The goal of health technology is to assist in more efficient data collection, increase connectivity, and foresee market changes. A push for a more healthy lifestyle across the United States has lead to an increasing need for innovative ideas that can lead to healthy behavior changes. Even with governmental efforts, life expectancy in the United States has decreased, inequities in healthcare persist in rural communities and healthcare costs are still at an all time high. Developing partnerships between healthcare communities and business technology firms has the possibility to strengthen the development of evidence-based research and HIPAA-compliant clinical resources that can contribute to an improved population health in the United States. Using these firms can also establish more reputable platforms, health analytics, and marketing expertise that could serve beneficial to the current healthcare issues.
Implementing technology into exercise devices has lead to success stories surrounding smart technology abound. Exercise using smart technology has been shown to increase physical activity compared to traditional healthcare models. Access to healthcare is also an issue due to the US healthcare industry structure. This can be improved through the use of online applications geared towards connecting clinicians with patients. Through businesses engagement, assistance in the development of technology services that increases access to low-cost care for patients in need is possible.
Innovative inventions in data collection would assist with patients having access to their own health information. This gives them the opportunity to make informed decisions about their own health. It also reduces redundant testing resulting in patients and clinicians saving time and money. Collaborating with businesses and technology firms on healthcare issues and projects could improve population health overall.
- Stey A, Kanzaria H, Brook R. How disruptive innovation by business and technology firms could improve population health [published online August 16, 2018]. JAMA.doi:10.1001/jama.2018.10782
I created the above map using the tutorials in ESRI website for ArcGIS & the content from GIS Tutorial for Health. The map explores the Mammography clinics in relations to counties in Pennsylvania. The pattern of high concentration around cities of Pittsburg and Philadelphia is evident from the maps.
Few other observations from the map are:
Potter and Sullivan counties have fewer women aged 40-74, but still, there are no clinics. They are obvious areas of the state where clinics are needed.
Monroe, Clearfield, Jefferson counties have higher women aged 40-74, but a relatively
small number of clinics.
Philadelphia & Pittsburg surrounding areas have enough clinics, but remote northwestern and northeastern counties need more clinics.
The power of GIS can be further explored to look into the cities that sound to have more mammography clinics, in the map below :
This map shows that though Allegheny county hosts Pittsburg, there is a pattern of concentration of clinics in the south relatively more urban part of the city. The pattern correlates with other healthcare facilities in the county that counts towards health equities in this county.
Granulation to the smallest unit possible brings in more refined data on what seems to be different in small scale.
In the midst of newsworthy Hepatitis A outbreaks in Kentucky, San Diego, and Michigan, this map depicts the number of Hepatitis A incidents across the United States from 2017-2018. The number fluctuations in each state over the last year is alarming considering that there are few national regulations being put into place in the realm of food safety. Catherine Huddle from Food Safety News explains that although the CDC recommends that all children should be vaccinated at the ages of 1 and 2, ” the CDC has not recommended Hepatitis A vaccinations for food service workers” (Huddle, 2018). We can only hope that more information and awareness of Hepatitis A outbreaks can help force a decline in it’s prevalence. For more information of state reported Hepatitis A incidents you can visit the Food Safety News web page.
Texas has one of the highest vaccination rates for childhood diseases overall, 97.4%, according to CDC. But the number of children not vaccinated because of their parents’ “personal beliefs”—as opposed to medical reasons—has risen since 2003, when such exemptions were introduced, to more than 44,000 so far in 2017 according to CDC. The 4:3:1:3:3:1:4 series is an overall measure that encompasses many vaccines that are recommended for children. Various demographic factors (sex, gender, race, availability of commercial health insurance) influence the decision to get vaccinated, were looked at.
The county-level data on the socioeconomic factors were obtained from US Census Bureau (American Factfinder). The health insurance data was obtained from Small Area Health Insurance Estimates (SAHIE). The vaccination rates were obtained from Texas Immunization registry through DSHS. The data was cleaned and geocoded to be analyzed in ArcGIS to produce maps as shown in Figure 1. Pearson’s correlation coefficient was used to analyze the relationship between vaccination rates and independent variable.
The non-vaccination rates are higher around the major cities of Dallas, Austin-San Antonio, Houston and some northwest Texas counties. Population density has a positive correlation with the non-vaccination rate. Other demographic factors have a positive correlation in certain counties as opposed to others.
Source: American FactFinder, Texas Immunisation Registry
The limitation on the immunization data is it being an optional registry so it would not be accurate to run statistics off this information to estimate an immunization rate. In future, it is productive to expand this concept to use regression analysis to try to find the odds of the relationship expressed in the maps and to find if there is a significant association.
Check out this map that shows the average fine particulate matter (PM 2.5) (µg/m³) by county for the year 2011. From the map we can see clusters with a higher average indicated by the darker shading. For instance, we can see a cluster consisting counties within for Colorado, Nebraska, Kansas and Wyoming. It is also apparent there are higher concentrations in many Southern, Midwestern and Northeastern states compared to western states. States such as, Indiana, Ohio, Tennessee and Kentucky predominantly have a higher average.
By Julia Watson
Check out this map that shows the number of National Health Service Corps (NHSC) sites in Tennessee as of 10/6/17 and the number of National Committee for Quality Assurance certified Patient Center Medical Home (PCMH) sites in Tennessee as of 1/23/18.
For more information on the Patient Centered Medical Home click here.
By Julia Watson
In recognition of National Birth Defects Prevention month check out this map of Tennessee which shows the rate of chromosomal birth defects by county per 10,000 live births by county for the state of Tennessee for the years 2008 to 2012. From the map we can see counties such as, Williamson, Johnson, Scott and Giles had a higher rate of chromosomal birth defects, ranging from 27.01 to 36.00 per 10,00 live births. Given a mothers age is a significant risk factor for certain types of chromosomal birth defects with older mothers having a higher risk it would be interesting to compare the age demographics of these counties.
By Julia Watson