Abstract

Insufficient knowledge and misconceptions regarding hospice remain significant barriers to hospice utilization. Hence, understanding how patients and caregivers learn about hospice is necessary to address disparities in hospice access and utilization. Given its recent rise as a health information source, the Internet is likely a principal method patients and caregivers use to learn about hospice. In this study, I used Google Trends to examine trends in search volumes for hospice- and palliative care-related search terms on Google and relationships between search volumes, hospice utilization, and mean per-patient hospice length of stay across U.S. regions and nationally. Overall search volumes for “hospice” and “palliative care” have increased from 2014 to 2018, while those for “hospice care”, “home hospice”, and “palliative” have waned. In all regions and the U.S., search volumes for all terms were positively associated with hospice utilization. Regarding mean per-patient hospice length of stay, the U.S., but not all regions, exhibited reciprocal positive associations between search volumes for certain hospice-related terms and negative associations for palliative care-related terms. These results suggest that, to some extent, searching for hospice-related information online may encourage patients and caregivers to consider hospice and engage earlier in the hospice utilization process. These findings may influence the extent to which disparities in online health information-seeking behaviors regarding hospice are considered relevant to addressing disparities in hospice utilization.

Introduction

Background

Hospice care in the U.S. is growing, with a nearly sixfold increase in enrollees from 1994 to 2014 (Finnigan-Fox et al., 2017). Timely utilization of hospice is associated with effective pain management at the end of life, preservation of patient dignity, improved quality of life for patients, and decreased rates of depression and complicated bereavement for caregivers (Finnigan-Fox et al., 2017; Wright et al., 2008). However, an estimated 40% of eligible patients do not receive hospice care (Cagle et al., 2016), and racial minority populations (Hispanics and African Americans particularly) are underrepresented in the hospice patient distribution (Haas et al., 2007). Among key factors contributing to reluctance toward hospice utilization are insufficient knowledge about hospice services and misconceptions about hospice philosophy. These misconceptions include equating hospice with “giving up”, assuming hospice is only for the final weeks or days of life, assuming hospice disrupts ongoing patient-physician relationships, failing to realize that hospice cannot provide concurrent curative care, among others (Cagle et al., 2016; Mrig & Spencer, 2018; Shalev et al., 2019; Vig et al., 2010). Hence, understanding the strategies that patients and caregivers use to learn about hospice is a necessary step toward promoting timely hospice utilization.

Recently, individuals are increasingly utilizing the Internet as a resource for obtaining health and healthcare information, given the easily accessible and ubiquitous nature of websites and a constantly updated variety of published content. Among respondents in one national survey, 81% of adults reported Internet use, and 72% of those respondents reported seeking health information online. Although Internet use generally declines with age, those age 65 or older represent the fastest growing population of Internet users (Fischer et al., 2014). While the Internet commonly functions as a secondary or tertiary source of health information behind healthcare professionals and family or friends for older adults (Fischer et al., 2014; Magsamen-Conrad et al., 2019), it is plausible that patients and caregivers routinely utilize online resources when seeking information about hospice. The breadth of hospice-related information available online corroborates this notion (Corn et al., 2011; Finnigan-Fox et al., 2017).

The current landscape for the integration of web-based resources in patient care warrants exploration of the relationship between Internet search volume for hospice- and palliative care-related terms and hospice utilization. Understanding disparities in access to or willingness to pursue online information about hospice could reveal new insights about and help address disparities in hospice utilization.

Objective and hypotheses

Here I explore trends in Google search volumes for hospice- and palliative care-related search terms across U.S. states, relationships between said search volumes and the normalized number of hospice users across regions and the aggregate U.S., and relationships between said search volumes and mean per-patient hospice length of stay (LOS) across the same geographical distribution. Although hospice and palliative care differ in that palliative care involves symptom management concurrent with curative treatment while hospice is provided in the last six months of life post-treatment termination (Shalev et al., 2019), I exploratorily included search terms for both in this study. Given the aforementioned connection between health information-seeking behaviors and healthcare utilization, I hypothesized that search volumes for hospice- and palliative care-related search terms would be positively and reciprocally associated with hospice utilization and mean LOS and that these associations would be more pronounced in areas of greater hospice utilization.

Materials and Methods

Google search terms and volumes

To obtain Google search volumes for hospice- and palliative care-related search terms, I accessed Google Trends via the gtrendsR package (v1.4.7) in R (v4.0.2). Google Trends provides search volumes, based on the number of searches for a given term normalized to the total number of searches in a geographic area during a specified time frame. These indices are then relativized to the highest measured volume across the time frame and presented on a 0-100 scale (Bail et al., 2018; Dreher et al., 2018; Effenberger et al., 2020). Search terms included “hospice”, “hospice care”, “home hospice”, “palliative care”, and “palliative”, which were among the most popular terms related to hospice and palliative care across U.S. states based on Google Trends data and in previous studies, such as Finnigan-Fox et al. (2017). For each year (defined as from January 1 to December 31) individually from 2014 to 2018, I made separate calls to the Google Trends database using the gtrends() function. In each call, I queried for all five search terms together, specified “web” as the Google product to query, and limited search results to the geographic U.S. and to the default U.S. English setting. I collected search volumes across each state and District of Columbia for each search term. When search volume levels were unavailable for a minority of states (i.e., Alaska, North Dakota, and Wyoming in the case of “home hospice” only), they were considered negligible and treated as 0.

Hospice utilization

As measures of hospice utilization, I collected data regarding the number of Medicare hospice beneficiaries and mean per-patient hospice LOS (the total number of covered days on hospice divided by the number of hospice beneficiaries) for each state and District of Columbia from 2014 to 2018. For 2014 to 2016, these data were available in the Centers for Medicare & Medicaid Services (CMS) Hospice Utilization and Payment Public Use Files for each year, archived under the Hospice Providers subsection of the Legacy Medicare Provider Utilization and Payment dataset series. For 2017 and 2018, these data were filed as Medicare Hospice Utilization by State datasets under the Medicare Utilization for Part A archive. Since normalization of hospice user count based on Medicare beneficiary count was necessary, I obtained the total number of Original Medicare (i.e., non-Medicare Advantage) enrollees for each state and District of Columbia from 2014 to 2018 via the Kaiser Family Foundation (KFF) Total Number of Medicare Beneficiaries dataset.

Race and socioeconomic status (SES)

Race and SES have been implicated as social determinants of hospice utilization. Specifically, racial and ethnic minorities and individuals with lower SES exhibit a lower likelihood of hospice utilization (Haas et al., 2007). To account for variations in hospice utilization and search volumes for hospice- and palliative care-related terms due to race and SES, I collected data on racial composition and the median household income for each state and District of Columbia. For the former, I obtained the proportion of Caucasian individuals for each location and year from 2014 to 2018 via the KFF Population Distribution by Race/Ethnicity dataset. For the latter, I collected median household incomes for the same duration based on 2018 U.S. dollars from the U.S. Bureau of Census Current Population Survey Annual Social and Economic (ASEC) Supplement for 2019.

Internet penetration rates

Additionally, Internet access is associated with greater health literacy (Bailey et al., 2015), while disparities or disruptions in Internet access are associated with decreased online health information-seeking behaviors (McCloud et al., 2016). Hence, to account for the likelihood of higher search volumes in areas of higher Internet penetration rates (Bail et al., 2018), I collected data regarding the proportion of households with Internet access in each state and District of Columbia from the National Center for Education Services (NCES) Digest of Education Statistics subsection of their Annual Reports. These data span 2015 to 2018 (data from 2014 were unavailable). Households were characterized as having Internet access if residents could access the Internet at home via paid subscription to a mobile device-based cellular data plan; broadband Internet service installed via cable, fiber optic, or digital subscriber line; satellite Internet service; dial-up Internet service; or other service.

Geographical categorization

I associated each state and District of Columbia to their corresponding U.S. Census Bureau-designated region (Northeast, Midwest, South, or West). For the generation of map diagrams, I retrieved Federal Information Processing Standards (FIPS) codes for each state and District of Columbia via the usmap package (v0.5.1) in R.

Data pre-processing

Both Medicare hospice user and total Medicare beneficiary data were skewed right. To generate more normal distributions, I log-transformed these data. Other variables were normal and did not necessitate transformation.

Estimation strategy

For the analysis of each of the following relationships, I generated separate ordinary least-squares multiple linear regression models for each region and combinatorially for each of the five search terms. Regression coefficients in all models were standardized by z-score using the lm.beta package (v1.5.1) in R. First, I generated a set of models predicting the log-transformed number of Medicare hospice users as a function of the search volume for a given term. Covariates included the log-transformed number of Medicare beneficiaries, the proportion of Caucasian individuals in state populations, and state median household incomes. Second, I queried the inverse relationship, predicting search volume for a given term based on the log-transformed number of Medicare hospice users. Covariates for this model included the log-transformed number of Medicare beneficiaries, the proportion of Caucasian individuals in state populations, state median household incomes, and state household Internet access rates. Third, I generated a set of models predicting the mean per-patient hospice LOS as a function of the search volume for a given term. Covariates included the log-transformed number of Medicare hospice users, the log-transformed number of Medicare beneficiaries, the proportion of Caucasian individuals in state populations, and state median household incomes. Fourth, I queried the inverse relationship, predicting search volume for a given term based on the mean per-patient hospice LOS. Covariates in this model included the log-transformed number of Medicare hospice users, the log-transformed number of Medicare beneficiaries, the proportion of Caucasian individuals in state populations, state median household incomes, and state household Internet access rates.

While log-transformation of certain data obfuscates determination of effect sizes, these models still provide insight into the general positive or negative directionality of queried relationships.

Results

Trends in search volume for hospice- and palliative care-related terms

Aggregately in the U.S., “hospice” was the most commonly searched term in each year except 2014, when it marginally trailed “palliative”. Only “hospice” and “palliative care” exhibited net increases in search volume from 2014 to 2018, with percent increases of 10.5% and 66.5% respectively. Meanwhile, “hospice care” and “palliative” experienced significant declines in search volume with percent decreases of 17.1% and 16.0% respectively; “home hospice” remained comparatively stable with a percent decrease of 5.5% (Table 1).

Fig. 1 highlights trends in search volume for hospice-related terms by state. Notably, search volume for “hospice” increased sharply in the Midwest and South from 2014 to 2015 before stabilizing in mostly the 80-100% range. States in the West exhibited the lowest volume; gradual increases have occurred, albeit not to the extent of other regions. States in the Northeast stably maintained high volume from 2014 to 2018. Contrastingly, trends for “hospice care” and “home hospice” were more variable, consistent with their lower search volume compared to “hospice”. For “hospice care”, all regions exhibited declines in mean search volume. These declines were most precipitous between 2017 and 2018, as the Northeast, South, and West experienced large (12.5-19.2%) decreases. For “home hospice”, the Northeast exhibited the highest concentration of moderate or high search volumes; however, in all regions, volumes were lower and more fluctuant than those for “hospice” or “hospice care”.

Fig. 2 highlights trends in search volume for palliative care-related terms by state. To a greater extent than for “hospice”, mean search volume for “palliative care” showed significant growth from 2014 to 2015 in all regions, ranging from a 52.1% increase in the Northeast to a 78.9% increase in the West. These volumes stabilized in subsequent years, with high-volume states concentrated particularly in the Northeast and Midwest. For “palliative”, significant declines in search volume occurred in all regions, with especially precipitous declines in the Midwest and Northeast between 2014 and 2015, before stabilizing in mostly the 41-60% or 61-80% range.

Predicting hospice utilization from search volumes

Fig. 3 summarizes linear regression models predicting the effect of search volumes for hospice- and palliative care-related terms on the log-transformed count of Medicare hospice users in each region and aggregately for the U.S. In all models, all variables had a statistically significant association (p < 0.01) with hospice user count, and the number of Medicare beneficiaries was expectedly the most significant predictor. In all U.S. models, each search term had a positive association with hospice user count. The strength of association was greatest for “hospice”, followed by “home hospice”, and lowest for “palliative”. In most regional models, hospice-related search terms exhibited a positive association with hospice user count (only “home hospice” was negatively associated in the models for the Northeast and South regions). For all three hospice-related terms, these associations were strongest in the Northeast and West, which marginally exhibited lower hospice user counts (normalized for Medicare beneficiary counts) compared to other regions. Regarding palliative care-related terms, “palliative care” had a positive association in all regions except the West, and “palliative” had negative associations, albeit of minimal strength, in all regions except the Midwest. These data suggest that despite the effects of Medicare population, race, and income, search volumes for hospice-related terms—more so than palliative care-related terms generally—correlate with hospice utilization to some extent.

Interestingly, in all regions except the Midwest, Caucasian proportion and median household income had slightly negative associations (p < 0.01) with hospice utilization, standing in contrast to existing literature (Haas et al., 2007; Wang et al., 2015). Refinement of models to include proportions of non-white races and other metrics of SES, including mean educational attainment levels, unemployment rates, and poverty rates, is warranted.

Predicting search volumes from hospice utilization

To examine the inverse effect of hospice utilization on search volumes for hospice- and palliative care-related terms, I generated the linear regression models summarized in Fig. 4. Log-transformed hospice user count did not have a statistically significant association with search volume for any term.

Interestingly, in all models, mean household income had a statistically significant association (p < 0.01)—negatively in models for all hospice-related terms (except in the West and aggregate U.S. for “home hospice”) and positively for both palliative care-related terms. Although this requires more substantiation, this may suggest that financial flexibility or insurance coverage for continued curative treatments may delay hospice-related information seeking. For the former, associations were strongest in the Midwest; for the latter, associations were strongest in the South. Caucasian proportion also exhibited a positive, statistically significant association (p < 0.01) with search volume for all terms except “hospice care” aggregately for the U.S. Regionally, only the Northeast consistently maintained this trend (p < 0.01). Notably, Internet penetration rates were not a significant predictor of volume for any term.

Predicting mean per-patient hospice LOS from search volumes

Fig. 5 summarizes linear regression models predicting the effect of search volumes on mean per-patient hospice LOS in each region and aggregately for the U.S. For “hospice” and “hospice care”, the U.S. exhibited a statistically significant positive association (p < 0.01); the West was the only region that showed a significant association, which was negative, for “hospice” (p < 0.05). For “home hospice”, the West and U.S. exhibited positive associations, while the Northeast exhibited a negative association (p < 0.01). Interestingly, for “palliative care” and “palliative”, the Midwest and U.S. had negative associations (p < 0.05).

Further, in all models, both Caucasian proportion and median household income had significant negative associations (p < 0.01 in most cases) with mean LOS in all regions, including the aggregate U.S., except the Northeast. This may further corroborate delays in hospice utilization as a result of financial flexibility.

Predicting search volumes from mean per-patient hospice LOS

To examine the inverse effect of mean per-patient LOS on search volumes, I generated the linear regression models summarized in Fig. 6. Among hospice-related terms, mean LOS had positive, statistically significant associations with volume for only “hospice” and “home hospice” for the West and U.S. (p < 0.01 in all cases, except p < 0.05 for “hospice” in the West), but not for other locations. Among palliative care-related terms, mean LOS had a negative association (p < 0.05) with volume for “palliative” in the Midwest and U.S.

In all models for “hospice” and “hospice care” and in most models for “home hospice” (except in the West and U.S.), median household income had a negative association with search volume (p < 0.01). This association was ubiquitously positive for palliative care-related terms (p < 0.01). For all terms in aggregate U.S. models, Caucasian proportion was positively associated with search volume (p < 0.01). Regionally, this relationship was positive and significant (p < 0.05 minimum) for all terms except “hospice care” in primarily the Midwest. Further, Internet penetration rates were insignificant in all models.

Discussion

In this study, I used Google Trends to explore the relationship between search volumes for hospice- and palliative care-related terms and hospice utilization Regionally and aggregately in the U.S., search volumes for “hospice” and “palliative care” increased rapidly from 2014 to 2018, while those for “hospice care”, “home hospice”, and “palliative” declined. Importantly, in the aggregate U.S., search volumes for each search term were positively associated with hospice utilization; however, there were variations regionally. The inverse relationship was insignificant. This may suggest that searching for hospice-related information online may be a precursor to hospice utilization. Although differences in normalized hospice user counts per region were marginal, this relationship appeared slightly more salient in areas where per capita hospice utilization was less. Although further corroboration is needed, this may suggest that patients or caregivers may turn to the Internet to obtain information about hospice when other information sources or support structures that conduce to hospice utilization are less readily available. Regarding the relationship between mean per-patient hospice LOS and search volumes, national, but mostly no regional, reciprocal associations were found. That search volumes for hospice-related terms exhibited some positive associations may suggest that searching for hospice-related information online may allow patients and caregivers to be more adequately informed about hospice and consequently more likely to engage in upstream conversations about hospice or consider it earlier. Albeit unsubstantiated, that some negative associations were found between search volume for palliative care-related search terms and mean LOS may suggest that searching for palliative care-related information online may encourage patients or caregivers to select palliative care over hospice, based on need. These relationships are further summarized in Fig. 7.

Nonetheless, this study has several limitations. First, Google Trends does not provide absolute search volumes. This limits the interpretability of search volume patterns longitudinally, as changes for states treated as the point of reference in a given time range may be more dramatic than they really are. Albeit less accessible, one useful alternative that warrants consideration in future investigations is Google AdWords, which reports average monthly search volumes for specified terms. Albeit reported as monthly averages in the form of 10-point ranges, these volumes represent absolute levels. Additional benefits of Google AdWords include the availability search volumes for low-frequency search terms, which Google Trends often does not, and the availability of search volumes at city and county levels. Lastly, Google AdWords provides search volume results from not only Google, but also other leading Internet search engines (Bail et al., 2018).

A second limitation pertains to algorithmic confounding: search algorithms are not static, and changes therein may influence the reliability of data derived from said algorithms. (Lazer et al., 2014). Of note, Google Trends introduced an improvement to their data collection system on January 1, 2016. It is possible that this update disproportionately impacted search volume levels for lower-frequency search terms, such as “home hospice”. Separately, an inability to capture user intention when performing searches may skew a minority of search result data. Because the Google search algorithm provides autocompleted search suggestions when users begin typing search queries (Bail et al., 2018), it remains possible that some queries may be misleading (e.g., inadvertently searching “hospice care” when “hospice” was intended, searching “palliative care” when “palliative” was intended, or conflating “home hospice” and “home health”). Although this possibility cannot be readily accounted for, the search terms included in this study were general, reducing the possibility of variability in specific search suggestions based on user demographic characteristics or search history.

A third limitation pertains to an inability to rigorously assess temporality in the relationships between search volume and hospice utilization. Despite aforementioned results, it remains unclear whether searches for hospice- or palliative care-related terms precede or follow the onset of hospice utilization (i.e., it is possible that prospective or pre-contemplative hospice patients or caregivers search for said terms to learn more about hospice prior to onboarding or that patients or caregivers already enrolled in hospice search for said terms). One method to address this limitation in future investigations involves employing Google AdWords to query low-frequency search terms that may indicate temporality, such as “how to enroll in hospice” or “starting hospice”. Further, the applicability of online behaviors to offline actions remains unverified (Bail, 2014; Golder & Macy, 2014), especially considering that user characteristics are unknown. Searching for hospice- or palliative care-related information does not guarantee access to reliable sources of information (Corn et al., 2011; Finnigan-Fox et al., 2017), limiting the generalizability of discussed relationships. Searching for information alone does not account for the impacts of Internet skill and health literacy on health information-seeking behaviors (Jacobs et al., 2017; McCloud et al., 2016); hence, integration of these data into models is warranted in future investigations.

Despite these limitations, these results suggest that, to some degree, variability in Internet search volumes for hospice- and palliative care-related terms can elucidate disparities in hospice utilization. These digital trace data can be helpful in identifying areas where health information-seeking behaviors regarding hospice lag and potentially conduce to clinical interventions to increase patient and caregiver agency in pursuing hospice-related information online in addition to their Internet skill and hospice-related health literacy. Proximately, clinicians should be encouraged to connect patients and caregivers with reliable online sources of information about hospice.

Figures and Tables

Region Year Number of Medicare hospice users Number of Medicare beneficiaries Mean per-patient hospice LOS % Caucasian Median household income % of households with internet access “Hospice” search volume “Hospice care” search volume “Home hospice” search volume “Palliative care” search volume “Palliative” search volume
Midwest 2014 26414.67 700487.9 62.25000 79.89167 59145.92 NA 69.75000 66.08333 28.00000 45.50000 62.08333
Midwest 2015 27379.50 703292.9 61.75000 79.47500 61114.50 76.77104 72.25000 63.16667 28.33333 67.50000 66.50000
Midwest 2016 28196.83 721959.5 62.75000 79.20000 61114.50 81.35707 71.00000 64.50000 32.91667 63.50000 69.25000
Midwest 2017 29570.50 722364.5 62.33333 78.73333 62398.08 83.14537 78.41667 54.50000 28.83333 60.75000 66.75000
Midwest 2018 30622.33 743275.4 64.58333 78.31667 64540.83 84.30274 77.41667 57.66667 27.25000 70.75000 70.58333
Northeast 2014 22604.44 772961.7 57.66667 76.30000 65176.56 NA 62.55556 73.22222 50.11111 52.77778 65.55556
Northeast 2015 23393.67 781461.4 57.66667 75.85556 67024.33 80.63928 64.11111 65.44444 50.66667 80.00000 75.66667
Northeast 2016 23928.89 797156.1 59.55556 75.53333 67024.33 83.42654 66.88889 76.66667 54.11111 75.22222 81.77778
Northeast 2017 24850.33 790873.6 60.55556 74.98889 68437.67 84.93527 70.11111 64.00000 45.66667 69.11111 75.55556
Northeast 2018 25455.22 602636.0 61.88889 74.60000 70830.22 86.42224 67.77778 62.55556 41.44444 74.44444 77.11111
South 2014 31390.59 848330.1 72.94118 63.21765 52948.06 NA 75.76471 69.70588 31.05882 36.94118 51.82353
South 2015 32896.06 856787.4 72.70588 62.87647 54180.53 72.94575 80.23529 68.29412 31.76471 61.00000 56.88235
South 2016 34057.35 877501.9 72.88235 62.62941 54180.53 78.44511 80.11765 72.47059 36.17647 55.23529 62.11765
South 2017 35679.29 878372.5 73.23529 62.26471 58308.53 80.62861 83.41176 63.05882 30.94118 54.58824 59.00000
South 2018 37302.47 914326.9 75.00000 62.01176 59017.18 82.83514 83.64706 62.58824 28.82353 59.05882 61.94118
West 2014 20295.85 540833.5 66.46154 62.95385 61061.69 NA 61.69231 51.76923 29.38462 36.46154 49.46154
West 2015 21344.00 557893.2 66.00000 62.60769 63391.31 79.71369 63.00000 52.15385 32.23077 57.30769 54.23077
West 2016 22328.46 578031.3 66.84615 62.33846 63391.31 83.44137 63.61538 57.46154 35.00000 56.38462 60.23077
West 2017 23536.23 590977.4 67.76923 61.84615 66485.77 85.02407 69.30769 47.23077 34.07692 48.61538 52.38462
West 2018 24627.77 630265.5 70.38462 61.43077 66899.92 86.72051 67.23077 47.07692 29.84615 55.46154 62.38462
U.S. 2014 25841.22 721861.9 66.07843 69.38235 58632.53 NA 68.43137 64.90196 33.27451 41.62745 56.05882
U.S. 2015 26976.51 731189.5 65.76471 69.00392 60426.45 76.92866 71.11765 62.47059 34.41176 64.94118 61.78431
U.S. 2016 27901.31 750389.4 66.60784 68.73137 60426.45 81.28291 71.43137 67.50980 38.27451 61.00000 66.78431
U.S. 2017 29235.65 752966.2 67.03922 68.27843 63142.67 83.10120 76.29412 57.17647 33.84314 57.07843 62.05882
U.S. 2018 30409.18 746667.4 69.05882 67.92157 64410.84 84.80386 75.19608 57.47059 30.94118 63.60784 66.76471

Table 1: Mean values for all variables categorized by year and by region or aggregately for the U.S.

**Fig. 1:** Relative Google search volume for hospice care-related terms across US states from 2014 to 2018. Search volumes shown for (A) "hospice", (B) "hospice care", and (C) "home hospice".

Fig. 1: Relative Google search volume for hospice care-related terms across US states from 2014 to 2018. Search volumes shown for (A) “hospice”, (B) “hospice care”, and (C) “home hospice”.

**Fig. 2:** Relative Google search volume for hospice care-related terms across US states from 2014 to 2018. Search volumes shown for (A) "palliative care" and (B) "palliative".

Fig. 2: Relative Google search volume for hospice care-related terms across US states from 2014 to 2018. Search volumes shown for (A) “palliative care” and (B) “palliative”.

**Fig. 3:** Standardized coefficients from ordinary least-squares linear regression model predicting number of hospice users (log-transformed) based on relative Google search volume for hospice- or palliative care-related terms across US states from 2014 to 2018. Covariates include number of Medicare beneficiaries (log-transformed), race (% Caucasian), and median household income for each state. Results are categorized per region and aggregately for the U.S. Dots representing standardized coefficients are depicted with 95% confidence intervals. Search terms include (A) "hospice", (B) "hospice care", (C) "home hospice", (D) "palliative care", and (E) "palliative".

Fig. 3: Standardized coefficients from ordinary least-squares linear regression model predicting number of hospice users (log-transformed) based on relative Google search volume for hospice- or palliative care-related terms across US states from 2014 to 2018. Covariates include number of Medicare beneficiaries (log-transformed), race (% Caucasian), and median household income for each state. Results are categorized per region and aggregately for the U.S. Dots representing standardized coefficients are depicted with 95% confidence intervals. Search terms include (A) “hospice”, (B) “hospice care”, (C) “home hospice”, (D) “palliative care”, and (E) “palliative”.

**Fig. 4:** Standardized coefficients from ordinary least-squares linear regression model predicting relative Google search volume for hospice- or palliative care-related terms across US states from 2015 to 2018 based on number of Medicare hospice users (log-transformed). Covariates include number of Medicare beneficiaries (log-transformed), race (% Caucasian), median household income, and internet penetration rate (% of households with internet access) for each state. Results are categorized per region and aggregately for the U.S. Dots representing standardized coefficients are depicted with 95% confidence intervals. Search terms predicted include (A) "hospice", (B) "hospice care", (C) "home hospice", (D) "palliative care", and (E) "palliative". The scale for standardized coefficients has been abbreviated to enhance resolution; however, some confidence intervals may extend beyond these limits.

Fig. 4: Standardized coefficients from ordinary least-squares linear regression model predicting relative Google search volume for hospice- or palliative care-related terms across US states from 2015 to 2018 based on number of Medicare hospice users (log-transformed). Covariates include number of Medicare beneficiaries (log-transformed), race (% Caucasian), median household income, and internet penetration rate (% of households with internet access) for each state. Results are categorized per region and aggregately for the U.S. Dots representing standardized coefficients are depicted with 95% confidence intervals. Search terms predicted include (A) “hospice”, (B) “hospice care”, (C) “home hospice”, (D) “palliative care”, and (E) “palliative”. The scale for standardized coefficients has been abbreviated to enhance resolution; however, some confidence intervals may extend beyond these limits.

**Fig. 5:** Standardized coefficients from ordinary least-squares linear regression model predicting mean per-patient hospice length of stay based on relative Google search volume for hospice- or palliative care-related terms across US states from 2014 to 2018. Covariates include number of Medicare hospice users (log-transformed), number of Medicare beneficiaries (log-transformed), race (% Caucasian), and median household income for each state. Results are categorized per region and aggregately for the U.S. Dots representing standardized coefficients are depicted with 95% confidence intervals. Search terms include (A) "hospice", (B) "hospice care", (C) "home hospice", (D) "palliative care", and (E) "palliative". The scale for standardized coefficients has been abbreviated to enhance resolution; however, some confidence intervals may extend beyond these limits.

Fig. 5: Standardized coefficients from ordinary least-squares linear regression model predicting mean per-patient hospice length of stay based on relative Google search volume for hospice- or palliative care-related terms across US states from 2014 to 2018. Covariates include number of Medicare hospice users (log-transformed), number of Medicare beneficiaries (log-transformed), race (% Caucasian), and median household income for each state. Results are categorized per region and aggregately for the U.S. Dots representing standardized coefficients are depicted with 95% confidence intervals. Search terms include (A) “hospice”, (B) “hospice care”, (C) “home hospice”, (D) “palliative care”, and (E) “palliative”. The scale for standardized coefficients has been abbreviated to enhance resolution; however, some confidence intervals may extend beyond these limits.

**Fig. 6:** Standardized coefficients from ordinary least-squares linear regression model predicting relative Google search volume for hospice- or palliative care-related terms across US states from 2015 to 2018 based on mean per-patient hospice length of stay. Covariates include number of Medicare hospice users (log-transformed), number of Medicare beneficiaries (log-transformed), race (% Caucasian), median household income, and internet penetration rate (% of households with internet access) for each state. Results are categorized per region and aggregately for the U.S. Dots represent standardized coefficients, and bars represent standard errors. Search terms predicted include (A) "hospice", (B) "hospice care", (C) "home hospice", (D) "palliative care", and (E) "palliative". The scale for standardized coefficients has been abbreviated to enhance resolution; however, some confidence intervals may extend beyond these limits.

Fig. 6: Standardized coefficients from ordinary least-squares linear regression model predicting relative Google search volume for hospice- or palliative care-related terms across US states from 2015 to 2018 based on mean per-patient hospice length of stay. Covariates include number of Medicare hospice users (log-transformed), number of Medicare beneficiaries (log-transformed), race (% Caucasian), median household income, and internet penetration rate (% of households with internet access) for each state. Results are categorized per region and aggregately for the U.S. Dots represent standardized coefficients, and bars represent standard errors. Search terms predicted include (A) “hospice”, (B) “hospice care”, (C) “home hospice”, (D) “palliative care”, and (E) “palliative”. The scale for standardized coefficients has been abbreviated to enhance resolution; however, some confidence intervals may extend beyond these limits.

**Fig. 7:** Correlation matrix of all variables. All variables are aggregated from state-level data from 2014 to 2018 (internet penetration data are from 2015 to 2018). Blue values indicate a positive relationship, while red values indicate a negative relationship. The size of each circle represents the magnitude of the Pearson correlation coefficient. Relationships shown are statistically significant (_p_ < 0.05); blank spaces represent insignificant relationships.

Fig. 7: Correlation matrix of all variables. All variables are aggregated from state-level data from 2014 to 2018 (internet penetration data are from 2015 to 2018). Blue values indicate a positive relationship, while red values indicate a negative relationship. The size of each circle represents the magnitude of the Pearson correlation coefficient. Relationships shown are statistically significant (p < 0.05); blank spaces represent insignificant relationships.

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