My Health Story (MHS) is a public health surveillance survey administered to residents in the Finger Lakes region of New York State. The primary focus of the My Health Story effort is to gather information on social drivers of health and health outcomes that is more nuanced and detailed than what is typically included in publicly available secondary data sources. My Health Story is a recurring survey first administered during the summer of 2018. My Health Story 2022 is the second version. With each iteration, the My Health Story research team shapes the survey guided by the expertise and lived experiences of Common Ground Health’s extensive community-embedded network. This includes gathering input and feedback from a variety of stakeholders in the design, administration, interpretation, and dissemination of the survey data. Each iteration also includes updated or new context-specific topics relevant to the current public health landscape. 

New to the survey in 2022, we added questions on perceived discrimination in health care settings, housing mobility and quality, children’s health and well-being, and language access needs. Additionally, the MHS 2022 survey team prioritized improving accessibility and inclusion of the perspectives of community members living outside of Monroe County, of those with Indigenous backgrounds, and of individuals for whom English is not their primary language. We also refreshed digital accessibility tools embedded in the data collection process. The 2022 survey included a text-to-speech reader in English and Spanish, buttons to increase font size, and survey links in three different languages: English, Spanish, and American Sign Language.

Below you will find frequently asked questions about our survey. For more detailed information, please see our white paper.


Frequently Asked Questions

My Health Story 2022 had 3,861 responses: 3,747 people in the 9-county Finger Lakes region and an additional 114 people in three additional counties adjacent to the Finger Lakes region, which were Genesee, Orleans, and Wyoming. The 9-county region includes the following counties: Monroe, Chemung, Livingston, Ontario, Schuyler, Seneca, Steuben, Wayne, and Yates.  The dataset is unweighted, meaning, the data in the dataset represents only the people who took the survey.  Results cannot be generalized to the population as a whole.

Here is a heat map showing the number of people who took the survey by ZIP Code in the 8-county Finger Lakes region outside of Monroe:

Here is a map showing the number of people who took the survey by ZIP Code within Monroe County with the City of Rochester boundary line identified:
For additional information on the people who took the My Health Story Survey, please see our white paper.

Yes. The project adheres to the oversight rules governed by the Institutional Review Board (IRB) at the Biomedical Research Alliance of New York (BRANY).

MHS 2022 survey questions were written and pilot tested with the help of many people ranging in age, lifestyle, racial and ethnic identity, and geographic residence.  From August 2021 through June 2022, the MHS research team edited the 2018 version of the survey based on feedback from over 150 individuals including residents and staff from approximately 25 organizations.  Feedback was incorporated and multiple drafts of specific sections of the survey were returned for a second and third review as needed. Community members continued to reflect and provide feedback as we incorporated changes that led to the final version of the survey.
We entered the MHS 2022 survey into REDCap, the online survey administration tool used to collect responses. Multiple internal and external stakeholders tested the online survey and additional edits were made to improve upon inclusion and accessibility of the survey-taking experience.  Skip logic was also tested numerous times as MHS 2022 research staff took the survey as though they were representing individuals with varying lifestyles and answer patterns.  After internal testing, additional community members tested skip logic and provided feedback on survey questions and answer choices. Finally, four live, virtual feedback sessions were held with groups of people who took the survey online, most of whom did not see a prior version of the My Health Story survey.  

Beginning in May 2022, Empire Justice Center worked with the MHS 2022 survey design team to incorporate a Language Access Needs section into the MHS 2022 survey.  The entire survey was then translated into Spanish and American Sign Language (ASL).  Dr. Lorne Farovitch, a co-investigator on the survey, supervised the translation into ASL videos and helped to recruit members of the Deaf community.  The Deaf Monroe County Language Access Coalition and other Deaf community members pilot tested the survey in ASL.  
Lastly, additional REDCap survey links were created for the Spanish language version and American Sign Language version of the MHS 2022 Survey.  These were pilot tested by 2-4 language speakers in each group and additional edits were made to the language translations as needed.  The Spanish and English versions of the survey went live during the first week of July 2022 while the ASL version of the survey went live a few months later. 

All language versions of the survey links were tracked to specific recruitment materials using TinyURL links and QR codes created and managed by the Common Ground Health communications team. The English and Spanish versions of the survey received a large amount of spam responses within the first few days of the survey being opened to the public.  After an 8-month cleaning period, spam entries were identified and removed from the dataset. If you have questions about the process taken to identify and remove spam survey responses, please reach out to Dr. Sarah Farash at

For additional information on the people who took the My Health Story Survey, please see our white paper.

The survey questions are analyzed using descriptive statistics, for example looking at the total count of people who answered “yes” or “no” to a particular question.  We also look at percentages of people who reported specific responses to particular questions.  When it makes sense to do so, we add up answers that can be grouped together.  For example, we might add up all of the responses of people who said they have either good, very good, or excellent physical health status and compare the numbers to those who said they have fair or poor physical health status. We have an internal dashboard that we have created that helps us to look at patterns in the responses counts by race and ethnicity, age, income level, socioeconomic status, gender, county, and poverty rate.  We can also look to see what the demographics are of people who answered a specific question.  There are many ways to look at the data and we will be analyzing the close-ended questions for many years to come. Sometimes we also use statistical techniques like correlation tests or regression tests to look at patterns in the data.  In order to do that we use a statistical testing software like SAS or SPSS.

In order to analyze the answers to open-ended questions asked of everyone who took the survey, like “What is the biggest concern for your own health and well-being?”, we entered all the data into a software called Dedoose.  Then, we reviewed every answer and tagged them with a short, paraphrased code that remained true to participants’ original language. This step is called “close coding”.  Each response could have multiple codes attached to it, if more than one concern was reported. Starting with the framework from the 2018 survey, we then identified themes and sub-themes among those codes, adding or subtracting from the 2018 framework as needed in an iterative and reflective fashion. A code dictionary was simultaneously created to define each code, inclusion and exclusion criteria, as well as examples of responses that would fit into each. This qualitative data analysis technique is also known as Summative Content Analysis. If you would like to see a copy of the code tree that has definitions of each branch summarized, please click here.
Other open-ended response questions that were not asked of every respondent are read through and coded using close coding or content analysis using other software like Microsoft Access or Microsoft Excel.

Yes.  Both the quantitative and qualitative data analyses are checked by independent team members. We have a list of data equity principles that we use to make sure that our review process is consistent and tells the complete story of the data with accuracy and transparency.

For qualitative data with a large number of responses, we sometimes use other quantitative survey questions to help place codes into the appropriate themes and sub-themes. For example, a quantitative question on the survey asked respondents whether or not they had diagnoses of various chronic conditions, one of which was obesity or overweight. For people who stated in their write-in answer that they had concerns about weight, we were able to code those into people who listed the concern and had checked that they had a chronic condition issue related to weight separately from those who checked that they were concerned about their weight but did not check that they have a chronic condition. Those who did not have a chronic condition checked were listed as having a lifestyle concern related to weight management.

The overall code dictionary created for the “What is the biggest concern for your health?” question was checked for consistency and to ensure that drift had been corrected.  Additionally, the theme of Chronic Conditions in the code dictionary was reviewed by a medical expert to determine accurate and appropriate placement of codes within that theme. If you would like to see a copy of the code tree that has definitions of each branch summarized, please click here.

Yes.  Agencies, organizations, and individuals who have a secure place to store data are eligible to receive survey records from this dataset for their own use in health equity related projects or advocacy efforts. 

A Data Use Agreement (DUA) is necessary if you are requesting access to record-level survey data.  MHS survey questions and answer items are available here.  Once you have reviewed the survey questions and have a general idea of the data you would like to request, please reach out to   Please include in your email a list of the questions you would like to see the data for and how you plan to use the data.  After reviewing your email, a meeting will be set up with you to talk through the request process in more detail. 

Alternatively, if you would like to know the percentage of people who responded with a specific answer to a question, but don’t need the individual survey response data, please indicate that in your email. 

In general, it takes approximately four to eight weeks to process MHS 2022 data requests.  The timeframe to process the request begins after the meeting occurs where your request is clarified and the scope of work to process the request established.