participants who did not have access to a routine place of care had a 20% higher risk of being unaware that they had hypertension (Calixte et al., 2024). Another study built upon these findings examined access to health care through a different lens by focusing on if not having a health care visit in the last year was associated with uncontrolled hypertension (Akinyelure et al., 2021). They found that adults in the United States who had not had a health care visit in the last year vs. those who did were less likely to have controlled hypertension and to be aware that they had hypertension in the first place (Akinyelure et al., 2021). Together, these studies emphasize the role of health care access in hypertension awareness and control by assessing different dimensions of access. 

A similar study explored the relationship between self-reported hypertension and multiple dimensions of health care access, including both geographic location and sociodemographic factors to assess participants’ access to care (Fang et al., 2014). They found that 20% of adults in the United States with hypertension reported barriers in accessing care (Fang et al., 2014).  Collectively, these studies reinforce the relationship between a lack of health access and hypertension status among adults in the United States. 

The relationship between a lack of access to health care and hypertension has already been well established by previous research. This study does not seek to fill a novel gap, but rather to revisit this topic in order to expand our knowledge on the subject and the research process, demonstrating the researchers’ ability to conduct, synthesize, and present epidemiological research. The aim or purpose of this study is to explore how a routine lack of access to care influences uncontrolled hypertension in the United States among adults previously diagnosed with high blood pressure.

Study design and setting

The data for this research study was collected from the National Health and Nutrition Examination Survey (NHANES), which is a repeated cross-sectional survey design  “that measures the health and nutrition of adults and children in the United States.” (CDC, 2024c). The aim of NHANES is to “help improve the health of Americans.” (CDC, 2024c). NHANES has collected continuous periodic data since 1999, but this research study specifically focuses on data collected from the 2017-2018 study cycle (CDC, 2024c). NHANES has a nationwide scope, as the target population is “noninstitutionalized civilian [residents]” of all 50 states and D.C. (CDC, 2024c). All ages, genders, races, and ethnicities are included to ensure a nationally representative sample.  Additional information on the sampling method and the inclusion and exclusion criteria for both NHANES and this research study is provided below. 

Sample size and sampling

During the 2017-2018 survey cycle of NHANES, 16,211 people were selected from 30 different survey locations (CDC, 2020a). Of those selected, 9,254 were interviewed, and 8,704 were examined in a mobile examination center (MEC) (CDC, 2020a). A complex, multistage probability design was used to sample the civilian, noninstitutionalized population residing in the 50 states and D.C (CDC, 2020). The stages of sample selection, as outlined by the CDC (2020a), proceed as follows: (1) Selection of primary sampling units (PSU), which consist of counties or small groups of contiguous counties. (2) Selection of segments from PSUs that constitute one or more blocks containing a cluster of households. (3) Selection of specific households within segments. (4) Selection of individuals within a household.  

To be selected as a participant, individuals had to be non-institutionalized civilian residents of the United States (CDC, 2020a). Institutionalized and non-citizen individuals were excluded from the survey. To increase the reliability and precision of estimates of health status indicators among population subgroups, multiple subgroups were oversampled during the 2017-2018 survey cycle, including Hispanic persons; Non-Hispanic Black persons; Non-Hispanic Asian persons; Non-Hispanic White and other persons (i.e. Non-Hispanic persons who reported races other than Black, Asian, and White) at or below 185 percent of the Department of Health and Human Services (HHS) poverty guidelines; and Non-Hispanic White and other persons aged 80 years and older (CDC, 2020a). For the purposes of this study, additional exclusion criteria included children under the age of 18 years and individuals with missing or unknown survey data or those who refused to respond. Both interview and health examination data were used in this study, limiting the population size to the 8,704 participants that completed both components of the survey. After applying this study’s exclusion criteria, the final sample size was 4,148 participants.  

Data collection methods

To collect survey data describing the health and nutrition of U.S. citizens, NHANES conducted household interviews and health examinations on individuals of all ages (CDC, 2020a). The household interview was distributed by a trained interviewer and consisted of a Screener Questionnaire, Relationship Questionnaire, Family Questionnaire, and Sample Person Questionnaire to collect household and individual level demographic information (CDC, 2020b). Health examinations were conducted in a mobile examination center (MEC), which provided a controlled environment for physical measurements to be conducted under standardized conditions at each survey location (CDC, 2020a). Examination component eligibility was determined based upon the participant’s age and sex at the time of the screening (CDC, 2020c). The collection, processing, storing, and shipping of biospecimens was performed within the controlled environment of the MEC (CDC, 2020c). 

The broad categories of data available in NHANES that are collected through a combination of household interviews, MEC questionnaires, and MEC health examinations include Demographics Data, Dietary Data, Examination Data, Laboratory Data, Questionnaire Data, and Limited Access Data. This study focused on demographic data (i.e. race/ethnicity, age, sex, education level, and income level) that was collected via the household interview by trained interviewers using the Computer-Assisted Personal Interview (CAPI) system; examination data (i.e. blood pressure measurements and BMI) that was collected in the MEC; and questionnaire data (i.e. routine access to care, hypertension status, cholesterol status, and smoking status) that was collected in the home via trained interviewers using the CAPI system.

Variable definitions

Dependent variable. The dependent variable for this research study was uncontrolled high blood pressure, which was defined as BPcontrol. BPcontrol was defined in two categories as having uncontrolled high blood pressure or not having uncontrolled high blood pressure. The BPcontrol variable was created by combining two variables from the NHANES dataset, systolic and diastolic blood pressure. According to the American College of Cardiology (ACC) and American Heart Association (AHA),  the guidelines of high blood pressure are having a reading of a systolic blood pressure of 130 mm Hg or higher or a diastolic blood pressure of 80 mm Hg or higher (130/80 mmHg) in adults (Carey et al., 2022). Therefore, participants whose blood pressure readings fell above or equal to 130/80 mmHg were defined as having uncontrolled high blood pressure and participants whose blood pressure readings fell below 130/80 mmHg were defined as not having uncontrolled high blood pressure. NHANES measured these variables through physician examiners obtaining three consecutive BP readings after participants were “resting quietly in a seated position for 5 minutes” and after their “maximum inflation level (MIL) [had] been determined.” (CDC, 2020d). Prior to data collection, physician examiners were “certified for BP measurement through a training program from Shared Care Research and Education Consulting” (CDC, 2020d). Additionally, each of the participants’ “ upper arm circumference [was] measured … to guide selection of cuff size. Details on the protocol for obtaining upper arm circumference [are] described in the Physician Examination Procedures Manual.” (CDC, 2020d). The systolic and diastolic variables were measured during the examination portion of NHANES data collection. 

Independent variable. The independent variable for this research study was access to routine health care defined in two categories as lack of access to routine health care, and access to routine health care. The questionnaire data section of NHANES measured if participants had a routine place to go for health care by asking “Is there a place that {you/SP} usually {go/goes} when {you are/he/she is} sick or {you/s/he} need{s} advice about {your/his/her} health?” with participants' responses ranging from “yes”, “there is more than one place”, “Don’t know” to “no.” For this research study, the variable was re-categorized into two categories, removing participants who responded “Don’t know” and combining answers of “yes” and “there is more than one place” into the category of having access to routine health care as they both indicate that a participant has access to a routine place of health care. 

Confounders. There were nine confounding variables identified for this research study, specifically age, gender, race or ethnicity, education level, household income, BMI, cholesterol, smoking status, and hypertension status. Age, gender, and race are all well established confounding variables through previous research. Specifically, age is a confounding variable for hypertension as high blood pressure is more common in older adults populations (Pacca et al, 2024). Based on the Behavioral Risk Factor Surveillance System (BRFSS) which groups adults into broad age categories, age was defined in three categories as adult (18-44), middle-aged (45-64), and senior (65+) (CDC, 2025). NHANES measured age by asking participants what their age was at the time of screening during the questionnaire section . Race and gender are important confounding variables as hypertension is more prevalent among men than women, and it is more prevalent among different racial and ethnic groups (Pacca et al, 2024). NHANES defined gender in two categories, as male and female (CDC, 2020e). NHANES measured gender by asking each participant what their gender was during the questionnaire section. Race was defined in seven categories by NHANES: Mexican American, other Hispanic, non-Hispanic White, non-Hispanic Black, non-Hispanic Asian, and other race - including multi-racial (CDC, 2020e). NHANES measured race by asking participants to select their race and Hispanic origin information during the questionnaire section. Education level and household income are additional important confounders because research has shown that having a higher education level and having a higher income is associated with lower prevalence of hypertension (Metlock et al., 2024). NHANES defined education in five categories, less than 9th grade, 9th-11th grade (includes 12th with no diploma), high school graduate/GED or equivalent, some college or AA degree, college graduate or above. NHANES measured participants' education level by asking participants what their highest level of school completed was during the questionnaire section. Household income was re-categorized into two categories, above or below $20,000 per year, which is an established way to filter for low income populations used by the National Health Interview Survey (NHIS) (CDC, 2024b). NHANES measured participants' household income by asking participants what their annual household income was during the questionnaire. BMI and cholesterol are two additional confounding variables that were paramount to consider as previous studies have found that having a higher BMI and having dyslipidemia, or an imbalance of cholesterol, are associated with an increased risk of hypertension (Tang et al., 2022). BMI was re-categorized into four categories: underweight (less than 18.5), normal weight (18.5 to less than 25), overweight (25 to less than 30), and obesity (30 or greater), in line with the four standard categories for adult BMI (CDC, 2024a). NHANES measured BMI by recording participants' measurements during the examination portion of the survey. Cholesterol was defined in two categories, yes and no. NHANES measured cholesterol by asking participants if they were told they have high cholesterol levels by a doctor. Smoking is a particularly important confounding variable as previous research has demonstrated that there is a positive association between smoking and hypertension (Jareebi, 2017). Smoking was defined in two categories, yes and no. NHANES measured smoking by asking participants if they had smoked at least 100 cigarettes in their life during the questionnaire section. Hypertension status, when defined by whether or not an individual has been told they have high blood pressure, is another confounding variable with significance in this study. Delayed diagnosis of hypertension is common and significantly associated with worse cardiovascular outcomes (Lu et al., 2025). However, delayed diagnosis does not result solely from lack of access to care. Findings suggest that a large proportion of delays or negative hypertension diagnoses result due to missed clinical opportunities by the provider, rather than the patient’s disengagement from care (Lu et al., 2025). NHANES measured hypertension status by asking participants if they have ever been told by a doctor or other health professional that they had hypertension. Hypertension status was defined in two categories, yes and no. Refused, unknown, and missing categories were removed for all variables. 

Statistical analyses

In total, there are eleven variables of interest present in this study. All included variables are categorical. Binary variables include: BPcontrol, routineaccess, HBPstatus, sex, income, cholesterol, and smoking. Nominal variables include: race. Ordinal variables include: age, BMI,  and education. The univariate summary measures used were frequency and proportion for each categorical variable.  

For the bivariate analysis, chi-square tests were used to examine associations between the dependent variable and the independent and confounding variables, all of which were categorical. Specifically, these tests assessed whether the prevalence of uncontrolled blood pressure varied by access to routine health care, as well as across racial/ethnic groups, genders, ages, education levels, household income levels, BMI groups, cholesterol levels, and smoking statuses.

Given that the dependent variable was binary, a binary logistic regression model was conducted to examine the relationship between uncontrolled blood pressure and access to routine health care, adjusting for confounding variables such as racial/ethnic groups, age, gender, education level, household income, BMI, cholesterol, and smoking status. Odds ratios and confidence intervals were reported from the logistic regression. 

Our research project is ongoing. 

Thank you to Dr. Saruna Ghimire for serving as our project mentor and capstone professor!

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1. Communication

The researchers clearly communicated their research methods and findings through written and oral presentations. Additionally, both researchers worked together to organize complex public health data into an accessible and understandable format. They effectively communicated with each other in a clear and organized way while working on the project.

2. Teamwork

Both researchers worked collaboratively as a team to complete all phases of the project. The researchers equally shared responsibilities, coordinated tasks, and supported one another to meet deadlines. They held each other accountable and split the work based on their personal strengths, working together to achieve their common goal to successfully develop and answer their research question.

3. Critical Thinking

The researchers developed a research question and study design using national survey data. They analyzed complex datasets, and used problem-solving skills to address any challenges they came across during the course of this project. The researchers summarized and interpreted their results to communicate their findings. 

For our research project, we used secondary data collected by the CDC. So, we did not need to seek IRB approval. Additional information on the CDC's Ethics Review Board Approval for NHANES can be found here https://www.cdc.gov/nchs/nhanes/about/erb.html