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> Phenomena > Human-caused Phenomenon or Process > Health Transition

Health Transition

Health Transition refers to the gradual shift in population health patterns, often characterized by a decline in infectious diseases and an increase in chronic, non-communicable conditions.
This transition is influenced by factors such as socioeconomic development, improved nutrition, and advances in medical technology.
Understanding the Health Transition is crucial for developing effective public health strategies and allocating resources to address the evolving healthcare needs of a population.
The process is typically observed across nations and regions as they progress through demographic and epidemiologic changes.
Reserchers studying Health Transition aim to idnetify the drivers and implications of this dynamic shift in order to inform policy decisions and improve overall population wellbeing.

Most cited protocols related to «Health Transition»

1
The Thai Health-Risk Transition Project
The Thai Health-Risk Transition Project began in 2004 with the aim of studying changes in the health status of the Thai population associated with rapid modernization and industrialization. Part of this study project has involved assembling a cohort of Thais who would be representative of the general population and whose health status could be followed through time along with their risk behaviour and socio-demographic and economic profiles. Our target population was persons studying by correspondence via Sukhothai Thammathirat Open University (STOU). This group was chosen because STOU students live throughout the country and display considerable variation in lifestyle, family structure, socio-economic status, domestic and occupational environment and personal behaviour. For almost all these factors STOU students are similar to the general Thai population[6 (link)]. To the best of our knowledge this type of nationwide representative cohort study has not been attempted before in Thailand with previous cohort studies on health risks being limited to specific population groups such as specific occupational groups,[7 (link)] sex workers,[8 (link)] drug users, [9 (link)] or prisoners[10 (link)].
In 2005 a questionnaire (Additional files 1, 2 and 3) was mailed to all of the approximately 200,000 students enrolled at STOU. We received back a total of 87,134 (44%) completed questionnaires which were used to gather information on various subjects associated with health, including demography, social networking, work, health services, disease and injury, environment, food and physical activity, smoking, alcohol and transport. Various methods were used to achieve this initial successful response rate. These included making clear our association with STOU by sending out our questionnaire together with other STOU materials as well as promoting ourselves on the STOU website and other University information outlets.
When people responded and returned the questionnaire we scanned the data and created a digital data file and linked image file for each completed questionnaire. The scanning was completed using intelligent character recognition and editing software developed in Thailand called Scandevet (Figure 1)[11 (link)]. The personal identifying information for each individual record was connected to the digitized response data by an encrypting key with the code available only to the lead investigators in Thailand and the key stored in a locked safe. As well we created an additional SQL database containing the name, sex, birth date, address, telephone numbers, email address, student ID number, Citizen ID number, and Thai Cohort Study identifying number. This name-address database was constructed to enable subsequent changes of name, address or phone numbers as person-time accumulated. Periodically the name-address database file was updated and each individual record contained an update flag variable indicating if name or address had been changed. We conducted a 4-year follow-up study of this cohort in 2008/2009 and we summarize here the procedures used to maintain this contact and ensure a successful follow-up.
Seubsman S.A., Kelly M., Sleigh A., Peungson J., Chokkanapitak J, & Vilainerun D. (2011). Methods used for successful follow-up in a large scale national cohort study in Thailand. BMC Research Notes, 4, 166.
Publication 2011
Character Childbirth Drug Abuser Ethanol Family Structure Fingers Food Health Transition Injuries Population Health Prisoners Sex Workers Student Target Population Thai Workers
2
Simulation Model of Knee Osteoarthritis
The OAPol Model is a validated, state-transition, computer simulation model of the natural history and management of knee OA (24 (link),26 (link),27 (link),31 (link),32 (link)). In the model, individuals transition among health states defined by structural severity of knee OA (K-L grades 0 to 4) and symptom status (33 (link)). Symptomatic knee OA is defined as radiographic knee OA (K-L grades 2 to 4) accompanied by pain on most days. In the beginning of each simulation, each hypothetical patient is assigned a K-L grade, age, sex, and BMI.
In addition to capturing the incidence and progression of knee OA, the OAPol Model tracks the development of other chronic conditions prevalent in persons with knee OA. Large cohorts are followed until death, which is determined in a probabilistic manner using US life tables adjusted with disease-specific relative risks of mortality (34 –36 ). Additional descriptions of the OAPol Model structure have been published previously (26 (link),32 (link),37 (link)).
Losina E., Paltiel A.D., Weinstein A.M., Yelin E., Hunter D.J., Chen S.P., Klara K., Suter L.G., Solomon D.H., Burbine S.A., Walensky R.P, & Katz J.N. (2015). Lifetime medical costs of knee osteoarthritis management in the United States: Impact of extending indications for total knee arthroplasty. Arthritis care & research, 67(2), 203-215.
Publication 2015
Developmental Disabilities Disease Progression Health Transition Knee Pain Patients X-Rays, Diagnostic
3
Dichotomized Health Transition Scale
We dichotomised the health transition scale as suggested by Lauridsen et al (and as is typical), categorising patients as improved if they were in the top two categories of recovery, using this as our external criterion for generating ROC curves. [20] (link) We then purposively sampled RMDQ data, and data from the modified von Korff disability scale, across all trial arms, and from the Best Care arm in particular, at three-months, and one-year follow-up time points.
Froud R, & Abel G. (2014). Using ROC Curves to Choose Minimally Important Change Thresholds when Sensitivity and Specificity Are Valued Equally: The Forgotten Lesson of Pythagoras. Theoretical Considerations and an Example Application of Change in Health Status. PLoS ONE, 9(12), e114468.
Publication 2014
Disabled Persons Health Transition Patients
4
Markov Model for Herpes Zoster Vaccination
The ZOster ecoNomic Analysis (ZONA) model (Fig. 5) was developed in MS Excel. It is a static multi-cohort Markov model. Cohorts are split into 5 age groups for people aged 50+ years (i.e., 50–59, 60–64, 65–69, 70–79, 80+). If the “50+ years combined” option is selected, the model assumes that all of the subjects in the 50–59, 60–64, 65–69, 70–79 and 80+ age cohorts are vaccinated, as in a one-off ‘catch-up’ campaign, at an age of 50, 60, 65, 70 and 80 y respectively. The model follows all subjects within a cohort over their remaining life-time from the year of vaccination with annual cycle lengths. As such all subjects remain in their initial cohort and all subsequent events are counted in that cohort only. Three different HZ vaccination strategies are compared; no vaccination (control), vaccination with ZVL, and vaccination with HZ/su. Within each vaccine arm/strategy, individuals can be fully compliant with the vaccine dosing schedule, partially compliant or not vaccinated at all, depending on the corresponding vaccine coverage and compliance rates assumed. An overview of the model structure is presented in Fig. 5. Transition probabilities between the health states HZ, natural death, HZ related deaths, recover, recurrent HZ,.. occur using an annual time step. PHN and NON-PHN complications are health states which occur within a HZ episode and as such occur within this annual time step. Probabilities of moving between health states are derived from Germany specific literature and are age-group specific.

Schematic overview of Markov structure – ZONA model. HZ: herpes zoster; PHN: postherpetic neuralgia.

In this analysis 3 age cohorts were considered, i.e., 50–59, 60–69 and 70+ years, i.e., combining results from the 60–64 and 65–69 cohorts and the 70–79 and 80+ cohorts, respectively, for presentation purposes. The age cohorts were selected to capture age-dependent differences in disease incidence, complications, outcomes, costs and potential public health decision making.
Curran D., Van Oorschot D., Varghese L., Oostvogels L., Mrkvan T., Colindres R., von Krempelhuber A, & Anastassopoulou A. (2017). Assessment of the potential public health impact of Herpes Zoster vaccination in Germany. Human Vaccines & Immunotherapeutics, 13(10), 2213-2221.
Publication 2017
Age Groups Health Transition Herpes Zoster Human Herpesvirus 3 Immunization Schedule Postherpetic Neuralgia Vaccination Vaccines
5
Cross-Cultural Adaptation of the SF-36 Questionnaire
The SF-36 is a generic questionnaire, widely used in various conditions and populations [14 -16 ]. The SF-36 consists of 36 questions that are clustered to yield 8 health status scales: physical functioning (PF), Role-Physical (RP), Bodily Pain (BP), General Health (GH), Vitality (VT), Social Functioning (SF), Role-Emotional (RE), Mental Health (MH), Reported Health Transition (HT). Two summary measures aggregate these status scales, namely the Physical and Mental health summary scales. The SF-36 is suitable for self-administration, computerized administration, or administration by a trained interviewer in person or by telephone, to persons age 14 and older. The health concepts described by the SF-36 range in score from 0 to 100, with higher scores indicating higher levels of function and/or better health. The subjects' responses are presented as a profile of scores calculated for each scale.
The translation and cultural adaptation of the SF-36 followed the International Quality of Life Assessment (IQOLA) methodology [46 (link)-48 (link)]. In the first phase, the SF-36 was translated by three bilingual individuals. All three were native Arabic speakers with excellent proficiency in English. Two individuals were graduate students at the American University. The third translator was a physican. Once the three translations were completed, discrepancies between them were resolved by a committee consisting of the translators and three further individuals not involved in the translation process (a sociologist and two epidemiologists). The committee created one unified translation of the SF-36. Because of the difficulties related to Arabic grammar and to the style of Arabic writing, two other Arabic linguistics experts also reviewed the translated version. Then, the Arabic version of the SF-36 was backtranslated by a native english speaker living in Lebanon, who was unaware of the original English language document. Once the backtranslation was completed the committee reconvened to review and resolve the discrepancies between the backtranslation and the original document. Finally, a pre-test was conducted with a group (30 subjects) of lay native Arabic speakers. For each item the group was asked to explain how it was understood. Overall, few problems were noted. Discrepancies were resolved by group consensus. The committee overseeing the translation process reviewed the final translation. Globally, the adaptation did not cause any particular problems. In view of cultural differences, certain items were modified in order to fit more closely into the context, consistent with the inherent norms of Lebanese society. Some expressions were modified to suit the context: for example, a mile, several blocks and one block were translated respectively by more than 1000 meters for long distances, a few hundred meters for moderate distances (200, 300, 500... meter) and less than 100 meters. On the other hand, both linguists and subjects understood "a good bit of the time" and "most of the time" identically in Arabic. "Bowling or playing golf" was translated to gardening or sport activities simply to represent moderately strenuous physical activities, in view of the differences in leisure traditions between both cultures. Finally, words relating to religious beliefs, such as "only God knows", (inchallah) were formulated for "I don't know". Indeed, some of the Lebanese subjects, in particular patients with chronic diseases, found that the questions related to general health were blasphemous, specifically item GH4: "I expect my health to get worse". This was viewed with scepticism, as the subjects maintained that they cannot predict, and that only God knows what lies in store for them. As for the items concerning social relations, some persons expressed the desire that a distinction be made between family relationships, relations with neighbours, and social relationships (i.e. with friends) due to the importance of the family in oriental traditions.
Sabbah I., Drouby N., Sabbah S., Retel-Rude N, & Mercier M. (2003). Quality of Life in rural and urban populations in Lebanon using SF-36 Health Survey. Health and Quality of Life Outcomes, 1, 30.
Publication 2003
Acclimatization Asian Persons BAD protein, human Cultural Evolution Disease, Chronic Emotions Epidemiologists Friend Generic Drugs Health Transition Interviewers Mental Health Pain Patients Physical Examination Self Administration Student

Most recents protocols related to «Health Transition»

1
Comparing IraPEN Program and Status Quo
Two separate Markov decision models were developed to compare the long-term costs and health benefits of the IraPEN program (primary CVD prevention) with the status quo (no prevention) in two distinct scenarios. In the base case scenario, individuals without diabetes were included, while patients with diabetes were included in the alternative scenario. Each Markov model has four health states with transitions between the states according to age, sex, and the CVD risk characteristics of participants (Figure 1). In contrast to the usual Markov models, which are structured based on cohorts with average profiles, we decided to categorize the individuals based on their CVD risks. As the intervention (treatment) varied according to CVD risk level, it is logical to model them separately. In this way, we can take into account their specific characteristics. Therefore, based on WHO/ISH CVD risk prediction charts for EMR B, four index cohorts were constructed (5 ). These hypothetical cohorts were used as a representative for individuals with low, moderate, high, and very high CVD risk profiles. The CVD risk state represents the starting point for all people who are 40 years old. It was assumed that people in this state may either remain in the same health state, move to the stroke state, or CHD (coronary heart disease) state, or die. As long as they are event-free, these individuals can stay in a healthy state, but after the first event, they move to the CHD or stroke state and stay there until their death.
In WHO/ISH CVD risk prediction charts, the CVD risk is calculated based on individuals' age and risk factors such as blood pressure, lipid profile, diabetes, and smoking status and categorized into the following five groups: below 10% (low-risk group), between 10 and 19% (moderate-risk group), between 20 and 29% (high-risk group), between 30 and 39%, and above 40% (very high-risk group). As the individuals in the two latter groups are treated the same, in the IraPEN program, whoever has a CVD risk above 30% is categorized as the very high-risk group.
Therefore, considering what was mentioned earlier, all the Iranians aged older than 40 years who did not have CHD or stroke events before were eligible for this program. According to the recent census (2016), 31.16% of Iranians were older than 40 years (6 ). By adding individuals aged older than 30 years with the aforementioned risk factors, we can conclude that this program is going to screen at least 25 million people yearly.
The healthcare perspective and a 40-year time horizon were adopted for this analysis. As the analysis is a comparison between IraPEN (intervention) and status quo (no intervention) which both have the same Markov structure and transition probabilities, it is not expected that half cycle correction (HCC) approach makes any difference in ICER results; therefore, HCC was not applied to this analysis (7 (link)).
The hypothetical cohorts were used as a representative for individuals with low, moderate, high, and very high CVD risk profiles (Table 1). Progressively, a proportion of the cohort can go to the CHD state, who are the survivors of the first CHD event, or to the stroke state who are the survivors of the first stroke event. Those CHD and stroke events that were fatal moved to the death state. In general, the people in these two states are at a higher risk of dying from CHD or stroke, but they may die from any other causes like the normal population. Table 2 summarizes the assumptions of this analysis.
Jamshidi A., Daroudi R., Aas E, & Khalili D. (2023). A cost-effectiveness analysis of risk-based intervention for prevention of cardiovascular diseases in IraPEN program: A modeling study. Frontiers in Public Health, 11, 1075277.
Publication 2023
Blood Pressure Cerebrovascular Accident Diabetes Mellitus Health Transition Heart Disease, Coronary Lipids Patients Population at Risk Primary Prevention Survivors
2
Transition Cow NEFA Monitoring Protocol
The farm enrolled in this study conducted pre-calving plasma NEFA analyses every 2 weeks for monitoring transition cow health. For this, ten primiparous and ten multiparous cows were randomly selected (www.graphpad.com/quickcalcs/randomSelect1/) for blood collection among all cows expected to calve in the following 7 to 14 d. Blood samples were collected at the time of daily delivery of fresh total mixed ration. Samples were obtained via puncture of coccygeal vessels using EDTA vacuum tubes (Monoject EDTA K3; Covidien, Minneapolis, MN). Immediately after collection, samples were centrifuged at 2,000 x g for 10 min. Plasma was removed and placed into 2-mL microcentrifuge tubes (Fisher Scientific, Waltham, MA). All samples were refrigerated, and plasma NEFA concentration was determined within 24 h by Central Star Cooperative (Grand Ledge, MI) using a 96-well plate protocol validated for cattle (14 (link)). The test has a sensitivity (95%CI) of 88.9% (67.2–96.9%) and a specificity (95% CI) of 100.0% (97.1–100.0%) for the identification of excessive lipid mobilization pre-calving (14 (link)). A convenience sample size that included the results from all the cows sampled between September 2015 and March 2020 whose actual calving date was between 4 and 14 days [mean (SD) = 7.4 (2.1) days] after sampling (n = 1,532) were included in the study. The details regarding calving date, calf identification, calf health, and calf performance were extracted from the farm record system (DairyComp 305, Valley Agricultural Software, Tulare CA).
Velasquez-Munoz A., Schuurmans E.J., Brester J.L., Starken K, & Abuelo A. (2023). Association of maternal late-gestation lipid mobilization and their offspring's disease risk during the pre-weaned period and performance through first lactation: A cohort study in a dairy herd. Frontiers in Veterinary Science, 10, 1102421.
Publication 2023
BLOOD Blood Vessel Cattle Coccyx Edetic Acid Health Transition Hypersensitivity Lipid Mobilization Nonesterified Fatty Acids Obstetric Delivery Plasma Punctures Vacuum
3
Anaemia Management Implementation Research
The IVON-IS will aim to test the routine implementation of two interventions along the management pathway for anaemia in pregnancy and postpartum (i.e. screening for anaemia and treating anaemia using FCM). The study will also examine the linkages and interactions of patients, health system actors, and processes within and between health facilities as women transition through the health system (Fig. 2).

Scope of the IVON implementation research

Eboreime E., Banke-Thomas A., Obi-Jeff C., Adelabu Y., Balogun M., Aiyenigba A.A., Oluwole E.O., Akinajo O.R, & Afolabi B.B. (2023). A continuous quality improvement strategy to strengthen screening practices and facilitate the routine use of intravenous iron for treating anaemia in pregnant and postpartum women in Nigeria: a study protocol. Implementation Science Communications, 4, 22.
Publication 2023
Anemia Health Transition Patients Pregnancy Woman
4
Markov Model for TAVI vs SAVR Outcomes
In the base case, clinical events over the short term (≤ 30 days) were based on PARTNER 3 trial outcomes from the 30 days AE data set (Supplementary Material, Table S1). Input data for permanent pacemaker implantation (PPI) at 30 days were based on PARTNER 3 data for SAVR [3 (link)] and a real-world cohort study to reflect more recently available SAPIEN 3 TAVI data specific to the German population [11 (link)].
Monthly transition probabilities between health states for the Markov model, reflecting complications occurring after 30 days, were estimated on the basis of data from PARTNER 3 (up to 2 year outcomes) or other literature sources where there were too few events in PARTNER 3 for reliable estimates (Supplementary Material, Table S1). Rehospitalization rates were based on data from the PARTNER 3 study [3 (link)]. Reintervention rates due to value deterioration were based on PARTNER 3 data up to 2 years [3 (link), 12 (link)] and by competing risk estimates for the 73 year-old cohort from a study by Bourguignon et al. from year 3 onwards [13 (link)]. The same reintervention rate was used for both TAVI with SAPIEN 3 and SAVR in the base case; this simplifying assumption allowed best use of the available data. We introduced multiple scenarios to test the robustness of our model. Scenario 1 assumed a higher reintervention rate for TAVI versus SAVR, based on data at 5 years from the PARTNER 2 trial [14 (link)] (Table 4). Scenario 2 assumed an increased risk of stroke with TAVI versus SAVR after 1 and 2 years, to align with PARTNER 3 outcomes [3 (link), 12 (link)]. A number of scenarios explored alternative PPI rates: scenario 3a assumed a new PPI rate of 13.2% for TAVI and 3.3% for SAVR based on a recent study of over 38,000 patients enrolled in the German Aortic Valve Registry who received TAVI or SAVR between 2011 and 2015 [15 (link)], and scenario 3b assumed a new PPI of 6.5% for TAVI and 4.0% for SAVR based on PARTNER 3 [3 (link)] (Table 4).
Kuck K.H., Leidl R., Frankenstein L., Wahlers T., Sarmah A., Candolfi P., Shore J, & Green M. (2023). Cost-Effectiveness of SAPIEN 3 Transcatheter Aortic Valve Implantation Versus Surgical Aortic Valve Replacement in German Severe Aortic Stenosis Patients at Low Surgical Mortality Risk. Advances in Therapy, 40(3), 1031-1046.
Publication 2023
Cerebrovascular Accident Health Transition Ovum Implantation Pacemaker, Artificial Cardiac Patient Readmission Patients Valves, Aortic
5
Assessing HIV Health-Related Quality of Life
The HRQL was measured using the Medical Outcomes Study HIV Health Survey (MOS-HIV), specifically for individuals with HIV [19 (link)]. The MOS-HIV is a 35-item questionnaire that assesses the following subscales: general health perceptions, pain, physical functioning, role functioning, social functioning, energy/fatigue, mental health, health distress, cognitive function, quality of life, and health transition. The scales of the MOS-HIV are scored as summed rating scale scores using a scale of 0 to 100; higher scores indicate better health.
Sekine Y., Kawaguchi T., Kunimoto Y., Masuda J., Numata A., Hirano A., Yagura H., Ishihara M., Hikasa S., Tsukiji M., Miyaji T., Yamaguchi T., Kinai E, & Amano K. (2023). Adherence to anti-retroviral therapy, decisional conflicts, and health-related quality of life among treatment-naïve individuals living with HIV: a DEARS-J observational study. Journal of Pharmaceutical Health Care and Sciences, 9, 9.
Publication 2023
Cognition Fatigue Health Transition Mental Health Pain Physical Examination

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More about "Health Transition"

Health Transition, also known as the Epidemiologic Transition or Demographic Transition, refers to the gradual shift in population health patterns.
This dynamic process is typically characterized by a decline in infectious, communicable diseases and a rise in chronic, non-communicable conditions.
Factors such as socioeconomic development, improved nutrition, and advancements in medical technology play a pivotal role in shaping this transition.
Understanding the Health Transition is crucial for developing effective public health strategies and allocating resources to address the evolving healthcare needs of a population.
Researchers studying this phenomenon aim to identify the key drivers and implications of this shift in order to inform policy decisions and improve overall population wellbeing.
The Health Transition is often observed across nations and regions as they progress through demographic and epidemiologic changes.
Seminal works, such as those from TreeAge Pro (2011, 2018, 2022 R1.2), SPSS (version 22.0), SAS (statistical software version 9.4, system version 9.4), and the R environment, have provided valuable insights into the dynamics and modeling of this transition.
By comprehending the nuances of the Health Transition, healthcare professionals and policymakers can make more informed decisions, optimize resource allocation, and devise targeted interventions to address the changing disease burden and healthcare needs of a population.
This knowledge is essential for improving overall population health and wellbeing.