STUDY DESIGN AND DATA SOURCE
This is a retrospective longitudinal observational analysis of medical consultations performed by FPs and Generalists in the public PHC system in the Rio de Janeiro municipality from January 2013 to December 2018. Each medical consultation (the unit of analysis) was considered as a binary event (diagnosed versus non-diagnosed, LT requested versus LT not requested, follow-up consultation versus non-follow up consultation). The Rio de Janeiro Practice-based Research Network (RioPBRN) provided the dataset for this research, compiling Information from 999.364 patients registered in one health district in Rio de Janeiro. This study was approved by the Rio de Janeiro Municipal Health Department (RJ-MHD) research ethics board and it is registered under the number 03795118.0.0000.5279.
SETTING
The Family Health Strategy (Estratégia de Saúde da Família – FHS) is the Brazilian federal policy for public PHC launched in 1994 to provide structural organization and financial support to the municipal Family Health Teams (FHTs).21 FHTs are formed by one physician, one nurse, and four to six community health workers (CHW) to provide care for up to 4.000 patients in a given catchment area.
Despite the positive results in public health of the FHS,22–25 its expansion depended entirely on the municipalities' adherence to the policy. Rio de Janeiro, the last Brazilian capital to adopt the FHS, expanded the number of FHTs from 2008 to 2016, increased its coverage from 3.5% to 70% of the population,26 created new community-based primary care clinics and made strong investments to expand RTFM.27 More than 600 FPs have graduated so far from the three FM residency programs established in the city, increasing the provision and fixation of FPs in PHC.
Today, 25% of the FHTs in Rio de Janeiro have trained FPs, 65% have physicians without RTFM (Generalists), and 10% have physicians enrolled at the More Doctors Program, a federal policy established in 2013 for provision and fixation of Brazilian and foreign physicians in PHC.28 This distribution created a convenient quasi-experimental design to address the effect of RTFM in the provision of PHC.
OUTCOMES
Three groups of outcomes were analyzed as dichotomous events: (1) detection of CHCs; (2) LTs requests; and (3) follow-up visits.
The risk of a patient being detected with a CHC was estimated for each condition considering only consultations among patients that have not had that specific condition diagnosed prior to the encounter. A list with 31 CHC was created combining the ICD-10 codes from three different frameworks: (a) the Brazilian list of ambulatory-care sensitive conditions,29 (b) the Charlson comorbidity index30 and (c) the Elixhauser comorbidity index.31 Chronic hepatitis was added to the list and Neoplastic diseases were divided into five subdomains: Cancer (general), Cancer in men (Neoplasia of male genital organs), Cancer in women (Neoplasia of female genital organs), Breast cancer (women only) and Metastatic cancer. Three conditions – Hypertension, Type 2 Diabetes Mellitus (T2DM), and Hypothyroidism – were categorized in two different ways: one using only ICD10 codes and the other using specific clinical criteria. Table 1 summarizes the list with the 31 CHCs, the respective ICD10 codes, and clinical criteria.
For the risk of a LT being requested in one consultation, a list with the 30 most requested LTs in the sample was created. For LH, FSH, Rubella IgG and Rubella IgM, only women were considered as patients at risk, while PSA considered only men. For each LT in the list, consultations were categorized as having the LT requested (event) or not having the LT requested (no-event).
The risk of patients with a CHC having a follow-up consultation was estimated considering all patients seen in a month by each physician and consultations were clustered and ordered among each individual physician. Undiagnosed patients’ consultations were considered as no-event and diagnosed patients’ consultations were considered as events. The consultation in which the patient was diagnosed for the specific CHC was excluded.
The outcomes were chosen to approach three common questions that policymakers in LMIC have about the necessity of RTFM to work in PHC: “RTFM will make doctors more capable of detecting CHC?”, “Will they order less LTs?” and “Will they provide more follow-up visits? These are all common events in ambulatory care and analyzing the effect that RTFM has in their occurrence may bring relevant evidence for policymakers and health managers. The rationale behind the choice for these outcomes goes aligned with the definition of FM from the Brazilian,10 Canadian20 and European19 curricula for FM, i.e., that experts in FM are skilled clinicians that are capable of managing a full range of health conditions, making efficient use of diagnostic and therapeutic interventions.
In Rio de Janeiro, a comprehensive list of LTs is available for all doctors and nurses in the public PHC system to make use of. With no restriction for requesting any LT, the decision to order it or not will rely mostly on the doctor’s clinical judgment facing the patient’s health needs. In this scenario, concerns that patients would not have access to LTs due to poor availability and scarce resources moves from “lack of healthcare” to the “overuse that does not add value for patients and may even cause harm”.32 Supported by several medical associations, campaigns such as Choosing Wisely33 and the concept of Quaternary Prevention34 try to engage physicians and patients in conversations about unnecessary tests, treatments and procedures, showing that “doing more does not mean doing better”. If RTFM helps doctors to make more efficient use of diagnostic tests, patients treated by FPs will probably have fewer LTs requested in their consultations.
Table 1: List of chronic health conditions according to their ICD10 codes and clinical criteria.
|
Hypertension (ICD10)
|
I10-I15
|
Hypertension clinical criteria
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At least one measurement > 140/90 mmHg
|
Diabetes Mellitus (ICD10)
|
E10-E14
|
Diabetes clinical criteria
|
At least one glycemia > 126 mg/dl or A1C hemoglobin > 6.5%
|
Hypothyroidism coded
|
E00-E03, E890
|
Hypothyroidism clinical criteria
|
ICD codes (E00-E03, E890) and at least one TSH > 5 mcg/dL
|
HIV/AIDS
|
B20-B24, Z21, F024, R75
|
Drug addiction
|
F11-F16, F18, F19, Z715, Z722
|
Depression
|
F204, F313-F315, F32, F33, F341, F412, F432
|
Psychosis
|
F20, F22-F25, F28, F29, F302, F312, F315
|
Alcohol abuse
|
F10, E52, G621, I426, K292, K700, K703, K709, T51, Z502, Z714, Z721
|
Cardiac arrythmias
|
I441-I443, I456, I459, I47-I49, R001, Z950
|
Ischemic heart disease
|
I20-I22, I238, I248, I249, I250-I252, I255, I256, I258, I259
|
Peripheral artery disease
|
I70, I71, I731, I738, I739, I771, I790, I792, K551, K558, K559, Z958, Z959
|
Heart failure
|
I099, I110, I130, I132, I420, I425-I429, I43, I50, P290, J81
|
Kidney failure
|
I120, I131, N032-N037, N052-N057, N18, N19, N250, Z490-Z492, Z992
|
Osteoarthritis
|
M15- M19, M2, M40-M43
|
Rheumatic disorders
|
L93, L940, L941, L943, M05, M06, M08, M10, M11, M120, M123, M30, M310, M313, M315, M32-M36, M45, M461, M468, M469
|
Neurological disorders
|
G10-G13, G20-G22, G254, G255, G312, G318, G319, G32, G35-G37, G931, G934, G43, G44, G45, G46, G47, G5, G6, G7, G8, R47
|
Epilepsy
|
G40, G41, R56
|
Stroke
|
G45, G46, H340, I6
|
Dementia
|
F00-F03, F051, G30, G310, G311
|
COPD
|
I278, I279, J40-J44, J47, J60-J67, J684, J701, J703
|
Asthma
|
J45, J46
|
Chronic hepatitis
|
B180, B181, B182, B188, B189, K713, K714, K715, K730, K731, K732, K738, K739
|
Cirrhosis of the liver
|
B18, K700-K704, K709, K713, K715, K717, K73, K74, K760, K762-K764, K768, K769, Z944, I85, I864, I982, K711, K721, K729, K765-K767
|
Cancer (general)
|
C0, C1, C2, C3, C4, C64-C69, C7, C8, C9
|
Cancer in men
|
C60-C63
|
Cancer in women
|
C51-C58
|
Breast cancer
|
C50
|
Metastatic Cancer
|
C77, C78, C79, C80
|
EXPOSURES
Physicians were divided into two categories: (1) Generalists – doctors without RTFM (reference group); and (2) Family physicians (FPs) – graduated FPs, FM preceptors and residents enrolled in the FM residency programs.
Residents in FM spend two years working 48 hours a week in a community-based primary care clinic under the full supervision of a senior FP (FM preceptor) sharing responsibilities for the same patients in one FHT. Their clinical and academic35,36 activities aim to develop the skills, competencies, and attitudes a FP must have to practice in PHC. They are aligned with the National Committee for Medical Residencies (CNRM)37 and with the Brazilian Society of Family and Community Medicine (SBMFC).10 Information about other forms of post-graduate training or specialization were not available in the database and were not taken into account, nor the number of years in practice for any doctor.
INDEPENDENT VARIABLES
Models were adjusted for information regarding the consultation context, such as patient’s age, patient’s Charlson Comorbidity Index30 (except when the dependent variable was a CHC component of the index, e.g., HIV/AIDS, heart failure, stroke or COPD, since the same information would be present on both sides of the equation), and medical category in charge of the consultation (generalists or FPs).
The Charlson Comorbidity Index added to the model’s information about patients’ morbidity burden, assuming that those with a higher morbidity burden would be more likely to have more follow-up visits and to develop other CHCs, and would require more LTs. A dichotomous variable identifying if the consultation was a prenatal care visit was also included. As for time effects dummy variables for months and years were regarded in all models.
Patients’ information that does not change over time, such as patient’s sex and the Social Development Index (SDI) of the neighborhood were also considered. The SDI is a composite indicator combining information about sanitation, schooling, income, and housing conditions from every household in the FHT catchment area.38 It represents the grade of social development of the neighborhood where the patient lives, varying from 0 (least developed) to 1 (most developed).
All clinics and FHTs in this sample have the same human resources, equipment, and physical structure. Generalists and FPs were evenly distributed across the neighborhoods, clinics, and FHTs.
STATISTICAL ANALYSES
Multilevel binomial regression models were built to estimate the relative risks between Generalists (reference group) and FPs of each one of the three types of outcomes happening in medical consultations in primary care.
For LTs requested in one consultation and the detection of CHCs, consultations were clustered and ordered per each individual patient. This created a hierarchical data structure in which consultations were nested within patients, hence taking into account the correlation among consultations from the same patient. These models were adjusted for first level covariates (consultation), i.e., patient’s age, patient’s Charlson Comorbidity Index, prenatal care consultation, time, and medical category; and for second level covariates – SDI and patient’s sex.
For follow-up consultations, the hierarchical data structure chosen had consultations (first level) nested within doctors (second level). This structure captures the availability of consultations (access to healthcare) for each CHC between Generalists and FPs. In this way, RRs represent the risk of FPs offer one consultation for a specific CHC in a given period, compared to generalists. These models were adjusted for first level covariates (consultation), i.e., patient’s age, patient’s Charlson Comorbidity Index, and prenatal care consultation; for second level covariates (months, SDI and patient’s sex) and third level covariates (medical category).
With the RRs from the previous models, Population Attributable Fractions (PAFs) for each LT and CHC were calculated to estimate the change in the number of LTs requested and in the number of incident cases of CHCs per year in the same health care district if all medical consultations were performed by trained FPs.39,40 Data processing and statistical analysis were performed using R version 3.6.2 and lme4 package.